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28,296 Article Results

A solar-powered autonomous power system for aquaculture: optimizing dual-battery management for remote operation

10.11591/ijece.v15i5.pp4376-4386
Thomas Yuven Handaka Laksi , Levin Halim , Ali Sadiyoko
In Indonesia, growing fish consumption demands necessitate expanded, yet sustainable, fish production without sacrificing quality. The process of feeding and the quality of the surrounding water are important factors influencing fish quality. To address this, Parahyangan Catholic University's Fishery 4.0 project pioneers a unique technology that integrates water quality monitoring with a fish feeding feature. The design and implementation of an independent, reliable power module, which is fundamental to the functionality of this system, is at the focus of this research. This study shows that a designed power module adapted to the specific needs of Fishery 4.0 is feasible. The system powers all modules with a 12 V battery and is recharged with a solar panel. The battery can be charged to 95% capacity, yielding 8550 mAh from a 9000 mAh capacity. A UC-3906 charger IC controls the charging process, deliberately managing the parameters required for optimal battery charging. Particularly, when exposed to ideal solar radiation, the charger recharges a 9 Ah battery from 30% to full capacity in about 10 hours and 10 minutes. This study proposes a novel to battery management, which is critical for the operation of aquaculture equipment at isolated locations.
Volume: 15
Issue: 5
Page: 4376-4386
Publish at: 2025-10-01

Prospective applications of assistive robotics for the benefit of population groups

10.11591/ijece.v15i5.pp4531-4541
Anny Astrid Espitia-Cubillos , Robinson Jimenez-Moreno , Javier Eduardo Martínez-Baquero
The development of robotics has reached various fields of application such as the assistance field, where robots support people with different abilities in different activities to provide independence, comfort and interaction, even improving their self-esteem and quality of life. The objective is to identify the main benefits of the application of assistive robotics achieved to project its future fields of action. For this purpose, the Scopus database is used to find documents related to assistive robotics, which are filtered by publication date and according to the elimination criteria determined by the authors, and then bibliometric networks are constructed using VOSviewer. Finally, the main findings are analyzed and presented according to their area of application. Five areas of application of assistive robotics are identified that benefit children, the elderly, provide hospital assistance, help people with disabilities or support therapy and rehabilitation work, developments that allow the formulation of areas for future study. It is concluded that there are many advances in assistive robotics that demonstrate robotic development and provide assistance to a particular population, but more work is still needed to increase the number of beneficiaries, reduce costs and expand research in the areas mentioned and to be developed.
Volume: 15
Issue: 5
Page: 4531-4541
Publish at: 2025-10-01

Strategic integration of social media in information technology sector communication: designing effective practices

10.11591/ijece.v15i5.pp4653-4661
Benu Kesar , Shaji Joseph
This paper explores the transformative role of social media in enhancing communication and workflow efficiency within the information technology (IT) sector. We have introduced the adaptive social media for information technology collaboration (ASMIT) framework. Its goal is to provide a holistic strategy for digital transformation in the IT sector. Employing a mixed method approach, the research combines a systematic literature review with case study of HCL Technologies. Thematic analysis categorizes findings under five core pillars of the ASMIT framework. Results indicate that AI-driven tools, when embedded within collaborative social media platforms, significantly enhance organizational agility, project coordination, and security. The study contributes to IT scholarship by bridging technological integration with human-centered collaboration strategies.
Volume: 15
Issue: 5
Page: 4653-4661
Publish at: 2025-10-01

Classifying the suitability of educational videos for attention deficit hyperactivity disorder students with deep neural networks

10.11591/ijece.v15i5.pp4889-4898
Alshefaa Emam , Eman Karam Elsyed , Mai Kamel Galab
This paper presents a comprehensive deep learning-based system to evaluate the educational videos' suitability for students with attention deficit hyperactivity disorder (ADHD). Current methods frequently ignore important instructional elements that are necessary for improving learning experiences for students with ADHD, such as instructor hand movements, video length, object variety, and audio-visual quality. We emphasize two key issues for how to address these difficulties, first, we present the ADHD online instructor (AOI) dataset, a particular benchmark for assessing instructional hand movement in video suitability to solve the absence of a reference dataset for classifying educational videos relevant to ADHD. Second, the system includes creating an enhanced multitask deep learning model that increases classification accuracy by using task-specific enhancements and optimized architectures. This solves the requirement for a strong model that can distinguish between suitable and unsuitable instructional content. Comprehensive tests using pretrained convolutional neural network (CNN) models indicate that the enhanced VGG16 model outperforms baseline methods by achieving a highest accuracy of 97.84%. The results highlight the value of integrating deep learning methods with structured benchmark datasets, exposing up the path to more resilient and flexible instructional materials designed for students with ADHD.
Volume: 15
Issue: 5
Page: 4889-4898
Publish at: 2025-10-01

Synthesis of nonlinear multilinked control systems of thermal power plants

10.11591/ijece.v15i5.pp4500-4507
Oksana Porubay , Isamiddin Siddikov
The paper addresses the synthesis of nonlinear control laws for the technological parameters of drum boiler steam generators in thermal power plants, based on a synergetic control approach. The controlled system is considered to be multidimensional and highly interconnected. The inherent nonlinearity and interdependence of the technological parameters in thermal power plants necessitate the use of nonlinear control laws to achieve effective regulation. This approach enables the expansion of the range of permissible variations in regulator parameters, thereby ensuring the desired dynamic behavior of the controlled variables. An analytical method for synthesizing nonlinear vector control laws for steam generators is proposed. A methodology is developed for designing dynamic regulators capable of compensating for uncertain disturbances while accounting for control constraints. A Lyapunov function is constructed to describe the internal state dynamics of the control object. The proposed method for constructing the dynamic regulator ensures the asymptotic stability of the control system and stabilization of the controlled parameters over a wide range of load variations.
Volume: 15
Issue: 5
Page: 4500-4507
Publish at: 2025-10-01

Analysis of partial discharge characteristics in transformer oil insulation media using needle-plane and plane-plane electrode systems

10.11591/ijece.v15i5.pp4445-4453
Teuku Khairul Murad , Abdul Syakur , Iwan Setiawan
Insulation failure is a common issue in electric power transmission. Insulation is necessary to separate two or more live conductors to prevent electrical arcing or sparking between them. Partial discharge (PD) is a phenomenon that can also occur in high-voltage equipment under pre-breakdown conditions. This PD activity can take place in liquid insulation, such as transformer oil, leading to a decrease in the quality and reliability of the transformer. This study aims to detect PD under various conditions and investigate its characteristics. Although various studies have been conducted on PD in liquid insulation, most of them focus on PD characterization under specific conditions without considering variations in electrode configurations that may influence the PD phenomenon. Therefore, this research is necessary to fill this gap by analyzing PD characteristics using a needle-plane and plane-plane electrode system. This study introduces the use of castor oil as an alternative liquid insulating material. In this study, PD testing will be conducted in a laboratory environment, and it is expected to produce reliable data regarding the capability of liquid insulation to withstand PD. The results obtained indicate that the PD phenomenon occurs more quickly in the needle-plane electrode configuration compared to the plane-plane configuration. PD in the needle-plane electrode occurs at an average voltage of 10.96 kV, while PD in the plane-plane electrode occurs at an average voltage of 12.5 kV.
Volume: 15
Issue: 5
Page: 4445-4453
Publish at: 2025-10-01

Enhancing software fault prediction using wrapper-based metaheuristic feature selection methods

10.11591/ijece.v15i5.pp4803-4812
Ha Thi Minh Phuong , Dang Thi Kim Ngan , Dao Khanh Duy , Nguyen Thanh Binh
The application of software fault prediction (SFP) to predict faulty components at the early stage has been investigated in various studies. Reducing feature redundancy is key to enhancing the predictive accuracy of SFP models. Feature selection methods are utilized to select and retain the features that contribute the most information while eliminating irrelevant or redundant features from software fault datasets. However, feature selection (FS) in the field of SFP remains a broad and continuously evolving field, encompassing a diverse range of techniques and methodologies. In this work, we study and perform empirical evaluation of ten wrapper FS methods, namely artificial butterfly optimization (ABO), atom search optimization (ASO), equilibrium optimizer (EO), Henry gas solubility optimization (HGSO), poor and rich optimization (PRO), generalized normal distribution optimization (GNDO), slime mold algorithm, Harris hawk’s optimization, pathfinder algorithm (PFA) and manta ray foraging optimization for resolving the data redundancy issue in SFP datasets. Experimental results on nine fault datasets from the PROMISE and AEEEM repositories show that the EO achieves the best performance, with PRO and HGSO ranking next. The comparative analysis revealed that ten wrapper-based FS methods demonstrated a substantial improvement in handling data redundancy issues for SFP.
Volume: 15
Issue: 5
Page: 4803-4812
Publish at: 2025-10-01

Enhancing internet of things network efficiency with clustering and random forest fusion techniques

10.11591/ijece.v15i5.pp4954-4964
Ahmed Gamal Soliman Soliman Deabes , Hani Attar , Jafar Ababneh , Hala Abd El-kader Mansour , Michael Nasief , Esraa M. Eid
The internet of things (IoT) is a key element of the future internet, enabling the acquisition and transfer of data to improve efficiency. One challenge in IoT networks is managing the energy consumption of nodes. IoT innovation constantly evolves dynamically, contributing significantly to sustainable cities and economies. Clustering techniques can help conserve energy and extend the operational lifespan of network nodes. Cluster heads (CH) manage all cluster member (CM) nodes within their group, establishing intra-cluster and inter-cluster connections. Enhancing the CH selection process can further prolong the network lifespan. Various algorithms aim to extend the active duration of IoT nodes and the overall network lifespan. A comparison of the five algorithms shows that one algorithm is better than the others in some cases. This paper discusses how fusion techniques using the random forest (RF) algorithm can enhance energy efficiency in IoT networks. Five algorithms are compared using RF, a robust machine-learning algorithm renowned for its ensemble learning capabilities. It selects the best one based on active nodes per round, residual energy for each round, and the average end-to-end delay.
Volume: 15
Issue: 5
Page: 4954-4964
Publish at: 2025-10-01

Remote sensing applied to cocoa crop identification, a thematic review

10.11591/ijece.v15i5.pp4848-4855
Luisa Fernanda Cuellar-Escobar , Vladimir Henao-Céspedes
This article presents a thematic review of 25 publications related to the use of remote sensing techniques for the identification of cocoa crops from 2000 to 2023. Although the use of remote sensing techniques is widely used for mapping different covers because it is very useful in discriminating them, the generation of maps of cocoa crops presents challenges due to their spectral behavior similar to that of forests. This is because cocoa cultivation, being an agroforestry system that is developed in association with timber trees, causes the classification algorithms used to fail to differentiate between forest cover and cocoa crops. For this reason, this study seeks to investigate the different remote sensing techniques used in the mapping of cocoa crops, as well as an analysis of the structure of the publications highlighting the connections between countries and the factors that motivated the authors to research this crop.
Volume: 15
Issue: 5
Page: 4848-4855
Publish at: 2025-10-01

Energy evaluation of dependent malicious nodes detection in Arduino-based internet of things networks

10.11591/ijece.v15i5.pp4983-4992
Moath Alsafasfeh , Abdullah Alhasanat , Samiha Alfalahat
Detection of malicious nodes in the internet of things (IoT) network consumes power, which is one of the main constraints of the IoT network performance. To evaluate the energy-security trade-off for malicious node detection, this paper proposes an Arduino-based system for dependent malicious nodes (DMN) detection. The experimental work using Arduino and radio frequency (RF) modules was implemented to detect dependent malicious nodes in an IoT network. The detection algorithms were evaluated in terms of energy efficiency. The experiment comprises a coordinator node with five sensor nodes and varying malicious nodes. The results assess the detection algorithms in terms of distinguishing between normal and malicious behaviors and their impact on energy efficiency. The experiment demonstrated that the detection system could identify the malicious nodes. Additionally, the effect of increasing the number of sensors or malicious nodes on the suggested detection algorithm’s energy usage is evaluated.
Volume: 15
Issue: 5
Page: 4983-4992
Publish at: 2025-10-01

Experimental validation of a dual-band printed antenna array operating at 2.45/5.8 GHz with a high efficiency for wireless power transmission applications

10.11591/ijece.v15i5.pp4662-4670
Walid En-Naghma , Mohamed Latrach , Hanan Halaq , Abdelghani El Ougli
This paper presents a simple dual-band antenna element with a rectangular patch designed for the industrial, scientific, and medical bands at 2.45/5.8 GHz. The antenna achieves satisfactory simulated performance at both resonant frequencies, including a reflection coefficient below -10 dB, a voltage standing wave ratio (VSWR) not exceeding 1.2, radiation efficiencies of approximately 90% at 2.45 GHz and 95.55% at 5.8 GHz, and bandwidths of 125.80 MHz around 2.45 GHz and 308.60 MHz around 5.8 GHz. Building on this single-element design, an antenna array configuration comprising two elements etched on a Taconic TLY-5 substrate is developed. The two rectangular patches are connected via a T-junction to a 50 Ω excitation port. The proposed array's effectiveness is validated through simulations using three electromagnetic solvers and experimental measurements. The fabricated antenna array demonstrates improved performance, including a measured return loss of -16.78 dB at 2.45 GHz and -20.61 dB at 5.8 GHz, a VSWR not exceeding 1.5 (1.34 and 1.22 at 2.45 and 5.8 GHz, respectively), input impedance close to 50 Ω, high gain exceeding 8 dBi, bandwidths of 179.50 MHz at 2.45 GHz and 462.90 MHz at 5.8 GHz, and high radiation efficiencies of 96.54% at 2.45 GHz and 98.65% at 5.8 GHz. With only two patches, the proposed antenna array offers a compact, efficient, and practical solution for wireless power transmission applications, particularly for small wireless devices like rectenna systems, due to its simplicity, compact design, and excellent radiation efficiency.
Volume: 15
Issue: 5
Page: 4662-4670
Publish at: 2025-10-01

A novel approach for recommendation using optimized bidirectional gated recurrent unit

10.11591/ijece.v15i5.pp5019-5030
Prakash Pandharinath Rokade , Swati Babasaheb Bhonde , Prashant Laxmanrao Paikrao , Umesh Baburao Pawar
In today's world, every one of us refreshes our mood and gets energy through entertainment and enjoyment. Human nature is to provide feedback through ratings or comments for products used, services received, or films viewed. The recommendation system serves the user with recommendations based on historical stored information of user preferences. These systems amass information about the user in order to provide personalized experiences. These systems put efforts into delivering personalized experiences by accumulating information about the user. Hybrid algorithms are necessary to address the issues recommendation systems confront, which include low prediction accuracy, output that exceeds range, and inadequate convergence speed. This study suggests building a movie recommendation system using the remora optimization algorithm (ROA) and the bidirectional gated recurrent unit (BiGRU), the most recent version of the recursive neural network (RNN). The proposed method's results are compared with those of the genetic algorithm (GA), feed forward neural network (FFNN), and multimodal deep learning (MMDL). In terms of movie recommendation, BiGRU with ROA performs better than GA, MMDL, and FFNN.
Volume: 15
Issue: 5
Page: 5019-5030
Publish at: 2025-10-01

Optimized fractional-order direct torque control with space vector modulation strategy for two-wheel-drive electric vehicles

10.11591/ijece.v15i5.pp4409-4420
Touhami Nawal , Ouled-Ali Omar , Mansouri Smail , Benhammou Aissa
Electric vehicles (EVs) are a sustainable and efficient transportation choice, offering zero emissions, lower operating costs, and advanced performance features like instant torque and regenerative braking. They promote energy independence, improve urban livability, and support the global shift toward cleaner, renewable energy-powered mobility, making them a future-proof investment. The electric motor is a critical component in electric vehicles (EVs), the importance of which lies in its high efficiency, instant torque delivery, and smooth operation, which enhances performance and energy use. This paper focuses on a two-wheel drive electric vehicle (TWD EV) configuration powered by an energy storage battery system (ESBS), driven by two permanent magnet synchronous motors (PMSMs), and controlled using direct torque control with space vector modulation (DTC-SVM). fractional-order proportional integral derivative (FOPID) controllers, optimized via the grey wolf optimizer (GWO) algorithm, are implemented for precise speed control of the PMSMs. An electronic differential (ED) is incorporated to ensure vehicle stability, safety, and performance. The simulation results show that the proposed GWO-FOPID controller gave super results by reducing electromagnetic torque overshoot by 33%, improves torque settling time by 55%, and achieves the lowest electromagnetic torque ripple of approximately ±1 Nm compared to conventional DTC-SVM and GWO-PID approaches. Additionally, it optimized speed overshoot and undershoot by 44%, significantly enhancing system performance, responsiveness, and drive smoothness. This novel combination of fractional-order control, metaheuristic optimization, and electronic differential integration marks a meaningful advancement in high-precision and efficient control for 2WD EVs.
Volume: 15
Issue: 5
Page: 4409-4420
Publish at: 2025-10-01

Efficient fall detection using lightweight network to enhance smart internet of things

10.11591/ijece.v15i5.pp5031-5044
Pinrolinvic D. K. Manembu , Jane Ivonne Litouw , Feisy Diane Kambey , Abdul Haris Junus Ontowirjo , Vecky Canisius Poekoel , Muhamad Dwisnanto Putro
Fall detection automatically recognizes human falls, mainly to monitor and prevent severe injury and potential fatalities. It can be developed by applying deep learning methods to recognize human subjects during fall incidents and implemented in the internet of things (IoT) to monitor patient and elderly individuals’ activity. The development of object detection presents you only look once v8 (YOLOv8) as an influential network, but its efficiency needs to be improved. A modified YOLOv8 architecture is proposed to introduce a novel lightweight network version called YOLOv8-Hypernano (YOLOv8h) that recognizes fall events. The backbone incorporates a combined spatial and channel attention module, which enhances focus on human subjects by concentrating on movement patterns to detect falls more accurately. This work also offers a consecutive selective enhancement (CSE) module to improve efficiency and effectiveness in feature extraction while reducing computational costs. The neck structure is modified by adding a lightweight bottleneck network. The proposed network reconstructs feature maps in depth, paying more attention to accurate human movement patterns and enhancing efficiency and effectiveness in feature extraction. Experimental results of YOLOv8h with the light bottleneck and consecutive selective enhancement modules show giga floating-point operations per seconds (GFLOPS) of 5.6 and 1,194,440 parameters. The model performance is calculated in mean average precision, achieving 0.603 and 0.732 on the Le2i and Fallen datasets, respectively. These results demonstrate that the optimized network improves accuracy performance while maintaining lightweight computing requirements that can run smoothly on IoT devices, achieving comparable speed and efficiency suitable for operation on low-cost computing devices.
Volume: 15
Issue: 5
Page: 5031-5044
Publish at: 2025-10-01

Dynamic head pose estimation in varied conditions using Dlib and MediaPipe

10.11591/ijece.v15i5.pp4581-4592
Rusnani Yahya , Rozita Jailani , Nur Khalidah Zakaria , Fazah Akhtar Hanapiah
This paper presents the formulation and validation of a dynamic head pose estimation (HPE) algorithm, addressing challenges related to diverse conditions, complex poses, and partial obstructions. The study aims to create a robust algorithm that maintains high accuracy in real-time applications across varying conditions. The algorithm was implemented and assessed using Dlib and MediaPipe models. The study involved 30 participants in face and head without obstacles, face with obstacles and head with obstacles conditions. The results demonstrated impressive performance in both controlled and spontaneous head movement categories. The algorithm achieved an average accuracy of 93% for head pose estimation and 88% in detecting visual attention under spontaneous head movement categories. A correlation coefficient of 0.866 indicates a strong positive linear association between performance and attention accuracy, indicating that performance improvements are intricately linked to proportional increases in attention accuracy. However, this does not necessarily imply causation. The findings provide valuable insights into the effectiveness of the proposed algorithms in assessing visual attention and demonstrate their potential applications in healthcare monitoring, educational intervention, and driver monitoring systems. The significance of these results lies in the ability to advance human-computer interaction, enhance healthcare diagnostics, and offer innovative solutions across various domains.
Volume: 15
Issue: 5
Page: 4581-4592
Publish at: 2025-10-01
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