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Open AccessEditorial

Preface of the ETLTC 2025 Conference Series

by Debopriyo Roy, George Fragulis and Peter Ilic

Eng. Proc. 2025, 107(1), 1; https://doi.org/10.3390/engproc2025107001 - 20 Aug 2025

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Abstract

We are pleased to present the proceedings of the ETLTC–ICETM2025 International Conference Series, held from January 20 to 26, 2025, in Aizuwakamatsu, Japan, and hosted in hybrid format by the University of Aizu [...] Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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12 pages, 958 KB

Open AccessProceeding Paper

Exploring the Limits of LLMs in Simulating Partisan Polarization with Confirmation Bias Prompts

by Masashi Sakurai, Kento Ueta and Yasuhiro Hashimoto

Eng. Proc. 2025, 107(1), 2; https://doi.org/10.3390/engproc2025107002 - 20 Aug 2025

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Abstract

In this study, we investigate the potential of large language models (LLMs) to simulate partisan political polarization through conversation experiments. While previous research has demonstrated that LLM agents fail to reproduce human-like partisan polarization due to their inherent biases, we hypothesized that incorporating [...] Read more.

In this study, we investigate the potential of large language models (LLMs) to simulate partisan political polarization through conversation experiments. While previous research has demonstrated that LLM agents fail to reproduce human-like partisan polarization due to their inherent biases, we hypothesized that incorporating confirmation bias prompts could help overcome these limitations. We conducted conversation simulations between LLM agents assigned Democratic and Republican ideologies, analyzing both intra-party and inter-party interactions. Results without confirmation bias prompts revealed that agents, particularly those with Republican ideologies, tended to shift toward Democratic positions, failing to replicate human partisan behavior. However, when confirmation bias prompts were introduced, agents maintained their initial political stances more consistently, especially in intra-party conversations. While some tendency toward moderation remained in cross-party discussions, the magnitude of position shifts was significantly reduced. These findings suggest that confirmation bias prompts can effectively mitigate LLMs’ inherent biases in partisan simulations, though additional refinements may be needed to fully replicate human polarization dynamics. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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12 pages, 1985 KB

Open AccessProceeding Paper

Enhancing the Haar Cascade Algorithm for Robust Detection of Facial Features in Complex Conditions Using Area Analysis and Adaptive Thresholding

by Dayne Fradejas, Vince Harley Gaba, Analyn Yumang and Ericson Dimaunahan

Eng. Proc. 2025, 107(1), 3; https://doi.org/10.3390/engproc2025107003 - 21 Aug 2025

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Abstract

Facial features are critical visual indicators for understanding what a person is experiencing, providing valuable insights into their emotions and physical states. However, accurately detecting these features under diverse conditions remains a significant challenge, especially in computationally constrained environments. This paper presents a [...] Read more.

Facial features are critical visual indicators for understanding what a person is experiencing, providing valuable insights into their emotions and physical states. However, accurately detecting these features under diverse conditions remains a significant challenge, especially in computationally constrained environments. This paper presents a facial feature extraction method designed to identify regions of interest for detecting facial cues, with a focus on improving the accuracy of eye and mouth detection. Addressing the limitations of standard Haar cascade classifiers, particularly in challenging scenarios such as droopy eyes, red eyes, and droopy mouths, this method introduces a correction algorithm rooted in normal human facial anatomy, emphasizing symmetry and consistent feature placement. By integrating this correction algorithm with a feature-based refinement process, the proposed approach enhances detection accuracy from 67.22% to 96.11%. Through this method, the accurate detection of facial features like the eyes and mouth is significantly improved, offering a lightweight and efficient solution for real-time applications while maintaining computational efficiency. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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12 pages, 1341 KB

Open AccessProceeding Paper

Lost by Over-Management: Adaptive Notification Model for Handling Weakly Planned Activities

by Angelita Gozaly and Evgeny Pyshkin

Eng. Proc. 2025, 107(1), 4; https://doi.org/10.3390/engproc2025107004 - 21 Aug 2025

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Abstract

The study explores the scenarios and approach to the design of the software for managing notifications about the fuzzily planned activities. Though many such scenarios can be solved by using traditional time and activity management tools such as organizers, diaries, planners, or schedulers, [...] Read more.

The study explores the scenarios and approach to the design of the software for managing notifications about the fuzzily planned activities. Though many such scenarios can be solved by using traditional time and activity management tools such as organizers, diaries, planners, or schedulers, practical situations often arise when people tend to avoid overmanagement for real-life situations, when the plans might be flexible, and the planned activities might depend on location, contextual, and time information, which may not necessarily be well known or configured in advance. In this contribution, we describe examples of such situations and define the concept of soft planning. Following the principles of the human-driven design paradigm, we conducted a small-scale survey to gather insights into user preferences and identify drawbacks of existing digitalized activity planning and decision-making tools, often based on configurable notification management software. The findings reveal that while notifications are useful, users often encounter issues such as information overload, lack of contextual awareness, and disruptions caused by the notifications arriving at inconvenient or even inappropriate times. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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13 pages, 1130 KB

Open AccessProceeding Paper

Impact of Technological Tools on Mathematics Pedagogy: Data-Driven Insights into Educators’ Practices in Math Classrooms

by Lailani Pabilario

Eng. Proc. 2025, 107(1), 5; https://doi.org/10.3390/engproc2025107005 - 21 Aug 2025

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Abstract

Teaching with technology enhances instructional effectiveness and student engagement, particularly in mathematics, accounting, and ICT education. Digital learning creates an interactive environment that fosters deeper understanding and keeps learners updated with current trends. For teachers, it offers tools to assess student strengths and [...] Read more.

Teaching with technology enhances instructional effectiveness and student engagement, particularly in mathematics, accounting, and ICT education. Digital learning creates an interactive environment that fosters deeper understanding and keeps learners updated with current trends. For teachers, it offers tools to assess student strengths and weaknesses better, guiding them to develop targeted interventions. However, successful technology integration depends on educators’ digital skills, an area where many still face challenges. This paper aims to assess teachers’ technological and pedagogical proficiency and identify barriers to integration. The study employed a mixed-method approach with 60 teacher respondents selected through stratified random sampling from both urban and rural schools. Data was collected through online interviews, classroom observations, and pre- and post-survey questionnaires focusing on confidence, competence, and willingness to use technology. Thematic analysis and paired sample t-tests using SPSS v.20 revealed a significant improvement in teachers’ technological skills following an intervention program. It also identified both internal and external factors hindering technological integration in the classroom. Findings emphasize that sustained support and training are essential for effective technology use in the classroom and recommend that school administrators embed technology in curriculum planning to enhance both instruction and extension activities. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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11 pages, 208 KB

Open AccessProceeding Paper

A Comprehensive Analysis on Computational Thinking in Education: Open Issues and Challenges

by Jethro Jarvis Roy Jyrwa, Chandra Jayaraman and Alwin Joseph

Eng. Proc. 2025, 107(1), 6; https://doi.org/10.3390/engproc2025107006 - 21 Aug 2025

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Abstract

Computational thinking (CT) is a cognitive approach for solving problems using the concepts of algorithmic thinking, decomposition of a problem into components, identifying patterns among commonly occurring activities, and abstraction. CT promotes interdisciplinary learning and enhances problem-solving and logical reasoning abilities. In this [...] Read more.

Computational thinking (CT) is a cognitive approach for solving problems using the concepts of algorithmic thinking, decomposition of a problem into components, identifying patterns among commonly occurring activities, and abstraction. CT promotes interdisciplinary learning and enhances problem-solving and logical reasoning abilities. In this study, a comprehensive analysis of the current issues and challenges of applying CT in the educational landscape is presented with a focus on the various assessment tools and their implementation in teaching methods. The study identifies the various techniques that can be used by educators to evaluate the skills of students based on their ability to solve problems that require CT. A systematic review of the available literature and related works was conducted to analyze their importance in CT, as well as their issues and challenges. This study finds that there is a need for a unified definition and implementation guidelines on CT. The available assessment tools mainly focus on programming constructs, leaving little room for evaluating abstract concepts as challenges in the field; hence, designing and developing assessment mechanisms are also required for effective implementation of CT in an academic context. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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13 pages, 1002 KB

Open AccessProceeding Paper

Transforming Language Learning with AI: Adaptive Systems, Engagement, and Global Impact

by Thi Kim Anh Vo

Eng. Proc. 2025, 107(1), 7; https://doi.org/10.3390/engproc2025107007 - 21 Aug 2025

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Abstract

Artificial Intelligence (AI) is transforming language education through adaptive learning, automated assessments, and interactive tutoring. This study analyzes 80 peer-reviewed articles (2020–2024) to explore AI’s pedagogical and ethical dimensions. Findings show that AI-driven learning boosts engagement and proficiency via real-time feedback, yet challenges [...] Read more.

Artificial Intelligence (AI) is transforming language education through adaptive learning, automated assessments, and interactive tutoring. This study analyzes 80 peer-reviewed articles (2020–2024) to explore AI’s pedagogical and ethical dimensions. Findings show that AI-driven learning boosts engagement and proficiency via real-time feedback, yet challenges such as algorithmic bias, data privacy, and teacher adaptation remain. This paper proposes a responsible AI integration framework, emphasizing educator–technologist collaboration, professional development, and ethical governance. Addressing these concerns requires robust policies and continued research to maximize benefits while minimizing risks in AI-enhanced education. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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15 pages, 1506 KB

Open AccessProceeding Paper

Artificial Intelligence for Historical Manuscripts Digitization: Leveraging the Lexicon of Cyril

by Stavros N. Moutsis, Despoina Ioakeimidou, Konstantinos A. Tsintotas, Konstantinos Evangelidis, Panagiotis E. Nastou and Antonis Tsolomitis

Eng. Proc. 2025, 107(1), 8; https://doi.org/10.3390/engproc2025107008 - 21 Aug 2025

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Abstract

Artificial intelligence (AI) is a cutting-edge and revolutionary technology in computer science that has the potential to completely transform a wide range of disciplines, including the social sciences, the arts, and the humanities. Therefore, since its significance has been recognized in engineering and [...] Read more.

Artificial intelligence (AI) is a cutting-edge and revolutionary technology in computer science that has the potential to completely transform a wide range of disciplines, including the social sciences, the arts, and the humanities. Therefore, since its significance has been recognized in engineering and medicine, history, literature, paleography, and archaeology have recently embraced AI as new opportunities have arisen for preserving ancient manuscripts. Acknowledging the importance of digitizing archival documents, this paper explores the use of advanced technologies during this process, showing how these are employed at each stage and how the unique challenges inherent in past scripts are addressed. Our study is based on Cyril’s Lexicon, a Byzantine-era dictionary of great historical and linguistic significance in Greek territory. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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12 pages, 637 KB

Open AccessProceeding Paper

Enhancing Cognitive and Metacognitive Domains of Autistic Children Using Machine Learning

by Dilmi Tharaki, Yashika Rupasinghe, Piyathma Ruhunage, Ama Pehesarani and Samadhi Chathuranga Rathnayake

Eng. Proc. 2025, 107(1), 9; https://doi.org/10.3390/engproc2025107009 - 21 Aug 2025

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Abstract

ASD poses special difficulty in both cognitive and metacognitive development, necessitating specialized educational strategies. This research proposes LearnMate, a web-based application powered by machine learning techniques that aims to improve the abilities of children with autism. Utilizing classification models learned from medical data, [...] Read more.

ASD poses special difficulty in both cognitive and metacognitive development, necessitating specialized educational strategies. This research proposes LearnMate, a web-based application powered by machine learning techniques that aims to improve the abilities of children with autism. Utilizing classification models learned from medical data, LearnMate forecasts skill acquisition and suggests personalized learning activities according to the strengths and developmental requirements of the child. The system permits instructors to monitor progress through real-time feedback, enabling adaptive learning approaches. Pilot application to more than 100 children showed significant gains in their skills. The results demonstrate the immense potential for change through machine learning in special education to facilitate data-driven, personalized learning opportunities that enhance the capabilities of both autistic students and teachers. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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12 pages, 707 KB

Open AccessProceeding Paper

Stance and Engagement: How Community Notes Influence HPV Vaccine Conversations on X in Japan

by Kento Ueta, Masashi Sakurai and Yasuhiro Hashimoto

Eng. Proc. 2025, 107(1), 10; https://doi.org/10.3390/engproc2025107010 - 22 Aug 2025

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Abstract

This study examines the impact of Community Notes on users’ stance and posting behavior regarding the human papillomavirus (HPV) vaccine on X. Unlike previous research focusing on affected posts and authors, this study analyzes users who viewed Community Notes and their posting behavior [...] Read more.

This study examines the impact of Community Notes on users’ stance and posting behavior regarding the human papillomavirus (HPV) vaccine on X. Unlike previous research focusing on affected posts and authors, this study analyzes users who viewed Community Notes and their posting behavior before and after exposure. We analyzed posts related to the HPV vaccine using X’s official Community Notes dataset (January 2021–July 2024). Posts were classified as “Support,” “Oppose,” or “Neutral” using a large language model (GPT-4o, OpenAI), and changes in stance and posting frequency were evaluated. Findings show that 73% of users maintained their stance after viewing Community Notes. However, posting frequency increased sharply immediately after the note was added, especially among opposing users. This suggests that, since most Community Notes support vaccination, opposing users may have actively responded by posting critical responses. This study contributes by examining viewer behavior, not just post authors. However, limitations include GPT-4o’s classification accuracy and the restricted scope of topics and users covered in this study. Future research should improve the evaluation of Community Notes by verifying users who viewed Community Notes and enhancing stance classification through better prompts and model comparisons. Additionally, expanding the analysis beyond the HPV vaccine will help assess the broader applicability of the findings. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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13 pages, 1923 KB

Open AccessProceeding Paper

Leveraging LSTM Neural Networks for Advanced Harassment Detection: Insights into Insults and Defamation in the U-Tapis Module

by Gerald Imanuel Wijaya and Marlinda Vasty Overbeek

Eng. Proc. 2025, 107(1), 11; https://doi.org/10.3390/engproc2025107011 - 22 Aug 2025

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Abstract

The prevalence of online harassment necessitates sophisticated automated systems that can accurately classify offensive content. In this work, we present a text classification system based on Long Short-Term Memory (LSTM) networks to categorize text into Neutral, Insult, and Defamation classes, thereby providing a [...] Read more.

The prevalence of online harassment necessitates sophisticated automated systems that can accurately classify offensive content. In this work, we present a text classification system based on Long Short-Term Memory (LSTM) networks to categorize text into Neutral, Insult, and Defamation classes, thereby providing a more granular understanding of abusive behavior in digital environments. The system was evaluated using two labeled datasets—150 samples generated by ChatGPT and 1000 samples from internet sources—achieving an accuracy of 85% on both. Notably, the model demonstrated strong performance in identifying Defamation, exhibiting high precision and recall. These findings underscore the effectiveness of LSTM networks in capturing complex linguistic features, highlighting their potential for improving content moderation tools and curbing online harassment. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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12 pages, 417 KB

Open AccessProceeding Paper

Autism Spectrum Disorder Classification in Children Using Eye-Tracking Data and Machine Learning

by Nikolaos Kaloforidis, Konstantinos-Filippos Kollias, Panagiotis Radoglou-Grammatikis, Panagiotis Sarigiannidis and George F. Fragulis

Eng. Proc. 2025, 107(1), 12; https://doi.org/10.3390/engproc2025107012 - 22 Aug 2025

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Abstract

Early Autism Spectrum Disorder (ASD) detection is important for early intervention. This study investigates the potential of eye-tracking (ET) data combined with machine learning (ML) models to classify ASD and Typically Developed (TD) children. Using a publicly available dataset, five ML models were [...] Read more.

Early Autism Spectrum Disorder (ASD) detection is important for early intervention. This study investigates the potential of eye-tracking (ET) data combined with machine learning (ML) models to classify ASD and Typically Developed (TD) children. Using a publicly available dataset, five ML models were evaluated: Support Vector Machine (SVM), Random Forest, Convolutional Neural Network (CNN), Artificial Neural Network (ANN), and Random Forest improved with Convolutional Filters (ConvRF). The models were trained and tested using a set of evaluation metrics, including accuracy, precision, recall, F1-score, and ROC Area Under the Curve (AUC). Among these, the ConvRF model attained superior performance, achieving a recall of 90% and an AUC of 88%, indicating its robustness in identifying ASD children. These results highlight the model’s effectiveness in ensuring high sensitivity, which is critical for early ASD detection. This study shows the promise of combining ML and eye-tracking technology as accessible non-invasive tools for enhancing early ASD detection, resulting in timely and personalized interventions. Limitations and recommendations for future research are also included. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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10 pages, 2374 KB

Open AccessProceeding Paper

Design and Development of RDI Monitoring System of RSU’s Funded Research Projects

by Preexcy B. Tupas, Nova Marie F. Rosas, Ana G. Gervacio and Garry Vanz V. Blancia

Eng. Proc. 2025, 107(1), 13; https://doi.org/10.3390/engproc2025107013 - 22 Aug 2025

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Abstract

This paper presents the design, development, and evaluation of the REDI Monitoring System, a web-based platform aimed at enhancing the management and monitoring of funded research projects at Romblon State University (RSU). The system provides streamlined functionalities for proposal creation, submission, collaborator management, [...] Read more.

This paper presents the design, development, and evaluation of the REDI Monitoring System, a web-based platform aimed at enhancing the management and monitoring of funded research projects at Romblon State University (RSU). The system provides streamlined functionalities for proposal creation, submission, collaborator management, and administrative oversight, tailored to the needs of both students and faculty members. The development process adhered to established software engineering standards to ensure robustness and usability. A comprehensive testing phase was conducted with 50 participants, including students and faculty, following the ISO/IEC/IEEE 29119 software testing framework. Results demonstrated high user satisfaction, with over 90% of participants finding the system user-friendly and reliable. Minor areas for improvement were identified in notification delivery and interface responsiveness for faculty users. The REDI Monitoring System presents an effective and efficient solution that supports RSU’s research administration processes, fostering greater collaboration and transparency in funded research activities. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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7 pages, 292 KB

Open AccessProceeding Paper

User Acceptance of IBON (Image-Based Ornithological Identification) Monitoring in a Mobile Platform: A TAM-Based Study

by Preexcy B. Tupas, Juniel G. Lucidos, Alexander A. Hernandez and Rossian V. Perea

Eng. Proc. 2025, 107(1), 14; https://doi.org/10.3390/engproc2025107014 - 22 Aug 2025

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Abstract

This study investigates user acceptance of the IBON Monitoring system, a mobile app that uses image recognition to identify bird species. Using the Technology Acceptance Model (TAM), it surveyed 100 faculty and students at Romblon State University to assess factors like perceived usefulness, [...] Read more.

This study investigates user acceptance of the IBON Monitoring system, a mobile app that uses image recognition to identify bird species. Using the Technology Acceptance Model (TAM), it surveyed 100 faculty and students at Romblon State University to assess factors like perceived usefulness, ease of use, computer literacy, and self-efficacy. Results showed that usefulness and ease of use significantly influence user attitudes and intentions. The findings suggest actionable recommendations for improving IBON system adoption, including training programs to enhance computer literacy and self-efficacy and strategies to demonstrate the system’s relevance to user needs. Future research should explore additional external factors, such as cultural influences and user experience design, and conduct longitudinal studies to assess sustained use and impact on biodiversity monitoring outcomes. This study underscores the importance of fostering user acceptance to maximize the potential of innovative technologies like IBON Monitoring in advancing biodiversity conservation efforts. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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13 pages, 558 KB

Open AccessProceeding Paper

A Proposal of “Echo Read” as an Interactive AI Reading System to Support Rereading

by Yulana Watanabe, Yuuha Tokomoto and Eiichi Yubune

Eng. Proc. 2025, 107(1), 15; https://doi.org/10.3390/engproc2025107015 - 25 Aug 2025

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Abstract

This study investigates how rereading and different annotation styles affect the reading experience, proposing a system called Echo Read. It compares traditional literary annotations with SNS-style ones and enables mode switching among plain text, traditional, and SNS annotations. A theoretical framework views [...] Read more.

This study investigates how rereading and different annotation styles affect the reading experience, proposing a system called Echo Read. It compares traditional literary annotations with SNS-style ones and enables mode switching among plain text, traditional, and SNS annotations. A theoretical framework views reading as a multidimensional process across temporal and spatial axes. An experiment with participants reading under three annotation modes collects data via surveys, logs, and interviews. This study aims to show how annotations enhance comprehension, reflection, and social engagement, contributing to new understandings of digital reading and practical applications in education and culture. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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12 pages, 922 KB

Open AccessProceeding Paper

FairCXRnet: A Multi-Task Learning Model for Domain Adaptation in Chest X-Ray Classification for Low Resource Settings

by Aminu Musa, Rajesh Prasad, Mohammed Hassan, Mohamed Hamada and Saratu Yusuf Ilu

Eng. Proc. 2025, 107(1), 16; https://doi.org/10.3390/engproc2025107016 - 22 Aug 2025

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Abstract

Medical imaging analysis plays a pivotal role in modern healthcare, with physicians relying heavily on radiologists for disease diagnosis. However, many hospitals face a shortage of radiologists, leading to long queues at radiology centers and delays in diagnosis. Advances in artificial intelligence (AI) [...] Read more.

Medical imaging analysis plays a pivotal role in modern healthcare, with physicians relying heavily on radiologists for disease diagnosis. However, many hospitals face a shortage of radiologists, leading to long queues at radiology centers and delays in diagnosis. Advances in artificial intelligence (AI) have made it possible for AI models to analyze medical images and provide insights similar to those of radiologists. Despite their successes, these models face significant challenges that hinder widespread adoption. One major issue is the inability of AI models to generalize data from new populations, as performance tends to degrade when evaluated on datasets with different or shifted distributions, a problem known as domain shift. Additionally, the large size of these models requires substantial computational resources for training and deployment. In this study, we address these challenges by investigating domain shifts using ChestXray-14 and a Nigerian chest X-ray dataset. We propose a multi-task learning (MTL) approach that jointly trains the model on both datasets for two tasks, classification and segmentation, to minimize the domain gap. Furthermore, we replace traditional convolutional layers in the backbone model (Densenet-201) architecture with depthwise separable convolutions, reducing the model’s number of parameters and computational requirements. Our proposed model demonstrated remarkable improvements in both accuracy and AUC, achieving 93% accuracy and 96% AUC when tested across both datasets, significantly outperforming traditional transfer learning methods. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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10 pages, 769 KB

Open AccessProceeding Paper

Smart Irrigation Based on Soil Moisture Sensors with Photovoltaic Energy for Efficient Agricultural Water Management: A Systematic Literature Review

by Abdul Rasyid Sidik, Akbar Tawakal, Gumilar Surya Sumirat and Panji Narputro

Eng. Proc. 2025, 107(1), 17; https://doi.org/10.3390/engproc2025107017 - 25 Aug 2025

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Abstract

A smart irrigation system based on soil moisture sensors supported by photovoltaic energy is an innovation to address water use efficiency in the agricultural sector, especially in remote areas. This technology utilizes photovoltaic panels as a renewable energy source to operate water pumps, [...] Read more.

A smart irrigation system based on soil moisture sensors supported by photovoltaic energy is an innovation to address water use efficiency in the agricultural sector, especially in remote areas. This technology utilizes photovoltaic panels as a renewable energy source to operate water pumps, while soil moisture sensors provide real-time data that is used to automatically manage irrigation according to plant needs. This technology not only increases the efficiency of water and energy use but also supports environmental conservation by reducing dependence on fossil fuels. This research was conducted using a Systematic Literature Review (SLR) approach guided by the PRISMA framework to analyze trends, benefits, and challenges in implementing this technology. The analysis results show that this system offers various advantages, including energy efficiency, reduced carbon emissions, and ease of management through the integration of Internet of Things (IoT) technology. Several challenges remain, such as high initial investment costs, limited network access, and obstacles. Technical matters related to installation and maintenance. Various solutions have been proposed, including providing subsidies for small farmers, implementing radiofrequency modules, and using modular designs to simplify implementation. This study contributes to the development of a conceptual framework that can be adapted to various geographic and socio-economic conditions. Potential further developments include the integration of artificial intelligence and additional sensors to increase efficiency and support the sustainability of the agricultural sector globally. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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6 pages, 298 KB

Open AccessProceeding Paper

An ML Framework for the Early Detection and Prediction of Hypertension: Enhancing Diagnostic Accuracy

by Muhammad Areeb, Attique Ur Rehman and Alun Sujjada

Eng. Proc. 2025, 107(1), 18; https://doi.org/10.3390/engproc2025107018 - 25 Aug 2025

Viewed by 1417

Abstract

A major worldwide health problem, hypertension can result in serious consequences such as stroke, renal failure, and cardiovascular illnesses if it is not identified and treated promptly. Reducing death rates and facilitating prompt therapies need the early identification of hypertension. This research examines [...] Read more.

A major worldwide health problem, hypertension can result in serious consequences such as stroke, renal failure, and cardiovascular illnesses if it is not identified and treated promptly. Reducing death rates and facilitating prompt therapies need the early identification of hypertension. This research examines if there are ways ML could enhance early identification of hypertension. Therefore, hypertension is still considered a global public health problem, and one of the most important preventive goals is its timely and accurate diagnosis. Leveraging a 99.92% accuracy rate, the present study therefore proposes a novel ML framework that significantly dwarfs the currently documented best accuracy of 99.5%. This achievement of correctly identifying the essentiality of hypertension in establishing our recommended paradigm highlights the robustness and trustworthiness of the proposed actions to ensure timely treatment and enhance patients’ quality of life the largest amount. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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12 pages, 1332 KB

Open AccessProceeding Paper

U-Tapis: A Hybrid Approach to Melting Word Error Detection and Correction with Damerau-Levenshtein Distance and RoBERTa

by Prudence Tendy and Marlinda Vasty Overbeek

Eng. Proc. 2025, 107(1), 19; https://doi.org/10.3390/engproc2025107019 - 25 Aug 2025

Viewed by 89

Abstract

In the current digital era, the demand for rapid news delivery increases the risk of linguistic errors, including inaccuracies in the usage of melting words. This research introduces the U-Tapis application, a platform designed to detect and correct such errors using the Damerau-Levenshtein [...] Read more.

In the current digital era, the demand for rapid news delivery increases the risk of linguistic errors, including inaccuracies in the usage of melting words. This research introduces the U-Tapis application, a platform designed to detect and correct such errors using the Damerau-Levenshtein Distance algorithm and the RoBERTa model. The system achieved an average recommendation accuracy of 92.84%, with performance ranging from 91.30% to 95.45% across 3000 news articles. Despite its effectiveness, the system faces limitations, such as the static nature of its dataset, which does not update dynamically with new entries in the Indonesian Language Dictionary, and its tendency to flag all words with “me-” and “pe-” prefixes, regardless of context. These challenges highlight opportunities for future enhancements to improve the platform’s adaptability and precision. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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11 pages, 527 KB

Open AccessProceeding Paper

Communitech: Empowering Barangay Officials in Rural Areas Through Enhanced Computer Skills

by Joan F. Ferranco, Ana G. Gervacio and Charevel F. Ferranco

Eng. Proc. 2025, 107(1), 20; https://doi.org/10.3390/engproc2025107020 - 25 Aug 2025

Viewed by 160

Abstract

As communities rely on technology for information dissemination, Barangays must keep pace. Training Barangay officials, a key source of local information, enhances their ability to promote and communicate efficiently. The project offered capability training in Records Management, Basic Computer Maintenance, and Multimedia Technology. [...] Read more.

As communities rely on technology for information dissemination, Barangays must keep pace. Training Barangay officials, a key source of local information, enhances their ability to promote and communicate efficiently. The project offered capability training in Records Management, Basic Computer Maintenance, and Multimedia Technology. Objectives included the following: providing knowledge on ICT, streamlining document processes, introducing multimedia tools, conducting hands-on training, and evaluating outputs. A framework was adapted to guide the project, from needs assessment to implementation. The training averaged a 4.64 rating, indicating success. This model supports digital literacy for marginalized sectors and can be replicated in other municipalities for greater impact. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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11 pages, 463 KB

Open AccessProceeding Paper

A Deep Convolutional Neural Network-Based Model for Aspect and Polarity Classification in Hausa Movie Reviews

by Umar Ibrahim, Abubakar Yakubu Zandam, Fatima Muhammad Adam, Aminu Musa, Mohamed Hassan, Mohamed Hamada and Muhammad Shamsu Usman

Eng. Proc. 2025, 107(1), 21; https://doi.org/10.3390/engproc2025107021 - 26 Aug 2025

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Abstract

Aspect-based sentiment analysis (ABSA) plays a pivotal role in understanding the nuances of sentiment expressed in text, particularly in the context of diverse languages and cultures. This paper presents a novel deep convolutional neural network (CNN)-based model tailored for aspect and polarity classification [...] Read more.

Aspect-based sentiment analysis (ABSA) plays a pivotal role in understanding the nuances of sentiment expressed in text, particularly in the context of diverse languages and cultures. This paper presents a novel deep convolutional neural network (CNN)-based model tailored for aspect and polarity classification in Hausa movie reviews, as Hausa is an underrepresented language with limited resources and presence in sentiment analysis research. One of the primary implications of this work is the creation of a comprehensive Hausa ABSA dataset, which addresses a significant gap in the availability of resources for sentiment analysis in underrepresented languages. This dataset fosters a more inclusive sentiment analysis landscape and advances research in languages with limited resources. The collected dataset was first preprocessed using Sci-Kit Learn to perform TF-IDF transformation for extracting feature word vector weights. Aspect-level feature ontology words within the analyzed text were derived, and the sentiment of the reviewed texts was manually annotated. The proposed model combines convolutional neural networks (CNNs) with an attention mechanism to aid aspect word prediction. The model utilizes sentences from the corpus and feature words as vector inputs to enhance prediction accuracy. The proposed model leverages the advantages of the convolutional and attention layers to extract contextual information and sentiment polarities from Hausa movie reviews. The performance demonstrates the applicability of such models to underrepresented languages. With 91% accuracy on aspect term extraction and 92% on sentiment polarity classification, the model excels in aspect identification and sentiment analysis, offering insights into specific aspects of interest and their associated sentiments. The proposed model outperformed traditional machine models in both aspect word and polarity prediction. Through the creation of the Hausa ABSA dataset and the development of an effective model, this study makes significant advances in ABSA research. It has wide-ranging implications for the sentiment analysis field in the context of underrepresented languages. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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12 pages, 452 KB

Open AccessProceeding Paper

Integrating Serious Games in Primary Education: A Comprehensive Analysis

by Argyro Sachinidou, Ioannis Antoniadis and George F. Fragulis

Eng. Proc. 2025, 107(1), 22; https://doi.org/10.3390/engproc2025107022 - 26 Aug 2025

Viewed by 535

Abstract

The significant development of technology has greatly influenced crucial sectors of society, including health, economy, public health, and business. Technological tools have become essential in daily life, impacting the educational process across all age groups. Previous research has demonstrated the pervasive integration of [...] Read more.

The significant development of technology has greatly influenced crucial sectors of society, including health, economy, public health, and business. Technological tools have become essential in daily life, impacting the educational process across all age groups. Previous research has demonstrated the pervasive integration of technology into everyday activities, emphasizing the compelling attraction that screens and mobile devices provide, particularly among younger generations. However, earlier studies have often overlooked the detailed impact and practical applications of these technologies within the educational sector, particularly through computer games. This study employs a comprehensive analysis of scientific articles available on the internet, examining global research on the use of computer games in education. The research methods include a systematic review of publications, focusing on primary education while also considering other educational levels to provide a holistic view. The analytical approach highlights the practices employed during the implementation of educational computer games and their effects on the learning process. The major findings reveal that educational computer games have become a highly popular pedagogical method, effectively capturing the interest of both students and educators. The study underscores the growing demand for these educational tools and the promise of continuous improvements and additions to this type of teaching. The results suggest that integrating computer games into education not only enhances engagement but also signifies a progressive shift in teaching methodologies, paving the way for innovative educational practices. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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11 pages, 818 KB

Open AccessProceeding Paper

Analysis of the Role of Temperature and Current Density in Hydrogen Production via Water Electrolysis: A Systematic Literature Review

by Panji Narputro, Prastiyo Effendi, Iqbal Maulana Akbar and Saefur Rahman

Eng. Proc. 2025, 107(1), 23; https://doi.org/10.3390/engproc2025107023 - 26 Aug 2025

Viewed by 1443

Abstract

The production of hydrogen through water electrolysis has emerged as a promising alternative to decarbonizing the energy sector, especially when integrated with renewable energy sources. Among the key operational parameters that affect electrolysis performance, temperature and current density play a critical role in [...] Read more.

The production of hydrogen through water electrolysis has emerged as a promising alternative to decarbonizing the energy sector, especially when integrated with renewable energy sources. Among the key operational parameters that affect electrolysis performance, temperature and current density play a critical role in determining the energy efficiency, hydrogen yield and durability of the system. The study presents a Systematic Literature Review (SLR) that includes peer-reviewed publications from 2018 to 2025, focusing on the effects of temperature and current density across a variety of electrolysis technologies, including alkaline (AEL), proton exchange membrane (PEMEL), and solid oxide electrolysis cells (SOEC). A total of seven high-quality studies were selected following the PRISMA 2020 framework. The results show that high temperatures improve electrochemical kinetics and reduce excess potential, especially in PEM and SOEC systems, but can also accelerate component degradation. Higher current densities increase hydrogen production rates but lead to lower Faradaic efficiency and increased material stress. The optimal operating range was identified for each type of electrolysis, with PEMEL performing best at 60–80 °C and 500–1000 mA/cm², and SOEC at >750 °C. In addition, system-level studies emphasize the importance of integrating hydrogen production with flexible generation and storage infrastructure. The review highlights several research gaps, including the need for dynamic modeling, multi-parameter control strategies, and techno-economic assessments. These findings provide a basic understanding for optimizing hydrogen electrolysis systems in low-carbon energy architectures. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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12 pages, 1259 KB

Open AccessProceeding Paper

Anomaly Detection in Geothermal Steam Production Time Series Using Singular Spectrum Analysis

by Keiya Azuma and Yasuhiro Hashimoto

Eng. Proc. 2025, 107(1), 24; https://doi.org/10.3390/engproc2025107024 - 25 Aug 2025

Viewed by 203

Abstract

Geothermal power generation offers a high availability factor and independence from weather conditions, yet steam production in geothermal wells often declines over time due to factors such as pressure depletion and scale deposition. To enable early detection of production anomalies and optimize maintenance, [...] Read more.

Geothermal power generation offers a high availability factor and independence from weather conditions, yet steam production in geothermal wells often declines over time due to factors such as pressure depletion and scale deposition. To enable early detection of production anomalies and optimize maintenance, this paper proposes an anomaly detection framework based on Singular Spectrum Analysis (SSA). First, a Butterworth low-pass filter reduces high-frequency noise; then, SSA decomposes the time series, focusing on the largest singular value’s corresponding vectors. An anomaly score measures the deviation between current and historical singular vectors, and Non-Maximum Suppression (NMS) aggregates consecutive peaks to reduce false positives. We apply this method to 14 years of data from nine geothermal wells, comparing two threshold strategies: a unified threshold and well-specific thresholds. Results show that while a unified threshold simplifies deployment, individual thresholds can improve detection in certain wells, underscoring the impact of well characteristics and class imbalance. Our findings demonstrate that SSA-based anomaly detection, combined with NMS and threshold optimization, can effectively support maintenance decisions in geothermal power plants. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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14 pages, 3720 KB

Open AccessProceeding Paper

A Novel Data-Driven Framework for Automated Migraines Classification Using Ensemble Learning

by Muhammad Owais Butt, Azka Mir and Alun Sujjada

Eng. Proc. 2025, 107(1), 25; https://doi.org/10.3390/engproc2025107025 - 26 Aug 2025

Viewed by 177

Abstract

Migraines are recurring and highly painful headaches with multiple associated symptoms that severely affect millions of people around the world. This condition is considered quite serious from a neurologist’s perspective because it is highly debilitating. Effective treatment of migraines begins with its diagnosis [...] Read more.

Migraines are recurring and highly painful headaches with multiple associated symptoms that severely affect millions of people around the world. This condition is considered quite serious from a neurologist’s perspective because it is highly debilitating. Effective treatment of migraines begins with its diagnosis but the subjective nature of clinical evaluations along with class imbalance in patient datasets makes this very complicated. This paper attempts to tackle these issues by developing a machine-learning framework for automated migraines classification by utilizing a Kaggle dataset of 400 samples with 23 independent attributes and 1 dependent attribute representing different types of migraines. Our framework starts with a detailed cleansing of the data, which includes filtering out all missing values. Then, through the use of SMOTE (Synthetic Minority Oversampling Technique), the issue of an imbalanced dataset is tackled. This is followed by optimized feature selection through forward selection and cross-validation with Naïve Bayes. Supervised machine-learning classifiers such as Random Forest (RF), decision tree (DT), K-nearest Neighbors (KNN), and Naïve Bayes (NB) are evaluated and voted on to predict the outcome. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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9 pages, 209 KB

Open AccessProceeding Paper

AI Detection in Academia: How Indian Universities Can Safeguard Academic Integrity

by Akash Gupta, Harsh Mahaseth and Arushi Bajpai

Eng. Proc. 2025, 107(1), 26; https://doi.org/10.3390/engproc2025107026 - 26 Aug 2025

Viewed by 782

Abstract

In recent times, the use of Artificial Intelligence (AI) technologies like ChatGPT-4o within the education sector has become an undisputed fact. AI has transformed the education sector, offering tools that enhance student research and writing. However, the use of AI raises concerns with [...] Read more.

In recent times, the use of Artificial Intelligence (AI) technologies like ChatGPT-4o within the education sector has become an undisputed fact. AI has transformed the education sector, offering tools that enhance student research and writing. However, the use of AI raises concerns with respect to academic integrity, originality, and authenticity. Indian Universities regulate traditional plagiarism with anti-plagiarism detection systems. Some Indian Universities have also subscribed to AI plagiarism detection systems, but not all of them have subscribed to AI plagiarism detection. The majority of Indian Universities are not sufficiently prepared to identify AI-generated content that is contextually relevant and original, thus bypassing these traditional checks. This study stresses the urgent need for the University Grants Commission (UGC) to introduce advanced AI detection systems across Indian universities. Unlike regular plagiarism checkers, these tools can identify unique writing patterns that suggest AI-generated content. Without such measures, universities risk students using AI to complete assignments and research dishonestly. Through this research, the authors will examine the ethical concerns surrounding AI in academia and highlight the importance of clear guidelines to ensure responsible use. Colleges and universities need proper policies to regulate AI-generated work in student submissions. This study will compare how India and other countries handle AI detection in education, elaborating on the challenges of dealing with AI-generated content. The paper will propose a structured framework for Indian universities, including the use of AI detection tools, ethical guidelines, and awareness programmes to help students use AI responsibly while maintaining academic integrity in a changing educational system. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

5 pages, 368 KB

Open AccessProceeding Paper

Literature Study of the Potential Natural Oil Extracts from Plants as Bio Lubricants Using Local Resources in Indonesia

by Agung Nugraha, Naya Achmad Lajuari, Muhammad Andi Fazar Hermawan, Lazuardi Akmal Islami and Sivakumar Nallappan Sellappan

Eng. Proc. 2025, 107(1), 27; https://doi.org/10.3390/engproc2025107027 - 27 Aug 2025

Viewed by 975

Abstract

Lubricants are useful for reducing the negative impacts of friction. An engine that is not properly lubricated will easily wear out, make noise, and produce excessive heat. The use of conventional petroleum-based lubricants still dominates, but the sustainability of fossil resources and the [...] Read more.

Lubricants are useful for reducing the negative impacts of friction. An engine that is not properly lubricated will easily wear out, make noise, and produce excessive heat. The use of conventional petroleum-based lubricants still dominates, but the sustainability of fossil resources and the environmental impacts they have are major concerns. Therefore, the development of lubricants based on natural materials, or bio lubricants, is increasingly gaining attention. This paper aims to analyze various studies that have been conducted related to bio lubricants, especially those based on Indonesian natural resources. With the plant resources available in Indonesia, this research can be developed by utilizing the local wealth that is available, especially in abundance in Sukabumi City or Regency. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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14 pages, 685 KB

Open AccessProceeding Paper

Predictive Analysis of Voice Pathology Using Logistic Regression: Insights and Challenges

by Divya Mathews Olakkengil and Sagaya Aurelia P

Eng. Proc. 2025, 107(1), 28; https://doi.org/10.3390/engproc2025107028 - 27 Aug 2025

Viewed by 308

Abstract

Voice pathology diagnosis is essential for the timely detection and management of voice disorders, which can significantly impact an individual’s quality of life. This study employed logistic regression to evaluate the predictive power of variables that include age, severity, loudness, breathiness, pitch, roughness, [...] Read more.

Voice pathology diagnosis is essential for the timely detection and management of voice disorders, which can significantly impact an individual’s quality of life. This study employed logistic regression to evaluate the predictive power of variables that include age, severity, loudness, breathiness, pitch, roughness, strain, and gender on a binary diagnosis outcome (Yes/No). The analysis was performed on the Perceptual Voice Qualities Database (PVQD), a comprehensive dataset containing voice samples with perceptual ratings. Two widely used voice quality assessment tools, CAPE-V (Consensus Auditory-Perceptual Evaluation of Voice) and GRBAS (Grade, Roughness, Breathiness, Asthenia, Strain), were employed to annotate voice qualities, ensuring systematic and clinically relevant perceptual evaluations. The model revealed that age (odds ratio: 1.033, p < 0.001), loudness (odds ratio: 1.071, p = 0.005), and gender (male) (odds ratio: 1.904, p = 0.043) were statistically significant predictors of voice pathology. In contrast, severity and voice quality-related features like breathiness, pitch, roughness, and strain did not show statistical significance, suggesting their limited predictive contributions within this model. While the results provide valuable insights, the study underscores notable limitations of logistic regression. The model assumes a linear relationship between the independent variables and the log odds of the outcome, which restricts its ability to capture complex, non-linear patterns within the data. Additionally, logistic regression does not inherently account for interactions between predictors or feature dependencies, potentially limiting its performance in more intricate datasets. Furthermore, a fixed classification threshold (0.5) may lead to misclassification, particularly in datasets with imbalanced classes or skewed predictor distributions. These findings highlight that although logistic regression serves as a useful tool for identifying significant predictors, its results are dataset-dependent and cannot be generalized across diverse populations. Future research should validate these findings using heterogeneous datasets and employ advanced machine learning techniques to address the limitations of logistic regression. Integrating non-linear models or feature interaction analyses may enhance diagnostic accuracy, ensuring more reliable and robust voice pathology predictions. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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7 pages, 572 KB

Open AccessProceeding Paper

The Effect of UV Light in Accelerating IoT-Based Hydroponic Plant Growth

by Riyan, Isep Teddy Kurniawan, Muhammad Irsyad Fauzan and Trisiani Dewi Hendrawati

Eng. Proc. 2025, 107(1), 29; https://doi.org/10.3390/engproc2025107029 - 27 Aug 2025

Viewed by 134

Abstract

Hydroponic agriculture based on the Internet of Things (IoT) is an innovative solution to face the challenges of land limitations and climate uncertainty. This study aims to analyze the role of IoT in accelerating the growth of hydroponic plants through monitoring and automation [...] Read more.

Hydroponic agriculture based on the Internet of Things (IoT) is an innovative solution to face the challenges of land limitations and climate uncertainty. This study aims to analyze the role of IoT in accelerating the growth of hydroponic plants through monitoring and automation of the planting environment, as well as evaluating its impact on productivity, especially for the planting process in land with minimal sunlight. The system integrates sensors to monitor environmental parameters such as pH, temperature, and humidity, which are then processed in real-time to optimize nutrient delivery and irrigation. The results show that the use of IoT in hydroponic systems is able to significantly improve the quality and quantity of crop yields compared to conventional methods. However, there are several challenges in implementation, such as high initial costs, limited infrastructure in certain areas, and potential cybersecurity threats. Nonetheless, innovation and collaboration opportunities between the public and private sectors can accelerate the adoption of these technologies in sustainable agriculture. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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10 pages, 399 KB

Open AccessProceeding Paper

A Systematic Literature Study on IoT-Based Water Turbidity Monitoring: Innovation in Waste Management

by Fawwaz Muhammad, Wildan Nasrullah, Rio Alfatih and Trisiani Dewi Hendrawati

Eng. Proc. 2025, 107(1), 30; https://doi.org/10.3390/engproc2025107030 - 27 Aug 2025

Viewed by 278

Abstract

Water quality monitoring is an important step in maintaining environmental sustainability and public health. Water turbidity is one of the main parameters in assessing water quality, because a high level of turbidity can indicate pollution that is harmful to aquatic ecosystems and humans. [...] Read more.

Water quality monitoring is an important step in maintaining environmental sustainability and public health. Water turbidity is one of the main parameters in assessing water quality, because a high level of turbidity can indicate pollution that is harmful to aquatic ecosystems and humans. In the digital era, Internet of Things (IoT) technology has been applied to improve the effectiveness of real-time monitoring of water turbidity. This study aims to examine IoT-based water turbidity monitoring strategies and technologies using the Systematic Literature Review (SLR) method with the PRISMA protocol. In the process of searching for literature, this study identified 222 articles from the Scopus database, which, after going through the screening stage based on relevance, document type, and accessibility, resulted in seven main articles for further analysis. The results of the review show that the utilization of IoT sensors and wireless communication enables real-time monitoring of water turbidity, improves early detection of pollution, and improves effectiveness in water monitoring. However, challenges such as data security, sensor reliability, and communication network stability still need to be overcome to ensure the system works optimally. This study confirms that IoT can be a more efficient and sustainable solution in monitoring water turbidity. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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12 pages, 1258 KB

Open AccessProceeding Paper

Visualization of Rainfall Using Participatory Mobile Sensing for Crop Cultivation Support

by Yuki Inoue, Masayuki Higashino, Takao Kawamura and Mitsuru Tsubo

Eng. Proc. 2025, 107(1), 31; https://doi.org/10.3390/engproc2025107031 - 27 Aug 2025

Viewed by 43

Abstract

In drylands, farmers practicing rain-dependent agriculture sow their seeds in accordance with the onset of the rainy season. Thus, the onset of the rainy season is extremely important, but inexperienced farmers cannot determine when it begins. To support farmers in determining the optimal [...] Read more.

In drylands, farmers practicing rain-dependent agriculture sow their seeds in accordance with the onset of the rainy season. Thus, the onset of the rainy season is extremely important, but inexperienced farmers cannot determine when it begins. To support farmers in determining the optimal sowing date, we propose a method that combines GSMaP estimates with actual measurements from a mobile application to present rainfall data and evaluate its usefulness. In the proposed method, GSMaP is used to visualize estimated rainfall data, but satellite-based estimates can differ from ground-based actual measurements. If farmers own smartphones, they can use a mobile application to record actual rainfall measurements. This allows farmers to selectively incorporate both estimated and actual rainfall data into their cultivation plans. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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7 pages, 347 KB

Open AccessProceeding Paper

Stroke Prediction Using Machine Learning Algorithms

by Nayab Kanwal, Sabeen Javaid and Dhita Diana Dewi

Eng. Proc. 2025, 107(1), 32; https://doi.org/10.3390/engproc2025107032 - 27 Aug 2025

Viewed by 33

Abstract

Stroke is a major global cause of death and disability, and improving outcomes requires early prediction. Although class imbalance in datasets causes biased predictions and inferior classification accuracy, machine learning (ML) techniques have shown potential in stroke prediction. We used the Synthetic Minority [...] Read more.

Stroke is a major global cause of death and disability, and improving outcomes requires early prediction. Although class imbalance in datasets causes biased predictions and inferior classification accuracy, machine learning (ML) techniques have shown potential in stroke prediction. We used the Synthetic Minority Oversampling Technique (SMOTE) to balance datasets and lessen bias in order to address these problems. Furthermore, we suggested a method that combines a linear discriminant analysis (LDA) model for classification with an autoencoder for feature extraction. A grid search approach was used to optimize the hyperparameters of the LDA model. We used criteria like accuracy, sensitivity, specificity, AUC (area under the curve), and ROC (Receiver Operating Characteristic) to guarantee a strong evaluation. With 98.51% sensitivity, 97.56% specificity, 99.24% accuracy, and 98.00% balanced accuracy, our model demonstrated remarkable performance, indicating its potential to improve stroke prediction and aid in clinical decision-making. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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10 pages, 515 KB

Open AccessProceeding Paper

Utilization of Solar Panels in Various Applications: A Systematic Literature Review

by Robby Pahlevi David, Irwan Faisal, Viery Bagja Alamsyah and Panji Narputro

Eng. Proc. 2025, 107(1), 33; https://doi.org/10.3390/engproc2025107033 - 27 Aug 2025

Viewed by 125

Abstract

The utilization of renewable energy, particularly solar panels, has rapidly developed as a solution to reduce dependence on fossil fuels and carbon emissions. This study examines the application of solar panels across various sectors, including transportation, residential, commercial, industrial, and agricultural, using a [...] Read more.

The utilization of renewable energy, particularly solar panels, has rapidly developed as a solution to reduce dependence on fossil fuels and carbon emissions. This study examines the application of solar panels across various sectors, including transportation, residential, commercial, industrial, and agricultural, using a systematic literature review (SLR) approach. The results indicate that solar panels provide significant benefits in supporting energy sustainability, such as high efficiency in electric vehicles, carbon emission reduction in the transportation sector, and energy cost savings in commercial buildings. In the agricultural sector, solar panels are used for irrigation and crop storage. Additionally, technological advancements such as bifacial panels and integration with energy storage systems enhance efficiency and application flexibility. However, challenges such as high initial costs, location limitations, and technological efficiency remain major barriers. Through an analysis of the advantages and disadvantages of three types of solar panels (monocrystalline, polycrystalline, and thin-film), this study provides strategic guidance for selecting the most suitable technology for specific needs. The study concludes that the adoption of solar panels can be accelerated through technological innovation, cost reduction, and government policy support. With optimal utilization, solar panels have significant potential to drive the transition toward sustainable energy in the future. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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6 pages, 342 KB

Open AccessProceeding Paper

Detection of Bank Transaction Fraud Using Machine Learning

by Muhammad Sami, Azka Mir and Gina Purnama Insany

Eng. Proc. 2025, 107(1), 34; https://doi.org/10.3390/engproc2025107034 - 28 Aug 2025

Viewed by 1500

Abstract

Bank transaction fraud detection has emerged as an important area of research in the economic sector, driven by the developing sophistication of fraudulent activities and the considerable economic losses they entail. This paper reviews numerous methodologies and technologies employed in the real-time identification [...] Read more.

Bank transaction fraud detection has emerged as an important area of research in the economic sector, driven by the developing sophistication of fraudulent activities and the considerable economic losses they entail. This paper reviews numerous methodologies and technologies employed in the real-time identification and mitigation of fraudulent transactions, including traditional statistical techniques, machine learning algorithms and advanced artificial intelligence strategies. It enhances the need to combine anomaly detection structures with behavioral analytics to enhance detection accuracy while addressing challenges like data privacy, the need to balance false positives and negatives and the need for adaptive systems. By evaluating the most recent developments and case studies, this study provides a comprehensive assessment of what is happening in bank transaction fraud detection and presents future directions for enhancing safety features. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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7 pages, 312 KB

Open AccessProceeding Paper

AI as Modern Technology for Home Security Systems: A Systematic Literature Review

by Rizki Muhammad, Muhammad Syailendra Aditya Sagara, Yaunarius Molang Teluma and Fikri Arif Wicaksana

Eng. Proc. 2025, 107(1), 35; https://doi.org/10.3390/engproc2025107035 - 28 Aug 2025

Viewed by 833

Abstract

The growing demand for innovative home security solutions has accelerated the integration of advanced technologies to enhance safety, convenience, and operational efficiency. Artificial intelligence (AI) has become a pivotal element in revolutionizing home security systems by enabling real-time threat detection, automated surveillance, and [...] Read more.

The growing demand for innovative home security solutions has accelerated the integration of advanced technologies to enhance safety, convenience, and operational efficiency. Artificial intelligence (AI) has become a pivotal element in revolutionizing home security systems by enabling real-time threat detection, automated surveillance, and intelligent decision-making. This study employs a systematic literature review (SLR) to explore recent advancements in AI-driven technologies, such as machine learning, computer vision, natural language processing, and the Internet of Things (IoT). These innovations enhance security by providing features like facial recognition, anomaly detection, voice-activated controls, and predictive analysis, delivering more accurate and responsive security solutions. Furthermore, this study addresses challenges related to data privacy, cybersecurity threats, and cost considerations while emphasizing AI’s potential to deliver scalable, efficient, and user-friendly systems. The findings demonstrate AI’s vital role in the evolution of home security technologies, paving the way for smarter and safer living environments. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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17 pages, 2153 KB

Open AccessProceeding Paper

Building-Integrated Photovoltaics: A Bibliometric Review of Key Developments and Knowledge Gaps

by Panji Narputro, Marina Artiyasa, Paikun, Utamy Sukmayu Saputri, Dio Damas Permadi, Muhammad Hidayat, Nita Kurnita Sari and Sofa Lailatul Marifah

Eng. Proc. 2025, 107(1), 36; https://doi.org/10.3390/engproc2025107036 - 27 Aug 2025

Viewed by 257

Abstract

Building-Integrated Photovoltaics (BIPV) is a transformative approach to sustainable energy, which integrates photovoltaic systems as integral elements of building structures, such as facades, roofs, and windows. This bibliometric review aims to comprehensively analyze the evolution, trends, and challenges in BIPV research by referencing [...] Read more.

Building-Integrated Photovoltaics (BIPV) is a transformative approach to sustainable energy, which integrates photovoltaic systems as integral elements of building structures, such as facades, roofs, and windows. This bibliometric review aims to comprehensively analyze the evolution, trends, and challenges in BIPV research by referencing more than 10,000 publications indexed in Scopus. Key findings highlight the growing importance of cross-disciplinary collaboration in engineering, architecture, and environmental science to improve BIPV efficiency, aesthetic integration, and economic viability. Despite substantial progress, challenges remain, including high initial costs, regulatory limitations, and the need for innovative materials and energy storage solutions. Emerging trends underscore the potential of BIPV in urban planning and sustainability initiatives, supported by increased collaboration and international adoption in regions with supportive policies. This review identifies research gaps in cost-effective production, adaptive materials, and integrated energy management solutions, which offer future pathways for BIPV innovation. This review serves as a reference for academics, practitioners, and policymakers aiming to advance the adoption of BIPV, contributing to global efforts towards energy sustainability and low-carbon urban development. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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7 pages, 182 KB

Open AccessProceeding Paper

Evaluation of AI Models for Phishing Detection Using Open Datasets

by Nur Aniyansyah, Rina Rina, Sarah Puspitasari and Adhitia Erfina

Eng. Proc. 2025, 107(1), 37; https://doi.org/10.3390/engproc2025107037 (registering DOI) - 28 Aug 2025

Viewed by 4

Abstract

Phishing is a form of cyber-attack that aims to steal sensitive information by impersonating a trusted entity. To overcome this threat, various artificial intelligence (AI) methods have been developed to improve the effectiveness of phishing detection. This study evaluates three machine learning models, [...] Read more.

Phishing is a form of cyber-attack that aims to steal sensitive information by impersonating a trusted entity. To overcome this threat, various artificial intelligence (AI) methods have been developed to improve the effectiveness of phishing detection. This study evaluates three machine learning models, namely Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM), using an open dataset containing phishing and non-phishing URLs. The research process includes data preprocessing stages such as cleaning, normalization, categorical feature encoding, feature selection, and dividing the dataset into training and test data. The trained models are then evaluated using accuracy, precision, recall, F1-score, and comparison score metrics to determine the best model in phishing classification. The evaluation results show that the Random Forest model has the best performance with higher accuracy and generalization of 98.64% compared to Decision Tree which is only 98.37% and SVM 92.67%. Decision Tree has advantages in speed and interpretability but is susceptible to overfitting. SVM shows good performance on high-dimensional datasets but is less efficient in computing time. Based on the research results, Random Forest is recommended as the most optimal model for machine learning-based phishing detection. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

5 pages, 305 KB

Open AccessProceeding Paper

Variation in Current Density of Aluminum Scrap-Based Propeller Anodization to Increase Surface Hardness

by Rifani Putri Nayla, Paulus Dara Bani, Hilmi Udzmatillah, Lazuardi Akmal Islami and Sivakumar Nallappan Sellappan

Eng. Proc. 2025, 107(1), 38; https://doi.org/10.3390/engproc2025107038 - 28 Aug 2025

Viewed by 366

Abstract

Aluminum has the advantages of being lightweight and rust-resistant, and having high strength and durability. Aluminum scrap is a recycled material and is reused in its production process, for example, for propellers. Because it is used in conditions that require good durability, a [...] Read more.

Aluminum has the advantages of being lightweight and rust-resistant, and having high strength and durability. Aluminum scrap is a recycled material and is reused in its production process, for example, for propellers. Because it is used in conditions that require good durability, a coating that can increase the hardness and strength of aluminum is introduced. This study used the anodization method with a H₂SO₄ electrolyte medium and variations in current density of 0.03 A/cm², 0.035 A/cm², and 0.04 A/cm². The anodization time was 45 min. It was found that the hardness of the specimen increased from the initial hardness of 189 HL. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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10 pages, 204 KB

Open AccessProceeding Paper

Setting the Boundaries for the Use of AI in Indian Arbitration

by Akash Gupta, Arushi Bajpai and Samanvi Narang

Eng. Proc. 2025, 107(1), 39; https://doi.org/10.3390/engproc2025107039 (registering DOI) - 1 Sep 2025

Abstract

If an arbitrator employs the use of AI to draft an arbitral award, or legal counsel uses AI and the data are leaked within that process, what is the legal consequence, and what will be the ethical concerns and enforceability issues? As artificial [...] Read more.

If an arbitrator employs the use of AI to draft an arbitral award, or legal counsel uses AI and the data are leaked within that process, what is the legal consequence, and what will be the ethical concerns and enforceability issues? As artificial intelligence (AI) is used in every field, it has undoubtedly been used within the legal domain. However, its use should be regulated and balanced as there is an adjudication involved between the parties to decide the rights and obligations of the parties. In recent times, AI in arbitration has revolutionized dispute resolution by enhancing efficiency, automating legal research, and expediting case management. However, its application has a different set of challenges attached to it, particularly concerning due process, algorithmic bias, evidentiary integrity, and the enforceability of AI-assisted arbitral awards. This paper critically examines these legal implications, assessing how AI aligns with Indian arbitration laws and international frameworks. It further explores regulatory safeguards, the balanced and ethical use of AI, and the evolving role of arbitrators and counsels in the era of AI. By addressing these concerns, this paper aims to provide a comprehensive analysis of AI’s impact on the legal landscape of arbitration in India. To conclude, this paper proposes an expressed provision within the Arbitration and Conciliation Act, 1996, with respect to disclosure related to the ethical use of AI. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

13 pages, 250 KB

Open AccessProceeding Paper

Incorporation of Scratch Programming and Algorithmic Resource Design in Primary Education

by Fatimazahra Ouahouda, Achtaich Khadija and Naceur Achtaich

Eng. Proc. 2025, 107(1), 40; https://doi.org/10.3390/engproc2025107040 (registering DOI) - 1 Sep 2025

Abstract

This paper examines the integration of Scratch programming software into primary education to enrich learning experiences and promote essential programming skills. It examines gender differences in attitudes towards programming, explores game-based learning (GBL) in the Curriculum for Excellence (CfE) in Scotland, and addresses [...] Read more.

This paper examines the integration of Scratch programming software into primary education to enrich learning experiences and promote essential programming skills. It examines gender differences in attitudes towards programming, explores game-based learning (GBL) in the Curriculum for Excellence (CfE) in Scotland, and addresses the design of algorithmic resources in France. Through qualitative analysis, it assesses theeffectiveness of Scratch in teaching and learning, thereby contributing to improvements in the educational program and the programming curriculum in primary schools. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

10 pages, 217 KB

Open AccessProceeding Paper

Gamified Learning in Education: How Online Quizzes like Kahoot Transform Classroom Dynamics

by Harsh Mahaseth, Arushi Bajpai and Akash Gupta

Eng. Proc. 2025, 107(1), 41; https://doi.org/10.3390/engproc2025107041 - 1 Sep 2025

Abstract

Online quizzes, such as Kahoot, are innovative tools reshaping modern education by boosting student engagement, enhancing memory retention, and encouraging collaboration. This study explores their role as a modern extension of the Socratic Method, highlighting their ability to combat challenges like reduced attention [...] Read more.

Online quizzes, such as Kahoot, are innovative tools reshaping modern education by boosting student engagement, enhancing memory retention, and encouraging collaboration. This study explores their role as a modern extension of the Socratic Method, highlighting their ability to combat challenges like reduced attention spans, exam anxiety, and unhealthy competition. With real-time feedback and gamified elements, quizzes make learning interactive and enjoyable, breaking the monotony of lectures. While technical limitations like time constraints and over-reliance on digital tools are noted, the findings advocate a balanced approach. Online quizzes foster inclusivity, improve learning outcomes, and prepare students for a tech-driven future. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

13 pages, 2865 KB

Open AccessProceeding Paper

Graph Neural Networks for Drug–Drug Interaction Prediction—Predicting Safe Drug Pairings with AI

by Uzair Nisar, Humaira Ashraf, NZ Jhanjhi, Farzeen Ashfaq, Uswa Ihsan and Arny Lattu

Eng. Proc. 2025, 107(1), 42; https://doi.org/10.3390/engproc2025107042 - 1 Sep 2025

Abstract

At present, polypharmacy—which is the use of several medications to treat a single case at the same time—has become a fairly common medical practice, particularly in chronic illnesses or with older patients. But this relatively ‘faster’ form of treatment brings the problem of [...] Read more.

At present, polypharmacy—which is the use of several medications to treat a single case at the same time—has become a fairly common medical practice, particularly in chronic illnesses or with older patients. But this relatively ‘faster’ form of treatment brings the problem of cumulative polypharmacy, which occurs when there is an increase in drug–drug interactions (DDIs) due to the large number of medicines taken. While the aftermath, such as the reduction in strength of medication taken or catastrophic and fatal responses to certain drugs, is clearly not worth the initial effort put into trying to ease the condition, attempting to resolve these issues requires excessive research. With these difficulties in mind, we describe our research that uses graph neural networks (GNNs) focused on DDI prediction by modeling drugs and their interactions in the form of graphs. The research is divided into two parts. In this research, the relevant literature is reviewed in order to understand how modern GNN-based algorithms can be applied for the detection of optimal drugs. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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11 pages, 1332 KB

Open AccessProceeding Paper

Comparative Analysis of Action Recognition Techniques: Exploring Two-Stream CNNs, C3D, LSTM, I3D, Attention Mechanisms, and Hybrid Models

by Arshiya, Gursharan Singh, Arun Malik and Nugraha

Eng. Proc. 2025, 107(1), 43; https://doi.org/10.3390/engproc2025107043 - 1 Sep 2025

Abstract

Action recognition actions in video are sophisticated processes that demand more and more explicitly captured spatial and temporal information. This paper gives a comparison of several advanced techniques for action recognition using the UCF101 dataset. We look at two-stream convolutional networks, 3D convolutional [...] Read more.

Action recognition actions in video are sophisticated processes that demand more and more explicitly captured spatial and temporal information. This paper gives a comparison of several advanced techniques for action recognition using the UCF101 dataset. We look at two-stream convolutional networks, 3D convolutional networks, long short-term memory networks, two-stream inflated 3D convolutional networks, attention mechanisms, and hybrid models. Their methods have been examined for each of the proposed options along with their architectures, as well as their pros and cons. The results of our experiments have revealed the performance of these approaches on the UCF101 dataset, including a focus on the tradeoffs between computational efficiency, data requirements, and recognition accuracy. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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7 pages, 1564 KB

Open AccessProceeding Paper

Explainable Artificial Intelligence for Object Detection in the Automotive Sector

by Marios Siganos, Panagiotis Radoglou-Grammatikis, Thomas Lagkas, Vasileios Argyriou, Sotirios Goudos, Konstantinos E. Psannis, Konstantinos-Filippos Kollias, George F. Fragulis and Panagiotis Sarigiannidis

Eng. Proc. 2025, 107(1), 44; https://doi.org/10.3390/engproc2025107044 - 1 Sep 2025

Abstract

In the automotive domain, object detection is pivotal for enhancing safety and autonomy through the identification of various objects of interest. However, insights into the influential image pixels in the detection process are often lacking. Recognizing these significant regions within the image not [...] Read more.

In the automotive domain, object detection is pivotal for enhancing safety and autonomy through the identification of various objects of interest. However, insights into the influential image pixels in the detection process are often lacking. Recognizing these significant regions within the image not only enriches our qualitative understanding of the model’s functionality but also empowers us to refine and optimize its performance. Employing Explainable Artificial Intelligence (XAI), we present an XAI component in this paper. This component explains the predictions made by a pre-trained object detection model for a given image by generating heatmaps that highlight the most critical regions in the image for the detected objects. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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13 pages, 3910 KB

Open AccessProceeding Paper

Grading Support System for Pear Fruit Using Edge Computing

by Ryo Ito, Shutaro Konuma and Tatsuya Yamazaki

Eng. Proc. 2025, 107(1), 45; https://doi.org/10.3390/engproc2025107045 - 1 Sep 2025

Abstract

Le Lectier pears (hereafter, Pears) are graded based on appearance, requiring farmers to inspect tens of thousands in a short time before shipment. To assist in this process, a grading support system was developed. The existing cloud-based system used mobile devices to capture [...] Read more.

Le Lectier pears (hereafter, Pears) are graded based on appearance, requiring farmers to inspect tens of thousands in a short time before shipment. To assist in this process, a grading support system was developed. The existing cloud-based system used mobile devices to capture images and analyzed them with Convolutional Neural Networks (CNNs) and texture-based algorithms. However, communication delays and algorithm inefficiencies resulted in a 30 s execution time, posing a problem. This paper proposes an edge computing-based system using Mask R-CNN for appearance deterioration detection. Processing on edge servers reduces execution time to 5–10 s, and 39 out of 51 Pears are accurately detected. Full article

(This article belongs to the Proceedings of The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society)

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