Bulletin of Electrical Engineering and Informatics
The Bulletin of Electrical Engineering and Informatics (BEEI), ISSN: 2089-3191, e-ISSN: 2302-9285, is open to submissions from scholars and experts in the wide areas of electrical engineering and informatics from around the world. The BEEI is an open-access peer-reviewed journal published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU) that continuously presents the results of original research, reviews, surveys, or new data/concepts across all electrical engineering, electronics, instrumentation, control, robotics, telecommunication, computer engineering, information systems, information technology, and informatics (computer science) fields of interest. Articles must be understandable and written in standard English. This journal is indexed by Scopus (Elsevier)/ScimagoJR and has CiteScore: 3.9, SNIP: 0.685, and SJR: 0.306. The Ministry of Education, Culture, Research, and Technology of the Republic of Indonesia has given this journal its SINTA 1 (S1) seal of approval. This journal is published bimonthly (February, April, June, August, October & December) in both print and online versions. All publications are available in full text and are free to download. Bulletin of Electrical Engineering and Informatics Open Access Publisher:Institute of Advanced Engineering and Science (IAES) ISSN:2089-3191E-ISSN:2302-9285 Subject area: Mathematics: Control and Optimization Computer Science: Computer Science (miscellaneous) Engineering: Control and Systems Engineering Computer Science: Computer Networks and Communications Computer Science: Information Systems Engineering: Electrical and Electronic Engineering Physics and Astronomy: Instrumentation Computer Science: Hardware and Architecture
Institute of Advanced Engineering and Science
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Enhancing urban EV integration: a data-driven hybrid approach to charging station optimization and energy management
Electric vehicles (EVs) are pivotal to sustainable urban mobility, but their large-scale adoption in developing cities depends on efficient charging infrastructure and grid stability. This study proposes a hybrid deep learning framework to optimize EV charging station placement and energy scheduling in Vijayawada, India, projected to host 70,000 EVs by 2028. A convolutional neural network (CNN) is employed to classify charger types (Fast vs. Level 2) based on spatial features such as geospatial coordinates, population density, and traffic volume, while a long short-term memory (LSTM) network forecasts hourly charging demand using synthetic 24-hour sequences. The dataset comprises 108 candidate locations, designed to mirror real usage patterns. Model performance is evaluated using classification accuracy and mean absolute error (MAE). Results indicate that the CNN achieved 92% accuracy in charger type prediction, while the LSTM produced an hourly demand forecast with an MAE of 25 sessions/hour. These outcomes demonstrate the framework’s ability to reduce grid stress by shifting peak loads and strategically placing chargers in high-demand zones. The study provides a scalable and adaptable solution for EV infrastructure planning, enabling resilient grid integration, and supporting sustainable urban energy systems.
Copyright (c) 2026 Shaik Mohammed Hussain, Ganapaneni Swapna, Kambhampati Venkata Govardhan Rao, Malligunta Kiran Kumar, Srungaram Ravi Teja https://creativecommons.org/licenses/by-sa/4.0
A framework for multi-interval optimal power flow under solar energy penetration
The increasing penetration of renewable energy introduces variability and uncertainty into power system operations, thus requiring accurate forecasting methods to ensure reliable and economical scheduling. This study presents a multi-interval day-ahead optimal power flow (OPF) analysis integrated with photovoltaic (PV) generation, where hourly PV forecasts are obtained using the seasonal autoregressive integrated moving average (SARIMA) (1,0,1)(4,0,3)24 model. The forecast results achieved low error values (root mean square error (RMSE)=0.354, normalized RMSE (NRMSE)=4.192%, mean absolute error (MAE)=0.202), successfully capturing the daily PV generation pattern and providing sufficiently accurate input for the OPF simulation. The forecasted PV profiles were then integrated into a multi-interval OPF framework using the MATPOWER interior point solver (MIPS) solver. Results show that PV integration reduces system operating costs compared to cases without PV, with cost savings observed at various time intervals (e.g., reduction from $802.22/hour to $780.65/hour during PV peak hours). Compared to the conventional single-interval OPF benchmark based on Weibull distribution assumptions for PV, the proposed framework achieves lower average costs ($790.97/hour vs. $869.70/hour) while also reflecting the real variability of solar dynamics and load. Overall, the integrated forecasting-optimization framework demonstrates that SARIMA-based PV forecasting provides reliable inputs for OPF and offers a practical tool to support future system planning and operation with higher renewable energy penetration.
Copyright (c) 2026 Ricky Maulana, Syafii, Aulia https://creativecommons.org/licenses/by-sa/4.0
Smart charging of electric vehicles at a charging station using machine learning and pressure pad energy harvesting
The rapid growth of electric vehicles (EVs) demands intelligent, cost-effective, and sustainable charging solutions. This paper introduces a smart EV charging station system that integrates machine learning (ML) with pressure pad–based energy harvesting. The system forecasts energy demand, predicts vehicle types and slot needs, and recommends optimal charging times using real-time data such as state of charge (SoC), battery health, and user behavior patterns. ML models such as long short-term memory (LSTM) and random forest are employed to ensure accurate scheduling and forecasting. A smart display, the display slot indicator (DSI), powered by sensors and station data, guides users with live cost, time, and slot availability, including alternate suggestions during peak demand. The pressure pad not only contributes to energy recovery but also aids in real-time vehicle detection and traffic regulation within the station. With scalable capacity and intelligent automation, this system can support more than 400 EVs per day, minimizing operational load and energy waste while maximizing convenience and sustainability.
Copyright (c) 2026 Kumara Swamy Tadi, Ganapaneni Swapna, Kambhampati Venkata Govardhan Rao, Malligunta Kiran Kumar, Srungaram Ravi Teja https://creativecommons.org/licenses/by-sa/4.0
PSO-tuned bidirectional converter for intelligent electric vehicle charging in vehicle-to-grid and grid-to-vehicle applications
Electric vehicles (EVs) can act as distributed energy storage units in smart grids through vehicle-to-grid (V2G) and grid-to-vehicle (G2V) operations. However, large-scale bidirectional EV charging introduces power quality issues, including harmonic distortion and DC-link voltage fluctuations. This paper presents a PSO-tuned modified dq (MDq) control strategy for a bidirectional EV charging system operating under V2G and G2V modes. A transformer-less bidirectional DC–DC converter and a grid-connected voltage source inverter with an LCL filter are modeled to enable controlled power exchange between the EV battery and the grid. Particle swarm optimization (PSO) is employed to optimally tune the controller gains using a multi-objective fitness function that minimizes grid current harmonics, DC-link voltage error, current ripple, and settling time. Simulation results obtained in MATLAB/Simulink demonstrate that the proposed MDq controller significantly outperforms conventional PI and MDq-PI controllers, achieving a grid current total harmonic distortion (THD) of 2.39% while maintaining stable DC-link voltage and fast dynamic response. The proposed approach enhances power quality, grid stability, and operational reliability, making it suitable for intelligent EV charging in smart grid applications.
Copyright (c) 2026 Devarakonda Balasubramanyam, Goda Ganesh Raja Sekhar, Tadanki Vijay Muni https://creativecommons.org/licenses/by-sa/4.0
Parameters affecting protection coordination of high voltage distribution network with distributed energy resources
Distributed energy resources (DERs) have become favorable sources of power against conventional power sources at distribution level because of their advantages like reduction in power loss and voltage drop, and environment friendliness. Integrating of DERs into high voltage distribution networks (HVDNs) introduces significant challenges to protection relay coordination because of their dynamic operational characteristics. This comprehensive study investigates key parameters influencing effective protection relay coordination in distribution networks (DNs), including fault current variability, DER penetration level, network topology, and relay settings. Through comprehensive simulation conducted on modified IEEE 4-bus HVDN with variable DER configurations through PSCAD/EMTDC 4.5 software, the research analysis evaluates the impact of these parameters on protection reliability and speed. Results indicate that high DER penetration leads to fault current increment by 10-20%, grid current contribution reduction by 10-15% as well as relay current setting alterations approximately by 30-40% which necessitates adaptive protection schemes. Network topology and relay coordination strategy significantly affect fault detection and isolation process. The finding provides understanding of parameters affecting protection coordination which helps in its optimization and having reliable and secure operation of DN.
Copyright (c) 2026 Ishan Desai, Nilesh Chothani, Paul Jacob https://creativecommons.org/licenses/by-sa/4.0
High-performance voltage control of ladder boost converters using robust backstepping sliding mode control
This paper presents a control-focused solution for high-gain DC-DC conversion by integrating a capacitor-diode ladder boost converter (CDLBC) with a backstepping sliding mode control (BSMC) scheme. While the CDLBC topology provides modular voltage gain at moderate duty ratios, the core contribution lies in the development of a robust control strategy to address the converter’s nonlinear and multistage dynamics. The proposed BSMC method combines the recursive design of backstepping with the finite-time convergence and disturbance rejection properties of sliding mode control (SMC). A saturation function is employed to suppress chattering and ensure smooth control action. The controller is derived based on Lyapunov stability theory and implemented on a digital signal processor without requiring hardware modification. Experimental results under various output voltage transitions show that the BSMC approach significantly improves transient performance, reduces voltage ripple, and enhances regulation accuracy compared to conventional SMC. These findings confirm the effectiveness and practicality of software-based robust control for efficient and adaptive DC-DC power conversion in renewable energy and embedded applications.
Copyright (c) 2026 Thanh-Lam Le, Nguyen Nhan Bon https://creativecommons.org/licenses/by-sa/4.0
Power sharing based on starfish optimization algorithm in DC microgrid
This paper presents a starfish optimization algorithm (SFOA) method for optimizing control parameters in DC microgrids. SFOA is a new metaheuristic inspired by biology to solve optimization problems, which simulates the behavior of starfish, including exploration, preying, and regeneration. SFOA consists of two main phases of exploration and exploitation. This paper evaluates the performance of SFAO on droop control of DC microgrids by comparing with walrus optimizer (WO) and grasshopper optimization algorithm (GOA). From the simulation, SFOA shows superior capability. Validation on DC microgrid control using integral of time-weighted absolute error (ITAE) and integral of time-weighted squared error (ITSE). Simulation results demonstrate that the proposed technique exhibits a superior ITAE relative to WO and GOA, which are 6.88% and 8%, respectively. The performance validation results demonstrate that the SFOA approach exhibits potential and effective performance. The proposed method on DC microgrid control has been successfully applied and shows promising performance. The proposed methodology is particularly suitable for renewable energy integration in isolated or resource-constrained regions.
Copyright (c) 2026 Widi Aribowo, Laith Abualigah, Diego Oliva, Abubakar Umar, Aliyu Sabo, Hisham A. Shehadeh https://creativecommons.org/licenses/by-sa/4.0
Incubator with food object detection based automatic heating process using internet of things technology
Conventional incubators for heating food generally can be operated manually by both its temperature level and heating duration. This condition provides risk that the food will be heated inappropriately both at its temperature level and heating duration which in turn can decrease food quality. The objective of this article is to develop an incubator for heating food which is equipped with a food object detection based automatic heating process and using internet of things (IoT) technology for distance monitoring and control, which detects the type of food and heating it automatically at both temperature level and duration. The incubator was developed using components available in the market including GY-906-DCI sensor, OV5647 camera, Raspberry Pi 4, NodeMCU microcontroller, and Telegram bot. Testing results showed that the developed incubator system was able to detect food types in real-time, as well as automatically set temperature and incubation time parameters based on it. It was able to maintain a stable incubation temperature, with an average error of 2.74% and a temperature deviation of 1.23 °C. The developed incubator potentially improves safety and user convenience where the food is heated properly both at its temperature level and heating duration.
Copyright (c) 2026 Heru Supriyono, Agus Supardi, Pramudya Kurnia, Mohammad Dwiki Aji Nugroho, Muhammad Satria Ananta, Helmi Hidayatullah https://creativecommons.org/licenses/by-sa/4.0
Model predictive control with safety barrier enforcement for dynamic obstacle avoidance of mobile robots
This research proposes a model predictive control (MPC) approach with additional barrier function constraint for safe navigation of a differential drive wheeled mobile robot (DDWMR) in the presence of static and dynamic obstacles. The proposed approach uses the kinematic model of DDWMR to initially constructs a stabilizing MPC on the basis of Lyapunov’s stability theory. To ensure safe navigation of the DDWMR when obstacles are present, the control barrier function (CBF) concept is subsequently constructed and integrated into the developed MPC framework. The integrated MPC-CBF approach is shown to guarantee both the stability and safety properties of the DDWMR while navigating towards a desired goal position through a workspace filled with obstacles. The good performance of the proposed framework is demonstrated through computer simulations and experimental validation on a Turtlebot3 DDWMR plat form. In the real robot experiments, the controller achieved final tracking errors of [ex ey] = [0.1286 0.0626] m and e0 = 0.021 rad, while the corresponding simulation errors were [ex ey] = [0.0824 0.0698] m and e0 = 0.0883 rad, respectively. These results demonstrate that the developed feedback control method ensures safe, stable, and collision-free motions of the DDWMR.
Copyright (c) 2026 Michael Andrianto Gunarso, Tua Agustinus Tamba, Jonathan Chandra https://creativecommons.org/licenses/by-sa/4.0
Resource optimization of approximate convolutional neural network-based accelerator on FPGA platform
A significant number of machine learning techniques use convolutional neural networks (CNN). The hardware acceleration is essential due to the tremendous computation demands of CNNs, as well as the need for improved energy performance and lower usage response time. This manuscript presents the resource-optimized CNN-based hardware accelerator on a field-programmable gate array (FPGA) platform. The design uses LeNet-5 architecture for handwritten digits classification using the MNIST dataset. The CNN accelerator uses three different optimization approaches in this work, including fixed-point (FP) data optimization with shortened bits, approximate multiply-accumulate (MAC) operations, and loop unrolling features with a pipelining mechanism to optimize the hardware resources. These approaches improve the latency and resources, and overall performance of the CNN architecture. The CNN-based accelerator is designed and implemented on the Xilinx Zynq platform with the high-level synthesis (HLS) tool. The proposed MAC unit obtains a latency of 3.58 ms, with a frequency of 120.69 MHz on chip. The design uses only 96 block random access memory (BRAMs), digital signal processing (DSP) units of 106 and obtains the accuracy of 99.01%. The proposed accelerator improves the performance over state-of-the-art accelerators in concerning the area, power and accuracy.
Copyright (c) 2026 Pooja Alana Puttaswamy, Kasaragod Poornima Kamath, Priyadarshini Jainapur, Karanam Sunil Kumar, Kumar Puttaswamy Gowda https://creativecommons.org/licenses/by-sa/4.0
A review of the hardware implementation of CRYSTALS-Kyber post-quantum cryptography algorithm
The development of quantum computing is escalating the vulnerability of conventional cryptography algorithms. To answer this challenge, researchers develop the post-quantum cryptography (PQC) algorithms. The PQC algorithms are immune from attacks deployed by quantum computers. CRYSTALS-Kyber (abbreviated as Kyber) is a PQC algorithm, originally constructed as public key encryption (PKE), then extended as the key encapsulation mechanism (KEM) algorithm to securely transfer a shared key to other parties over unsecured communication channels. The implementation of Kyber algorithm in hardware ensures a better security standard for securing systems that prioritize performance. This study provides a literature review of the Kyber hardware implementations. The review is delivered by a systematic literature review method to discuss resource and performance optimization, key design constraints, performance trade-offs, and future research directions in hardware implementation of Kyber based on existing studies. Area utilization and energy efficiency are achieved through the optimization of memory and architecture. The trade-off between performance, flexibility, and utilization remains relevant in the deployment context. Future work should accommodate holistic solutions, security, and performance enhancements as well as fabrication for real-world solutions.
Copyright (c) 2026 Ardhi Wijayanto, Nabihah Ahmad, Salman Ahmed https://creativecommons.org/licenses/by-sa/4.0
Optimizing LTE-Advanced performance in urban networks using intra-band and inter-band carrier aggregation
The rapid increase in mobile data consumption has significantly impacted network performance, especially in densely populated urban areas. Long-term evolution (LTE)-Advanced, an enhancement of LTE under 3GPP Release 10, incorporates carrier aggregation (CA) to optimize spectral efficiency and data throughput. This study investigates the effectiveness of intra-band and inter-band CA methods in improving LTE-Advanced network performance in Cirangrang-Cibaduyut Kidul, a high-density urban area in Bandung, Indonesia. Using a combination of drive tests and network simulations in Atoll 3.3.0, Key performance indicators such as reference signal received power (RSRP), signal-to-interference-plus-noise ratio (SINR), and throughput were analyzed. The results indicate that inter-band CA outperforms intra-band aggregation, providing significant improvements in signal quality and data rates. These findings suggest that inter-band CA is a viable solution for optimizing LTE-Advanced networks in urban environments with high traffic demand. This research provides practical recommendations for network operators to enhance service quality efficiently without extensive infrastructure expansion.
Copyright (c) 2026 Hasanah Putri, Radial Anwar, Alfin Hikmaturokhman https://creativecommons.org/licenses/by-sa/4.0
An efficient spectrum sensing method using convolutional neural network and filter banks for cognitive radio
The rapid advancement of connected vehicle technologies has intensified the need for efficient spectrum utilization. Cognitive radio (CR) enables dynamic access to underutilized spectrum, with spectrum sensing playing a key role in detecting primary users (PU). This study introduces a novel spectrum sensing approach that integrates filter bank (FB) signal decomposition with convolutional neural networks (CNNs)—referred to as filter bank decomposition and convolutional neural network (FB-CNN)—to enhance detection performance compared to conventional methods. Unlike traditional techniques such as energy detection (ED), the proposed FB-CNN leverages the frequency components extracted by the FB as CNN inputs, enabling robust identification of PU signals across multiple modulation schemes, including BPSK, QAM, FSK, and GMSK. Simulation results demonstrate substantial gains, particularly in low-SNR scenarios: for example, at an SNR of 5 dB, FB-CNN achieves a detection probability of 89% for 2-FSK, compared to only 22% with ED—representing a fourfold improvement. These findings highlight the novelty and effectiveness of FB-CNN in significantly improving spectrum sensing reliability for connected vehicle networks operating in challenging signal environments.
Copyright (c) 2026 Hamza Ouamna, Noureddine El-Haryqy, Anass Kharbouche, Zhour Madini, Younes Zouine https://creativecommons.org/licenses/by-sa/4.0
Advanced real-time face detection and recognition in MATLAB
Face detection and recognition technologies are increasingly vital in security and surveillance. This article covers two main areas with real-time application: the basics of image processing, such as edge detection and filters, and an overview of global methods for face detection and recognition. The Viola-Jones algorithm, based on Haar-like features and a cascade of classifiers, has been utilized for detecting objects within the images. MATLAB’s toolbox has been used to further enhance face detection performance by identifying human facial patterns in webcam-captured frames. For face recognition, the algorithm compares a detected face with reference images, counting zero-valued differences. If these zero elements exceed a certain threshold, a match is confirmed, indicating a high similarity between the captured face and the reference image. This study presents a low-cost MATLAB prototype emphasizing practical, educational demonstration of real-time face analysis.
Copyright (c) 2026 Walid El Fezzani, Mahdi H. Miraz, Mohammad Arif Sobhan Bhuiyan https://creativecommons.org/licenses/by-sa/4.0
Real-time object detection and XAI-based activation map visualization using YOLOv8s
This study introduced a methodology for real-time object detection and interpretability using YOLOv8s, trained on the MS common objects in context (COCO) dataset. The system captured live webcam footage, processes frames resized to 640×384, and applies YOLOv8s to detect objects with bounding boxes, labels, and confidence scores. YOLOv8s architecture comprising a CSPDarknet53-based backbone, neck, and head ensures efficient feature extraction and accurate detection. To enhance model transparency, activation map generation is implemented by attaching forward hooks to intermediate convolutional layers. Feature maps are captured during the forward pass, averaged, normalized, and resized to match the original image dimensions. This visualization highlights regions influencing the model’s predictions, aligning with explainable artificial intelligence (XAI) principles. Experimental results demonstrate high detection accuracy and effective interpretability in indoor environments, making the framework suitable for robotics applications requiring both precision and transparency. The proposed method offers a practical and explainable solution for real-time scene understanding in intelligent systems.
Copyright (c) 2026 Ashaari Yusof, Muhammad Hishamuddin, Md Jakir Hossen https://creativecommons.org/licenses/by-sa/4.0
Vertex and edge labelling strategies for graph-based computed tomography image denoising
Low-dose computed tomography (LDCT) reduces radiation exposure but introduces elevated noise and streak artifacts that degrade structural fidelity. This paper proposes a graph-based LDCT denoising framework that stabilizes graph construction through explicit vertex and edge labelling guided by paired full-dose CT (FDCT) data. The overlapping LDCT patches are modeled as vertices, and FD-guided affinities are used to build a structurally consistent adjacency matrix and a Laplacian spectrum that are less sensitive to noise. Denoising is performed by spectral filtering via spectral graph wavelet transform (SGWT), followed by overlap–add patch aggregation for image reconstruction. Experiments on paired LDCT/FDCT slices (318 pairs) show that FD-guided labelling improves denoising quality compared with conventional filters and non-guided graph baselines. Quantitative results demonstrate higher peak signal-to-noise ratio (PSNR)/structural similarity index measure (SSIM) with improved edge and feature preservation, indicating better structural boundary retention under noise suppression.
Copyright (c) 2026 Iwan Setiawan, Didi Rosiyadi, Rina Ratianingsih, Maulidyani Abu, Edy Tri Baskoro https://creativecommons.org/licenses/by-sa/4.0
Fine-tuned LayoutLMv3 for Indonesian receipts extraction
Shopping is a transaction that generates a record as a payment receipt. Typically, a receipt is given as a small piece of paper that can be easily lost. It is essential to store the transaction information in the receipt digitally. Keeping the information in a digital form will make it easily accessible and will overcome the problem of easily lost receipts. Currently, the process of transferring receipt information into digital form is still being done manually. Having a system that can extract this information helps speed up the digitalization process tremendously. This research proposes a method that applies finetuning to the LayoutLMv3 model and with the help of optical character recognition (OCR) from Google Vision, can be used to extract transaction information contained in the receipt. The system works by using Google Vision to parse and segment every word contained within the receipt and its bounding box The LayoutLMv3 model will then assign labels to each word, and important words will be extracted. The finetuned LayoutLMv3 model successfully achieved an accuracy of 97.98% on training data and 90% accuracy on real-time test scenarios for extracting information on receipts written in the Indonesian.
Copyright (c) 2026 Oka Sudana, Ayu Wirdiani, Andre Dwi Winama Putra https://creativecommons.org/licenses/by-sa/4.0
Hybrid ARMA-LSTM model for adaptive link prediction in dynamic underwater sensor networks
Underwater wireless sensor network (UWSN) is highly vulnerable to packet loss due to varied features of underwater channels, including multipath fading, high latency, and environmental interference. Accurate prediction of packet loss is critical for improving data reliability and network performance. Our research presents a new approach to forecasting using a combination of autoregressive moving average (ARMA) and long short-term memory (LSTM) networks which are statistical models. A synthetic dataset was generated to facilitate model development and evaluation, simulating realistic UWSN conditions by varying key parameters such as signal-to-noise ratio (SNR), received signal strength indicator (RSSI), depth, distance, and temperature. The ARMA model captures linear temporal trends, while the LSTM network is trained on the ARMA residuals to learn nonlinear correction patterns. The findings indicate that the hybrid ARMA-LSTM model exhibits a marked superiority over the standalone ARMA model, achieving an approximate 85.4% reduction in mean absolute error (MAE), an 83.6% enhancement in root mean square error (RMSE), a significant boost in predictive accuracy as reflected by the R² score, which improved from -43.93 to -0.20. The results highlight the hybrid method a strong and precise solution for predicting packet loss in UWSN, directly impacting the improvement of reliability in underwater communication.
Copyright (c) 2026 Ritu Bhardwaj, Ashwani Kush https://creativecommons.org/licenses/by-sa/4.0
FruitNet: a deep hybrid model for fruit detection and yield estimation
In recent years, intelligent agriculture monitoring systems have attracted considerable interest for yield estimation and fruit quality inspection. This paper introduces FruitNet, a deep hybrid model integrating object detection, classification, and regression for automated apple detection and yield prediction. The architecture employs YOLOv8 for real-time detection, a convolutional neural network (CNN) for quality assessment across four categories (excellent, good, average, and bad), and random forest regression for estimating yield based on extracted features. To enhance classification robustness, the CNN features are further refined using a support vector machine (SVM) classifier, tuned via grid search for optimal performance. The system is implemented using Python and Django, with a preprocessing pipeline incorporating noise removal, data augmentation, and normalization. The model is trained and evaluated on the MinneApple dataset containing over 18,000 annotated images. Experimental results demonstrate high generalization performance with over 98% training accuracy and 94–95% validation accuracy across 15 epochs. Visual analytics including confusion matrices and detection overlays confirm robust detection and classification. The proposed FruitNet framework shows strong potential for deployment in real-time precision agriculture, supporting mobile integration, orchard-level insights, and scalable smart farming applications.
Copyright (c) 2026 Komal Baburao Bijwe, Ajay B. Gadicha https://creativecommons.org/licenses/by-sa/4.0
Taylor series linearization for fully fuzzy multi-objective fractional programming in educational systems
This study examines the fully fuzzy multi-objective linear fractional programming problem (FFMOLFPP), whereby both the objective functions and restrictions incorporate fuzzy parameters represented as triangular fuzzy numbers (TFN), without converting them into crisp values. A hybrid solution approach is presented to tackle the intrinsic nonlinearity and uncertainty. Initially, the imprecise numbers are transformed into parametric representations via the y- cut method. A first-order Taylor series expansion is subsequently utilized to linearize each fractional objective function around a fuzzy decision point. The linearized objectives are then consolidated by the weighted sum approach, transforming the multi-objective fuzzy model into a single-objective linear program. Numerical examples validate the strategy and demonstrate the improved accuracy and efficiency of the proposed methodology.
Copyright (c) 2026 Anitha Ganesan, Ganesan Kandasamy https://creativecommons.org/licenses/by-sa/4.0
Attention-enhanced wasserstein GAN for agricultural market data imputation
Crop price prediction in ASEAN markets is hindered by incomplete and inconsistent data, making data imputation essential. This study introduces the Wasserstein generative adversarial imputation network with attention (WGAIN+Att) to improve data quality for forecasting. Four configurations—GAIN, GAIN+Att, WGAIN, and WGAIN+Att—were evaluated on rice, corn, and soybean datasets (1961–2023). Results show that WGAIN+Att, particularly when attention is applied across all matrices (x, m, and z), achieved the best imputation performance, minimizing mean absolute error (MAE) and preserving statistical distributions, with optimal results at a 0.1 missing rate and 0.9 hint rate. In predictive tasks, GAIN-based imputations combined with convolutional neural networks (CNN)-long short-term memory networks (LSTM)-gated recurrent units (GRU) models consistently outperformed others in forecasting accuracy, achieving lower MAE and root mean squared error (RMSE). The findings highlight the role of attention in stabilizing imputation and ensuring realistic reconstructions, while also showing that aligning imputation with forecasting objectives improves agricultural price predictions.
Copyright (c) 2026 Ulima Inas Shabrina, Riyanarto Sarno, Ratih Nur Esti Anggraini, Agus Tri Haryono https://creativecommons.org/licenses/by-sa/4.0
Design and analysis of metamaterial-inspired annular ring patch antenna for 5G applications
This work presents an annular ring patch antenna (ARPA) on the basis of metamaterial (MTM) for 5G applications. ARPA has become a popular choice for a range of wireless applications owing to its low profile, miniature size, simplicity in integrating with printed circuit boards (PCBs), and compatibility with contemporary fabrication techniques. Nevertheless, the bandwidth, gain, and efficiency restrictions that the ARPA frequently experiences are crucial for fulfilling the stringent needs of 5G communication systems. To overcome these obstacles, researchers have tuned to materials known as MTMs, which are synthetic materials having special electromagnetic (EM) characteristics absent from natural materials. By including complementary split ring resonators (CSRR) structures into microstrip patch antenna (MPA) the important characteristics like bandwidth, gain, and efficiency are enhanced. An EM simulation software named computer simulation technology (CST) Microwave Studio is employed for the evaluation of the antenna prototype. Rogers RT5880, a commercially accessible substrate material is used to develop the prototype. Comparative analysis is conducted between conventional antennas and ARPA, in which the proposed antenna attains low electrical loss, uniform electrical properties, thermal stability, and dimensional stability with gain of 5.65 dB. The developed work proves that the addition of CSRR structure is the solution to the development of antennas with superior performance characteristics.
Copyright (c) 2026 Vishu Vidyadharan, Sivagnanam Sivaramalingam, Lekshmi Kanthan Padma Suresh https://creativecommons.org/licenses/by-sa/4.0
A new approach for sentiment and geopolitical stance analysis of Reddit comments on the Israel-Palestine conflict
The paper focuses on the persistent conflict between Israel and Palestine. of world discussion, with such sites as Reddit containing a great deal medium for public expression. This study uses natural language processing (NLP) including valence aware dictionary and sentiment reasoner (VADER) for sentimental analysis and latent Dirichlet allocation (LDA) for topic modelling of geopolitical positions in comments of 400,000 posts belonging to the topics subreddits. A sentiment analysis using VADER shows that negative sentiments observed and indicate that such sentiments prevail throughout fear of aggression, human suffering, and threat. media biases. LDA analysis for topic modelling. The process of LDA reveals such important topics as humanitarian ones. media discourses, polemizing and appeals for calm. Furthermore, the study which divides comments into geopolitical attitudes – support for or yet, Israelis, supporters of Palestine, opponents of both, and neutrals, offering a contingency comprehensive conception of public perspectives. The results emphasize the effectiveness of large-scale. Of the big data pre-processing techniques, NLP can be used to understand geopolitics and its intricate dynamics. educate policy makers, researchers, and media planners.
Copyright (c) 2026 Gopal D. Upadhye, Hrushikesh Joshi, Satpalsing Devising Rajput, Ranjana S. Jadhav, Pranav Moralwar, Tejas Muttyalwar, Yash Mehendale, Siddharth Mane, Shivam Mishal https://creativecommons.org/licenses/by-sa/4.0
A techno-analytical insight on federated learning methodologies towards diagnosis of brain tumor
Artificial intelligence (AI) has made rapid progress in addressing complex medical challenges, including life-threatening conditions such as brain tumors. Recent years have witnessed significant contributions from machine learning (ML) and deep learning (DL), yet practical deployment remains limited due to privacy concerns, data heterogeneity, and lack of collaborative training. Federated learning (FL) offers a promising alternative by enabling distributed training across institutions without data sharing, thereby improving detection accuracy while preserving patient privacy. This paper systematically reviews FL in the context of brain tumor diagnosis, with a focus on its mathematical foundations, core modelling approaches, and emerging research trends. The analysis highlights that while FL demonstrates strong potential in enhancing classification, detection, and segmentation tasks, major gaps remain in handling non-independent and identically distributed (non-IID) data, cross-modal integration, scalability, and real-world deployment. The key insight of this study is that future progress will rely on hybrid FL systems, fairness-aware aggregation, and security-enhanced frameworks to achieve clinically viable, equitable, and scalable diagnostic solutions.
Copyright (c) 2026 Kavitha Cholenahalli Rangaswamyshetty, Maya Bevinahalli Somashankaregowda https://creativecommons.org/licenses/by-sa/4.0
Energy-efficient spectrum sensing using a novel adaptive hybrid learning for CR-IoT networks
The rapid expansion of internet of things (IoT) networks has intensified spectrum scarcity due to the massive growth in wireless device connectivity. Cognitive radio sensor networks (CRSNs) offer a promising solution by enabling dynamic access to underutilized spectrum bands. However, existing spectrum sensing techniques in CRSNs often suffer from high energy consumption, low adaptability, and limited prediction accuracy posing challenges in energy-constrained environments. This paper proposes an energy-efficient spectrum sensing (EESS) framework using an adaptive hybrid learning model (AHLM) that integrates wavelet transform-based signal decomposition (WT-SD), deep reinforcement learning (DRL), entropy-based hierarchical clustering (EHC), and meta-learning-based transfer learning (ML-TLM). WT-SD extracts key spectral features, while DRL with policy-gradient optimization dynamically predicts spectrum availability. The EHC mechanism clusters sensor nodes to minimize redundant sensing, and ML-TLM enhances adaptability with minimal retraining. The proposed model achieves substantial improvements over traditional methods. Experimental results show a 36% reduction in sensing time, 60% lower energy consumption than energy detection (ED) methods, and an 18.3% increase in network lifetime. The model also achieves a probability of detection of 0.998 and accuracy of 98.1%. These results confirm that the proposed EESS-AHLM framework provides a scalable and intelligent solution for energy-aware spectrum sensing in next-generation cognitive radio (CR)-IoT environments.
Copyright (c) 2026 Pravin Jaronde, Archana Vyas, Mahendra Gaikwad https://creativecommons.org/licenses/by-sa/4.0
Challenges in applying DeepInsight for cyber threat detection
As the world suffers from intrusions and malware extensively nowadays, intru-sion detection systems (IDS) play a critical role in protecting cyberspace from attacks. However, attacks become more complex every day, leading to the neces-sity of developing new techniques that can protect our digital infrastructure from cyber-attacks. Deep learning (DL) is one of the techniques that are investigated to fight against cyber-attacks. However, due to the nature of traffic data, most of the techniques focus on the deep neural network (DNN) as the performance of the DNN dependsonthetraining data. In this paper, we investigate the effective-ness of using convolutional neural networks (CNN) to detect malware apps and network intrusions. The cybersecurity datasets are converted from tabular data into images using the DeepInsight technique. Experiments are conducted using two datasets, NSL-KDD and CICMaldroid20 datasets. The proposed method demonstrates that converting cybersecurity datasets from tabular data into im-ages may decrease the model’s accuracy. Furthermore, this approach introduces additional challenges in the detection of network intrusions and malware. More-over, the added architectural complexity may cause a dilution or distortion of feature representations, making it harder for the model to preserve the original semantic meaning of critical features.
Copyright (c) 2026 Malik AL-Essa, Mohammad Qatawneh, Nidal Turab, Yazeed Alsarhan https://creativecommons.org/licenses/by-sa/4.0
DeepFloyd-IF via diffusion and U-Net based cross-model attention for semantic coherence
Text to image synthesis is getting harder in artificial intelligence, impacting gaming, advertising, and multimedia. The practical use of current Text to Image models is limited by the trade-off between semantic coherence and visual quality. To address this, this work presents stable diffusion cross-modal attention with multi-head attention (SD-CMA-MHA), a framework for the DeepFloyd-IF task. This combines stable diffusion with U-Net based cross-modal attention and multi-head attention (MHA) to improve DeepFloyd-IF, a standard for high quality image synthesis. This allows the model to capture subtle semantic relationships between text and images while dynamically focusing on relevant input features. Experiments on LAION-1.2B and MS-COCO datasets show that the model achieves 80% generation accuracy, 70% text-image alignment similarity and reduced divergence from real images, better than previous methods. This shows that SD-CMA-MHA improves semantic alignment and fidelity. The conclusion is that by enabling more reliable and context aware visual generation, this work not only bridges the gap between text and visual modalities but also has implications for creative industries, education and human-computer interaction.
Copyright (c) 2026 Kowsalya Veilumuthu, Divya Chandrasekar, Sakthidevi Shunmugalingam Parvathi https://creativecommons.org/licenses/by-sa/4.0
Remote sensing in armed conflict studies: a bibliometric analysis
This article addresses the potential use of remote sensing in conducting studies related to armed conflict. To carry out this approach, a bibliometric analysis was carried out, applying the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, through which we sought to identify the most relevant authors, the geographical distribution of productivity, and the research trends in the area. The analysis was carried out with information obtained from the Scopus reference database in August 2024. The results indicate that productivity growth began in the 2000s and is still under development. Additionally, the review of the articles indicated that the use of remote sensing in relation to armed conflicts has focused mainly on the study of the effects of armed conflict on land cover, environmental stability and, more recently, on the impact on agricultural areas from a food security perspective.
Copyright (c) 2026 Maritza Yulieth Tangarife–Marulanda, Vladimir Henao–Céspedes https://creativecommons.org/licenses/by-sa/4.0
A firewall model for attack detection using machine learning and metaheuristic feature selection algorithms
This research presents a firewall model designed to enhance network attack detection by integrating machine learning (ML) and advanced feature selection techniques. The study introduces a union-based (DAUBA) feature selection method that combines the exploratory capability of the Dragonfly Algorithm (DA) with the exploitation efficiency of the Bat Algorithm (BA). By combining these two bio-inspired optimizers, the method generates complementary feature subsets that enhance both accuracy and efficiency. The proposed DA?BA feature selection method is incorporated into a ML–based firewall and evaluated on the UNSW-NB15 dataset using three classifiers: adaptive boosting (AdaBoost), K-nearest neighbor (KNN), and Naïve Bayes (NB). Experimental results demonstrate that the approach achieves near-perfect accuracy (100% with AdaBoost), along with strong precision, recall, and F1-scores, while maintaining computational costs compatible with real-time deployment. These findings highlight the novelty and practical value of combining DA and BA in feature selection for next-generation firewall systems.
Copyright (c) 2026 Mosleh M. Abualhaj, Sumaya Nabil Al-Khatib, Nida Al-Shafi, Mohammad O. Hiari, Mohammad Sh. Daoud, Mohammed Anbar, Mahran M. Al-Zyoud https://creativecommons.org/licenses/by-sa/4.0
Digital maturity assessment models in public administration: a systematic review
Digital transformation (DT) is accelerating societal change and creating major challenges for public organizations seeking to improve efficiency through digital technologies. However, its measurement remains a conceptual and methodological challenge. This study presents a systematic literature review (SLR), conducted under the PRISMA protocol and PICOC strategy, focusing on digital maturity models applied to public administration (PA) between 2020 and 2024. The review covers both scientific databases and institutional gray literature. Five critical aspects were analyzed: included dimensions, internal structural relationships, empirical validation, predictive capacity, and contextual conditions of applicability. Results reveal a recurrent set of dimensions—technology, processes, data, people, and governance—yet with high heterogeneity in levels and approaches. Only a minority of models incorporate causal structures, and fewer than half have been empirically validated. Predictive capacity is almost absent, except for one Bayesian network model. Institutional factors such as digital leadership, budget, and regulatory frameworks strongly influence applicability. Unlike previous reviews, this study integrates a bibliometric analysis and a critical synthesis of enablers and barriers. It concludes that current models are useful for diagnosis but require improvements in structure, validation, and anticipation, providing an updated reference framework for researchers and policymakers in digital governance.
Copyright (c) 2026 Miguel Abdias Mendoza Dionicio, Miguel Ángel Cano Lengua, Ciro Rodríguez https://creativecommons.org/licenses/by-sa/4.0
A modified one-to-one algorithm for optimizing sustainable lot sizing multi-item models
Multi-item inventory management in modern production environments faces major challenges related to stochastic demand, transportation costs, and carbon emissions. This study aims to develop a sustainable lot sizing model that integrates economic and environmental aspects, and proposes the one-to-one based optimization (OOBO) algorithm as a problem-solving approach. The methodology used includes non-linear programming (NLP) formulation that considers stochastic demand, ordering and storage costs, carbon emissions, energy consumption, and vehicle capacity constraints. The model is then optimized using OOBO and compared with the Aquila, particle swarm optimization (PSO), and genetic algorithm (GA) algorithms in three case scale scenarios (6, 30, and 50 items). The experimental results show that OOBO consistently outperforms the comparison algorithms, with cost savings of up to 40.9% in the 50-item case. OOBO also demonstrated high exploration resilience without premature convergence and competitive computational time efficiency. These findings confirm that OOBO is effective in simultaneously optimizing total costs and carbon emissions, making it an adaptive solution for sustainable supply chain management. The theoretical implications include the expansion of OOBO's application to multidimensional stochastic systems, while in practical terms, this model supports decision-makers in formulating environmentally friendly and efficient inventory policies.
Copyright (c) 2026 Dana Marsetiya Utama, Yuan Rafika, Shanty Kusuma Dewi https://creativecommons.org/licenses/by-sa/4.0
Multi objective energy aware integrated cloud scheduling with a consensus-based security
This research presents a multi-objective, energy-aware workflow scheduling framework for heterogeneous cloud–edge environments that addresses both efficiency and data integrity challenges. Conventional encryption-based security mechanisms, although effective in protecting data during task offloading, often introduce significant computational and communication overhead, leading to degraded system performance. To overcome this limitation, this work proposes the consensus security-integrity and quality-aware workflow scheduler (CSIQA-WS), which integrates energy-aware scheduling with a lightweight, consensus-driven security mechanism. The model incorporates automatic service management and an attack prevention module to detect and mitigate malicious behavior during inter-node data transmission while maintaining quality of service (QoS) constraints. A dynamic coordination between edge and cloud resources enables efficient workload distribution and robust resource utilization. Experimental evaluation using scientific workflow benchmarks demonstrates that CSIQA-WS significantly reduces processing time and energy consumption compared to existing approaches. The proposed model achieves up to 92.29% reduction in processing time and consistently improves overall QoS while preserving data integrity in dynamic execution environments. These results indicate that CSIQA-WS provides an effective and scalable solution for secure and energy-efficient workflow scheduling in modern cloud–edge systems.
Copyright (c) 2026 Fairoz Pasha, Jayapandian Natarajan https://creativecommons.org/licenses/by-sa/4.0
Ethylene gas-based fruit expiry predictor: a sustainable solution to fruit wastage
Globally, environmental and economic issues are very apparent regarding food wastage. Fruits play a significant role since they can be damaged easily. Ethylene gas is one of the significant gases produced by fruits when they begin to ripen; it tests whether the fruit is ripe or else spoilable. This work presents a sustainable solution for fruit wastage: an ethylene gas-based fruit expiry predictor (FEP). The system is designed and developed around advanced sensing and artificial intelligence (AI) models for real-time monitoring of ethylene levels and temperature in forecasting the spoilage of fruits. The system consists of non-invasive sensors for detecting ethylene and temperature, a data processing microcontroller, and an AI model trained on a large dataset to make accurate predictions regarding the expiry of fruit. The AI model processes the information collected by the sensors and then displays the grade (level of ripeness of the fruit) on a liquid crystal display (LCD) screen. This solution improves fruit management with reduced wastage in line with international sustainability targets. These would enable real-time and highly accurate predictions of fruit spoilage, allowing end-users informed choices that will eventually lead to reducing the carbon footprint from food waste and increasing food security.
Copyright (c) 2026 Olabode Idowu-Bismark, Anyiam Chinemere Eberchukwu, Oluwadamilola Oshin, Ibiang Emmanuel, Tonwe Mary, Emmanuel Adetiba https://creativecommons.org/licenses/by-sa/4.0
Comparative analysis of ensemble learning algorithms in enhanced confidence-based assessments
This paper provides a comparative analysis of ensemble learning (EL)algorithms to enhance the confidence-based assessment (CBA) in evaluating student performance. Traditional CBA often suffers from misclassification caused by overconfidence and underconfidence, limiting its accuracy and fairness. To address these challenges, an enhanced CBA-EL model integrating bagging and boosting ensemble algorithms is proposed. Five bagging algorithms, which are random forest (RF), decision tree (DT)support vector machine (SVM), K-nearest neighbors (KNN), Naïve Bayes(NB), and four boosting algorithms, which are adaptive boosting(AdaBoost), eXtreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost), were evaluated using a dataset of 276 responses collected from Pre- and Post-Quiz CBA in a discrete structures course. Algorithm performance was evaluated using accuracy, correlation, weighted mean precision (WMP), and weighted mean recall (WMR). RF achieved 73.19% accuracy, 0.725 correlation, 0.751 WMP, and 0.766 WMR, while CatBoost outperformed all with 86.23% accuracy and the highest correlation, WMP, and WMR values, with 0.842, 0.843, and 0.862, respectively. The findings indicate that integrating EL into CBA improves prediction accuracy and supports bias-aware student evaluation. This research advances reliable assessment practices and informs the development of adaptive learning systems.
Copyright (c) 2026 Nur Maisarah Nor Azharludin, Khyrina Airin Fariza Abu Samah, Mohamad Faiz Dzulkalnine, Ahmad Firdaus Ahmad Fadzil, Mohd Nor Hajar Hasrol Jono, Lala Septem Riza https://creativecommons.org/licenses/by-sa/4.0
Forecasting graduate student enrollment in university using regression analysis
The government ensures educational quality in universities through a quality assurance (QA) system implemented via accreditation, which evaluates both study programs and institutions. A key concern in accreditation is the decline in new student enrollment, making accurate predictions of enrollment numbers essential for quality assessment. This study proposes a linear regression (LR) model to forecast future university student enrollments based on enrollment figures from the previous year as input feature. Using a dataset from one of Indonesia’s leading university spanning 2013 to 2023, the experimental results demonstrate that the LR model outperforms other regression techniques, including multi-layer perceptron (MLP), K-nearest neighbors (KNN), decision tree (DT), and random forest (RF). The LR model achieves R² values between 0.87 and 0.95, reflecting a strong linear relationship between current and future student numbers. It also delivers high accuracy, with root mean square error (RMSE) values ranging from 11.72 to 41.21 per year. The trained LR model has been integrated into a web-based system, offering data visualization and enrollment predictions to support university management in monitoring quality, addressing enrollment challenges, and facilitating informed decision-making.
Copyright (c) 2026 Anggraini Dwiansyah, Imam Fahrurrozi, Muhammad Fakhrurrifqi, Umar Farooq, Ganjar Alfian https://creativecommons.org/licenses/by-sa/4.0
Real-time sleep posture classification using wearable accelerometers and machine learning models
Sleep posture plays a critical role in sleep quality and health, influencing conditions such as sleep apnea. Accurate classification of sleep postures is essential for diagnosing and treating sleep-related disorders. The sleep posture can be detected by using wearable acceleromter. This paper presents an realtime classification system for four sleep postures by integrating accelerometer data with a machine learning (ML) model. The proposed system was tested with various ML models, including decision trees (DT), random forest (RF), K-nearest neighbors (KNN), support vector classifier (SVC), and logistic regression (LR), across multiple performance metrics. The results demonstrate that the LR model, when combined with accelerometer data, significantly outperforms other methods, achieving a classification accuracy of 91%. This paper also discusses the system’s potential for real-time deployment on embedded devices, contributing to advancements in sleep posture monitoring.
Copyright (c) 2026 Thi Thu Nguyen, Bao Bo Quoc, Kolla Bhanu Prakash, Duc-Tan Tran https://creativecommons.org/licenses/by-sa/4.0
Deep neural networks for predicting kidney health: focus on cyst, stone, tumor, and normal classification
Kidney diseases affect individuals across all age groups and are a major global health concern. Pathological and other conditions, such as tumors, cysts, and stones, along with normal states of the kidneys, need to be detected as early as possible to improve treatment outcomes and quality patient care. This study looks into the use of computed tomography (CT) images for deep learning-based kidney disease classification. We evaluated four widely used convolutional neural networks (CNNs) such as VGG16, MobileNetV2, ResNet50, and InceptionV3 on a dataset of 12,456 CT images. Among the individual models, MobileNetV2 achieved the highest validation accuracy of 99.64%. As a novel contribution, we propose a hybrid deep learning model that combines MobileNetV2 and ResNet50 to enhance diagnostic performance. The hybrid architecture design led to superior results: 99.88% validation accuracy, 99.50% precision, 99.50% recall, 99.25% F1-score, and a reduced validation loss of 0.0090. Performance was further validated using confusion matrices, receiver operating characteristic (ROC) curves, classification reports, and 6-fold cross-validation to assess generalization. The proposed model demonstrates strong robustness and generalizability across kidney condition categories. As far as we are aware, not many research have looked into a hybrid combination of MobileNetV2 and ResNet50 for multi-class kidney CT classification.
Copyright (c) 2026 Abdey Rabby, Jannatun Naima Jannat, Md Assaduzzaman, Rahmatul Kabir Rasel Sarker, Raja Tariqul Hasan Tusher https://creativecommons.org/licenses/by-sa/4.0
Multitask deep learning for sentiment analysis with sarcasm detection in bilingual code-mixed social media content
Sentiment analysis in social media often hindered by sarcasm, which can reverse text meaning, and bilingual code-mixing, which adds complexity in non-English primary context. Existing approaches extract separate features for each language and translate them into a single language, resulting in the loss of contextual meaning and omission of crucial features. This paper proposes a multitask learning model for sentiment analysis with sarcasm detection tailored to bilingual code-mixed social media content. A hybrid feature engineering technique is integrated into a multitask deep learning architecture designed to capture the nuances of sentiment and sarcasm while addressing the complexities of processing bilingual code-mixed content. The hybrid technique combines domain-knowledge-based natural language processing (NLP) with a deep learning-based embedding approach. It includes rule-based preprocessing, normalization, spellchecking, feature extraction and selection, and feature representation. The engineered features are integrated into a multitask deep learning network using bidirectional long short-term memory (Bi-LSTM) combined with gated recurrent units (GRU). Using a public dataset that contains bilingual code-mixed social media content related to public security, our proposed model achieved a higher F1score compared to two baseline models that employ single task and multitask approaches.
Copyright (c) 2026 Mohd Suhairi Md Suhaimin, Adi Wibowo, Ervin Gubin Moung, Patricia Anthony, Mohd Hanafi Ahmad Hijazi https://creativecommons.org/licenses/by-sa/4.0
Dual-domain analysis of CGM data for personalized metabolic health management
Time-domain metrics on a continuous glucose monitoring (CGM) miss hidden dynamics, but frequency-domain features detect up on oscillating patterns, which makes it easier to measure glucose variability and gain clinical understanding. This study aimed to analyze the time and frequency domain features of CGM data for three patients (PtID 65, PtID 109, and PtID 134) to assess glucose variability and control. CGM data were collected over a specified period, with key metrics such as mean glucose, standard deviation (SD), time in range (TIR), and spectral features being computed. The analysis included both time domain and frequency domain features. PtID 109 exhibited the highest mean glucose (207.86 mg/dL) and greatest variability, reflected in the highest SD (85.57 mg/dL) and the lowest TIR (38.24%). PtID 134 showed the most stable glucose levels with the highest TIR (73.60%) and the lowest SD (59.81 mg/dL). Frequency domain analysis revealed that PtID:134 had the highest spectral flatness, indicating more variability in the glucose signal’s frequency content. PtID 109 had the lowest TIR at 38.24%, whereas PtID 134 achieved 73.60%, indicating a 24% enhancement and demonstrating superior glucose stability. Dual-domain analysis offers thorough framework for comprehending glucose variability, facilitating individualized treatment and enhanced metabolic health care.
Copyright (c) 2026 Selvaraj Gowriswari, Saminathan Brindha https://creativecommons.org/licenses/by-sa/4.0
THD reduction in novel asymmetrical 21 level MLI using FLO-MOA algorithm optimized cascaded controller technique
Since multi-level inverters (MLIs) may operate at lower switching frequencies and reduce switching losses, they are frequently used in high and medium electric drives for renewable energy applications. Lower-order harmonics could result from using a lower switching frequency, which would increase line current distortion. MLIs provide fewer output harmonics than traditional converters. This research suggested a hybrid strategy for creating a 21-level MLI. The suggested cascaded fractional-order tilt integral fractional-order proportional tilt derivative (FOTI-FOPTID) controller is used to adjust the switching pulse for the 21-level MLI. In this work, the Mother Optimization algorithm (MOA) is combined with the Frilled Lizard optimization (FLO) method to improve it. This results in the FLO-MOA algorithm, which is used to adjust the controller's gain parameters. This strategy reduces the number of switches and asymmetrical sources of dc voltage. The inverter's efficiency is raised, power loss is minimized, total harmonic distortion (THD) is decreased, and output voltage levels are improved.
Copyright (c) 2026 Mohandas Ramaiah, Bharath Vaniyambadi Sathyanarayanan https://creativecommons.org/licenses/by-sa/4.0
Detecting respiratory diseases using spectrogram-based deep features and machine learning algorithms
Early diagnosis of respiratory diseases is difficult as lung sound analysis requires the skills of medical professionals. Respiratory diseases are one of the leading causes of death in the world, so early detection is critical. Automatic identification is made possible by artificial intelligence. However, lung sound data is unstructured, while artificial intelligence often requires structured data. Therefore, feature extraction is required to structure the voice data. Traditional techniques such as mel-frequency cepstral coefficients (MFCC) often produce fewer features and information. This research uses a deep feature approach, which produces more features, as a solution. This research applies three convolutional neural network (CNN) architectures as deep features, namely VGG-16, DenseNet-121, and ResNet50, with machine learning classifications, namely random forest, support vector machine (SVM), Naïve Bayes, and K-nearest neighbors (KNN). This research will identify the optimal combination of methods. The results of this study show that respiratory disease classification can be effectively achieved by combining deep features and machine learning classification. The results of 10-fold cross-validation show that the three CNN architectures perform best on SVM with a linear kernel. The accuracy of VGG-16 is 70.63%, ResNet-50 is 64.93%, and DenseNet-121 is 73.58%.
Copyright (c) 2026 Elvina Nur Hana, Mohammad Reza Faisal, Dwi Kartini, Muhammad Itqan Mazdadi, Setyo Wahyu Saputro, Fatma Indriani, Kenji Satou https://creativecommons.org/licenses/by-sa/4.0
Comparative analysis of ResNet backbones in single shot detector for visual-based waste detection
Waste has become a serious environmental issue that requires effective and efficient management systems. This study compares three residual network (ResNet) variants (ResNet-34, ResNet-50, and ResNet-101) within the single shot detector (SSD) framework for visual waste detection. The dataset consists of 800 images in four categories—food, plastic, paper, and wood—with a 70:20:10 split for training, validation, and testing. The backbone architecture, optimizer (stochastic gradient descent (SGD) and Adam), and learning rate are varied to evaluate fifteen experimental configurations. Model performance is assessed using precision, recall, F1-score, and mean average precision (mAP). The results show that SSD–ResNet-34 with SGD and a learning rate of 0.0005 works best, with a mAP of 91.02%, which is better than deeper backbones. Deeper backbone architectures do not consistently improve accuracy; instead, they increase the risk of overfitting on small datasets. These findings highlight that lightweight architecture, when used with the right hyperparameter settings, strikes a better balance between accuracy, computational efficiency, and generalization for small-scale waste detection tasks.
Copyright (c) 2026 Zahra Khalila Salsabila, Nurcahya Pradana Taufik Prakisya, Febri Liantoni https://creativecommons.org/licenses/by-sa/4.0
A robust model for early detection of chronic kidney disease leveraging machine learning and data balancing techniques
Chronic kidney disease (CKD) requires reliable early screening, yet clinical datasets are often highly class imbalanced, which can bias machine learning models and reduce detection quality. This study presents a unified evaluation of two imbalance mitigation strategies, synthetic minority over-sampling technique (SMOTE), and cost-sensitive learning, across six classifiers: decision tree (DT), K-nearest neighbors (KNN), logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost). Experiments were conducted on a public CKD dataset with 1,659 records and 54 features using a consistent pipeline including preprocessing, feature selection, imbalance handling, and stratified k-fold cross-validation. Models were assessed with accuracy, precision, recall, and F1-score. Results show that the imbalance strategy materially changes model behavior: cost-sensitive learning generally improves precision, while SMOTE more often increases recall and F1-score. The best overall performance was achieved by XGBoost with cost-sensitive learning, reaching 93% accuracy and 92% precision, outperforming prior reports on the same dataset. RF remained stable across both strategies, whereas KNN was sensitive to SMOTE induced distribution shifts. These findings provide practical guidance for selecting imbalance handling methods to improve healthcare machine learning for CKD detection.
Copyright (c) 2026 Helmi Imaduddin, Siti Agrippina Alodia Yusuf, Muhammad Syahriandi Adhantoro https://creativecommons.org/licenses/by-sa/4.0
Transfer learning-based texture-enhanced convolutional neural networks over plant disease identification
The global agricultural productivity and food security take serious threats due to the presence of plant diseases; thus, early and accurate diagnosis becomes the key to successful management of the disease. The traditional diagnosis techniques that rely on visual observation are time-based, subjective, and cannot be implemented on a large scale. Recent development in machine learning and computer vision provides possible solutions to automated plant disease detection. This paper suggests a plant disease identification with transfer learning (PDD-TL) model with the preprocessing, segmentation, feature extraction, and disease prediction phases. In the initial stages, median filtering is used to simplify the image quality, after which cells affected by the disease are segmented with the help of the integration of adaptive pixels in joint segmentation (IAPJS) algorithm. Multi-texton and pyramid histogram of oriented gradients (PHOG) are the discriminative features extracted. The classification of the disease is done with a new triple convolutional activation CNN with transfer learning (CNN-TCA-TL). In contrast to the current methods that use either a pure deep learning method or handcrafted features, the framework proposed explicitly employs both the use of texture descriptors and transferable deep representations, which retain fine-grained structural details. The experimental findings prove that CNN-TCA-TL has an accuracy of 0.92 which will prove that it is effective.
Copyright (c) 2026 Nilesh N. Thorat, Mangesh D. Salunke, Aarti P. Pimpalkar, Mayuresh B. Gulame, Babeetta Bbhagat, Sumit Hirve, Saleha Saudagar, Madhura Eknath Sanap https://creativecommons.org/licenses/by-sa/4.0
Analysis of brain activity to methamphetamine stimulus using electroencephalography technology with Naive Bayes algorithm
The increasing use of methamphetamine among young generations has led to significant alterations in brain function, affecting both behavior and mental health. However, scientific understanding of the neural activity changes induced by methamphetamine remains limited. This study aims to analyze brainwave patterns using electroencephalography (EEG) and classify addiction response levels through the Naive Bayes algorithm. The experimental procedure involved presenting each subject with visual stimuli related to methamphetamine while recording their brain activity using EEG for three minutes. The extracted EEG features were then analyzed with the Naive Bayes classifier. The results demonstrated a classification accuracy of 97.9%. The proposed method successfully categorized brain activity patterns into five levels of response: non-addicted, mildly addicted, moderately active, addicted, and highly addicted. These findings indicate that the Naive Bayes algorithm is effective in distinguishing subtle variations in brainwave patterns associated with different levels of methamphetamine addiction response.
Copyright (c) 2026 Suci Rahmalia Putri, Amanda Khalishah Hasibuan, Cindy Ananda Sinaga, Ernest Natanael Manullang, Arjon Turnip, Abdi Dharma, Mardi Turnip https://creativecommons.org/licenses/by-sa/4.0
Improved no-line-of-sight static and dynamic sensor data classification using KNN algorithm with PLS model
The rising cases of structural collapses across the world have aggravated the problem of finding people under rubble as part of search and rescue (SAR) effort. The conventional search techniques, namely drill operation and the use of dog searching, are usually slow, labour-intensive and unsuccessful in difficult debris setting. Radar systems, though non-invasive, are limited by attenuation and multipath interference in non-line-of-sight (NLOS) environments. The proposed research will contribute to the improvement of victim recognition by creating an advanced machine learning (ML) model that will operate in the most challenging environmental settings. It suggests a modular prediction model combining both the K-nearest neighbor (KNN) and partial least squares (PLS) to extract features and reduce the dimensions. The procedure includes the derivation of essential signal characteristics, dataset validation, PLS application to get limited and discriminative feature amounts, and KNN classification under conditions of both fixed and dynamic conditions. Experimental findings indicate classification scores of 87.87 and 75.70 respectively in case of static and dynamic data. These results validate the practicability of the suggested solution in enhancing the forecasting accuracy during NLOS circumstances and emphasize its possible use in enhancing quicker, more dependable, and evidence-based official choices during the actual SAR operations.
Copyright (c) 2026 Enoch Adama Jiya, Ilesanmi B. Oluwafemi, Francis Ayoleke Ibikunle, Nik Syahrim Nik Anwar https://creativecommons.org/licenses/by-sa/4.0
Optimizing grouper catch efficiency using AI-controlled light fishing in Pamekasan
Light fishing techniques are widely used to improve marine fish catches by adjusting the spectrum and intensity of light-emitting diodes (LEDs) to attract specific species. Grouper (Epinephelus spp.) are known to respond well to red light, although the optimal light intensity remains unclear. This study proposes a dual system consisting of a pulse-width modulation (PWM)-based red LED brightness controller and an artificial intelligence (AI) fish detection module using the YOLOv4 algorithm. LED brightness was varied at duty cycles of 50, 100, 150, 200, and 250 (1 kHz), producing luminous flux values between 120 and 650 lm. Field experiments conducted at the Pamekasan coast using 10 grouper samples showed that 80% of fish aggregated at a duty cycle of 150 (~420 lm at 1.5 m). ANOVA results (p<0.05) indicated significant differences among light intensities, with the highest attraction observed at this level. These findings suggest the existence of an optimal light intensity threshold. Further studies with larger samples and multi-site experiments are required to validate these results and evaluate ecological impacts.
Copyright (c) 2026 Bambang Sampurno, Galih Sukma Adjie, Mashuri, Liza Rusdiyana, Suhariyanto https://creativecommons.org/licenses/by-sa/4.0
Evaluating the effectiveness of digital filtering techniques in electronic stethoscopes: a study of Kalman and Butterworth filters
Heart sound, or phonocardiogram (PCG), signals are often distorted by noise from respiration, movement, and the surrounding environment, which complicates accurate cardiac feature extraction in portable monitoring systems. This study aims to design and evaluate an effective digital filtering method to enhance PCG signal quality obtained from a low-cost acquisition system based on a condenser microphone sensor and an ESP32 microcontroller. The main contribution of this work is the implementation and comparison of two noise-reduction approaches: Butterworth band-pass filters of various orders and the Kalman filter applied to PCG signals acquired from mannequin-based simulations. Data were recorded for 10 seconds at a 1000 Hz sampling rate, processed in MATLAB, and analyzed using the fast Fourier transform (FFT) to determine the optimal frequency ranges. Experimental results demonstrate that the 8th-order Butterworth band-pass filter achieved the highest signal-to-noise ratio (SNR) improvement, averaging 25.659 dB, outperforming other configurations. These findings indicate that an appropriately tuned Butterworth filter provides a simpler yet robust solution for real-time PCG denoising in embedded systems. Future work will integrate the filtering process directly into the ESP32 firmware and evaluate its performance on human subjects to enhance clinical applicability.
Copyright (c) 2026 Endang Dian Setioningsih, Sumber, Triwiyanto, Vugar Abdullayev, Farid Amrinsani, Bima Maulana Raharjo, Tetrik Fa’altin https://creativecommons.org/licenses/by-sa/4.0
Toward a business intelligence framework for e-government interoperability: data integration and decision support
In e-government systems, achieving smooth data interoperability is still a major challenge, especially in judicial systems where fragmented data management affects decision-making and service quality. In order to facilitate real-time data integration and interoperability, this paper suggests a novel service-oriented business intelligence (SOBI) framework coupled with a common central data warehouse (CCDW). Our approach, which uses the Moroccan legal system as a use case, makes use of materialized views and extract, transform, and load (ETL) procedures to guarantee scalable, secure, and efficient exchange of information between disparate systems. The framework provides a scalable model for e-governance while improving decision support, transparency, and service delivery. Findings highlight enhanced system performance and data sharing, with implications for broader e-government applications. The proposed approach provides a foundation for future extensions toward internet of thing-based urban planning and real-time data integration for smart and sustainable urban ecosystems.
Copyright (c) 2026 Oumkaltoum Barakat, Omar Elbeqqali, Loqman Chakir https://creativecommons.org/licenses/by-sa/4.0
Flood mapping using Res-Q and machine learning on imbalanced data
Flood disaster mapping requires accurate methods to support early warning and mitigation planning. To address common issues such as imbalanced data distribution and limited attribute handling, this study proposes an improved approach. The methodology includes: i) modification of the spatial sort filter skyline method with reverse normalization based on attribute preferences, applied when an attribute has minimal preference to ensure balanced consideration during skyline filtering; ii) data labeling and balancing, where initial flood potential labeling is generated using Res-Q, followed by K-Means clustering to group data into four classes (low, moderate, high, and very high) and SMOTE to further balance the dataset with 558 data points per class; iii) model evaluation using the C5.0 algorithm under three schemes, showing high and consistent accuracy with 89.24% on imbalanced data (Schema 2) and 93.3% on balanced data (Schema 3), while Schema 1 shows overfitting due to extreme imbalance; and iv) the main contribution, integrating reverse normalization with skyline filtering combined with clustering and resampling, enhancing both accuracy and robustness in identifying flood-prone areas. This structured approach highlights methodological improvements, reliable results, and practical contributions for effective flood disaster management.
Copyright (c) 2026 Siti Yuliyanti, Vega Purwayoga, Andi Nur Rachman, Zakwan Gusnadi https://creativecommons.org/licenses/by-sa/4.0
Conditional toggle algorithm: an adaptive metaheuristic and its implementation on handling engineering problems
There have been numerous new metaheuristic algorithms in this decade. Unfortunately, the attention on taking stagnation is still less considered so that it is difficult to find new metaheuristic algorithms that are enriched with stagnation taking mechanism. This work introduces a new method called conditional toggle algorithm (CTA). CTA is designed to be adaptive on facing enhancement and stagnation during iteration as its novelty. When enhancement occurs, the exploitation-focused look is applied. Meanwhile, the exploration-focused look is applied when stagnation occurs. The efficacy of CTA is then measured by implementing to solve three cases: 23 functions, 4 engineering design problems, and economic emission dispatch (EED) problem in Java-Bali power system in Indonesia. CTA is compared with five new metaheuristic algorithms. The evidence provides that CTA is supreme in taking high dimension functions and competing in taking fixed dimension functions. CTA is also supreme in taking pressure vessel and speed reducer design problems and the EED problem. But its performance is average in taking welded beam and spring design problems. In the future, CTA can be modified with other metaheuristic algorithms to enhance its performance and challenged to take broader problems, especially in electrical engineering fields.
Copyright (c) 2026 Purba Daru Kusuma, Helmy Widyantara https://creativecommons.org/licenses/by-sa/4.0
Leakage current mitigation and efficiency enhancement in transformerless 3-level NPC PV inverters
In this research explores the design and performance of a transformerless (TL) three-level neutral point clamped (NPC) inverter for grid-connected photovoltaic (PV) systems. In the push toward more compact, cost-effective, and efficient renewable energy systems, TL inverter topologies have emerged as a strong alternative to conventional systems that use line-frequency transformers. The proposed topology includes two separate PV sources interfaced through dedicated boost converters, which then feed into a common NPC inverter, enhancing flexibility and maximum power point tracking (MPPT) performance. Comprehensive simulations were performed using MATLAB/Simulink to evaluate grid compatibility, total harmonic distortion (THD), and transient behavior under fluctuating irradiance levels. Results validate the efficacy of the system in maintaining grid compliance, lowering leakage current, and achieving high-quality power output. Compared to traditional two-level inverters or transformer-based alternatives, the proposed system demonstrates a clear performance advantage in both efficiency and reliability.
Copyright (c) 2026 Revathi Dhanapalan, Sivasankari Balan, Jayapriya Mangalaraj, Edwin Singh Chinna Thurai https://creativecommons.org/licenses/by-sa/4.0
Uplink power control for multi-path channel estimation in massive multiple-input multiple-output systems
Multi-path communication channels provide a realistic representation of transmitter-receiver communication in practical propagation environments. This work investigates uplink (UL) multi-path channel estimation (CE) in a massive multiple-input multiple-output (M-MIMO) multi-cell multi-user system, where each user communicates with its serving base station (BS) through a multi-path channel. The network operates using time-division duplex (TDD), exploiting channel reciprocity between the uplink and downlink. The impact of multi path propagation on CE is analyzed using two approaches: the ideal minimum mean square error (MMSE) estimator and a proposed simplified estimator. The MMSE estimator assumes prior knowledge of the large-scale fading (LSF) coefficients of interfering users, which is impractical in real systems. To overcome this limitation, a simplified estimator is proposed that does not require such information while achieving asymptotic performance close to that of the MMSE estimator. Realistic propagation scenarios are also considered, where channels may include either non-line-of-sight (NLoS) components or a combination of line-of-sight (LoS) and NLoS paths depending on the user’s distance from the BS. Furthermore, a heuristic power control strategy is introduced to mitigate pilot contamination, particularly for cell-edge users, thereby reducing inter-cell interference and improving overall system performance. Analytical and simulation results validate the proposed approach.
Copyright (c) 2026 Jamal Amadid, Asma Khabba, Zakaria El Ouadi, Lahcen Sellak, Abdelmouttalib Bousrout, Abdelouhab Zeroual, Tole Sutikno https://creativecommons.org/licenses/by-sa/4.0
Towards energy-efficient 5G networks: coordination solutions for macro and pico cells
The increasing demand for high-speed, low-latency connectivity has driven the rapid deployment of fifth-generation (5G) networks. However, the enhanced performance of 5G systems comes at the cost of higher energy consumption, posing a significant challenge to sustainability goals. This study explores energy-efficient coordination strategies for macro and pico cells to optimize power usage while maintaining network performance. By employing a systematic mapping study (SMS) and a systematic literature review (SLR), we analyze current research trends, challenges, and emerging solutions in energy-efficient 5G network design. Key strategies, including AI-driven resource allocation, dynamic spectrum management, and interference mitigation techniques, are examined to assess their effectiveness in reducing energy consumption. The findings highlight the critical role of intelligent coordination mechanisms in achieving a balance between energy efficiency and service quality. This research contributes to the development of sustainable 5G architectures by identifying optimal methodologies for macro- and pico-cell integration, paving the way for greener and more adaptive next-generation networks.
Copyright (c) 2026 Hasanah Putri, Rendy Munadi, Sofia Naning Hertiana, Alfin Hikmaturokhman https://creativecommons.org/licenses/by-sa/4.0
A modified transfer function for frequency sampling filters: theory, design, and applications
The article is devoted to frequency sampling filters (FSF). FSFs are highly efficient linear-phase digital filters that can be significantly more computationally efficient than Parks–McClellan FIR filters. FSFs are well-suited for implementing filter banks, which can be used to construct high-performance spectrum analyzers. Although these filters have been known for a long time, they have not been widely adopted despite their efficiency. This article presents the authors’ perspective on the reasons behind the limited use of FSFs. A new original method for forming the transfer function (TF) of FSFs using an ideal analog prototype is proposed. The proposed method fundamentally differs from the longestablished classical approach. The TF of an analog filter that meets the require-ments of absolute linearity in the phase response (PR) was considered. The bilinear z-transform method was applied to this ideal TF. By applying L’Hˆopital’s rule, an analytical expression for determining the weighting coefficients of the resulting TF was obtained. The article presents examples of calculated FSFs and a filter bank based on them. Tables of the optimized weighting coefficients and plots of their attenuation characteristics are provided. An improved block diagram of the FSF is also presented. The characteristics of the classical FSF TF and the proposed (alternative) FSF TF are analyzed and compared to determine their advantages and disadvantages.
Copyright (c) 2026 Serhii Rybka, Ivan Varava https://creativecommons.org/licenses/by-sa/4.0
Smart aquaculture system: internet of things-based contextual e-service for sustainable aquafarming
A creative and contextual e-service for aquaculture system is developed to handle threats that climate change poses to global ecology and sustainable aquaculture. Therefore,"ICTization framework model," is based on capacity, connectivity, and context across three layers—core, and management, and distribution— has served as the study's guide. These layers support elements of centralization, decentralization, and parallelization hence reduce misconceptions about infrastructure, development, and communication. It incorporates a four-tier conceptual framework for an e-service system based on the internet of things (IoT) that is aimed at appropriately collect and manage environmental data in real time. Each tier of the framework is further elaborated for e-services using contextual information with existing infrastructure preserved while improving synchronization of data, administration, and services. It is able to predict and respond to variations in the quality, temperature, and other vital parameters of the water. The architecture realizes aquaculture as a service by orchestrating all the stakeholders – farmers, researchers, and network operators. E-service based system shall include remote monitoring, automated feeding and water quality assessment in real-time which will significantly improve efficiency reduce cost and increase income. Moreover, improved resource efficiency and less ecological impact made possible through the proposed conceptual framework enables sustainable aquaculture practices.
Copyright (c) 2026 Md Abdullah Al Rahib, Md Redwan Hussain, Md. Zubayer Chowdhury, Jarin Tias Meraj, Abdus Sattar https://creativecommons.org/licenses/by-sa/4.0
Embedded deployment of traffic sign detection and recognition systems
Traffic sign (TS) detection and recognition are essential components of advanced driver assistance systems (ADAS), contributing to safer and more reliable driving. However, deploying deep learning–based vision models on embedded platforms is challenging due to constraints in computational power and energy consumption. In this work, a comparative deployment of you only look once version 7 (YOLOv7) and YOLOv7-tiny deep learning algorithms is conducted on embedded NVIDIA platforms, namely Jetson Nano and Jetson Xavier NX, to evaluate their suitability for real-time TS detection. Following the detection stage, a convolutional neural network (CNN) is integrated to perform TS recognition, enabling a complete detection–recognition pipeline. Experimental results show that YOLOv7-tiny achieves higher detection precision of 97%, while providing better speed and computational cost on resource-constrained devices, with Jetson Nano reaching 18.8 frames per second (FPS) and, on Jetson Xavier NX reaching 43 FPS. The integrated CNN model ensures reliable classification of detected TS with an accuracy of 99.54%. This work highlights the trade-offs between precision, speed, and power consumption and provides practical guidance for selecting detection and recognition architectures for embedded ADAS applications.
Copyright (c) 2026 Imane Taouqi, Mohamed Lamane, Abdessamad Klilou, Assia Arsalane, Kebir Chaji https://creativecommons.org/licenses/by-sa/4.0
Recency, frequency, quality: novel feature from sentiment analysis for clustering and ranking in tourism big data analytics
Understanding tourist perceptions has been a key benefit of sentiment analysis in tourism data. However, its outcomes can be further utilized to gain insights into the characteristics of tourist attractions and hotels. This study aims to develop a new feature, called recency, frequency, quality (RFQ), derived from sentiment analysis results to cluster and rank tourist attractions and hotels in Bali. RFQ consists of three components: review recency, review frequency, and review quality. These dimensions reflect the recentness of reviews, the popularity based on the number of reviews, and the review quality measured by the ratio of positive to negative sentiment polarity. Using big data analytics through clustering and ranking, the study finds that the quality of tourist attractions and hotels is primarily concentrated in Badung and Gianyar regencies. More tourist attractions are found in the silver cluster than in the gold, indicating the need to enhance quality. In the hotel sector, the diamond cluster dominates among star-rated hotels, suggesting overall high quality. Budget hotels show fairly good quality, with most falling under the gold cluster.
Copyright (c) 2026 Ni Wayan Sumartini Saraswati, I Ketut Gede Darma Putra, Made Sudarma, I Made Sukarsa, I Gusti Ayu Agung Mas Aristamy https://creativecommons.org/licenses/by-sa/4.0
Machine learning techniques for analyzing student’s performance in Islamic Studies
Many learning institutions and organizations are currently faced with the acute burden of trying to forecast the academic performance of their students. This paper reflects the application of machine learning tools to discuss the potential and performance of students in Islamic Studies. The framework suggested in this paper, will start with the acquisition of the historical data of the students in the input dataset. First, the forward selection wrapper method is used to select the most meaningful features thus eliminating the redundant qualities in the set of student data. Three types of classifiers are then used to create a classification model based on fuzzy support vector machines (SVM), K-nearest neighbors (K-NN), and Naive Bayes. In such a methodological approach, academic performance is predicted and results measured according to certain criteria. According to results of the experiment, it is noted that feature selection-fuzzy support vector machine (FS-fuzzy SVM) has an excellent accuracy of 99.9% with a sensitivity of 98.50% and a specificity of 98.50% and it is therefore seen to be more effective in predicting the academic performance of students in Islamic Studies.
Copyright (c) 2026 Laili Ramadani, Eva Ardinal, Muhiddinur Kamal, Mahyudin Ritonga, Julhadi, Juliwis Kardi, Nuraiman https://creativecommons.org/licenses/by-sa/4.0
Performance evaluation of a novel blockchain consensus mechanism (PoDIPA) for decentralized microgrid networks
The increasing demand for sustainable and decentralized energy systems has driven the adoption of blockchain technology in microgrid networks. However, conventional consensus mechanisms, such as proof of work (PoW) and proof of stake (PoS), suffer from high energy consumption, limited adaptability, and fairness issues, which hinder their suitability for dynamic microgrid environments. This paper proposes a novel consensus mechanism, proof of dynamic influence and participation activity (PoDIPA), which integrates prosumers’ real-time participation activity and historical influence into the validator selection process. The proposed mechanism is evaluated through deterministic simulations and compared with PoW and PoS in terms of energy efficiency, transaction processing time, and security resilience. Simulation results demonstrate that PoDIPA significantly reduces average energy consumption and adapts more rapidly to network dynamics while maintaining security performance comparable to existing consensus mechanisms under majority attack scenarios. Although PoDIPA exhibits higher short-term variability due to its adaptive nature, the overall efficiency–stability trade-off remains favorable. These results indicate that PoDIPA is a promising consensus solution for supporting fair, energy-efficient, and decentralized energy trading in future microgrid systems.
Copyright (c) 2026 Sadly Syamsuddin, Salama Manjang, Muhammad Bachtiar Nappu, Ady Wahyudi Paundu https://creativecommons.org/licenses/by-sa/4.0
Exponential smoothing-based forecasting of self-similar internet of things traffic
The rapid growth of internet of things (IoT) devices generate highly variable and self-similar traffic patterns, creating challenges for maintaining quality of service (QoS) in modern telecommunication networks. Accurate short-term forecasting of such traffic is essential for efficient resource allocation, yet its fractal characteristics and long-range dependence complicate prediction. This study investigates the use of simple exponential smoothing for short-term forecasting of self-similar IoT traffic by evaluating three smoothing coefficients (a=0.1, 0.5, and 0.9). The Hurst exponent (H=0.5) confirms the presence of self-similarity in the observed traffic. Experimental results show that a=0.1 provides the highest prediction accuracy, achieving a mean absolute percentage error (MAPE) of 25.82% when forecasting traffic values within a 32-minute horizon. The method effectively captures underlying trends while reducing noise sensitivity. These findings demonstrate that exponential smoothing offers a lightweight, interpretable, and practical solution for real-time IoT traffic forecasting, supporting dynamic network load management under highly variable traffic conditions.
Copyright (c) 2026 Almira Mukhamejanova, Katipa Chezhimbayeva, Samal Kaliyeva, Eleonora Lechshinskaya, ?umussay Tumanbayeva, Yuliya Garmashova, Tolganay Abisheva, Inkar Zhumay https://creativecommons.org/licenses/by-sa/4.0
Combined analysis of the importance of factors in agricultural process management tasks
The article presents a combined approach for analyzing the significance of factors in the agro-industrial sector using Shapley additive explanations (SHAP), simple combination, and principal component analysis (PCA)+combination methods. The study addresses the pressing need for efficient agricultural resource management under constrained and changing climatic conditions. The proposed methodology evaluates the impact of various factors on key performance indicators such as yield, income, and operating costs. SHAP analysis identified critical determinants, with "Land Area (ha)" contributing significantly to "Market Capacity" (59.5%) and "Sales Revenue" (57.2%), highlighting the importance of production scale. The simple combination method, integrating gradient boosting (GB), mutual information (MI), and recursive feature elimination (RFE) with Lasso, revealed a more balanced factor distribution, assigning 14.5% to "Land Area" and 12.8% and 10.7% to “Seed Use” and “Fertilizer Cost,” respectively. The PCA+combination method emphasized global trends, identifying "Yield per Hectare" (22.5%) and "Field Size" (11.5%) as key contributors to variance. This integrative approach captures localized effects and global interdependencies, offering comprehensive data interpretations. The findings are instrumental in optimizing resource management and strategic planning and enhancing agricultural production efficiency.
Copyright (c) 2026 Gulzira Abdikerimova, Moldir Yessenova, Aizhan Zharkimbekova, Zhanar Beldeubayeva, Aigulim Bayegizova, Nurgul Uzakkyzy, Ainagul Alimagambetova, Gulden Murzabekova https://creativecommons.org/licenses/by-sa/4.0
Analytical broadband impedance matching using modified approximating functions with embedded transmission zeros
This paper proposes a modified approximating function (MAF)-based analytical method for broadband impedance matching in radio-electronic systems. Unlike traditional Chebyshev and Butterworth approaches, which rely on fixed pole distributions and predefined amplitude responses, the proposed method analytically embeds load-specific transmission zeros directly into the approximation function. This modification enables more accurate reconstruction of frequency-dependent impedance behavior without increasing the network order or circuit complexity. The method establishes a unified analytical synthesis framework linking impedance modeling, ladder-network realization, and constrained optimization. Validation was performed over the 1–10 GHz band using numerical simulations, Monte Carlo tolerance analysis, and prototype measurements. Compared with classical Chebyshev and Butterworth designs, the MAF-based approach achieves a 15–25% reduction in maximum reflection coefficient, a 30–40% decrease in optimization iterations, and improved robustness, with reflection variations remaining within 2% under ±10% parameter deviations. The results confirm that the proposed method provides superior analytical flexibility, improved matching accuracy, and reduced computational effort, making it suitable for automated broadband radio frequency (RF) design applications.
Copyright (c) 2026 Assel Yerzhan, Zhanat Manbetova, Gulzada Mussapirova, Gayni Karnakova, Almira Mukhamejanova, Manat Imankul, Zhanybek Kaliyev, Nagima Bakirova https://creativecommons.org/licenses/by-sa/4.0
Optimized chaos based encryption-decryption of multimedia data
These days, a major concern is the security of multimedia data exchange over the internet. Compared to conventional methods, chaos-based encryption has proven increasing role and superiority in current multimedia cryptography. This research proposes an innovative multimedia encryption technique utilizing optimized chaotic systems and simple rearrangement operation. Data blocking will be used to optimize the chaotic model and to reduce the key generation time. To increase the private key length and to raise the level of data security the presented method will be implemented in multiple rounds, where each round will be treated as an independent task and the selected sequence of rounds to be executed depends on the user wish. The method can be efficiently used to cipher-decipher short messages, images, and digital speech signal, and it will use variable length of private key, variable block size and variable number of rounds. The output of simulation confirms effectiveness of proposed technique in giving strong security, and good speed up in ciphering and deciphering process comparing with other existing standard and chaotic based methods. The proposed technique shows high resistance to attacks while the quality of encrypted/decrypted digital data remains acceptable.
Copyright (c) 2026 Rashad J. Rasras, Mutaz Rasmi Abu Sara, Jihad Nader, Ziad Alqadi https://creativecommons.org/licenses/by-sa/4.0
The role of technology acceptance model in evaluating educational games for higher education
This systematic review investigates the application of the technology acceptance model (TAM) in evaluating the acceptance of educational games within higher education. Educational games have become prominent tools for enhancing learning outcomes through interactive and engaging experiences. Guided by TAM, this review analyzes how key constructs (perceived ease of use (PEOU), perceived usefulness (PU), attitude toward use (ATU), behavioral intention (BI), and perceived enjoyment (PE) influence students’ acceptance of educational games. Using preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, relevant studies published between 2020 and 2024 were systematically identified from Scopus and Web of Science (WoS) databases, with 20 empirical studies meeting the inclusion criteria. The synthesis reveals that PU and PEOU are the most significant predictors of acceptance, while hedonic factors such as enjoyment and flow enhance engagement and sustained use. Key challenges include technical limitations, cultural misalignment, inconsistent TAM extensions, and a lack of longitudinal evidence. The review highlights the need for context-aware and inclusive design approaches that integrate both cognitive and affective factors. Overall, the findings position TAM not only as an evaluative framework but also as a design-oriented model for developing effective, engaging, and pedagogically grounded educational games in higher education.
Copyright (c) 2026 Mohamad Firdaus Che Abdul Rani, Nor Hafizah Adnan, Ahmad Zamri Mansor, Melor Md. Yunus https://creativecommons.org/licenses/by-sa/4.0
Anonymization techniques for privacy-preserving data publishing: a comprehensive survey
Data-driven innovation in healthcare, finance, and smart cities increasingly depends on sharing rich datasets, but such sharing raises severe privacy risks and regulatory challenges. Privacy-preserving data publishing (PPDP) seeks to release useful data while preventing re-identification and inference attacks. This paper presents a comprehensive survey of anonymization techniques for PPDP, spanning traditional models (k-anonymity, l-diversity, t-closeness, and pseudonymization) and modern approaches (differential privacy (DP), synthetic data generation, federated learning (FL), secure multi-party computation (SMPC), homomorphic encryption (HE), blockchain-based schemes, and quantum-safe cryptography). We propose a taxonomy that organizes these methods by privacy guarantees, data utility, scalability, and computational cost, and we provide a comparative analysis of their strengths, limitations, and typical application domains. The survey also reviews legal and ethical frameworks, with particular attention to general data protection regulation GDPR, health insurance portability and accountability act (HIPAA), and related regulations, and highlights emerging trends such as artificial intelligence (AI-driven) anonymization and privacy risks from large language models (LLMs) and quantum computing. Overall, the study shows that various techniques fail to protect all data scenarios so we need to create hybrid systems which will provide explainable anonymization solutions at different scales to protect privacy and maintain useful data utility.
Copyright (c) 2026 Sami Smadi, Nader Abdel Karim, Hasan Kanaker, Waleed K. Abdulraheem https://creativecommons.org/licenses/by-sa/4.0
Markerless versus marker-based augmented reality: comparative usability insights from a museum context
Augmented reality (AR) has become a pivotal tool for enhancing museum engagement through interactive experiences. This study developed and evaluated an Android-based AR application for the Popa Eyato Archaeological Museum, comparing marker-based (Vuforia), and markerless (EasyAR) tracking methods. While previous studies often focus on technical development, this research provides empirical insights into usability within the Southeast Asian Museum context. Using a mixed-methods approach with 35 participants, the application was evaluated using the system usability scale (SUS) and handheld augmented reality usability scale (HARUS). Results show that the application achieved an average SUS score of 78, classified as “Good” and “Acceptable”. The comparative analysis reveals that while marker-based AR offers higher stability, markerless AR provides greater flexibility for spatial exploration. However, environmental factors like museum lighting significantly impact markerless tracking performance. These findings offer practical recommendations for museum curators and AR designers in selecting appropriate tracking technologies based on infrastructural constraints and visitor demographics. The study concludes that balancing technical stability with user mobility is essential for optimizing digital heritage preservation and educational engagement in regional museums.
Copyright (c) 2026 Lanto Ningrayati Amali, Muhammad Rifai Katili, Arif Dwinanto, Rasim https://creativecommons.org/licenses/by-sa/4.0
Integrating multi-criteria decision making and public sentiment analysis for sustainable urban green space planning
Sustainable planning of green open spaces (GOS) requires decision-making models that combine expert evaluation with public input. This study proposes a novel hybrid framework that integrates multi-criteria group decision making (MCGDM) with public sentiment analysis to support community-based and data-driven urban planning. The workflow consists of evaluating 25 community-proposed GOS locations using stepwise weight assessment ratio analysis (SWARA) for criteria weighting and MABAC-BORDA for multi-criteria ranking, resulting in 11 feasible alternatives. To incorporate community perspectives, a term frequency-inverse document frequency-support vector machine (TF-IDF–SVM) classifier was applied to 1500 public comments, where SVM achieved the highest accuracy (0.80–0.96). The integrated approach improves ranking stability, reduces decision ambiguity, and strengthens alignment between expert judgment and community sentiment. This study contributes a transparent, participatory decision-support model that unifies MCGDM and sentiment analysis to enhance the effectiveness of sustainable GOS planning.
Copyright (c) 2026 Muhammad Syafaat S. Kuba, Muhammad Faisal, Nurnawaty, Titik Kwaha Abd Rahman, Andi Makbul Syamsur, Muhyiddin AM Hayat, Rizki Yusliana Bakti https://creativecommons.org/licenses/by-sa/4.0
Gamified learning in virtual reality: a scoping review (2010-2025)
This paper provides a scoping review of gamified learning in virtual reality (VR) from 2010-2025, which combines trends from various disciplines, game types, and learning approaches. This paper differs from previous ones, which mainly focus on bibliometric results, by critically examining 108 publications to determine the dominant types of VR games, their correlation with learning outcomes, and the impact on the learning process. This paper follows the preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (PRISMA-ScR) protocol. The findings suggest that simulation, role-playing, and problem-solving VR games are the most common, particularly in healthcare and science, technology, engineering, and mathematics (STEM) fields, improving engagement, retention, and skill development. The use of VR games in other fields and the needs of neurodiverse or physically disabled learners have yet to be explored. The review discusses the use of big data and cloud computing for VR deployment and adaption, and the significance of low and no-code technologies for educators developing VR without programming knowledge. Through the synthesis of patterns, research gaps, and cross-disciplinary challenges, this study provides a roadmap for VR-based gamification research, with emphasis on inclusivity, ethical considerations, long-term learning effectiveness beyond novelty effects, and sustainable educational integration.
Copyright (c) 2026 Fuad Manna Fataftah, Siti Hazyanti Mohd Hashim https://creativecommons.org/licenses/by-sa/4.0
Thermal mode modeling using neural network technologies and the finite element method
This study presents the analysis and modeling of the thermal regime of a furnace lining at an industrial copper smelting facility using a combined approach based on neural network (NN) technologies and the finite element method (FEM). Experimental temperature data were collected from a laboratory setup equipped with three thermocouples (TP-2488/1 and TCRosemount 0065), with a sampling frequency of 1 Hz over a total duration of 5 hours, resulting in 18,000 measurement points. The measurement uncertainty of the thermocouples did not exceed ±1.5 °C. These data were used both for model development and for validating the numerical FEM simulations. A feedforward neural network was trained using 70% of the dataset, while 15% and 15% were used for validation and testing, respectively. The prediction error of the neural network remained within 3% with a 95% confidence interval of [2.6%, 3.4%]. The results show that the proposed hybrid approach improves temperature prediction accuracy and reduces static control error by 15% when combined with a proportional-integral controller. The methodology demonstrates significant potential for improving thermal process stability and reducing energy consumption in high-temperature metallurgical systems.
Copyright (c) 2026 Nazarbek Mussabekov, Irbulat Utepbergenov, Zhanybek Kaliyev, Zhazira Issayeva, Galiya Ybytayeva, Gulbakyt Ansabekova, Gayni Karnakova, Karlygash Butabaeva https://creativecommons.org/licenses/by-sa/4.0
Optimized resource management system for precision farming using cluster based wireless sensor network
The deployment of wireless sensor networks (WSNs) in precision agriculture is essentially guarded by the energy limitations of sensor nodes, which can impede long-term, autonomous field monitoring. This paper introduces a hierarchical, cluster-based resource management system intended to prevail over these limitations. The central part of our approach is a dynamic clustering algorithm that intelligently groups sensor nodes to balance energy consumption and streamline data transmission across the network. Within each cluster, intra-cluster data aggregation is performed to fuse raw sensor data—encompassing critical parameters like soil temperature, humidity, and pH—thereby minimizing redundant packet transmissions. This aggregated data drives a predictive control model that automates decision-making for the precise actuation of irrigation and fertigation systems. Empirical validation demonstrates that our methodology achieves a dual objective: it significantly extends the network's operational lifespan by enhancing energy efficiency and throughput while reducing latency. Concurrently, this optimized resource allocation directly correlates with increased crop yield, presenting a robust and scalable framework for sustainable, high-efficiency agriculture, particularly in resource-intensive environments like urban and vertical farms.
Copyright (c) 2026 Sandeep B. Kallur, Kishan Singh https://creativecommons.org/licenses/by-sa/4.0
Ransomware and artificial intelligence: a comprehensive systematic review of reviews
This study provides a comprehensive synthesis of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL) in ransomware defense. Using a “review of reviews” methodology based on the PRISMA, this paper gathers insights on how AI is transforming ransomware detection, prevention, and mitigation strategies in the past five years (2020-2024). The findings highlight the effectiveness of hybrid models, which combine multiple analysis techniques such as code inspection (static analysis) and behavior monitoring during execution (dynamic analysis). The study also explores anomaly detection and early warning mechanisms before encryption that tackle ransomware’s growing complexity. It also examines key challenges in ransomware defense, such as techniques designed to deceive AI driven detection, and the lack of strong and diverse datasets. It highlights AI’s role in early detection and real-time response systems, enhancing scalability and resilience. With the systematic review of reviews approach, the contributions of this study are systematically consolidating research insights from multiple review articles, identifying effective AI models, and bridging theory with practice to foster collaboration among academia, industry, and policymakers. Future research directions are anticipated and practical recommendations for cybersecurity practitioners are provided. Finally, it presents a roadmap for advancing AI-driven countermeasures, for the protection of key systems and infrastructures against evolving ransomware threats.
Copyright (c) 2026 Therdpong Daengsi, Phisit Pornpongtechavanich, Paradorn Boonpoor, Kathawut Wattanachukul, Korn Puangnak, Kritphon Phanrattanachai, Pongpisit Wuttidittachotti, Paramate Horkaew https://creativecommons.org/licenses/by-sa/4.0
Hybrid analytical framework for evaluating socio-economic factors in regional development
This study aims to develop and validate a hybrid analytical framework for evaluating the influence of socio-economic factors on regional development. The framework combines correlation analysis, principal component analysis (PCA), and fuzzy inference modeling into a unified approach, applied to 2023 data from the city of Taraz, Kazakhstan, covering 16 socio-economic indicators across demographic, economic, social, and industrial domains. The findings reveal that investments in fixed assets (r=0.8963 and q=0.000010), average monthly salary (r=0.8907 and q=0.000010), and retail trade (r=0.8885 and q=0.000010) exert the strongest positive influence, while migration balance and manufacturing show weak or negative effects. The results demonstrate that the hybrid model offers more comprehensive insights compared to single-method approaches, validating its effectiveness in capturing complex and uncertain dependencies. Practically, the model provides policymakers with a robust decision-support tool for identifying priority areas, designing targeted strategies, and ensuring sustainable regional growth, with adaptability to other regions and datasets.
Copyright (c) 2026 Ayman Akynbekova, Raikhan Muratkhan, Zhanar Lamasheva, Ayagoz Mukhanova, Gulbakhar Yussupova, Serik Eslyamov, Saya Santeyeva, Alfiya Abdrakhmanova https://creativecommons.org/licenses/by-sa/4.0
Cloud security: an upcoming development in hybrid automated storage protection systems
The need for cloud storage is growing daily, despite its apparent reliability and scalability. Because of the complexity of data security, including privacy and confidentiality, it is difficult to store and manage user data remotely. An untrustworthy insider could be the cloud third party responsible for managing the data security issues occurring within the cloud services. An automated hybrid cryptographic algorithm for cloud data storage is provided by the current research to improve data protection and guarantee information confidentiality without any disruption from cloud third parties. Two encryption methods that are employed to attain high performance and efficiency are enhanced automated concurrent encryption for data blocks (CEDB) and improved automated asynchronous encryption for data streams (AEDS). By converting the fixed S-box in the traditional AES to a modifying S-box using a ternary Galois field (GF) and complementary ternary GF, we present a novel encryption technique in enhanced automated concurrent encryption. Furthermore, the matrix utilized for the AES mix column approach is converted into a dynamic matrix using the ternary GF and complementary ternary GF. This cryptographic approach aims at providing cloud data security, ensuring confidentiality, and integrity for the user’s information even in compromised situations.
Copyright (c) 2026 Srividya Bharadwaja VenkataSubbu, Smitha Sasi, Meharunnisa Sakkar Sab Pakeer Saheb, Harikeerthan Mysore Keshava Rao, Soumya Seetharamaiah https://creativecommons.org/licenses/by-sa/4.0
Bibliometrics on the use of remote sensing and machine learning in crop classification
This study presents a bibliometric analysis of global research on crop classification using remote sensing and machine learning (ML), a field critical to advancing precision agriculture. A systematic search in Scopus identified 2,122 peer-reviewed articles published between 2014 and 2023. The analysis employed VOSviewer and the Bibliometrix package in R to assess publication trends, citation impact, and keyword co-occurrence networks. Results reveal a marked increase in scientific production after 2017, coinciding with the availability of high-resolution satellite imagery and the adoption of deep learning algorithms, particularly convolutional neural networks (CNNs). China emerged as the leading contributor, followed by the United States and India, reflecting strong investments in agricultural modernization and remote sensing infrastructure. Thematic mapping highlights both traditional research areas, such as vegetation indices and land cover classification, and emerging themes, including AI-supported algorithms and food security. Despite this growth, disparities persist, with most countries contributing fewer than 100 publications, underscoring the need to promote participation in underrepresented regions. Findings demonstrate the field’s rapid evolution, emphasize the integration of AI-driven methods in crop monitoring, and suggest future directions combining remote sensing, ML, and internet of things (IoT) technologies to address global challenges in food security and sustainable agricultural management.
Copyright (c) 2026 Andrea del Pilar Sánchez-Chavez, Vladimir Henao-Cespedes, Yeison Alberto Garcés-Gómez https://creativecommons.org/licenses/by-sa/4.0