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Open AccessEditorial

Preface: 7th International Conference on Advanced Technologies for Humanity

by Brahim El Bhiri and Ayman S. Mosallam

Eng. Proc. 2025, 112(1), 84; https://doi.org/10.3390/engproc2025112084 - 24 Mar 2026

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Abstract

It is with great pleasure that I present this preface to the proceedings of the 7th International Conference on Advanced Technologies for Humanity (ICATH 2025), held from 9 to 11 July 2025, at the National School of Applied Sciences (ENSA), Ibn Tofaïl University, [...] Read more.

It is with great pleasure that I present this preface to the proceedings of the 7th International Conference on Advanced Technologies for Humanity (ICATH 2025), held from 9 to 11 July 2025, at the National School of Applied Sciences (ENSA), Ibn Tofaïl University, Kenitra, Morocco [...] Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

2 pages, 132 KB

Open AccessEditorial

Statement of Peer Review

by Brahim El Bhiri and Ayman S. Mosallam

Eng. Proc. 2025, 112(1), 85; https://doi.org/10.3390/engproc2025112085 - 24 Mar 2026

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Abstract

In submitting conference proceedings to Engineering Proceedings, the Volume Editors of the proceedings would like to certify to the publisher that all papers published in this volume have been subjected to peer review by the designated expert referees and were administered by [...] Read more.

In submitting conference proceedings to Engineering Proceedings, the Volume Editors of the proceedings would like to certify to the publisher that all papers published in this volume have been subjected to peer review by the designated expert referees and were administered by the Volume Editors in strict accordance with the policies defined on the conference website [...] Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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11 pages, 3762 KB

Open AccessProceeding Paper

Multi-Layer Perceptron Neural Networks for Concrete Strength Prediction: Balancing Performance and Optimizing Mix Designs

by Younes Alouan, Seif-Eddine Cherif, Badreddine Kchakech, Youssef Cherradi and Azzouz Kchikach

Eng. Proc. 2025, 112(1), 1; https://doi.org/10.3390/engproc2025112001 - 14 Oct 2025

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Abstract

Optimizing concrete production requires balancing ingredient ratios and using local resources to produce an economical material with the desired consistency, strength, and durability. Compressive strength is crucial for structural design, yet predicting it accurately is challenging due to the complex interplay of various [...] Read more.

Optimizing concrete production requires balancing ingredient ratios and using local resources to produce an economical material with the desired consistency, strength, and durability. Compressive strength is crucial for structural design, yet predicting it accurately is challenging due to the complex interplay of various factors, including component types, water–cement ratio, and curing time. This study employs a Multi-layer Perceptron Neural Network (ANN_MLP) to model the relationship between input variables and the compressive strength of normal and high-performance concrete. A dataset of 1030 samples from the literature was used for training and evaluation. The optimized ANN_MLP configuration included 16 neurons in a single hidden layer, with the ‘tanh’ activation function and ‘sgd’ solver. It achieved an R² of 0.892, an MAE of 3.648 MPa, and an RMSE of 5.13 MPa. The model was optimized using a univariate sensitivity analysis to measure the impact of each hyperparameter on performance and select optimal values to maximize the accuracy and robustness. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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10 pages, 294 KB

Open AccessProceeding Paper

Towards an Industry 5.0 Enhanced by AI: A Theoretical Framework

by Ayoub Belkadi and Mustapha Bachiri

Eng. Proc. 2025, 112(1), 2; https://doi.org/10.3390/engproc2025112002 - 14 Oct 2025

Cited by 1 | Viewed by 2200

Abstract

The advent of artificial intelligence marks a decisive turning point in the evolution of Industry 5.0, redefining the paradigms of industrial performance. This holistic transformation affects not only technological aspects but also the entire industrial ecosystem. Industrial performance is amplified by AI through [...] Read more.

The advent of artificial intelligence marks a decisive turning point in the evolution of Industry 5.0, redefining the paradigms of industrial performance. This holistic transformation affects not only technological aspects but also the entire industrial ecosystem. Industrial performance is amplified by AI through two major axes: operational excellence and strategic differentiation of solutions. These drivers of performance are structured around concrete strategic advantages, particularly in terms of technological leadership and operational resilience. However, this transformation raises significant challenges on both the human, technical, and financial levels. The managerial implications require a structured approach to the adoption of AI, supported by appropriate organizational development. Future prospects suggest an ever-deeper integration of AI within the industrial ecosystem, paving the way for new models of performance and innovation. In this paper, we strive to make a scientific contribution aimed at shedding light on the impact of artificial intelligence on Industry 5.0, highlighting its implications for the pillars of industrial transformation: operational efficiency and optimization of industrial processes, technological innovation, and competitiveness. We have opted for a theoretical analysis of research related to the integration of AI into industrial systems, in order to provide a synthetic and critical review of this phenomenon. Full article

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11 pages, 3386 KB

Open AccessProceeding Paper

AI-Driven Semantic Framework for Automated Construction Planning and Scheduling with BIM and Digital Twin Integration

by Qais Amarkhil, Mohamed Hegab and Anwar Alroomi

Eng. Proc. 2025, 112(1), 3; https://doi.org/10.3390/engproc2025112003 - 14 Oct 2025

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Abstract

Construction planning and scheduling, including task sequences, constraints, and interdependencies, is poorly structured within digital models such as BIM and Digital Twin and lacks effective integration with planning documents to support scheduling analysis, logic-based reasoning, and automation. To address this gap, this paper [...] Read more.

Construction planning and scheduling, including task sequences, constraints, and interdependencies, is poorly structured within digital models such as BIM and Digital Twin and lacks effective integration with planning documents to support scheduling analysis, logic-based reasoning, and automation. To address this gap, this paper develops an AI-enabled framework organized into three core dimensions: (1) enriching BIM and integrating reality data with activity, spatial, and resource attributes; (2) formalizing planning logic using a planning ontology to represent execution relationships; and (3) applying AI techniques to extract planning knowledge, validate constraints, and generate automated schedules. The framework supports logic-based planning, progress tracking, and coordination across construction processes. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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6 pages, 188 KB

Open AccessProceeding Paper

TTS and STT in Service of Education

by Zakaria El Fakir, Oussama Kaich, El Habib Benlahmar, Sanaa El Filali and Omar Zahour

Eng. Proc. 2025, 112(1), 4; https://doi.org/10.3390/engproc2025112004 - 14 Oct 2025

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Abstract

This article explores how Text-to-Speech (TTS) and Speech-to-Text (STT) technologies are being harnessed in education to enhance accessibility, language development, and overall learner engagement. Drawing upon theoretical frameworks in linguistics and educational psychology, we highlight the benefits TTS and STT can offer to [...] Read more.

This article explores how Text-to-Speech (TTS) and Speech-to-Text (STT) technologies are being harnessed in education to enhance accessibility, language development, and overall learner engagement. Drawing upon theoretical frameworks in linguistics and educational psychology, we highlight the benefits TTS and STT can offer to diverse student populations, including students with disabilities, language learners, and those seeking personalized or self-paced instruction. We discuss methods for integrating TTS and STT into the classroom (hardware, software, and practical considerations) and offer case studies of effective implementations in areas such as literacy support, foreign language acquisition, and assessment. We then address the pedagogical benefits these tools provide—such as differentiated instruction, immediate feedback, and a heightened sense of learner autonomy—along with limitations and challenges that educators may encounter. In conclusion, we suggest future directions for research and practice, underscoring the importance of teacher training, ethical considerations, and ever-evolving advancements in natural language processing. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

8 pages, 553 KB

Open AccessProceeding Paper

User Perception of Content Credibility in E-Commerce Websites: Insight from Behavioral Economics Theories

by Brahim Sabiri and Asmahane Tahiri

Eng. Proc. 2025, 112(1), 5; https://doi.org/10.3390/engproc2025112005 - 14 Oct 2025

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Abstract

This study investigates the factors influencing the perceived credibility of advertising content on e-commerce platforms, drawing on behavioral economics and communication theories. Through a quasi-experimental design involving 156 participants, we analyzed how message features, product importance, and socio-demographic variables affect user perceptions. The [...] Read more.

This study investigates the factors influencing the perceived credibility of advertising content on e-commerce platforms, drawing on behavioral economics and communication theories. Through a quasi-experimental design involving 156 participants, we analyzed how message features, product importance, and socio-demographic variables affect user perceptions. The results reveal that users assign higher credibility to simple, essential content and that gender plays a significant role, with women perceiving paramedical and technical content as more credible. Age, however, showed no significant influence. The discussion highlights the psychological mechanisms behind these behaviors, such as risk and ambiguity aversion, and proposes implications for digital marketing strategies and future research. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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9 pages, 1042 KB

Open AccessProceeding Paper

FDM Process Parameters Impact on Roughness and Dimensional Accuracy of PLA Parts

by Niama Arreda, Hamza Isksioui, Haitam Boutahri, Anasse L’kadiba and Haj Elmoussami

Eng. Proc. 2025, 112(1), 6; https://doi.org/10.3390/engproc2025112006 - 16 Oct 2025

Cited by 2 | Viewed by 1648

Abstract

Interest in research on FDM systems using inexpensive materials like PLA and ABS is constantly increasing. In this regard, the scope of this study is narrowed to exclusively focus on PLA. To improve the surface finish of PLA printed products, it is important [...] Read more.

Interest in research on FDM systems using inexpensive materials like PLA and ABS is constantly increasing. In this regard, the scope of this study is narrowed to exclusively focus on PLA. To improve the surface finish of PLA printed products, it is important to have optimal values of the most important process parameters, notably layer height, temperature, and printing speed. The surface roughness is a critical aspect of additive manufacturing that directly impacts the functionality, aesthetics, and overall performance of printed parts. To accomplish the improvement of surface quality, the statistical method ANOVA (Analysis of Variance) is used to analyze data and identify the most relevant process parameters that impact roughness and dimensional precision. The response variables are identified during this study in order to define the optimal printing parameters for improving part quality and ensuring the best surface finishes. Additionally, the dimensional accuracy of the parts is analyzed in order to check the reliability and effectiveness of the optimum parameters. The results are validated through this additional assessment, which also provides insight into the capabilities and limitations of inexpensive FDM machines when the optimized parameters are used. In conclusion, this study emphasizes the significance of enhancing parameters to improve the performance of 3D printed components, providing insightful information about the potential of PLA as an inexpensive material for applications that need both high surface quality and precise dimensional control. According to the analysis, the thickness of the layers and printing speed have a significant role in the roughness for a better desired surface quality. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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10 pages, 767 KB

Open AccessProceeding Paper

Thematic and Geographic Trends in Studying Smart Cities and PPP Projects: A Bibliometric Review

by Mohammed Amine Benarbi

Eng. Proc. 2025, 112(1), 7; https://doi.org/10.3390/engproc2025112007 - 7 Oct 2025

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Abstract

This study explores the relationship between public–private partnerships (PPPs) and smart cities in the literature that use information and communication technologies (ICTs) for sustainable urban development. This study explores, based on VOSviewer software 1.6.20, thematic and geographic patterns in academic articles (from Scopus) [...] Read more.

This study explores the relationship between public–private partnerships (PPPs) and smart cities in the literature that use information and communication technologies (ICTs) for sustainable urban development. This study explores, based on VOSviewer software 1.6.20, thematic and geographic patterns in academic articles (from Scopus) to identify central themes and knowledge gaps. The key findings highlight a lack of consideration of the African context in studying this subject and the prioritization of technological, governmental, and financial aspects more than social dimensions. The aim of this research is to guide policymakers, planners, and researchers by addressing these gaps to use as future recommendations. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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13 pages, 2101 KB

Open AccessProceeding Paper

Emotion Recognition and Soft Skills Prediction: A Bibliometric Exploration

by Nouhaila Farajy, Ahmed Remaida, Benyoussef Abdellaoui and Aniss Moumen

Eng. Proc. 2025, 112(1), 8; https://doi.org/10.3390/engproc2025112008 - 14 Oct 2025

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Abstract

This study, based on a 25-year dataset (2000–2025) collected from the Scopus database, provides a comprehensive bibliometric analysis of the intellectual structure of research on emotion recognition, prediction, and skills. Using bibliographic coupling as the primary method, the analysis examines the titles, abstracts, [...] Read more.

This study, based on a 25-year dataset (2000–2025) collected from the Scopus database, provides a comprehensive bibliometric analysis of the intellectual structure of research on emotion recognition, prediction, and skills. Using bibliographic coupling as the primary method, the analysis examines the titles, abstracts, keywords, frameworks, and review literature, presenting the most significant articles in this area, along with the headings of 202 relevant papers. The study investigates the temporal distribution of research outputs, focusing particularly on trends from the last decade. To visualize the scientific landscape, the study uses VOSviewer to map co-authorship, keyword co-occurrence, and citation networks. The analysis highlights the most prolific journals, influential authors, dominant subject areas, and frequently used keywords. Additionally, it identifies the algorithms used for emotion recognition in predicting soft skills, along with the objectives of the studies, as well as the data and results involved. The study also identifies the leading countries and educational institutions contributing to this research domain. The findings offer a detailed overview of the field’s development and intellectual trends, providing insights and recommendations for future research directions. This research also helps to understand how emotion recognition can contribute to human development across various domains, as discussed in this article. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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15 pages, 565 KB

Open AccessProceeding Paper

Assessing the Effects of Dust on Solar Panel Performance: A Comprehensive Review and Future Directions

by Abdelali Abdessadak, Hicham Ghennioui, Brahim El Bhiri, Nadège Thirion-Moreau, Mounir Abraim and Safae Merzouk

Eng. Proc. 2025, 112(1), 9; https://doi.org/10.3390/engproc2025112009 - 14 Oct 2025

Cited by 3 | Viewed by 5731

Abstract

Accumulation of dust on PV panels is a big challenge, especially in dry and semi-arid environments like Morocco, where the number of dust particles in the atmosphere diminishes the efficiency of solar panels severely. The review analyzes 30 recent studies, which provide insight [...] Read more.

Accumulation of dust on PV panels is a big challenge, especially in dry and semi-arid environments like Morocco, where the number of dust particles in the atmosphere diminishes the efficiency of solar panels severely. The review analyzes 30 recent studies, which provide insight into performance degradation by dust, as well as the search for solutions that mitigate this effect. Results show that dust reduced solar panel efficiency by between 10% and 40% based on environmental conditions, including dust density, composition, and length of exposure. Many technological approaches have been provided for the problem, including autonomous cleaning systems and advanced coatings, yet economic and scalability barriers are still in existence. Also, using AI in predictive maintenance provides good opportunities to optimize solar panel cleaning schedules to enhance energy production. This review concludes with the observation that, going forward, more research on long-term solutions and the development of sustainable and cost-effective cleaning technologies is urgently needed in order to better exploit solar energy in dusty environments. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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14 pages, 4118 KB

Open AccessProceeding Paper

Use of Artificial Neural Networks for the Evaluation of Thermal Comfort Based on the PMV Index

by Naoual Ben Yachrak and Driss Taoukil

Eng. Proc. 2025, 112(1), 10; https://doi.org/10.3390/engproc2025112010 - 14 Oct 2025

Cited by 1 | Viewed by 1033

Abstract

This study aims to develop an artificial neural network (ANN) model to predict the predicted mean vote (PMV) index, a key Indicator of thermal comfort. Based on the ASHRAE II dataset, our approach uses the six PMV variables: air temperature, relative humidity, air [...] Read more.

This study aims to develop an artificial neural network (ANN) model to predict the predicted mean vote (PMV) index, a key Indicator of thermal comfort. Based on the ASHRAE II dataset, our approach uses the six PMV variables: air temperature, relative humidity, air velocity, radiative mean temperature, clothing insulation, and metabolic rate. However, accurately calculating PMV to determine the thermal comfort of a space can be complex due to the non-linear relationships between these different parameters. Sensitivity analysis of these parameters, performed by the Spearman rank method, identifies the most influential parameters on thermal comfort. The ANN model is trained and tested on 26,805 datasets. The results demonstrate a strong predictive capacity of the ANN, attested by a coefficient of determination R² of 0.99 and a low root mean square error RMSE. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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8 pages, 671 KB

Open AccessProceeding Paper

Dynamic Pricing for Load Balancing in Electric Vehicle Charging Stations: An Integration with Open Charge Point Protocol

by Ayoub Abida, Mourad Zegrari and Redouane Majdoul

Eng. Proc. 2025, 112(1), 11; https://doi.org/10.3390/engproc2025112011 - 14 Oct 2025

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Abstract

Given the environmental threats, the adoption of green and clean mobility is crucial for decarbonizing the mobility sector. Green mobility will bring a mass integration of electric vehicle charging stations (EVCSs) to ensure sufficiency for electric vehicle (EVs) users. To achieve this, intelligently [...] Read more.

Given the environmental threats, the adoption of green and clean mobility is crucial for decarbonizing the mobility sector. Green mobility will bring a mass integration of electric vehicle charging stations (EVCSs) to ensure sufficiency for electric vehicle (EVs) users. To achieve this, intelligently distributing the charging load of EVs is essential to prevent stress on local electrical grids. The uneven distribution of EV charging at specific EVCSs leads to load imbalances compared to underutilized stations, necessitating dynamic load-balancing (in real time) mechanisms to optimize grid demands and prevent overloading. To address this challenge, the authors propose an algorithm for balancing EV loads at EVCSs using dynamic charging prices. This algorithm is intended to be integrated into the OCPP. Simulation results indicate that lower pricing at Station A (0.22 $/kWh) attracts more users, reducing congestion at higher-priced Stations B (0.31 $/kWh) and E (0.29 $/kWh). The proposed model encourages users to utilize less crowded stations, achieving a fairer distribution of EV charging demand while providing cost benefits to users selecting those stations. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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10 pages, 936 KB

Open AccessProceeding Paper

Machine Learning Techniques for Water Resources in Morocco

by Rachid El Ansari, Mohammed El Bouhadioui, Hicham Boutracheh, Jamal Elhassan, Rissouni Youssef, Jamil Hicham, Aboutafail Moulay Othman and Aniss Moumen

Eng. Proc. 2025, 112(1), 12; https://doi.org/10.3390/engproc2025112012 - 14 Oct 2025

Cited by 1 | Viewed by 1402

Abstract

Machine learning is emerging as a powerful tool across many scientific fields, including water resource management. In Morocco, growing challenges such as climate change, population growth, and high water demand—especially in agriculture—have led researchers to apply these techniques to water-related issues. This study [...] Read more.

Machine learning is emerging as a powerful tool across many scientific fields, including water resource management. In Morocco, growing challenges such as climate change, population growth, and high water demand—especially in agriculture—have led researchers to apply these techniques to water-related issues. This study reviews recent research conducted in Morocco, highlighting major trends, scientific contributions, and progress in machine learning applications for hydrological challenges. Following the PRISMA framework, a systematic search was carried out in the Scopus database, resulting in 103 relevant publications affiliated with Moroccan institutions. Using NVIVO and SPSS software, key themes were identified, including water quality, groundwater management, and groundwater level prediction. The most frequently used models include Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and Artificial Neural Networks (ANN). This article presents a comparative analysis of nine highly cited Moroccan studies, focusing on application areas, models, parameters, and performance. Findings show a clear rise in AI-related hydrological research in Morocco, especially in water quality monitoring, smart irrigation optimization, and groundwater level forecasting. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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9 pages, 1066 KB

Open AccessProceeding Paper

Measuring Sustainability Through Business Processes with GaiaTool

by Raimel Sobrino-Duque, Juan A. Plasencia Soler, Begoña Moros Valle and Joaquín Nicolás Ros

Eng. Proc. 2025, 112(1), 13; https://doi.org/10.3390/engproc2025112013 - 14 Oct 2025

Viewed by 635

Abstract

Sustainability assessments enable the obtention of insights that foster continuous improvement in organizations. Evaluating sustainability can play a key role in enhancing decision-making, especially if performed from the top-level stages of the organization. Nevertheless, tools supporting sustainability assessment are scarce in the literature, [...] Read more.

Sustainability assessments enable the obtention of insights that foster continuous improvement in organizations. Evaluating sustainability can play a key role in enhancing decision-making, especially if performed from the top-level stages of the organization. Nevertheless, tools supporting sustainability assessment are scarce in the literature, and there is no one that evaluates sustainability from a business process approach. The aim of this work is to present GaiaTool, a supporting tool for a Green Business Process Management (Green BPM) approach called Gaia. This automated support allows organizations to assess their sustainability level by means of a set of sustainability indicators related to business processes. GaiaTool has been validated through a case study conducted in a higher education organization. The case study has served to redefine objectives, establish action plans, and redesign business processes to improve the sustainability levels of the organization under study. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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9 pages, 1700 KB

Open AccessProceeding Paper

Implementation of Industry 5.0 in SME: Scoping Review

by Zineb Bentassil, Anass Ben Abdelouahab and Aniss Moumen

Eng. Proc. 2025, 112(1), 14; https://doi.org/10.3390/engproc2025112014 - 14 Oct 2025

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Abstract

Industry 5.0 (I5) represents a significant evolution in the trajectory of industrial development, emphasizing a human-centric approach that integrates advanced technologies with the goal of promoting sustainable growth, resilience, and enhanced human well-being. While Industry 4.0 already posed considerable challenges for industrial organizations, [...] Read more.

Industry 5.0 (I5) represents a significant evolution in the trajectory of industrial development, emphasizing a human-centric approach that integrates advanced technologies with the goal of promoting sustainable growth, resilience, and enhanced human well-being. While Industry 4.0 already posed considerable challenges for industrial organizations, particularly in terms of technological integration, workforce adaptation, and strategic realignment, the shift toward Industry 5.0 has introduced additional complexities. The accelerated pace of innovation and the evolving expectations for human–machine collaboration have intensified these challenges. Large manufacturing corporations are already facing difficulties in adapting to this new paradigm; thus, the question arises: how are Small and Medium-sized Enterprises (SMEs), which typically operate with limited resources, infrastructure, and financial capacity, managing this transition? This paper presents a scoping review of 17 research papers, chosen from an initial set of 37 publications sourced from Scopus, Web of Science and ScienceDirect on the implementation of Industry 5.0 in SMEs. A comprehensive synthesis of existing research was conducted to elucidate the current state of the topic, identify the research questions addressed, and outline future directions for this emerging paradigm. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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10 pages, 244 KB

Open AccessProceeding Paper

Adversarial Attacks in IoT: A Performance Assessment of ML and DL Models

by Hamza Jamiri and Abdellah Zyane

Eng. Proc. 2025, 112(1), 15; https://doi.org/10.3390/engproc2025112015 - 14 Oct 2025

Cited by 1 | Viewed by 1695

Abstract

The rise of IoT devices has led to significant advancements but also new security challenges. This paper assesses the performance of various machine learning (ML) models—Decision Trees, Naïve Bayes, Support Vector Machines (SVMs), and a deep learning model (CNN)—against adversarial attacks using the [...] Read more.

The rise of IoT devices has led to significant advancements but also new security challenges. This paper assesses the performance of various machine learning (ML) models—Decision Trees, Naïve Bayes, Support Vector Machines (SVMs), and a deep learning model (CNN)—against adversarial attacks using the IoT-23 dataset. Attacks tested include the Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD). Findings show that Decision Trees are the most robust, while CNNs are the most vulnerable, highlighting the need for improved defenses in IoT systems and suggesting new research avenues in adversarial learning. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

11 pages, 2292 KB

Open AccessProceeding Paper

Development and Application of Self-Sensing Materials for Structural Health Monitoring of Civil Engineering Infrastructures

by Rosa Penna, Annavirginia Lambiase, Gerarda Landi, Giuseppe Lovisi and Luciano Feo

Eng. Proc. 2025, 112(1), 16; https://doi.org/10.3390/engproc2025112016 - 14 Oct 2025

Cited by 1 | Viewed by 1440

Abstract

This study examines advanced cementitious composites incorporating Multi-Walled Carbon Nanotubes (MWCNTs), combining experimental investigations and analytical modeling for enhanced Structural Health Monitoring (SHM) applications. The experimental phase assessed the electrical properties of specimens with varying MWCNT contents, identifying a percolation zone between 0.05 [...] Read more.

This study examines advanced cementitious composites incorporating Multi-Walled Carbon Nanotubes (MWCNTs), combining experimental investigations and analytical modeling for enhanced Structural Health Monitoring (SHM) applications. The experimental phase assessed the electrical properties of specimens with varying MWCNT contents, identifying a percolation zone between 0.05 wt% and 0.5 wt%. A dispersion protocol using ultrasonic agitation and a surfactant ensured the uniform distribution of CNTs. Furthermore, a novel micromechanical model, based on established polymer matrix approaches, was used to predict electrical conductivity behavior, accounting for nanotube geometry, concentration, waviness, and tunneling effects. Model predictions confirmed its effectiveness in analyzing structure–property relationships in CNT-based cementitious materials. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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8 pages, 427 KB

Open AccessProceeding Paper

Virtual Laboratories in STEM Education: A Scoping Literature Review on E-Learning Innovation

by Hajar Hanine, Nouhaila Farajy and Aniss Moumen

Eng. Proc. 2025, 112(1), 17; https://doi.org/10.3390/engproc2025112017 - 14 Oct 2025

Cited by 1 | Viewed by 4057

Abstract

As digital learning continues to expand, virtual laboratories have become increasingly prominent in STEM education. This scoping review explores the development and application of virtual labs within online and blended learning settings. It looks into how these resources encourage experiential learning, increase student [...] Read more.

As digital learning continues to expand, virtual laboratories have become increasingly prominent in STEM education. This scoping review explores the development and application of virtual labs within online and blended learning settings. It looks into how these resources encourage experiential learning, increase student interest, and offer substitutes for conventional laboratory limitations. The evaluation concentrates on important aspects such as learning objectives, instructional techniques, technology infrastructure, and real-world implementation difficulties. It also identifies recurring limitations in the current body of research, including the lack of adaptable virtual lab designs and limited empirical evaluation. The study highlights the essential role of virtual laboratories in advancing e-learning innovation and outlines future research directions aimed at maximizing their educational impact in STEM fields. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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10 pages, 700 KB

Open AccessProceeding Paper

An Overview of the Transformation Towards Quality 4.0: Technological Needs, Challenges, and Benefits

by Anass Hafid, Fatima Ezzahra Sebtaoui and Ahmed Mouchtachi

Eng. Proc. 2025, 112(1), 18; https://doi.org/10.3390/engproc2025112018 - 14 Oct 2025

Viewed by 3359

Abstract

Quality 4.0 is a new concept that integrates Industry 4.0 technologies into traditional quality management systems in order to reduce operational cost, time and improve efficiency. This article presents an overview of the transformation towards Quality 4.0 and reviews articles published in Scopus, [...] Read more.

Quality 4.0 is a new concept that integrates Industry 4.0 technologies into traditional quality management systems in order to reduce operational cost, time and improve efficiency. This article presents an overview of the transformation towards Quality 4.0 and reviews articles published in Scopus, ScienceDirect, and Web of Science in order to have accurate, up-to-date, and relevant information. This work focuses on the Industry 4.0 technologies applied in Quality management systems that can support the transformation to Quality 4.0, the challenges of these technologies can address, and the benefits of implementing Quality 4.0. The results analyze the critical benefits of implementing digital technologies, including operational efficiency and improved innovation. Practical cases and real-life experiences are discussed. Finally, we identified a quantitative matrix reflecting the frequency of specific technologies associated with particular quality challenges. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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11 pages, 2243 KB

Open AccessProceeding Paper

Cost and QoS Analysis in IoT: An Optimization Approach Based on the OneM2M Standard

by Jamal Et-Tousy, Samira Abourriche and Abdellah Zyane

Eng. Proc. 2025, 112(1), 19; https://doi.org/10.3390/engproc2025112019 - 14 Oct 2025

Viewed by 946

Abstract

Ensuring excellent Quality of Service (QoS) while limiting financial expenses is seriously challenged by the growing scope and complexity of the Internet of Things (IoT). Though established under ETSI, the OneM2M standard provides a consistent middleware foundation but does not include a thorough [...] Read more.

Ensuring excellent Quality of Service (QoS) while limiting financial expenses is seriously challenged by the growing scope and complexity of the Internet of Things (IoT). Though established under ETSI, the OneM2M standard provides a consistent middleware foundation but does not include a thorough QoS management technique. This work presents a dynamic optimization method based on the MAPE-K model to solve traffic- and resource-oriented QoS aspects. We balance QoS with operational costs by providing cost modeling—including cloud resource pricing and workload offloading. Supported by mathematical modeling and real-world workload situations, the results show the possibilities of cost-aware QoS techniques for scalable and efficient IoT systems based on the OneM2M standard. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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9 pages, 2371 KB

Open AccessProceeding Paper

Advanced Tolerance Optimization for Freeform Geometries Using Particle Swarm Optimization: A Case Study on Aeronautical Turbine Blades

by Oubrek Mohamed, Bellat Abdelouahad, Salih Abdelouahab and Jalid Abdelilah

Eng. Proc. 2025, 112(1), 20; https://doi.org/10.3390/engproc2025112020 - 14 Oct 2025

Viewed by 562

Abstract

This study introduces a novel approach to optimizing geometric tolerances on freeform surfaces, specifically turbine blades, by leveraging a global tolerance framework. Unlike traditional methods that rely on multiple local tolerances, this research proposes a unified model to streamline design complexity while maintaining [...] Read more.

This study introduces a novel approach to optimizing geometric tolerances on freeform surfaces, specifically turbine blades, by leveraging a global tolerance framework. Unlike traditional methods that rely on multiple local tolerances, this research proposes a unified model to streamline design complexity while maintaining functional integrity and cost efficiency. A turbine blade, reconstructed from 3D-scanned point cloud data, serves as the basis for this investigation. The reconstructed geometry was analyzed to define deviation distributions, followed by the application of a global tolerance model. Using genetic algorithms, the tolerances were optimized to balance manufacturing costs and performance penalties. Results demonstrate a substantial simplification in quality control processes, with a reduction in manufacturing costs by up to 20%, while preserving aerodynamic and structural performance. The study highlights the potential of global tolerance strategies to transform tolerance allocation in industries such as aerospace and energy, where freeform surfaces are prevalent. The integration of optimization techniques and advanced surface analysis offers a forward-looking perspective on enhancing manufacturing precision and efficiency. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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10 pages, 1067 KB

Open AccessProceeding Paper

Adaptive Q-Learning in Noisy Environments: A Study on Sensor Noise Influence

by Mouna El Wafi, My Abdelkader Youssefi, Rachid Dakir, Mohamed Bakir and Younes El Koudia

Eng. Proc. 2025, 112(1), 21; https://doi.org/10.3390/engproc2025112021 - 14 Oct 2025

Viewed by 790

Abstract

Reinforcement learning, particularly Q-learning, has demonstrated significant potential in autonomous navigation applications. However, the environments of the real world introduce sensor noise, which can impact learning efficiency and decision-making. This study examines the influence of sensor noise on Q-learning performance by simulating an [...] Read more.

Reinforcement learning, particularly Q-learning, has demonstrated significant potential in autonomous navigation applications. However, the environments of the real world introduce sensor noise, which can impact learning efficiency and decision-making. This study examines the influence of sensor noise on Q-learning performance by simulating an agent navigating an environment with noise. We compare two learning strategies: one with fixed hyperparameters and another with dynamically adjusted hyperparameters. Our results show that high sensor noise degrades learning performance, increasing convergence time and sub-optimal decision-making. However, adapting hyperparameters over time improves resilience to noise by optimizing the balance between exploration and exploitation. These findings highlight the importance of robust learning strategies for autonomous systems under uncertain conditions. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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10 pages, 3739 KB

Open AccessProceeding Paper

Detection of Cracks and Deformations Through Moment Transform Techniques

by Hind Es-sady, Hassane Moustabchir and Mhamed Sayyouri

Eng. Proc. 2025, 112(1), 22; https://doi.org/10.3390/engproc2025112022 - 14 Oct 2025

Viewed by 443

Abstract

Ensuring the structural integrity of mechanical components is a key challenge in industries such as automotive, aerospace, and energy. Conventional techniques for defect identification, including non-destructive testing (NDT) and the Finite Element Method (FEM), offer reliable solutions—yet FEM often requires intensive modeling work [...] Read more.

Ensuring the structural integrity of mechanical components is a key challenge in industries such as automotive, aerospace, and energy. Conventional techniques for defect identification, including non-destructive testing (NDT) and the Finite Element Method (FEM), offer reliable solutions—yet FEM often requires intensive modeling work and high computational cost. To streamline the detection process, this study proposes a method based on orthogonal moment transforms applied to digital images. This fast and automated technique is particularly suited for integration into industrial vision systems. The approach consists in encoding the visual features of a component using continuous orthogonal moments (e.g., Zernike, Chebyshev, or Fourier), and analyzing the extracted descriptors to identify irregularities associated with surface cracks or structural flaws. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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14 pages, 1052 KB

Open AccessProceeding Paper

Artificial Intelligence Models for Balancing Energy Consumption and Security in 5G Networks

by Hammad Lazrek, Hassan El Ferindi, Meryam El Mouhtadi, Mohammed Zouiten and Aniss Moumen

Eng. Proc. 2025, 112(1), 23; https://doi.org/10.3390/engproc2025112023 - 14 Oct 2025

Cited by 1 | Viewed by 1107

Abstract

Fifth-generation (5G) networks represent a paradigm shift in telecommunications, offering ultra-reliable low-latency communication, massive connectivity of devices, and unparalleled data rates. While these advantages also present significant complications surrounding energy consumption and cybersecurity, requiring new approaches to maintain operational effectiveness and network fidelity. [...] Read more.

Fifth-generation (5G) networks represent a paradigm shift in telecommunications, offering ultra-reliable low-latency communication, massive connectivity of devices, and unparalleled data rates. While these advantages also present significant complications surrounding energy consumption and cybersecurity, requiring new approaches to maintain operational effectiveness and network fidelity. This study proposes a new hybrid artificial intelligence (AI) framework consisting of explainable AI (XAI) for transparent resource allocation, convolutional neural networks (CNNs) for real-time anomaly detection, and recurrent neural networks (RNNs) for predictive energy optimization. Experiments and real-world case studies illustrate this framework’s scalability and efficiency by achieving improved network resource management, a detection accuracy of 99.7% for anomalies, and energy savings of up to 65%. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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6 pages, 219 KB

Open AccessProceeding Paper

Digital Reconstruction of Historical Scenes for History Teaching

by Oussama Kaich, Zakaria El Fakir, El Habib Benlahmar, Sanaa El Filali and Omar Zahour

Eng. Proc. 2025, 112(1), 24; https://doi.org/10.3390/engproc2025112024 - 15 Oct 2025

Viewed by 1564

Abstract

This article examines the role of digital reconstructions of historical scenes in the teaching of history, highlighting their theoretical foundations, their methods, and the educational benefits they offer. Drawing from perspectives in educational sciences and digital humanities, we explore how the use of [...] Read more.

This article examines the role of digital reconstructions of historical scenes in the teaching of history, highlighting their theoretical foundations, their methods, and the educational benefits they offer. Drawing from perspectives in educational sciences and digital humanities, we explore how the use of 3D modeling, virtual reality (VR), and augmented reality (AR) can create immersive environments that enhance learners’ engagement, curiosity, and critical thinking. After outlining the epistemological and didactic underpinnings—namely constructivism and the investigative approach to history—we detail the practical steps involved in reconstructing historical scenes (documentary research, iconographic analysis, 3D modeling). Two case studies illustrate how virtual reconstructions can bring historical contexts to life, improve knowledge retention, and encourage interdisciplinary collaboration. We then discuss the benefits for students, including improved understanding, motivation, and the development of critical analysis skills. Finally, we address the limitations and challenges associated with this pedagogical approach, such as technical and financial constraints, scientific validation, and teacher training. We conclude by identifying research perspectives, especially regarding the potential of artificial intelligence and collaborative international projects. Ultimately, digital reconstructions can be a powerful educational tool, enabling learners not only to “see” the past but also to reflect upon its complexities and debates. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

11 pages, 3349 KB

Open AccessProceeding Paper

Enhancing Grid-Connected Photovoltaic Power System Performance Using Fuzzy P&O Approach

by Zerouali Mohammed, Talbi Kaoutar, El Ougli Abdelghani and Tidhaf Belkacem

Eng. Proc. 2025, 112(1), 25; https://doi.org/10.3390/engproc2025112025 - 14 Oct 2025

Viewed by 807

Abstract

Solar energy solutions have become increasingly popular worldwide due to the growing need for renewable energy. This article presents a photovoltaic (PV) system connected to a three-phase power grid, modeled under varying climatic conditions. It consists of two conversion stages, a DC-DC Boost [...] Read more.

Solar energy solutions have become increasingly popular worldwide due to the growing need for renewable energy. This article presents a photovoltaic (PV) system connected to a three-phase power grid, modeled under varying climatic conditions. It consists of two conversion stages, a DC-DC Boost converter and a DC-AC inverter. The former uses a variable-step P&O based on fuzzy logic control to maximize the power of the photovoltaic panels, allowing for greater tracking accuracy than traditional P&O techniques. Inverters with phase-locked loop technology improve the performance of grid-connected PV systems by using a conventional PI controller that has a faster response. Using Matlab/Simulink environments, the entire system and control techniques are evaluated and verified. The simulation results confirm the effectiveness and robustness of the proposed system. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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8 pages, 218 KB

Open AccessProceeding Paper

Towards an Explainable Linguistic Approach to the Identification of Expressive Forms Within Arabic Text

by Zouheir Banou, Sanaa El Filali, El Habib Benlahmar, Fatima-Zahra Alaoui and Laila El Jiani

Eng. Proc. 2025, 112(1), 26; https://doi.org/10.3390/engproc2025112026 - 15 Oct 2025

Viewed by 710

Abstract

This paper presents a rule-based negation and litotes detection system for Modern Standard Arabic. Unlike purely statistical approaches, the proposed pipeline leverages linguistic structures, lexical resources, and dependency parsing to identify negated expressions, exception clauses, and instances of litotic inversion, where rhetorical negation [...] Read more.

This paper presents a rule-based negation and litotes detection system for Modern Standard Arabic. Unlike purely statistical approaches, the proposed pipeline leverages linguistic structures, lexical resources, and dependency parsing to identify negated expressions, exception clauses, and instances of litotic inversion, where rhetorical negation conveys an implicit positive meaning. The system was applied to a large-scale subset of the Arabic OSCAR corpus, filtered by sentence length and syntactic structure. The results show the successful detection of 5193 negated expressions and 1953 litotic expressions through antonym matching. Additionally, 200 instances involving exception prepositions were identified, reflecting their syntactic specificity and rarity in Arabic. The system is fully interpretable, reproducible, and well-suited to low-resource environments where machine learning approaches may not be viable. Its ability to scale across heterogeneous data while preserving linguistic sensitivity demonstrates the relevance of rule-based systems for morphologically rich and structurally complex languages. This work contributes a practical framework for analyzing negation phenomena and offers insight into rhetorical inversion in Arabic discourse. Although coverage of rarer structures is limited, the pipeline provides a solid foundation for future refinement and domain-specific applications in figurative language processing. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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8 pages, 5640 KB

Open AccessProceeding Paper

Sustainable Thermal Insulation Composites Based on Alfa Plant Fibers and Wood Waste

by Youssef Cherradi, Omar Ennaya, Younes Alouan, Seifeddine Cherif, Hamid El Qarnia, Reda Sadouri and Mustafa Benyoucef

Eng. Proc. 2025, 112(1), 27; https://doi.org/10.3390/engproc2025112027 - 14 Oct 2025

Viewed by 753

Abstract

This study focuses on the development, characterization, and numerical simulation of novel composite materials based on natural vegetable fibers for applications in civil engineering. Three different bio-based composites were formulated using Alfa plant fibers, wood waste, and an equal mixture of both (50% [...] Read more.

This study focuses on the development, characterization, and numerical simulation of novel composite materials based on natural vegetable fibers for applications in civil engineering. Three different bio-based composites were formulated using Alfa plant fibers, wood waste, and an equal mixture of both (50% Alfa, 50% wood), with polyvinyl acetate (PVAc), a non-polluting polymer matrix, as the binder. The performance of these composites is strongly influenced by the fiber morphology, structural characteristics, and the nature of the matrix. Thus, understanding and optimizing these parameters is crucial for tailoring materials to meet specific design requirements. The experimental approach began with the morphological and structural characterization of the raw and treated fibers, followed by the evaluation of the thermal a properties of the resulting composites. Furthermore, thermal conductivity simulations were performed using COMSOL Multiphysics to validate the experimental results and gain deeper insights into heat transfer behavior within the composites. A comparative analysis with conventional synthetic insulation materials revealed that the developed bio-composites offer competitive thermal performance while being more environmentally sustainable. These findings highlight the potential of Alfa and wood waste fibers as effective, eco-friendly alternatives for thermal insulation in building applications. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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9 pages, 594 KB

Open AccessProceeding Paper

Expansive Soils Stabilized with Recycled Polypropylene Fibers: An Assessment Based on Laboratory and Life Cycle Data for Mechanical and Environmental Performance

by Ahlam El Majid, Khadija Baba and Yassine Razzouk

Eng. Proc. 2025, 112(1), 28; https://doi.org/10.3390/engproc2025112028 - 15 Oct 2025

Cited by 2 | Viewed by 1566

Abstract

This study explores the use of recycled polypropylene fibers as sustainable reinforcement materials for stabilizing expansive clayey soils. Laboratory testing revealed that the optimal fiber combinations enhanced ductility, post-peak behavior, strength, and swelling properties. A cradle-to-grave life cycle assessment (LCA) also showed the [...] Read more.

This study explores the use of recycled polypropylene fibers as sustainable reinforcement materials for stabilizing expansive clayey soils. Laboratory testing revealed that the optimal fiber combinations enhanced ductility, post-peak behavior, strength, and swelling properties. A cradle-to-grave life cycle assessment (LCA) also showed the environmental advantages of fiber-reinforced soil over traditional methods. The results suggest that incorporating recycled fibers into soil stabilization techniques can improve performance and promote sustainability in civil engineering applications. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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9 pages, 861 KB

Open AccessProceeding Paper

Deep Learning for Transformer-Based Plant Disease Detection: A Bibliometric Analysis

by Raghiya Elghawth, Wafae Abbaoui, Anass Ariss and Soumia Ziti

Eng. Proc. 2025, 112(1), 29; https://doi.org/10.3390/engproc2025112029 - 15 Oct 2025

Cited by 2 | Viewed by 2443

Abstract

Agriculture, food security, and economic stability are impacted by plant diseases, making their identification and diagnosis essential. This article illustrates the research trends in plant disease detection using transformers through a bibliometric analysis based on visualization. The publications used in this work were [...] Read more.

Agriculture, food security, and economic stability are impacted by plant diseases, making their identification and diagnosis essential. This article illustrates the research trends in plant disease detection using transformers through a bibliometric analysis based on visualization. The publications used in this work were collected from Scopus and Web of Science databases. For visualization, programs such as Biblioshiny and VOSViewer 1.6.20 were used. The results demonstrate that China is the most productive country, accounting for 11 total publications and 126 citations. China Agricultural University was the most productive institute, with six publications, while the Frontiers in Plant Science journal was the most productive journal, with six publications and 102 citations. It also demonstrates that the most used research topics in this field are “deep learning”, “plant disease”, and “vision transformer”. This study provides insights into the application of transformers for plant disease detection, enabling researchers to better understand and explore this field. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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10 pages, 2016 KB

Open AccessProceeding Paper

The Impact of Implementing Supply Chain X.0: A Bibliometric Literature Review Using PRISMA Protocol

by Fatima Zahra Hilal and Abdelhak Yaacoubi

Eng. Proc. 2025, 112(1), 30; https://doi.org/10.3390/engproc2025112030 - 15 Oct 2025

Viewed by 837

Abstract

The evolution of supply chain management (SCM) into Supply Chain X.0 reflects the integration of advanced technologies and adaptive strategies that redefine operational efficiency, sustainability, and resilience. This systematic literature review examines the impact of implementing Supply Chain X.0, focusing on operational efficiency, [...] Read more.

The evolution of supply chain management (SCM) into Supply Chain X.0 reflects the integration of advanced technologies and adaptive strategies that redefine operational efficiency, sustainability, and resilience. This systematic literature review examines the impact of implementing Supply Chain X.0, focusing on operational efficiency, economic outcomes, environmental sustainability, and social implications. Following the PRISMA protocol, 83 peer-reviewed articles from 1998 to 2025 were analyzed and sourced from Scopus. Findings reveal that Supply Chain X.0 enhances performance through automation, real-time visibility, predictive analytics, and sustainability initiatives. However, challenges such as high implementation costs, workforce adaptation, data quality, and security persist. This review provides a comprehensive synthesis for understanding these impacts and identifies research gaps and future research directions for smart supply chain development. Furthermore, it offers novelty by synthesizing the entire Supply Chain X.0 evolution (0.0 to 5.0) in one systematic review combining performance and sustainability impacts across all stages with quantified metrics, thus providing a holistic view that bridges historical and modern smart supply chains. It also identifies underexplored research gaps, such as the applicability of X.0 stages in developing economies and the need for standardized eco-metrics. Finally, the review introduces a novel visual framework using VOSviewer to illustrate the interconnectedness of keywords related to the supply chain, performance, sustainability, and AI, offering a tool to guide future integrative research. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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10 pages, 2051 KB

Open AccessProceeding Paper

Analyzing Film Reviews to Unearth Fluctuations in Netflix Stock Closing Prices

by Mariame Tarsi, Samira Douzi and Abdelaziz Marzak

Eng. Proc. 2025, 112(1), 31; https://doi.org/10.3390/engproc2025112031 - 14 Oct 2025

Viewed by 1120

Abstract

During trading sessions, the stock market experiences a multitude of fluctuations that are influenced by an extensive array of factors. This is especially pertinent to businesses that attract substantial international interest, such as streaming platforms that have shown a noteworthy increase in popularity [...] Read more.

During trading sessions, the stock market experiences a multitude of fluctuations that are influenced by an extensive array of factors. This is especially pertinent to businesses that attract substantial international interest, such as streaming platforms that have shown a noteworthy increase in popularity in recent times. The objective of this research is to examine the fluctuations in the value of Netflix shares during the time period that correlates with the release of particular films that elicited significant reactions. The research utilizes natural language processing (NLP) and sentiment analysis approaches to preprocess the movie evaluations and calculate the sentiment score. The study yields thought-provoking findings concerning the correlation between movie release dates and the closing price. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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11 pages, 2843 KB

Open AccessProceeding Paper

CI/CD Pipeline Optimization Using AI: A Systematic Mapping Study

by Redouan Farihane, Imane Chlioui and Maryam Radgui

Eng. Proc. 2025, 112(1), 32; https://doi.org/10.3390/engproc2025112032 - 15 Oct 2025

Cited by 1 | Viewed by 3961

Abstract

Continuous Integration/Deployment (CI/CD) pipelines are essential in the software engineering field, and improving automation and efficiency in this field requires optimizing them. AI has become a crucial tool for optimizing these pipelines at different stages. This research examines the application of AI approaches [...] Read more.

Continuous Integration/Deployment (CI/CD) pipelines are essential in the software engineering field, and improving automation and efficiency in this field requires optimizing them. AI has become a crucial tool for optimizing these pipelines at different stages. This research examines the application of AI approaches to CI/CD pipeline optimization using a systematic mapping study (SMS) to give a global overview of this field. We examined 92 papers published between 2015 and 2025 based on five main criteria: publication year and channel, research type, CI/CD pipeline stage, empirical method, and AI techniques used. The results show a notable increase in research efforts since 2018; conferences and journals are the most popular publication channels, and most of them are the solutions proposal type, which uses hypothesis-based experimentation as an empirical method for evaluation. Moreover, the testing stage of CI/CD is the most targeted using reinforcement learning algorithms. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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9 pages, 427 KB

Open AccessProceeding Paper

Generative AI for Code Translation: A Systematic Mapping Study

by Aymane Rgaguena, Imane Chlioui and Maryam Radgui

Eng. Proc. 2025, 112(1), 33; https://doi.org/10.3390/engproc2025112033 - 15 Oct 2025

Cited by 1 | Viewed by 1679

Abstract

Generative artificial intelligence (AI) has greatly advanced the code translation process, particularly through large language models (LLMs), which translate source code from one programming language into another. This translation has historically been error-prone, labor-intensive, and highly dependent on manual intervention. Although traditional tools [...] Read more.

Generative artificial intelligence (AI) has greatly advanced the code translation process, particularly through large language models (LLMs), which translate source code from one programming language into another. This translation has historically been error-prone, labor-intensive, and highly dependent on manual intervention. Although traditional tools such as compilers and transpilers have restrictions in managing complex programming paradigms, recent generative AI models most importantly those based on transformer architectures, have shown promise. This systematic mapping study intends to evaluate and compile studies on Generative AI applications in code translation released between 2020 and 2025. Using five main criteria, the publication year and channel, research type, publication type, empirical study type, and AI models. A total of 53 relevant articles were chosen and examined. The results show that conferences and journals are the most often used publishing venues. Although historical-based evaluations and case studies were the empirical methodologies most often used, researchers have primarily focused on implementing transformer-based artificial intelligence models. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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9 pages, 416 KB

Open AccessProceeding Paper

Application of Artificial Intelligence in Stock Market Prediction

by Aya Bellaly, Sara Belattar and El Khatir Haimoudi

Eng. Proc. 2025, 112(1), 34; https://doi.org/10.3390/engproc2025112034 - 15 Oct 2025

Cited by 1 | Viewed by 3127

Abstract

Because of the inherent volatility and non-linearity of financial markets, precise forecasting is a constant struggle. Using historical data, this study examines how well intelligent algorithms in forecast trading market trends. It contrasts a number of machine learning methods, such as Random Forest, [...] Read more.

Because of the inherent volatility and non-linearity of financial markets, precise forecasting is a constant struggle. Using historical data, this study examines how well intelligent algorithms in forecast trading market trends. It contrasts a number of machine learning methods, such as Random Forest, K-Nearest Neighbors (KNN), XGBoost, and Decision Tree, with a deep learning methodology based on Long Short-Term Memory (LSTM) networks. It is possible to assess these models’ capacity to identify intricate temporal patterns because they are trained directly on historical price data rather than using specially designed technical indicators. The findings provide insights into successful data-driven forecasting techniques by highlighting the advantages and disadvantages of each approach in various market scenarios. Supporting the creation of predictive tools for well-informed decision-making in trading environments is the goal of this research. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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9 pages, 1053 KB

Open AccessProceeding Paper

A Review of Concrete Strengthening Methods Using Synthetic and Natural Composites

by Yasmina Ed-Dariy, Brahim El Bhiri and Ahmed Deifalla

Eng. Proc. 2025, 112(1), 35; https://doi.org/10.3390/engproc2025112035 - 15 Oct 2025

Viewed by 1271

Abstract

Currently, the need to repair and strengthen structures is very important. The reinforcement of structures aims to repair or bring existing structures into conformity, either for reasons of loss of initial properties or for reasons of refurbishment level linked to new standards or [...] Read more.

Currently, the need to repair and strengthen structures is very important. The reinforcement of structures aims to repair or bring existing structures into conformity, either for reasons of loss of initial properties or for reasons of refurbishment level linked to new standards or new uses. One of the methods which has met with great success in the field of upgrading reinforced concrete structures is exterior bonding using composite materials. This article summarizes some of the methods used for the strengthening of concrete elements, and compares them from a technical and environmental point of view. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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9 pages, 1157 KB

Open AccessProceeding Paper

Assessing Value Created by IT in Moroccan Healthcare: Perspectives from Professionals

by Rachid Oumlil and Fatima Makhoukh

Eng. Proc. 2025, 112(1), 36; https://doi.org/10.3390/engproc2025112036 - 16 Oct 2025

Viewed by 804

Abstract

This exploratory study examines how 67 healthcare professionals across Morocco perceive the value generated by the use of IT in healthcare organizations through semi-structured interviews. The results reveal that most of these professionals perceive IT positively, emphasizing its contribution to quality, communication, and [...] Read more.

This exploratory study examines how 67 healthcare professionals across Morocco perceive the value generated by the use of IT in healthcare organizations through semi-structured interviews. The results reveal that most of these professionals perceive IT positively, emphasizing its contribution to quality, communication, and patient satisfaction. Specifically, 97% of them recognize its value, highlighting its benefits in terms of efficiency and cost management. However, challenges such as cost and complexity were identified, highlighting the need for customized IT solutions that meet local needs and constraints. This study provides insights for decision-makers and administrators aiming to enhance IT integration in the healthcare sector. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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9 pages, 473 KB

Open AccessProceeding Paper

Optimization of Forecasting Performance in the Retail Sector Using Artificial Intelligence

by Hoda Jatte, Sara Belattar and El Khatir Haimoudi

Eng. Proc. 2025, 112(1), 37; https://doi.org/10.3390/engproc2025112037 - 16 Oct 2025

Viewed by 2955

Abstract

In the retail industry, demand forecasting is absolutely crucial for guaranteeing efficient inventory and supply chain control. Different artificial intelligence (AI) techniques have been used lately to improve forecasting performance. Demand fluctuation, seasonal patterns, and outside influences continue to create difficulties, though. Using [...] Read more.

In the retail industry, demand forecasting is absolutely crucial for guaranteeing efficient inventory and supply chain control. Different artificial intelligence (AI) techniques have been used lately to improve forecasting performance. Demand fluctuation, seasonal patterns, and outside influences continue to create difficulties, though. Using several machine-learning techniques Linear Regression, XGBoost, Random Forest, Decision Tree, Prophet, and LSTM this paper offers a comparative study to forecast product demand. A retail dataset obtained from Kaggle served as the basis for training and testing the forecasting models. The experimental results demonstrate that the LSTM model outperforms all others with accuracy, precision, recall, and F1-score of 92.31%, 92.31%, 100.00%, and 96.00%, respectively, followed by Prophet with 85.71%, 92.31%, 92.31%, and 92.31%, respectively, Decision Tree with 93.05%, 75.76%, 76.13%, and 75.94%, respectively, Random Forest with 91.99%, 66.86%, 88.08%, and 76.02%, respectively, XGBoost with 83.21%, 45.70%, 87.84%, and 60.12%, respectively, and Linear Regression with 60.67%, 25.46%, 89.75%, and 39.67%, respectively. These results verify that ensemble and deep learning models can greatly help retailers in raising operational efficiency and notably improve forecasting accuracy. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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9 pages, 295 KB

Open AccessProceeding Paper

An In-Depth Analytical Cryptanalysis for Substitution Boxes: Odd Size Case Study

by Ismail El Gaabouri, Mostafa Belkasmi, Mohamed Senhadji and Brahim El Bhiri

Eng. Proc. 2025, 112(1), 38; https://doi.org/10.3390/engproc2025112038 - 15 Oct 2025

Viewed by 700

Abstract

From hieroglyphic writing in ancient Egypt to the post-quantum edge, cryptology is usually seen as an immortal concept that evolves within the enhancement of human civilization. However, modern cryptography primitives try to make revealing ciphered information as tough as possible for attackers. As [...] Read more.

From hieroglyphic writing in ancient Egypt to the post-quantum edge, cryptology is usually seen as an immortal concept that evolves within the enhancement of human civilization. However, modern cryptography primitives try to make revealing ciphered information as tough as possible for attackers. As a sort of enhancement, substitution boxes play an important role in leveraging security, especially for symmetric-based algorithms. The S-box concept is integrated internally into the encryption process for block ciphers and added as a strengthened layer for stream ciphers. Consequently, in-depth analytical considerations are always needed to gather the required information if any S-box wants to be integrated. For this reason, this paper is about providing a scrutiny cryptanalysis for these S-boxes and, more precisely, size ones, since they are not widely investigated. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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10 pages, 727 KB

Open AccessProceeding Paper

Narrative Review on Symbolic Approaches for Explainable Artificial Intelligence: Foundations, Challenges, and Perspectives

by Loubna Meziane, Wafae Abbaoui, Soukayna Abdellaoui, Brahim El Bhiri and Soumia Ziti

Eng. Proc. 2025, 112(1), 39; https://doi.org/10.3390/engproc2025112039 - 17 Oct 2025

Cited by 1 | Viewed by 3535

Abstract

The review “Symbolic Approaches for Explainable Artificial Intelligence” discusses the potential of symbolic AI to improve transparency, contrasting it with opaque deep learning systems. Though connectionist models perform well, their poor interpretability means that they are of concern for bias and trust in [...] Read more.

The review “Symbolic Approaches for Explainable Artificial Intelligence” discusses the potential of symbolic AI to improve transparency, contrasting it with opaque deep learning systems. Though connectionist models perform well, their poor interpretability means that they are of concern for bias and trust in high-stakes fields such as healthcare and finance. The authors integrate symbolic AI methods—rule-based reasoning, ontologies, and expert systems—with neuro-symbolic integrations (e.g., DeepProbLog). This paper covers topics such as scalability and integrating knowledge, proposing solutions like dynamic ontologies. The survey concludes by advocating for hybrid AI approaches and interdisciplinary collaboration to reconcile technical innovation with ethical and regulatory demands. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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12 pages, 717 KB

Open AccessProceeding Paper

Leveraging Large Language Models and Data Augmentation in Cognitive Computing to Enhance Stock Price Predictions

by Nassera Habbat, Hicham Nouri and Zahra Berradi

Eng. Proc. 2025, 112(1), 40; https://doi.org/10.3390/engproc2025112040 - 17 Oct 2025

Viewed by 1601

Abstract

Precise stock price forecasting is essential for informed decision-making in financial markets. This study examines the combination of large language models (LLMs) with data augmentation approaches, utilizing improvements in cognitive computing to enhance stock price prediction. Traditional methods rely on structured data and [...] Read more.

Precise stock price forecasting is essential for informed decision-making in financial markets. This study examines the combination of large language models (LLMs) with data augmentation approaches, utilizing improvements in cognitive computing to enhance stock price prediction. Traditional methods rely on structured data and basic time-series analysis. However, new research shows that deep learning and transformer-based architectures can effectively process unstructured financial data, such as news articles and social media sentiment. This study employs models, such as RNN, mBERT, RoBERTa, and GPT-4 based architectures, to illustrate the efficacy of our suggested method in forecasting stock movements. The research employs data augmentation techniques, including synthetic data creation using Generative Pre-trained Transformers, to rectify imbalances in training datasets. We assess metrics like accuracy, F1-score, recall, and precision to verify the models’ performance. We also investigate the influence of preprocessing methods like text normalization and feature engineering. Extensive tests show that transformer models are much better at predicting how stock prices will move than traditional methods. For example, the GPT-4 based model got an F1 score of 0.92 and an accuracy of 0.919, which shows that LLMs have a lot of potential in financial applications. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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11 pages, 1076 KB

Open AccessProceeding Paper

Moroccan Institutional Chatbots: A Hybrid Approach with LLMs, Semantic Matching, and Dialect Adaptation for DARIJA

by Oumaima Ennasri, Brahim El Bhiri and Yann Ben Maissa

Eng. Proc. 2025, 112(1), 41; https://doi.org/10.3390/engproc2025112041 - 20 Oct 2025

Viewed by 1569

Abstract

With the rapid growth of LLM-based chatbots and their applications in fields such as health, education, and entertainment, there is a growing interest in developing systems capable of mimicking human behavior through conversation and natural language interaction. These chatbots are available in several [...] Read more.

With the rapid growth of LLM-based chatbots and their applications in fields such as health, education, and entertainment, there is a growing interest in developing systems capable of mimicking human behavior through conversation and natural language interaction. These chatbots are available in several languages, such as English, French, and Spanish. Unfortunately, Arabic chatbots—especially those that understand Arabic dialects—are still very limited. In this paper, we develop a chatbot for the Moroccan Arabic dialect, specifically designed for the public sector, such as the fiscal domain and government administration. These institutions require tools to reduce communication loads, limit human assistance, and minimize the time needed to find documents or complete payment procedures. Our optimized chatbot combines recent technologies like LLMs and semantic similarity. It supports Moroccan citizens by providing responses in the Moroccan dialect (Darija), both in text and speech, without requiring extensive resources. It also supports other citizens in French, Spanish, and English. Our chatbot was tested in a real use case in the tax domain, and the results were satisfactory, especially considering the general complexity of the Arabic language and the particular challenges of the Moroccan dialect. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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8 pages, 261 KB

Open AccessProceeding Paper

Ethical Prioritization Framework for the Responsible Integration of AI- and IoMT-Enabled Smart Medical Devices

by Mustapha El Ansari, Abdelouahad Achmamad, Abdelhadi El Falaki, Ibtissam Youlyouz Marfak and Saad El Madani

Eng. Proc. 2025, 112(1), 42; https://doi.org/10.3390/engproc2025112042 - 20 Oct 2025

Viewed by 954

Abstract

The integration of smart medical devices (SMDs) driven by artificial intelligence (AI) and the internet of medical things (IoMT) is revolutionizing healthcare through improved diagnostics and continuous monitoring. However, their deployment raises significant ethical concerns, including patient safety, data privacy, informed consent, fairness, [...] Read more.

The integration of smart medical devices (SMDs) driven by artificial intelligence (AI) and the internet of medical things (IoMT) is revolutionizing healthcare through improved diagnostics and continuous monitoring. However, their deployment raises significant ethical concerns, including patient safety, data privacy, informed consent, fairness, bias, and regulatory compliance. This paper presents a structured prioritization framework that assesses these ethical considerations according to their severity, contextual impact, and relevance to clinical practice. The usefulness of this prioritization lies in its ability to guide stakeholders to focus on high-impact areas, ensuring that resources and interventions address the most critical ethical risks first. Targeted mitigation strategies support the application of this framework in practice. By aligning innovation with ethical responsibility, this approach promotes safer, fairer, and more reliable healthcare solutions. This ultimately enables the sustainable and socially responsible integration of SMDs into modern medical systems. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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11 pages, 1590 KB

Open AccessProceeding Paper

Topological Feature Extraction for Interpretable Cancer Tissue Classification

by Ilhame Fadli and Jaouad Dabounou

Eng. Proc. 2025, 112(1), 43; https://doi.org/10.3390/engproc2025112043 - 20 Oct 2025

Viewed by 974

Abstract

Traditional deep learning methods for histopathological analysis suffer from a lack of interpretability, which limits their use in the clinic despite their high accuracy. This paper proposes a Topological Data Analysis (TDA) framework for interpretable colorectal cancer tissue classification. We used persistent homology [...] Read more.

Traditional deep learning methods for histopathological analysis suffer from a lack of interpretability, which limits their use in the clinic despite their high accuracy. This paper proposes a Topological Data Analysis (TDA) framework for interpretable colorectal cancer tissue classification. We used persistent homology to extract topological features from 5000 histological images representing eight tissue classes, combining persistence landscapes with Support Vector Machine (SVM) classification. This method achieved an overall accuracy rate of 82.70%, while providing biologically interpretable features that are directly related to tissue morphology. Topological features successfully represented cellular connectivity as well as structural patterns, enabling perfect classification of morphologically distinct tissue pairs. This research demonstrates that topological data analysis (TDA) represents a promising alternative to non-transparent methods, offering competitive efficiency while ensuring interpretability, a crucial aspect for its clinical integration in computational pathology. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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11 pages, 457 KB

Open AccessProceeding Paper

Federated Learning-Driven Digital Twin: A Privacy-Preserving AI Approach for Crisis Logistics

by Hafsa El Mouhsine, Rajaa Saidi and Walid Cherif

Eng. Proc. 2025, 112(1), 44; https://doi.org/10.3390/engproc2025112044 - 21 Oct 2025

Cited by 1 | Viewed by 1881

Abstract

In emergency situations, rapid action is critical to save lives, yet humanitarian logistics often grapple with challenges like information dispersion, tight deadlines, and strict privacy regulations. This research introduces FL-DT-HSC, a novel approach integrating Federated Learning (FL) and Digital Twins (DTs). Federated Learning [...] Read more.

In emergency situations, rapid action is critical to save lives, yet humanitarian logistics often grapple with challenges like information dispersion, tight deadlines, and strict privacy regulations. This research introduces FL-DT-HSC, a novel approach integrating Federated Learning (FL) and Digital Twins (DTs). Federated Learning enables the management of sensitive data across multiple sites without centralization, while Digital Twins offer live simulations to guide decision-making. Tested through a fictional case based on the 2022 Pakistan floods, FL-DT-HSC shows promise for faster, more efficient, and privacy-conscious responses. Though still a concept, it leverages established ideas from healthcare and industrial applications, laying the groundwork for real-world experiments to transform crisis logistics. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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8 pages, 200 KB

Open AccessProceeding Paper

A Review of Assistive Technology in Special Education

by Nada Saabi, Imane Chlioui and Maryam Radgui

Eng. Proc. 2025, 112(1), 45; https://doi.org/10.3390/engproc2025112045 - 22 Oct 2025

Cited by 2 | Viewed by 5517

Abstract

Assistive technology (AT) is transforming special education by making learning more accessible and inclusive for students with disabilities. By leveraging emerging technologies, AT enhances engagement, personalizes learning experiences, and fosters integration into mainstream education. This paper explores the role of AT in supporting [...] Read more.

Assistive technology (AT) is transforming special education by making learning more accessible and inclusive for students with disabilities. By leveraging emerging technologies, AT enhances engagement, personalizes learning experiences, and fosters integration into mainstream education. This paper explores the role of AT in supporting diverse learning needs and improving educational outcomes, highlighting its potential to create more inclusive and adaptive learning environments, and exploring some of the challenges and future hopes. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

11 pages, 1116 KB

Open AccessProceeding Paper

IoT Architecture for Inclusive Urban Mobility: A Design Science Research Approach to Sustainable Transportation in Morocco

by Tarik Abdennasser, Souad Alaoui, Imane Chlioui and Abdelhalim Hnini

Eng. Proc. 2025, 112(1), 46; https://doi.org/10.3390/engproc2025112046 - 22 Oct 2025

Viewed by 1029

Abstract

We introduce an IoT architecture that addresses critical mobility challenges in Morocco’s urban transportation ecosystem. Using Design Science Research methodology, we developed a complete system integrating smart infrastructure, edge computing, and accessible interfaces to enhance service quality while prioritizing inclusivity for vulnerable populations. [...] Read more.

We introduce an IoT architecture that addresses critical mobility challenges in Morocco’s urban transportation ecosystem. Using Design Science Research methodology, we developed a complete system integrating smart infrastructure, edge computing, and accessible interfaces to enhance service quality while prioritizing inclusivity for vulnerable populations. Our five-layer architecture targets institutional capacity limitations, inadequate service levels, and accessibility barriers present in Morocco’s transportation landscape. An evaluation of our proposed solution shows how technology integration can advance eco-friendly transport goals while accommodating limited resources in developing contexts. The research contributes novel insights into IoT architectural models for inclusive design alongside practical recommendations for transportation authorities seeking to leverage digital transformation for more equitable urban mobility. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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10 pages, 789 KB

Open AccessProceeding Paper

Smarter Cities, Stronger Heritage: Toward Participatory and Knowledge-Driven Governance for Cultural Preservation in Morocco

by Douae El Hadraoui and Naila Amrous

Eng. Proc. 2025, 112(1), 47; https://doi.org/10.3390/engproc2025112047 - 22 Oct 2025

Viewed by 1057

Abstract

This paper explores how Smart Urban Governance, Smart Knowledge Management (Smart KM), and participatory digital tools can reshape cultural heritage rehabilitation in Morocco. Using a qualitative, multi-method approach including comparative case studies, document analysis, and thematic synthesis, it examines the institutional, technological and [...] Read more.

This paper explores how Smart Urban Governance, Smart Knowledge Management (Smart KM), and participatory digital tools can reshape cultural heritage rehabilitation in Morocco. Using a qualitative, multi-method approach including comparative case studies, document analysis, and thematic synthesis, it examines the institutional, technological and civic forces at play. While Morocco shows strong political will, governance remains fragmented, citizen participation low, and Smart KM underdeveloped. By drawing lessons from Spain and Indonesia, the study highlights transferable innovations in digital storytelling and participatory governance. It concludes with a roadmap for Morocco emphasizing institutional reform, Smart KM investment, and inclusive, digital engagement strategies. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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11 pages, 484 KB

Open AccessProceeding Paper

RF Energy-Harvesting Systems: A Systematic Review of Receiving Antennas, Matching Circuits, and Rectifiers

by Mounir Bzzou, Younes Karfa Bekali and Brahim El Bhiri

Eng. Proc. 2025, 112(1), 48; https://doi.org/10.3390/engproc2025112048 - 24 Oct 2025

Cited by 3 | Viewed by 4877

Abstract

The widespread integration of low-power electronic devices in IoT, biomedical, and sensing applications has intensified the demand for energy-autonomous solutions. Radio Frequency Energy Harvesting (RFEH) offers a promising alternative by leveraging ambient RF signals available in both indoor and outdoor environments. Despite its [...] Read more.

The widespread integration of low-power electronic devices in IoT, biomedical, and sensing applications has intensified the demand for energy-autonomous solutions. Radio Frequency Energy Harvesting (RFEH) offers a promising alternative by leveraging ambient RF signals available in both indoor and outdoor environments. Despite its conceptual appeal, practical deployment still faces major challenges. This systematic literature review (SLR) examines 25 recent studies, following the PRISMA methodology, to provide a comprehensive overview of current RFEH architectures. It focuses on three critical components: receiving antennas, impedance matching circuits (IMCs), and RF-to-DC rectifiers. Design strategies are reviewed and compared across antenna types, matching techniques, and rectifier configurations. The review also highlights persistent challenges and outlines directions for the development of compact, efficient, and robust energy-harvesting systems for next-generation wireless technologies. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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12 pages, 1655 KB

Open AccessProceeding Paper

Evaluating Flood Risk Assessment in Turkey: Advancing Climate Change Adaptation and Resilience

by Lina A. Khaddour, Ceren Kazbek and Ismail Elhassnaoui

Eng. Proc. 2025, 112(1), 49; https://doi.org/10.3390/engproc2025112049 - 24 Oct 2025

Cited by 1 | Viewed by 2465

Abstract

Flooding in Turkey is intensifying due to both climate change and unregulated development. Despite national frameworks, local-level gaps persist in risk assessment, infrastructure, and adaptation planning. This study evaluates Turkey’s flood vulnerability using a mixed-methods approach, combining GIS-based spatial analysis, remote sensing, expert [...] Read more.

Flooding in Turkey is intensifying due to both climate change and unregulated development. Despite national frameworks, local-level gaps persist in risk assessment, infrastructure, and adaptation planning. This study evaluates Turkey’s flood vulnerability using a mixed-methods approach, combining GIS-based spatial analysis, remote sensing, expert surveys, and policy review. Results highlight rapid urbanization, infrastructure deficits, and institutional fragmentation as key drivers of risk. Current policies remain reactive and disconnected from long-term climate resilience goals. The study advocates for data-driven, inclusive strategies that integrate AI, GIS, and nature-based solutions to build scalable, adaptive frameworks aligned with Turkey’s climate and sustainability objectives. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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8 pages, 561 KB

Open AccessProceeding Paper

Connected Health Revolution: Deployment of an Intelligent Chatbot for Efficient Management of Online Medical Information Requests

by Achraf Berrajaa, Issam Berrajaa and Naoufal Rouky

Eng. Proc. 2025, 112(1), 50; https://doi.org/10.3390/engproc2025112050 - 27 Oct 2025

Viewed by 851

Abstract

Within the rapidly advancing disciplines of natural language processing (NLP) and artificial intelligence (AI), this paper introduces an innovative approach aimed at improving access to health-related information. Fueled by the growing reliance on digital platforms for health inquiries, our research unveils an intelligent [...] Read more.

Within the rapidly advancing disciplines of natural language processing (NLP) and artificial intelligence (AI), this paper introduces an innovative approach aimed at improving access to health-related information. Fueled by the growing reliance on digital platforms for health inquiries, our research unveils an intelligent chatbot designed to categorize health-related queries and deliver personalized advice. By leveraging a diverse dataset and employing advanced NLP techniques, our models, which include Support Vector Machines, Random Forest, Bagging Classifier, among others, assist in building a flexible conversational agent. The evaluation metrics demonstrate that the Bagging Classifier delivers outstanding results, reaching an accuracy of 99%. The study concludes with a comparative analysis, positioning the Bagging Classifier as a benchmark for accuracy and performance in the classification of health-related queries. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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9 pages, 399 KB

Open AccessProceeding Paper

Urban Mobility Modeling: Application to Seoul Bike-Sharing Data

by Farouk Mselmi, Mouhsene Fri and Naoufal Rouky

Eng. Proc. 2025, 112(1), 51; https://doi.org/10.3390/engproc2025112051 - 27 Oct 2025

Viewed by 1133

Abstract

This study applies a model from the normal variance–mean mixture family to capture daily demand in urban bike sharing. We fit both a mixture-based model and a standard Gaussian model to the logarithmic returns of total daily rental counts from the Seoul Bike-Sharing [...] Read more.

This study applies a model from the normal variance–mean mixture family to capture daily demand in urban bike sharing. We fit both a mixture-based model and a standard Gaussian model to the logarithmic returns of total daily rental counts from the Seoul Bike-Sharing Demand dataset. Parameter estimation is performed, and model performance is assessed using mean squared error (MSE). Using one year of hourly rental data aggregated to daily counts from the Seoul Bike dataset, we find that the mixture-based model substantially outperforms the Gaussian counterpart, achieving a lower MSE. These results suggest that models from the normal variance–mean mixture family are more effective at capturing the large fluctuations and outliers inherent in bike-sharing demand data compared to models assuming normally distributed returns. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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12 pages, 635 KB

Open AccessProceeding Paper

Trustworthy Multimodal AI Agents for Early Breast Cancer Detection and Clinical Decision Support

by Ilyass Emssaad, Fatima-Ezzahraa Ben-Bouazza, Idriss Tafala, Manal Chakour El Mezali and Bassma Jioudi

Eng. Proc. 2025, 112(1), 52; https://doi.org/10.3390/engproc2025112052 - 27 Oct 2025

Cited by 2 | Viewed by 2023

Abstract

Timely and precise identification of breast cancer is crucial for enhancing clinical outcomes; however, current AI systems frequently exhibit deficiencies in transparency, trustworthiness, and the capacity to assimilate varied data modalities. We introduce a reliable, multi-agent, multimodal AI system for individualised early breast [...] Read more.

Timely and precise identification of breast cancer is crucial for enhancing clinical outcomes; however, current AI systems frequently exhibit deficiencies in transparency, trustworthiness, and the capacity to assimilate varied data modalities. We introduce a reliable, multi-agent, multimodal AI system for individualised early breast cancer diagnosis, created on the CBIS-DDSM dataset. The system consists of four specialised agents that cooperatively analyse diverse data. An Imaging Agent employs convolutional and transformer-based models to analyse mammograms for lesion classification and localisation; a Clinical Agent extracts structured features including breast density (ACR), view type (CC/MLO), laterality, mass shape, margin, calcification type and distribution, BI-RADS score, pathology status, and subtlety rating utilising optimised tabular learning models; a Risk Assessment Agent integrates outputs from the imaging and clinical agents to produce personalised malignancy predictions; and an Explainability Agent provides role-specific interpretations through Grad-CAM for imaging, SHAP for clinical features, and natural language explanations customised for radiologists, general practitioners, and patients. Predictive dependability is assessed by Expected Calibration Error (ECE) and Brier Score. The framework employs a modular design with a Streamlit interface, facilitating both comprehensive deployment and interactive demonstration. This paradigm enhances the creation of reliable AI systems for clinical decision assistance in oncology by the integration of strong interpretability, personalised risk assessment, and smooth multimodal integration. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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9 pages, 1355 KB

Open AccessProceeding Paper

Modeling and Forecasting the Real Effective Exchange Rate in Morocco: A Comparative Analysis by ARIMA, Random Forest and the Dynamic Factor Model

by Souad Baya, Abdellali Fadlallah, Hamza El Baraka, Khalil Bourouis and Majdouline Ezzraouli

Eng. Proc. 2025, 112(1), 53; https://doi.org/10.3390/engproc2025112053 - 28 Oct 2025

Viewed by 1440

Abstract

This paper presents a comparative empirical analysis of three modeling approaches applied to the forecasting of Morocco’s Real Effective Exchange Rate (REER): the ARIMA model, the Random Forest algorithm, and the Dynamic Factor Model (DFM). Utilizing a comprehensive macroeconomic quarterly dataset spanning from [...] Read more.

This paper presents a comparative empirical analysis of three modeling approaches applied to the forecasting of Morocco’s Real Effective Exchange Rate (REER): the ARIMA model, the Random Forest algorithm, and the Dynamic Factor Model (DFM). Utilizing a comprehensive macroeconomic quarterly dataset spanning from 1999Q4 to 2021Q3, the study assesses the out-of-sample predictive performance of these models over a structurally dynamic period, including the transition to a more flexible exchange rate regime in 2018 and the global shock induced by the COVID-19 pandemic. The findings reveal that the Random Forest model significantly outperforms both ARIMA and DFM in terms of accuracy and adaptability to structural breaks. Variable importance analysis highlights the dominant role of real economic fundamentals, particularly industrial value added, inflation, and exports in explaining REER movements. In contrast, the ARIMA model underreacts to exogenous shocks due to its univariate structure, while the DFM suffers from a loss of predictive power likely caused by excessive dimensionality reduction. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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10 pages, 831 KB

Open AccessProceeding Paper

Bridging the Gap Between Traditional Process Mining and Object-Centric Process Mining

by Hamza Moumad and Maryam Radgui

Eng. Proc. 2025, 112(1), 54; https://doi.org/10.3390/engproc2025112054 - 28 Oct 2025

Viewed by 1583

Abstract

Process mining has become an essential technique for analyzing and optimizing business processes by leveraging digital traces recorded by enterprise systems. However, traditional process mining methods rely heavily on the concept of case identifiers, assuming that each event is associated with only one [...] Read more.

Process mining has become an essential technique for analyzing and optimizing business processes by leveraging digital traces recorded by enterprise systems. However, traditional process mining methods rely heavily on the concept of case identifiers, assuming that each event is associated with only one process instance. This assumption often limits their applicability in complex, real-world environments where multiple objects interact concurrently. This study seeks to connect conventional process mining approaches with the growing domain of object-centric process mining, which provides a broader perspective by considering events linked to multiple business entities. We review the conceptual foundations of both approaches and identify the challenges in transitioning from a case-centric to an object-centric perspective. Our findings demonstrate that object-centric process mining provides richer insights into interconnected process behavior. We conclude that object-centric paradigms mark a significant advancement in process analytics, paving the way for more adaptive and intelligent process improvement frameworks. This study not only bridges conventional process mining approaches with the emerging field of object-centric process mining (OC-PM) but also explores how recent advancements, particularly in Generative AI, are being leveraged within OC-PM frameworks. Specifically, we highlight approaches that integrate Generative AI techniques, including Large Language Models (LLMs), to enhance process understanding and prediction. The integration of AI—especially Generative AI—enables researchers and practitioners to move beyond the limitations and challenges of classical, case-centric process mining, offering more flexible, intelligent, and context-aware process analysis capabilities. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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10 pages, 853 KB

Open AccessProceeding Paper

Enhancing Machine Learning Model Prediction with Feature Selection for Botnet Intrusion Detection

by Marwa Baich and Nawal Sael

Eng. Proc. 2025, 112(1), 55; https://doi.org/10.3390/engproc2025112055 - 29 Oct 2025

Cited by 1 | Viewed by 1065

Abstract

Increased vulnerabilities brought about by the explosive growth of the Internet of Things (IoT) call for improved security measures to protect systems from attacks. Intrusion Detection Systems (IDS) that use machine learning (ML) are essential for identifying vulnerabilities. Among various threats, botnets are [...] Read more.

Increased vulnerabilities brought about by the explosive growth of the Internet of Things (IoT) call for improved security measures to protect systems from attacks. Intrusion Detection Systems (IDS) that use machine learning (ML) are essential for identifying vulnerabilities. Among various threats, botnets are particularly challenging due to their persistence and complexity. This study explores the application of ML techniques (RF, NB, DT, KNN, LR, and XGBoost) for intrusion detection in IoT networks, with a focus on handling imbalanced data and applying feature selection methods. On the Bot-IoT dataset, the study used Lasso feature selection and the SMOTE data balancing technique to obtain a high accuracy of 99.99% with low execution times using the XGBoost model. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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12 pages, 3409 KB

Open AccessProceeding Paper

Urban Traffic in Casablanca: A Novel Dataset and Its Application to Congestion Analysis via Fuzzy Clustering

by Naoufal Rouky, Abdellah Bousouf, Mouhsene Fri, Othmane Benmoussa and Mohamed Amine El Amrani

Eng. Proc. 2025, 112(1), 56; https://doi.org/10.3390/engproc2025112056 - 30 Oct 2025

Cited by 1 | Viewed by 2296

Abstract

Understanding traffic congestion in urban areas is crucial for ensuring mobility, especially in metropolitan cities of developing countries. This study presents new spatial and temporal data to analyze congestion in Casablanca. Spatial data, collected using QGIS, covers 22 ZIP code areas and includes [...] Read more.

Understanding traffic congestion in urban areas is crucial for ensuring mobility, especially in metropolitan cities of developing countries. This study presents new spatial and temporal data to analyze congestion in Casablanca. Spatial data, collected using QGIS, covers 22 ZIP code areas and includes built environment factors such as land use, road types, and public transport stations. Temporal data consists of 440 randomly generated trajectories per commune, with real-time travel data collected hourly over one week using the Waze Route Calculator. A Python script was used to compute the Travel Time Index (TTI) for each zone. To classify zones based on congestion patterns, we applied fuzzy c-means clustering, allowing for nuanced grouping and interpretation of overlapping characteristics. This dataset supports traffic modeling, simulation, and congestion analysis in developing urban contexts. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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9 pages, 1474 KB

Open AccessProceeding Paper

Comparative Study of MRI Modality Embeddings for Glioma Survival Prediction

by Fatima-Ezzahraa Ben-Bouazza, Saadia Azeroual, Bassma Jioudi and Zakaria Hamane

Eng. Proc. 2025, 112(1), 57; https://doi.org/10.3390/engproc2025112057 - 30 Oct 2025

Viewed by 1252

Abstract

Accurately predicting survival within patients diagnosed with diffuse glioma remains one of the most difficult issues in neuro-oncology. While most prior research has focused on multimodal fusion or clinical data, we introduce a modality-specific deep learning framework that employs preoperative MRI only to [...] Read more.

Accurately predicting survival within patients diagnosed with diffuse glioma remains one of the most difficult issues in neuro-oncology. While most prior research has focused on multimodal fusion or clinical data, we introduce a modality-specific deep learning framework that employs preoperative MRI only to predict mortality outcomes using patient MRI scans. Using the UCSF-PDGM dataset containing structural, diffusion, and perfusion imaging of 495 glioma patients, we trained VGG16 models on every MRI modality individually, including T1, T2, FLAIR, SWI, DWI, ASL, HARDI-derived metrics, and segmentation maps. Our findings revealed that segmentation-based and diffusion-derived features, particularly FA or tensor eigenvalues, possessed the greatest predictive strength, surpassing those obtained from standard structural MRI in binary survival classifications. This approach of modality-specific model training allows for clearer explanations of the prediction process compared to fused approaches and is more practical in scenarios where not all types of MRI are performed on patients. This approach demonstrates the strong predictive power of individual MRI sequences for mortality in glioma cases, providing a modular, adaptable, and clinically actionable deep-learning framework. Additional enhancements can incorporate volumetric models, longitudinal imaging, and non-imaging datasets, including genomic and clinical information. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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9 pages, 1263 KB

Open AccessProceeding Paper

New Hospital Management in the Light of Informational Intelligence and Knowledge Management

by Mohammed Ibrahimi and Bouchra Debbagh

Eng. Proc. 2025, 112(1), 58; https://doi.org/10.3390/engproc2025112058 - 30 Oct 2025

Viewed by 1197

Abstract

Today, the right to easy access to medical care for all citizens is one of the universal rights promoted by the WHO to achieve the health goals of sustainable development. Furthermore, an intelligent reevaluation of hospital management is becoming an absolute necessity in [...] Read more.

Today, the right to easy access to medical care for all citizens is one of the universal rights promoted by the WHO to achieve the health goals of sustainable development. Furthermore, an intelligent reevaluation of hospital management is becoming an absolute necessity in light of the pressure that healthcare and public health establishments worldwide face. A management based on the capitalization and exploitation of vast quantities of knowledge. In the 21st century, medicine has already moved to the “in silico” phase, where healthcare professionals must use knowledge bases to make clinical decisions. Indeed, knowledge gains greater value when it’s actively engaged with through dynamic knowledge bases. There is a plethora of research on hospital management, but few studies have approached it from the angle of informational intelligence governed by knowledge management. In this article, we adopt a positivist posture, using deductive logic and the Delphi method based on expert opinion and consensus. We aim to approach hospital management from an informational intelligence perspective, inspired by knowledge representation systems and the object approach. We present an initial vision of the intelligent hospital management model, showing its strengths in relation to its predecessors, as well as its potential uses. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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16 pages, 3690 KB

Open AccessProceeding Paper

Mapping Green Hydrogen Research in North Africa: A Bibliometric Approach for Strategic Foresight

by Hicham Boutracheh, Mouhssine Yassine, Rachid El Ansari and Aniss Moumen

Eng. Proc. 2025, 112(1), 59; https://doi.org/10.3390/engproc2025112059 - 31 Oct 2025

Cited by 1 | Viewed by 1000

Abstract

This bibliometric analysis aims to map the evolution, disciplinary structure, and collaboration dynamics of green hydrogen (GH) research in North Africa from 2019 to 2025. Drawing on a corpus of ~39,000 global publications, indexed in Scopus and analysed through SciVal, we isolate and [...] Read more.

This bibliometric analysis aims to map the evolution, disciplinary structure, and collaboration dynamics of green hydrogen (GH) research in North Africa from 2019 to 2025. Drawing on a corpus of ~39,000 global publications, indexed in Scopus and analysed through SciVal, we isolate and examine the contributions of Egypt, Morocco, Algeria, Tunisia, and Libya. Egypt leads the region with 842 publications and a field-weighted citation impact of 2.42, followed by Morocco (232 Pubs., FWCI 2.30) and Algeria (184 Pubs., FWCI 1.65). Notably, Tunisia exhibits the highest growth factor (41 times since 2019), while Libya remains marginal with only 18 publications in the GH field. The region is well represented in Energy and Environmental fields but is underrepresented in trendy areas such as Materials and Chemical Engineering, highlighting critical gaps in consistency, sophistication, and technical infrastructure. While international collaboration exceeds 69% for most countries, it rarely translates into a high impact compared to the global average. Conversely, the limited industrial collaboration shows the highest citation impact (e.g., Tunisia: 68 citations/publications). A thematic analysis reveals shared strengths in electrolytic hydrogen production and renewable energy integration, with Egypt showing diversification into microalgae and nanocomposites and Morocco excelling in techno-economic assessments and ammonia-based systems. By revealing patterns in research quality, collaboration, and thematic positioning, this study offers evidence-based insights to inform national science strategies, enhance regional cooperation, and position North Africa more strategically in the emerging global green hydrogen economy. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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10 pages, 1756 KB

Open AccessProceeding Paper

Enhancing Urban Mobility: Integrating Multi-LIDAR Tracking and Adaptive Motion Planning for Autonomous Vehicle Navigation in Complex Environments

by Mohamed Bakir, My Abdelkader Youssefi, Rachid Dakir, Mouna El Wafi and Younes El Koudia

Eng. Proc. 2025, 112(1), 60; https://doi.org/10.3390/engproc2025112060 - 3 Nov 2025

Viewed by 1092

Abstract

Deploying autonomous vehicles in urban mobility systems promises significant improvements in safety, efficiency, and sustainability. On the other hand, running these vehicles in the continuously changing and often uncertain conditions of modern cities turns out to be a major challenge. These cars need [...] Read more.

Deploying autonomous vehicles in urban mobility systems promises significant improvements in safety, efficiency, and sustainability. On the other hand, running these vehicles in the continuously changing and often uncertain conditions of modern cities turns out to be a major challenge. These cars need advanced systems that can continuously change in order to observe conditions. This paper puts forward a new way that brings together multiple LIDAR sensors for the real-time spotting and following of objects, along with adaptive motion planning methods made to handle the difficulties of city traffic. Using LIDAR-based mapping for environmental modeling and predictive tracking techniques helps the system build a richly detailed, consistently updating depiction of surroundings that supports accurate and quick decisions. Another feature of the system is dynamic path planning that ensures safe navigation by considering traffic, pedestrian movement, and road conditions. Simulations carried out in highly dense urban scenarios show improvement in collision avoidance, path-planning optimization, and response to environmental dynamics. Such outcomes prove that combining multi-LIDAR tracking and adaptive motion planning contributes significantly to the performance and safety of an autonomous vehicle when operating in very complex urban conditions. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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10 pages, 1268 KB

Open AccessProceeding Paper

A Comprehensive Review of Human Factors and Ergonomics in Industry 5.0

by Hajar Mouhib, Sara Amar, Samah Elrhanimi and Laila El Abbadi

Eng. Proc. 2025, 112(1), 61; https://doi.org/10.3390/engproc2025112061 - 3 Nov 2025

Cited by 1 | Viewed by 4501

Abstract

The transition from Industry 4.0 to Industry 5.0 marks a pivotal shift toward human-centric manufacturing, where advanced technologies are designed to enhance rather than replace human capabilities. Human Factors and Ergonomics (HFE) play a vital role in ensuring that this transformation balances both [...] Read more.

The transition from Industry 4.0 to Industry 5.0 marks a pivotal shift toward human-centric manufacturing, where advanced technologies are designed to enhance rather than replace human capabilities. Human Factors and Ergonomics (HFE) play a vital role in ensuring that this transformation balances both worker well-being and system performance. However, existing research reveals a notable gap in fully understanding HFE’s role across the different levels of human–system interactions. This paper presents a comprehensive literature review, offering key insights and future directions to address this gap and highlighting the need for broader perspectives in designing human-centric systems. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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8 pages, 1010 KB

Open AccessProceeding Paper

Evaluation of Innovative and Sustainable Fire Protection Systems for Reinforced Concrete Structures

by Louai Wafa, Ayman Mosallam and Ashraf Abed-Elkhalek Mostafa

Eng. Proc. 2025, 112(1), 62; https://doi.org/10.3390/engproc2025112062 - 4 Nov 2025

Viewed by 571

Abstract

This study presents a comprehensive overview of recent advancements in fire protection technologies for reinforced concrete (RC) structures, with a focus on sustainable and high-performance solutions. As climate change and urban densification continue to shape modern construction, the need for fire-resilient and environmentally [...] Read more.

This study presents a comprehensive overview of recent advancements in fire protection technologies for reinforced concrete (RC) structures, with a focus on sustainable and high-performance solutions. As climate change and urban densification continue to shape modern construction, the need for fire-resilient and environmentally responsible building systems has never been more urgent. This study examines traditional fire protection practices and contrasts them with emerging innovations. Emphasis is placed on their thermal performance, structural integrity post-exposure, and long-term durability. Case studies and laboratory findings highlight the effectiveness of these systems under standard and severe fire scenarios. This paper will present the results of a research study on the assessment of different fire protection systems for RC columns retrofitted with fiber-reinforced polymer (FRP) jacketing. To quantify how insulation can preserve confinement, three commercial fire protection schemes were tested on small-scale CFRP- and GFRP-confined concrete cylinders: (i) a thin high-temperature cloth + blanket (DYMAT™-RS/Dymatherm), (ii) an intumescent epoxy-based coating (DCF-D + FireFree 88), and (iii) cementitious mortar (Sikacrete™ 213F, 15 mm and 30 mm). Specimens were exposed to either 60 min of soaking at 200 °C and 400 °C or to a 30 min and 240 min ASTM E119 standard fire; thermocouples recorded interface temperatures and post-cooling uniaxial compression quantified residual capacity. All systems reduced FRP–interface temperatures by up to 150 °C and preserved 65–90% of the original confinement capacity under moderate fire conditions (

\leq

400 °C and

\leq

30 min ASTM E119) compared to 40–55% for unprotected controls under the same conditions. The results provide practical guidance on selecting insulation types and thicknesses for fire-resilient FRP retrofits. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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11 pages, 744 KB

Open AccessProceeding Paper

A Deep Learning Framework for Early Detection of Potential Cardiac Anomalies via Murmur Pattern Analysis in Phonocardiograms

by Aymane Edder, Fatima-Ezzahraa Ben-Bouazza, Oumaima Manchadi, Youssef Ait Bigane, Djeneba Sangare and Bassma Jioudi

Eng. Proc. 2025, 112(1), 63; https://doi.org/10.3390/engproc2025112063 - 31 Oct 2025

Viewed by 1089

Abstract

Heart murmurs, resulting from turbulent blood flow within the cardiac structure, represent some of the initial acoustic manifestations of potential underlying cardiovascular anomalies, such as arrhythmias. This research presents a deep learning framework aimed at the early detection of potential cardiac anomalies through [...] Read more.

Heart murmurs, resulting from turbulent blood flow within the cardiac structure, represent some of the initial acoustic manifestations of potential underlying cardiovascular anomalies, such as arrhythmias. This research presents a deep learning framework aimed at the early detection of potential cardiac anomalies through the analysis of murmur patterns in phonocardiogram (PCG) signals. Our methodology employs a spectro-temporal feature fusion technique that integrates Mel spectrograms, Mel Frequency Cepstral Coefficients (MFCCs), Root Mean Square (RMS) energy, and Power Spectral Density (PSD) representations. The features are derived from segmented 5-second phonocardiogram (PCG) windows and subsequently input into a two-dimensional convolutional neural network (CNN) for the purpose of classification. In order to mitigate class imbalance and enhance generalization, We employ data augmentation techniques, including pitch moving and noise injection. The model under consideration has undergone training and evaluation utilizing a carefully selected subset of the CirCor DigiScope dataset. The experimental findings indicate a robust performance, with a classification accuracy recorded at 92.40% and a cross-entropy loss measured at 0.2242. The results indicate that an analysis of PCG signals informed by murmurs may function as an effective non-invasive method for the early screening of conditions that may include arrhythmias, particularly in clinical environments with limited resources. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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7 pages, 884 KB

Open AccessProceeding Paper

Medical Specialty Classification: An Interactive Application with Iterative Improvement for Patient Triage

by Anas Chahid, Ismail Chahid, Mohamed Emharraf and Mohammed Ghaouth Belkasmi

Eng. Proc. 2025, 112(1), 64; https://doi.org/10.3390/engproc2025112064 - 4 Nov 2025

Viewed by 566

Abstract

The challenge of accurately identifying the appropriate medical specialty based on patient symptoms leads to delays in diagnosis and treatment. This paper presents an AI model developed to classify medical specialties from symptom descriptions. The model, implemented with BERT, hosted via a Python-based [...] Read more.

The challenge of accurately identifying the appropriate medical specialty based on patient symptoms leads to delays in diagnosis and treatment. This paper presents an AI model developed to classify medical specialties from symptom descriptions. The model, implemented with BERT, hosted via a Python-based Flask API v3, and integrated with an interactive frontend application, allows users to input symptoms textually or interactively select affected body parts and answer multiple choice questions. Following deployment, feedback data from doctors and residents was collected and utilized to enhance the model performance, supplemented by additional data from online medical forums. This study demonstrates significant improvements in finding the correct medical specialty, contributing to more efficient patient triage, reducing the time to diagnose and treat patients, and eliminating the presence of doctors in the initial process as they are often busy in emergency departments. The use of generative AI and large language models, notably BERT, is highlighted as a key factor in the model’s success. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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13 pages, 4149 KB

Open AccessProceeding Paper

A Multimodal Deep Learning Pipeline for Enhanced Detection and Classification of Oral Cancer

by Idriss Tafala, Fatima-Ezzahraa Ben-Bouazza, Manal Chakour El Mezali, Ilyass Emssaad and Bassma Jioudi

Eng. Proc. 2025, 112(1), 65; https://doi.org/10.3390/engproc2025112065 - 4 Nov 2025

Viewed by 1706

Abstract

Oral cancer represents a life-threatening malignancy with profound implications for patient survival and quality of life. Oral squamous cell carcinoma (OSCC), the predominant histological variant of oral cancer, constitutes a substantial healthcare challenge wherein early detection remains critical for therapeutic efficacy and enhanced [...] Read more.

Oral cancer represents a life-threatening malignancy with profound implications for patient survival and quality of life. Oral squamous cell carcinoma (OSCC), the predominant histological variant of oral cancer, constitutes a substantial healthcare challenge wherein early detection remains critical for therapeutic efficacy and enhanced survival outcomes. Recent advances in deep learning methodologies have demonstrated superior performance in medical imaging applications. However, existing investigations have predominantly employed unimodal image data for oral lesion classification, thereby neglecting the potential advantages of multimodal data integration. To address this limitation, we propose a comprehensive multimodal pipeline for the classification of OSCC versus leukoplakia through the integration of histopathological imagery with tabular data encompassing anatomical characteristics and behavioral risk factors. Our methodology achieved a precision of 0.97, F1-score of 0.97, recall of 0.98, and accuracy of 0.97. These findings demonstrate the enhanced diagnostic precision and efficacy afforded by multimodal approaches in oral cancer classification, suggesting a promising avenue for improved diagnostic accuracy and treatment planning optimization. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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14 pages, 610 KB

Open AccessProceeding Paper

Integrated Production–Distribution Planning for Paper Manufacturing Under Fuzzy Uncertainty

by Yassine Boutmir, Rachid Bannari, Fayçal Fedouaki, Abdelfettah Bannari and Achraf Touil

Eng. Proc. 2025, 112(1), 66; https://doi.org/10.3390/engproc2025112066 - 5 Nov 2025

Cited by 1 | Viewed by 880

Abstract

The paper manufacturing industry faces significant challenges in coordinating production and distribution decisions under uncertain market conditions. This research presents an integrated production–distribution planning model for paper manufacturing that addresses demand uncertainty through fuzzy set theory. The model considers multiple paper grades, production [...] Read more.

The paper manufacturing industry faces significant challenges in coordinating production and distribution decisions under uncertain market conditions. This research presents an integrated production–distribution planning model for paper manufacturing that addresses demand uncertainty through fuzzy set theory. The model considers multiple paper grades, production facilities, warehouses, and customer zones while minimizing total supply chain costs. A hybrid intelligent algorithm combining genetic algorithms with fuzzy simulation is developed to solve the complex optimization problem. The approach handles fuzzy demand parameters using credibility theory and employs Monte Carlo simulation for fuzzy variable evaluation. Computational experiments demonstrate the effectiveness of the proposed methodology, achieving cost reductions of 12–18% compared to traditional deterministic approaches. The results indicate that the fuzzy–genetic algorithm approach provides robust solutions that perform well under various uncertainty scenarios, making it suitable for practical implementation in paper manufacturing supply chains. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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9 pages, 257 KB

Open AccessProceeding Paper

Integrating Model-Driven Engineering with Machine Learning for Intelligent Systems: Literature Review

by Kaouthar Elgueddari, Zineb Aarab, Achraf Lyazidi and Adil Anwar

Eng. Proc. 2025, 112(1), 67; https://doi.org/10.3390/engproc2025112067 - 5 Nov 2025

Viewed by 1705

Abstract

The rapid development of intelligent systems has created a need for techniques capable of handling complexities and developing in an automatic way. The integration of machine learning in model-driven engineering (MDE) offers several advantages for the development and improvement of complex and intelligent [...] Read more.

The rapid development of intelligent systems has created a need for techniques capable of handling complexities and developing in an automatic way. The integration of machine learning in model-driven engineering (MDE) offers several advantages for the development and improvement of complex and intelligent systems. While machine learning (ML) also offers robust techniques and MDE has systematic approaches aimed at code generation and abstraction, in this review, while presenting the principles of MDE and ML, the article also critically explores the integration of ML in MDE. Starting with the fundamental concepts of MDE, then the principles and algorithms of ML, the focus of the discussion is on how machine learning techniques can improve model-driven engineering processes. By presenting the motivations for their combined use in the development of intelligent systems, based on the recent literature, the article describes the challenges and potential future directions, noting that the integration of machine learning into model-driven engineering not only accelerates development but also enhances the adaptability and performance of intelligent and complex systems, making it an increasingly relevant approach to addressing the complexities of modern intelligent systems. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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14 pages, 345 KB

Open AccessProceeding Paper

Bi-Objective Production–Distribution Planning for Paper Manufacturing: A Credibility-Based Expected Value Approach

by Yassine Boutmir, Rachid Bannari, Abdelfettah Bannari, Fayçal Fedouaki and Achraf Touil

Eng. Proc. 2025, 112(1), 68; https://doi.org/10.3390/engproc2025112068 - 12 Nov 2025

Viewed by 507

Abstract

The paper manufacturing industry faces increasing challenges in balancing operational costs with service quality under uncertain market conditions. This research presents a bi-objective credibility-based expected value model for integrated production–distribution planning that simultaneously minimizes total costs and maximizes service-level performance. The model considers [...] Read more.

The paper manufacturing industry faces increasing challenges in balancing operational costs with service quality under uncertain market conditions. This research presents a bi-objective credibility-based expected value model for integrated production–distribution planning that simultaneously minimizes total costs and maximizes service-level performance. The model considers multiple paper grades, production facilities, warehouses, and customer zones while handling demand uncertainty through credibility theory. Three additional constraints are introduced: service time limitations, capacity expansion decisions, and quality assurance requirements. The Torabi–Hassini (TH) method is employed to solve the bi-objective optimization problem effectively. Computational experiments demonstrate the model’s capability to provide balanced trade-off solutions between cost efficiency and service quality, achieving service-level improvements of 8–13% with cost increases of 5–9% compared to cost-only optimization, and cost reductions of 10–15% compared to service-only optimization. The results show that the credibility-based expected value approach provides robust and practical solutions for paper manufacturing supply chain optimization. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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13 pages, 285 KB

Open AccessProceeding Paper

Multi-Objective Optimization for After-Sales Service Technician Scheduling: An Integrated Mixed-Integer Programming Approach

by Chaimaa Essabar and Achraf Touil

Eng. Proc. 2025, 112(1), 69; https://doi.org/10.3390/engproc2025112069 - 8 Nov 2025

Viewed by 621

Abstract

This paper presents an after-sales service optimization problem (ASOP) that integrates technician scheduling with spare parts inventory management, multi-day planning horizons, team coordination requirements, and customer satisfaction modeling. We developed a comprehensive mixed-integer programming model that simultaneously optimizes technician assignments, inventory allocation, team [...] Read more.

This paper presents an after-sales service optimization problem (ASOP) that integrates technician scheduling with spare parts inventory management, multi-day planning horizons, team coordination requirements, and customer satisfaction modeling. We developed a comprehensive mixed-integer programming model that simultaneously optimizes technician assignments, inventory allocation, team collaborations, and emergency response capabilities while maintaining service-level agreement (SLA) compliance. The model includes novel constraints for equipment availability, certification requirements, spare parts consumption, and dynamic customer priority adjustments. Computational experiments on test instances demonstrated 50.0% SLA compliance with 25.8% average technician utilization across 12 service requests, 6 technicians, and a 3-day planning horizon. The integrated approach achieved an 18% cost reduction compared to sequential optimization while improving the customer satisfaction scores by 13.9%. A sensitivity analysis revealed critical trade-offs between inventory holding costs, team coordination benefits, and service quality metrics. The proposed framework provides comprehensive decision support for modern after-sales service operations requiring integrated resource management. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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10 pages, 565 KB

Open AccessProceeding Paper

Predictive Maintenance Approaches: A Systematic Literature Review

by Zeineb El Hammoumi, Dounia Tebr, Youssef Charkaoui, Imane Satauri and Omar El Beqqali

Eng. Proc. 2025, 112(1), 70; https://doi.org/10.3390/engproc2025112070 - 11 Nov 2025

Cited by 1 | Viewed by 5940

Abstract

Since increasing attention has been given to predictive maintenance (PdM) of industrial equipment, in order to enhance operational efficiency, improve reliability, and reduce downtime, this powerful strategy offers significant benefits, holds clearly great promises, and is now regarded as a key for future [...] Read more.

Since increasing attention has been given to predictive maintenance (PdM) of industrial equipment, in order to enhance operational efficiency, improve reliability, and reduce downtime, this powerful strategy offers significant benefits, holds clearly great promises, and is now regarded as a key for future perspective in Industry 4.0. There are various approaches to PdM, each offering its own set of advantages and disadvantages which are single and hybrid approaches to carrying out diagnostics and prognostics in PdM. In this paper we will compare these approaches according to different aspects such as complexity of data and interpretability of results. Moreover, we also discuss the barriers to successful adoption, such as data quality, system complexity, and the need for workforce training. Finally, this paper concludes by identifying future research directions in response to scientific problems, which will drive the next wave of innovation in predictive maintenance solutions. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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9 pages, 433 KB

Open AccessProceeding Paper

Contextual Modeling and Intelligent Decision-Making for IoT Systems: A Combined Ontology and Machine Learning Approach

by Sanaa Mouhim

Eng. Proc. 2025, 112(1), 71; https://doi.org/10.3390/engproc2025112071 - 18 Nov 2025

Viewed by 855

Abstract

In the context of the Internet of Things (IoT), this article proposes an innovative approach combining ontologies and the Apache Spark MLlib library to design an intelligent system capable of dynamically adapting to its environment. The aim is to model the context including [...] Read more.

In the context of the Internet of Things (IoT), this article proposes an innovative approach combining ontologies and the Apache Spark MLlib library to design an intelligent system capable of dynamically adapting to its environment. The aim is to model the context including users, devices, events, and environmental conditions, and exploit massive sensor data to generate intelligent, contextualized predictions. The architecture relies on two pillars: an ontology as a formal way to structure and semantically annotate knowledge and Spark MLlib in order to execute big data machine learning algorithms and notably random forest regression. The solution is targeted to real-time applications such as energy or air quality management in smart homes. The results demonstrate the value of combining ontology and machine learning in order to improve contextual knowledge and automatic decision-making. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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10 pages, 960 KB

Open AccessProceeding Paper

SQL and NoSQL Databases: A Comparative Study with Perspectives on IA-Based Migration Approach

by Soukayna Abdellaoui, Wafae Abbaoui, Loubna Meziane, Brahim El Bhiri and Soumia Ziti

Eng. Proc. 2025, 112(1), 72; https://doi.org/10.3390/engproc2025112072 - 20 Nov 2025

Viewed by 5638

Abstract

Traditional relational databases have been widely used for many years and have been the go-to choice for the industry. However, the emergence of NoSQL databases coincided with the increasing popularity of the Internet, social networks, and cloud computing. Data migration has become a [...] Read more.

Traditional relational databases have been widely used for many years and have been the go-to choice for the industry. However, the emergence of NoSQL databases coincided with the increasing popularity of the Internet, social networks, and cloud computing. Data migration has become a crucial topic, due to the rapid growth of applications and the ever-expanding amount of data being collected. This has led to a shift from relational database management systems (RDBMS) to NoSQL databases, also known as not only SQL. This article compares the characteristics of both database architectures, SQL and NoSQL, focusing on aspects such as scalability and performance, flexibility, and security. Additionally, the role of AI in streamlining the migration process will be explored. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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8 pages, 369 KB

Open AccessProceeding Paper

A Comparative Analysis of Immunity-Inspired Cybersecurity Approaches

by Abir Bala, Brahim El Bhiri, Ayoub Bahnasse and Mouaad Mohy-Eddine

Eng. Proc. 2025, 112(1), 73; https://doi.org/10.3390/engproc2025112073 - 27 Nov 2025

Viewed by 1321

Abstract

Cybersecurity has evolved significantly over the years, with a growing interest in biologically inspired models that emulate the immune system’s defense mechanisms. This paper provides a comparative analysis of various immunity-based approaches in cybersecurity, tracking their progression from their inception to the present. [...] Read more.

Cybersecurity has evolved significantly over the years, with a growing interest in biologically inspired models that emulate the immune system’s defense mechanisms. This paper provides a comparative analysis of various immunity-based approaches in cybersecurity, tracking their progression from their inception to the present. It explores the strengths and limitations of these methods across different cybersecurity areas, such as intrusion detection, malware analysis, and network protection. By reviewing foundational research, recent advancements, and existing challenges, this study aims to offer a well-rounded perspective on the effectiveness and constraints of immunity-driven strategies in protecting modern digital infrastructure. Additionally, it highlights emerging trends and future directions, stressing the importance of integrating these approaches with machine learning and other advanced technologies to strengthen cybersecurity resilience. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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10 pages, 1458 KB

Open AccessProceeding Paper

Blockchain and Industrial Traceability: Insights from a Systematic Literature Review Within Industry 4.0 Contexts

by Khira Belgada and Laila El Abbadi

Eng. Proc. 2025, 112(1), 74; https://doi.org/10.3390/engproc2025112074 - 3 Dec 2025

Cited by 1 | Viewed by 1299

Abstract

In the context of Industry 4.0, industrial firms are encountering new challenges related to data management, flow traceability, security and process transparency. Blockchain, as a distributed ledger technology, offers innovative solutions to meet these challenges. This study proposes a systematic literature review (SLR) [...] Read more.

In the context of Industry 4.0, industrial firms are encountering new challenges related to data management, flow traceability, security and process transparency. Blockchain, as a distributed ledger technology, offers innovative solutions to meet these challenges. This study proposes a systematic literature review (SLR) on the recent contributions of blockchain in industrial environments. A total of 20 scientific articles, published over the last ten years, were analyzed to better understand how this technology is being integrated into production processes and supply chains. The analysis identified four major areas in which blockchain is being mobilized: traceability of production processes, transparency of supply chains, integration into digital industrial systems, and its role in decision support. The results show that blockchain enables reliable, real-time monitoring of industrial operations, particularly when coupled with technologies such as IoT, smart contracts or event-driven databases. It also promotes better coordination between players, reinforces trust, and facilitates audits in complex or multi-actor environments. However, despite its potential, several limitations remain. Barriers related to scalability, implementation costs, system interoperability and the integration of manual tasks still limit its widespread adoption. Furthermore, in many cases, blockchain is treated as a secondary technology, reducing the depth of analysis available. This review offers a structured vision of the contributions and limitations of blockchain in industry while identifying future research prospects, particularly around hybrid models and concrete implementation cases. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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10 pages, 488 KB

Open AccessProceeding Paper

Enhancing Critical Industrial Processes with Artificial Intelligence Models

by Karim Amzil, Rajaa Saidi and Walid Cherif

Eng. Proc. 2025, 112(1), 75; https://doi.org/10.3390/engproc2025112075 - 8 Dec 2025

Cited by 1 | Viewed by 1241

Abstract

This review explores the deployment of Artificial Intelligence (AI) technologies to augment key industry processes in the new paradigm of Industry 5.0. Based on a handpicked collection of 35 peer-reviewed articles and leading resources, the study integrates the latest breakthroughs in Machine Learning [...] Read more.

This review explores the deployment of Artificial Intelligence (AI) technologies to augment key industry processes in the new paradigm of Industry 5.0. Based on a handpicked collection of 35 peer-reviewed articles and leading resources, the study integrates the latest breakthroughs in Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), and Federated Learning (FL) with their applications in predictive maintenance, process planning, real-time monitoring, and operational excellence. The results emphasize AI’s central role in making manufacturing smarter, minimizing system downtime, and facilitating decision-making based on information in various industries like aerospace, energy, and intelligent manufacturing. Yet, the review also highlights significant challenges, ranging from data heterogeneity to model interpretability, security risks, and the ethics of automation. Solutions in the making, including Explainable AI (XAI), privacy-enhancing collaborative models, and enhanced cybersecurity protocols, are postulated to be the key drivers for the development of dependable and resilient industrial AI systems. The study concludes by postulating directions for further research and practice to secure the safe, transparent, and human-centered deployment of AI in industrial settings. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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11 pages, 569 KB

Open AccessProceeding Paper

Adopting the Internet of Things and Big Data in Real-Time for Customer Acquisition in a Cloud Environment: An Exploratory Literature Review

by Youssef Charkaoui, Dounia Tebr, Zeineb El Hammoumi, Imane Satauri and Omar El Beqqali

Eng. Proc. 2025, 112(1), 76; https://doi.org/10.3390/engproc2025112076 - 8 Dec 2025

Viewed by 1044

Abstract

In this age of consumerism, most companies are doing their utmost to convince their customers of their products and to attract new customers. The IT development we see today is a perfect solution for strengthening the relationship between companies and their customers, giving [...] Read more.

In this age of consumerism, most companies are doing their utmost to convince their customers of their products and to attract new customers. The IT development we see today is a perfect solution for strengthening the relationship between companies and their customers, giving them the opportunity to expand their customer base. The Internet of Things refers to an inter-connected system of smart devices that communicate and exchange data and big data analytics over the internet. As this involves the process of the treating data to unlock hidden information, patterns, and insights, the combination of both tools creates a revolution in customer relations and gives us the opportunity to understand our customers’ needs before they do themselves. This article presents an exploratory literature review of studies analyzing the relationship between IOT and big data in marketing. It provides a deep analysis of various scholars’ works that examine the methodology used by these tools to reinforce customer relations and acquire new ones. This review provides an overview of the most interesting research on this topic and the methods and techniques employed as well as an analysis of the obstacles and challenges involved. The results of this research show that IOT and big data analytics are key factors for an efficient analysis of clients’ needs. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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10 pages, 419 KB

Open AccessProceeding Paper

Digital Transformation Meets Process Improvement: Driving Enterprise Continuous Efficiency-Seeking

by Najib Ouradi, Rajaa Saidi and Walid Cherif

Eng. Proc. 2025, 112(1), 77; https://doi.org/10.3390/engproc2025112077 - 11 Dec 2025

Viewed by 983

Abstract

Automation and digitization have gained every aspect of our life. Performance and digital are precepted as twins, and thus process improvement (i.e., performance continuous seek) has fatally met digital. Digital transformation implies a good understanding of processes, their optimization, and the common leitmotiv [...] Read more.

Automation and digitization have gained every aspect of our life. Performance and digital are precepted as twins, and thus process improvement (i.e., performance continuous seek) has fatally met digital. Digital transformation implies a good understanding of processes, their optimization, and the common leitmotiv behind the methods used to improve them (continuous improvement). Beyond the existing methods and tools for process improvement lays the need to work under the same “roof” process optimization and digital transformation. This paper presents the methods for process assessment and process improvement and highlights the link between the process of digital transformation. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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8 pages, 348 KB

Open AccessProceeding Paper

A PSO-Driven Hyperparameter Optimization Approach for GRU-Based Traffic Flow Prediction

by Imane Briki, Rachid Ellaia and Maryam Alami Chentoufi

Eng. Proc. 2025, 112(1), 78; https://doi.org/10.3390/engproc2025112078 - 12 Dec 2025

Viewed by 802

Abstract

Smart cities increasingly rely on intelligent technologies to improve urban infrastructure, sustainability, and quality of life. Traffic flow prediction is essential for the optimization of the transportation system, reducing congestion and improving mobility. However, real-world traffic data are often noisy, limited in size, [...] Read more.

Smart cities increasingly rely on intelligent technologies to improve urban infrastructure, sustainability, and quality of life. Traffic flow prediction is essential for the optimization of the transportation system, reducing congestion and improving mobility. However, real-world traffic data are often noisy, limited in size, and lack sufficient features to capture the flow dynamics and temporal dependencies, making accurate prediction a significant challenge. Previous studies have shown that recurrent neural network (RNN) variants, such as LSTM and GRU, are well-suited for time series forecasting tasks, but their performance is highly sensitive to hyperparameter settings. This study proposes a hybrid approach that integrates GRU with a metaheuristic optimization algorithm to address this challenge. After effective preprocessing steps and a sliding time window are applied to structure the data, particle swarm optimization (PSO) is utilized to optimize the hyperparameters of the GRU. The model’s performance is evaluated using RMSE, MAE, and

R^{2}

, and compared against several baseline approaches, including LSTM, CNN-LSTM, and a manually configured GRU. According to the experimental findings, the GRU model that was manually adjusted performed the best overall. However, the PSO-GRU model demonstrated competitive results, confirming that metaheuristics offer a promising alternative when manual tuning is not feasible despite the higher computational costs. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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14 pages, 990 KB

Open AccessProceeding Paper

Localization of Unknown Nodes on UWSN Using the Linear Constraint Optimization Technique Based on Energy and Distance (LUCOTED)

by Hamid Ouidir, Amine Berqia and Siham Aouad

Eng. Proc. 2025, 112(1), 79; https://doi.org/10.3390/engproc2025112079 - 16 Dec 2025

Cited by 1 | Viewed by 449

Abstract

Underwater Wireless Sensor Networks (UWSNs) are widely used technologies in aquatic environments. However, these types of networks face several constraints caused by the mobility of nodes, energy consumption, and constraints due to acoustic communication. In light of this, the location of nodes appears [...] Read more.

Underwater Wireless Sensor Networks (UWSNs) are widely used technologies in aquatic environments. However, these types of networks face several constraints caused by the mobility of nodes, energy consumption, and constraints due to acoustic communication. In light of this, the location of nodes appears as a promising axis for improving the services expected from these networks. To address these, we suggest the LUCOTED approach—a Linear Constraint Optimization Technique for estimating unknown node positions by selecting anchor nodes with the highest energy and shortest distance, based on randomly initialized conditions. It achieves 98% accuracy, exceeding Gradient Descent and Trilateration methods. Moreover, our method LUCOTED outperforms the DEEC algorithm in terms of error when the number of anchor nodes is below 80 and achieves higher accuracy than the EPRP technique when the number exceeds 100. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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11 pages, 218 KB

Open AccessProceeding Paper

Predictive Maintenance in Pharma Manufacturing: Challenges and Strategic Directions

by Oumaima Manchadi, Fatima-Ezzahraa Ben-Bouazza, Aymane Edder, Idriss Tafala and Bassma Jioudi

Eng. Proc. 2025, 112(1), 80; https://doi.org/10.3390/engproc2025112080 - 14 Jan 2026

Cited by 2 | Viewed by 1563

Abstract

Predictive maintenance (PdM) has emerged as a critical enabler for improving reliability, operational efficiency, and regulatory compliance in pharmaceutical manufacturing. Despite its proven effectiveness in several industrial sectors, PdM adoption within the pharmaceutical industry remains comparatively limited due to sector-specific technical, regulatory, and [...] Read more.

Predictive maintenance (PdM) has emerged as a critical enabler for improving reliability, operational efficiency, and regulatory compliance in pharmaceutical manufacturing. Despite its proven effectiveness in several industrial sectors, PdM adoption within the pharmaceutical industry remains comparatively limited due to sector-specific technical, regulatory, and organizational constraints. This paper presents a structured technical analysis of recent academic and industrial works addressing PdM implementation in pharmaceutical manufacturing systems. The analysis examines applied AI and machine learning techniques, sensor and data acquisition strategies, implementation maturity, and regulatory considerations relevant to highly regulated environments. The findings indicate that, while PdM solutions can significantly improve equipment availability and reduce maintenance-related costs, major barriers persist, including limited failure data, validation and re-validation requirements, and organizational resistance to data-driven maintenance practices. Based on this analysis, the paper argues that hybrid approaches combining physics-based models (PBMs) with data-driven methods and explainable artificial intelligence (XAI), supported by digital twins and robust data governance frameworks, represent a practical and regulation-aware pathway for the broader adoption of predictive maintenance in pharmaceutical manufacturing Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

10 pages, 452 KB

Open AccessProceeding Paper

A Generic Model Integrating Machine Learning and Lean Six Sigma

by Fadwa Farchi, Chayma Farchi, Badr Touzi and Charif Mabrouki

Eng. Proc. 2025, 112(1), 81; https://doi.org/10.3390/engproc2025112081 - 19 Jan 2026

Cited by 1 | Viewed by 563

Abstract

With rapid urbanization and population growth, efficient transportation systems are increasingly crucial, particularly in sectors like healthcare and pharmaceutical logistics, which face unique challenges. In Morocco, there is a lack of studies on pharmaceutical transport, especially regarding costs and delivery conditions, creating a [...] Read more.

With rapid urbanization and population growth, efficient transportation systems are increasingly crucial, particularly in sectors like healthcare and pharmaceutical logistics, which face unique challenges. In Morocco, there is a lack of studies on pharmaceutical transport, especially regarding costs and delivery conditions, creating a need for a specialized model. This research presents the development and validation of a predictive model for optimizing urban transport in Morocco. Tested across key sectors—pharmaceuticals, agri-food, electronics, and manufactured goods—the model demonstrated strong performance, though variations emerged based on product complexity. Notably, the agri-food sector presented greater logistical challenges, while the manufacturing and electronics sectors yielded higher prediction accuracy. By integrating statistical process control (SPC) and Lean Six Sigma principles, the model ensures ongoing performance monitoring and continuous improvement. It supports cost reduction, time optimization, and lower environmental impact through enhanced route planning and delivery efficiency. The pharmaceutical sector was selected as a case study due to its critical logistical constraints, such as cold chain requirements and the need for high reliability. Python was used for model development, enabling rapid iteration and collaborative validation. The results confirm the model’s adaptability and generalizability to similar urban environments across North and Sub-Saharan Africa. The study offers a robust and scalable framework for improving transport efficiency while aligning with sustainability and smart mobility goals. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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10 pages, 1591 KB

Open AccessProceeding Paper

A Comprehensive Sustainable Performance Assessment in Morocco’s Mining Sector Using Artificial Neural Networks and the Fuzzy Analytic Network Process

by Chayma Farchi, Fadwa Farchi, Badr Touzi and Ahmed Mousrij

Eng. Proc. 2025, 112(1), 82; https://doi.org/10.3390/engproc2025112082 - 6 Feb 2026

Viewed by 356

Abstract

This article provides an in-depth evaluation of sustainability performance within the mining sector by employing the Fuzzy Analytic Network Process (FANP). The assessment centers on five fundamental dimensions: economic, social, environmental, operational, and stakeholder-related factors. FANP facilitates a comprehensive prioritization of both these [...] Read more.

This article provides an in-depth evaluation of sustainability performance within the mining sector by employing the Fuzzy Analytic Network Process (FANP). The assessment centers on five fundamental dimensions: economic, social, environmental, operational, and stakeholder-related factors. FANP facilitates a comprehensive prioritization of both these broad categories and their associated sub-criteria, enabling a well-structured and balanced appraisal of sustainable performance. The methodology is further strengthened by integrating machine learning techniques, specifically a multilayer perceptron, which improves the accuracy and reliability of the multidimensional performance evaluation. Although the study concentrates on the mining industry in Morocco, the developed model is flexible and can be adapted to various other industries and research fields. By filling a significant gap in holistic sustainability assessment, this work offers valuable practical insights to support enhanced management practices and contributes meaningfully to the advancement of sustainable development goals. The findings and approach presented are pertinent to both industry professionals and the academic community alike. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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9 pages, 678 KB

Open AccessProceeding Paper

Building a Holistic Performance Index for Construction Projects

by Emad Elwakil and Mohamed Hegab

Eng. Proc. 2025, 112(1), 83; https://doi.org/10.3390/engproc2025112083 - 24 Feb 2026

Viewed by 577

Abstract

In the building sector, time and cost overruns are still ongoing difficulties; hence, good project management depends critically on accurate evaluation of project performance. Usually, project success is measured in several performance criteria: cost, schedule, quality, safety, and others as well. This research [...] Read more.

In the building sector, time and cost overruns are still ongoing difficulties; hence, good project management depends critically on accurate evaluation of project performance. Usually, project success is measured in several performance criteria: cost, schedule, quality, safety, and others as well. This research suggests the construction of a thorough Project Performance Index (PPI) methodically combining these important performance criteria. One finds the relative weight of every element by means of a frequency-based analysis of their occurrence in the current literature. The final index presents a complete method for assessing and contrasting the performance of building projects, giving researchers and practitioners trying to improve project results a helpful instrument. Full article

(This article belongs to the Proceedings of 7th Edition of the International Conference on Advanced Technologies for Humanity (ICATH 2025))

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