Engineering Proceedings

6 pages, 1456 KB

Open AccessProceeding Paper

Action Research on Problem-Based Learning in Development of Engineering Curriculum in Design Departments: A Case Study of Mechanism Design

by Ting-Chien Lu

Eng. Proc. 2025, 103(1), 26; https://doi.org/10.3390/engproc2025103026 (registering DOI) - 8 Sep 2025

Abstract

Mechanism design, a fundamental course of the Department of Design, presents a significant challenge in learning product design. To address this issue, problem-based learning (PBL) was applied to teaching the “tree-climbing mechanism.” In the course, students designed and constructed a gear-based phone holder, [...] Read more.

Mechanism design, a fundamental course of the Department of Design, presents a significant challenge in learning product design. To address this issue, problem-based learning (PBL) was applied to teaching the “tree-climbing mechanism.” In the course, students designed and constructed a gear-based phone holder, assembled and evaluated a linkage mechanism using LEGO, designed and prototyped a tree-climbing mechanism through hands-on modeling, and tested the tree-climbing mechanism with handcrafted models or LEGO, incorporating batteries and motors. In each subtopic, students leveraged the department’s digital fabrication equipment and workshop to create functional prototypes. The instructor provided foundational knowledge of mechanisms to guide students to relevant resources and adapt the course content based on student reflections and assessments. Full article

► Show Figures

Figure 1

6 pages, 918 KB

Open AccessProceeding Paper

Prediction of Torque Arm Fatigue Life by Fuzzy Logic Method

by Caner Baybaş, Mustafa Acarer and Fevzi Doğaner

Eng. Proc. 2025, 104(1), 83; https://doi.org/10.3390/engproc2025104083 (registering DOI) - 7 Sep 2025

Abstract

In this study, a fuzzy-logic-based decision support model is developed to predict the fatigue life of load-bearing system elements such as torque arm. Traditional methods for fatigue life prediction are mostly based on certain mathematical expressions and fixed parameters and do not adequately [...] Read more.

In this study, a fuzzy-logic-based decision support model is developed to predict the fatigue life of load-bearing system elements such as torque arm. Traditional methods for fatigue life prediction are mostly based on certain mathematical expressions and fixed parameters and do not adequately take into account the uncertainties caused by many factors such as material structure, surface condition, loading pattern and heat treatment. In order to overcome these deficiencies, the fuzzy logic method is preferred. The model is based on a fuzzy logic system and predicts outputs according to specific input conditions using rules derived from expert knowledge and experience. The input parameters of the model are material type, surface hardness, maximum applied stress level, and type of heat treatment. Although these parameters can be expressed numerically in the classical sense, the relationship between them is often imprecise and based on experience and engineering interpretation. Therefore, a more realistic and flexible prediction model has been created with the linguistic variables and rule-based approach of fuzzy logic. Full article

► Show Figures

Figure 1

8 pages, 1554 KB

Open AccessProceeding Paper

Magnetic Nanoparticles for Toxic Wastewater Cleaning:Experimental Study on Phenol

by Lacramioara Oprica, Larisa Popescu-Lipan, Liviu Sacarescu, Mihai Costache, Cosmin Hincu and Dorina Creanga

Eng. Proc. 2025, 104(1), 87; https://doi.org/10.3390/engproc2025104087 (registering DOI) - 6 Sep 2025

Abstract

This study focuses on the possibility of cleaning of industrial wastewater with catalytically active magnetic nanoparticles. Cobalt ferrite synthesized by the co-precipitation method was used, as prepared or after surface modification with a silica precursor. Electronic absorption spectra were recorded and analyzed to [...] Read more.

This study focuses on the possibility of cleaning of industrial wastewater with catalytically active magnetic nanoparticles. Cobalt ferrite synthesized by the co-precipitation method was used, as prepared or after surface modification with a silica precursor. Electronic absorption spectra were recorded and analyzed to obtain the phenol degrading rate for various experimental design variants. Treating with pristine magnetic nanoparticles under simultaneous exposure to ultraviolet radiation resulted in similar degrading rates for 4 g/L and 8 g/L pristine nanoparticles, while, for silanized nanoparticles, the degrading rates were slightly increased. Along with ultraviolet irradiation and magnetic nanoparticles, hydrogen peroxide was also added, which led to significant enhancement of phenol degradation, for both pristine and silanized nanoparticles. It is proposed that photo-Fenton processes, triggered by metal ions at the nanoparticle surface and water photolysis and sustained by hydrogen peroxide decomposition, occurred to gradually decompose phenol to simpler compounds. Full article

► Show Figures

Figure 1

12 pages, 1156 KB

Open AccessProceeding Paper

Energetic Analysis for the Improvement of a Cupola Furnace

by Axel Vargas Sánchez, Ricardo Galindo Bulos, Juan C. Prince, Asunción Zárate and Miguel A. Gijón

Eng. Proc. 2025, 104(1), 86; https://doi.org/10.3390/engproc2025104086 (registering DOI) - 6 Sep 2025

Abstract

Cupola furnaces rank among the oldest melting technologies in steelmaking, relying predominantly on coke as the primary fuel. In this study, a detailed energy analysis was conducted on a cupola unit used for gray and ductile iron production. Energy and thermal analyses were [...] Read more.

Cupola furnaces rank among the oldest melting technologies in steelmaking, relying predominantly on coke as the primary fuel. In this study, a detailed energy analysis was conducted on a cupola unit used for gray and ductile iron production. Energy and thermal analyses were performed on the furnace to improve efficiency and minimize energy losses in the system. Computational simulations with an equation solving program quantified an exhaust-gas heat loss of 1.5 Gigajoules. To recover this waste heat, a heat exchanger was proposed to preheat the incoming combustion air. Numerical simulations of the modified system demonstrate a 3% increase in overall furnace efficiency and a reduction of about ten percent of coke per charge, equivalent to 716 kg per day for the unit under evaluation. Full article

► Show Figures

Figure 1

1371 KB

Open AccessProceeding Paper

Design of a Forklift Hydraulic System with Unloading Valves for Load Handling

by Yordan Stoyanov, Atanasi Tashev and Penko Mitev

Eng. Proc. 2025, 104(1), 85; https://doi.org/10.3390/engproc2025104085 (registering DOI) - 6 Sep 2025

Abstract

This paper presents the design and analysis of a forklift hydraulic system utilizing an open-center configuration equipped with unloading (safety-overflow) valves and an emergency lowering mechanism. The hydraulic system includes an external gear pump, double-acting power cylinders, hydraulic distributors, and control valves. A [...] Read more.

This paper presents the design and analysis of a forklift hydraulic system utilizing an open-center configuration equipped with unloading (safety-overflow) valves and an emergency lowering mechanism. The hydraulic system includes an external gear pump, double-acting power cylinders, hydraulic distributors, and control valves. A comprehensive approach is undertaken to select system components based on catalog data and to model the flow rate, required torque, and power characteristics of the pump, along with load handling performance as a function of cylinder dimensions and hydraulic pressure. System behavior under various operating conditions is simulated using Automation Studio, enabling performance optimization and fault response assessment. The inclusion of unloading valves and an emergency button enhances system safety by enabling controlled pressure relief and emergency actuation. The impact of thermal effects, filter efficiency, and reservoir design on hydraulic fluid integrity is also addressed. This study aims to improve reliability, efficiency, and safety in hydraulic forklift systems while supporting informed design decisions using simulation-driven methodologies. Full article

► Show Figures

Figure 1

8 pages, 1451 KB

Open AccessProceeding Paper

Development of a System for Flexible Feeding of Parts with Robot and Machine Vision

by Penko Mitev

Eng. Proc. 2025, 104(1), 84; https://doi.org/10.3390/engproc2025104084 (registering DOI) - 6 Sep 2025

Abstract

This article presents a design solution for feeding cylindrical parts with axial orientation. A working algorithm was developed to control and synchronize the main components, which was verified via a simulation. The pneumatic and electrical circuits were designed using a software platform for [...] Read more.

This article presents a design solution for feeding cylindrical parts with axial orientation. A working algorithm was developed to control and synchronize the main components, which was verified via a simulation. The pneumatic and electrical circuits were designed using a software platform for engineering purposes. Based on the CAD project created, a real prototype was built. The energy consumption of the system was tested and evaluated. The results from the prototype verified the solution. This article emphasizes the use of specific sensors for detecting part orientation and their role in improving process reliability. The system is suitable for industrial implementation due to its functionality, stable operation, low energy consumption, and ability to be integrated into automated production systems. Full article

► Show Figures

Figure 1

13 pages, 2651 KB

Open AccessProceeding Paper

Automatic Evaluation Visual Characteristics of Corn Snacks Using Computer Vision

by Angel Danev, Atanaska Bosakova-Ardenska, Radoslava Gabrova and Hristina Andreeva

Eng. Proc. 2025, 104(1), 81; https://doi.org/10.3390/engproc2025104081 - 5 Sep 2025

Abstract

The process of extrusion is a part of many modern manufacturing technologies that are applied in various industrial productions. In the food industry, the process of extrusion is used to produce popular foods such as cereal mixes, confectionery products, pet foods, etc. The [...] Read more.

The process of extrusion is a part of many modern manufacturing technologies that are applied in various industrial productions. In the food industry, the process of extrusion is used to produce popular foods such as cereal mixes, confectionery products, pet foods, etc. The advantages of extrusion technology are continuously applied in many studies in order to develop foods with more significant functional properties that could be produced in a time-effective and cost-effective way. In recent years, computer vision has become one of the preferred technologies in the development of new methods for quality control of food product production. This paper proposes a system for the evaluation of the quality parameters of extruded foods using a computer vision method. This system combines hardware and software modules that are developed for the discussed topic. A justification of the proposed system is provided on the basis of an experiment using extruded corn snacks made with different ingredients. Full article

► Show Figures

Figure 1

12 pages, 1813 KB

Open AccessProceeding Paper

An Efficient Approach for Mining High Average-Utility Itemsets in Incremental Database

by Ye-In Chang, Chen-Chang Wu and Hsiang-En Kuo

Eng. Proc. 2025, 108(1), 32; https://doi.org/10.3390/engproc2025108032 (registering DOI) - 5 Sep 2025

Abstract

Traditional high-utility itemset (HUI) mining methods tend to overestimate utility for long itemsets, leading to biased results. High average-utility itemset (HAUI) mining addresses this problem by normalizing utility with itemset length. However, uniform utility thresholds fail to account for varying item importance. Recently, [...] Read more.

Traditional high-utility itemset (HUI) mining methods tend to overestimate utility for long itemsets, leading to biased results. High average-utility itemset (HAUI) mining addresses this problem by normalizing utility with itemset length. However, uniform utility thresholds fail to account for varying item importance. Recently, HAUI mining with multiple minimum utility thresholds (MMU) has been used for flexible utility evaluation. While the generalized HAUIM (GHAUIM) algorithm performs well, it requires two database scans and is limited to static datasets. Therefore, we developed a novel tree-based method that scans the database only once to improve efficiency by reducing storage and eliminating costly join operations. Additionally, pruning strategies and incremental updates were introduced to enhance scalability. The developed method outperformed GHAIM in efficiency. Full article

► Show Figures

Figure 1

11 pages, 4573 KB

Open AccessProceeding Paper

Markov Modelling and Cluster-Based Analysis of Transport Layer Traffic Using Quick User Datagram Protocol Internet Connections

by Zoltan Gal, Marcell B. Gal and Gyorgy Terdik

Eng. Proc. 2025, 108(1), 31; https://doi.org/10.3390/engproc2025108031 - 5 Sep 2025

Abstract

Quick User Datagram Protocol Internet Connection (QUIC) is a modern transport protocol leveraging the User Datagram Protocol (UDP) to improve latency, security, and mobility. In this study, we analyzed QUIC traffic by uploading a 10 MB file under varied maximum transmission unit (MTU), [...] Read more.

Quick User Datagram Protocol Internet Connection (QUIC) is a modern transport protocol leveraging the User Datagram Protocol (UDP) to improve latency, security, and mobility. In this study, we analyzed QUIC traffic by uploading a 10 MB file under varied maximum transmission unit (MTU), bandwidth, and segment size conditions. Interarrival times (IAT) at both client and server were captured and analyzed using ordering points to identify the clustering structure (OPTICS) clustering and Markov modelling. Transition matrices and eigenvalue spectra revealed steady states, convergence behavior, and spectral gaps. The results showed that parameter variations significantly affected the traffic state diversity and flow dynamics, optimizing QUIC performance in real-world deployments. Full article

► Show Figures

Figure 1

17 pages, 3051 KB

Open AccessProceeding Paper

Review and Comparative Analysis of Modern Knee Prostheses with Development of a Conceptual Design

by Akhmejanov Sayat, Zhetenbayev Nursultan, Nurgizat Yerkebulan, Sultan Aidos, Uzbekbayev Arman, Sergazin Gani, Ozhikenov Kassymbek and Nurmangaliyev Asset

Eng. Proc. 2025, 104(1), 80; https://doi.org/10.3390/engproc2025104080 - 4 Sep 2025

Abstract

This paper provides a comprehensive review of the structural features and biomechanical functions of modern passive and semi-active knee prostheses, followed by comparative analysis. Based on findings from scientific literature and engineering practice, a new conceptual knee prosthesis was developed using a modular [...] Read more.

This paper provides a comprehensive review of the structural features and biomechanical functions of modern passive and semi-active knee prostheses, followed by comparative analysis. Based on findings from scientific literature and engineering practice, a new conceptual knee prosthesis was developed using a modular design approach. The proposed structure was modeled in SolidWorks, and its kinematic behavior and structural integrity were quantitatively evaluated through finite element analysis (FEA). The knee module was specifically designed to integrate with previously developed ankle and foot prosthetic components via an adapter interface. This modular approach allows the prosthesis to be configured according to the individual clinical needs of the patient. Simulation results confirmed that the proposed design meets the requirements for motion accuracy and structural reliability. In future work, the physical prototype will be manufactured using 3D printing with PLA plastic as an initial test material, followed by fabrication with high-strength engineering plastics or metal alloys. This study represents a critical early step toward the development of a fully functional, adaptive lower-limb prosthetic system. Full article

► Show Figures

Figure 1

7 pages, 561 KB

Open AccessProceeding Paper

Hybrid 3D Mesh Reconstruction Models of CT Images for Deep Learning Based Classification of Kidney Tumors

by Muhammed Ahmet Demirtaş, Alparslan Burak İnner and Adnan Kavak

Eng. Proc. 2025, 104(1), 79; https://doi.org/10.3390/engproc2025104079 - 4 Sep 2025

Abstract

We present a comparative analysis of three hybrid methodologies for transforming 3D kidney tumor segmentations of volumetric NIfTI data into highly accurate network representations. Exploiting the KiTS23 dataset, we evaluate edge-preserving reconstruction pipelines integrating anisotropic diffusion, multiscale Gaussian filtering and KNN-based network optimisation. [...] Read more.

We present a comparative analysis of three hybrid methodologies for transforming 3D kidney tumor segmentations of volumetric NIfTI data into highly accurate network representations. Exploiting the KiTS23 dataset, we evaluate edge-preserving reconstruction pipelines integrating anisotropic diffusion, multiscale Gaussian filtering and KNN-based network optimisation. Model 1 uses Gaussian smoothing with Marching Cubes, while Model 2 uses spline interpolation and Perona-Malik filtering for improved resolution. Model 3 extends this structure with normal sensitive vertex smoothing to preserve critical anatomical interfaces. Quantitative metrics (Dice score, HD₉₅) demonstrated the advantage of Model 3, which achieved a 22% reduction in the Hausdorff distance error rate compared to conventional methods while maintaining segmentation accuracy (Dice > 0.92). The proposed unsupervised pipeline bridges the gap between clinical interpretability and computational accuracy, providing a robust infrastructure for further applications in surgical planning and deep learning-based classification. Full article

► Show Figures

Figure 1

8 pages, 2108 KB

Open AccessProceeding Paper

Development of a Software Tool for Hall Parameter Evaluation in Semiconductor Structures

by Gergana Mironova and Goran Goranov

Eng. Proc. 2025, 104(1), 78; https://doi.org/10.3390/engproc2025104078 - 4 Sep 2025

Abstract

The Hall effect is widely used in magnetic field sensors and contactless measurement systems. Accurate modeling of Hall-effect elements is essential for optimizing performance, especially in high-sensitivity applications under controlled conditions like vacuum. This paper introduces a graphical software tool for calculating key [...] Read more.

The Hall effect is widely used in magnetic field sensors and contactless measurement systems. Accurate modeling of Hall-effect elements is essential for optimizing performance, especially in high-sensitivity applications under controlled conditions like vacuum. This paper introduces a graphical software tool for calculating key electrical parameters of Hall elements, such as Hall voltage, Hall coefficient, and carrier mobility. Users can input variables like semiconductor thickness, current, and magnetic field, with built-in models for materials like silicon, germanium, and gallium arsenide. Designed for vacuum operation, the tool supports simulation-based analysis, aiding researchers and educators in understanding and evaluating Hall-effect devices. Full article

► Show Figures

Figure 1

13 pages, 3205 KB

Open AccessProceeding Paper

Overview of Memory-Efficient Architectures for Deep Learning in Real-Time Systems

by Bilgin Demir, Ervin Domazet and Daniela Mechkaroska

Eng. Proc. 2025, 104(1), 77; https://doi.org/10.3390/engproc2025104077 - 4 Sep 2025

Abstract

With advancements in artificial intelligence (AI), deep learning (DL) has become crucial for real-time data analytics in areas like autonomous driving, healthcare, and predictive maintenance; however, its computational and memory demands often exceed the capabilities of low-end devices. This paper explores optimizing deep [...] Read more.

With advancements in artificial intelligence (AI), deep learning (DL) has become crucial for real-time data analytics in areas like autonomous driving, healthcare, and predictive maintenance; however, its computational and memory demands often exceed the capabilities of low-end devices. This paper explores optimizing deep learning architectures for memory efficiency to enable real-time computation in low-power designs. Strategies include model compression, quantization, and efficient network designs. Techniques such as eliminating unnecessary parameters, sparse representations, and optimized data handling significantly enhance system performance. The design addresses cache utilization, memory hierarchies, and data movement, reducing latency and energy use. By comparing memory management methods, this study highlights dynamic pruning and adaptive compression as effective solutions for improving efficiency and performance. These findings guide the development of accurate, power-efficient deep learning systems for real-time applications, unlocking new possibilities for edge and embedded AI. Full article

► Show Figures

Figure 1

11 pages, 1535 KB

Open AccessProceeding Paper

Automated Control of Dynamic Loads in Drive Systems

by Alina Fazylova, Kuanysh Alipbayev, Teodor Iliev and Alisher Aden

Eng. Proc. 2025, 104(1), 76; https://doi.org/10.3390/engproc2025104076 - 4 Sep 2025

Abstract

This article discusses the automated control of dynamic loads in drive systems using the example of a wind turbine screw drive. A mathematical model was developed, including differential equations of system motion, the voltage balance of the electric motor, and transfer functions of [...] Read more.

This article discusses the automated control of dynamic loads in drive systems using the example of a wind turbine screw drive. A mathematical model was developed, including differential equations of system motion, the voltage balance of the electric motor, and transfer functions of the control system. The Laplace transform was applied to obtain the system’s frequency and time characteristics. Numerical calculations and simulation results are presented, demonstrating the system’s stability and the effectiveness of the proposed control method. The generated amplitude–frequency and transient response graphs confirm the system’s operability. The proposed approach enhances the reliability of the screw drive, reduces mechanical loads, and extends the equipment’s service life. Full article

► Show Figures

Figure 1

9 pages, 664 KB

Open AccessProceeding Paper

Concrete Innovation Using Tree Branch Waste as Coarse Aggregate and Stone Ash as Fine Aggregate

by Irsad Fauzan Sunarlan, Okky Lutfi Fauzi, Usep Saepudin and Utamy Sukmayu Saputri

Eng. Proc. 2025, 107(1), 65; https://doi.org/10.3390/engproc2025107065 - 4 Sep 2025

Abstract

Concrete is a widely used construction material. This research investigates the effect of adding tree branch waste and stone dust as substitutes for coarse and fine aggregates on concrete’s physical and mechanical properties. The results show that these additives significantly impact weight and [...] Read more.

Concrete is a widely used construction material. This research investigates the effect of adding tree branch waste and stone dust as substitutes for coarse and fine aggregates on concrete’s physical and mechanical properties. The results show that these additives significantly impact weight and compressive strength. The weight comparison for 10% additive concrete was 7.28 kg at 7 and 14 days, while for 20% additive concrete, it was 7.02 kg at 7 days and 7.06 kg at 14 days. Normal concrete weighed 7.50 kg at 7 days and 7.66 kg at 14 days. The planned compressive strength (K250 or F’c: 20 MPa) for 28 days was met, with samples containing 10% and 20% additives exceeding the planned strength. However, increased use of these materials led to a reduction in compressive strength. Therefore, the addition of tree branches and stone dust should be limited to 10%, as the highest compressive strength was obtained at this percentage. This research suggests that using tree branch waste and stone dust as partial substitutes for aggregates can reduce concrete’s weight while maintaining its strength. Limiting the addition to 10% is recommended for optimal results. Full article

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

► Show Figures

Figure 1

13 pages, 1463 KB

Open AccessProceeding Paper

Improving the Mechanical Performance of TPU95A Filament in FDM 3D Printing via Parameter Optimization Using the Taguchi Method

by Abdelrahman Albardawil, Aden Robby Muhamad Aditya, Muchammad Yusup Mubarok, Lazuardi Akmal Islami and Dani Mardiyana

Eng. Proc. 2025, 107(1), 62; https://doi.org/10.3390/engproc2025107062 - 4 Sep 2025

Abstract

This study explores the mechanical characteristics of 3D-printed specimens fabricated using TPU-95A filament, with a focus on the influence of key printing variables—temperature, speed, and layer height—on tensile strength, toughness, and surface hardness. Through systematic testing, the tensile evaluation revealed a peak tensile [...] Read more.

This study explores the mechanical characteristics of 3D-printed specimens fabricated using TPU-95A filament, with a focus on the influence of key printing variables—temperature, speed, and layer height—on tensile strength, toughness, and surface hardness. Through systematic testing, the tensile evaluation revealed a peak tensile strength of 329.02 kgf/cm² and toughness of 1.56 under conditions of elevated temperatures and optimized layer configurations. Similarly, the hardness assessment indicated a maximum average value of 74.9 Shore A, emphasizing the substantial effect of process parameters on material integrity and resilience. A detailed variance analysis confirmed the pivotal roles of temperature and layer height in enhancing mechanical properties. Using a statistical optimization approach, optimal printing conditions were identified, demonstrating that higher temperatures, moderate speeds, and reduced layer heights significantly improve the balance between strength, flexibility, and durability. These findings contribute to the development of tailored fabrication strategies, offering practical insights for applications where precision and mechanical reliability are critical. Full article

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

► Show Figures

Figure 1

11 pages, 832 KB

Open AccessProceeding Paper

Heart Failure Prediction Through a Comparative Study of Machine Learning and Deep Learning Models

by Mohid Qadeer, Rizwan Ayaz and Muhammad Ikhsan Thohir

Eng. Proc. 2025, 107(1), 61; https://doi.org/10.3390/engproc2025107061 - 4 Sep 2025

Abstract

The heart is essential to human life, so it is important to protect it and understand any kind of damage it can have. All the diseases related to hearts leads to heart failure. To help address this, a tool for predicting survival is [...] Read more.

The heart is essential to human life, so it is important to protect it and understand any kind of damage it can have. All the diseases related to hearts leads to heart failure. To help address this, a tool for predicting survival is needed. This study explores the use of several classification models for forecasting heart failure outcomes using the Heart Failure Clinical Records dataset. The outcome contrasts a deep learning (DL) model known as the Convolutional Neural Network (CNN) with many machine learning models, including Random Forest (RF), K-Nearest Neighbors (KNN), Decision Tree (DT), and Naïve Bayes (NB). Various data processing techniques, like standard scaling and Synthetic Minority Oversampling Technique (SMOTE), are used to improve prediction accuracy. The CNN model performs best by achieving 99%. In comparison, the best-performing ML model, Naïve Bayes, reaches 92.57%. This shows that deep learning provides better predictions of heart failure, making it a useful tool for early detection and better patient care. Full article

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

► Show Figures

Figure 1

7 pages, 385 KB

Open AccessProceeding Paper

COVID-19 Prediction Using Machine Learning

by Ali Raza, Attique Ur Rehman and Imam Sanjaya

Eng. Proc. 2025, 107(1), 60; https://doi.org/10.3390/engproc2025107060 - 4 Sep 2025

Abstract

The COVID-19 virus caused unprecedented global disruption. There have been millions of cases and deaths reported worldwide. Accurate prediction of COVID-19 trends is crucial for effective decision-making, resource allocation, and policy formulation. ML has been shown to be an excellent method for projecting [...] Read more.

The COVID-19 virus caused unprecedented global disruption. There have been millions of cases and deaths reported worldwide. Accurate prediction of COVID-19 trends is crucial for effective decision-making, resource allocation, and policy formulation. ML has been shown to be an excellent method for projecting the virus’s growth and impact as it can analyze vast datasets, discover trends, and develop predictive models. This study examines the use of various machine learning techniques for the prediction of COVID-19 such as time series analysis, regression models, and classification techniques. This paper further addresses the problems and constraints of applying the ML model to this context and suggests possible enhancements for future forecasting endeavors. The overall intention of this work is to enlighten people as to how this ML-based method contributes to pandemic forecasting in terms of improvements in pandemic preparation and response schemes. Full article

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

► Show Figures

Figure 1

12 pages, 1419 KB

Open AccessProceeding Paper

A Real-Time Intelligent Surveillance System for Suspicious Behavior and Facial Emotion Analysis Using YOLOv8 and DeepFace

by Uswa Ihsan, Noor Zaman Jhanjhi, Humaira Ashraf, Farzeen Ashfaq and Fikri Arif Wicaksana

Eng. Proc. 2025, 107(1), 59; https://doi.org/10.3390/engproc2025107059 - 4 Sep 2025

Abstract

This study describes the creation of an intelligent surveillance system based on deep learning that aims to improve real-time security monitoring by automatically identifying suspicious activity. By using cutting-edge computer vision techniques, the suggested system overcomes the drawbacks of conventional surveillance that depends [...] Read more.

This study describes the creation of an intelligent surveillance system based on deep learning that aims to improve real-time security monitoring by automatically identifying suspicious activity. By using cutting-edge computer vision techniques, the suggested system overcomes the drawbacks of conventional surveillance that depends on human observation to spot irregularities in public spaces. The system successfully completes motion detection, trajectory analysis, and emotion recognition by using the YOLOv8 model for object detection and DeepFace for facial emotion analysis. Roboflow is used for dataset annotation, model training with optimized parameters, and visualization of object trajectories and detection confidence. The findings show that abnormal behaviors can be accurately identified, with noteworthy observations made about the emotional expressions and movement patterns of those deemed to be threats. Even though the system performs well in real time, issues like misclassification, model explainability, and a lack of diversity in the dataset still exist. Future research will concentrate on integrating multimodal data fusion, deeper models, and temporal sequence analysis to further enhance detection robustness and system intelligence. Full article

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

► Show Figures

Figure 1

Journal Description

Engineering Proceedings

Vol. 107

ETLTC 2025

Vol. 102

ITS APF 2025

Vol. 100

TechSys 2025

Vol. 99

EEM 2025

Vol. 98

SSIM 2024

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Further Information

Guidelines

MDPI Initiatives

Follow MDPI