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29,196 Article Results

Optimal design, decoding, and minimum distance analysis of Goppa codes using heuristic method

10.11591/ijece.v15i6.pp5411-5421
Bouchaib Aylaj , Said Nouh , Mostafa Belkasmi
Error-correcting codes are crucial to ensure data reliability in communication systems often affected by transmission noise. Building on previous successful applications of our heuristic method degenerate quantum simulated annealing (DQSA) to Bose–Chaudhuri–Hocquenghem (BCH) and quadratic residue (QR) codes. This paper proposes two algorithms designed to address two coding problems for Goppa codes. DQSA-dmin computes the minimum distance (dmin) while DQSA-Dec, serves as a hard decoder optimized for additive white gaussian noise (AWGN) channels. We validate DQSA-dmin comparing its computed minimum distances with theoretical estimates for algebraically constructed Goppa codes, showing accuracy and efficiency. DQSA-dmin further used to find the optimal Goppa codes that reach the lower bound of dmin for linear codes known in the literature and stored in Marcus Grassl's online database. Indeed, we discovered 12 Goppa codes reaching this lower bound. For DQSA-Dec, experimental results show that it obtains a bit error rate (BER) of 10-5 when SNR=7.5 for codes with lengths less than 65, which is very interesting for a hard decoder. Additionally, a comparison with the Paterson algebraic decoder specific to this code family shows that DQSA-Dec outperforms it with a 0.6 dB coding gain at BER=10-4. These findings highlight the effectiveness of DQSA-based algorithms in designing and decoding Goppa codes.
Volume: 15
Issue: 6
Page: 5411-5421
Publish at: 2025-12-01

Flow-guided long short-term memory with adaptive directional learning for robust distributed denial of service attack detection in software-defined networking

10.11591/ijece.v15i6.pp5484-5496
Huda Mohammed Ibadi , Asghar A. Asgharian Sardroud
A software-defined networking (SDN) architecture is designed to improve network agility by decoupling the control and data planes, but while much more flexible, also makes networks more vulnerable to threats, such as distributed denial of service (DDoS) attacks. In this study we present a novel detection model, the flow-guided long short-term memory (LSTM) network with adaptive directional learning (ADL), for the mitigation of DDoS attacks in software defined networking (SDN) environments. While the methodology is based on a flow direction algorithm (FDA), which analyzes traffic patterns and detects anomalies from directional flow behavior. The proposed method integrates FDA in LSTM-based threat detection frameworks within internet of things (IoT) networks, thereby yielding enhanced detection accuracy, as well as a real-time security threat response. The experimental evaluation on two benchmark datasets, namely the InSDN dataset and a real-time dataset utilizing a Mininet and POX controller setup, shows that a detection rate of 99.85% and 99.72%, respectively, thereby showcasing the proposed model’s ability to differentiate between legitimate and malicious network traffic.
Volume: 15
Issue: 6
Page: 5484-5496
Publish at: 2025-12-01

Improving time-domain winner-take-all circuit for neuromorphic computing systems

10.11591/ijece.v15i6.pp5173-5182
Son Ngoc Truong , Tu Tien Ngo
With the rapid advancements of information processing systems, winner- take-all (WTA) circuits have emerged as essential components in a wide range of cognitive functions and decision-making applications. Neuromorphic computing systems, inspired by the biological brain, utilize WTA circuits as selective mechanisms that identify and retain the strongest signal while suppressing all others. In this study, we present an effective time-domain WTA circuit with optimized multiple-input NOT AND (NAND) gate and delay circuit for neuromorphic computing applications. The circuit is evaluated using sinusoidal current inputs with varying phase delays, which successfully demonstrating precise winner selection. When applied to neuromorphic image recognition task, the enhanced time-domain WTA achieves an improvement of 0.2% in precision while significantly reducing power consumption, yielding a low figure of merit (FoM) of 0.03 µW/MHz, compared to the previous study with FoM of 0.25 µW/MHz. The optimized WTA circuit is highly promising for large-scale neuromorphic applications.
Volume: 15
Issue: 6
Page: 5173-5182
Publish at: 2025-12-01

Combination of rough set and cosine similarity approaches in student graduation prediction

10.11591/ijece.v15i6.pp6001-6011
Ratna Yulika Go , Tinuk Andriyanti Asianto , Dewi Setiowati , Ranny Meilisa , Christine Cecylia Munthe , R. Hendra Kusumawardhana
Higher education institutions must deliver high-quality education that produces graduates who are knowledgeable, skilled, creative, and competitive. In this system, students are a vital asset, and their timely graduation rate is an important factor to consider. In the department of computer science, a challenge arises in distinguishing between students who graduate on time and those who do not. With a low on-time graduation rate of just 1.90% out of 158 graduates, this issue could negatively affect the institution's accreditation evaluation. This research employs the Case-Based Reasoning method, enhanced with an indexing process using rough sets and a prediction process utilizing cosine similarity. The testing, conducted using k-fold validation with 60%, 70%, and 80% of the data, produced average accuracy rates of 64.2%, 66.3%, and 65.6%, respectively. The test results indicate that the highest average accuracy of 66.3% was achieved with 70% of the cases.
Volume: 15
Issue: 6
Page: 6001-6011
Publish at: 2025-12-01

Fine-tuning pre-trained deep learning models for crop prediction using soil conditions in smart agriculture

10.11591/ijece.v15i6.pp5667-5678
Praveen Pawaskar , Yogish H K , Pakruddin B , Deepa Yogish
Agriculture is the backbone of the Indian economy, with soil quality playing a crucial role in crop productivity. Farmers often struggle to select the appropriate crop based on soil type, leading to significant losses in yield and productivity. To address this challenge, deep learning techniques provide an efficient solution for automated soil classification. In this study, a dataset of 781 original soil images, including clay soil, alluvial soil, red soil, and black soil, was collected from Kaggle and augmented to 3,702 images to enhance model training. Several deep learning models were employed for soil classification, including pretrained architectures and a proposed model, SoilNet. Experimental results demonstrated that DenseNet201 achieved 100% validation accuracy, ResNet50V2 98%, VGG16 99%, MobileNetV2 99%, and the proposed SoilNet model 97%. The proposed approach outperformed existing work by surpassing 95% accuracy. Additionally, model performance was evaluated using precision, recall, and F1-score, ensuring a comprehensive analysis of classification effectiveness. These findings highlight the potential of deep learning in improving soil classification accuracy, aiding farmers in making informed crop selection decisions.
Volume: 15
Issue: 6
Page: 5667-5678
Publish at: 2025-12-01

Computational modelling under uncertainty: statistical mean approach to optimize fuzzy multi-objective linear programming problem with trapezoidal numbers

10.11591/ijece.v15i6.pp5708-5716
Arti Shrivastava , Bharti Saxena , Ramakant Bhardwaj , Aditya Ghosh , Satyendra Narayan
This study presents a comprehensive approach to solving fuzzy multi-objective linear programming problems (FMOLPP) under uncertainty using trapezoidal fuzzy numbers. The authors propose a novel integration of Yager’s ranking method, the Big-M optimization technique, and Chandra Sen’s statistical mean methods to effectively convert fuzzy objectives into crisp values and optimize them. The methodology allows for managing multiple fuzzy objectives by ranking and aggregating them using various statistical means such as arithmetic, geometric, quadratic, harmonic, and Heronian averages. The model is implemented using TORA software and demonstrated through a detailed numerical example. The results validate the robustness and practicality of the proposed approach, showcasing consistent optimal solutions across all statistical methods. This research significantly enhances decision-making processes in uncertain environments by offering a structured, computationally efficient solution strategy for complex real-world optimization problems.
Volume: 15
Issue: 6
Page: 5708-5716
Publish at: 2025-12-01

Power loss reduction and stability enhancement of power system through transmission network reconfiguration

10.11591/ijece.v15i6.pp6012-6026
Titus Terwase Akor , Theophilu Chukwudolue Madueme , Chibuike Peter Ohanu , Tole Sutikno
The power network faces several challenges as electricity usage rises and the frequency of partial and total grid disruptions is of great concern. This paper addresses the problem of voltage instability and high-power losses in transmission network, which threatens the stability of the power grid. The MATLAB R2023a/MATPOWER 5.0 is used to develop a model and analyze using the Newton-Raphson load flow method. The analysis reveals a marginal voltage violation at Bus 13 (below 0.95 p.u.). To enhance stability and efficiency, the network was reconfigured using a hybrid whale algorithm and particle swarm optimization (WAPSO) approach, incorporating new transmission lines (5-8 and 13-14) to improve connectivity and reduce congestion. The reconfiguration reduced active power losses by 29.5% (from 36.013 to 25.371 MW) and reactive power losses by 29.8% (from 301.30 to 211.59 MVAr). The system demonstrated first swing stability, with rotor angles remaining below π/2 (1.5669 rad maximum deviation) and fault clearance within the critical clearing time (0.2 s). Optimized exciter gains and a damping coefficient of 1.5 p.u. ensured effective oscillation suppression and stable generator voltages at 1.05 p.u. The hybrid WAPSO approach proved effective in optimizing voltage and rotor angle stability, enabling the network to meet a 24.086 p.u. load demand while enhancing overall grid reliability.
Volume: 15
Issue: 6
Page: 6012-6026
Publish at: 2025-12-01

A systematic review of heuristic and meta-heuristic methods for dynamic task scheduling in fog computing environments

10.11591/ijece.v15i6.pp5986-6000
Hamed Talhouni , Noraida Haji Ali , Farizah Yunus , Saleh Atiewi , Yazrina Yahya
The distributed fog node network and variable workloads make task distribution difficult in fog computing. Optimizing computing resources for dynamic workloads with heuristic and metaheuristic algorithms has shown potential. To address changing workloads, these algorithms enable real-time decision-making. This systematic review examines heuristic, meta-heuristic, and real-time dynamic job scheduling strategies in fog computing. Static methods like heuristic and meta-heuristic algorithms can help modify dynamic task scheduling in fog computing situations. This paper covers a current study area that stresses real-time approaches, meta-heuristics, and fog computing environments' dynamic nature. It also helps build reliable and scalable fog computing systems by spotting dynamic task scheduling trends, patterns, and issues. This study summarizes and analyzes the latest fog computing research on task-scheduling algorithms and their pros and cons to adequately address their issues. Fog computing task scheduling strategies are detailed and classified using a technical taxonomy. This work promises to improve system performance, resource utilization, and fog computing settings. The work also identifies fog computing job scheduling innovations and improvements. It reveals the strengths and weaknesses of present techniques, paving the way for fog computing research to address unresolved difficulties and anticipate future challenges.
Volume: 15
Issue: 6
Page: 5986-6000
Publish at: 2025-12-01

Enhanced ankle physiotherapy robot with electromyography - triggered ankle velocity control

10.11591/ijece.v15i6.pp5314-5326
Dimas Adiputra , Radithya Anjar Nismara , Muhammad Rafli Ramadhan Lubis , Nur Aliffah Rizkianingtyas , Kensora Bintang Panji Satrio , Rangga Roospratama Arif , Annisa Salsabila
Previous ankle physiotherapy robots, called picobot rely on predefined trajectories continuous passive movement without considering patient intent, limiting the encouragement of user-intent motion. This study then integrates electromyography (EMG) signals as triggers into picobot with an ankle velocity-based control system. The upgraded robot activates movement in specific gait phases based on muscle activity, synchronizing therapy with the patient’s intent. Functionality test on 7 young male healthy subjects investigates leg muscles, such as Tibialis Anterior, Soleus, and Gastrocnemius muscles for the most significantly contribute to ankle movements. Then, the muscle is tested to trigger picobot movements. Functionality tests revealed the Tibialis muscle significantly contributes to gait phases 2, the Soleus is prominent in phases 3 and 4, and gastrocnemius is active on phase 1. The robot successfully performs plantarflexion when EMG signals exceed a 1.58 V threshold, reaching a target position of -0.11 rad at a constant velocity of -0.62 rad/s. These findings establish a foundation for future trials since patient testing has not yet been conducted. By promoting active participation, this innovation has the potential to enhance rehabilitation outcomes. Incorporating user-intent triggers may accelerate recovery and improve healthcare accessibility in Indonesia, offering a significant advancement in physiotherapy technologies.
Volume: 15
Issue: 6
Page: 5314-5326
Publish at: 2025-12-01

Machine learning model for accurate prediction of coronary artery disease by incorporating error reduction methodologies

10.11591/ijece.v15i6.pp5655-5666
Santhosh Gupta Dogiparthi , Jayanthi K. , Ajith Ananthakrishna Pillai , K. Nakkeeran
Coronary artery disease (CAD) remains a leading cause of mortality worldwide, with an especially high burden in developing countries such as India. In light of increasing patient loads and limited medical resources, there is an urgent need for accurate and reliable diagnostic support systems. This study introduces a machine learning (ML) framework that aims to enhance CAD prediction accuracy by specifically addressing the reduction of false negatives (FN), which are critical in medical diagnostics. Utilizing a stacked ensemble model comprising five base classifiers and a meta-classifier, the framework integrates cost-sensitive learning, classification threshold tuning, engineered features, and manual weighting strategies. The model was developed using a clinically acquired dataset from the Jawaharlal Institute of postgraduate medical education and research (JIPMER), consisting of 428 patient records with 36 original features. Evaluation metrics show that the proposed model achieved an accuracy of 92.19%, sensitivity of 98%, and an F1-score of 95.15%. These improvements are significant in a clinical context, potentially reducing missed diagnoses and improving patient outcomes. The model is intended for deployment in cardiology outpatient settings and demonstrates a scalable, adaptable approach to medical diagnostics.
Volume: 15
Issue: 6
Page: 5655-5666
Publish at: 2025-12-01

Hardware efficient multiplier design for deep learning processing unit

10.11591/ijece.v15i6.pp5205-5214
Jean Shilpa V. , Anitha R. , Anusooya S. , Jawahar P. K. , Nithesh E. , Sairamsiva S. , Syed Rahaman K.
Deep learning models increasing computational requirements have increased the demand for specialized hardware architectures that can provide high performance while using less energy. Because of their high-power consumption, low throughput, and incapacity to handle real-time processing demands, general-purpose processors frequently fall short. In order to overcome these obstacles, this work introduces a hardware-efficient multiplier design for deep learning processing unit (DPU). To improve performance and energy efficiency, the suggested architecture combines low-power arithmetic circuits, parallel processing units, and optimized dataflow mechanisms. Neural network core operations, such as matrix computations and activation functions, are performed by dedicated hardware blocks. By minimizing data movement, an effective on-chip memory hierarchy lowers latency and power consumption. According to simulation results using industry-standard very large-scale integration (VLSI) tools, compared to traditional processors, there is a 25% decrease in latency, a 40% increase in computational throughput, and a 30% reduction in power consumption. Architecture’s scalability and modularity guarantee compatibility with a variety of deep learning applications, such as edge computing, autonomous systems, and internet of things devices.
Volume: 15
Issue: 6
Page: 5205-5214
Publish at: 2025-12-01

Design and development of home-grown biometric fingerprint device and software for attendance and access control

10.11591/ijece.v15i6.pp5616-5632
Jumoke Soyemi , Ogunyinka Olawale Ige , Olugbenga Babajide Soyemi , Ajibodu Franklin Ademola , Adaramola Ojo Jayeoba , Afolayan Andrew Olumide , Habeeb O. Amode , Mukail Aremu Akinde
This study details the design, development, and deployment of an Android-based Biometric Fingerprint system tailored for institutional access control, attendance tracking, exam monitoring, and staff management. Developed collaboratively by the Innovation Centre and departments across engineering and information and communication technology (ICT), the system integrates custom hardware and software. Hardware includes fingerprint sensors connected to an ATMEGA8 microcontroller and Android interfaces for portability. The software uses modular architecture, comprising a Kotlin-based mobile app with Jetpack Compose, a Laravel-powered web admin panel, and a secure backend API hosted on a virtual private server (VPS). Fingerprint data is safely stored using base64 encoding, enabling accurate user authentication and real-time tracking. A functional prototype was built, tested, and refined, with 95 units deployed in a pilot phase. The system supports multiple fingerprint profiles, secure data handling, and integration with existing institutional platforms. Emphasizing customization, modularity, and adherence to ICT policies, the research also serves as a training tool for staff and students, enhancing operational efficiency and supporting local technology development. Performance evaluation showed a FAR of 0.5%, FRR of 1.2%, and an average authentication time of 2.3 seconds. Post-deployment, student attendance increased by 15%, fee compliance by 10%, and 89% of users rated the system as easy to use. This work demonstrates effective hardware-software co-design for scalable biometric authentication in educational settings.
Volume: 15
Issue: 6
Page: 5616-5632
Publish at: 2025-12-01

Detection of breast cancer with ensemble learning using magnetic resonance imaging

10.11591/ijece.v15i6.pp5371-5379
Swati Nadkarni , Kevin Noronha
Despite notable progress in medicine along with technology, the deaths due to breast cancer are increasing steadily. This paper proposes a framework to aid the early detection of lesions in breast with magnetic resonance imaging (MRI). The work has been carried out using diffusion weighted imaging (DWI) and dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI). Data augmentation has been incorporated to enlarge the data set collected from a reputed hospital. Deep learning has been implemented using the ensemble of convolutional neural network (CNN). Amongst the individual CNN models, the you only look once (YOLO) CNN yielded the highest performance with an accuracy of 93.4%, sensitivity of 93.44%, specificity of 93.33%, and F1-score of 93.44%. Using Hungarian optimization, appropriate selection of individual CNN architectures to form the ensemble of CNN was possible. The ensemble model enhanced performance with 95.87% accuracy, 95.08% sensitivity, 96.67% specificity, and F1-score of 95.87%.
Volume: 15
Issue: 6
Page: 5371-5379
Publish at: 2025-12-01

A new algorithm for quality-of-service improvement in mobile ad hoc networks

10.11591/ijece.v15i6.pp5466-5483
Hanafy M. Ali , Adel F. El-Kabbany , Yahia B. Hassan
The quality of service (QoS) in mobile ad hoc networks (MANETs) plays a crucial role in optimizing overall network resource utilization. MANET routing protocols, fundamental to QoS, demand adaptive and swift solutions for efficient path searching. In this context, our paper introduces a novel algorithm based on MANETs, employing a hybrid approach that combines ant colony optimization (ACO) with hybrid multipath quality of service ant (HMQAnt) routing protocols. Our algorithm emphasizes bandwidth optimization as a pivotal factor for providing effective paths. By incorporating bandwidth as a significant parameter in the MANETs algorithm, we aim to enhance its overall properties. The proposed routing protocol, focusing on bandwidth optimization, is anticipated to improve the delivery of total network traffic. Evaluation of the algorithm's performance is conducted through QoS metrics, which are overhead, end-to-end delay, and jitter, throughputs, utilizing a MATLAB simulator. Simulation results indicate that our proposed routing protocol holds a distinct advantage compared to ad hoc on-demand distance vector (AODV), destination- sequenced distance (DSDV), dynamic source routing (DSR), and hybrid ant colony optimization-based (ACO) routing protocol called (ANTMANET) algorithms.
Volume: 15
Issue: 6
Page: 5466-5483
Publish at: 2025-12-01

Improving electrical load forecasting by integrating a weighted forecast model with the artificial bee colony algorithm

10.11591/ijece.v15i6.pp5854-5862
Ani Shabri , Ruhaidah Samsudin
Nonlinear and seasonal fluctuations present significant challenges in predicting electricity load. To address this, a combination weighted forecast model (CWFM) based on individual prediction models is proposed. The artificial bee colony (ABC) algorithm is used to optimize the weighted coefficients. To evaluate the model’s performance, the novel CWFM and three benchmark models are applied to forecast electricity load in Malaysia and Thailand. Performance is assessed using mean absolute percentage error (MAPE) and root mean square error (RMSE). The experimental results indicate that the proposed combined model outperforms the single models, demonstrating improved accuracy and better capturing seasonal variations in electricity load. The ABC algorithm helps in finding the optimal combination of weights, ensuring that the model adapts effectively to different forecasting scenarios.
Volume: 15
Issue: 6
Page: 5854-5862
Publish at: 2025-12-01
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