Bulletin of Electrical Engineering and Informatics
The Bulletin of Electrical Engineering and Informatics (BEEI), ISSN: 2089-3191, e-ISSN: 2302-9285, is open to submissions from scholars and experts in the wide areas of electrical engineering and informatics from around the world. The BEEI is an open-access peer-reviewed journal published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU) that continuously presents the results of original research, reviews, surveys, or new data/concepts across all electrical engineering, electronics, instrumentation, control, robotics, telecommunication, computer engineering, information systems, information technology, and informatics (computer science) fields of interest. Articles must be understandable and written in standard English. This journal is indexed by Scopus (Elsevier)/ScimagoJR and has CiteScore: 3.9, SNIP: 0.685, and SJR: 0.306. The Ministry of Education, Culture, Research, and Technology of the Republic of Indonesia has given this journal its SINTA 1 (S1) seal of approval. This journal is published bimonthly (February, April, June, August, October & December) in both print and online versions. All publications are available in full text and are free to download. Bulletin of Electrical Engineering and Informatics Open Access Publisher:Institute of Advanced Engineering and Science (IAES) ISSN:2089-3191E-ISSN:2302-9285 Subject area: Mathematics: Control and Optimization Computer Science: Computer Science (miscellaneous) Engineering: Control and Systems Engineering Computer Science: Computer Networks and Communications Computer Science: Information Systems Engineering: Electrical and Electronic Engineering Physics and Astronomy: Instrumentation Computer Science: Hardware and Architecture
Institute of Advanced Engineering and Science
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An internet of things-based weather system for short-term solar and wind power forecasting using double moving average
This article presents the design and implementation of an internet of things (IoT)-based weather forecasting system aimed at optimizing operational planning for renewable energy generation. The system leverages a Raspberry Pi as its central controller, integrating pyranometer and anemometer sensors for real-time data collection and predictive analytics. Utilizing the double moving average method, the system provides accurate short-term forecasts of solar and wind power outputs, which are crucial for addressing the intermittency challenges of renewable energy sources. The integration with the Blynk platform ensures user-friendly data visualization and accessibility. Results from a three-day testing phase reveal the system's high accuracy, with prediction errors of 8.79% for solar power and 16.49% for wind power. These findings underscore the system's potential to enhance energy planning, improve efficiency, and support sustainability goals. By enabling data-driven decision-making, this IoT-based forecasting system offers a scalable solution for advancing renewable energy integration into the power grid.
Copyright (c) 2025 Syafii, Imra Nur Izrillah, Aulia, Muhammad Ilhamdi Rusydi https://creativecommons.org/licenses/by-sa/4.0
The effect of the number of NACA 4412 airfoil blades on the performance of a horizontal axis wind turbine
This work reports an experimental investigation of the effect of blade number on the performance of a small-scale horizontal-axis wind turbine (HAWT) using NACA 4412 airfoil blades. Two turbine prototypes (one with 7 blades and one with 9 blades) were fabricated and tested under controlled wind speeds (3.4–6.0 m/s). The turbine outputs were measured using INA219 current/voltage sensors and a TCRT5000 rotations per minute (RPM) sensor interfaced to an Arduino-based system for real-time data acquisition. Results show that the 9-blade turbine consistently generated higher electrical power and achieved a higher power coefficient than the 7-blade design. For example, at 3.4 m/s the 7-blade turbine produced about 0.0297 W versus 0.0471 W for the 9-blade turbine. The peak power coefficient reached ≈0.198 for the 9-blade rotor (vs. ≈0.195 for 7 blades) at the same wind speed. Sensor calibration indicated high accuracy (errors <1.2%), confirming the reliability of the measurements. These findings suggest that, for the tested design, increasing the number of blades improves small-HAWT performance. The developed wireless monitoring system and experimental results provide guidance for optimizing blade count in future small turbine designs.
Copyright (c) 2025 Ahmad Fauzan Adziimaa, Jasmine Fazrin Widi Putri, I Putu Eka Widya Pratama, Ahmad Firyal Adila https://creativecommons.org/licenses/by-sa/4.0
Feasibility study of solar-diesel generation hybrid power systems: a case study of rural electrification in Papua, Indonesia
This study presents a contextually tailored off-grid hybrid energy system consisting of solar photovoltaic (PV), diesel generator (DG), and battery storage, designed for the remote mountains village of Jifak in Papua, Indonesia. The objective is to evaluate the technical, economic, environmental, and social feasibility of electrification in underserved regions. A comprehensive feasibility analysis was conducted using HOMER software, incorporating realistic communal load profiles, National Aeronautics and Space Administration (NASA) climate data, and field-based social assessments. The optimized system achieved a net present cost (NPC) of $10.43 million, a levelized cost of energy (LCOE) of $0.5095/kWh, and annual emissions of 7,965 kg CO₂. Sensitivity analysis was performed on fuel cost, discount rate, and inflation rate to assess system robustness. Beyond the technical metrics, the study assessed the socio-economic impacts of electrification, revealing improvements in lighting quality, education, productivity, income generation, and environmental awareness. The findings provide a replicable model and decision-making framework for policymakers and practitioners aiming to deploy low-carbon, sustainable electrification strategies in similarly remote regions.
Copyright (c) 2025 Marthen Liga, Aris Sampe, Johni Jonatan Numberi https://creativecommons.org/licenses/by-sa/4.0
Agrivoltaic systems: a literature review
Agrivoltaic systems integrate solar energy generation with agricultural production to achieve efficient land use and mitigate climate change. This study presents a bibliometric analysis of the scientific literature on this topic, published from 2013 to 2023, to identify key trends, research areas, and emerging topics. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology and data from the Scopus database, the analysis was conducted with the R package bibliometrix and VOSviewer software. The results show remarkable growth in scientific output since 2020, with the United States, China, and Germany as the leading countries. The findings reveal the benefits of agrivoltaic systems, such as increased crop productivity, water-use efficiency, and income diversification for farmers. Emerging topics include the optimization of panel configurations and socioeconomic implications. Despite these benefits, challenges like high initial costs, social acceptance, and the need for adaptable designs persist. The conclusions underscore the importance of specific policies and incentives to support the adoption of these technologies. This analysis provides a comprehensive overview of the state of agrivoltaic systems, offering valuable insights for researchers, policymakers, and other stakeholders.
Copyright (c) 2025 Mariajosé Durán-Cabezas, Catalina Gómez-Gaviria, Vladimir Henao-Céspedes https://creativecommons.org/licenses/by-sa/4.0
Optimized electric vehicle charging: solar-driven wireless power transfer system
Wireless power transfer (WPT) is emerging as a transformative solution to overcome the limitations of conventional plug-in charging for electric vehicles (EVs). This study aims to design and implement an efficient and reliable wireless charging system using inductive coupling with low requirements on the primary circuit. The proposed system integrates an Arduino-controlled high-frequency converter along with sensors and relays to optimize power flow, ensure safety, and reduce energy wastage. The methodology involves experimental rearrangement of transformers and frequency elements to achieve maximum efficiency while maintaining compact circuit design. Results demonstrate that the system can achieve efficient energy transfer suitable for short charging intervals, particularly beneficial for shuttle buses at stations and rental taxis at parking hubs. The findings highlight that wireless charging not only reduces total charging time but also supports cost-effective battery sizing, enabling improved vehicle turnaround and operational efficiency. In conclusion, this work contributes a weather-resistant, safe, and economically viable charging approach that sets new standards for EV infrastructure. Its implications lie in redefining charging stations along predetermined routes and stops, ultimately advancing sustainable and user-friendly electric transportation.
Copyright (c) 2025 Vipin D. Bondre, Shweta V. Bondre, Uma Yadav, Priya Dasarwar (Maidamwar), Rashmi Sharma https://creativecommons.org/licenses/by-sa/4.0
Design and development of harmonic filters for harmonics reduction in polluted distribution network
Recently due to development in the power electronics sector, there is a tremendous increase in nonlinear loads. These nonlinear loads cause distortion in the system current and result in degrading quality of power. The poor power quality causes technical and financial losses in the system which necessitates adoption of techniques to reduce the harmonic distortion to meet IEEE-standards and improve system efficiency. As per literature, passive, active and hybrid filter techniques are implemented to mitigate the harmonics. Each has merits and demerits. Constructive reduction in current harmonics improves the life and efficiency of equipment’s also assists to improve power quality and relieves penalties imposed by utilities. In this work, an attempt is made to give a detailed approach used in the designing of harmonic filters. This study will provide a broad outline to the engineer, researcher and consultant working in the field of power quality to design filters for the case under study. The steps to design the filters are well explained with mathematical equations and examples for greater insight. To validate the performance of the filter a MATLAB/Simulink platform is utilized. The outcome of the simulation result proved that current harmonics are minimized with a substantial amount.
Copyright (c) 2023 Pranita R. Chavan, Babasaheb R. Patil https://creativecommons.org/licenses/by-sa/4.0
Online PID-neural network for tracking lower limb rehabilitation exoskeleton angular position
Gait trajectory tracking control is an essential component of a lower limb rehabilitation exoskeleton (LLRE). Meanwhile, the proportional-integral-derivative (PID) controller remains popular for a variety of applications, including LLRE. Nonetheless, employing PID presents a significant issue, namely determining how to choose or tune the parameters. This paper addresses the LLRE’s hipknee angular position tracking system based on an online PID-NN controller, i.e., a PID controller, whose parameters are online modified by a trained neural network (NN). A proposed framework for designing the PID-NN controller is elaborated. Numerical verifications are carried out by comparing the performance of the PID-based control system, whose parameters have been tuned using Ziegler-Nichols (ZN), without and using NN. Performance comparisons involving the presence of external disturbance are also carried out. The simulation results show that the proposed PID-NN-based control system provides better performance with lower mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) values.
Copyright (c) 2025 Ummi Hanifah, Aura Adinda, Akif Rahmatillah, Imam Sapuan, Khusnul Ain, Harry Septanto, Rifai Chai https://creativecommons.org/licenses/by-sa/4.0
The neural network adaptive behaviour model for localization and speed control in autonomous rescue mobile robot operation
In robotic operation, an autonomous operation for a mobile robot is needed to operate smoothly, hence, a control system is needed. Numerous architectures for robotics control systems have been put forth. Regretfully, creating a control system architecture is very challenging and occasionally results in inaccuracy in control. An alternative to conventional mobile robot control has emerged to address this issue: behavior-based control system architectures. This paper addresses the behavior of an autonomous mobile robot (AMR) control system in an outdoor rescue operation. The AMR behavior will be governed by the neural network methods, which are a computational intelligence to generate a dependable control algorithm. The architecture is used to coordinate behavior, especially to localize the victims, and for speed control to find the victim location with fast timing. In localization parameters to find the victim in the disaster area, this neural network adaptive model has the smallest error, which is 3.27, compared with other models such as free space model 43.46, and empirical model 4.735. While in robot speed parameter has a low error value, which is 1.47. With this small error, we can conclude that the neural network adaptive behaviour control architecture model for rescue mobile robot operation has been successfully developed.
Copyright (c) 2025 Muhammad Hafizd Ibnu Hajar, Galang Persada Nurani Hakim, Ketty Siti Salamah, Diah Septiyana https://creativecommons.org/licenses/by-sa/4.0
Optimal tuning of robust proportional integral derivative based on sliding mode controller for an AVR system
The primary objective of the automated voltage regulator (AVR) is to maintain the terminal voltage of the synchronous generator at the specified level with great precision in power production systems. Accurate voltage regulation improves the longevity of equipment intended for operation at the specified voltage within a power system network. This study presents a robust control of an AVR system utilizing proportional integral derivative (PID) control based on sliding mode techniques. The suggested control method is implemented by utilizing the particle swarm optimization (PSO) technique to tune the parameters of the proposed controller in the AVR system. A comparative performance analysis is conducted between the proposed controller, PID controller, and (PSO-fractional order proportional integral derivative (FOPID) controller. The comparison is derived using transient response characteristics and parameter uncertainty. The results reveal that the proposed PSO-PID-sliding mode control (SMC) controller has superior performance, characterized by rapid convergence, reduced overshoot, stability achievements in time domains, and robustness against parameter fluctuations. The proposed controller has markedly enhanced the performance of the AVR system and can be effectively implemented inside it.
Copyright (c) 2025 Lemita Abdallah, Lemita Ines, Kahla Sami https://creativecommons.org/licenses/by-sa/4.0
An internet of things-enabled wearable device for stress monitoring and control
The development of a wearable sensor device integrated into the internet of things (IoT) infrastructure is presented, with functionality aimed at continuous measurement of the user's physiological parameters and their intelligent processing for real-time stress level assessment. The system enables continuous monitoring of physiological parameters, allowing early detection of stress signals and supporting adaptive behavioral responses. The hardware platform is designed to consolidate various biomedical sensors, enabling continuous acquisition and intelligent processing of physiological data in real time. During testing, heart rate (HR) ranged from 68 to 89 beats per minute (bpm), respiratory rate varied from 11 to 15 breaths per minute, and skin conductivity ranged from 63 to 77 µS. Acquired physiological data were uploaded to a cloud-based infrastructure to enable advanced processing and analysis. The system achieved an overall stress detection accuracy of 87%, and signal stability remained high even under changing conditions. The proposed wearable solution demonstrates strong potential for use in healthcare, education, and occupational environments. It also offers scalability through the integration of intelligent algorithms and additional sensor modules.
Copyright (c) 2025 Gulnur Tyulepberdinova, Ainur Abduvalova, Murat Kunelbayev, Gulshat Amirkhanova, Saltanat Adilzhanova https://creativecommons.org/licenses/by-sa/4.0
Adaptive voltage controller based on extreme learning machine for DC-DC boost converter
This study presents an adaptive voltage controller for a DC-DC boost converter using the extreme learning machine (ELM) algorithm to address the limitations of conventional control techniques under varying load and reference voltage conditions. The ELM is implemented to predict the optimal parameters of a PI controller (Kp and Ki), enabling real-time adaptability of the system. Simulation results in MATLAB/Simulink demonstrate that the proposed ELM-based proportional-integral controller (PI-ELM) outperforms both traditional PI controllers and those optimized using metaheuristic algorithms. Specifically, the controller achieved a maximum absolute error of only 0.0185 for Kp and 0.0294 for Ki across a range of operating conditions, with corresponding mean squared errors (MSE) of 0.01861 and 0.02798, respectively. These findings confirm the effectiveness of the ELM in enhancing the dynamic response and robustness of boost converter voltage regulation systems.
Copyright (c) 2025 Herlambang Setiadi, Darmansyah, Awan Uji Krismanto, Sulthon Yusuf Abdillah https://creativecommons.org/licenses/by-sa/4.0
Improved load frequency control with chess algorithm-driven optimization of 3DOF-PID controller
In contemporary hybrid power systems, persistent load fluctuations disrupt the delicate balance between electrical output and mechanical torque, thereby compromising frequency stability. Load frequency control (LFC) mechanisms are indispensable in maintaining this equilibrium, particularly in systems integrating renewable and thermal energy sources. This study introduces a three-degree-of-freedom proportional-integral-derivative (3DOF-PID) controller optimized via the novel chess optimization algorithm (COA) and evaluates its efficacy against the ant lion optimizer (ALO) and Harris Hawks optimization (HHO). Extensive MATLAB/Simulink simulations were conducted on a hydrothermal system, with performance assessed through objective functions—integral of absolute error (IAE) and integral of time-weighted absolute error (ITAE). The COA consistently yielded the lowest cumulative error values (IAE=0.1548 and ITAE=0.2965), demonstrating its superiority in steady-state performance. However, COA exhibited substantial dynamic deviations, including an overshoot of 387.79% and undershoot of 4513.8% in ∆ftie. Conversely, HHO offered a significantly enhanced transient response, achieving 0% undershoot in ∆ftie with minimal oscillatory behavior. ALO displayed moderate performance but struggled with higher undershoots and prolonged settling time. The findings underscore the criticality of algorithm selection in controller design. While COA excels in minimizing long-term errors, HHO is preferable for applications requiring heightened dynamic stability and responsiveness.
Copyright (c) 2025 Kittipong Ardhan, Natpapha Chansom, Sitthisak Audomsi, Worawat Sa-Ngiamvibool, Jagraphon Obma https://creativecommons.org/licenses/by-sa/4.0
Enabling SECS/GEM in legacy equipment: a proof of concept
The rapid adoption of Industry 4.0 (I4.0) has driven the need for automated machine-to-machine (M2M) communication in manufacturing. However, legacy equipment remains a challenge due to its incompatibility with modern protocols like semiconductor equipment and materials international (SEMI) equipment communication standard/generic equipment model (SECS/GEM). Replacing these machines is costly, making retrofitting a more viable solution. This paper proposes a modular automation software framework that enables SECS/GEM integration for legacy machines without extensive hardware modifications. The system is implemented using Raspberry Pi and Arduino, acting as an intermediary between legacy equipment and modern factory networks. The framework facilitates real-time data exchange, remote monitoring, and enhanced automation while ensuring scalability and cost-effectiveness. Experimental evaluation demonstrates improved interoperability and reduced manual intervention. This solution provides a practical and adaptable approach to integrating legacy systems into (I4.0) environments.
Copyright (c) 2025 Muhammad Syahir Kamal Fitri, Selvakumar Manickam, Shams Ul Arfeen Laghari, Siang Kok Chia, Mohamad Khairi Ishak, Shankar Karuppayah https://creativecommons.org/licenses/by-sa/4.0
Enhanced sensing of liquid levels: multipoint detection using twisted polymer optical fiber in cylindrical configurations
Recent interest in water level detection is driven by the need for accurate monitoring in sectors such as agriculture, flood management, and environmental conservation. The measuring of liquid levels and solution concentrations is critical in a variety of industrial sectors. This research is to design, implement, and test the use of plastic optical fiber (POF) as an optical sensor for sensing multi-point liquid levels and densities. The developed device includes a multi-point liquid level sensor that uses refractive index modification of macro bend POFs selectively twisted around a cylindrical column. When the POFs were submerged in various mediums such as pure water, seawater, saltwater, and cooking oil, a set of U-shaped detecting heads was designed to detect changes in liquid levels. The experimental setup included an 850 nm optical light source and a power meter to measure the output power. A comparison of the performance of straight, bent, and twisted POF forms revealed that the twisted and bent POF topologies attained a sensitivity of 30%, exceeding bending-only 21% and twisting-only 12% configurations. In summary, changes in liquid levels resulted in an increase in output power for all liquid media, highlighting the potential of POF as a reliable sensor for sensing liquid levels.
Copyright (c) 2025 Siti Khadijah Idris@Othman, Hazura Haroon, Hanim Abdul Razak https://creativecommons.org/licenses/by-sa/4.0
Comparative analysis of 5G network performance at Thailand's premier shopping centers
This paper evaluates 5G network performance across three well-known shopping malls in Bangkok: Icon Siam, Siam Paragon, and CentralWorld. The study focuses on assessing key quality of service (QoS) metrics, consisting of download (DL) speed, upload (UL) speed, and latency. Measurements were taken in various zones within each mall; including high, ground, and outdoor areas through field tests using two different mobile network operators (MNO-1 and MNO-2). The findings indicate noticeable differences in performance, with Icon Siam recording the highest average DL speed of 273.6 Mbps (MNO-1) and the outdoor zone at Siam Paragon having the lowest at 11.2 Mbps (MNO-2). While MNO-1 provided more stable UL speeds, MNO-2 showed greater variability. Latency results also highlighted MNO-1’s stronger network efficiency, often staying below 20 ms, apart from a slight increase in outdoor areas. Statistical analyses, using ANOVA and t-Test, revealed significant disparities in QoS parameters depending on location and MNO, with outdoor areas often underperforming. These results underline the importance of in-building distributed antenna systems (IB-DAS) and improved infrastructure for boosting 5G performance. Furthermore, this study offers insights that can be useful to improve network quality in high-traffic locations.
Copyright (c) 2025 Therdpong Daengsi, Pachara Srimuk, Korn Puangnak, Nattapong Phanthuna, Amnaj Prajong, Phisit Pornpongtechavanich https://creativecommons.org/licenses/by-sa/4.0
Performance analysis of a proximity-coupled triangular slot microstrip patch antenna for ship radar applications
Microstrip patch antennas are extensively utilized in modern communication systems because of their small size and simple fabrication process. Among the different patch geometries, triangular patches offer size reduction compared to their rectangular and circular counterparts, making them suitable for space-constrained applications. This study focuses on the design and analysis of an equilateral triangular microstrip antenna (ETMSA) using proximity coupled feed with a triangular slot, targeting optimal performance at 2.2 GHz. The antenna is constructed using two FR4 substrates of identical permittivity but different thicknesses (h1 and h2), with a 50-ohm microstrip line feed positioned between them. The aim is to determine the optimal values of patch surface area, slot dimensions, and upper substrate thickness to achieve maximum bandwidth, minimal return loss, and ideal voltage standing wave ratio (VSWR). Simulations and measurements confirm that the antenna achieves a 120 MHz bandwidth achieving a return loss of –42 dB and a VSWR of 1.03, demonstrating excellent agreement. These results confirm the antenna's effectiveness for fixed-beam applications in wireless communication systems, highlighting its potential for efficient and compact antenna solutions.
Copyright (c) 2025 Ismahene Ikhlef, Karima Chemachema, Farouk Grine https://creativecommons.org/licenses/by-sa/4.0
An innovative design of a frequency-tunable UHF RFID antenna for identification applications
This paper introduces the design of a new frequency-reconfigurable ultra-high frequency radio frequency identification (UHF RFID) antenna, demonstrating an innovative approach that enables dynamic adjustment of its resonance frequency. The proposed antenna design features a central dipole structure, enhanced by two hexagonal split-ring resonators (H-SRR) at each end. A T-match network is integrated into the center of the dipole, which is essential for achieving impedance matching between the antenna and the Alien Gen2 H4 RFID microchip. The antenna is designed using a Rogers 4350B substrate, a high-performance dielectric material ideal for RFID applications. With dimensions of 68×32.6×1.524 mm3, the compact antenna maintains full UHF band (860 MHz to 930 MHz) coverage compliant with International Telecommunications Union (ITU) RFID standards. This ensures that the antenna can be used in different regions around the world, offering broad compatibility with various RFID systems. The antenna's frequency reconfigurability is achieved through the integration of localized capacitors with variable values, which plays a key role in enabling precise adjustments to the antenna's center frequency across the entire UHF band. Extensive simulation results validate the effectiveness of this reconfigurable design, demonstrating that the antenna can dynamically adjust its frequency while maintaining excellent performance metrics, including impedance matching, radiation efficiency, and bandwidth. This makes the proposed antenna an ideal choice for modern RFID applications.
Copyright (c) 2025 Zakaria Errachidi, Jamal Zbitou, Noha Chahboun, Otman Oulhaj, Ahmed Lakhssassi https://creativecommons.org/licenses/by-sa/4.0
A metamaterial inspired multi band antenna using complementary split ring resonator for wireless applications
This research introduces a new printed metamaterial antenna with triple and quad bands for wireless applications. The suggested antenna is constructed of FR4 material, with two slots created in the radiating element. In addition, a circular complementary split ring resonator (C-CSRR), is carved from the ground plane. HFSS simulation software is being put into use to design, model, and measure the suggested antenna parameters in a real-world environment. The measured results indicate that an antenna with C-CSRR behind the radiating patch resonates at three distinct frequencies, including 3.5 GHz, 7.5 GHz, and 8.2 GHz, and an antenna with C-CSRR and slots on the radiating patch resonates at four different frequencies, including 3.5 GHz, 7.5 GHz, 8.8 GHz, and 9.32 GHz. An antenna without complementary split ring resonator (CSRR), or a conventional antenna, resonates at 9.6 GHz. The metamaterial antenna results in a 65% diminution in antenna size in contrast to a regular microstrip antenna. The simulated outcome demonstrates that the suggested metamaterial antenna's peak gain is around 6 dB to 8 dB and it has a resonance frequency for C-band applications, including weather radar systems and 5G applications.
Copyright (c) 2025 Harshavardhan Reddy, Rajendra R. Patil https://creativecommons.org/licenses/by-sa/4.0
Fixed and fair power allocation in downlink and uplink NOMA: outage probability analysis and bit error rate comparative study
Non-orthogonal multiple access (NOMA) is a crucial technology for upcoming radio access networks since it allows several users to use the same time and frequency resources. It is positioned as a viable option for next-generation communication systems because to its capabilities to increase system capacity and spectrum efficiency. This essay investigates the effects of fair and fixed power allocation (PA) techniques on NOMA systems' uplink and downlink performance. It specifically assesses bit error rate (BER) and outage probability (OP), two crucial performance parameters. The paper provides a thorough comparison of the fixed and fair PA approaches, highlighting the advantages, and disadvantages of each. While fixed PA is easier to deploy, results show that it performs poorly in dynamic situations, increasing BER and OP, particularly for users with less reliable channels. Fair PA, on the other hand, improves system dependability, and user fairness by dynamically allocating power depending on user situations, thus reducing OP and BER. Future wireless networks will benefit greatly from its enhanced spectrum efficiency and up to 78% reduction in outage likelihood. With fair PA's higher flexibility and effectiveness in real-world, varied circumstances, the results underline the significance of selecting appropriate PA techniques for NOMA systems.
Copyright (c) 2025 Abdelbari Falloun, Abdessalam Ait Madi https://creativecommons.org/licenses/by-sa/4.0
Artificial intelligence based on fuzzy logic for a long-range radio frequency identification reader antenna
The radiation pattern of radio frequency identification (RFID) antennas is influenced by various factors such as design, operating frequency, and polarization, which determine characteristics like directionality, omnidirectionality, beam width, and gain. Achieving precise readings over extended distances is crucial for the effectiveness of RFID systems, enabling faster item retrieval and delivery. The read distance, a critical aspect of RFID system performance, depends on factors like transmitted power, frequency, and antenna gain. Passive backscatter RFID setups particularly benefit from optimizing read distance for efficient operation. Fuzzy logic, as a soft computing technique, addresses uncertainties inherent in RFID systems effectively. This paper presents a novel approach to RFID antenna design, utilizing fuzzy logic to dynamically adjust frequency and power transmission. By enhancing field distribution, polarization, and received signal strength, this approach aims to optimize tag readings at extended distances, thereby improving overall system effectiveness. The methodology involves implementing algorithms in a C program to control the long-range distance aspects of the RFID system. Incorporating fuzzy rule algorithms into the RFID system's control logic enhances its ability to respond intelligently to changes in the operating environment, contributing to improved performance and reliability in long-range RFID applications.
Copyright (c) 2025 Hanane Sefraoui, Abdechafik Derkaoui, Abdelhak Ziyyat https://creativecommons.org/licenses/by-sa/4.0
MIMO-enhanced distributed spectrum sensing with diffusion based algorithms for cognitive radio systems
Spectrum sensing (SS) is a fundamental function in cognitive radio (CR) networks, enabling efficient spectrum utilization by identifying available channels. However, existing SS methods face challenges such as low accuracy in dynamic and low signal-to-noise ratio (SNR) environments, as well as high computational complexity. To address these issues, this paper presents a distributed SS technique that combines multiple-input multiple-output (MIMO) technology with a diffusion-based (DB) cooperative algorithm. MIMO enhances spatial diversity to improve detection performance, while the DB algorithm enables efficient collaboration among secondary users, reducing both sensing time (ST) and computational time (CT). Simulations over Rayleigh (RL) and Rician (RC) fading channels evaluated metrics such as probability of detection and false alarm. Results demonstrate that the proposed MIMO-DB method outperforms existing approaches, including honey badger remora optimization (HBRO)-AlexNet, by reducing ST by 18 seconds and CT by 45 seconds at 5 dB SNR, while achieving higher detection accuracy across varying SNR levels. These findings highlight the method’s robustness and efficiency, making it a promising solution for dynamic spectrum management in 5G, internet of thing (IoT) and other next-generation wireless systems.
Copyright (c) 2025 Srikantha Kandhgal Mochigar, Rohitha Ujjini Matad https://creativecommons.org/licenses/by-sa/4.0
Enhanced Semarang batik classification using deep learning: a comparative study of CNN architectures
Batik is an important part of Indonesia’s cultural heritage, with each region producing unique designs. In Central Java, Semarang is known for its distinctive batik patterns that reflect rich local traditions. However, many people are still unfamiliar with these designs, which threatens their preservation. This study develops an automated system to classify Semarang batik patterns, showing how technology can help safeguard cultural heritage. A convolutional neural network (CNN) approach was used to recognize ten batik types, including Asem Arang, Asem Sinom, Asem Warak, Blekok, Blekok Warak, Gambang Semarangan, and Kembang Sepatu. Pre-processing steps—such as image resizing, cropping, flipping, and rotation—improved model performance and reduced complexity. Five CNN architectures (MobileNetV2, ResNet-50, DenseNet-121, VGG-16, and EfficientNetB4) were tested using 224×224 input size, Adam optimizer, ReLU activation, and categorical cross-entropy loss. Results show VGG-16, ResNet-50, and DenseNet-121 achieved perfect accuracy (1.0) on a dataset of 3,000 locally collected images. These findings highlight CNN models’ strong potential for batik pattern recognition, supporting digital preservation of Indonesian culture.
Copyright (c) 2025 Edy Winarno, Achmad Solichan, Aditya Putra Ramdani, Wiwien Hadikurniawati, Anindita Septiarini, Hamdani Hamdani https://creativecommons.org/licenses/by-sa/4.0
Multi-feature fusion framework for enhanced image deduplication accuracy using adaptive deep learning
Image deduplication is a critical task in domains such as digital asset management, content-based image retrieval (CBIR), and data storage optimization. This paper presents a novel method for improving deduplication accuracy by integrating multiple feature types. A comprehensive framework is proposed that combines visual, semantic, and structural image elements. The system employs deep learning architectures, including convolutional neural networks (CNNs) and transformers, to extract high-level features, which are fused through an adaptive weighting mechanism that dynamically adjusts based on image content. Experimental results across diverse datasets demonstrate that the proposed multi-feature fusion approach significantly outperforms traditional single-feature methods, achieving an average improvement of 15% in deduplication accuracy. By overcoming limitations in handling complex visual similarities, this study introduces a more robust and efficient solution for image deduplication.
Copyright (c) 2025 Rahul Shah, Ashok Kumar Shrivastava https://creativecommons.org/licenses/by-sa/4.0
Integration of deep learning algorithms for real-time vehicle accident detection from surveillance videos
Major road accidents have increased due to the rapid rise of vehicles on the roads due to affordability and accessibility. While minor accidents can be resolved without the need for escorting to hospitals, significant accidents that involve the deployment of airbags necessitate the immediate attention of authorities. Thus, subsequent action of first aid and proper communication to concerned medical personnel can avoid most fatalities from accidents. The system involves the automatic detection of traffic accidents from videos extracted by closed-circuit television (CCTV) surveillance. In case of an accident, the system will detect and information about the accident will be instantly relayed to the nearest medical center. We have implemented different machine learning models such as Resnet-18, VGG-16, LeNet, and Inception V1 to ensure the accuracy of accident detection. From comparing all these models, the convolutional neural network (CNN) model shows the highest accuracy of 98%. The quick response will be an important step toward a safer and more secure transportation landscape.
Copyright (c) 2025 Riya Mota, Renuka Wankhade, Gitanjali Rahul Shinde, Rutuja Rajendra Patil, Grishma Bobhate, Gagandeep Kaur https://creativecommons.org/licenses/by-sa/4.0
The use of fiber bragg grating coated with polyimide for CO2 gas sensor
This study presents the application of fiber bragg grating (FBG) sensors coated with polyimide for detecting carbon dioxide (CO₂) gas, employing both theoretical and experimental approaches. The basic FBG components were coated with polyimide layers of varying thicknesses. Subsequently, the fabricated FBG sensors were characterized using an optical interrogator system with four channels. Furthermore, the sensor was tested for CO₂ detection at a working temperature of 47 °C. Experimental data showed that the FBG sensor coated with polyimide layers of 10 nm, 15 nm, and 20 nm demonstrated sensitivities of 1.9 ppm, 1.84 ppm, and 1.8 ppm, respectively. In contrast, the uncoated FBG sensor exhibited a higher sensitivity of 3 ppm. Increasing the coating thickness beyond 20 nm leads to a decrease in sensor sensitivity. The findings suggest that an optimal polyimide coating thickness for CO₂ detection using FBG sensors is around 20 nm. Achieving high sensitivity in CO₂ gas sensors is crucial for their effective use across a broad range of applications.
Copyright (c) 2025 Dedi Irawan, Saktioto, Azhar, Sutoyo, Muhammad Sahal, Dwi Hanto, Bambang Widiyatmoko https://creativecommons.org/licenses/by-sa/4.0
Optimizing gaussian filter implementation for canny edge detection using graph-based MCM algorithms
This study presents an optimized implementation of the gaussian filter in the Canny edge detection algorithm, focusing on reducing computational complexity while balancing power, timing, and resource utilization. Traditional implementations rely on the common subexpression elimination (CSE) algorithm for multiplierless constant multiplication, which results in high logic operations and resource consumption. To address this, we explore the constant array vector multiplication (CAVM) technique with two graph-based algorithms (exact GB and approximate GB). These algorithms offer a novel graph-structured approach to constant multiplication, differing from existing methods by modeling multiple paths to achieve optimal adder reuse. The architectures were implemented using Xilinx system generator (XSG) and evaluated in Vivado 2018.1. Experimental results reveal that both exact GB and approximate GB reduce logic operations and improve timing performance compared to CSE_csd. Among them, approximate GB achieves the fastest computation and lowest LUT utilization, making it the most hardware-efficient design. However, it exhibits the highest power consumption, whereas exact GB offers the best trade-off between speed and power efficiency. This optimization framework shows potential not only in image processing but also in embedded vision systems and low-power digital signal processing (DSP) applications. These findings demonstrate that GB Algorithms can effectively optimize gaussian filter design for real-time image processing applications.
Copyright (c) 2025 Lowkya Chandaka, Madhavi Dunna https://creativecommons.org/licenses/by-sa/4.0
Handling partial occlusions in facial expression recognition with variational autoencoder
Facial expression recognition (FER) is essential in various domains such as healthcare, road safety, and marketing, where real-time emotional feedback is crucial. Despite advancements in controlled settings such as well-lit, frontal, and unobstructed conditions, FER still faces significant challenges in natural, unconstrained environments. One of the most difficult issues is the presence of occlusions, which obscure key facial features. To overcome this, multiple strategies have been proposed, generally falling into two categories: those focused on analyzing visible facial regions and those aimed at reconstructing hidden facial features. In this study, we present a variational autoencoder (VAE)-based solution designed to reconstruct facial features obscured by occlusions. Experimental results show our VAE model optimized with the structural similarity index measure (SSIM) cost function achieves superior performance, with recognition rates of 91.2% for eye occlusions and 89.7% for mouth occlusions. The SSIM-optimized VAE effectively reconstructs occlude facial features while preserving structural details, demonstrating significant improvements over conventional approaches. This VAE-based solution proves particularly robust for real-world scenarios involving common facial obstructions like masks or sunglasses, making it valuable for applications in healthcare monitoring, driver safety systems, and human-computer interaction.
Copyright (c) 2025 Abdelaali Kemmou, Adil El Makrani, Ikram El Azami, Moulay Hafid Aabidi https://creativecommons.org/licenses/by-sa/4.0
Optimized weighted non-local mean filter for enhanced denoising and improved quality of medical images
Image quality is significantly influenced by noise, light, and artifacts, particularly in medical images where precision is essential for accurate diagnosis. Denoising is a significant pre-processing for enhancing the overall quality of images to enable efficient classification, feature extraction, and segmentation. Conventional denoising filters smooth out boundaries and lose texture because they are ineffective to process color images. To address these limitations, a weighted factor-based non-local means (WF+NLM) filter is proposed as an improvement over the non-local means (NLM) filter, with an additional weight factor based on pixel similarity. This addition reduces blurring while maintaining fine details, resulting in improved quality. The proposed filter performs effectively in blood smear images, with a peak signal-to-noise ratio (PSNR) of 39.6904, SSIM of 0.9551, and gradient SSIM of 0.9889. Statistical tests indicates that the WF+NLM filter improves image quality in terms of structure, gradients, and feature similarity. Statistical inference for a one-tailed paired t-test validates statistical significance with the highest t value of 9.323829 with p-value 0.00037 by the wavelet-based non-local moment mean (W-NMM) filter asserts higher image restoration quality.
Copyright (c) 2025 Aiswarya Senthilvel, Krishnaveni Marimuthu, Subashini Parthasarathy https://creativecommons.org/licenses/by-sa/4.0
Cross-cultural prediction of marital satisfaction using machine learning algorithms and generic needs
Marital satisfaction is crucial for individual well-being and family stability. Prior research has predominantly focused on Western contexts using traditional statistical models, limiting the generalizability of findings across cultures. This study addresses a significant gap by employing machine learning algorithms Naive Bayes, support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) on a diverse dataset comprising responses from 7,178 participants across 33 countries. Our methodology includes a robust data preprocessing pipeline, feature selection, and algorithm evaluation, emphasizing their practical application in relationship interventions. Using predictors derived from Maslow's generic needs, including love, respect, and pride in one's spouse, we demonstrate that these factors are significant cross-cultural predictors of marital satisfaction. Our results show that pride in spouse, love, and respect for spouse are the most significant predictors of marital satisfaction across cultures. This demonstrates the effectiveness of machine learning in capturing complex relationships, offering more accurate predictions than traditional methods. These findings suggest that fostering love, respect, and sacrifice in early relationships can significantly enhance marital satisfaction across diverse cultural contexts.
Copyright (c) 2025 Khye Sponge, Kok-Why Ng, Choo-Yee Ting, Ian Chai https://creativecommons.org/licenses/by-sa/4.0
MAS-TENER: a modified attention score transformer encoder for Indonesian skill entity recognition
Skill entity recognition is a crucial task for aligning educational curricula with the evolving needs of the industry, particularly in multilingual job markets. This study introduces modified attention score transformer encoder (MAS-TENER), a novel transformer-based model designed to enhance the recognition of skill entities from Indonesian job descriptions. The proposed model modifies the attention mechanism by integrating relative positional embeddings and removing the scaling factor in self-attention. These improvements enhance the context of tokens, allowing for the accurate establishment of hard skills, soft skills, and technology skills. The MAS-TENER model was pre-trained and fine-tuned using a combinF.ation of job description datasets and additional corpora, achieving an F1-score of 90.46% at the entity level. The experimental results demonstrate the model's ability to handle unstructured, mixed-language job descriptions, with significant potential for curriculum reform and the development of new workforce capabilities. The study demonstrates the efficacy of the MAS-TENER model as an effective response for any natural language processing (NLP) task in low-resource languages. Moreover, the scope of long-term job market analytics in action research has been a key skill set in the education policy arena, demonstrating collaborative workforce capabilities.
Copyright (c) 2025 Meilany Nonsi Tentua, Suprapto, Afiahayati https://creativecommons.org/licenses/by-sa/4.0
Low-cost internet of things system for water metering in smart campus
Internet of things (IoT) technologies are transforming the monitoring of water distribution networks (WDN) and urban water infrastructure (UWI), as well as smart campus infrastructures, which has the same problems as an urban water network, such as leaks, inaccurate readings, and unnecessary expenses. Smart water meters (SWM) represent an economical IoT solution for remotely monitoring system parameters such as flow rate, pressure, and water quality to reduce losses. This paper introduces an IoT-based smart water metering solution employing message queuing telemetry transport (MQTT), long range (LoRa), a middleware for IoT, and low-cost sensors, implemented at the Federal Institute of Para´ıba, Brazil, as an initial effort toward establishing a smart campus. The evaluation of the IoT device showed a measurement performance index (MPI) of 97.83%, with a flow sensor error margin (FS400A) below 2% for calibrated ranges. The quality of the wireless link yielded an average RSSI of-89 dBm and a packet error rate of 0.35%. The IoT system demonstrated potential as a feasible smart campus application
Copyright (c) 2025 Átila de Souza Medeiros, Ruan Delgado Gomes, Anderson F. B. F. da Costa https://creativecommons.org/licenses/by-sa/4.0
Performance analysis of 3D assets in virtual reality simulations for climate change: a case study in sustainable energy systems
This study investigates the performance impact of 3D assets in a virtual reality (VR) simulation designed for climate change education, aiming to balance visual fidelity and system efficiency on standalone headsets. Using a case study modeled on a sustainable energy environment, key performance metrics frames per second (FPS), triangle count, and draw calls were measured to assess the effect of object density, material transparency, and batching strategies. Experimental results show that configurations with 20 trees and 20 characters maintained 101 FPS, while denser scenes with 30 trees and 30 characters dropped to 79 FPS approaching the minimum usability threshold for VR. Transparent tree foliage with alpha-cutout materials imposed higher graphics processing unit (GPU) loads than high-triangle opaque character models, highlighting the performance cost of material complexity. These findings offer practical guidelines for optimizing asset configurations in immersive educational VR content. Future work may explore integration of artificial intelligence (AI) behavior and user interaction to assess broader system performance.
Copyright (c) 2025 Cahya Miranto, Ardiman Firmanda, Hestiasari Rante, Sritrusta Sukaridhoto, Muhammad Agus Zainuddin, Haolia Rahman https://creativecommons.org/licenses/by-sa/4.0
An optimized deep learning framework based on LEE for real time student performance prediction in educational data
Predicting student performance in real-time remains a critical challenge in educational data mining (EDM), especially with large, noisy, and high-dimensional datasets. This study proposes an advanced deep learning framework that integrates learning entropy estimation (LEE) with models such as support vector machines (SVM), you only look once (YOLO), recurrent convolutional neural networks (RCNN), and artificial neural networks (ANN) to enhance feature selection and classification accuracy. The framework follows a systematic pipeline involving data preprocessing, LEE-based feature extraction, and model training on a real-time academic dataset comprising student demographics, attendance, and performance metrics. Among the proposed models, the LEE-based YOLO (LBYOLO) achieved the highest testing accuracy of 93% and the fastest execution time of 1.84 seconds, while the LEE-based ANN (LBANN) demonstrated consistent performance across precision, recall, and F1-score. The results confirm the superiority of deep learning methods over traditional machine learning techniques for educational prediction tasks. This approach enables early detection of at-risk students and supports timely, data-driven educational interventions. Future work will focus on adaptive learning systems and multi-platform student behavior analysis to support personalized education strategies.
Copyright (c) 2025 Ramaraj Muniappan, Sowmya Devi Devarajan, Lavanya Subbarayalu Ramamurthy, Ayshwarya Balakumar, Prathap Gunaseelan, Shyamala Palanisamy, Srividhya Selvaraj, Dhendapani Sabareeswaran, Ilango Bhaarathi https://creativecommons.org/licenses/by-sa/4.0
Optimizing job scheduling on cloud resources using the first-come, first-served-SlotFree method
Cloud computing environments encounter significant challenges in job scheduling, particularly due to excessive waiting times and inefficient resource utilization associated with conventional algorithms such as first-come, first-served (FCFS) and backfilling. This study introduces FCFS-SlotFree, a novel scheduling algorithm that enhances resource allocation efficiency by dynamically sorting jobs based on their arrival times and workloads, and subsequently assigning them to a fixed set of virtual machines (VMs) without relying on rigid time-slot constraints. This flexible scheduling approach facilitates better adaptation to heterogeneous workloads. Extensive experiments conducted under realistic cloud scenarios demonstrate that FCFS-SlotFree significantly reduces average waiting time (AWT) by approximately 32.78% compared to FCFS and by 9.68% compared to backfilling, while concurrently improving resource utilization by 3.58% and 1.27%, respectively. The results substantiate the algorithm’s effectiveness in optimizing scheduling performance and resource efficiency within complex cloud environments.
Copyright (c) 2025 Ardi Pujiyanta, Fiftin Noviyanto, Taufiq Ismail https://creativecommons.org/licenses/by-sa/4.0
Comparative performance analysis of LSTM, GRU, and bidirectional neural networks for political ideology classification
Political ideology classification is crucial for understanding social polarization, monitoring democratic processes, and identifying bias on online platforms. This study compares the performance of long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional GRU (Bi-GRU) neural network models in classifying liberal and conservative political ideologies from social media text data. The Bi-GRU achieved the best results with 88.75% accuracy and 89.16% F1-score, highlighting its strength in contextual analysis. These findings suggest their applicability in areas such as election monitoring and the analysis of political discourse. This study contributes to the field of political text classification by offering a comparative analysis of deep learning architectures. The dataset utilized covers a wide range of issues, including social, political, economic, religious, and racial topics, demonstrating its comprehensive nature. Visualizations using WordCloud and uniform manifold approximation and projection (UMAP) reveal distinct ideological patterns, validating the dataset’s quality for training models. The findings underscore the importance of utilizing advanced bidirectional architectures for nuanced tasks, such as ideology classification, where contextual understanding is crucial. These insights open avenues for future research, such as the application of Bi-GRU in analyzing multilingual political ideologies or real-time sentiment tracking during election campaigns.
Copyright (c) 2025 Lasmedi Afuan, Nurul Hidayat, Ipung Permadi, Iqbal, Didit Suprihanto, Panky Bintang Pradana Yosua, Reyno Alfarez Marchelian https://creativecommons.org/licenses/by-sa/4.0
Enhancing data integrity in internet of things-based healthcare applications: a visualization approach for duplicate detection
This study addresses the critical issue of data duplication in healthcare-related internet of things (IoT) datasets, which can compromise the reliability of analyses and patient outcomes. A Python-based visualization framework using Pandas and Matplotlib was developed to detect and represent duplicate records. The methodology was applied to six cancer-related datasets sourced from Kaggle, ranging from 300 to 55,000 records, encompassing numerical, textual, and categorical data types. The visualization technique provided clear insights into duplication patterns, identifying specific counts such as 7 duplicates in the wearable device dataset, 19 in the thyroid recurrence dataset, and 534 in the synthetic healthcare electronic health record (EHR) dataset. Compared to traditional detection methods, the visualization tool facilitated faster and more intuitive initial data assessment, demonstrating its effectiveness for rapid quality checks in healthcare datasets. However, scalability limitations were observed in larger datasets, where visual clarity declined. These findings highlight the value of visualization as a preliminary data quality assessment tool and suggest future integration with advanced detection algorithms to enhance robustness and scalability.
Copyright (c) 2025 Siti Noor Basirah Md Isa, Nurul A. Emran, Norharyati Harum, Logenthiran Machap, Azlin Nordin https://creativecommons.org/licenses/by-sa/4.0
Evaluating digital competency among statistical educators: a comparative analysis of input-oriented DEA models
As the educational landscape shifts towards online learning, assessing educators' digital competencies has become crucial. This study aims to evaluate the digital competencies of university educators using data envelopment analysis (DEA), specifically comparing the banker, charnes, and cooper (BCC) input-oriented models (super efficiency and Bi-O multi-criteria data envelopment analysis (MCDEA) super efficiency BCC models). The research was conducted in three phases. Initially, the BCC model assessed educators' digital competencies. Subsequently, the Bi-O MCDEA model evaluated these competencies within an online learning context. Finally, the effectiveness of the two models was compared. Data was collected through a survey administered to 30 educators from Universiti Teknologi MARA, with a response rate of 75%. Results showed that while the BCC model identified 23 out of 30 educators as efficient, the Bi-O MCDEA model recognized only two as efficient. This discrepancy highlights the different stringencies of the models and their impact on assessing digital competencies. The super efficiency (SE) model was then used to rank the efficient educators to determine the most proficient. The study underscores the need for precise assessment tools in online education to enhance digital competencies effectively. It suggests that integrating advanced DEA models can significantly improve the identification and training of educators, thereby enriching the educational outcomes in digital environments.
Copyright (c) 2025 Nor Faezah Mohamad Razi, Jufiza A. Wahab, Anis Zafirah Azmi, Norhayati Baharun, Suraya Masrom, Nor Aslily Sarkam https://creativecommons.org/licenses/by-sa/4.0
Text clustering for analyzing scientific article using pre-trained language model and k-means algorithm
Text clustering is a technique in data mining that can be used for analyzing scientific articles. In Indonesia-accredited journals, SINTA, there are two languages used, Indonesian and English. This is the first research focusing on clustering Indonesian and English texts into one cluster. In this research, bidirectional encoder representations from transformers (BERT) and IndoBERT are used to represent text data into fixed feature vectors. BERT and IndoBERT are pre-trained language models (PLMs) that can produce vector representations that take care of the position and context in a sentence. To cluster the articles, the K-Means algorithm is implemented. This algorithm has good convergence and adapts to the new examples, which helps in improved clustering performance. The best k-value in the K-Means algorithm is defined by using the silhouette score, the elbow method, and the Davies-Bouldin index (DBI). The experiment shows that the silhouette score can produce the most optimal k-value in clustering the articles, which has a mean score of 0.597. The mean score for the elbow method is 0.425, and for the DBI is 0.412. Therefore, the silhouette score optimizes the performance of PLMs and the K-Means algorithm in analyzing scientific articles to determine whether in scope or out of scope.
Copyright (c) 2025 Firdaus, Siti Nurmaini, Novi Yusliani, Muhammad Naufal Rachmatullah, Annisa Darmawahyuni, Yesi Novaria Kunang, Muhammad Fachrurrozi, Risky Armansyah https://creativecommons.org/licenses/by-sa/4.0
Driver activity recognition using deep learning based on multi-step batch size up
The increasing popularity of electric motorbikes in Indonesia, while promoting sustainable mobility, also raises concerns regarding traffic safety. Given the high incidence of motorcycle-related accidents, there is a critical need for systems capable of monitoring and recognizing driver behavior. This study proposes a driver activity recognition system for electric motorbikes, utilizing an event data recorder (EDR) to capture seven key sensor signals: three-axis acceleration, voltage, current, power, and speed. A custom dataset was constructed using data collected from 10 subjects, each performing five driving activities including forward drive, brake, stop, left turn, and right turn for over three-minute intervals per activity. The classification model is based on a long short-term memory (LSTM) neural network. To optimize training efficiency, a multi-step batch size up (MSBU) strategy was introduced, which accelerates training time by 1.84× compared to a fixed batch size of 32. The best performance was achieved using a segment length of 75 time-steps, yielding an accuracy and macro F1-score of 0.9873. These results demonstrate the effectiveness of the proposed system for real-time driver behavior monitoring and activity recognition in electric motorbike applications.
Copyright (c) 2025 Darmawan Utomo, Natanael Indria Prambodo, Budihardja Murtianta https://creativecommons.org/licenses/by-sa/4.0
Explainable artificial intelligence for multiclass prediction model of suicide attempt
Suicide attempt prediction is a challenging classification problem that involves a variety of risk factors in individuals with various medical conditions. Accurate risk stratification prediction is hampered by the absence of reasons for those who have attempted suicide and developing prediction model is challenged to be explained. Therefore, this work aimed to develop a multiclass prediction model for suicide attempts and to use Shapley additive explanations (SHAP), an explainable artificial intelligence (XAI) method to analyze the prediction model for suicide attempts in explaining the decision of the model. The prediction model is trained using machine learning approaches, random forest (RF) and gradient boosting (GB), on a clinical dataset of patients with chronic diseases. GB demonstrated higher accuracy with 0.81 than RF with 0.78 for multiclass classification results (no risk, low risk, moderate risk, and high risk). By analyzing the SHAP explanations, clinicians can gain valuable insights into the factors contributing to suicide attempt predictions in patients with chronic diseases. This enhanced understanding can facilitate more informed and appropriate treatment decisions, potentially leading to improved patient outcomes and targeted interventions.
Copyright (c) 2025 Noratikah Nordin, Mohd Halim Mohd Noor, Zurinahni Zainol, Chan Lai Fong, Ryna Imma Buji https://creativecommons.org/licenses/by-sa/4.0
From Interaction Flow Modeling Language to Symfony: an automated model transformation methodology
Web application development has become increasingly complex with the rise of modern frameworks and user-centric architectures. Ensuring efficient and reliable development processes requires adopting structured methodologies that bridge abstract models with platform specific implementations. This paper presents a methodology for automating the transformation of Interaction Flow Modeling Language (IFML) models into Symfony models using the Atlas Transformation Language (ATL). The proposed approach supports model-driven architecture (MDA) principles and bridges the gap between abstract user interaction models in platform independent model (PIM) level and platform specific model (PSM) level. By leveraging IFML’s user-centered modeling capabilities and Symfony’s model-view-controller (MVC) framework, our solution enables the automatic generation of reliable, structured front-end architectures. Two case studies demonstrate the feasibility of the approach. This work contributes to the automation of model driven web development by offering a scalable and reusable transformation process from IFML to Symfony.
Copyright (c) 2025 Abir Sajji, Yassine Rhazali, Youssef Hadi https://creativecommons.org/licenses/by-sa/4.0
Fast lightweight convolutional neural network for Turkish sentiment analysis
This study presents a fast, lightweight, and high-performing fast convolutional neural network (Fast CNN) model tailored for Turkish sentiment analysis (SA). The agglutinative morphology of Turkish, combined with the limited availability of high-quality linguistic resources, introduces significant challenges for conventional approaches. To address these issues, we propose a streamlined Fast CNN architecture consisting of an embedding layer, global max-pooling, dropout, and fully connected layers. Despite its simplicity, the model outperforms seven state-of-the-art convolutional neural network (CNN)-based systems across four benchmark Turkish sentiment datasets. It achieves an average area under the curve (AUC) of 0.94, representing a 6.8% improvement over the strongest baseline and a gain of over 80% relative to several deeper architectures. In addition to its superior accuracy, the model demonstrates reduced computational complexity, making it well-suited for real-world deployment in resourceconstrained environments. Potential applications include customer feedback mining and digital marketing analytics in Turkish-language domains.
Copyright (c) 2025 Saed Alqaraleh, Abdul Hafiz AbdulHafiz https://creativecommons.org/licenses/by-sa/4.0
A deep Q-learning approach for adaptive cybersecurity threat detection in dynamic networks
Cybersecurity faces persistent challenges due to the rapid growth and complexity of network-based threats. Conventional rule-based systems and classical machine learning approaches often lack the adaptability required to detect advanced and dynamic attacks in real time. This study introduces a deep Q-learning framework for autonomous threat detection and mitigation within a simulated network environment that reflects realistic traffic, malicious behaviors, and system conditions. The framework incorporates experience replay and target network stabilization to strengthen learning and policy optimization. Evaluation was performed on a synthesized dataset containing benign traffic and multiple attack categories, including distributed denial of service (DDoS), phishing, advanced persistent threats, and malware. The proposed system achieved 96.7% detection accuracy, an F1-score of 0.94, and reduced detection latency to 50 ms. These results surpassed the performance of rule-based methods and traditional classifiers such as support vector machines, random forests, convolutional neural networks, and recurrent neural networks. A central contribution lies in combining dynamic feature selection with reinforcement learning (RL), allowing the agent to adapt to evolving threats and diverse network conditions. The findings demonstrate the potential of deep reinforcement learning (DRL) as a scalable and efficient solution for real-time cybersecurity defense.
Copyright (c) 2025 P. Shyamala Bharathi, Sathish Kumar Selvaperumal, Narendran Ramasenderan, V. Thiruchelvam, Deepak Arun Annamalai, M. Jaya Bharatha Reddy https://creativecommons.org/licenses/by-sa/4.0
Improvement on the handover technique for 5G network using fuzzy logic algorithm
Beyond 5G (B5G) networks require advanced handover algorithms to guarantee seamless connectivity and optimum quality of service. Traditional handover methods are not sufficient to meet the stringent latency and reliability requirements of next-generation networks. To meet these challenges, the integration of fuzzy logic into handover algorithms offers a viable solution. The proposed approach utilizes parameters such as reference signal received power (RSRP), reference signal received quality (RSRQ), signal-to-interference plus noise ratio (SINR), and user equipment (UE) speed as inputs, while dynamically adjusting the time-to-trigger (TTT) and handover margin (HOM) as outputs. To assess the effectiveness of this algorithm, handover latency (HOL) and handover interruption time (HIT) are evaluated and compared with existing algorithms in the literature. The results show better and more efficient performance in both terms of latency and interruption time.
Copyright (c) 2025 Samia Hakkou, Tomader Mazri, Nabil Hmina https://creativecommons.org/licenses/by-sa/4.0
Prediction of postpartum depression in Zacatecas Mexico using a machine learning approach
Postpartum depression (PPD) is a silent disorder, difficult to detect by the mother who suffers from it. In this research project, we propose a classification model of PPD using machine learning (ML) techniques, following a supervised learning approach. This is model allows the prediction of PPD using sociodemographic and medical data through a dataset of 100 Zacatecan mothers previously classified with the result of Edinburgh Test. We use eight ML algorithms such as adaptative boosting classified (ABC), principal component analysis (PCA) boosting, decision trees (DT), k-nearest neighbors (KNN), support vector machines (SVM), random forests (RF), and boosting. Our results show that the proposed ML model based on ABC algorithm can outperform other classifiers yielding a precision of 90%, a recall of 90%, a F1-score of 78% and 74% for area under curve (AUC), illustrating a correct capability in the prediction of this disorder.
Copyright (c) 2025 Lopez-Veyna J. Ivan, Ortiz-Garcia Mariana, Diaz-Diaz Alvaro Moises, Bermejo-Sabbagh Carlos https://creativecommons.org/licenses/by-sa/4.0
Evaluating the impact of risk management and cybersecurity on decision-making in the Peruvian National Informatics System
This research addresses the influence of risk management (RM) and cybersecurity (CIB) on decision making (DM) of the Peruvian State's National Informatics System (SNIEP) in the year 2024, in line with sustainable development goal 9. Using a quantitative, non-experimental, cross-sectional design approach, a sample of 487 CIB analysts was analyzed to explore the relationship between these critical variables. The findings show uneven implementation of RM and CIB practices, which significantly impact the DM and quality of information (Sig 0.000), processes (Sig 0.001), and the effectiveness of system decisions (Sig 0.060). In addition, key areas were identified to strengthen the integration of RM and CIB strategies in the state's digital environment, highlighting their importance to ensure informed and resilient decisions in the face of growing cyber threats. The study provides empirical evidence on their impact on the quality, effectiveness and security of DM in government digital environments. The research contributes both to the development of a theoretical framework that articulates concepts of RM, CIB, and DM in the public sector, and to the formulation of strategies and policies that promote a secure and efficient digital infrastructure, aimed at improving public services and citizen trust in the contemporary digital environment.
Copyright (c) 2025 Frank Agustín Olivos Estrada, Willian Sebastian Flores-Sotelo, Carmen Rosa Barrera-Avalos, Williams Arturo Martínez-Aberga, Richard Sulca-Guillen, Jorge Miguel Chávez- Díaz https://creativecommons.org/licenses/by-sa/4.0
Sinusoidal modelling for efficient source coding of phonocardiogram signals in cardiac monitoring devices
This study focuses on developing an efficient and cost-effective approach for compressing Phonocardiogram (PCG) signals without compromising their quality. The method utilizes two data compression techniques, capturing heart sounds and transforming them into the frequency domain to extract essential features such as frequency, phase, and amplitude peaks. The compressed signals are subsequently reconstructed to faithfully replicate the original heart sounds. The findings contribute to advancements in biomedical signal processing and compression methodologies, with potential applications in telemedicine and remote sustainable healthcare systems. Compressed PCG signals enable real-time remote consultations and continuous cardiac health monitoring, particularly in underserved regions with limited medical resources. This research holds significant potential for improving access to cardiovascular healthcare and promoting overall health and well-being.
Copyright (c) 2025 Samer Alabed, Mohammad Al-Rabayah, Bahaa Al-Sheikh, Lama Bou Farah https://creativecommons.org/licenses/by-sa/4.0
Optimized XGBRF-CatBoost model for accurate polycystic ovary syndrome prediction using ultrasound imaging
Polycystic ovary syndrome (PCOS) is a multifactorial endocrine disorder characterized by hyperandrogenism, anovulation, oligomenorrhea, and ovarian microcysts, often resulting in infertility, obesity, and dermatological issues. This study proposes a hybrid machine learning (ML) framework for accurate PCOS prediction using ovarian ultrasound imaging and clinical parameters. A gradient regression-based multilayer perceptron neural network (GRMPNN) is employed for feature selection, followed by a stacked ensemble classifier combining extreme gradient boosted random forest (XGBRF) and CatBoost for final diagnosis. The dataset comprises 541 anonymized patient records from Ghosh Dastidar Institute for Fertility Research (GDIFR), incorporating 45 clinical, hormonal, and imaging features. Preprocessing includes normalization, noise reduction, and random oversampling to address class imbalance. Feature selection using univariate statistical testing and chi-square ranking identified 13 key attributes. The proposed XGBRF–CatBoost model achieved accuracy, precision, recall, and F1-score exceeding 98% across both benchmark datasets, outperforming principal component analysis (PCA) and neural fuzzy rough subset evaluating (NFRSE)-based models. This framework enhances diagnostic precision, reduces computational complexity, and supports scalable integration into clinical workflows. The findings underscore the potential of artificial intelligence (AI)-assisted tools in reproductive medicine and present a reproducible, interpretable approach for early PCOS detection.
Copyright (c) 2025 Boobalan Annamalai, Sudhakar Periyasamy https://creativecommons.org/licenses/by-sa/4.0
The A3C-CCTSO-R2N2 algorithmic framework for precise edge-cloud parameter estimation
Efficient resource allocation is crucial in fog computing environments due to dynamic conditions and different user requirements; this work addresses the scheduling issues of internet of things (IoT) applications in such situations. Our proposed method, chaotic crossover tuna swarm optimizer (CCTSO), is based on metaheuristics and aims to reduce energy usage, reaction time, and SLA breaches; it should help with these problems. Improved system responsiveness and dependability are outcomes of the suggested approach's use of machine learning models for scheduling decision prediction and dynamic workload adaptation. The framework achieves a good balance between performance and energy efficiency by adjusting critical parameters and application settings. By reducing energy usage, reaction time, and operational cost while retaining reduced service level agreement (SLA) violation rates, our solution greatly outperforms previous techniques, according to experimental assessments. In real-world implementations, our results demonstrate that CCTSO is a strong solution for fog-based IoT scheduling, providing greater scalability and adaptability. Taken together, the results of this study provide a strong algorithmic foundation for better resource management in cloud, fog, and edge computing environments.
Copyright (c) 2025 Gangadharan Manonmani, Krishnasamy Ponmozhi, Krishnasamy Balasubramanian https://creativecommons.org/licenses/by-sa/4.0
CODE NET: COVID-19 segmentation and detection via deep learning based networks
Humans with COVID-19 have an infectious condition that affects the respiratory system. In addition to more serious conditions, headaches may be fatal for those who have the disease. Our difficulty with COVID-19 detection stems from the unreliability of computed tomography (CT) and magnetic resonance imaging (MRI) scans in identifying lung abnormalities. COVID-19 detection is a time-consuming process. In this research, a novel CODE NET model is proposed for the detection of COVID-19 virus from the gathered lung chest X-ray (CXR) images. The images are pre-processed utilizing an adaptive trilateral filter to improve the quality of the images. A reverse edge attention network (RE-Net) uses enhanced images to segment the CXR images for accurate virus detection. The segmented images are fed into a Link Net to extract relevant features and classify the COVID-19 cases. The classified cases are fed into the Grad-CAM model to generate heat maps for accurately detecting the virus. According to the result, the proposed model attains 99.75% of accuracy rate for the COVID-19 detection. The proposed CODE NET enhances the overall accuracy by 1.78%, 1.51%, and 2.20% over combined domain features-random forest (CDF-RF), Bayes-SqueezeNet, and bidirectional long short-term memory (Bi-LSTM) respectively.
Copyright (c) 2025 Fareesa Amina, Krishnanaik Vankdoth https://creativecommons.org/licenses/by-sa/4.0
Harmony haven: usability evaluation of matrimonial websites for cultural sensitivity in Bangladesh
In the digital age, matrimonial websites have become essential platforms for matchmaking, particularly in culturally diverse societies like Bangladesh. This study explores the usability and cultural sensitivity of five popular matrimonial websites—Biyeta, OrdhekDeen, SensibleMatch, Shaadi.com, and Bangladeshi Matrimony—tailored for Bangladeshi users. Using Nielsen's Heuristics, the research identifies key usability issues and cultural misalignments. Data were collected via a questionnaire with 105 respondents, focusing on usability and cultural relevance. The results revealed that OrdhekDeen was the most used platform, with 40% of users preferring it, followed by Bangladeshi Matrimony at 23.8%. Usability issues were categorized, with 46.7% of users reporting challenges in navigating websites and 44.8% finding design consistency lacking. However, error prevention was highly rated, with 54.3% stating they never mistakenly sent messages due to unclear prompts. The study concludes with recommendations for improving cultural alignment and usability to enhance user satisfaction.
Copyright (c) 2025 Firoz Hasan, Md. Hasan Moon, Tanvir Ahmed Momin, Sumaya Akter, Md. Awal Hadi, Dewan Mamun Raza, Noman Al Badhon https://creativecommons.org/licenses/by-sa/4.0
Autoregressive integrated moving average-long short-term memory optimized hybrid model for cybercrime forecasting
Cybercrime represents a growing global threat with adverse impacts on citizen security, the digital economy, and quality of life. In this context, an optimized hybrid model was developed that combines autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) for the monthly forecast of cybercrime complaints, applying the cross industry standard process for data mining (CRISP-DM) methodology and applying Python based data science techniques. The model combines the capabilities of the ARIMA statistical approach to capture linear components with the power of LSTM neural networks to address nonlinear temporal relationships. The architecture was trained on a set of 60,378 official records of complaints registered by the National Police of Peru between 2018 and 2023, achieving a mean absolute percentage error (MAPE) of 10.73%, which represents a significant improvement over the singular ARIMA and LSTM predictive models. Compared to previous studies in crime, health, and agriculture, this approach showed a greater ability to generalize over complex time series. It is concluded that the application of the proposed model is a relevant contribution for the police and other security agencies to anticipate crime trends and design preventive and effective strategies to combating cybercrime.
Copyright (c) 2025 Manuel Martin Morales-Barrenechea, Ciro Rodriguez, Ernesto David Cancho-Rodriguez, Ricardo Richard Huamantingo Navarro https://creativecommons.org/licenses/by-sa/4.0
Memory faults using open and short defect models for nano technology applications
As technology progresses from sub-micron to nanometer scales, memory-based systems are increasingly prone to faults. Consequently, developing robust methodologies to achieve defect-free embedded static random-access memory (SRAM) has become a critical challenge in modern very large scale integration (VLSI) design. Also, the increased integration of layout layers leads to form unknown defects. From the existing literature, observed that huge parametric variation is present whenever technology is changed. This is the key issue addressed in this paper, by representing an analysis on the impact of open and short defect models that uses parasitic extraction method while drawing various fault models. Possible open/short defects between the existing nodes are considered for the development of fault models using 45 nm, 32 nm, and 7 nm technologies. The total number of fault models of both kinds observed are 147. Also observed that besides to the existing faults, few undetectable faults are found named as undefined short faults (USF), undefined write after read fault (UWARF), and few faults with multiple faulty behavior.
Copyright (c) 2025 Parvathi Muddapu, Maddela Venkatesh https://creativecommons.org/licenses/by-sa/4.0
Feature separation of music across diverse dataset: a comparative perspective
In music, feature separation is the process of separating distinguishable auditory characteristics, such as pitch, timbre, rhythm, and harmonic content, from a complicated, mixed signal. Virtual reality (VR), gaming, music transcription, karaoke systems, audio restoration, music information retrieval (MIR), music education, and audio forensics, are just a few of the areas where the topic has attracted a lot of attention. Feature extraction is crucial in music separation as it identifies and isolates sound elements, improving accuracy, and reducing noise. It simplifies raw audio into meaningful data for efficient processing and effective model learning. Without it, clean separation of audio components is very difficult. In this research, extracting features from mixed audio sources enables clean and accurate isolation of musical elements, enhancing quality, supporting precise evaluations, and boosting neural network performance across varied datasets including DSD100, MUSDB, and MUSDB18-HQ, which collectively afford rich musical content for making evaluations and benchmarks. Evaluation metrics, such as F1-score, precision, and recall, are utilized to demonstrate the performance data of the extracted features. The MUSDB18-HQ dataset yielded an overall increase of 17.86% in the F1-score metrics with significant increases in drums (+25.05%) and vocals (+20.04%), showing that the dataset was highly effective for feature separation.
Copyright (c) 2025 Sakthidevi Shunmugalingam Parvathi, Divya Chandrasekar https://creativecommons.org/licenses/by-sa/4.0
Few-shot brain tumor classification: meta- vs metric-learning comparison
Medical imaging requires accurate brain tumor recognition because precise classification is essential for early diagnosis and effective treatment planning. A major challenge in medical applications is that deep learning models typically require extensive amounts of labeled data to perform well. To address this, this research evaluates three few-shot learning (FSL) approaches-prototypical networks, Siamese networks, and model-agnostic meta-learning (MAML)-for brain tumor classification using the Figshare brain tumor dataset. The results show that prototypical networks consistently outperform the other approaches, achieving 89.07% accuracy (95% CI: 88.12–89.96%), 88.73% precision, and 88.67% recall, making them the optimal solution for this task. Siamese networks achieve 83.73% accuracy (95% CI: 82.64–84.76%), while MAML demonstrates significantly reduced performance, with 43.70% accuracy (95% CI: 42.10–45.22%). This study demonstrates that FSL can be applied effectively for medical image classification, with prototypical networks achieving the best performance in brain tumor detection. The inclusion of confidence intervals further validates the robustness and reliability of the results. Future research will focus on improving feature representation and exploring hybrid approaches to better handle rare tumor classes, thereby enhancing the clinical applicability of FSL models.
Copyright (c) 2025 Shynar Akhmetzhanova, Azamat Serek, Ruslan Kashayev, Aizhan Kozhamuratova https://creativecommons.org/licenses/by-sa/4.0
Hybrid 3D CNN–transformer model for early brain tumor detection with multi-modal magnetic resonance imaging
Accurate and early diagnosis of brain tumors using multi-modal magnetic resonance imaging (MRI) remains a critical challenge due to tumor heterogeneity and complex spatial representation. This study proposes a novel hybrid deep learning framework that integrates a 3D convolutional neural network (3D CNN) with swin transformer blocks and an attention-based feature fusion module (ABFFM). The model leverages multi-modal MRI inputs—T1, T1Gd, T2, and fluid-attenuated inversion recovery (FLAIR)—and features a dual-branch classification head for binary tumor detection and multi-label tumor sub-region classification: enhancing tumor (ET), tumor core (TC), and whole tumor (WT). Experiments conducted on the BraTS2023-GLI dataset demonstrate that the proposed model achieves a superior classification accuracy of 96.51%, with precision of 97.98%, recall of 97.04%, and F1-score of 97.61%, outperforming state-of-the-art methods. Furthermore, intrinsic attention weights offer interpretability by highlighting modality-specific contributions. The proposed model establishes a clinically promising approach for brain tumor analysis, with strong implications for early diagnosis and treatment planning.
Copyright (c) 2025 Vivek Kumar Sharma, Gaurav Kumar Ameta https://creativecommons.org/licenses/by-sa/4.0
Digital twins and IIoT: comparison of Prometheus and InfluxDB
This article presents a comparative analysis of data monitoring and visualization tools—Prometheus and InfluxDB—in the context of digital twins (DTs) applied to industrial settings. DTs optimize production processes using industrial internet of things (IIoT) technologies. Mathematical models assessed the tools based on response time, resource consumption, throughput, and reliability. Prometheus is better suited for high-frequency monitoring, achieving a response time of 0.01 seconds and processing up to 10,000 metrics per second—10–15% better than InfluxDB. It consumes 1.5 times less memory (100 MB versus 150 MB), making it faster and more resource-efficient. Conversely, InfluxDB excels in long-term storage and analytics, handling up to 8,000 metrics per second with a response time of 0.09 seconds. However, it requires more resources, including higher CPU usage (20% versus 15%). Both tools integrate seamlessly with Grafana for visualization, offering flexibility for real-time monitoring and decision-making. The study provides actionable insights for selecting monitoring systems based on project-specific requirements, highlighting Prometheus’s efficiency in dynamic scenarios and InfluxDB’s strength in analytics-focused tasks.
Copyright (c) 2025 Bauyrzhan Amirkhanov, Timur Ishmurzin, Murat Kunelbayev, Gulshat Amirkhanova, Azim Aidynuly, Gulnur Tyulepberdinova https://creativecommons.org/licenses/by-sa/4.0
Smart virtual rotor for frequency stability enhancement considering inverter-based renewable energy sources
This paper proposes a novel smart virtual rotor controller (VRC) that combines the Bat Algorithm (BA) with extreme learning machine (ELM) to enhance frequency stability in power systems. To reflect the impact of renewable integration, inverter-based power plants are incorporated to simulate high levels of penetration from power-electronics-based generation. The proposed method first tunes the virtual rotor parameters (virtual inertia and damping control) using BA under varying operating conditions. These parameters are then trained with ELM to enable adaptive control across different scenarios. Time-domain simulations demonstrate that the proposed approach outperforms existing methods in terms of frequency nadir and settling time, while also achieving a significant reduction in execution time, requiring only 0.0033 seconds.
Copyright (c) 2025 Herlambang Setiadi, Baity Nuris Syifa, Muhammad Abdillah, Yusrizal Afif https://creativecommons.org/licenses/by-sa/4.0
Replay attacks and sniffing in Bluetooth low energy communications with mobile phone
This article analyzes vulnerabilities in Bluetooth low energy (BLE) connections in smartphones against replay and tracking attacks using software defined radio (SDR), evaluating four scenarios with BLE headsets and smartphones from different manufacturers through HackRF one, GNU radio, and Wireshark. In scenario 1, the advertising message ADV_NONCONN_IND was captured and retransmitted, generating persistent and deceptive pairing pop ups on smartphones. In scenario 2, fake pairing request signals were replicated to simulate a connection attempt, causing interface errors and deceptive notifications for the user. In scenario 3, complete pairing sequences were captured and replayed, producing false connection alerts and fabricated information such as battery level indicators from non existent devices. In scenario 4, passive tracking enabled the extraction of sensitive data during the pairing process, including ADV_IND packets, media access control (MAC) addresses, frequencies, manufacturer identifiers, and transmission power levels. A total of 93 successful and 123 failed attacks were recorded, with abnormal behaviors observed such as false pairing requests and manipulated device data, exposing users to risks of identity spoofing, denial of service (DoS) attacks, or targeted interference. The results highlight BLE protocol weaknesses against radio frequency (RF) based attacks and demonstrate the potential of SDR tools as powerful instruments for wireless protocol validation and cybersecurity research.
Copyright (c) 2025 Juan Sebastian Orozco Duran, Edith Paola Estupiñan Cuesta, Juan Carlos Martínez Quintero https://creativecommons.org/licenses/by-sa/4.0
Autism detection using facial and motor analysis using machine learning
This paper proposes a method for detecting autism spectrum disorders (ASD) through the analysis of facial and motor features using machine learning. The aim is to develop an algorithm for automatic ASD diagnosis based on spatiotemporal behavioral patterns. Traditional diagnostic methods rely on subjective expert observations, often delaying intervention. To address this, a hybrid convolutional neural network and long short-term memory (CNN+LSTM) model was employed. Convolutional layers extracted spatial features from video frames, while recurrent layers tracked temporal dynamics. Using MediaPipe face mesh, pose, and hands models, 1,639 parameters were obtained, including facial and pose coordinates, hand landmarks, mouth aspect ratio (MAR), and motion energy. The dataset comprised 100 children, aged 5–9 years (50 with ASD, 50 typically developing (TD)). Stratified cross-validation was applied to ensure subject-independent evaluation. Results showed 90% accuracy on the training set, 85–90% on validation, and an area under the curve (AUC) greater than 0.90, confirming model stability. Data visualization highlighted significant differences in motor activity and emotional expression between groups. The proposed approach demonstrates the potential for robust and objective ASD detection. It can be applied in clinical and educational contexts to improve early diagnosis and timely intervention.
Copyright (c) 2025 Aizat Amirbay, Nurlan Baigabylov, Ayagoz Mukhanova, Kuralay Mukhambetova, Elyor Zaitov, Roza Burganova, Khayriniso Khusanova, Feruza Akhmedova https://creativecommons.org/licenses/by-sa/4.0
Hybrid DL and ML approach for MRI-based classification of bone marrow changes in lumbar vertebrae
Alterations in the bone marrow changes lumbar vertebrae (BMCLVB) are considered important markers of chronic low back pain severity, particularly among patients with coexisting conditions like osteoporosis or cancer. Realizing these associations informs healthcare and insurance frameworks but also supports early intrusion planning for high-risk populations. This study aims to classification (BMCLVB) as normal or abnormal used magnetic resonance imaging (MRI) with machine learning (ML) model. A novel dataset comprising 1,018 BMCLVB MRI images was utilized to extract deep features via a pre-trained ConvNeXt-XLarge model. These features were then classified using different types in individual and ensemble ML algorithms. To ensure a comprehensive performance evaluation, all models were tested using accuracy, precision, recall, and F1-score. The combination of ConvNeXt-XLarge and logistic regression (LR) achieved a classification accuracy 93.14%, precision 93.22%, recall 94.83%, and F1-score 94.02%. These results highlight that the proposed model provides a fast and cost-efficient solution supporting the diagnosis of BMCLVB and potential to significantly improve clinical decision-making and patient care outcomes.
Copyright (c) 2025 Yasir Hussein Shakir, Tiong Sieh Kiong, Chai Phing Chen, Sachin Sharma Ashok Kumar https://creativecommons.org/licenses/by-sa/4.0
A review on radio-frequency transceiver architectures for low-power wireless sensor networks
Wireless sensor networks (WSNs) have garnered significant scientific attention because of their many uses, but their power usage is a fundamental barrier to their deployment. Energy constraints have a direct effect on important design elements including battery capacity, energy harvester effectiveness, and network longevity. To enable sustainable WSN operation, radio-frequency (RF)-based transceiver (TR) design has become a key area of study. A thorough examination of current RF-TR architectures is given in this paper, with a focus on low-power (LP) implementations designed for WSN applications. Amplifier-sequenced hybrid (ASH), superheterodyne (SHD), zero-intermediate frequency (Zero-IF), low-intermediate frequency (Low-IF), sliding-intermediate frequency (Sliding-IF), and super-regenerative (SRG) architectures are among the TR system designs that are categorized, with an emphasis on the performance trade-offs associated with each. Comparative evaluation shows that Zero-IF and SRG architectures are more energy efficient than other designs that were studied, which makes them viable options for ultra-low-power (ULP) WSN installations. Along with outlining important research issues in RF-TR design, such as hardware minimization, security, synchronization, and energy optimization, this review also suggests possible future paths to improve the sustainability and performance of WSN-based RF-TRs.
Copyright (c) 2025 Ambika Narenahalli Ashok Kumar, Geetishree Mishra https://creativecommons.org/licenses/by-sa/4.0
Improving COVID-19 chest X-ray classification via attention-based learning and fuzzy-augmented data diversity
This paper presents a hybrid deep learning (DL) framework that combines model-level and data-level enhancements to improve classification performance without compromising clinical relevance. The proposed framework consisted of an EfficientNetB0 model with a hybrid attention module, which focused attention both spatially and channel-wise, and a VGG-16 model that was trained on training data augmented using a fuzzy-logic-based contrast and brightness enhancement. The attention module focused the model by recalibrating the features in an adaptive manner. The fuzzy-logic augmentation increased data diversity while maintaining the anatomical fidelity of the medical image domain. In addition, an uncertainty-aware ensemble approach was utilized to combine both models' predictions, which considered model confidence and entropy of the predictions, to enhance the reliability of the predictions. The proposed framework achieves a classification accuracy of 99.6%, outperforming several existing approaches.
Copyright (c) 2025 Girish Shyadanahalli Cheluvaraju, Jayasri Basavapatna Shivasubramanya https://creativecommons.org/licenses/by-sa/4.0
An improved round robin time sharing algorithm for optimizing data mapping in cloud computing environments
Cloud computing in recent years has been widely applied in a wide number of applications and fields. However, allocating tasks to virtual machines (VMs) remains a part that needs enhancement. Task scheduling algorithms in heterogeneous computing system are required to satisfy high-performance data mapping requirements. The efficient allocation between resources and tasks decreases waiting time (WT), turnaround time (TT) and maximizes resource utilization. Various task scheduling algorithms, including round robin (RR) and some improved RR algorithm are used for cloud environment. A novel time-sharing algorithm (NRRTSA) is introduced, demonstrating enhancements in WT and TT. Simulation findings indicate that the NRRTSA algorithm effectively schedules multiple requests (cloudlets) among several VMs, the proposed NRRTSA outperforms RR and other algorithms in terms of the average of both TT and WT. The average turnaround time (ATT) is enhanced with a ratio of 10.8% to 45%, the average waiting time (AWT) is enhanced with a ratio of 10.9% to 45%.
Copyright (c) 2025 Afaf Abdelkader, Asmaa Mohamed, Nermeen Ghazy https://creativecommons.org/licenses/by-sa/4.0
Mobile application to optimize appointment management in a specialized dental center
The objective of the research was to implement a mobile application for the management of appointments in a specialized dental center, to improve patient care, allowing them to make their reservation from the place and at the time they want. The research has a quantitative approach, of experimental type with a pre-experimental design. The population consisted of 70 patients, with a total sample of 60. The SPSS statistical software was used for the elaboration of the results, obtaining positive results. With all the above mentioned in this research work, it is concluded that the implementation of the mobile application for appointment management for the dental center will facilitate patients to have better attention, which allows a reduction of time and satisfaction with the service. In indicator 1, referring to appointment registration time, a reduction of 44.13% was obtained. In indicator 2 on the number of patients presenting for appointments, an increase of 17.58% was obtained, and finally, in indicator 3 on the level of satisfaction, an increase of 65% was obtained.
Copyright (c) 2025 Joel Urbina-Novoa, Michael Cabanillas-Carbonell https://creativecommons.org/licenses/by-sa/4.0
The utilization of the Taguchi method on microring resonator design parameters to enhance the value of the quality factor
This study uses the Taguchi method to optimize the quality factor (Q-factor) of microring resonators (MRRs) for sensor applications. The MRRs are compact optical components widely used in biosensors and environmental monitoring due to their sensitivity to refractive index changes. The Q-factor, a key performance metric for MRRs, is significantly influenced by structural parameters such as ring radius (R), gap (g), waveguide width (W), and waveguide height (h). We employed a finite difference time domain (FDTD) simulation to model light propagation within the MRR and compute the corresponding Q-factor to identify the optimal combination of these parameters. An L9 orthogonal array (OA) is used in the Taguchi method to analyze each factor's influence with three levels systematically. The optimization resulted in a Q-factor of 6208.44, significantly higher than the baseline value, indicating a substantial improvement. Compared to previous works, this research highlights the advantages of combining FDTD-based electromagnetic modeling with statistical optimization, offering a structured yet efficient approach to MRR design. The proposed method enhances Q-factor performance and provides scalability for practical applications in biomedical and environmental sensing. These findings underscore the utility of Taguchi-based design in advancing the field of photonic sensor optimization.
Copyright (c) 2025 Ahmad Aminudin, Lilik Hasanah, Harbi Setyo Nugroho, Chandra Wulandari, Budi Mulyanti, Roer Eka Pawinanto, Ahmad Rifqi Md Zain, Gandi Sugandi, Ida Hamidah, Widyaningrum Indrasari, Jumril Yunas https://creativecommons.org/licenses/by-sa/4.0
Automated real-time cervical cancer diagnosis using NVIDIA Jetson Nano
Cervical cancer is a global health concern, making early detection critical for ensuring effective treatment outcomes. Screening technique, the Papanicolaou test (Pap test), has been adopted globally for timely detection. Nevertheless, the process of screening is subjective. The current study aims to advance the development of an automated real time framework for cervical cell analysis for early-stage diagnosis using supervised classification on NVIDIA Jetson Nano platform. Our approach, leveraging adaptive fuzzy k-means (AFKM) clustering and k-means clustering, extracts distinctive features from cervical cell images for accurate classification. Utilizing multilayer perceptron (MLP) and support vector machine (SVM) classifiers, we achieved a classification accuracy of 97%, highlighting the potential of our system for real-time applications in cervical cancer investigation. Validation by two expert pathologists further supports the system’s practical utility.
Copyright (c) 2025 Pallavi Mulmule, Swati Shilaskar, Shripad Bhatlawande, Vedant Mulmule, Vaishali H Kamble, Jyoti Madake https://creativecommons.org/licenses/by-sa/4.0
Enhancing skin cancer detection using transfer learning and AdaBoost: a deep learning approach
Skin cancer is one of the most prevalent types of cancer worldwide, with early detection playing a critical role in improving patient outcomes. In this study, we propose a deep learning model based on LeNet-7 combined with adaptive boosting (AdaBoost) to classify skin lesions as either benign or malignant using the International Skin Imaging Collaboration (ISIC) dataset. We evaluate the proposed model alongside other well-established deep learning architectures, such as residual network (ResNet), VGGNet, and the traditional LeNet model, through various performance metrics including precision, recall, F1-score, specificity, Matthew’s correlation coefficient (MCC), area under the receiver operating characteristic curve (AUC-ROC), and testing accuracy. Our results demonstrate that the proposed model (LeNet-7+AdaBoost) significantly outperforms the other models, achieving a testing accuracy of 91.3%, precision of 0.92, recall of 0.91, and AUC-ROC of 0.93. The model successfully addresses issues of overfitting and generalization, providing a robust solution for skin cancer classification. However, some misclassifications of visually similar benign and malignant lesions highlight areas for future improvement. The proposed model shows promise in real-world medical applications and paves the way for further research into optimizing deep learning models for skin cancer detection.
Copyright (c) 2025 Hersatoto Listiyono, Retnowati, Purwatiningtyas, Eko Nur Wahyudi, Ali Maskur https://creativecommons.org/licenses/by-sa/4.0
Assessing external factors of the agro-industrial complex efficiency based on data
Modern agriculture faces the challenge of increasing production efficiency in the context of limited resources and variable climatic conditions. This article presents an approach to assessing the impact of various factors on agro-industrial indicators using machine learning methods. The primary focus is on the development and application of a hybrid analysis that includes techniques such as gradient boosting (GB), mutual information (MI), and recursive feature elimination (RFE). The study was conducted using data from agro-industrial enterprises in the North Kazakhstan region for the period 2020–2022, encompassing production, climatic, and economic indicators. It was found that crop area, average crop weight, and precipitation are the most significant factors, accounting for up to 93% of the correlation with yield increase. The use of the proposed methods made it possible to reduce forecast uncertainty by 28% and increase the accuracy of key indicator predictions by 15–20%. The results of the analysis, visualized as correlation matrices and feature significance maps, confirm the possibility of applying the proposed approach to optimize the management of agro-industrial production. The application of the developed methodology contributes to the development of strategies aimed at the sustainable development of the agro-industrial complex.
Copyright (c) 2025 Gulalem Mauina, Ulzada Aitimova, Ainagul Kadyrova, Saltanat Adikanova, Aigul Syzdykpayeva, Zhanat Seitakhmetova, Ainagul Alimagambetova, Ainur Shekerbek https://creativecommons.org/licenses/by-sa/4.0
Research on optimal design of surface permanent magnet synchronous generator
The more fossil energy is used, the less this energy source will become because it is not an infinite energy and it pollutes the environment, so there is a need for solutions with new and infinite energy sources such as wind energy. This paper designs and focuses on optimizing a floating magnet synchronous generator (SG) for a wind power generation system using finite element analysis (FEA) with ANSYS Maxwell software. This generator is compared with other types of generators such as squirrel cage induction generator (SCIG), wound rotor induction generator (WRIG), SG, doubly-fed induction generator (DFIG), and switched reluctance generator (SRG). Throughout the analysis and design process, the paper emphasizes the significant benefits of surface-mounted permanent magnet (SPM) motors in increasing efficiency and reliability while reducing supply costs. The research results of the paper aim to demonstrate that SPM can meet the needs of high efficiency and low cost in the industrial and civil fields. The results of this study by the authors will provide new contributions to serve as a basis for the design, manufacture, calculation and control of Halbach permanent magnet (Halbach PM) electric machines based on optimization techniques such as genetic algorithms (artificial intelligence) and sustainable optimization (for electrical equipment).
Copyright (c) 2025 Phan Hoai Nam, Tran Duc Chuyen https://creativecommons.org/licenses/by-sa/4.0
A hybrid extreme machine learning model for predicting heart disease
Heart disease (HD), the leading cause of death for adults over 65, can affect anyone at any time. Additionally, modern lifestyles, poor diets, and other factors have led to an increased risk of HD among teenagers. One significant challenge is managing and analysing vast amounts of data, often surpassing terabytes, which is crucial for researching, diagnosing, and predicting cardiovascular diseases quickly. To enhance primary health care, especially in early and rapid diagnosis of heart attacks and to assist less experienced doctors in understanding clinical HD data, we propose a hybrid method called the "hybrid extreme machine learning model (HEMLM)". This technique combines the strengths of multi-layer perceptron (MLP), random layers, and logistic regression (LR). The model offers various feature patterns and multiple classification techniques. Compared to support vector machine (SVM), LR, and Naive Bayes (NB), the HEMLM algorithm demonstrates superior performance and efficiency. Testing results show identification accuracies of 94.91%, 94.77%, 92.42%, and 87.14% for data splitting ratios of 85:15, 80:20, 70:30, and 60:40, respectively.
Copyright (c) 2025 Abdelmoty M. Ahmed, Bilal Bataineh, Ghazi Shakah, Marwa O. Al Enany, Maie M. Aboghazalah, Mahmoud M. Khattab https://creativecommons.org/licenses/by-sa/4.0
New approach to measuring researcher expertise using cosine similarity algorithm and association rules
This study proposes a new method to assess researcher expertise using publication data. The quality of research publications is an important indicator in the ranking of universities that are undergoing diversification. Research publications have become an important indicator in the university ranking system and have a major impact on the reputation of universities as a lens for the study of expertise and prestige for human resources. Expertise is often difficult to verify objectively, as a result, many people claim to be experts or are considered experts without evidence and correct data. To ensure the expertise of researchers, it must be proven with valid data support through measurable and presentable expertise parameters. The model built uses the cosine similarity and association rule approaches. The publication variables attached to the researcher are formulated in the collaboration of the algorithm to assess the level of researcher expertise. Validation of important points of publications as parameters for measuring expertise has been identified as the main factor contributing to the measurement of researcher expertise and its impact on university reputation. The model built successfully validated researcher expertise up to 72% which is relevant to its support for university rankings up to 75%.
Copyright (c) 2025 Ali Firdaus, Deris Stiawan, Samsuryadi, Rahmat Budiarto https://creativecommons.org/licenses/by-sa/4.0
Design and implementation of a solar-powered IoT-based real-time air quality monitoring system
Air pollution has become a global issue due to rapid urbanization and industrialization. Air quality monitoring is essential for mitigating the adverse effects of air pollution on public health and the environment. This study presents a solar-powered internet of thing (IoT)-based air quality monitoring system designed for autonomous operation in outdoor settings. The prototype integrates an ESP32 microcontroller with low-cost sensors for PM2.5, PM10, temperature, humidity, and heat index. Powered by a solar panel and battery, the system ensures off-grid functionality, while Wi-Fi transmission to the Blynk platform, enables real-time visualization, historical record storage, and instant user access through mobile dashboards. The system was calibrated against reference instruments and deployed for 14 consecutive days. Results confirmed stable data transmission and reliable performance that suitability for outdoor use without reliance on grid power under real-world conditions. Furthermore, correlation analysis showed a strong relationship between PM2.5 and PM10, and moderate associations with humidity. Regression analysis further identified humidity and heat index as the most significant predictors, while temperature exhibited only minor influence. These findings demonstrate the feasibility of a low-cost, portable, and energy-autonomous IoT monitoring system, providing accurate real-time insights to support evidence-based air quality management.
Copyright (c) 2025 Chaiyong Soemphol, Taweesak Thongsan, Sakuntala Ninkaew, Piyapat Panmuang https://creativecommons.org/licenses/by-sa/4.0