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: 4.2, SNIP: 0.609, and SJR: 0.326. 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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Design and analysis of peaking capacitor for generation of high voltage fast risetime pulses
High voltage pulses of short duration are required in certain applications, here precise and rapid electrical energy delivery is required. These include applications like pulsed power system, high speed switching circuits, plasma generation circuits, high speed imaging, non-thermal food processing. One of the best and cheap methods of producing the high voltage fast rise time pulses is using Peaking capacitor. Designing a high-voltage peaking capacitor requires careful consideration of various factors such as voltage rating, capacitance value, energy storage capacity, physical size, dielectric material, and safety measures. In this paper, designing of 200 pF, 300 kV peaking capacitor is presented and simulation is carried out in PSPICE. The maximum peak voltage across the peaking capacitor, the time to reach peak voltage, and the maximum peaking current obtained are 290 kV, 98.9 ns, and 22.41 kA, respectively. Also, to study the electric field of the medium used in peaking capacitors, Quick field software is used. The maximum electric field in Perspex, used as a dielectric medium, was 13 kV/mm.
Copyright (c) 2026 Madhu Palati, Manjunatha Babu Pattabhi, Ozwin Dominic Dsouza https://creativecommons.org/licenses/by-sa/4.0
Enhancing wind speed forecasting accuracy: comparative insights into recurrent neural networks for short-term prediction
Short-term wind speed forecasting is essential for maintaining grid stability and supporting the integration of renewable energy, yet the strong variability of wind makes accurate prediction difficult. Sudden fluctuations and nonlinear atmospheric behavior often reduce the performance of conventional artificial intelligent models. To address this challenge, this study evaluates three forecasting methods which include a gated recurrent unit (GRU) model, a temporal convolutional networks (TCNs) model, and a hybrid GRU–TCN design that enables prolonged term forecasting while enabling quick identification of localized weather changes across various meteorological parameters. The researchers used Laayoune, Morocco data to build their model training process. The hybrid method exceeded all other models because it achieved an R² value of 0.99 and a root mean square error (RMSE) of 0.16 m/s and a mean absolute error (MAE) of 0.03 m/s. The system successfully manages sudden shifts in wind patterns while maintaining accurate site-specific physical behavior. The hybrid GRU–TCN design functions as a dependable and expandable system, which delivers real-time wind forecasting capabilities that enable effective smart grid operations and facilitate the growth of wind energy systems.
Copyright (c) 2026 Sara Fennane, Houda Kacimi, Hamza Mabchour, Fatehi Altalqi, Aziz El Hazmir, Adil Echchelh https://creativecommons.org/licenses/by-sa/4.0
Assessment of annual aggregate commercial energy loss due to day-on road lights: a field research study
The electrical vitality utilization is at a quick rate as compared to its era driving towards the need for vitality reserve. Every nation is concerned about addressing the energy security issue and most of their attention during the past few years has been focused on the very essence of energy conservation opportunities. It is the responsibility of all the electricity consumers to ensure that no portion of electrical vitality gets wasted either hastily or intentionally. Unfortunately, as a result of day-on-road lights (DORL), electricity is being wasted in several villages and towns. This claim is validated through the performed field research (FR) presented in this manuscript. From the FR, it is inferred that approximately 13.2% of the total road lights were found turned-on during the day hours, resulting in an annual vitality loss of approximately 55.75 MWh. It is being wasted because of the mismanagement of switches and manmade errors; because of this, it forms a part of the overall commercial loss of 4.8 tonnes of oil equivalent (toe) and accounts for the release of about 47.4 tonnes of CO? (tCO2) emissions per annum. This paper presents on broad investigation on the FR performed to recognize a yearly total commercial misfortune of vitality with techno-commercial analysis.
Copyright (c) 2026 Sadda Jayachandra, Shaik Rafikiran https://creativecommons.org/licenses/by-sa/4.0
Optimizing the economic evaluation method for calculating the total owning cost of induction motors
This paper presents an approach for estimating the total ownership cost (TOC) of induction motors (IMs) by applying five different economic evaluation methods, each based on specific assumptions. These methods are examined and compared in detail to demonstrate the difference in their implementation and outcomes. In addition to the technical data of the analyzed IM, the methods also consider significant economic factors that affect TOC assessments, such as electricity tariffs, initial investment cost, operating hours, discount rate, and the anticipated lifespan. The proposed approach offers a practical tool to support decision-making when selecting the most economical among different alternatives offered by various manufacturers or venders. A numerical example is presented to demonstrate the application of the proposed work, including detailed calculations with results summarized in tables. The paper concludes by offering practical guidance on selecting the most appropriate economic method for the evaluated scenarios of IM, offering direction to site engineers and decision- makers in balancing technical aspects with long-term economic considerations. The calculation is straightforward and can be performed using standard mathematical tools, illustrating how various factors influence costs and enhancing the reliability of the result. For large-scale analyses, MATLAB provides additional computational efficiency and scalability.
Copyright (c) 2026 Ayman Agha, Khaled Alzaareer, Hudefah Alkashashneh https://creativecommons.org/licenses/by-sa/4.0
AC and DC power transfer optimization research using numerical models for wireless heavy-duty EV charging
Wireless power transfer (WPT) can enable automated charging of heavy-duty electric vehicles (HDEVs), where high power levels require high efficiency and stable operation. This paper compares AC- and DC-based WPT architectures for the TEMSA LD SB E bus using MATLAB/Simulink simulations. The anal ysis is conducted at three operating frequencies (80, 85, and 90 kHz) and for coupling coefficients ranging from 0.10 to 0.90 with 0.10 increments. The AC system uses an eight-switch resonant inverter, whereas the DC system employs a direct DC/DC converter. Transient behavior is solved using a fourth-order Runge–Kutta scheme, and electromagnetic interactions are verified via finite element analysis (FEM). Peak efficiencies of 96.10% (AC) and 97.44% (DC) are achieved at 85 kHz and a coupling coefficient of 0.90. Normalized sensi tivity indices and linear regression identify coupling and frequency as dominant factors, while the DC architecture exhibits reduced sensitivity. The results pro vide quantitative guidance for designing reliable high-power WPT chargers for commercial fleets.
Copyright (c) 2026 Mustafa Karavus, Ahmet Arif Ergin, Gulten Polat, Bunyamin Esiyok https://creativecommons.org/licenses/by-sa/4.0
MPPT controller for a solar PV array with temperature-compensated P&O algorithm
Photovoltaic (PV) systems must operate at the maximum power point (MPP) to maintain efficiency under varying environmental conditions. This study develops a temperature-compensated perturb-and-observe (P&O), maximum power point tracking (MPPT) controller integrated with a boost converter in MATLAB. A 100 kW PV array was modeled using realistic module parameters; baseline experiments without MPPT revealed power losses up to 70% and incorrect power–temperature correlations due to fixed-duty cycle operation. With MPPT enabled, the controller dynamically adjusted the duty ratio, achieving proportional scaling with irradiance and an inversely proportional response to temperature. Results confirm a peak summer output of 48.78 kW compared to 15 kW without MPPT, improving annual energy yield by a factor of three under realistic Sakarya operating conditions.
Copyright (c) 2026 Abdulaziz H. Yusuf, Ceyda Aksoy Tirmikci, Eltahir Idris Eltahir Mohamed https://creativecommons.org/licenses/by-sa/4.0
Comparative study of CNN and fused 2D CNN-LSTM with CWT and STFT for power quality disturbance classification
The integration of solar and wind energy has increased electricity generation but also introduced power quality disturbances (PQDs) that threaten grid stability. This study examines the detection and classification of five PQD types—voltage sag, swell, interruption, harmonics, and normal conditions—across noisy environments (0, 10, 20, and 30 dB) signal-to-noise ratio (SNR). Traditional methods— support vector machine (SVM), random forest (RF), artificial neural networks (ANN), and 1D convolutional neural networks (1D CNN)—are evaluated on raw signal data, while advanced models—2D CNN and fused 2D CNN-LSTM—utilize time-frequency representations (continuous wavelet transform (CWT) and short-time Fourier transform (STFT)). Results show that deep learning (DL) models achieve high accuracy even in noisy environments, with the fused 2D CNNLSTM using CWT outperforming all other methods. Noise adversely affects feature extraction, with CWT consistently outperforming STFT under low SNR conditions. These findings demonstrate that combining DL models with robust time-frequency analysis and temporal modeling enhances PQD classification and supports dependable monitoring in smart grid environments.
Copyright (c) 2026 Bouchra Feriel Khaldi, Fatma Zohra Dekhandji, Abdelmadjid Recioui https://creativecommons.org/licenses/by-sa/4.0
An RBF neural network–based MPPT with sliding mode and fuzzy control for PV systems using buck converter
This paper proposes an integrated control strategy for maximum power point tracking (MPPT) in photovoltaic (PV) systems using a buck converter. The controller combines a radial basis function (RBF) neural network for uncertainty approximation, sliding mode control (SMC) for robustness, and fuzzy logic for adaptive tuning of the switching gain to reduce chattering. The complete RBF–SMC–fuzzy control law is derived, and closed-loop stability is proven using Lyapunov theory. Simulation results in MATLAB/Simulink under both resistive and battery charging loads show that the proposed method achieves fast tracking with a settling time of about 20 ms, a tracking efficiency higher than 99%, and a voltage ripple of approximately 1.2%. Compared with conventional methods, the proposed controller significantly reduces chattering and improves power extraction performance under irradiance and load variations.
Copyright (c) 2026 Anh Van Le, Minh Van Pham, Linh Thi To Vu https://creativecommons.org/licenses/by-sa/4.0
Design and realization of a fuzzy logic-based MPPT controller for PV systems using microcontroller
This study presents a microcontroller-based fuzzy logic control method for maximum power point tracking (MPPT) in photovoltaic systems under varying temperature and solar irradiation. The proposed controller is implemented on an 8-bit microcontroller and regulates the duty cycle of a pulse-width-modulation-driven DC-DC converter to extract maximum power from the photovoltaic array. Unlike conventional MPPT methods, the fuzzy logic approach provides faster response, improved flexibility, and stronger robustness against nonlinear current-voltage characteristics and converter switching effects. The system includes a photovoltaic array, sensing circuits, a DC-DC converter, and an embedded controller programmed with optimized C code for real-time operation. Experimental results show that the proposed method reaches the maximum power point quickly and maintains stable performance during environmental changes. It also improves energy conversion efficiency compared with traditional algorithms. Its low-cost hardware and simple embedded implementation make it suitable for practical photovoltaic applications and sustainable energy generation in renewable energy systems.
Copyright (c) 2026 Mohan P. Thakre, Badal Kumar, Alok Kumar, Supriya Nilesh Thakur, Krupali Kanekar, Pranali M. Thakre, Prashant K. Magadum https://creativecommons.org/licenses/by-sa/4.0
Enhancing solar power generation efficiency through chaotic beetle swarm optimization for constant power generation
The necessity for more and cleaner sustainable energy sources to generate power is increasing because of the reduction of fossil fuel supplies and their negative impacts on the environment. This research addresses the requiring crucial for optimized solar power systems in the weather change issues. In the beginning, the photovoltaic (PV) system’s voltage is enhanced by dual stage XBoost converter (DSXBC) with little voltage stress and maximum voltage gain. Also, the radial bias function neural network (RBFNN)maximum power point tracking (MPPT) tracks the PV system’s uppermost power and its parameters are fine-tuned by chaotic beetle swarm optimization (CBSO) algorithm. By integrating chaotic dynamics within the optimization process, CBSO runs a robust and efficient approach to navigating the complex search space related with MPPT. The MATLAB tool is utilized to reveal the efficacy of developed approach for allowing constant power generation in solar power generation systems with efficacy of 99.74% and tracking efficiency of 98.9% in steady state condition, thereby enabling continuous power generation.
Copyright (c) 2026 Sreenivasan Ramachandran, Ramkumar Ravindran https://creativecommons.org/licenses/by-sa/4.0
Modified integral sliding mode speed controller for predictive stator flux and torque controlled induction motor drive
The paper deals with utilization of integral sliding mode (SM) in speed controller of predictive torque and flux control (PTC) of induction motor (IM). At first, the model-based prediction of the motor torque and stator flux is presented. Then, the voltage vector of the inverter is selected to minimize the errors of the torque and the flux. The proportional-integral (PI) speed controller is used to provide the reference torque for the torque controller. However, the PI speed controller is not achieved high performance especially in cases of high load torque and low speed commands. Then, the integral SM one is employed to enhance the speed control performance. Simulations of two speed controllers in PTC drive are carried out. Evaluations using criterions relevant to speed error confirm the expected performance of the integral SM speed controller.
Copyright (c) 2026 Hau Huu Vo, Dat Vinh Phat Tran https://creativecommons.org/licenses/by-sa/4.0
Securing electric vehicle charging stations from adversarial cyber attacks using hybrid detection models
Electric vehicle charging infrastructure (EVCI) has become essential. However, these infrastructures are increasingly vulnerable to cyber threats, particularly through spoofing and adversarial attacks on charging ports. This paper introduces a robust anomaly detection framework leveraging long short-term memory (LSTM) based autoencoders to identify anomalies in electric vehicle (EV) charging port current magnitudes. A simulated EVCI setup is developed in MATLAB/Simulink to capture charging behaviors under normal and adversarial scenarios. To generate adversarial data, the fast gradient sign method (FGSM) is employed. The reconstructed outputs from the LSTM-autoencoder (LSTM-AE) are statistically compared to real-time observations using the Kolmogorov–Smirnov (KS) test to detect anomalies. The framework achieves a high detection accuracy of 98.5%, demonstrating strong resilience against cyber-injected data anomalies and setting a foundation for enhanced EVCI cybersecurity.
Copyright (c) 2026 Ravindra Babu Jaladanki, Pavan Kumar Kolluru, Nagul Shaik, Kamparapu V V Satya Trinadh Naidu, Duggineni Veeraiah, Anita Pradhan, Rallabandi Ch. S. N. P. Sairam https://creativecommons.org/licenses/by-sa/4.0
FA-optimized fractional order PI control for bidirectional V2G and G2V systems with integrated subsystem management
This paper presents a Firefly algorithm (FA)-optimized, fractional order proportional-integral (FOPI), control strategy for bidirectional vehicle-togrid (V2G), and grid-to-vehicle (G2V) systems. The optimization focuses on minimizing key performance indices—integral of time-weighted absolute error (ITAE), integral of squared error (ISE), and integral of absolute error (IAE)—to achieve optimal dynamic response and energy efficiency. The control system incorporates converter with battery controller. The FA is employed to fine-tune the FOPI parameters, leveraging its robust global search capability to balance trade-offs between fast response, minimal overshoot, and stability. Simulation results validate the effectiveness of the proposed approach, demonstrating superior performance compared to conventional proportional–integral (PI) controllers, with ITAE reduced by 63.4% compared to PI controller. This study provides a scalable and efficient solution for advanced energy management in V2G/G2V systems, contributing to the sustainable integration of electric vehicles (EVs) with smart grids.
Copyright (c) 2026 Mohamed Shiek Mothi Ismail Mujupur, Mohamed Ismail Mohamed Mustafa https://creativecommons.org/licenses/by-sa/4.0
Simulation and optimization of inverse kinematics algorithms for real-time target tracking in inertial stabilization platforms
Two-axis gimbal systems are engineered to maintain visual lock on target objects by actively counterbalancing movements, regardless of whether these originate from the target or the mounting platform. This research investigates the creation and refinement of inverse kinematics (IK) algorithms for achieving accurate, instantaneous target tracking in inertial stabilization platforms (ISPs), commonly referred to as gimbal systems. Such platforms are essential in applications requiring exceptional stability and precision, including surveillance operations, navigational systems, and scientific investigations. The study commences with a comprehensive analysis of the mathematical foundations underlying IK, with particular attention to the challenges posed by real-time processing requirements. To address these obstacles, sophisticated optimization methods are employed, with an emphasis on reducing computational latency and improving tracking accuracy. The developed algorithms are tested within a Simscape Multibody simulation environment, enabling thorough evaluation across various operational scenarios. Validation incorporates both simulated conditions and practical field tests to confirm the algorithms' durability and functional effectiveness. Results demonstrate significant improvements in both tracking precision and system reactivity, providing a foundation for more efficient and reliable gimbal systems in challenging dynamic environments.
Copyright (c) 2026 Abderahman Kriouile, Soufiane Hamida, Abdoul Latif Abdou Moussa https://creativecommons.org/licenses/by-sa/4.0
Adaptive bidirectional heuristic rapidly exploring random tree* for efficient path planning
Sampling-based path planning algorithms such as rapidly exploring random tree* (RRT*) are widely used for autonomous navigation in complex environments. However, many RRT variants suffer from slow initial exploration, suboptimal convergence, and search inefficiency in dense spaces. Based on this, adaptive bidirectional heuristic-RRT* (ABH-RRT*) is proposed. It is a novel method introduced as a unified path planner. ABHRRT* integrates bidirectional tree growth, heuristic-based parent selection, fast-informed hybrid sampling, and adaptive reordering to improve exploration efficiency and path optimality. The algorithm speeds up the initial path recovery caused by the presence of dual tree expansion and fast sampling. In addition, the algorithm also refines the solution using informed sampling and adaptive reordering to improve convergence toward near-optimal paths. The performance of ABH-RRT* is evaluated in four environments with different complexity levels and compared with RRT, RRT*, Fast-RRT*, Smart-RRT*, and Informed-RRT*. Experimental results show that ABH-RRT* consistently produces shorter paths and faster convergence, reduces path cost by 2–24% and increases convergence speed by 40–58% in dense and constrained environments. These results show that ABH-RRT* is a better and adaptive solution for path planning in complex scenarios.
Copyright (c) 2026 Heru Suwoyo, Ahmad ‘Athif Mohd Faudzi, Andi Adriansyah, Yudhi Gunardi, Julpri Andika, Yinzhong Tian https://creativecommons.org/licenses/by-sa/4.0
Faults detection in SOC using TCRMCN based MBIST with optimization based BISR
Memory takes up much of the chip area in modern system on chip (SOC). It is more challenging to repair these memories with a traditional external equipment test method. In this paper, SOC devices using temporal channel reconfiguration multi-graph convolution networks (TCRMCN), based memory built-in self-test (MBIST), with optimization based built-in-self-repair (BISR) (TCRMCN-MBIST-BISR-SOC) is proposed. Further the results of the test are analyzed by TCRMCN, which detects different types of faults. Once faults are detected, the data is passed to BISR, where the faulty memory cells are replaced using redundant memory cells and optimized by multi-objective fitness dependent optimization algorithm (MOFDOA). The proposed method demonstrates significant improvements in delay, power consumption, and access time, outperforming existing approaches like adaptive dynamic k-nearest neighbor (ADKNN) fostered BIST and Namib beetle optimization approach (NBOA) espoused BISR for SOC-based devices (ADKNNF-BIST-NBOA-BISR), deep q-learning with bit-swapping-based linear feedback shift register fostered BIST and BISR for static random access memory (SRAM) (DQL-BSL-BIST-BISR), and design of a fast and energy-efficient MBIST architecture using Verilog (DFEC-MBIST), the proposed method achieves 9.28%, 8.78%, and 9.29% higher accuracy while reducing delay by 9.45%, 5.36%, and 8.28%, respectively.
Copyright (c) 2026 Madhava Rao Jillella, Prashanth Narayanappa Ananda https://creativecommons.org/licenses/by-sa/4.0
Electrocardiogram signal denoising and heart disease classification
Electrocardiogram (ECG) recordings are often contaminated by baseline wander (BLW), power-line interference, and motion or muscular noise, reducing the reliability of both manual and automated diagnosis. The paper presents a light and reproducible MATLAB pipeline applying finite-impulse-response (FIR) filters designed using Kaiser and Hamming windows for ECG denoising, which after R-peak detection follows an RR-interval analysis for classification of heart rate as tachycardia, bradycardia, or normal. In the experiments, 15 MIT-BIH records with added Gaussian noise at several SNR levels were used for benchmarking the performance of denoising. FIR band-pass and low-pass windowed filters improved the clarity of the waveform and supported robust R-peak detection; RR-interval-based classification reached a mean accuracy of ~98.7% on the study set. The approach is computationally lightweight and quite suitable for embedded real-time deployment but is restricted to the small sample of records and synthetic noise modeling. Future work will compare the efficacy of windowed FIR filtering against modern deep-learning denoisers (CNN/RNN/GAN architectures) and assess the pipeline in larger clinical datasets.
Copyright (c) 2026 K. Venkata Siva Reddy, M. Balaji https://creativecommons.org/licenses/by-sa/4.0
Performance analysis of adaptive channel coding with OFDM modulation over Rayleigh fading channels
In the contemporary wireless communication system, the orthogonal frequency division multiplexing (OFDM) in conjunction with the use of the error correction code scheme is important towards ensuring a reliable transmission of data through the fading channel. This work is a complete analysis of performance of OFDM modulation combined with adaptive channel coding schemes are low-density parity check (LDPC), turbo, and convolutional code while using the Rayleigh fading channel. Error correction schemes such as LDPC, turbo, and convolutional code are used with a variety of trade-offs on their error correction performance. The system is coded in MATLAB/Simulink and run with 64 subcarriers, a length of cyclic prefix of 16 and a 16-quadrature amplitude modulation (16-QAM) modulation, channel coding techniques which are preferred upon the levels of signal-to-noise ratio (SNR) between 0 to 20 dB and the level of performance is measured by bit error rate (BER). The simulation findings prove that LDPC code achieves the highest performance at high SNR values, convolutional code has moderate BER at mid-SNR range, and turbo code has superior performance at low-SNR range and outperforming both LDPC and convolutional codes. These findings indicate that adaptive channel coding in the OFDM system is effective over Rayleigh fading.
Copyright (c) 2026 Venkatesan Sangeetha, Pattipati Naga Sudha https://creativecommons.org/licenses/by-sa/4.0
Sustainable greenhouse using IoT and machine learning to optimize the microclimate for lettuce cultivation
Sustainable agriculture faces increasing challenges due to climate variability, which affects crop productivity and resource efficiency. This study proposes a sustainable greenhouse system that integrates internet of things (IoT) sensors and machine learning models to optimize the microclimate for lettuce cultivation. Environmental data, including temperature, humidity, and light intensity, were collected through IoT sensors and processed using machine learning algorithms, specifically neural networks and support vector machines (SVM), implemented on the Orange data mining platform. The results indicate that the neural network model achieved superior performance, reaching an accuracy of 99.99% in predicting optimal greenhouse climate conditions, outperforming the SVM model. The best-performing model was subsequently implemented on an Arduino-based IoT system to automatically regulate greenhouse conditions. The proposed system improved resource efficiency and supported optimal lettuce growth while promoting sustainable agricultural practices. These findings demonstrate that integrating IoT and machine learning can enhance greenhouse management, contributing to climate-resilient agriculture and improved food production systems.
Copyright (c) 2026 Rudy Ivan Jamjachi Yauri, Jorge Raul Herbozo Ramirez, Christian Ovalle https://creativecommons.org/licenses/by-sa/4.0
Exact outage and intercept analysis of physical layer security in half-duplex energy harvesting relay systems
This paper investigates physical layer security (PLS) in a half duplex (HD) relay network with radio frequency energy harvesting (EH). We consider an amplify and forward (AF) protocol in which both the source and the relay harvest energy from a dedicated power beacon, while a passive eavesdropper attempts to intercept the confidential message. Unlike prior studies that rely on asymptotic analysis or numerical integration, we derive exact closed form expressions for the outage probability (OP) and the intercept probability (IP). The proposed expressions characterize the reliability and secrecy trade-off in energy limited cooperative relaying and enable efficient performance evaluation. Monte Carlo simulations validate the analysis and match the theoretical results. Moreover, the closed form results reveal how channel conditions and harvesting duration jointly shape outage and interception behaviors. We further quantify the impacts of normalized transmit power, the time allocation factor between harvesting and information transmission, and the EH efficiency. The results provide practical guidance for selecting system parameters to meet target reliability and secrecy levels, and they show that the time allocation factor must be carefully chosen to balance harvested energy and transmission time.
Copyright (c) 2026 The-Anh Ngo, Xuan-Truong Tran, Viet-Thanh Le, Thien P. Nguyen, Duy-Hung Ha https://creativecommons.org/licenses/by-sa/4.0
Risk-aware dynamic spectrum allocation using learning-augmented RIS control for cognitive radio networks
The proposed RADIANT-CRN framework introduces a risk-aware dynamic spectrum allocation approach for reconfigurable intelligent surface (RIS)-enabled cognitive radio networks while maintaining reliable protection for primary users (PUs). It incorporates our previously developed bidirectional long short-term memory and adaptive manta-ray foraging optimization (BiLSTM-AMRFO) spectrum prediction and deep channel estimation models into a bilevel optimization framework, where semidefinite relaxation (SDR) adjusts RIS phase shifts, geometric programming (GP) allocates transmit power, and entropy-regularized assignment performs channel selection. A primal-dual actor–critic framework coordinates these modules. On a 120 MHz testbed with 256-element RIS, RADIANT-CRN gets a sum rate of 846±18 Mbps with a 0.7±0.2% PU-violation and 99.3% chance-constraint coverage. This is about 25% higher than a greedy non-risk baseline and about 43% higher than a no-RIS optimizer. It also lowers interference CVaR_0.95 from 9.1 mW to 2.4 mW. These results demonstrate that RADIANT-CRN is the first framework to enforce both chance and CVaR guarantees in RIS-assisted CRNs, achieving high spectral efficiency with statistically certifiable PU protection that aligns with Federal Communications Commission interference requirements. The framework is validated on a prototype SDR testbed (Rician fading, K=6 dB, bursty PU activity); implementation uses Python 3.11/PyTorch 2.x with convex optimization python (CVXPy/MOSEK), and synthetic testbed traces are available upon request.
Copyright (c) 2026 Mallikarjun Dheshmuk, Santosh B. Kumbalavati https://creativecommons.org/licenses/by-sa/4.0
An open-source low-cost TDOA-based sound source localization system
Sound source localization (SSL) has many civilian and military applications, such as robotics, gunshot localization in nature reserves and warfare, surveillance, wild animal tracking, and rescue missions. One effective method of SSL is measuring the time difference of arrival (TDOA) for an array of microphones. The TDOA method requires designated hardware that is quite expensive and might require specific proprietary software; this makes SSL research expensive and difficult to develop software for, especially for hobbyists and educators. This paper presents a low-cost and easy-to-build SSL system that is built using 4 USB sound cards along with 4 microphones and a USB hub. The code for the system is made open for the public, and it is based on open-source applications, which are Linux, GStreamer, and Octave, to provide an open environment for studying and researching SSL. The localization process relies on finding the TDOA using generalized cross correlation (GCC) thresholding and finding the intersection point of three two-sheeted hyperboloids. The theoretical and experimental descriptions of the system are presented in this paper, along with some of the challenges, such as the microphones’ timing and position calibration. Despite the low sampling rate and high noise of the used USB sound cards, the system was able to locate the sounds within a 2.4 m radius with a root mean square error (RMSE) of 7.23% and a mean error of 6.12%.
Copyright (c) 2026 Mahmoud A. Alnaanah, Amir Abu-Al-Aish, Mohd H.S. Alrashdan, Mohammad Zayed Ahmed, Haitham A. Alasha’ary, Hamzah Hmeidi https://creativecommons.org/licenses/by-sa/4.0
Object detection for waste management: a comparative review of models, challenges, and future directions
Despite growing interest in automated waste detection, existing surveys either focus on a narrow set of models or lack systematic comparisons across object detection paradigms. This review addresses that gap by examining recent advances in deep learning for waste management, spanning two-stage detectors (Faster region-based convolutional neural network (Faster R-CNN) and Mask region-based convolutional neural network (Mask R-CNN)), single-shot frameworks (you only look once version 1 (YOLO)v1 to YOLOv11), and emerging Transformer-based models (ViT-WM and AL-DETR). Faster R-CNN achieved category-level accuracy of 91.68% and overall accuracy of 89.68%, while Mask R-CNN reported AP values between 26.2% and 34.5% across varied datasets. YOLO models demonstrated strong real-time capability, with YOLOv5 reaching a mAP@0.5 of 92.96% and YOLOv8 achieving 97.63% accuracy with precision and recall above 93%. Transformer-based approaches are especially promising: ViT-WM achieved 98.17% accuracy, the highest among reviewed models, and AL-DETR reported a mAP of 58.9% while integrating active learning (AL) strategies to reduce reliance on extensive labeled data. These results emphasize YOLO’s efficiency for real-time waste sorting and the potential of Transformer architectures for handling complex, cluttered environments. Remaining challenges include dataset variability, computational demand, and limited standardized benchmarks. Future research should prioritize developing comprehensive datasets, optimizing Transformers for real-time use, and leveraging AL to enhance generalizability with reduced annotation effort.
Copyright (c) 2026 Owen Tamin, Ervin Gubin Moung, Ali Farzamnia https://creativecommons.org/licenses/by-sa/4.0
Enhanced face recognition with nuclear norm-based angle 2D-PCA using QR decomposition
Several approaches based on two-dimensional principal component analysis (2DPCA) have shown limitations in terms of classification performance. To enhance its robustness, an angular variant of 2DPCA has been proposed, establishing a relationship between reconstruction error and data variance through the Frobenius norm. However, this technique still encounters certain challenges. To overcome these shortcomings and further strengthen resilience to data variations, we propose a novel framework: nuclear norm-based angular 2DPCA using QR-decomposition (AN2DPCA-QR). This new formulation leverages the nuclear norm to optimize a variance-related criterion by maximizing the ratio of projected to original variance, aiming to improve the discriminative capacity of the projection space. The method employs a non-greedy iterative algorithm to solve the optimization problem, incorporating adaptive mean centralization for bias reduction, and QR decomposition instead of singular value decomposition (SVD) for numerical stability and reduced complexity. Compared to its predecessor, AN2DPCA-QR offers enhanced robustness, and interpretability. Results obtained on various public benchmark datasets clearly demonstrate the practical relevance and resilience of the proposed method.
Copyright (c) 2026 Jamal Elalji, Driss Gretete, Khalid Chougdali https://creativecommons.org/licenses/by-sa/4.0
A novel hybrid model for brain tumor classification leveraging U-Net segmentation and ResNet50 architecture
Brain tumors are life-threatening conditions requiring accurate and timely diagnosis for effective treatment. This paper proposes a novel hybrid model combining U-Net for tumor segmentation and residual network 50 (ResNet50) architecture for classification to achieve performance in brain tumor classification from magnetic resonance imaging (MRI) images. This paper proposes a novel hybrid model that integrates U-Net for tumor segmentation with ResNet50 architecture for classification, enabling robust multi-class classification across glioma, meningioma, pituitary tumor, and no tumor classes. Utilizing a diverse dataset of 7,023 MRI images, the model achieves a remarkable accuracy of 99.78±0.05%, outperforming existing methods. Compared to related works, the proposed model demonstrates superior accuracy and scalability. This hybrid approach addresses key challenges in medical imaging, providing a robust and interpretable solution for real-world clinical applications.
Copyright (c) 2026 Nattavut Sriwiboon, Songgrod Phimphisan https://creativecommons.org/licenses/by-sa/4.0
Kernel PCA-enhanced BoVW representation for SIFT-based face recognition using SM
This study proposes a face recognition pipeline that integrates scale-invariant feature transform (SIFT) descriptors, the bag of visual words (BoVW) model, Kernel principal component analysis (KPCA), and support vector machine (SVM) classification. It starts by extracting local keypoint descriptors from preprocessed face images using SIFT. These descriptors are subsequently vector-quantized into a visual vocabulary through MiniBatch K-Means clustering, yielding fixed-length BoVW histograms for each image. Nonlinear dimensionality reduction is achieved by applying KPCA with a radial basis function, addressing the complexity of the feature space. The resulting compact feature representations are subsequently classified using a linear SVM. The proposed method is evaluated on labelled faces in the wild (LFW) dataset with filtered 100 classes, demonstrating notable classification accuracy and reliable generalization across training, validation, and testing splits. Our experimental evaluation confirms that integrating local invariant features, nonlinear feature reduction, and discriminative classification allows the proposed method to exceed state-of-the-art face recognition performance. In addition, this proposed method is particularly suitable for scenarios with limited training data and computational resources, providing a lightweight but robust alternative to deep learning-based models.
Copyright (c) 2026 Chen-Han Chong, Siew-Chin Chong, Lee-Ying Chong https://creativecommons.org/licenses/by-sa/4.0
Cocoon quality assessment and silk yield estimation YOLOv8 and EfficientNet-B0
Silk production depends heavily on accurate cocoon grading, yet manual inspection is slow, inconsistent, and varies between operators. This creates problems in quality control and affects the final yield of raw silk. To address this, we present an automated system that uses computer vision to detect, separate, and grade silk cocoons without human involvement. The system combines a you only look once version 8 (YOLOv8) model for segmenting individual cocoons from tray images and an EfficientNetB0 classifier for identifying defects across six categories, including one qualified class and five defect types. After detection and grading, the pipeline also estimates the percentage of good cocoons and predicts silk yield based on standard industry measures. The model was trained on 3,068 cocoon samples and achieved 96.1% mean average precision (mAP) for segmentation and 97% accuracy for classification. The system can count cocoons, assess quality distribution, and provide batch-level yield estimates. This automated approach improves reliability, reduces manual effort, and offers consistent grading suitable for both farm-level and industrial environments. With low operating cost and simple deployment, the system supports modern, scalable, and data-driven sericulture.
Copyright (c) 2026 Shivananda Shivanna, Lincy Meera Mathews, Omraj Ravindra Bhalke, Aryan Vats, Krrish Agrawal, Aditya Singh https://creativecommons.org/licenses/by-sa/4.0
Osprey optimization algorithm for VGG16 hyperparameter optimization in breast cancer detection
Globally, breast cancer is one of the reason for mortality among women and accurate automated diagnosis remains a critical research challenge. This research is used to improve breast cancer classification performance by optimizing deep learning (DL) model hyperparameters using a bio-inspired optimization technique. The osprey optimization algorithm (OOA) is applied to fine-tune the hyperparameters of the VGG16 convolutional neural network (CNN) for histopathological breast cancer image classification. The optimized model is evaluated using a curated dataset and compared with established DL architectures, including AlexNet, Xception, InceptionV3, and ResNet50. Performance is assessed using standard evaluation metrics such as accuracy, precision, recall, F1-score, specificity, AUC-ROC, Matthews correlation coefficient (MCC), log loss, and inference time. Experimental results indicate that the OOA-optimized VGG16 model achieves superior performance, with an accuracy of 97.7%, precision of 96.71%, recall of 97.79%, AUC-ROC of 99.92%, and MCC of 0.9449, while maintaining competitive computational efficiency. The results demonstrate that bio-inspired hyperparameter optimization significantly enhances classification reliability and diagnostic accuracy. In summary, integrating OOA optimization with the VGG16 architecture yields a dependable framework for breast cancer identification, making it a promising candidate for deployment in automated diagnostic support systems.
Copyright (c) 2026 Sabura Banu Urundai Meeran, Nafeena Abdul Munaf, Vengadeshwaran Velu https://creativecommons.org/licenses/by-sa/4.0
Hybrid XAI and deep learning architecture for trustworthy dental diagnostics
Dental periodontal disease is a persistent an inflammatory disorder affecting tooth supporting tissues and stays a main motive of tooth loss. Although dental radiographs are essential for early diagnosis, their interpretation is often subjective and inconsistent due to reliance on clinician expertise. This study proposes an automated and interpretable diagnostic framework using a convolutional neural network (CNN) integrated with gradient-weighted class activation mapping (Grad-CAM). The CNN performs binary classification of periapical radiographs into periodontal and normal categories, while Grad-CAM provides visual explanations of the model’s decision-making process. Experimental results show that the proposed model achieves a classification accuracy of 94.17%, indicating reliable diagnostic performance. The generated heatmaps consistently highlight clinically relevant regions, particularly alveolar bone loss in periodontal cases, whereas normal images exhibit no pathological activation. These findings demonstrate that the proposed CNN–Grad-CAM framework enhances both diagnostic accuracy and interpretability. The study contributes a transparent and trustworthy artificial intelligence solution to support objective periodontal disease diagnosis in dental radiology.
Copyright (c) 2026 Yusra Fadhillah, Muhammad Noor Hasan Siregar, Ade Ismail Abdul Kodir, Khairur Rizki https://creativecommons.org/licenses/by-sa/4.0
An innovative deep learning approach for Arabic race recognition
In computer vision, human race detection has become a critical application across many domains, such as security and customized marketing. Deep learning approaches, such as convolutional neural network (CNN), have played an essential role in improving human race detection. Nevertheless, detecting Arabic race is still a field that has received little attention. In this paper, an Arabic human race dataset comprising the following classes: Gulf, Levant, Sudan, Egypt, and North Africa (excluding Egypt) has been collected and proposed as a starting point for Arabic race classification. This dataset has been evaluated using a simple CNN-based model and other transfer learning models: DenseNet121, VGG16, and ResNet50. The difficulty in classifying these regions lies in the similarity of border areas in people’s features and in intermarriage between different regions, which helps transfer genetic traits that distinguish one region from another. The best results in recall, F1-score, precision, and accuracy were obtained by the DenseNet121 model, which achieved an average accuracy of 0.746 across five folds.
Copyright (c) 2026 Amal Saif, Rahmeh Ibrahim, Eman Alnagi, Ashraf Ahmad, Abdullah Aref https://creativecommons.org/licenses/by-sa/4.0
Deep learning based identification of Crocidolomia pavonana larvae on mustard plants using Grad-CAM
Mustard greens are an important vegetable commodity, but their production is often affected by pest attacks, especially the cabbage worm Crocidolomia pavonana (C. pavonana). The larvae damage leaf tissues and cause significant yield losses, while chemical control is often ineffective due to differences in insecticide sensitivity across larval instars. This study proposes a deep learning based classification approach combined with gradient weighted class activation mapping (Grad-CAM) to identify larval instars of C. pavonana on mustard plants. A dataset of 684 images covering instars 1 to 4 was collected from laboratory rearing and field observations, then processed using resizing and augmentation techniques and divided into training, validation, and testing sets with an 8 to 1 to 1 ratio. Two convolutional neural network (CNN) models, visual geometry group 19 (VGG19), and Xception, were implemented with additional fully connected layers. The VGG19 model achieved 94.20% accuracy and outperformed Xception. Grad-CAM successfully highlighted larval regions and supported visual interpretation. The results show that the proposed method can improve pest identification accuracy and support more effective pest management.
Copyright (c) 2026 Diana Tri Susetianingtias, Sarifuddin Madenda, Risnawati, Maukar, Eka Patriya, Rodiah https://creativecommons.org/licenses/by-sa/4.0
Cross-lingual deep learning model for gender detection
Speech recognition is transforming the way humans interact with technology and automatic gender recognition is an essential part of this evolution. This study develops a multilingual deep learning (DL) model for gender detection using three audio datasets: RAVDESS (English), Berlin EmoDB (German), and IITKGP-SEHSC (Hindi). These datasets provide linguistic diversity, enabling the development of a multi-lingual gender identification model. The mel-frequency cepstral coefficients (MFCC) and VGGish embeddings and other audio features were used to process raw audio data into something meaningful. The findings show the machine learning (ML) models (random forest (RF) and extreme gradient boosting) achieved good results in the monolingual (98.26% using Hindi and 96.90% using cross-lingual) setup. In DL models, convolutional neural network (CNN) outperformed other models in both monolingual and cross-lingual scenarios, with 99.33% accuracy for Hindi and 98.11% accuracy in cross-lingual setup. These findings show how well DL works for gender detection in multilingual and emotionally complex settings. It outperforms traditional models. The experiment describes the potential of DL in speech-based artificial intelligence (AI) applications, which enhances the performance in real-life scenarios.
Copyright (c) 2026 Kavita Jhajharia, Ginika Mahajan, Dhondi Samrudh, Koustubh Patel https://creativecommons.org/licenses/by-sa/4.0
Smart city solutions: internet of things-enabled ambulances for enhanced post-earthquake resilience in Morocco
Disasters such as earthquakes pose severe threats to sustainable urban development, causing loss of life, infrastructure collapse, and long-term disruption. In the critical first hours after an earthquake, delays in emergency response significantly reduce survival chances for vulnerable populations. This paper proposes an integrated framework that leverages internet of things (IoT)-enabled ambulances, geographic information systems (GIS), and optimization algorithms to enhance post-earthquake emergency response. The framework addresses two key challenges: i) optimal allocation of ambulances under resource constraints and ii) dynamic routing in disrupted road networks. Using real data from the 2023 Al Haouz earthquake in Morocco, the study compares deterministic approaches (Dijkstra, A*) with metaheuristics (particle swarm optimization (PSO), ant colony optimization (ACO), and Tabu Search (TS)). Results show that PSO reduced ambulance requirements by 40% while rescuing more citizens, and ACO achieved the highest route reliability (0.96). These findings demonstrate the practical applicability of IoT-enabled smart ambulances in improving resilience, efficiency, and equity in urban disaster management.
Copyright (c) 2026 Sara Tahiri, Mouad Choukhairi, Youssef Fakhri, Mohamed Amnai https://creativecommons.org/licenses/by-sa/4.0
A BERT-based modular framework for automated English essay scoring via trait analysis
Automated essay scoring (AES) systems are commonly implemented using holistic scoring, which limits interpretability and prevents assessment at the writing trait level. As a result, such systems provide limited diagnostic and actionable feedback. To address this limitation, this study proposes a modular trait-based AES framework that separates structure and grammar evaluation while maintaining an integrated scoring mechanism. The proposed framework consists of two modules. The structure module evaluates the ideas, organization, and style traits using a bidirectional encoder representations from transformer-bidirectional long short-term memory (BERT-BiLSTM-Attention) architecture trained on the automated student assessment prize (ASAP) dataset. The grammar module evaluates the Conventions trait by applying a BERT-based grammatical acceptability classifier trained on the Corpus of linguistic acceptability (CoLA) dataset, followed by multinomial logistic regression to convert grammatical patterns into interpretable grammar scores. Experiments were conducted on the ASAP dataset and evaluated using the quadratic weighted Kappa (QWK) metric. The structure module achieved a QWK score of 0.7906 on the test set, while the grammar module obtained a QWK of 0.3923. The integrated holistic score reached a QWK of 0.7847. These results demonstrate that the proposed modular framework improves interpretability and scoring performance, supporting more objective and actionable essay evaluation for formative assessment in English language education.
Copyright (c) 2026 Jasman Pardede, Rizka Milandga Milenio, Thalita Zharifa Nathania https://creativecommons.org/licenses/by-sa/4.0
Hybrid metaheuristic algorithms for feature selection in classification: a systematic literature review
Feature selection (FS) is a popular technique for improving machine learning (ML) model's effectiveness by eliminating irrelevant and redundant features. It is challenging because of the intricate relationship between features and large search space. Recent studies have focused on using hybrid metaheuristics to solve FS problem. This systematic literature review (SLR) is performed on three significant databases that explores recent studies from 2019 to 2024 that used hybrid metaheuristics for FS in classification. This paper aims to understand the existing hybrid algorithms, hybridization goal, hybridization type, and application domains. Moreover, crucial parameters, fitness and transfer functions, initial population method, traditional FS approach, classification algorithm, evaluation criteria, and statistical test are investigated in this paper. The qualitative findings derived from the systematic review encompassed 646 publications, systematically categorized based on predefined inclusion and exclusion criteria. Consequently, 35 papers were analyzed to develop new insights in the domain of FS in classification, focusing on single-objective metaheuristics. Hybrid metaheuristics surpass the efficacy of their individual components in enhancing algorithmic performance to attain optimal or near-optimal solutions. The limitations of hybrid metaheuristics and research gaps are identified for scholars interested in developing metaheuristic algorithms for FS.
Copyright (c) 2026 Manal Othman, Ku Ruhana Ku-Mahamud https://creativecommons.org/licenses/by-sa/4.0
Ultra-lightweight hybrid authentication for MQTT/MQTT-SN internet of thing security
The rapid growth of internet of thing (IoT) has increased the need for secure communication among resource-constrained devices using lightweight protocols such as message queuing telemetry transport (MQTT) and message queuing telemetry transport for sensor network (MQTT-SN). Traditional certificate-based solutions introduce significant computational and memory overhead for low-power devices. This paper proposes the hybrid lightweight protocol (HLP), a certificate-free approach combining elliptic-curve key exchange, hash-based message authentication code (HMAC)-based authentication, and ChaCha20-Poly1305 encryption. HLP uses pre-shared keys to reduce handshake complexity while maintaining confidentiality, integrity, and mutual authentication across MQTT and MQTT-SN environments. A Python-based implementation using paho-mqtt was evaluated in a constrained-device testbed. Experimental results show that HLP achieves lower handshake latency (-20–24 ms) and reduced bandwidth overhead (-130 bytes) compared with elliptic curve Diffie-Hellman ephemeral-pre-shared key (ECDHE-PSK) and elliptic curve Diffie-Hellman ephemeral-elliptic curve digital signature algorithm (ECDHE-ECDSA), while still supporting forward secrecy. These findings demonstrate that HLP is an efficient and practical solution for securing IoT communications on constrained devices.
Copyright (c) 2026 Nabeel Alassaf, Selvakumar Manickam, Ammar Odeh, Mohammed Anbar https://creativecommons.org/licenses/by-sa/4.0
Leveraging defense-in-depth through a deception-driven security model for smart university
Cybersecurity remains one of the biggest challenges to address as the education sector shifts to the smart university concept. The education sector has experienced in recent years a noticeable rise in cyberattacks, revealing limitations in relying solely on traditional defense-in-depth (DID) security strategies. In response, the study implements a deception-driven security model (D-DSM) designed for a campus network environment. The proposed model incorporates decoy resources managed through a centralized deception mechanism to mislead attackers, divert malicious activities away from critical assets, and provide meaningful indicators of attack behavior, resulting in a more effective way to mitigate attacks. Rather than replacing existing defenses, the model complements current security controls by improving the visibility of advanced and lateral attacks while helping reduce false alerts. Adding deception as an active security layer is a useful way to make networks more resilient and helps build smarter, safer, and more sustainable university infrastructures.
Copyright (c) 2026 Marlon A. Naagas, Anazel P. Gamilla, Mary Camille D. Rabang https://creativecommons.org/licenses/by-sa/4.0
Cybersecurity in property digitisation: a taxonomy and evaluation of current security methods
The proliferating methods of digitization for documents related to real estate, land, property, and tax, encountered with critical security challenges. Such documents, which have an inclusion of high-value information, such as transaction history, geographic boundaries, tax assessment, and ownership details, are quite prime targets for various types of cyber threats. It is observed that with security domains advancing in faster pace, their implications towards securing such legal documents are questionable, especially in the presence of emerging lethal threats. This manuscript presents a comprehensive and highly compact exhibit of curated information about the effectiveness of all identified security solutions. The presented study has discussed the taxonomy of six mechanisms of securing property-related digitized document. It was found that although the impersonation attacks and forgery can be addressed with multifactor authentication, digital signatures and blockchain, they are not reportedly found to be mitigating modern-day threats like social engineering, and insider collusion. The outcome of this study infers that hybrid approaches fusing with zero-trust models, anomaly detection, and artificial intelligence (AI) demands more resilient security system. The paper contributes towards some novel findings of widely deployed security solutions, research trends, and gaps which will offer definitive guidelines towards designing a novel solution.
Copyright (c) 2026 Anuradha Uppar, Nagaveni Veerakyatharayappa, Parvathi Chikkanna https://creativecommons.org/licenses/by-sa/4.0
Profiling student performance for multi-agent personalization in virtual reality
This study uses the open university learning analytics dataset (OULAD) to cluster student performance data to improve personalized learning. Three main aspects are the focus of the analysis: instructional involvement, behavior, and demographics. To create significant, comprehensible student profiles, the clustering algorithms k-means, k-modes, and k-prototypes were used for each dimension independently. In order to forecast student categories from input features, supervised classification models, such as support vector machines (SVMs) and random forests, were trained using these profiles as targets. Accuracy, F1-score, and cross-validation were used to assess the categorization models' performance. The outcomes demonstrate how well unsupervised and supervised learning strategies may be combined for adaptive learning. These profiles serve as a foundation for the future design of a multi-agent virtual reality (VR)-learning environment. In this envisioned system, specialized agents would handle behavioral adaptation, demographic personalization, and pedagogical coordination, offering a personalized learning experience tailored to each learner’s profile.
Copyright (c) 2026 Ghalia Mdaghri Alaoui, Ilhame Khabbachi, Abdelhamid Zouhair, El Mokhtar En-naimi https://creativecommons.org/licenses/by-sa/4.0
Metaheuristic nurse scheduling with hospital clustering using flower pollination algorithm
Effective nurse scheduling is essential to ensure balanced workloads, reduce fatigue, and maintain healthcare service quality. However, the nurse scheduling problem (NSP) is complex due to constraints related to nurse skills, task requirements, and legal working-hour limits. This study proposes an integrated framework combining a mathematical optimization model with metaheuristic algorithms to generate optimal daily nurse activity schedules. Genetic algorithm (GA) and simulated annealing (SA) are employed to produce near-optimal solutions for nurse populations ranging from 3 to 50 individuals, considering skill-level compatibility, workload balance, and maximum working hours. Experimental results using real scheduling data from 30 nurses across three skill levels demonstrate that all generated schedules satisfy the imposed constraints, with no nurse exceeding the 12hour daily working limit. Comparative analysis shows that GA achieves lower scheduling costs for larger nurse populations, while SA consistently requires significantly shorter computation times, making it suitable for time-sensitive applications. In addition, the flower pollination algorithm (FPA) is used to cluster 3,155 hospitals based on bed capacity, service variety, and workforce size, supporting data-driven workforce distribution analysis. The proposed framework integrates operational scheduling optimization with hospital-level clustering, providing practical decision support for healthcare workforce planning.
Copyright (c) 2026 Muhammad Khahfi Zuhanda, Hartono, Sayuti Rahman, Prana Ugiana Gio, Erianto Ongko https://creativecommons.org/licenses/by-sa/4.0
Edge-iterated graph parameters: theory and applications to wireless sensor networks
In this paper, we study three graph parameters–the iterated chromatic number xn(G), the iterated domination number yn(G), and the iterated covering num- ber Bn(G)—through the line-graph transformations applied successively on a connected graph G. These edge-iterated parameters track how coloring, domination, and covering structures evolve as the graph undergoes successive linegraph transformations. For standard graph families such as paths, cycles, and grid graphs, we derive exact formulas and establish upper and lower bounds, revealing both periodic and divergent behaviours depending on graph structure. Since exact computation of these parameters becomes intractable for large networks, we propose a BFS-based greedy algorithm for estimating xn(G), and benchmark it against the Welsh–Powell and DSATUR algorithms. The simulation results validate the theoretical bounds and show that the proposed method is computationally efficient without significant loss in coloring quality. We further show that these parameters have natural interpretations in wireless sensor networks (WSNs): xn(G) informs frequency and time-slot assignment under multi-hop interference, yn(G) identifies minimal supervisory structures for fault tolerance, and Bn(G) guides energy-aware link monitoring. The framework thus connects iterated graph theory to concrete design problems in sensor network optimization.
Copyright (c) 2026 Sreelatha Kakkattumadathil Sreenivasan, Janardhanan Suresh Kumar, Indulekha Thekkethuruthel Sadanandan, Narayanankutty Karuppath https://creativecommons.org/licenses/by-sa/4.0
Fast and accurate cheat detection using deep learning algorithms
The rapid expansion of online education, accelerated by the global health crisis of 2020, has introduced significant challenges in maintaining academic integrity due to the absence of physical supervision during remote examinations. As digital learning becomes a permanent component of modern education, ensuring fairness and credibility in online assessments has become a critical concern for educational institutions. This study proposes an intelligent deep learning (DL)–based framework for detecting non-compliant behaviors during online examinations using standard webcam video streams. The proposed system integrates real-time video monitoring with automated behavioral analysis by combining object detection and classification models. In particular, you only look once version 5 (YOLOv5) is employed for efficient facial and object detection, while a convolutional neural network (CNN) is used to classify cheating and non-cheating behaviors from extracted visual features. Experimental results demonstrate that the integrated YOLOv5–CNN architecture achieves high detection accuracy and low inference latency, making it suitable for real-time and scalable deployment in online proctoring systems. By enabling objective and automated monitoring, the proposed framework contributes to strengthening fairness, transparency, and trust in digital assessment environments, thereby supporting the long-term sustainability of online education.
Copyright (c) 2026 Ilhame Khabbachi, Ghalia Mdaghri Alaoui, Abdelhamid Zouhair https://creativecommons.org/licenses/by-sa/4.0
Multi domain, multi-scale diagnostic modeling for histopathological breast cancer classifications
Breast cancer detection through histopathological imaging remains challenging due to complex tissue morphology, observer variability, and subtle differences between invasive and pre-invasive lesions. Conventional computer-aided diagnostic systems often rely on single-domain feature extraction, restricting multi-scale representation and clinical interpretability. To overcome these limitations, we propose a verified diagnostic framework integrating five analytical components for efficient and explainable breast cancer classification. The adaptive multi-level histopathological feature selection using cross-domain mutual information maximization (AMFSCDMIM) extracts highly informative morphological and frequency features with minimal redundancy. The deep hierarchical hybrid morphological– frequency encoding network (DH-HMFEN) refines spatial–spectral representations, while the multi-scale morphological attention classification network (MS-MACNet) applies adaptive attention across tissue structures for improved discrimination. The adaptive ensemble validation for breast cancer classification (AEV-BCC) calibrates confidence levels for enhanced reliability, and the comparative analytical performance validation with interpretability integrated metrics (CAPV-IIM) quantitatively evaluates model explainability using expert annotations. Experimental results on benchmark datasets achieve 96% accuracy, 0.98 area under the receiver operating characteristic curve (AUROC), and a 0.88 interpretability alignment score, outperforming existing methods. The proposed confidence-calibrated, multi-domain, and multi-scale framework enhances diagnostic precision and clinical trust in histopathology-based breast cancer detection.
Copyright (c) 2026 Komal S. Gandle, Kshirsagar Dhananjay Bhanudas https://creativecommons.org/licenses/by-sa/4.0
Hybrid dual-stream deep learning for breast cancer ultrasound detection
The heterogeneity of breast tissue and subtle morphological variations in ultrasound images make breast cancer detection a challenging task. This study proposes a hybrid deep learning framework that integrates EfficientNetB4 and ConvNeXt within a dual-stream architecture enhanced by advanced attention mechanisms. The model combines multi-scale texture representation with spatial feature extraction to improve classification performance. A two-stage preprocessing pipeline, consisting of adaptive median filtering and bilateral filtering, is applied to reduce speckle noise while preserving important structural details. The proposed method is evaluated on BUSI and UDAIT datasets, achieving 87.82% accuracy, 87.33% precision, and 85.33% recall on BUSI, and 85.69% accuracy, 84.00% precision, and 78.00% recall on UDAIT. These results outperform several baseline models, including ResNet-50, DenseNet-121, and vision transformers. Error analysis shows limitations in detecting small lesions and cross-modal generalization, with reduced performance on mammography images. Attention visualization demonstrates strong agreement with radiologist annotations, supporting model interpretability. The findings highlight the effectiveness of hybrid architectures for ultrasound-based breast cancer detection while emphasizing the need for modality-specific optimization.
Copyright (c) 2026 Musab Mahmoud Iqtait, Marwan Harb Alqaryouti, Ala Eddin Sadeq, Jafar Ababneh, Suhaila Abuowaida, Nawaf Alshdaifat, Muath Alali https://creativecommons.org/licenses/by-sa/4.0
Integrating hybrid deep learning and CSI for multi-interval hydrological data in enhanced flood prediction
Flood prediction accuracy is often constrained by heterogeneous and asynchronous hydrological data collected at different time intervals. This study proposes a hybrid deep learning–based flood prediction framework that integrates long short-term memory (LSTM), convolutional neural network (CNN), and cubic spline interpolation (CSI) to address these challenges. Rainfall, river discharge, and water level data representing upstream, midstream, and downstream conditions of the Bengawan Solo watershed were utilized. CSI was applied as a preprocessing step to harmonize multi-interval data, reduce noise, and recover missing observations, thereby improving data consistency. The experimental results show that the proposed hybrid LSTM–CNN model enhanced with CSI outperforms baseline LSTM and non-interpolated hybrid models, achieving a mean absolute percentage error (MAPE) of 5.84%, root mean square error (RMSE) of 0.125 m, mean absolute error (MAE) of 0.082 m, and R² of 0.948. The integration of spatio-temporal feature learning with data harmonization enables more accurate flood level prediction and supports timely flood early warning systems. The proposed approach demonstrates strong potential for improving flood risk management and disaster preparedness in flood-prone regions.
Copyright (c) 2026 Indrastanti Ratna Widiasari, Eko Sediyono, Rissal Efendi https://creativecommons.org/licenses/by-sa/4.0
Generating synthetic strike-through handwritten text using generative adversarial networks
The evaluation of handwritten text documents is a significant research field in text analysis. Deep learning classifiers' effectiveness tends to decline when faced with variations in text style and the presence of strike-out text components. These strike-outs can occur at the character, word, paragraph, or page level. When these documents undergo optical character recognition (OCR) processing, they often yield inaccurate results. Data unavailability, imbalance poses a strong risk in this area. Hence usage of synthetic datasets for conducting research can solve the issue to an extent. Despite using traditional data augmentation techniques like rotation, position shifting, zooming, and shearing, the issue of imbalanced class distribution remained unchanged. In order to tackle this challenge this paper demonstrates the creation of a synthetic dataset comprising of strikethrough text using modified version of generative adversarial networks (GANs) that has an auxiliary network for text recognition. IAM and RIMES dataset are used as base for the GAN to generate synthetic images of handwritten text. A line segmentation technique is also implemented to create strike outs on random words selected from the synthetic image set thereby increasing the number of strikes. The work believes that the simulated dataset will significantly improve the quality of handwriting text recognition models.
Copyright (c) 2026 Dheemanth Urs Raje, Chethan Hasigala Krishanappa https://creativecommons.org/licenses/by-sa/4.0
Performance analysis of classification models to determine the health status of edge computing devices
Artificial intelligence (AI) has contributed to the development of autonomous systems in the healthcare field by integrating machine learning models, whose evaluation on resource-limited hardware devices is important to ensure their efficiency. This research evaluates the performance of classification models in edge computing (EC) systems, considering metrics such as accuracy, latency, memory consumption, and energy efficiency on low-power microcontrollers using TinyML techniques. The processes involved include the development, implementation, and testing of algorithms on embedded hardware using differentiated preprocessing techniques and the validation of hypotheses through statistical analysis. The results show that the decision tree (DT) model is more efficient in terms of prediction time and energy consumption, while random forests (RFs) stand out for their greater accuracy. Furthermore, memory analysis reveals that models based on fully connected neural networks are more efficient in terms of RAM usage. This provides guidelines for selecting algorithms in resource-constrained environments.
Copyright (c) 2026 Ricardo Yauri, Nora Bertha La Serna Palomino https://creativecommons.org/licenses/by-sa/4.0
An AI-enhanced hybrid project management model: a comparative evaluation using the weighted sum model
Projects today face increasing complexity, uncertainty, and regulatory constraints that often limit the effectiveness of traditional and strictly Agile project management (PM) methods. Current hybrid methods remain largely theoretical and lack integrated decision-support tools that combine predictive analytics with transparent multi-criteria evaluation. To address this gap, this paper presents an artificial intelligence (AI)-enhanced hybrid project management model (AI–HPMM) that combines AI functionalities with the weighted sum model (WSM) to support adaptive, data-driven, and transparent decision-making. A systematic literature review was conducted according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines (2019–2025), identifying ten success factors aligned with project management body of knowledge (PMBOK) domains. These factors were weighted using WSM to compare Agile, Waterfall, and AI-enhanced hybrid methods. AI–HPMM achieved the highest WSM score (9.96/10), outperforming Agile (7.66/10) and Waterfall (7.90/10). The reviewed literature also reports a 15% reduction in schedule deviations, a 12–20% improvement in resource efficiency, and a 25–30% increase in decision-making speed. Overall, the proposed model improves adaptability, transparency, and responsiveness in complex and dynamic project environments.
Copyright (c) 2026 Issam Talkam, Ibrahim Hamzane, Abdessamad Belangour https://creativecommons.org/licenses/by-sa/4.0
Hybrid deep learning ensemble with score-based feature optimization for cyber attack detection in IoT systems
The rapid growth of internet of things (IoT) devices have improved connectivity but also exposed networks to cyber threats. This study proposes a prediction-scoring-based ensemble deep learning model with prediction-scoring-optimized feature selection (EDLM-PSOFS) for intrusion detection in IoT systems. The model integrates random forest (RF) feature extraction with ant lion optimization (ALO)-tuned convolutional neural networks (CNNs) to balance accuracy and computational efficiency. Using the KDD Cup ’99 dataset containing 4.9 million traffic records and 41 features, the framework achieved 97% accuracy, 0.99 precision, and 0.97 recall within five epochs. Comparative evaluation shows faster convergence and reduced complexity than gated recurrent units (GRU), long short-term memory (LSTM), and support vector machine (SVM) baselines, demonstrating suitability for real-time, resource-constrained IoT deployments.
Copyright (c) 2026 John Manoranjini, Venugopal Gaddam, Kotla Venkata Raghavender, Hanumantha Rao Battu, Pamarthi Sunitha, Sathish Kumar Shanmugam https://creativecommons.org/licenses/by-sa/4.0
Hybrid deep learning architecture for autism spectrum disorder detection from gait kinematic data
Early detection of autism spectrum disorder (ASD) is essential for enabling timely interventions that significantly impact developmental outcomes. Traditional diagnostic procedures often rely on clinical observations and subjective assessments, which can reduce objectivity and delay diagnosis. This study presents an automated classification approach for detecting ASD in children using gait kinematic data obtained through video-based skeletal tracking. We propose a hybrid deep learning architecture that combines one-dimensional convolutional layers (Conv1D), bidirectional long short-term memory (BiLSTM) networks, and a multi-head attention mechanism to capture complex spatiotemporal patterns in motion data. The dataset includes 100 participants—50 diagnosed with ASD and 50 typically developing (TD) peers. Preprocessing steps included Euclidean norm transformation, logarithmic scaling, Z-score normalization, and sliding window segmentation. The proposed model achieved 94.9% accuracy, 0.91 recall, 0.86 precision, and an area under the curve (AUC) of 0.97, outperforming a baseline long short-term memory (LSTM) architecture. These findings demonstrate the potential of gait-based kinematic features and hybrid neural networks for objective and reproducible ASD screening. The developed system can contribute to AI-assisted decision support tools in clinical and educational environments, enhancing diagnostic accuracy, and supporting inclusive developmental care.
Copyright (c) 2026 Gulzat Ziyatbekova, Svetlana Beglerova, Dinara Zhukenova, Alokhon Alikarieva, Madi Akhmetzhanov, Nuriddin Alikariev, Quvvatali Rakhimov, Yersultan Tulebayev https://creativecommons.org/licenses/by-sa/4.0
Differential quality game with fuzzy information for assessing financial resources for air quality monitoring in cities
Rapid urbanization intensifies pressure on city air quality, making costeffective monitoring a governance priority. Existing sensor-placement approaches optimize coverage but often ignore strategic behavior of polluters and budget uncertainty, leading to fragile deployments. We propose a decision model that allocates monitoring funds via a bilinear differential game with fuzzy information between an environmental defender and a polluter. Unlike linear differential games solvable via the Cauchy formula, bilinear dynamics and non-measurable adversary strategies require a novel discreteapproximation method within a positional game scheme. The model captures dynamic financial interactions through membership functions and yields analytical characterizations of the defender's preference set and optimal pure strategies. Computational experiments on realistic scenarios illustrate stable funding regimes and support actionable guidance for urban planners: how much to invest, when, and where to expand monitoring stations to achieve resilient oversight under uncertainty. The framework can be embedded in intelligent decision-support tools for smart-city environmental management.
Copyright (c) 2026 Arkadii Chikrii, Volodimir Malyukov, Valery Lakhno, Inna Malyukova, Adlet Kassymbekov, Raissa Uskenbayeva, Gabit Shuitenov, Bauyrzhan Tynymbayev https://creativecommons.org/licenses/by-sa/4.0
Hybrid machine learning model towards neuroimaging analysis for detection and grading brain tumors
Neuroimaging analysis enables detailed observation of brain tumors, with growing adoption of advanced imaging techniques in clinical practice. Limitations of conventional approaches in supporting proactive decisionmaking and reliable grading are increasingly addressed through machine learning. However, earlier models often faced challenges of limited generalization and computational burden. To overcome these issues, this study introduces a hybrid convolution neural network–support vector machine (CNN–SVM) framework that combines ResNet-50 feature extraction with a feature weighting (FW) strategy and SVM-based classification for improved diagnostic precision. The system is further enhanced with a clinically guided grading scheme, mapping classification outputs into malignant, benign, and healthy categories for greater interpretability. The proposed model was evaluated on three benchmark neuroimaging datasets (Figshare, Kaggle magnetic resonance imaging (MRI), and BraTS-2019) and achieved up to 98.6% accuracy with high sensitivity and specificity, while retaining low computational cost and rapid inference, outperforming conventional CNN-only methods.
Copyright (c) 2026 Lakshminarayana Munirathnam, Rashmi Shivaswamy, Suraksha Prakash, Reshma Shafi, Karanam Sunil Kumar https://creativecommons.org/licenses/by-sa/4.0
An innovative approach to identifying triangular arbitrage opportunities in financial markets using the Bellman-Ford algorithm
Existing arbitrage detection techniques rely on exhaustive search or linear programming, which are computationally expensive and often miss profitable cycles in dynamic markets. Triangular arbitrage is a profitable trading strategy that exploits discrepancies in currency exchange rates, but common algorithms detect only a limited number of loops and cannot find non-loop opportunities. To address these gaps, this study presents a realtime, graph-based framework for identifying triangular arbitrage opportunities in cryptocurrency markets using an optimized implementation of the Bellman–Ford algorithm. By modeling currency exchange rates as a directed graph and detecting negative-weight cycles, the framework efficiently identifies profitable arbitrage opportunities under realistic trading conditions. The proposed framework achieves an average detection latency of 0.002 milliseconds, providing empirical performance benchmarks for single-exchange cryptocurrency trading systems. Experiments on a six-month historical dataset yielded a detection accuracy of 92%, while additional validation on live cryptocurrency market data streams confirmed the framework’s real-time performance and low latency. This high-speed detection is crucial in high-frequency trading (HFT), where brief pricing inefficiencies can yield significant profits before being corrected. The experimental pipeline is designed to support reproducibility and comparative evaluation in applied FinTech research.
Copyright (c) 2026 Issam Akouaouch, Anas Bouayad https://creativecommons.org/licenses/by-sa/4.0
Design of an educational platform based on augmented reality for the special education of children with dyscalculia
Dyscalculia is a learning disorder that affects between 3% and 8% of the world's population and is characterized by difficulty in acquiring basic mathematical concepts, mainly in children. In view of this, several technological tools have been explored that can help improve the education of these children, with augmented reality (AR) being a promising alternative. The objective of this research is to design an AR-based platform to support the special education of children with dyscalculia. An agile methodology, Scrum, was used for the development, which allowed for an interactive approach to the design of the prototypes. Consequently, these were evaluated and validated by six experts in the field. The result was an acceptance rate of 81.2%, demonstrating the viability of the prototype. It is concluded that the prototype using AR is an effective tool to support children with dyscalculia aged 8 to 10, as it allows for an interactive and beneficial learning experience to nurture their knowledge.
Copyright (c) 2026 Jhair Baltodano-Payahua, Sebastian Ramos-Cosi, Alicia Alva-Mantari, Gustavo Villar-Mayuntupa, Enrique Lee Huamani, Meyluz Paico-Campos, Laberiano Andrade-Arenas https://creativecommons.org/licenses/by-sa/4.0
Feature extraction and machine learning methods for biometric recognition based on fusion of ECG and fingerprint
This research introduces a multimodal biometric authentication framework by amalgamating electrocardiogram (ECG) and fingerprint modalities through the utilization of diverse feature extraction methodologies and machine learning classifiers. The proposed methodology aspires to augment precision and mitigate spoofing vulnerabilities in contrast to traditional single-modality systems. Among the feature extraction techniques assessed—grayscale, binary, Sobel edge detection, and minutiae—Naïve Bayes (NB) in conjunction with minutiae features exhibited superior performance, attaining an accuracy rate of 96.25%. Supplementary experiments employing random forest (RF) and support vector machine (SVM) also revealed commendable classification efficacy, underscoring the robustness of the fusion methodology. This investigation provides a pragmatic and secure biometric framework by harnessing complementary biometric characteristics to enhance authentication dependability. The proposed system presents promising applications in real-world contexts, particularly concerning mobile security and healthcare access control. Future research endeavors will tackle challenges associated with ECG signal variability, computational efficiency, and extensive deployment.
Copyright (c) 2026 Hafiz Ilhami, Dodon Turianto Nugrahadi, Mohammad Reza Faisal, Irwan Budiman, Andi Farmadi, Dwi Kartini, Puput Dani Prasetyo Adi, Jumadi Mabe Parenreng https://creativecommons.org/licenses/by-sa/4.0
Territorial disparity index: a GIS-integrated data framework for territorial disparities measurement in rural Morocco
Despite availability of numerous indicators for territorial evaluation and the measurement of their deprivation or advantage, the results remain confined to a coarse-grained spatial scale and cannot be applied at finer levels of territories like to Moroccan rural entities known as douars. This paper presents a bottom-up framework termed territorial disparity index (TDI) that quantitatively conceptualize territories as entities shaped through data-driven technological processes in order to better control and develop them and above all to reduce their multi-scale disparities. Through this study, we propose a geographic information system (GIS)-integrated data for composite index through two key dimensions: socio-economic and spatial-environmental aspects, quantified as scores based on 32 variables translated into indicators, then converted into decile ranks through a systematic data processing pipeline. The framework of TDI not only enhances spatial sensitivity often overlooked, but also provides a highly objective tool for mapping territorial disparity. Applied to actual data of a real Moroccan territory, in Berkane, this research introduces a novel methodology for territorial data collection, combining cross-sectional and spatial data analysis to digitally perceive these territories according to their levels of disadvantage. Principal component analysis (PCA) is then used to ensure robustness and objectivity of TDI index.
Copyright (c) 2026 Nassima Tabloul, Mohcine Kodad, Mimoun Boukhidous, El Houcine Addou https://creativecommons.org/licenses/by-sa/4.0
Optimization of cyber attack detection model using deep learning algorithm based on convolutional neural network
The increasing intensity and complexity of cyber threats demand more adaptive intrusion detection mechanisms. Conventional approaches are often limited in capturing complex and non-linear attack patterns in network traffic data. This study develops and evaluates a convolutional neural network (CNN)-based model for multi-class cyberattack detection. The proposed architecture integrates convolutional, pooling, and fully connected layers with rectified linear unit (ReLU) and SoftMax activation functions to improve classification performance. The network security laboratory-knowledge discovery and data mining (NSL-KDD) dataset is used for training and evaluation. Experimental results show that the CNN model achieves 96.34% accuracy and an F1-score of 0.99, outperforming several traditional machine learning methods, including Naïve Bayes (NB), decision tree (DT), support vector machine (SVM), and random forest (RF). The superior performance is attributed to the model’s capability to automatically learn and extract meaningful spatial representations from network data without manual feature engineering. These findings demonstrate the effectiveness of deep learning techniques in improving cyberattack detection and contribute to the development of reliable AI-driven network security systems with strong potential for real-world cybersecurity applications and evolving threat mitigation strategies.
Copyright (c) 2026 Hondor Saragih, Jonson Manurung https://creativecommons.org/licenses/by-sa/4.0
Classifier comparison benchmark for machine learning weather prediction enhancement
Artificial intelligence (AI) and data mining can improve next-generation weather forecasting for urban planning, agriculture, and disaster management. This study investigates how machine learning (ML) classifiers can reduce forecast errors and support decision-making in sectors that require accurate predictions, including agriculture and transportation. We evaluate four classifiers—K-nearest neighbor (KNN), random forest (RF), Naive Bayes (NB), and multilayer perceptron (MLP)—using Waikato environment for knowledge analysis (WEKA) and Orange3 to compare their performance in identifying rain. A 10-fold cross-validation approach is applied to reduce overfitting, and model effectiveness is measured using key performance indicators including accuracy, precision, sensitivity (recall), and F-measure. Results show that classifier performance varies across tools, indicating that the analytical framework can influence outcomes. Among all models, the RF classifier performs best, achieving 99.92% accuracy in WEKA and 99.9% in Orange3. The MLP also shows strong performance with 99.20% accuracy in WEKA and 98.7% in Orange3. KNN and NB exhibit comparable performance, but lower precision and F-measure in WEKA. Overall, the findings suggest that RF is the most effective approach for rain prediction using data mining tools, with practical relevance for agriculture, transportation, and power systems.
Copyright (c) 2026 Areen Arabiat, Mohammad Hassan https://creativecommons.org/licenses/by-sa/4.0
Design and evaluation of a secure key exchange protocol using the Kyber-Dilithium algorithm
Over 90% of the billions of people who use the internet globally use it through the transport layer security (TLS) protocol. TLS is a security standard that performs network authentication and data encryption when accessing the internet. Authenticated key exchange (AKE) is the protocol TLS uses for network authentication and key establishment during the TLS Handshake process. The AKE protocol utilizes a public key cryptosystem (PKC) and digital signatures with algorithms commonly used, namely elliptic curve cryptography (ECC) and Rivest-Shamir-Adleman (RSA). Future advancements in quantum computing may compromise the security of the widely used ECC and RSA algorithms. This research conducts an implementation and comparative analysis of post-quantum algorithms resistant to quantum computer attacks, specifically Kyber-Dilithium, in the context of the AKE protocol. The implementation is performed at three security levels: 128-bit, 192-bit, and 256-bit. The results show that the Kyber-Dilithium is greater than those of the RSA variant and much larger than those of the ECC variant. In contrast to the ECC and RSA variants, the Kyber-Dilithium algorithm variants perform better across all security levels, even if their byte sizes are greater.
Copyright (c) 2026 Bambang Harjito, Muhammad Defaroyan, Fajar Muslim, Ery Permana Yudha, Endra Pratama https://creativecommons.org/licenses/by-sa/4.0
Moderate guerrilla for the user’s understanding of the artificial intelligence-based user security awareness prototype
To enhance user security awareness in response to the numerous cyberattacks targeting users, we developed an artificial intelligence (AI)based prototype utilizing the naive retrieval augmented generation (RAG) method. In pursuit of this objective, we conducted user testing employing the usability testing method to evaluate users' comprehension of the developed prototype. We integrated moderate and guerrilla techniques in usability testing by engaging 20 randomly selected respondents from the government, private sectors, and industries. The majority of participants were male, aged 26-35 years, holding a bachelor's degree, and possessing 510 years of computer experience. The test data were analyzed using the USE assessment matrix, which includes four assessment parameters: usefulness, satisfaction, ease of use, and ease of learning (USE). The data were presented in tabulated form, with total and average values. The test results indicate that usefulness, satisfaction, ease of USE achieved a total value exceeding 4.00 and an average value of 4.29, within an interval range of 4.20-5.00, categorized as very good. The findings of this study have implications for enhancing user security awareness and provide feedback for refining the framework and prototype in subsequent research.
Copyright (c) 2026 I Putu Agus Eka Pratama, I Made Oka Widyantara, Linawati, Nyoman Gunantara https://creativecommons.org/licenses/by-sa/4.0
Analysis of arrhythmia detection and classification using electrocardiogram signals with decision tree algorithm
Heart disease remains the primary cause of death globally, with arrhythmia diagnosis often limited by restricted access to medical personnel and the complexity of electrocardiogram (ECG) interpretation. Accurate arrhythmia classification is essential to prevent cardiovascular complications. The proposed method successfully categorized classify ECG signals into five categories: normal, abnormal, potentially arrhythmia, moderate arrhythmia risk, and highly potentially arrhythmia. Data were collected from 30 subjects under three activity scenarios: sitting, walking, and running. The proposed model achieved an accuracy of 99.4%, demonstrating strong potential for real-time monitoring applications. Performance evaluation was conducted using accuracy, precision, recall, and F1-score for each class. Although the dataset size remains relatively small, the findings highlight the effectiveness of decision tree as an efficient and interpretable classification method. Future research will involve validation using large-scale public databases like the arrhythmia database at MIT-BIH and comparisons with advanced methods including convolutional neural network (CNN), transformer-based models, and explainable artificial intelligent (XAI) frameworks.
Copyright (c) 2026 Putri Juniarti Marpaung, Nia Anggreni Matondang, Rince Margaretta Siregar, Angelica Ester Novelia Siahaan, Abdi Dharma, Arjon Turnip, Mardi Turnip https://creativecommons.org/licenses/by-sa/4.0
Exploring mobile health service adoption among diabetic patients using an integrated UTAUT-PMT model
Mobile health services (MHS) have emerged as vital tools for delivering accessible and cost-effective healthcare, particularly for managing chronic conditions such as diabetes. This study investigates the behavioral factors influencing MHS adoption by integrating the unified theory of acceptance and use of technology (UTAUT) and protection motivation theory (PMT) into a unified model. A total of 189 valid responses were collected through both offline surveys (23 diabetic outpatients from 4 clinics) and online surveys (166 members of an Indonesian diabetes forum). Structural equation modeling was used to test the proposed relationships. The results show that self-efficacy (SE), effort expectancy (EE), and perceived vulnerability (PV) significantly influence behavioral intention (BI), while BI strongly affects MHS usage behavior (UB). Conversely, perceived severity (PS) and response cost (RC) were not significant. The study offers a novel theoretical contribution by demonstrating how the interaction between cognitive and motivational factors affects technology adoption in healthcare. Practical implications are provided for app developers and policymakers to improve MHS engagement through personalized interventions.
Copyright (c) 2026 Ragil Saputra, Satriyo Adhy, Hasbi Yasin https://creativecommons.org/licenses/by-sa/4.0
Empowering energy management: anomaly detection in smart meter data for proactive consumption control
The increasing deployment of smart energy meters (SEMs) has enabled real-time monitoring of energy consumption, but the vast data generated makes it challenging to detect anomalies that may indicate inefficiencies, faults, or unauthorized usage. This study aims to enhance energy management by developing a hybrid anomaly detection framework that improves accuracy while providing actionable insights for consumers. The proposed method integrates statistical and machine learning (ML) approaches, specifically Z-score, local outlier factor (LOF), one-class support vector machine (SVM), and isolation forest (iForest), to analyze simulated smart meter data. An anomaly is flagged only when identified by all four methods, thereby reducing false positives and improving reliability. The framework is implemented in an interactive dashboard built with streamlit, offering real-time visualization, peak-time alerts, usage forecasts, and personalized consumption suggestions. Results demonstrate that the hybrid approach outperforms single-method models, achieving higher detection accuracy and practical applicability. The findings highlight the potential of combining complementary detection techniques with proactive feedback to empower consumers, reduce energy wastage, and support sustainable energy management. This work provides a scalable foundation for future real-time deployment in smart grids and microgrid environments.
Copyright (c) 2026 Batchalakuri Jyothi, Bhavana Pabbuleti, Beeravalli Mounika, Hrushitha Kalapala, Meda Uma Santhosh Chandra, Sanaboina Sai Srilakshmi, Bommasani Ganesh Babu, Kambhampati Venkata Govardhan Rao, Malligunta Kiran Kumar, Rami Reddy Chilakala https://creativecommons.org/licenses/by-sa/4.0
Two-stage random search–Bayesian optimization for CNN-based short-term load forecasting
This study proposes a two-stage hyperparameter optimization pipeline for convolutional neural network (CNN)–based short-term electricity load forecasting. In the first stage, random search is used to broadly explore candidate configurations, including the number of filters in each convolutional layer, batch size, training epochs, and the loss function. In the second stage, Bayesian optimization based on the tree-structured Parzen estimator (TPE), implemented in Optuna, refines promising regions of the hyperparameter space to obtain a better-performing model. The optimized CNN is evaluated using half-hourly (30-minute) electricity demand data from New South Wales (NSW), Victoria (VIC), and Queensland (QLD), and is benchmarked against a baseline CNN, a multilayer perceptron (MLP), an extended short-term memory network, and single-stage optimization variants. Across the three regions, the proposed approach achieves mean absolute percentage error (MAPE) values between 1.05% and 1.14%, representing an improvement of approximately 58% over the baseline CNN. Statistical robustness is examined using paired Wilcoxon signed-rank tests with Holm–Bonferroni correction on per-timestamp errors. Overall, the results indicate that combining random search with Bayesian optimization improves CNN forecasting accuracy across the three studied regions and provides a transparent tuning framework for future replication.
Copyright (c) 2026 Tuan Anh Nguyen, Thanh Ngoc Tran https://creativecommons.org/licenses/by-sa/4.0
Toward optimal bankruptcy prediction: evaluating ensemble methods using Taiwan and U.S. financial bankruptcy data
Bankruptcy filings have increased significantly in many countries, causing widespread concern across society and triggering various economic issues. One contributing factor to the global rise in corporate bankruptcies is the unstable nature of companies’ growth. This issue often driven by unclear financial strategies and weak business direction. Thus, bankruptcy prediction plays a vital role, enabling earlier intervention and allowing business owners to improve their financial strategies proactively. This research investigates the effectiveness of ensemble machine learning (ML) methods using random forest (RF), stacking, and adaptive boosting (AdaBoost) for the prediction of corporate bankruptcy using datasets from Taiwan and the U.S. In the experimental phase, the performance is assessed using accuracy, precision, recall, F1 score, relative absolute error, and time. RF scored the highest accuracy in the classification of Taiwan’s bankruptcy data with 97.067%, meanwhile, AdaBoost M1 obtained the highest accuracy in the classification of the U.S.’s bankruptcy data with 94.0075%. The research shows that these methods, particularly AdaBoost M1, can improve early-warning systems and provide actionable insights for financial risk management. The main contribution of this research is its cross-country comparison of ensemble methods for bankruptcy prediction.
Copyright (c) 2026 Nur Farahaina Idris, Mohd Arfian Ismail https://creativecommons.org/licenses/by-sa/4.0
Detecting anomalies in MQTT/MQTT-SN traffic using intelligent learning models
The widespread adoption of the internet of things (IoT) has heightened demand for secure, efficient communication across constrained devices. Lightweight protocols such as message queuing telemetry transport (MQTT) and its variant MQTT-sensor networks (SN) are widely used for IoT messaging but lack intrinsic security mechanisms, leaving them vulnerable to denial-of-service, spoofing, and injection attacks. This study presents a machine learning (ML)-based anomaly detection framework designed to enhance the security of MQTT and MQTT-SN traffic. We emulate realistic IoT environments to generate both benign and malicious traffic, extracting protocol-specific features such as packet length, topic length, quality of service (QoS) levels, and publish frequency. Three supervised models—random forest (RF), eXtreme gradient boosting (XGBoost), and long short-term memory (LSTM)—were trained and evaluated using cross-validation and statistical performance metrics. Experimental findings demonstrate that XGBoost achieved the best overall results, with 97.4% accuracy, 95.9% F1-score, and low false-positive and false-negative rates. Furthermore, the framework was successfully deployed on edge devices such as Raspberry Pi Zero W and ESP32, confirming its real-time feasibility and efficiency. The proposed approach highlights the potential of intelligent learning models to deliver lightweight, deployable, and effective intrusion detection for IoT systems utilizing MQTT and MQTT-SN communication protocols.
Copyright (c) 2026 Nabeel Mustafa Alassaf, Selvakumar Manickam, Ammar Odeh, Mohammed Anbar https://creativecommons.org/licenses/by-sa/4.0
Allergen detection based on food packaged products for eczema patients using optical character recognition method
Eczema, also known as dermatitis, is a chronic skin condition that causes recurring episodes of dry and itchy skin. It can be managed through medication and by avoiding triggers like stress and certain foods. To help patients avoid food-related triggers, researchers conducted a study to detect allergenic food compositions in packaged products using optical character recognition (OCR) techniques, specifically open computer vision (OpenCV) and Tesseract. The study involved analyzing 120 images of food labels. The process included several steps: preprocessing the images by converting them to a text-friendly format (gray scaling, denoising, and thresholding), using Tesseract for text detection, followed by case folding and tokenization. The results showed that the system achieved an average text detection accuracy of 61.88% and an average allergen detection accuracy of 83.06%. The highest accuracy for text detection was 78.52%, and the highest accuracy for allergen detection was 100%. These findings suggest that OCR techniques can be a useful tool for helping eczema patients manage their diet and minimize flare-ups.
Copyright (c) 2026 Dian Palupi Rini, Riska Tri Mardilah, Muhammad Qurhanul Rizqie, Dwi Rosa Indah, Alvi Syahrini Utami, Jovanic Morgan https://creativecommons.org/licenses/by-sa/4.0
Comparing the novel Dhouib-Matrix-SPP to genetic algorithm for autonomous mobile robot path planning problem
Autonomous mobile robots (AMRs) are becoming integral to applications ranging from industrial automation to urban mobility. A core challenge in deploying AMRs effectively is the path planning problem determining an optimal and collision-free path from a start to a goal location within a given environment. This paper proposes a novel method, Dhouib-Matrix-SPP (DM-SPP), that enhances path planning efficiency and adaptability for AMRs operating in different statistical environment. Basically, DM-SPP is developed to unravel the shortest path in a graph and based on columns-rows structure with polynomial computational time. Here, the DM-SPP method is adapted to plan the shortest feasible path between two positions while avoiding obstacles. In order to prove the validity of the proposed DM-SPP method, it is applied to different environments and compared to different case studies taken from the literature. The simulation results show that the DM-SPP method was able to find, with a significantly lower number of iterations, the optimal solutions in comparison with other results obtained by the genetic algorithm (GA) method. DM-SPP presents an overall average improvement in computation time of (37882.55%) compared to the GA, which can reduce search and execution time.
Copyright (c) 2026 Souhail Dhouib, Danijela Pezer, Tole Sutikno https://creativecommons.org/licenses/by-sa/4.0
Food traceability model using blockchain technology
The complexity and globalisation of food supply chains raises concerns over food safety, authenticity and provenance. Data fragmentation, fraud and lack of transparency mean traditional traceability solutions are often ineffective and diminish consumer confidence. Blockchain technology can solve these limitations as it is decentralized, transparent and immutable. This study shows that blockchain technology has the potential to change the food traceability landscape by allowing food products to be securely and verifiably traced from raw ingredients to consumers. We discuss how such a system can improve openness, immutability, and information integrity. We will discuss the issues of scalability, interoperability, data security and stakeholder collaboration in the food sector in the implementation and adoption of blockchain. Preferred reporting items for systematic reviews and meta-analyses (PRISMA) and design science research (DSR) methods were used to develop a blockchain-based food traceability model, ChickenTrax, from 33 papers from 2020 to 2025. Its goal is to enable real-world deployment within the poultry sector and to improve the knowledge of the advantages and limitations of the use of the blockchain technology for a more robust and reliable food traceability system (FSTS).
Copyright (c) 2026 Rudy Tjahyadi, Meyliana, Harco Leslie Spits Warnars, Richard Wiputra https://creativecommons.org/licenses/by-sa/4.0
Novel framework and reference architecture for artificial intelligence models for stock markets
This original research article proposes, as its contribution, a novel unified framework and reference architecture for stock market prediction in postCOVID financial markets, which exhibit unprecedented volatility and nonlinear dynamics, demanding more robust predictive approaches than traditional models can provide. This original framework integrates artificial intelligence (AI) and machine learning (ML) models, ranging from classical techniques support vector machine (SVM) to deep learning (DL) architectures such as long short-term memory (LSTM) neural networks and gated recurrent unit (GRU) models, within a modular system encompassing data ingestion, sentiment processing, predictive optimization, reinforcement learning (RL), and cloud-based portfolio management. Another key original contribution is the synthesis of standards (ISO 23053, ISO 38505, ISO 20546) with big data methodological frameworks (REBD and Biggy), forming a unified meta-framework that orchestrates predictive signals from sentiment analysis (SA) and macroeconomic indicators. Experimental realworld stock market validation on mining-sector stocks demonstrated, with a 100% success rate, consistent investment outperformance over passive Buy & Hold baselines, yielding investment optimizations of up to +11.11 pp: the evaluated portfolios achieved 23.57% and 8.25% returns versus their 19.94% and 5.41% baselines, respectively. These results confirm the validity of the proposed novel framework as a reproducible reference architecture, an original contribution empirically grounded and experimentally validated for the development of future financial AI systems.
Copyright (c) 2026 Ernesto Cancho-Rodriguez, Miguel Angel Cano Lengua https://creativecommons.org/licenses/by-sa/4.0
SignVerse: bridging communication through a bi-directional sign language translation system
This study introduces SignVerse, a novel bi-directional sign language translation (SLT) system, to enhance communication between the hearing-impaired community and the general public. SignVerse makes real-time, two-way conversations easy for Indian Sign Language (ISL) users—no special hardware needed. The system uses smart artificial intelligence (AI) tech: computer vision, deep learning, and natural language processing (NLP). When someone types or speaks, the text/speech-to-sign module runs the input through NLP-based syntactic reordering and shows the ISL translation using a lively 3D avatar. On the flip side, the sign-to-text/speech module leverages MediaPipe to spot hand landmarks in real time, and the convolutional neural network-long short-term memory (CNN-LSTM) model accurately recognizes each gesture. Everything works together to help ISL users connect smoothly with others 94.8% recognition accuracy, less than 1.8-second translation latency, and more than 90% gesture clarity in user studies are all demonstrated by experimental evaluations. The lightweight model, which is optimized through knowledge distillation, guarantees excellent performance even on common consumer devices. With significant potential for societal impact, SignVerse is a significant step toward real-time, AI-driven ISL translation. When everything is taken into account, it is a dependable, scalable, and reasonably priced choice for inclusive communication.
Copyright (c) 2026 Gopal Dadarao Upadhye, Shalini Wankhade, Umbare Rupali Tukaram, Ankita Kakade, Mayur Agarwal, Dhanshree Shinde, Manesh Mahale, Nujaim Maindargi https://creativecommons.org/licenses/by-sa/4.0
Rapid and reliable approach for detecting milk spoilage
Milk spoilage poses a major challenge to food safety, public health, and sustainability, often resulting in unnecessary waste across households and dairy supply chains. Traditional detection methods, such as smelling or boiling, are subjective, delay early identification, and frequently lead to misjudgment. This study proposes a machine learning (ML)–based spoilage detection framework that integrates real-time pH and carbon monoxide (CO) sensor data to classify milk as fresh, not fresh, or spoiled. multiple supervised learning models, including random forest, eXtreme gradient boosting (XGBoost), support vector machine (SVM), and decision tree, were trained and evaluated using datasets collected from raw and boiled milk samples under varying conditions. Performance was assessed using coefficient of determination (R²), mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) metrics to identify the most reliable model for shelf-life prediction. Experimental results show that random forest and XGBoost outperform traditional threshold-based approaches, with random forest demonstrating superior consistency and operational efficiency. The findings highlight the potential of intelligent, low-cost sensor–ML systems to significantly enhance early spoilage detection, strengthen food safety, and reduce milk wastage across domestic and industrial environments.
Copyright (c) 2026 Srushti Jamewar, Ritika Patki, Nishad Gharote, Tanika Mathur, Mridula Korde https://creativecommons.org/licenses/by-sa/4.0
Comparative analysis of switching frequency in DTC-CBSVM and FOC of three-level NPC PMSM drive for EV applications
This paper presents a comparative analysis of switching frequency performance in direct torque control with carrier-based space vector modulation (DTC-CBSVM) and field-oriented control (FOC) of permanent magnet synchronous motor (PMSM) drives fed by a three-level neutral point clamped (NPC) inverter. PMSM drives are widely utilized in electric vehicles (EVs) due to their high efficiency, torque density, and reliability. However, the selection of an appropriate control strategy significantly influences inverter switching frequency, system efficiency, and overall drive performance. The DTC-CBSVM approach provides fast torque response and reduced torque ripple, whereas the FOC technique ensures precise control of flux and torque with smoother operation. In this study, both methods are implemented and evaluated under identical operating conditions. Simulation results highlight the trade-offs between dynamic response, switching frequency, and harmonic performance. The findings serve as a reference for selecting optimal control strategies for efficient PMSM-based NPC inverter drives in EV applications.
Copyright (c) 2026 Rakesh Shriwastava, Balajee Maram, Yogesh S. Pawar, Jyoti P. Rothe, Dinesh S. Wankhede, Hema Kale https://creativecommons.org/licenses/by-sa/4.0