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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Artificial neural network maximum power point tracking for mitigation photovoltaic harmonic distortion
This study introduces a novel methodology aimed at minimising total harmonic distortion (THD) in grid-connected photovoltaic (PV) systems (GCPVs) through the implementation of a maximum power point tracking (MPPT) approach based on artificial neural networks (ANN). High THD levels in PV systems can lead to inefficiencies, power quality issues, and potential damage to the grid infrastructure. Although traditional MPPT methods effectively optimise the power output, they often fail to address harmonics. The proposed ANN-based MPPT algorithm improves PV power harvesting while actively minimising the harmonic distortions. The ANN was trained using a comprehensive dataset that included various environmental conditions, ensuring robust performance in diverse operational scenarios. Simulation results demonstrate that the ANN-based MPPT approach significantly reduces THD to below 1% across various irradiance levels, in contrast to the 1.18% to 2.72% observed with conventional methods such as perturb and observe (P&O), while simultaneously preserving optimal power output. Reducing harmonic distortion improves the power quality, system efficiency, and lifespan of grid-connected components. This study highlights ANN-based control strategies for addressing the challenge of maximising energy harvesting and maintaining power quality in modern PV systems, offering a solution for the sustainable integration of solar energy into the grid.
Copyright (c) 2025 Adel Bouledroua, Tarek Mesbah, Samia Kelaiaia https://creativecommons.org/licenses/by-sa/4.0
Parameter tuning of PIDG controller on maximum photovoltaic power point for battery charging system
Maximum photovoltaic power point (MPVPP) based on DC-DC buck converter is supplied by photovoltaic module. A controller method is needed to control the signal that it drives the switching component of DC-DC buck converter. The previous researcher conducts proportional integral derivative (PID) controller applying the DC-DC buck converter, but only its parameters (proportional, KP, integral, KI, and derivative, KD) are studied. This paper presents MPVPP based on PID with gain (PIDG) controller on the DC-DC buck converter by tuning the parameters of KP. KI and KD and adding a gain, G connected to PIDG controller for charging 12 V, 7 Ah battery. The DC-DC buck converter is designed for the output voltage of 14.7 V and output power of 150 W and modelled using Simulink MATLAB. The simulation results show that the parameters of KP=0.0032, KI=1, and KD=4×10-7 are suitable to control the switching component. The gain, G gives significant effect on the settling time and the time to reach their steady state value of output voltage of 14.7 V. The battery SOC can increase 1.36% per second, if the initial SOC is 25%, thus it needs arround 55 seconds to reach the fully charging condition.
Copyright (c) 2025 Muhammad Irwanto, Togar Timoteus Gultom, Habib Satria, Baharuddin Ismail, Christin Erniati Panjaitan, Mahdi Syukri https://creativecommons.org/licenses/by-sa/4.0
Equilibrium optimizer-based double integral sliding mode maximum power point tracking for wind energy
Wind energy is an effective renewable energy source. However, when it comes to harnessing its power because of its variability and nonlinearity, traditional controllers have limitations. This work proposes the design of two nonlinear maximum power point tracking (MPPT) methods to track the maximum power point for stochastic wind in the below-rated wind speed zone. These methods are the sliding mode controller (SMC) and the double integral sliding mode controller (DISMC). A benchmark model of a 4.8 MW wind turbine (WT) is subjected to random wind profiles in the MATLAB/Simulink environment. The equilibrium optimizer (EO) is used here and contrasted with particle swarm optimization (PSO) and grey wolf optimizer to achieve a good design of the controller in the sliding plane and change the switching control in sliding mode. The proposed optimization methodology and DISMC improved the smoothening of the control of angular speed, and specifically, the EO outperformed the rest of the techniques.
Copyright (c) 2025 Shrabani Sahu, Sasmita Behera, Nimay Chandra Giri, George Uwadiegwu Alaneme, Fathy Abdelaziz Syam, Fawzan Salem https://creativecommons.org/licenses/by-sa/4.0
Improved power quality and reduced losses in DFIG-based WECS using third-order sliding mode control
The study presents a comparative analysis of two advanced high-order sliding mode control (HOSMC) strategies—super-twisting sliding mode control (STSMC) and third-order sliding mode control (TOSMC)—for enhancing the performance of doubly-fed induction generator (DFIG)-based wind energy conversion systems (WECS). The key goals are to maximize energy efficiency, minimize the total harmonic distortion (THD) in the stator current, and reduce electrical losses within the system. Both control strategies are integrated into a direct field-oriented control (DFOC) scheme using space vector modulation (SVM) to improve dynamic response and control accuracy. MATLAB/Simulink simulations show that TOSMC consistently outperforms STSMC in multiple performance aspects. TOSMC ensures better energy efficiency through precise tracking of active and reactive power references while mitigating transient oscillations (chattering effects).Furthermore, TOSMC significantly reduces harmonic distortion, achieving a THD of 0.21%, compared to 0.33% for STSMC, and surpasses conventional controllers, which exhibit a minimum recorded THD of approximately 0.46%. The mitigation of transient phenomena also contributes to reduced switching losses and ohmic heating, thereby enhancing the generator’s thermal stability and overall reliability.
Copyright (c) 2025 Abdelaziz Belhait, Messaoud Louafi, Sihem Ghoudelbourk https://creativecommons.org/licenses/by-sa/4.0
Designing an A+ LED solar simulator: spectrum optimization and its impact on silicon solar cells
The development of light-emitting diode (LED)-based solar simulators that comply with the updated IEC 60904-9:2020 standard, particularly achieving a Class A+ irradiance spectrum, remains a significant challenge. This necessitates careful consideration of two key spectral quality indicators: spectral deviation (SPD) and spectral coverage (SPC). This study proposes a method to achieve a Class A+ solar simulator spectrum using a minimal number of LED types while optimizing SPD and SPC. It also examines the influence of SPD and SPC on the photogenerated current density (Jph) and short-circuit current density (Jsc) of crystalline silicon and multi-crystalline silicon solar cells. By selectively adding ultraviolet (UV) and near-infrared (NIR) LEDs to the original six-type LED configuration, the simulator’s spectral performance was enhanced to more closely align with the AM1.5G standard. The configuration incorporating both UV and NIR LEDs demonstrated the highest performance. It achieved an SPC of 97.521% and the lowest SPD at 26.088%. Simulation results confirmed that higher SPC and lower SPD values contribute to reduced errors in the calculated Jsc and Jph for both crystalline silicon (c-Si) and multi-crystalline silicon (mc-Si) solar cells. These findings highlight the importance of well-balanced spectral design and demonstrate that accurate spectral simulation is achievable using only essential LED wavelengths.
Copyright (c) 2025 Chaiyant Boonmee, Patcharanan Sritanauthaikorn, Saichol Chudjuarjeen, Paiboon Kiatsookkanatorn, Khanittha Wannakam, Jeerawan Homjan, Kreeta Sukthang, Panet Sooksing, Napat Watjanatepin https://creativecommons.org/licenses/by-sa/4.0
Analysis of voltage drop using transformer tap changer and placement of capacitor bank with genetic algorithm
The demand for electrical energy is increasing due to high economic growth and population. The impact is that electrical energy operates excessively to meet the required demand. Unbalanced loads, higher power losses on the line, and voltage drops that are higher than allowed are just a few of the issues that may result from this. Adding tap changers and capacitor banks is one method of improving the voltage profile and power losses. To conduct this study, tap changers and capacitor banks were added to the IEEE 33 bus network system. The value, capacity, and location of the tap changers and capacitor banks in the system were ascertained using the genetic algorithm (GA) approach. According to the simulation results, the voltage profile, which initially had 21 buses outside the IEEE standard limits, may be ideal by installing two tap changers and two capacitor banks. Additionally, reactive power losses decreased from 41.8 kVar to 93.3 kVar, and active power losses decreased from 202.7 kW to 130.7 kW, a decrease of 72 kW.
Copyright (c) 2025 Yulianta Siregar, Agus Kivander Saragi, Issarachai Ngamroo https://creativecommons.org/licenses/by-sa/4.0
Thermal analysis of li-ion battery pack using phase change materials based on climate conditions
The development of lithium-ion batteries necessitates improved management of these systems, particularly with regard to thermal aspects. They operate optimally between 35 °C and 45 °C. Temperatures exceeding 50 °C accelerate cell aging, while those surpassing 60 °C can trigger thermal runaway, potentially leading to catastrophic failure. To mitigate these risks, phase change materials (PCMs) are employed in battery thermal management systems (BTMS). They absorb heat during charging or discharging, transitioning from solid to liquid, then release the stored energy during periods of low demand, solidifying to help regulate battery temperature. This study conducts a thermal analysis of a lithium-ion (LiFePO4) battery pack delivering a 24 V load, using COMSOL MULTIPHYSICS software. The objective is to evaluate and compare the thermal behavior of different PCMs, RT27, Paraffin Wax 58-60, and HM030, against air as a baseline reference. Simulations are performed using the integrated finite element method (FEM), with a discharge rate of 4 C. A correlation is proposed between the choice of PCM and the climate in specific locations, with the choice being made based on the disparities in the results obtained.
Copyright (c) 2025 Ishak Mohammed Mokhtar Benounnane, Ahmed Wahid Belarbi, Mohammed El Bachir Ghribi https://creativecommons.org/licenses/by-sa/4.0
Optimized electric vehicle charging allocation with overload management and vehicle to grid support
The rapid proliferation of electric vehicles (EVs) in residential distribution networks poses significant challenges, particularly in managing peak demand and maintaining grid stability during the peak demand periods. This study employs a day-ahead EV charging framework in compliance with valley-filling technique to align charging during off-peak periods for a centralized residential charging station that balances grid stability with customer satisfaction. To mitigate network overloading, vehicle to grid support is integrated through optimization based on genetic algorithm (GA), enabling optimal scheduling of both charging and discharging activities under operational constraints. Simulation outcomes substantiate the efficacy of the proposed charging scheme in preventing overloads and demonstrate a notable enhancement in the load factor from 70.68% to 82.24%, reflecting enhanced utilization of energy resources. The approach offers technical and economic benefits for both utilities and EV users, highlighting its potential for scalable and efficient grid management.
Copyright (c) 2025 S. J. Hamim, Imran Rahman, Md. Yeamin, Abdullah Saleh, Tareq Aziz https://creativecommons.org/licenses/by-sa/4.0
Beyond a simple filter: transient and steady state analysis of first-order resistor-resistor-capacitor circuits
This paper presents a quantitative analysis of a first-order resistor-resistor-capacitor (RRC) circuit, detailing its transient, steady state, and frequency-domain behaviors through computational modeling. The study confirms that the circuit's time constant (τ) governs its dynamic response, with the capacitor charging to 63.2% of its final voltage in one τ. The key finding is the circuit's fundamental distinction from a simple resistor-capacitor (RC) filter: under a 100 V step excitation, the RRC topology stabilizes with a non-zero steady-state current of 0.35 A, following a controlled transient inrush of 1.0 A. Frequency analysis further characterizes the circuit as a stable low-pass filter with a predictable -20 dB/decade roll-off. This work elucidates a critical engineering trade-off, demonstrating that the RRC's components dually define its transient speed and its final steady state operating point, providing a quantitative framework for advanced power management and signal conditioning applications.
Copyright (c) 2025 Soumia Djelaila, Ali Abderrazak Tadjeddine, Ridha Ilyas Bendjillali, Mohammed Sofiane Bendelhoum https://creativecommons.org/licenses/by-sa/4.0
Machine learning based annual solar energy forecasting for enhanced grid integration of photovoltaic systems
The increase in electricity demand is witnessed by many nations due to the rise in population and ongoing developments. To cope with energy requirements, countries are looking towards cleaner alternatives to reduce overreliance on energy generation from conventional resources. The introduction of artificial intelligence (AI) in real-world applications is acknowledged positively by experts as it enhances the performance and efficiency of the system. This paper reports the advancement of AI in harnessing renewable energy sources (RESs) to their true potential by leveraging their response when the grid is not able to fulfill the power requirement from conventional resources. Moreover, the prediction also remains a challenge with renewables due to their volatile behavior, especially with solar-based energy generation. This issue is also addressed by interfacing AI-enabled applications and the difference between true and predicted values for one year is observed. The result reveals that the true response aligns with the predicted response, which ensures the ability of AI to harness solar energy by consuming minimal time. The proposed approach is also promising from the utility operators’ and end users’ perspectives in designing any large-scale renewable projects for sustainable development and also encourages the utilization of renewables to a larger extent.
Copyright (c) 2025 Nandini K. Krishnamurthy, Anubhav Kumar Pandey, Sumana Sreenivasa Rao https://creativecommons.org/licenses/by-sa/4.0
Influence of installing a virtual synchronous generator control on Lombok Island power grid with high penetration of PV plants
Indonesia is a country with several islands, and providing clean energy in islanded power systems connected to a single main grid would be economy challenging. On the other hand, absence of inertia, system strength, and damping value in islanded power systems due to inverter interfaced renewable energy (RE) resources can cause significant decline of power system stability. The primary concern with integrating large scale photovoltaic (PV) power plant in an islanded power system is maintaining frequency and voltage stability. This research investigates the application of virtual synchronous generator (VSG) in Lombok’s Islanded power system, considering high penetration of PV. A thorough time domain simulation is performed with a detailed modelling of power system in Lombok Island to study the dynamic voltage and frequency stability. The simulation results show that the VSG improves both frequency and voltage stability in transient and steady state stages, ensuring smoother operation and faster stabilization time. It is found that the frequency deviation can be curtail up to 0.5% and the steady state can be increased up to 0.1%.
Copyright (c) 2025 Herlambang Setiadi, Nadarajah Mithulananthan, Baity Nuris Syifa, Yoshiko Ricky Ananda, Riski Cahya Anugrah Haebibi, Yusrizal Afif https://creativecommons.org/licenses/by-sa/4.0
Intelligent maximum power point tracking control for solar photovoltaic systems using fuzzy and neuro-fuzzy techniques
A solar-photovoltaic (PV) system cannot optimize power transfer from the generator to the load due to the nonlinear characteristics of the PV arrays. Maximum power point tracking (MPPT) approaches are necessary to optimize the power output of PV arrays. This study introduces a dual intelligent MPPT framework using fuzzy-logic controller (FLC) and neuro-fuzzy controller (NFC) to enhance solar PV efficiency under dynamic environmental conditions. The FLC uses 49 fuzzy rules with seven membership functions (MFs) in a fuzzy interface system (FIS). The NFC is an extension of FLC and is constructed using the artificial neuro-fuzzy interface system (ANFIS). The work analyzes the simulation results and performance realization, including % power loss, system efficiency, and MPPT efficiency under variable irradiance and temperatures. The solar-PV system utilizes FLC and NFC to achieve MPPT efficiencies of 97.89% and 98.61%, respectively. Similarly, the solar-PV system employing FLC and NFC yields system efficiencies of 98.24% and 99.23% respectively. The proposed system using both FLC and NFC is compared with existing MPPT approaches, with better improvement in system efficiency.
Copyright (c) 2025 Mohankumar Venugopal, Madhusudhan Mahadevaswamy, Manjunath Bimbitha Swarna Gowri https://creativecommons.org/licenses/by-sa/4.0
A high-efficiency transformerless buck-boost inverter with fuzzy logic control for grid-connected solar PV systems
Transformerless inverters are increasingly favored in grid-connected photovoltaic (PV) systems due to their higher efficiency, reduced size, and lower cost. This paper presents a novel transformerless inverter topology that integrates buck boost conversion with an advanced fuzzy logic controller (FLC) to enhance energy extraction and power quality under dynamically changing solar conditions. The proposed system employs a sine triangle pulse width modulation (PWM) scheme in conjunction with the FLC to improve waveform quality and system responsiveness. By dynamically adapting to variations in irradiance and load, the control strategy reduces the total harmonic distortion (THD) from 36.51% to 1.51%, significantly enhancing compliance with international grid standards. Additionally, a novel grounding technique is implemented to mitigate common mode leakage currents, a typical issue in transformerless systems, without the need for galvanic isolation. Comprehensive MATLAB/Simulink simulations validate the inverter’s performance, demonstrating superior dynamic behavior, harmonic suppression, and overall reliability. The proposed architecture offers a compact, cost effective, and high performance solution for next generation grid integrated solar PV systems.
Copyright (c) 2025 Bodapati Venkata Rajanna, Kondragunta Rama Krishnaiah, Veerlapati Ramaiah, Shaik Hasane Ahammad, Mohammad Najumunnisa, Syed Inthiyaz, Nitalaksheswara Rao Kolukula, Ambarapu Sudhakar https://creativecommons.org/licenses/by-sa/4.0
Application of traction force observer and sliding mode controller for speed in enhancing the stability of electric vehicles
With the rapid advancement of electric vehicle (EV) technology, optimizing control and stability has become a key research focus. One major challenge is efficiently distributing traction force while minimizing disturbances under real-world conditions. This paper proposes a traction force observation method combined with a sliding mode speed controller to enhance EV performance. The observation method estimates the traction force from the motor to the wheels and detects disturbances affecting force transmission. This enables optimal traction force distribution among the wheels, reducing slip, improving road grip, and enhancing stability in complex driving conditions. Meanwhile, the sliding mode controller flexibly adjusts traction force as the vehicle navigates various terrains, ensuring stability and safety in hazardous situations. Simulations conducted using MATLAB Simulink and CarSim demonstrate that the proposed system significantly improves EV stability and control performance. Although these results are promising, further studies are necessary to address real-world implementation challenges and optimize the method for practical applications, including parameter tuning, sensor integration, and experimental validation. Overall, this research provides a practical framework for enhancing traction control and vehicle dynamics in future intelligent electric mobility systems.
Copyright (c) 2025 An Thi Hoai Thu Anh, Nguyen Van Hoa https://creativecommons.org/licenses/by-sa/4.0
Research on PMSM control without speed sensorless applied to industrial electric drive system based on ADSMC method
The paper research, calculates, and designs an industrial electric drive system control such as: computer numerical control (CNC) machining machines, milling machines, and grinding machines, with sensorless permanent magnet synchronous motors (PMSM) based on measuring current components, axial position and applied voltage to obtain information about rotation angle and speed for PMSM based on adaptive sliding mode control (ADSMC) method. Here an optimal sliding surface will be designed to demonstrate faster convergence than conventional sliding mode control. Then, an adaptive law is researched and developed to make the control parameters, especially the switching gain, updated quickly online. Therefore, the motor noise can be effectively reduced and the system can be better eliminated from noise, Chattering, and nonlinear noise. Finally, a reference model was created, the exponential decay curve was applied to track the angular position error. The ADSMC system with model reference proposed by the authors in the paper has combined the advantages of sliding mode control method and adaptive control method according to the sample model. The simulation results show that the performance is achieved faster and the control process is more accurate, the error of speed and angular position (less than 0.01%) compared to other control methods.
Copyright (c) 2025 Ha Van Chinh, Tran Duc Chuyen, Nguyen Hoai Nam https://creativecommons.org/licenses/by-sa/4.0
Improvement of load frequency control performance for shipboard microgrid system
This research studies the shipboard microgrid (MG) scheme's frequency fluctuations problem contrary to the impulsiveness of renewable resources, load instabilities, and the uncertainty of the parameters in the ship MG plant. A shipboard MG system consists of some of the renewable energy resource s (RESs) such as photovoltaic (PV), wind turbine generator (WTG), battery energy storage system (BESS), ship diesel generator (DG), fuel cell (FC), aqua electrolyzer (AE), and loads. A new fuzzy proportional integral derivative (FPID) controller is established to attain the desired frequency stability for the shipboard MG system. Additionally, various scenarios are executed in this research to validate the robustness of the anticipated controller to various load disturbances, parameter changes of plant, and fluctuations of solar irradiance and wind speed. The numerical simulation results obtained in three scenarios compared with those of the conventional PID controller and the existing time-varying derivative fractional order PID (TVD-FOPID) controller in literatures to validate the high usefulness and applicability of the planned control strategy. In brief, the established load frequency controller (LFC) based on FPID technique can improve frequency deviation in shipboard MG plant effectively.
Copyright (c) 2025 Cong-Trang Nguyen, Nguyen Nghia Tin, Thai Pham Thien Hao, Phan Tan Liem https://creativecommons.org/licenses/by-sa/4.0
Intelligent building automation system using ESP32, Azure and internet of things technologies
The adoption of home automation systems in buildings faces limitations due to their cost, integration complexity, and protocol heterogeneity, which hinders the development of accessible solutions based on embedded devices to improve interaction in environments within buildings or homes. The literature review indicates that the selection of hardware and communication protocols in home automation systems considers factors such as cost, available infrastructure, and application context. In addition, approaches are identified that prioritize security, wired or wireless connectivity, and affordability. This paper presents the development of an affordable home automation system for building automation in Lima, using the ESP32 microcontroller and internet of things (IoT) technologies. The objectives focus on hardware design, implementation of control algorithms, remote monitoring interface, and validation in a simulated environment. The solution includes Wi-Fi connectivity, a cloud-based MySQL database, and a web interface. Key findings include the home automation system, integrated with Flask technology and web services, enabling monitoring and control via a responsive web interface, demonstrating its operability and ensuring lossless data transmission.
Copyright (c) 2025 Fernando Cardoza, Cristy Samamé, Ricardo Yauri, Antero Castro https://creativecommons.org/licenses/by-sa/4.0
Modeling broken rotor bar faults in induction motors: a combined SSFR and FEM approach
The fault of broken rotor bar (BRB) yields to high levels of stress in induction motor drive (IM) and being a common fault. This paper proposes a novel hybrid approach combining standstill frequency response (SSFR) testing and finite element method (FEM) modeling to improve fault diagnosis accuracy. The findings were verified experimentally using a 7.5 kW three-phase IM by SSFR approach under various failure scenarios. Reliability of SSFR method is confirmed by the use of FEM, flux 2D magnetic analysis software is employed on the same IM using in SSFR to determine the magnetic field under different fault and load conditions. The work is finished by current harmonics analyses and the outcomes of the BRB model demonstrate that the combined method enhances fault detectability, particularly for incipient and partial bar breakages, reducing false alarms compared to conventional techniques.
Copyright (c) 2025 Abdelhakim Mabrek, Zaidi Elyazid, Bachir Selmoune, Kamel Eddine Hemsas https://creativecommons.org/licenses/by-sa/4.0
Path planning and obstacle avoidance for UAVs using Theta* and modulated velocity obstacle avoidance with 2D LiDAR
This paper proposes a novel framework for autonomous unmanned aerial vehicle (UAV) navigation in complex environments, seamlessly integrating Theta* for global path planning with a simplified modulated velocity obstacle avoidance (MVOA) algorithm for local obstacle avoidance. Theta* generates optimal, smooth paths, while MVOA processes 2D LiDAR data as a single obstacle block to compute modulated velocities, enabling efficient avoidance of static and dynamic obstacles with minimal computational overhead. Compared to MVOA-only navigation, the integration of Theta* and MVOA produced shorter trajectories and faster mission completion with smoother velocity adjustments, demonstrating clear improvements in efficiency and stability. Simulation results show the framework maintains a 0.6 m safety distance and operates at 10 Hz, underscoring its robustness and reliability. The resulting control velocity is transmitted to an ArduPilot-based flight controller via MAVLink, ensuring precise, real-time execution. The current implementation focuses on 2D navigation in a planar environment as a foundation for future 3D expansion, with all results obtained through high-fidelity simulation. Building on these findings, the framework shows strong potential for real-time applications such as swarm UAV coordination, terrain surveying, and indoor navigation, offering a scalable solution for autonomous systems in dynamic settings.
Copyright (c) 2025 Hoang Thuan Tran, Dong LT. Tran, Chi Thanh Vo https://creativecommons.org/licenses/by-sa/4.0
3D mapping for unmanned aerial vehicle combining LiDAR and depth camera in indoor environments
Indoor reconnaissance missions for unmanned aerial vehicles (UAVs) pose significant challenges in scene reconstruction, mapping, and environmental feature extraction. Relying on a single type of sensor often results in limited accuracy, increased susceptibility to environmental noise, and a lack of comprehensive spatial information. To address these issues, this study proposes a mapping method that combines light detection and ranging (LiDAR) and depth camera data. The method collects data from both LiDAR and a depth camera integrated on the UAV, then performs preprocessing on both data sources to construct local 3D maps using the real-time appearance-based mapping (RTAB-Map) algorithm. Subsequently, the local maps are merged using a filtering method to generate a detailed and complete global map. Real-time experiments conducted on Ubuntu 20.04 using the robot operating system (ROS) Noetic libraries demonstrate that this multi-sensor fusion approach provides richer and more comprehensive environmental information, thereby enhancing the effectiveness of mapping tasks in unknown indoor environments.
Copyright (c) 2025 Hoang Thuan Tran, Chi Thanh Vo, My Duyen Ha, Nong Trong Tu, Du Van Ngan, Nam Hoai Le, Duong Van Hoa https://creativecommons.org/licenses/by-sa/4.0
Internet of things-based fuzzy controller for automatic irrigation and NPK nutrient monitoring of grapes
Grape cultivation has gained increasing attention due to its short growing period and the high market value of its sweet, refreshing fruits. However, achieving optimal growth requires precise environmental and nutrient management, which can be challenging under conventional farming practices. This research aims to develop an automatic watering system that integrates soil moisture and nutrient monitoring to optimize grape cultivation. The system utilizes Nitrogen Phosphorus Potassium (NPK) sensors, soil moisture sensors, and a camera for growth observation, all connected through the internet of things (IoT) for remote monitoring via Android devices. A fuzzy logic controller is implemented to regulate watering duration based on environmental conditions such as temperature and humidity. Experimental results show that the system effectively adjusts watering duration to approximately six seconds when the temperature is between 25–32 °C and humidity is around 60%. The DS18B20 temperature sensor achieved an average error rate of only 0.12%, while the humidity sensor demonstrated 0.2% error, indicating high accuracy levels of 99.8%. Despite minor limitations related to internet stability and sensor calibration, the system demonstrates strong potential for commercial-scale smart farming applications, promoting resource-efficient and data-driven grape cultivation.
Copyright (c) 2025 Moechammad Sarosa, Septriandi Wirayoga, Mila Kusumawardani, Dimas Firmanda Al Riza, Yunia Mulyani Azis https://creativecommons.org/licenses/by-sa/4.0
Analog artificial intelligence hardware for neural networks: design trends and considerations
The increasing deployment of artificial intelligence (AI) in real-time and edge applications intensified the demand for energy-efficient hardware capable of high-throughput processing. Conventional digital processors were constrained by sequential data processing, memory bandwidth limitations, and high-power consumption, making them suboptimal for edge-based AI. This review presented a comprehensive analysis of analog very-large-scale integration (VLSI) design approaches for neural network (NN) implementation focusing on circuit-level architectures including in-memory analog computing, current-mode circuits, switched-capacitor (SC) techniques, and operational transconductance amplifier (OTA)-based designs. Significant hardware design considerations such as process variation, crossbar scalability, precision–linearity trade-offs, and mixed-signal interface challenges were critically examined. Furthermore, training methodologies—spanning offline learning, circuit calibration, and programmability were discussed in the context of analog AI hardware. The review incorporated case studies, recent developments in edge deployment, and a comparative analysis of advanced analog VLSI chips. Key performance evaluation metrics such as accuracy, calibration overhead, noise robustness, and energy per inference, were also addressed. Circuit-level design aspects that impacted the performance, precision, and reliability of analog computing blocks were discussed. The paper concluded by identifying research gaps and future directions for the development of analog AI hardware suitable for real-world edge applications.
Copyright (c) 2025 Kanchan S. Gorde, Sonali M. Sonavane, Sonal Hutke, Ankush Hutke https://creativecommons.org/licenses/by-sa/4.0
Embedded system with automatic control for solar energy capture using photovoltaic panels
Solar energy harvesting addresses challenges related to environmental variability and the limitations of fixed systems, which affect the energy yield obtained from photovoltaic panels. To improve efficiency, tracking systems are being developed using control algorithms or algorithmic energy management strategies. Reviewed research has explored methodologies such as software modeling, experimental testing, and integration of embedded systems with fuzzy logic and internet of things (IoT) for energy monitoring and management, using both single-axis and dual-axis technologies, demonstrating improvements in energy harvesting efficiency. This paper presents the development of a microcontroller-based system with automatic control to optimize solar energy capture in photovoltaic panels using light-dependent sensors and integrating control algorithms into low-cost hardware. The tests carried out demonstrated the operation of the tracking algorithm, confirming that the integration of light-dependent sensors, servo motors and the Arduino UNO microcontroller orient the solar panel based on the detected light, determining that with the generation of 900 mA with 6.98 V in full sunlight, the 5 V and 4400 mAh battery is charged, obtaining an autonomy of up to 3.65 days without solar recharging.
Copyright (c) 2025 Frank Rojas, Fabrizzio Contreras, Juan Alvarez, Ricardo Yauri, Rafael Espino https://creativecommons.org/licenses/by-sa/4.0
Design and analysis of an asymmetrical star-shaped fractal antenna with meta-surface integration at 5.2 GHz
Wireless communication requires optimized antenna designs to ensure maxi mum signal reception and transmission in today’s rapidly advancing technolo gies. Recent research emphasizes improving antenna efficiency and directivity to support higher data rates, extended coverage, and reliable connectivity. How ever, conventional antenna structures often suffer from narrow bandwidth, low radiation efficiency, and high return loss, which degrade signal quality and re strict operational range, particularly in complex electromagnetic environments. This study introduces an innovative asymmetrical star-shaped fractal antenna coupled with a metasurface layer consisting of periodic split-ring resonator (SRR) unit cells on a FR4 substrate to overcome these restrictions. The SRR based metasurface plays a critical role in suppressing surface waves, improving impedance matching, and enhancing radiation directivity. Experimental evalu ations were performed across 4.5–10 GHz, focusing on key performance mea sures such as gain, return loss, and voltage standing wave ratio (VSWR). The suggeted antenna achieved a stable return loss below −10 dB and demonstrated a strong operational peak at 5.2 GHz, with improved directivity and radiation efficiency compared to conventional patch designs. The integration of asym metrical star-shaped fractal geometry with SRR-based metasurface technology effectively addresses the shortcomings of traditional antennas, establishing the proposed design as a compact, efficient, and reliable candidate for mid-band wireless communication systems.
Copyright (c) 2025 Piyush Dalsania, Jagdish M. Rathod https://creativecommons.org/licenses/by-sa/4.0
Isolation enhancement of four port multiple-input multiple-output antenna for sub-6 GHz 5G communication
For 5G communication, this research suggests a small, broad band, 4-port multiple-input multiple-output (MIMO) antenna with an impedance bandwidth of 2.0 GHz (or 3.0-5.0 GHz). The n77, n78, and n79 bands are covered. The single antenna is realized by inserting the stubs and creating the ‘HI’ slot on the rectangle patch with the defect in the ground plane, using FR-4 substrate. Next, four MIMO antennas are built utilizing the reference antenna. Due to mutual interaction, implementing MIMO systems presents a substantial challenge: achieving good isolation between antenna parts in the confined space. To increase isolation with decoupling procedures, the four antennas are placed orthogonally to one another. Because the antennas are positioned orthogonally, the MIMO antenna has an isolation of 28.0 dB. The diversity gains (DG) and envelop correlation coefficient (ECC) are used to analyze the recommended antenna's diversity performance characteristics, and the results show that the values are 9.99 dB and 0.0003, respectively. The simulated S-parameters have been compared with orthogonal and adjacent positions of quad port MIMO antenna. Anritsu MS2037C VNA is used to measure the parameters, and HFSS software is used to simulate it.
Copyright (c) 2025 Ramesh Manikonda, Annapantula Sudhakar, Govindarao Tamminaina https://creativecommons.org/licenses/by-sa/4.0
Development of an internet of things microstrip antenna for turbo code off-grid emergency communications
Technologies and other things are fully automated, meaning signal processing and microstrip antennas for communication are essential because of their compact size and versatility. Due to inefficiency and other factors, traditional communication methods fail, which means some emergency communication systems encounter difficulties. MATLAB was used to simulate a microstrip antenna for turbo code off-grid emergency communication and signaling of internet of things (IoT) devices. A criterion is followed to determine whether a microstrip antenna’s behavior meets the emergency communication requirements. The results show that the system’s transfer function satisfies the required conditions to meet efficient communication and signaling, especially in emergencies. The step response peaked at 1.04 and an overshoot of 4.6%, meeting the conditions for efficient communication. Besides that, the generated Bode plot and Nyquist plot display the required behavior, meaning that the microstrip antenna can function as a communication device for emergency situations.
Copyright (c) 2025 Fredelino A. Galleto Jr., Aaron Don M. Africa, Rhen Anjerome Bedruz, Marnel Peradilla, Samuelle P. Barja, Sean Bono L. Macariola, Matthew Luigi N. Manalo, Angiolo C. Ongkinglok https://creativecommons.org/licenses/by-sa/4.0
Design of compact dual-resonance multiple-input-multiple-output antenna array for internet of things application
This work presents a compact dual-resonance multiple-input-multiple-output (MIMO) antenna array for internet of things (IoT)-enabled smart devices requiring both 5G and Wi-Fi connectivity. The antenna operates at 3.6 GHz (5G smartphones) and 5.4 GHz (high-speed Wi-Fi), using a dual-resonance MIMO configuration for reliable multi-device communication. An integrated electromagnetic band-gap (EBG) structure suppresses surface waves, reducing mutual coupling and achieving >35 dB isolation with an envelope correlation coefficient (ECC) of 0.05. A fabricated prototype validated the design, with measurements aligning closely with simulations. The proposed antenna offers compactness, dual-band operation, low coupling, and strong MIMO performance, making it well-suited for next-generation IoT systems.
Copyright (c) 2025 Mahesh Kadu, Pankaj Pramod Chitte, Sandip R. Udawant, Vilas S. Ubale https://creativecommons.org/licenses/by-sa/4.0
Iris-based lung cancer pre-scanning for mobile platforms
Lung cancer remains one of the leading causes of cancer-related mortality globally, with early detection being critical for improving survival rates. Traditional diagnostic methods such as computed tomography (CT) scans and biopsies are effective but often costly, invasive, and inaccessible in resource-limited settings. In this study, we evaluate suitable deep learning models for mobile platforms and propose an application for early detection of lung cancer based on iris images. Through experimentation and comparison, the results show that the MobileNet model family achieves high performance while maintaining a light-weight architecture. The positive results of this study further strengthen the potential application of iris in the pre-diagnosis of lung cancer via mobile platforms.
Copyright (c) 2025 Hung Ho-Dac, Tuan Anh Le, Trong Thua Huynh https://creativecommons.org/licenses/by-sa/4.0
Evaluating random–Nyquist sampling ratios in combined compressed sensing magnetic resonance imaging
Compressed sensing (CS) has been widely applied in magnetic resonance imaging (MRI) to accelerate the image acquisition without significantly reducing its image quality. In Cartesian MRI, acquisition time can be reduced by skipping phase-encoding steps for faster data acquisition. However, the balance between random under-sampling and Nyquist sampling at the k-space center strongly determines image quality. In this study, we systematically evaluate the impact of different random-to-Nyquist sampling ratios for both single-coil (CS-MRI) and multi-coil (CS-pMRI) reconstructions. Simulation results reveal that dense Nyquist sampling around the k-space center is essential for maintaining image fidelity, whereas reconstruction quality deteriorates sharply when random sampling exceeds approximately 60% of the total under-sampled data. Moreover, CS-pMRI consistently outperforms CS-MRI under equivalent under-sampling factors, benefiting from additional coil sensitivity information that improves resilience against aliasing and noise. These findings provide practical guidelines for hybrid under-sampling design, emphasizing that sufficient Nyquist sampling coverage of central k-space is crucial for achieving high-quality reconstructions while enabling high acceleration in CS-MRI.
Copyright (c) 2025 Duc Khanh Pham, Duc-Tan Tran, Anh Quang Tran https://creativecommons.org/licenses/by-sa/4.0
Brain tumor classification using PCA-NGIST features with an enhanced RELM classifier
Brain tumours may cause severe health risks because of abnormal cell growth, which may result in organ malfunctions and death in adulthood. As precise identification of the tumour type is required for effective treatment. Magnetic resonance imaging (MRI) has recently been provided as an effective method for brain tumour diagnosis by computer-based based systems. To categorize brain tumours from MRI images, the paper offered a fusion model integrating an enhanced regularized extreme learning machine (RELM) classifier with principal component analysis (PCA) and normalized GIST (NGIST) feature extraction. While NGIST extracts strong spatial and texture features essential for modelling the tumour, PCA reduces the dimension of the input features without sacrificing significant data patterns. The improved RELM efficiently categorizes brain tumours into three categories: pituitary, meningioma, and glioma. It is optimized to improve learning capacity and generalization. The novelty of this study lies in the integration of NGIST descriptors with PCA-driven dimensionality reduction and an enhanced RELM classifier in a single lightweight framework. Unlike conventional methods that trade accuracy for computational cost, the proposed model ensures high precision and recall while remaining computationally efficient. This unique fusion demonstrates significant improvements in both diagnostic accuracy of 96% and clinical applicability, offering a balanced solution for real-time brain tumor classification.
Copyright (c) 2025 Bukkapatnam Rakesh Babu, Vullanki Rajesh, Bodapati Venkata Rajanna, Shaik Hasane Ahammad https://creativecommons.org/licenses/by-sa/4.0
New perspective in enhancing Papanicolaou-smear image using CLAHE and spider monkey optimization
High-quality Papanicolaou (Pap) smear images are essential for reliable early detection of cervical cancer, yet low contrast and noise often hinder accurate interpretation. This study introduces spider monkey optimization (SMO)-contrast-limited adaptive histogram equalization (CLAHE), an optimized CLAHE framework guided by the SMO algorithm. A novel signal contrast (SC) objective function is proposed, combining perceptual enhancement contrast enhancement-based image quality (CEIQ) with fidelity preservation peak signal-to-noise ratio (PSNR) to adaptively tune CLAHE parameters. Experiments on the publicly available SIPaKMeD and Mendeley LBC datasets demonstrate that SMO-CLAHE consistently outperforms manual settings and flower pollination algorithm (FPA)-based optimization, and achieves performance comparable to pelican optimization algorithm (POA) across key quality metrics including entropy, structural similarity index (SSIM), PSNR, enhancement measure estimation (EME), root mean square contrast (RMSC), standard deviation (STD-DEV), and CEIQ. Furthermore, downstream evaluation using a MobileNetV3-S classifier shows that the enhanced images lead to improved cervical cancer classification performance. These results highlight SMO-CLAHE as a robust and clinically relevant preprocessing framework, offering a new perspective for Pap smear image enhancement and diagnostic support.
Copyright (c) 2025 Ach Khozaimi, Wuryansari Muharini Kusumawinahyu, Isnani Darti, Syaiful Anam, Ulfatun Nahdhiyah https://creativecommons.org/licenses/by-sa/4.0
Generating data for predicting court decisions in Kazakhstan using machine learning
This study presents the development of a synthetic dataset and machine learning models for predicting court decisions in Kazakhstan. The dataset contains 100,000 cases generated from the Code of the Republic of Kazakhstan, covering both administrative and criminal offenses. Each record includes attributes such as the age of the accused, offense type and severity, and mitigating or aggravating factors. Regression models were applied to estimate offense severity, level of guilt, and likelihood of penalties, while classification models predicted the offense category, relevant law articles, and sentencing type. Predictions addressed both general outcomes—classifying cases as criminal or administrative—and specific judicial decisions, including fines, imprisonment terms, and other penalties. Classification models achieved 92% accuracy in determining offense category and sentencing type, and regression models reached a root mean squared error (RMSE) of 0.12 for offense severity. Using synthetic data preserves confidentiality while enabling pattern discovery for decision support. The results demonstrate the potential of artificial intelligence (AI) to improve sentencing prediction, prioritize case processing, and enhance transparency in Kazakhstan’s judicial system. Beyond transparency in decision support, the proposed approach also shows potential in crime prevention, workload optimization, and fostering digital transformation within judicial operations.
Copyright (c) 2025 Artyom Ignatovich, Anar Yessengeldina, Gulzhakhan Baidullayeva, Dinara Ussipbekova, Baktykul Jakhanova, Gulmira Saduakassova, Bulat Serimbetov, Assemgul Tynykulova https://creativecommons.org/licenses/by-sa/4.0
Advancements in machine learning techniques for precise detection and classification of lung cancer
Lung cancer remains one of the most prevalent and lethal malignancies worldwide, necessitating early detection and accurate classification for effective treatment. In this work, we present a unique machine learning (ML) model that uses medical imaging data to detect and classify lung cancer. Utilizing a dataset of 613 images which obtained from Kaggle, our model combines sophisticated feature extraction methods with three essential algorithms: AdaBoost, stochastic gradient descent (SGD), and random forest (RF). Orange3 data mining software was used to classify the model after it was preprocessed and features were extracted using MATLAB. Nonetheless, the model showed good performance in identifying lung cancer lesions in four different categories: squamous cell carcinoma, big cell carcinoma, adenocarcinoma, and normal. With an accuracy of 0.998 and an AUC range of 1.000, AdaBoost notably produced the best results. Overall, ensemble ML techniques demonstrated notable benefits over single classifiers, indicating its potential to aid in the creation of accurate instruments for the diagnosis of lung cancer in its early stages.
Copyright (c) 2025 Hamza Abu Owida, Areen Arabiat, Muhammad Al-Ayyad, Muneera Altayeb https://creativecommons.org/licenses/by-sa/4.0
Adaptive AI-driven framework for digital mental health interventions in low-resource universities
Mental health problems affect nearly half of university students worldwide, with around 20% reporting depressive symptoms and over 40% showing signs of anxiety. This burden is particularly acute in low-resource universities, where limited infrastructure and minimal investment in mental health restrict access to effective care. To address this gap, this study applies a projective research approach, defined as the design of evidence-based solutions without immediate empirical implementation. A systematic review of 402 scientific articles was carried out across major databases, from which 15 met strict inclusion criteria. The analysis identified recurrent barriers such as unstable internet connectivity, devices with less than 2 GB RAM, and the absence of regulatory frameworks governing AI in education. Based on these findings, an adaptive intervention model was proposed, integrating artificial intelligence (AI), machine learning (ML), and deep learning (DL) to deliver personalized psychological support directly on local devices, without requiring permanent connectivity. The proposed system demonstrated potential to reduce anxiety and depression scores by 15–25% in controlled studies and achieved prediction accuracies above 80% for stress and loneliness detection. This framework provides a scalable foundation for universities in developing contexts, contributing to equity in access to digital mental health services.
Copyright (c) 2025 Rubén Baena-Navarro, Yulieth Carriazo-Regino, Richard Crawford-Vidal, Alexander Fernández-Arango, Francisco Barreiro-Pinto https://creativecommons.org/licenses/by-sa/4.0
Dispersion compensation in single and multi-channel DWDM using chirped apodized fiber Bragg gratings
Chromatic dispersion (CD) is a key limiting factor in long-haul optical fiber communication, particularly in multi-channel dense wavelength division multiplexing (DWDM) systems, where it introduces signal distortion and inter-symbol interference (ISI). This paper proposes a low-dispersion-offset compensation (LDOC) scheme employing Gaussian-apodized linear chirped fiber Bragg gratings (CFBGs) to enhance dispersion management in single and multi-channel DWDM optical fiber communication systems. Simulations were performed in OptiSystem 7.0 for 10 Gbps single-channel transmission over standard single-mode fiber (SSMF) spanning 110–210 km, and were extended to 4- and 8-channel DWDM systems with a 0.8 nm channel spacing. System performance was evaluated in terms of quality factor (Q-factor), bit error rate (BER), and eye height under varying fiber lengths, input powers, and chirp coefficients. The LDOC-enhanced CFBG achieved a Q-factor of 7.04 with a BER of 9.82×10⁻¹³ for single-channel transmission at 180 km, 13.83 with a BER of 5.57×10⁻⁴¹ for a 4-channel system at 150 km, and 7.56 with a BER of 7.76×10⁻¹¹ for an 8-channel system at 150 km. These results confirm significant improvements compared to conventional CFBGs, demonstrating that the proposed LDOC-based approach is a compact and effective solution for next-generation metro-core, long-haul, DWDM, and 5G/6G optical networks.
Gopalakrishna Murthy Chinnammahalli Ramappa
Copyright (c) 2025 Kripa Kalkala Balakrishna, Gopalakrishna Murthy Chinnammahalli Ramappa, Karthik Palani https://creativecommons.org/licenses/by-sa/4.0
Enhanced security and performance through permutation-byte key cipher with reduced-round AES
This paper introduces the permutation-byte key cipher with reduced-round advanced encryption standard (PBKC-RRAES), a novel enhancement of the AES designed to significantly improve both security and performance. The proposed algorithm integrates key modifications; i) replacing the computationally intensive MixColumns function with an efficient bit permutation technique that achieves superior diffusion while reducing computational overhad by eliminating complex matrix multiplication operations. This substitution enhances security through improved bit-level scrambling patterns, while simultaneously accelerating processing speed through simpler bitwise operations; ii) the addition of AddRoundKey operations between cipher states, iii) enhanced byte substitution operations and round constant additions in the key schedule algorithm before key expansion, and iv) reducing rounds from 10 to 6. These innovations yield heightened sensitivity to plaintext changes, evidenced by a 54.214% avalanche effect, surpassing the standard 50% threshold. Performance evaluations reveal PBKC-RRAES operates 26.90% improvement in encryption time and a 22.73% improvement in decryption time than standard AES, alongside throughput enhancements of 39.48% in encryption and 31.27% in decryption compared to the original AES, critical improvements for bandwidth-constrained applications. These results demonstrate that PBKC-RRAES is a robust and effective alternative for cryptographic applications, particularly beneficial for real-time video streaming, secure cloud storage, mobile payment systems, and IoT device where both security and processing effectivity are paramount.
Copyright (c) 2025 Jerico S. Baladhay, Alvincent E. Danganan, Edjie M. De Los Reyes, Heidilyn V. Gamido, Marlon V. Gamido https://creativecommons.org/licenses/by-sa/4.0
Design of a machine learning model for predicting credit risk in microfinance using environmental data
Agricultural microfinance is a sector that is significantly impacted by climate-related risks, such as temperature fluctuation, soil degradation, and irregular rainfall. These environmental factors have not only impact on crop yield but also results in influencing borrowers’ ability to repay agricultural loans. Traditional credit scoring models lack in predicting due to the complex interplay between environmental and borrower-specific variables. This research study proposes a new predictive machine learning model based on XGBoost for assessing the credit risks in agricultural microfinance. This model utilizes environmental indicators, borrower characteristics, and loan attributes for computing the continuous credit risk score. The model was trained utilizing a real-world dataset of 142,017 loan applications with a 70/30 split. When compared with other traditional models, the results of the model showcases an accuracy of 99%, a recall of 84%, a precision of 89%, and an F1-score of 86%, outperforming traditional algorithms such as logistic regression and decision tree. This model has substantial implications for microfinance organizations. With this model, borrowers can evaluate risk accurately during the loan application stage by utilizing environmental data, resulting in better loan targeting, enhanced financial inclusion, and better risk mitigation for vulnerable farming communities in climate-sensitive regions.
Copyright (c) 2025 Eladio Alfredo Soto Alvarez, Miguel Angel Cano Lengua https://creativecommons.org/licenses/by-sa/4.0
Meta-learning for malaria diagnosis: evaluating stacking models for enhanced classification performance
Accurate malaria detection is crucial for effective disease management, particularly in regions with limited medical resources. Deep learning models have shown promising results in automated diagnosis, yet real-world deployment often faces challenges such as computational cost and model interpretability. This study evaluates multiple deep learning architectures—VGG16, ResNet50, InceptionV3, MobileNetV2, and DenseNet121—on the publicly available National Institutes of Health (NIH) malaria cell image dataset (27,558 images), and enhances their performance using stacking ensemble learning with different meta-learners. Among individual models, DenseNet121 achieved the highest accuracy of 88.00%, while MobileNetV2 had the lowest at 84.80%. Implementing stacking with logistic regression as the meta-learner improved accuracy to 89.40%, while random forest increased it to 90.10%. The best performance was achieved with XGBoost as the meta-learner, attaining an accuracy of 91.20%, precision of 92.10%, recall of 90.80%, and an F1-score of 91.40%—representing a 3.2% accuracy improvement over the best individual model. The classification report further confirms superior performance in distinguishing infected and uninfected cases. These results highlight the potential of stacking with advanced meta-learners to support health workers in rapid, reliable malaria diagnosis, ultimately aiding timely treatment, and improving patient outcomes in clinical and field settings.
Copyright (c) 2025 Komal Kumar Napa, Sangeetha Murugan, Sathya Subramanian, Durga Devi Saravanan, Devana Nageswari, Battula Krishna Prasad https://creativecommons.org/licenses/by-sa/4.0
Artificial intelligence in smart home security: balancing innovation with ethics
Because of the evolution of artificial intelligence (AI), home security has progressed from a basic security system to an active architecture that is responsive and adaptive to real world situations. Due to the rapid adoption of AI in smart systems, there is increasing suspicion surrounding privacy issues and ethical ambiguity, as well as gaps when it comes to regulating these technologies. We provide an overview of AI in smart home security applications and examine the area of security, access control, intrusion detection, human action recognition, and research on intelligent automation. We summarize the last decade of evolution, with some summaries of previous on computer vision, authentication systems, and finding unusual patterns recently. Our key findings include the development of approaches to improve real time security monitoring, dramatic reductions in false alarms, and customization of home access using AI. Improvements in security have also increased risk with respect to ethical ambiguity as well as technical issues in certain cases. In this paper, we offers pathways for improved AI system design, proposed formal data protection regulations, and examples of simplifying complex system for user comprehension, which also establishes the groundwork for future efforts. Home security should balance new opportunities with ethical considerations.
Copyright (c) 2025 Ashraf Al Sharah, Tareq A. Alawneh, Hamza Abu Owida, Jawdat S. Alkasassbeh, Zahid Iqbal https://creativecommons.org/licenses/by-sa/4.0
Enhancing cloud resource management: leveraging adversarial reinforcement learning for resilient optimization
This paper introduces the first adversarial reinforcement learning (ARL) framework for resilient cloud resource optimization under dynamic and adversarial conditions. While traditional reinforcement learning (RL) methods improve adaptability, they fail when faced with sudden workload surges, security threats, or system failures. To address this, we propose an ARL-based approach that trains RL agents using simulated adversarial perturbations, such as workload spikes and resource drops, enabling them to develop robust allocation policies. The framework is evaluated using synthetic and real-world Google Cluster traces within an OpenAI Gym-based simulator. Results show that the ARL model achieves 82% resource utilization and a 180 ms response time under adversarial scenarios, outperforming static policies and conventional RL by up to 12% in terms of cost-effectiveness. Statistical validation (p<0.05) confirms significant improvements in resilience. This work demonstrates the potential of ARL for self-healing cloud schedulers in production environments.
Copyright (c) 2025 Agariadne Dwinggo Samala, Soha Rawas, Santiago Criollo-C https://creativecommons.org/licenses/by-sa/4.0
A powerful machine learning method for detecting phishing threats
Phishing threats exploit social engineering and deceptive web infrastructure to steal sensitive personal information, often by mimicking legitimate websites. With the proliferation of online services and the increasing prevalence of cybercrime, detecting phishing websites has become a critical challenge. This study presents a comprehensive machine learning (ML)-based approach for detecting phishing websites. A total of 48 discriminative features were extracted from 10,000 web pages—comprising 5,000 phishing and 5,000 legitimate sites. Nine ML classifiers were initially evaluated, including random forest (RF), support vector machine (SVM), and XGBoost. Ensemble models based on soft voting and stacking were then constructed to improve detection performance. Among the models, the soft voting classifier (VC) achieved the best performance with an accuracy and F1-score of 98.82%. The results indicate that ensemble learning offers a robust solution for the automated detection of phishing websites.
Copyright (c) 2025 Mahmoud Baklizi, Jamal Zraqou, Mohammad Alkhazaleh, Issa Atoum, Faisal Alzyoud, Musab B. Alzghoul https://creativecommons.org/licenses/by-sa/4.0
System dynamics modeling for strategic management of information technologies in universities
This study seeks to answer the question: how can system dynamics (SD) modeling contribute to the strategic management of information technology (IT) in universities? The objective of the research is to analyze the importance of incorporating IT into university strategic management through the application of SD methodology. To this end, a model was designed that integrates variables related to resource allocation, the quality of the educational process, and the interaction between institutional actors. The methodology made it possible to simulate technological implementation scenarios and examine their effects on operational efficiency and academic performance. The results show that the strategic integration of IT promotes better resource planning, optimizes the interaction between administrative and academic processes, and contributes to raising the quality of teaching. In conclusion, the proposed model demonstrates that SD is an effective tool for anticipating and understanding the internal dynamics of universities, facilitating more efficient strategic management in today's digital context.
Copyright (c) 2025 Laberiano Andrade-Arenas, Margarita Giraldo Retuerto, Cesar Yactayo-Arias https://creativecommons.org/licenses/by-sa/4.0
Cyber security threats and web vulnerability analysis of higher educational institutions in Bangladesh
This paper presents a comprehensive analysis of cyber security threats and web vulnerabilities in the context of higher educational institutions in Bangladesh, including twenty public and private universities. Educational institutions are highly vulnerable due to their negligence in maintaining a functional network, mainly owing to budgetary constraints. As a result, they have become a hacker playground for many ambitious adversaries to boast their technical skills, regardless of the harm they may inflict. Through the use of vulnerability assessment and penetration testing (VAPT), we conducted a methodical analysis of the institutions’ web infrastructures, identify and categorize the prevalent security threats and vulnerabilities that may compromise the integrity, confidentiality, and availability of information systems. Our findings reveal significant disparities in the security strength of both public and private universities, with the latter demonstrating a higher degree of vulnerability due to varying factors, such as budget constraints, policy enforcement, and awareness levels. This study underscores the urgent need for robust cyber security frameworks tailored to the higher educational sector’s unique requirements, advocating for proactive measures to mitigate potential cyber threats. The implications of this research extend beyond the academic domain, offering insights into national cyber security strategies and the safeguarding of critical information infrastructures.
Copyright (c) 2025 Shadiqul Hasan Khan Janny, Md. Asadujjaman Noor, Mohammad Enan Al Harun Sahan, Sheikh Nafez Sadnan, Muhammad Towfiqur Rahman, Abu Saleh Md Bakibillah https://creativecommons.org/licenses/by-sa/4.0
IoT-based real-time monitoring of agricultural wastewater using Raspberry Pi, Node-RED, and Grafana
This study introduces an internet of things-based agricultural wastewater monitoring system (IoT-AWMS) designed to enhance water management through real-time monitoring and advanced sensor integration. The system employs a Raspberry Pi for centralized control, node-RED for automation, InfluxDB for data storage, and Grafana for visualization. A key innovation is the integration of an alternative sensing approach for estimating electrical conductivity (EC), complementing conventional sensors for total dissolved solids (TDS), water temperature (DS18B20), and ambient conditions (DHT11). The system achieves over 85% accuracy in estimating EC across diverse water samples, including drinking water, agricultural runoff, and fertilizer-enriched solutions. Compared with conventional approaches, IoT-AWMS demonstrates superior accuracy, scalability, and cost-effectiveness. Its modular design supports applications in nutrient runoff detection, contamination monitoring, and optimized water resource utilization, with broader potential in precision farming and environmental monitoring. This work contributes a robust, adaptable IoT framework for sustainable agricultural water management.
Copyright (c) 2025 Nur’in Batrisyia Mohd Faizu, Ahmad Muzammil Roslizar, Muhammad Aizat Zaim Zaini, Fakrulradzi Idris, Zulkarami Berahim, Anas Abdul Latiff https://creativecommons.org/licenses/by-sa/4.0
Development of a machine learning-based framework for predicting failures in heat supply networks
The increasing complexity and scale of heat supply systems leads to a higher risk of failures, which may cause significant economic and environmental consequences. This study develops a predictive mathematical framework for the early detection of emergency conditions in heat supply networks (HSNs) using machine learning (ML). The proposed approach is based on the LightGBM gradient boosting (GB) algorithm, chosen for its high accuracy and efficiency in handling large datasets. Real operational data (temperature, pressure, flow, and vibration) were considered. Data preprocessing, feature engineering (including SHAP analysis), and hyperparameter tuning with grid search and 5-fold cross-validation improved prediction quality. The model achieved accuracy of 85%, F1-score of 0.82, and receiver operating characteristic (ROC)-area under the curve (AUC) of 0.96, outperforming logistic regression (LR) and decision trees. The framework may be integrated into monitoring systems for predictive maintenance, reducing downtime and optimizing costs.
Copyright (c) 2025 Dauren Darkenbayev, Gulnar Balakayeva, Uzak Zhapbasbayev, Mukhit Zhanuzakov https://creativecommons.org/licenses/by-sa/4.0
MVC in machine learning: a decade of algorithmic advances, challenges, and applications–a systematic review
This systematic review evaluates the developments in multi-view clustering (MVC), its challenges, and applications from 2009 to 2024 and synthesizes 157 studies selected according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 guidelines. MVC overcomes the shortcomings of the traditional single-view approaches by using complementary information provided by heterogeneous data sources. We used a strict search strategy in the ACM Digital Library, IEEE Xplore, and Scopus, and then carefully examined the quality of the found articles. The significant results suggest that the MVC research has grown explosively, with China as the major contributor and IEEE/Elsevier as the leading publishers. Developments in algorithms include deep learning, graph-based models, and factorization. Ongoing issues include managing incomplete views, scalability, successful fusion strategies, and interpretability. The review points out the wide range of applications of MVC in various areas, including bioinformatics, social network analysis, and multimedia. Future research must create adaptive frameworks, improve the interpretability of models, and develop strong evaluation measures, thus unlocking the full potential of MVC in real-life data applications.
Copyright (c) 2025 Pankaj Kumar, Rashmi Agarwal https://creativecommons.org/licenses/by-sa/4.0
Evaluating maintainability metrics in microservices-based student registration systems
As governments redefine educational policy and schools evolve their priorities, more schools must have software that recalibrates with minimal friction. To provide objective guidelines, this study rigorously measures maintainability attributes in a microservices-styled student registration platform, framing the assessment with the ISO/IEC 25010 maintainability specification. We steered each of the standard's maintainability sub-characteristics into defined quantitative constructs, executed in the context of a production microservices topology. Architectural and behavioural views were analysed using Structure101 in static tool runs, and unified modeling language (UML) model inspection anchored the derivation of key metrics, ensuring that stakeholder-defined structures and live microservices concurrency both shaped the evaluation. Results indicate moderate system modularity with average component dependency (ACD) of 2.14, propagation cost (PC) of 10.2%, and identification of one non-trivial cycle group involving three classes. Cohesion analysis revealed structural improvement opportunities in core classes such as admin and candidate lack of cohesion in methods 4 (LCOM4)≥2). The inheritance structure shows optimal characteristics with shallow depth (depth of inheritance tree (DIT)≤1), and controlled breadth (number of children (NOC)=2), supporting both analyzability and modifiability. These findings provide actionable insights for enhancing system maintainability in microservices architectures, particularly for educational domain applications requiring frequent policy adaptations.
Copyright (c) 2025 Gintoro, Eko Cahyo Nugroho https://creativecommons.org/licenses/by-sa/4.0
On-edge 2D-to-3D generative pipeline for seamless instance transformation
Despite ongoing challenges with fragmented workflows, latency in device imports, and the main issue of limitations in object reconstruction functionality, relying on imperfect extraction networks remains an impractical solution for scalable object generation. To deal with these constraints, we proposed an end-to-end pipeline that leverages a re-designed self-consistency mechanism—aimed at reducing discrimination, along with the beneficial enhancement from level-set projection and gradient-surface orthogonality. In addition, our approach designs dynamic 3D object creation with minimal manual effort by unifying surface topology and optimizing data loading, enabling a streamlined reconstruction process and more flexible object projection. Our method supports rapid, resource-efficient mesh reconstruction and consistently demonstrates performance improvements across multiple instance benchmarks, covering virtual projection tasks. Improvements in mesh topology reconstruction, as measured by the L1 Chamfer distance (CD) metric, are consistently higher, while the system also achieves significant transmission speedups—up to 56.5×—near-instant importing—along with lowering latency in practical rendering on virtual reality (VR) devices. This result highlights that refining mesh binding improves re-creation fidelity. Our approach to scalability leads to faster user engagement and allows automated deployment without requiring human intervention during importing.
Copyright (c) 2025 Jirayu Petchhan, Surasachai Doungtap https://creativecommons.org/licenses/by-sa/4.0
Enhanced speech recognition in natural language processing
Speech recognition is crucial for helping individuals with physical disabilities access digital content. However, current systems have significant flaws that hinder user experience and complicate daily tasks. Environmental disturbances can cause misinterpretation, and existing automatic speech recognition (ASR) systems struggle with comprehending acoustic and linguistic nuances and handling diverse speaking styles and accents. To address these issues, a new model integrates bidirectional encoder representations from transformers (BERT) and transformer features with natural language processing (NLP) capabilities. This model aims to consolidate semantic, linguistic, and acoustic information extracted from the Kaldi speech recognition toolkit and improve accuracy by rescoring the list of N-best hypotheses. The innovative approach leverages advancements in NLP to enhance speech recognition's accuracy and robustness across various scenarios. Evaluations on the LibriSpeech dataset show that integrating BERT, transformer encoder, and generative pretrained transformer 2 for rescoring N-best hypotheses significantly improves transcription accuracy. The proposed model achieves a word error rate (WER) of 17.98%, outperforming other models. This development paves the way for advancements in speech recognition technology, offering better user experiences in real-world applications.
Copyright (c) 2025 Siu-Hong Chang, Kok-Why Ng, Su-Cheng Haw, Yih-Jian Yoong https://creativecommons.org/licenses/by-sa/4.0
Design of a secure-cloud remote medical monitoring system using the P-QRS-T electrocardiogram detection algorithm
The COVID-19 pandemic has highlighted the limitations of traditional healthcare, resulting in higher mortality rates among children, the elderly, and healthcare workers. This situation has created a pressing need for urgent medical care from healthcare professionals. This paper presents a secure cloud-based remote medical monitoring system that integrates the internet of things (RMMS-IoT) with advanced P-QRS-T electrocardiogram (ECG) detection algorithms to enable real-time, accurate vital sign analysis. The system combines microcontroller devices, wearable sensors, and medical-grade equipment, leveraging hypertext transfer protocol secure (HTTPS) and Blynk bridge cloud technologies to ensure data security and interoperability. The RMMS-IoT system demonstrated high accuracy in monitoring vital signs by comparing its results with data from actual measuring devices, showing errors in body temperature readings below 1% and heart rate (HR) measurements below 2.8%. The algorithm used to detect P-QRS-T features from the ECG exhibited robust performance in differentiating between normal and abnormal ECG patterns in patients, and it achieved an accuracy rate of 90% in ECG classification.
Copyright (c) 2025 Rostom Khalef, Fateh Moulahcene, Ammar Merazga https://creativecommons.org/licenses/by-sa/4.0
Comparative analysis of Haar Cascade, OpenCV, and you only look once algorithms for vehicle detection
Object detection is one of the substantial tasks in computer vision and has a wide range of applications ranging from autonomous driving to monitoring systems. This study presents a comparative analysis of vehicle detection approaches, contrasting traditional methods (OpenCV contour analysis and Haar Cascade) with modern deep learning-based you only look once version 8 (YOLOv8) and its variants. Vehicles were identified and localized within video frames using bounding boxes, with performance assessed through accuracy, F1-score, mean average precision (mAP), and inference speed. YOLOv8 consistently achieved superior accuracy (up to 98% in specific scenarios) and real-time processing speeds (155 FPS), confirming its suitability for safety-critical applications such as intelligent transport systems and autonomous navigation. However, its higher computational and memory demands highlight deployment trade-offs, where lighter variants like YOLOv8s remain feasible for embedded or low-power devices. In contrast, Haar Cascade and contour analysis offered faster execution and smaller memory footprints but lacked robustness under complex environmental conditions. The study also acknowledges limitations such as dataset bias, adverse weather effects, and scalability challenges, which may impact generalization in real-world deployments. By analyzing these trade-offs, the work provides essential insights to guide practitioners in selecting suitable vehicle detection solutions across diverse application environments.
Copyright (c) 2025 Gagandeep Kaur, Shital Pawar, Rutuja Rajendra Patil, Amol Vijay Patil, Anuradha V. Yenkikar, Nikita Bhandari, Kalyani Dhananjay Kadam https://creativecommons.org/licenses/by-sa/4.0
Route splitting and adaptive mutation in genetic algorithms for the capacitated vehicle routing problem
The capacitated vehicle routing problem (CVRP), where vehicle capacity constraints limit the load carried per route for multiple vehicles, is addressed using an optimized genetic algorithm (GA) framework. This work focuses on finding the best configuration of GA by systematically evaluating 12 distinct GA variants, differing in adaptive mutation rates and route-splitting strategies. The framework integrates adaptive mutation rates and novel route-splitting approaches—greedy, dynamic programming (DP), and heuristic—to enhance computational efficiency and solution quality. Experiments on six CVRP instances of varying complexity, encompassing differences in problem size, vehicle capacity, and geographical distribution, demonstrate the heuristic approach’s effectiveness. It achieves solutions within 2%–5% of the optimal cost of DP while being 3–4 times faster. Adaptive techniques reduce costs by up to 20% compared to standard GAs and heuristics. The framework’s scalability is evident in large-scale instances such as the 200-customer case, where the heuristic method balances cost (414.17) and computation time (0.003 seconds). The developed software is openly available at GitHub, providing a robust tool for addressing practical logistics challenges.
Copyright (c) 2025 Shirali Kadyrov, Cemil Turan https://creativecommons.org/licenses/by-sa/4.0
A hybrid random forest and particle swarm optimization model for early preeclampsia detection
Preeclampsia has become a serious medical problem in the world. Currently, there is no routine or comprehensive screening program in place for preeclampsia, which means that preventive measures are not as effective as they could be, potentially resulting in higher rates of illness and death among mothers and infant. The main purpose of this study is to predict early of preeclampsia using random forest algorithms. This study used a quantitative approach with samples 504. The data were analyzed using random forest with particle swarm optimization (PSO). Random forest have been an accuracy rate of 96.08%, for the area under the curve (AUC), precision, sensitivity, and specificity each (0.971; 97.06%; 97.06%; and 94.12%). Model significantly increased 1.39% after optimize from 94.69% to 96.08%. The design process model algorithm has been validated that have a high level of accuracy based on literature reviews. The quality of services offered will certainly influence people to utilize technology-based services more than conventional ones. Recommendation for field technology and health is building an application model for early prediction of preeclampsia based on machine learning (ML) which is an effort for health workers to provide optimal antenatal care and step in changing technology-based pregnancy checks as initial prevention for pregnant women so that preeclampsia can be avoided.
Copyright (c) 2025 Esti Yuandari, R. Topan Aditya Rahman, Ika Avrilina Haryono, Nurul Hidayah, Novita Dewi Iswandari, Siti Hateriah https://creativecommons.org/licenses/by-sa/4.0
Soil erosion analysis based on machine learning method
Soil erosion poses a serious environmental and agricultural threat that undermines land productivity, sustainability, and ecosystem stability. This study develops a robust machine learning framework for predicting and analyzing soil erosion across diverse landscapes by integrating advanced remote sensing data, climate indicators, and soil characteristics. Spectral indices such as the normalized difference vegetation index (NDVI), moisture stress index (MSI), and surface albedo were employed to assess vegetation condition, moisture levels, and surface reflectance. The proposed model, based on the extreme gradient boosting (XGBoost) algorithm, classifies erosion stages with up to 99% accuracy, ranging from healthy land to severely degraded areas. The methodology includes comprehensive feature engineering, dataset preprocessing, and model evaluation. Furthermore, a comparative analysis with traditional models (USLE and RUSLE) highlights the superior predictive performance of the proposed approach. The findings offer valuable insights for sensor-based monitoring systems and cloud-based decision-support tools, supporting sustainable land use management, erosion risk mitigation, and effective soil conservation strategies.
Copyright (c) 2025 Mukhammed Bolsynbek, Gulzira Abdikerimova, Sandugash Serikbayeva, Ardak Batyrkhanov, Dana Shrymbay, Zhazira Taszhurekova, Gulkiz Zhidekulova, Gulmira Shraimanova https://creativecommons.org/licenses/by-sa/4.0
The extraction of a brief summary from scientific documents using machine learning methods
This study proposes a machine learning-based approach for automatic summarization of scientific documents using a fine-tuned DistilBART model a lightweight and efficient version of the bidirectional and auto-regressive transformers (BART) architecture. The model was trained on a large corpus of 12,540 scientific articles (2015–2023) collected from the arXiv repository, enabling it to effectively capture domain-specific terminology and structural patterns. The proposed pipeline integrates advanced text preprocessing techniques, including tokenization, stopword removal, and stemming, to enhance the quality of semantic representation. Experimental evaluation demonstrates that the fine-tuned DistilBART achieves high summarization performance, with ROUGE-2=0.472 and ROUGE-L=0.602, outperforming baseline transformer-based models. Unlike conventional approaches, the method shows strong applicability beyond academic research, including automated indexing of technical documentation, metadata extraction in digital libraries, and real-time text processing in embedded natural language processing (NLP) systems. The results highlight the potential of transformer-based summarization to accelerate scientific knowledge discovery and improve the efficiency of information retrieval across various domains.
Copyright (c) 2025 Gulden Murzabekova, Galiya Mukhamedrakhimova, Zhazira Taszhurekova, Yerbol Yerbayev, Zhanagul Doumcharieva, Valentina Makhatova, Moldir Tolganbaeva, Sandugash Serikbayeva https://creativecommons.org/licenses/by-sa/4.0
Evaluating the effectiveness of Havij for structured query language injection exploitation in web applications
Structured query language injection (SQLi) is still one of the most critical risks to web application security, as it allows attackers to interfere with sensitive data and even a complete database infrastructure. Although many automated tools are available, previous studies usually achieve only descriptive briefs, which do not offer empirical assessments that measure the performance and the usability. This research fills this void by a systematic five-stage experimental analysis of the Havij automated SQLi tool under a controlled and ethical test setup. Confirmation of vulnerability, automated exploitation, data extraction and benchmarking of performance were performed as the methodology, and the results were compared against the industry standard SQLmap tool. It was found that in less than a minute Havij was able to locate the target database, scan its structure, and steal authentication credentials, which is quite efficient and user-friendly. In contrast to the literature, our work presents not only quantitative measures (time-to-exploit, request volume, and success rate) but also a qualitative evaluation (user accessibility and limitations), which gives a comprehensive evaluation. The results highlight trade-offs between the depth and accessibility, the continued dangers of SQLi in practice, and provide recommendations that developers and security experts can implement.
Copyright (c) 2025 Mahmoud Baklizi, Mohammad Alkhazaleh, Musab Bassam Yousef Alzghoul, Adi Maaita, Jamal Zraqou, Mohammad AlShaikh-Hasan https://creativecommons.org/licenses/by-sa/4.0
Feature selection to predict COVID-19 new patients in the four southern border provinces of Thailand
This paper presents a machine learning-based prediction framework that utilizes ensemble feature selection techniques to accurately forecast the number of new coronavirus disease (COVID-19) infections in Thailand’s four southern border provinces. The framework used include multiple linear regression (MLR), mul tilayer perceptron neural networks (MLP-NN), and support vector regression (SVR), to classify short-term trends in new patient cases. The study evaluates the effectiveness of these models across different provinces and demonstrates how integrating feature selection methods: forward selection (FS), backward elimination (BE), and genetic algorithms (GA) enhances prediction accuracy. The findings highlight the adaptability of the models, with each province ben efiting from tailored model-feature selection strategies. The results show that the predictive models align closely with real patient data, enabling authorities to anticipate outbreaks and implement timely interventions. Moreover, the pro posed methodology can be applied to other epidemics, making it a valuable tool for public health planning and preparedness. The study offers actionable in sights for decision-makers, emphasizing the importance of predictive modeling in community-level outbreak management.
Copyright (c) 2025 Chadaphim Photphanloet, Sherif Eneye Shuaib, Siriprapa Ritraksa, Pakwan Riyapan https://creativecommons.org/licenses/by-sa/4.0
Alzheimer's disease detection based on MR images using the quad convolutional layers CNN approach
Alzheimer’s disease is a progressive neurodegenerative disorder requiring early and accurate detection for effective intervention. Deep learning (DL) techniques, particularly convolutional neural networks (CNNs), have shown promise in medical image classification. However, conventional CNN models often suffer from high computational complexity and inefficiency in handling imbalanced datasets. This study proposes a quad convolutional layers-CNN (QCL-CNN) for Alzheimer’s disease detection using magnetic resonance images (MRI) scans from the open access series of imaging studies (OASIS) dataset, which includes four dementia stages, non-dementia, very mild dementia, mild dementia, and moderate dementia. The QCL-CNN model employs four sequential convolutional layers for enhanced multi-level feature extraction, ensuring efficient classification while minimizing computational overhead. The experimental results demonstrate that QCL-CNN outperforms traditional CNN architectures, achieving an accuracy of 99.90%, recall of 99.89%, specificity of 99.93%, and an F1-score of 99.52%. The model surpasses VGG19, Xception, ResNet50, and DenseNet201 while maintaining a significantly lower parameter count (4.2M), making it computationally efficient. These findings confirm that network optimization is more crucial than model depth, ensuring robust performance even with fewer layers. Future research should explore multi-modal imaging, class balancing techniques, and real-world clinical validation to further improve the model’s diagnostic capabilities. The QCL-CNN model offers a promising artificial intelligence (AI)-powered approach for early Alzheimer’s detection, enabling precise, and efficient medical diagnosis.
Copyright (c) 2025 Yuri Pamungkas, Achmad Syaifudin, Wawan Yunanto, Uda Hashim https://creativecommons.org/licenses/by-sa/4.0
Memory management principle for dynamic isolation in agent-based epidemic modeling
This paper presents a new epidemiological modeling approach that adapts the working set (WS) concept from computer memory management to the dynamics of infectious diseases. Traditional compartmental models provide valuable insights but are limited in their ability to capture dynamic isolation and heterogeneous contact patterns. In contrast, the WS model conceptualizes a time-varying subset of agents actively participating in social interactions, allowing for dynamic adjustments to the rate of infection and the explicit identification of superspreaders. By incorporating isolation states for both susceptible and infected individuals, the model more realistically captures quarantine and targeted interventions. Including an incubation period reduces epidemic peaks by nearly 40% and delays them by more than three weeks, providing critical time for public health response. Within the WS model, moderate isolation reduces peak infection rates by more than three times compared to uncontrolled scenarios, while high isolation almost completely prevents large-scale spread. These results highlight the model's ability to estimate the intensity and timing of interventions with greater accuracy than traditional models. By integrating the time window parameter and computer resource management principles, the adapted WS model represents a robust and adaptable tool for analyzing epidemic dynamics. The results highlight its potential for advancing epidemic modeling and supporting real-time public health decision-making.
Copyright (c) 2025 Aslanbek Murzakhmetov, Gaukhar Borankulova https://creativecommons.org/licenses/by-sa/4.0
Cybersecurity challenges in healthcare: mitigating risks in a rapidly evolving digital landscape
The rapid digital transformation of the healthcare sector brings significant benefits but also exposes institutions to escalating cybersecurity risks. This study analyzes vulnerabilities such as ransomware, supply chain compromises, and insider threats, drawing on international reports from World Health Organization (WHO), Healthcare Information and Management Systems Society (HIMSS), Ponemon, and Verizon. The paper contributes a unique mitigation framework that consolidates three strategic pillars: i) continuous cybersecurity training for medical staff, ii) deployment of advanced technological safeguards, and iii) establishment of collaborative incident reporting mechanisms. Beyond mapping current threats, the study provides policy-oriented guidance for strengthening resilience in both developed and emerging healthcare systems. With healthcare breaches costing an average of $10.1 million per incident, the findings highlight the urgent need for coordinated action to ensure patient safety, service continuity, and institutional trust.
Copyright (c) 2025 Arina Alexei, Ion Bolun, Anatolie Alexei https://creativecommons.org/licenses/by-sa/4.0
Real-time browser-integrated phishing uniform resource locator detection via deep learning and fuzzy matching
Phishing attacks through deceptive URLs remain a critical cybersecurity threat, particularly in financial transactions and online payment systems. This study evaluates multiple deep learning (DL) models on the PhiUSIIL dataset of 235,795 URLs, with bidirectional gated recurrent unit (BiGRU) achieving the best performance—99.82% accuracy at a 60:40 split, along with high precision, F1-score, and the lowest test loss. To further improve detection of obfuscated URLs, an enhanced BiGRU variant is proposed using an expanded 366-character vocabulary. For real-time deployment, a Chrome extension is developed, integrating exact and fuzzy matching via the Ratcliff–Obershelp algorithm with cloud-based whitelist and blacklist checks. When fuzzy matching is inconclusive, the BiGRU model performs the final classification. By combining an adaptive browser-side tool with a robust DL backend, the proposed system ensures high accuracy, scalability, and efficiency for phishing detection in practical web environments.
Copyright (c) 2025 Dam Minh Linh, Han Minh Chau, Huynh Trong Thua, Tran Cong Hung https://creativecommons.org/licenses/by-sa/4.0
Smart tourism application: towards software development for artificial intelligence in tourism management
Artificial intelligence (AI) can manage tourism by optimizing, personalizing the experience, and enhancing user interactions. This research presents the Ayutthaya tourism platform independent model (ATPiM), an intelligent tourism application that integrates a domain-specific language (DSL) designed for chatbot development with machine learning algorithms that generate personalized recommendations based on user preferences, historical data, and real-time contextual influences. This pre-experimental design measures performance on parameters such as response time, recommendation accuracy, and system latency. The outcomes indicate that the mean time taken to respond to a user's query was 2.3 seconds, with 88.5% recommendation accuracy, and no latency. The AI-based recommendation system achieved 89.7% accuracy at destinations, 87.2% at accommodations, 90.3% at itineraries, and 85.6% at activities, with corresponding recalls of 85.4%, 83.5%, 88.1%, and 80.2% respectively. Although these results are promising, a 6.2% error rate for the advanced search, along with data security are some of the remaining issues. The findings reveal that the development of new user-centric and sustainable solutions for tourism, which leverage state-of-the-art natural language processing approaches, can enhance data security and provide additional new technologies, such as augmented reality (AR) and blockchain, for use in tourism.
Copyright (c) 2025 Parinya Natho, Adisak Sarathum, Amnaj Sookjam, Anek Putthidech, Salinun Boonmee https://creativecommons.org/licenses/by-sa/4.0
Load frequency control of multi-source power system using PID+DD controller based on chess algorithm
This article presents load frequency control for a nonlinear multi-source power system divided into three areas, consisting of thermal reheat power plants, hydropower, and wind generation, while considering generation rate constraints (GRC). A proportional–integral–derivative (PID) plus second-order derivative (PID+DD) controller optimized using the chess algorithm (CA) is proposed. The effectiveness of CA is validated against hippopotamus optimization (HO), grey wolf optimizer (GWO), and ant lion optimizer (ALO) under two scenarios: a 10% step load perturbation (SLP) and a random load pattern (RLP). Simulation results indicate that the proposed CA significantly improves dynamic performance. In scenario 1 (10% SLP), CA achieves a reduction of approximately 30.5% in integral weight time absolute error (ITSE) compared to GWO and 43.7% compared to HO, while also reducing frequency undershoot in Area 2 by 15.2% compared to HO. In scenario 2 RLP, CA maintains robustness, limiting tie-line power deviations to ±8 MW, whereas HO exhibits deviations exceeding ±12 MW. Overall, the CA-tuned PID+DD controller demonstrates superior damping, reduced overshoot and undershoot, and enhanced stability across multi-area interconnected renewable systems, making it a promising approach for future real-time load frequency control (LFC) applications with higher renewable penetration.
Copyright (c) 2025 Chatmongkol Areeyat, Sittihsak Audomsi, Jagraphon Obma, Xiaoqing Yang, Worawat Sa-ngiamvibool https://creativecommons.org/licenses/by-sa/4.0
Design and emulation of an SDN network with opendaylight to improve QoS in a peruvian financial institution
This study presents the design and emulation of a software-defined networking (SDN) architecture using the OpenDaylight controller to enhance the quality of service (QoS) in a Peruvian financial institution. The main objective is to overcome limitations of traditional networks, including high latency, limited bandwidth, and packet loss, which hinder the efficiency of financial services. The proposed SDN architecture was implemented and tested through simulations in the Eve-NG platform, where key performance parameters—latency, throughput, and packet loss—were measured. Results demonstrated significant improvements, with latency reduced by up to 40%, stable throughput maintained at 10 Mbps across all branches, and a noticeable reduction in packet loss. These outcomes validate the feasibility of adopting SDN in financial environments to support critical services and ensure operational continuity. Furthermore, the findings emphasize SDN’s role in modernizing network infrastructures, improving user experience, and aligning local financial institutions with international technological trends. Future research may explore alternative SDN controllers, scalability in larger topologies, and integration with emerging technologies such as network function virtualization (NFV).
Copyright (c) 2025 Juan David Indigoyen Roncal, Christian Ovalle Paulino https://creativecommons.org/licenses/by-sa/4.0
Prediction of asphalt performance based on plastic waste using machine learning
The incorporation of plastic waste into asphalt mixtures offers a promising solution to address the growing environmental concerns while enhancing the performance of road materials. Traditional methods, such as the Marshall test, are costly and time-consuming, thus highlighting the need for more efficient prediction techniques. Machine learning (ML) models, including random forest (RF), extreme gradient boosting (XGBoost), and artificial neural networks (ANN), have shown significant potential in predicting asphalt performance, optimizing material compositions, and reducing the dependence on labor-intensive laboratory tests. Key influencing factors such as bitumen content, plastic size, and temperature have been identified as crucial for improving asphalt properties. This systematic review emphasizes the potential of ML in streamlining the development of plastic-modified asphalt, offering a sustainable and cost-effective approach to road construction. Furthermore, it supports the advancement of green infrastructure and lays the foundation for future innovations in sustainable pavement engineering, contributing both to academic research and practical applications in the construction industry.
Copyright (c) 2025 I Gusti Agung Ananda Putra, Darpan Rokade https://creativecommons.org/licenses/by-sa/4.0
5G cellular network planning in Parepare City
The telecommunications industry is rapidly advancing, particularly in cellular network communications that use air as the transmission medium, with 5G new radio (NR) emerging as a key global technology including in Indonesia. Defined by enhanced mobile broadband (eMBB) offering speeds up to 10 Gbps, ultra-reliable low-latency communications (URLLC) with latency below 1 millisecond, and massive machine-type communications (mMTC) supporting large-scale internet of things (IoT) connectivity, 5G plays a crucial role in modern digital infrastructure. This study focuses on the city of Parepare in South Sulawesi, an area driven by trade, port operations, fisheries, shipbuilding, and natural tourism highlighting the need for high-speed and reliable data services. The research aims to develop a comprehensive 5G NR network plan for Parepare through coverage and capacity analyses evaluating synchronization signal-reference signal received power (SS-RSRP), signal-to-interference-plus-noise ratio (SS-SINR), and throughput performance. Using Atoll software to design and map next-generation Node B (gNodeB) placements, the study offers a scientific approach to optimizing 5G deployment and supporting the city’s economic growth and tourism potential.
Copyright (c) 2025 Yuniarti, Sulwan Dase, Nurul Khaerunnisa, Arni Litha, Nurhayati, Muhira Dzar Faraby, Asma Amaliah, Isminarti, Martina Pineng, Sandryones Palinggi https://creativecommons.org/licenses/by-sa/4.0
Optimizing tilt angle of the roof for the best performance ratio of rooftop photovoltaic
Rooftop photovoltaic (RPV) systems are becoming increasingly popular as a source of renewable energy. One key factor that significantly affects the performance of RPV systems is the tilt angle of the solar panels. This study aims to determine the optimal tilt angle to maximize electricity production for the RPV system installed on academic building, Department of Electrical Engineering, Faculty of Engineering, University of Riau. The research method used is the PVSyst simulation. The simulation data input was used as daily weather for one year. In this study, variations in roof tilt angles of 5°, 10°, 15°, 20°, and 25° were examined. The results show that the optimal tilt angles for this location are 5° and 10°. At a 5° tilt angle, the RPV can generate 247,128 kWh per year with a performance ratio (PR) of 83%. And then at a 10° tilt angle, the system can generate 248,012 kWh per year with a PR of 82%. Based on the simulation results, other tilt angles also produced higher energy outputs but yielded lower PR values. This study provides practical recommendations for designing RPV systems in regions with similar weather conditions.
Copyright (c) 2025 Nurhalim Dani Ali, Syaiful Bahri, Yusni Ikhwan Siregar, Suwondo, Anhar, Noveri Lysbetti Marpaung, Juandi Muhammad, Iswadi Hasyim Rosma https://creativecommons.org/licenses/by-sa/4.0
Real-time vehicle detection and speed estimation system using Raspberry Pi and camera module
In the era of intelligent transportation systems, real-time vehicle detection and distance estimation play a crucial role in enhancing road safety and traffic efficiency. This study proposes a low-cost, real-time system that integrates you only look once–version 8 (YOLOv8)-based deep learning for vehicle detection with monocular vision techniques for distance estimation, implemented on a Raspberry Pi embedded platform. The objective is to provide a scalable, affordable solution for traffic monitoring and collision avoidance in resource-constrained environments. The methodology involves using a camera module connected to Raspberry Pi for live video capture, YOLOv8 for object detection, and a calibrated monocular distance estimation algorithm based on bounding box dimensions and known vehicle sizes. Experimental results show that the system achieves over 90% detection accuracy under standard lighting conditions and maintains a distance estimation error below 10% for vehicles within 15 meters. The model processes video frames in real time (~0.17 seconds per frame), proving its effectiveness for embedded deployment. In conclusion, the proposed system offers a robust, power-efficient alternative to high-cost light detection and ranging (LiDAR) or stereo vision systems. Its modular design supports future enhancements such as speed estimation or multi-camera integration, making it highly relevant for smart city applications and low-cost vehicular safety systems.
Copyright (c) 2025 B. Jyothi, Bhavana Pabbuleti, Gadi Sanjeev, Kambhampati Venkata Govardhan Rao, S. Sai Srilakshmi, Atul Jee, Malligunta Kiran Kumar, Thulasi Bikku, Ch. Rami Reddy https://creativecommons.org/licenses/by-sa/4.0
Modeling the process of magma rising in the bowels of the Earth and its eruption to the surface
This paper presents a numerical model for simulating magma ascent in the Earth’s interior and its eruption to the surface, aimed at improving earthquake prediction. Magmatic flows are modeled as highly viscous fluids with a low Reynolds number (Re) using simplified Navier–Stokes equations. The approach incorporates hydrodynamic instability arising from density differences between magmatic and asthenospheric layers. Initial and boundary conditions were formulated for magma outflow from a narrow crack, and a dimensionless Euler–Reynolds (ER) parameter was introduced to characterize flow behavior. Numerical experiments for different ER values revealed that at low ER, magma spreads slowly, forming stable layers, while higher ER values accelerate vertical rise, increase pressure gradients, and enhance instability. The model identifies zones of stress accumulation that may precede seismic events. An additional method—monitoring fluid levels in deep wells—showed correlation with seismic fluctuations, supporting its potential for early warning. The results confirm the reliability of the proposed approach, demonstrating good agreement with seismological data. The developed methodology can be applied to enhance early warning systems and reduce risks in seismically active regions.
Copyright (c) 2025 Ainur Taurbekova, Balnur Karimsakova, Orken Mamyrbayev, Ainur Toigozhinovа, Aliya Tergeussizova, Kuntugan Doshtaev, Zarina Aidaraliyeva, Elvira Utepova https://creativecommons.org/licenses/by-sa/4.0
Advances in artificial intelligence-driven 3D model generation: a review of GAN and VAE methodologies
This paper offers a comprehensive review of current developments in artificial intelligence (AI)-based 3D model creation, with an emphasis on techniques utilizing variational autoencoders (VAEs) and generative adversarial networks (GANs). 3DGAN, paired 3D model generation with GAN, conditional GAN, FaceVAE, voxel-based 3D object reconstruction, and 3D-VAE-SDFRaGAN are the six main techniques that are studied in this work. Each method is discussed, highlighting its architectural framework, data representation, and specific approach to generating 3D models. First, the paper introduces basic terms and classical 3D modeling techniques and provides a comparative analysis of them based on their workflow, purpose and field of application. In subsequent chapters, methods for generating 3D models based on the use of GANs and VAEs are reviewed, describing its methodology, experimentation technique, results, and comparison with other methods. The review outlines the strengths and limitations of each approach and their applications in object reconstruction, shape generation, and maintaining model consistency. It concludes by emphasizing how AI-driven methods can advance 3D modeling, underscoring the need for further research to enhance quality, control, and training reliability. The findings show AI’s significant impact on automating complex modeling tasks and enabling new creative opportunities in 3D content development.
Copyright (c) 2025 Shyngys Adilkhan, Madina Alimanova, Lei Shi, Aiganym Soltiyeva https://creativecommons.org/licenses/by-sa/4.0
Mapping global research trends in power quality for industrial electrical systems: a bibliometric analysis (2016–2024)
This paper analyzes trends in electrical power quality (PQ) in industrial systems through a bibliometric approach to identify key topics, prominent authors, and patterns of international collaboration that may guide future research. PQ disturbances can significantly affect operational continuity, energy efficiency, and equipment lifespan in industrial electrical systems (IES), making it essential to map the research landscape to support technological and strategic responses. The study reviews 103 articles from the Scopus database for the period 2016–2024, applying relevance and currency criteria. VOSviewer® was used to conduct the analysis, employing keyword co-occurrence networks and bibliographic coupling to visualize thematic, collaborative, and citation relationships. Results indicate a strong research focus on harmonic distortion, voltage disturbances, and artificial intelligence applications for diagnosis and mitigation. India leads in scientific production, while IEEE Access is the most influential source. Despite growing interest, the study identifies limited international collaboration and thematic fragmentation, which may hinder comprehensive solutions. The findings highlight the need to expand collaboration networks, standardize methodologies, and integrate underexplored topics into mainstream PQ studies, strengthening the ability of industrial systems to address emerging challenges and improve performance, resilience, and reliability.
Copyright (c) 2025 Eliana Noriega Angarita, Vladimir Sousa Santos, Pablo D. Donolo, Edgar Solorio Aguila, Endy Garcia Garcia, Johan Hernandez Tomas, Victor Garduno Leon, Jose Rivera Ramos https://creativecommons.org/licenses/by-sa/4.0
Integrating RPA, BPM, and DT in the context of Industry 4.0 and 5.0: a strategic approach for modern enterprises
This paper examines the interaction between robotic process automation (RPA), business process management (BPM), and digital transformation (DT)-three critical components in improving operational efficiency and driving business modernization. RPA automates repetitive tasks, reduces errors, accelerates processing, and optimizes resource use. When combined with artificial intelligence (AI) and machine learning (ML), it further enhances data analysis and decision-making. BPM focuses on analyzing, designing, and optimizing business processes to ensure organizational agility. DT provides a technological foundation for broader innovation in processes and structures. The paper contributes a comprehensive and updated perspective on how RPA, BPM, and DT interrelate—not only functioning independently but also reinforcing one another to create greater business value. It emphasizes that their integration is a strategic approach to improving performance, responsiveness, and continuous innovation. Importantly, the research is relevant to both Industry 4.0 and Industry 5.0. While Industry 4.0 (I4.0) prioritizes automation and data-driven systems, Industry 5.0 (I5.0) highlights human–technology collaboration for more adaptive and human-centric organizations. This study enriches theoretical insights and offers practical guidance for building effective and sustainable DT strategies.
Copyright (c) 2025 Truong Bui Quang, Huu Dang Quoc, Van Nguyen Thi Cam, Anh Nguyen-Duc https://creativecommons.org/licenses/by-sa/4.0
Bilateral transactions impact voltage stability and nodal pricing in power networks
This study investigates the impact of bilateral transactions on voltage stability and nodal pricing in the Indian power grid using a modified IEEE 30-bus system. A high voltage direct current (HVDC) link is integrated into the network to enhance control and system flexibility. Two advanced transmission pricing mechanisms— megawatt (MW)-Mile and megavolt-ampere (MVA)-Mile—are employed to allocate costs based on power flow magnitude and distance. The analysis incorporates hybrid AC-DC optimal power flow (OPF) modeling under various transaction levels. Simulation results show that a 100 MW bilateral transaction reduces the voltage at the receiving bus (bus 28) by 2% (from 1.05 to 1.03 p.u.) and increases the nodal price by 6.25% (from ₹4.80 to ₹5.10/kWh). The use of HVDC technology reduces total generation cost by approximately 8.2% (from ₹85 lakhs to ₹78 lakhs) and decreases real power loss from 70 MW to 50 MW. These findings confirm that bilateral transactions influence voltage profiles and market pricing. Moreover, MW-Mile and MVA-Mile methods demonstrate effective cost allocation capabilities. The proposed approach offers a practical framework for improving grid reliability and economic transparency in evolving power markets.
Copyright (c) 2025 Ganesh Wakte, Mukesh Kumar, Mohammad Aljaidi, Ramesh Kumar, Manish Kumar Singla https://creativecommons.org/licenses/by-sa/4.0
Predicting player skills and optimizing tactical decisions in football data analysis using machine learning methods
This study investigates the integration of machine learning (ML) techniques into football analytics to predict player skills and optimize tactical decisions. A dataset of over 150,000 professional match actions from various leagues and seasons was analyzed using deep neural networks, convolutional neural networks (CNNs), and gradient boosting machines (GBM) algorithms on biometric, contextual, and match data. The valuing actions by estimating probabilities (VAEP) metric indicated scores from +1.8 to +3.0 for key players, enabling detailed performance evaluation. CNN models achieved up to 91% precision, 88% recall, and a receiver operating characteristic – area under the curve (ROC-AUC) of 0.94, confirming their effectiveness in predicting player actions and contributions. Injury risk prediction using eXtreme gradient boosting (XGBoost) reached an F1-score of 0.87 and a ROC-AUC of 0.92, offering actionable insights for injury prevention and optimal player rotation. The findings highlight artificial intelligences (AI)’s capacity to support individualized preparation, tactical adjustments, and cost-effective recruitment strategies. While computational demands and data quality remain challenges, the results demonstrate the transformative potential of AI in modern football, providing a practical framework for data-driven decision-making to enhance team performance and strategic planning
Copyright (c) 2025 Akmaral Kassymova, Tolegen Aibatullin, Shynar Yelezhanova, Assem Konyrkhanova, Ainur Mukhanbetkaliyeva, Assemgul Tynykulova, Ulzhan Makhazhanova, Gulmira Azieva https://creativecommons.org/licenses/by-sa/4.0
Multi-attribute based optimal location and sizing of solar power plant in radial distribution system
Advancements in renewable energy sources (RES) have significantly increased power generation and reduced emissions. Optimally integrating RES into distribution systems can minimize power losses, emissions, and enhance voltage profile and stability. Therefore, determining the optimal location and size of RES is crucial for their effective integration. This paper presents a novel approach for identifying the optimal location and size of a solar power plant (SPP) in a distribution system, considering system power losses, voltage profile, voltage stability, and emissions simultaneously. A simple yet effective methodology combining repeated load flow and fuzzy systems is proposed. Repeated load flow is used to calculate the relevant attributes, while fuzzy decision-making is employed to determine the optimal solution. The effectiveness of the proposed method is demonstrated through its application to the IEEE-33 bus system. The results illustrate that integrating a SPP at the optimal location and size can significantly reduce power losses and emissions while improving voltage profile and stability.
Copyright (c) 2025 Ramesh Kumar, Digambar Singh, Mohammad Aljaidi, Manish Kumar Singla, Shashank Tripathi https://creativecommons.org/licenses/by-sa/4.0