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31,456 Article Results

Stability analysis and robust control of cyber-physical systems: integrating Jacobian linearization, Lyapunov methods, and linear quadratic regulator control via LMI techniques

10.11591/ijece.v15i6.pp5276-5285
Rachid Boutssaid , Abdeljabar Aboulkassim , Said Kririm , El Hanafi Arjdal , Youssef Moumani
Stability issues in cyber-physical systems (CPS) arise from the challenging effects of nonlinear dynamics relation to multi-input, multi-output systems. This research proposed a robust control framework that combines Jacobian linearization, Lyapunov stability analysis, and linear quadratic regulator (LQR) control via linear matrix inequalities (LMIs). The robust methodology does the following: it applies linearization on the dynamics of the CPS; it establishes the stability of the system using Lyapunov functions and LMIs; and it designs an LQR controller. The proposed framework was validated through a comparison between the behavior of a linearized and nonlinear model. The autonomous vehicle application showed: a settling time of 20 seconds; an overshoot of 3.8187%; and a steady-state error of 2.688×10⁻⁷. The proposed framework is robustly demonstrated and has applications to areas in automation and smart infrastructure. Future work includes optimizing the design of weighting matrices and developing adaptive control features.
Volume: 15
Issue: 6
Page: 5276-5285
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

Citizens’ electronic satisfaction factors in electronic government services: an empirical study from Kuwait

10.11591/ijece.v15i6.pp5690-5698
Abdullah Alshehab , Ali Alfayly , Naser Alazemi
This study investigates the dimensions of service quality provided by Kuwait’s “Sahel” electronic government (e-government) application and their impact on user satisfaction among citizens and residents. Adopting a quantitative methodology based on the modified electronic government quality (e-GovQual) model, data were collected from 1,064 respondents over four weeks, assessing user experiences across usability, reliability, responsiveness, security, and efficiency dimensions. Results indicate moderate overall satisfaction, with particularly high ratings for transparency and ease of use, yet notable concerns regarding trust and data security. Satisfaction with reliability and technical support was moderate, signaling areas for improvement. The study recommends enhancing the user interface for intuitive navigation, improving real-time data synchronization between governmental entities, providing efficient technical support, and strengthening security measures to build user trust. These recommendations are crucial for advancing Kuwait’s e-government effectiveness. Future research should explore causal relationships among service quality dimensions and incorporate technical assessments by information and communication technology (ICT) experts to further enhance user satisfaction.
Volume: 15
Issue: 6
Page: 5690-5698
Publish at: 2025-12-01

Explainable fault diagnosis using discrete grey wolf optimization algorithm for photovoltaic system

10.11591/ijece.v15i6.pp5286-5296
Slimani Hassina , Chouhal Ouahiba , Beddiaf Yassine , Mahdaoui Rafik , Haouassi Hichem , Hamdi Roumaissa
The present article introduces the discrete grey wolf optimization algorithm (DGWOA), a novel variant of the grey wolf optimizer (GWO). DGWOA integrates discrete optimization techniques with explainable artificial intelligence (XAI) methodologies. This approach aims to overcome limitations associated with traditional fault diagnosis methods, such as limited accuracy in identifying complex patterns and low interpretability. Furthermore, it mitigates early convergence problems commonly encountered in optimization algorithms and enhances adaptability to discrete classification challenges. The DGWOA algorithm is designed to generate interpretable classification rules for fault detection through a stochastic search strategy. The explainability provided by the model not only enhances decision-making transparency but also improves diagnostic efficiency and predictive accuracy. The proposed algorithm was evaluated using a photovoltaic system dataset and benchmarked against established rule-based classifiers. DGWOA consistently achieved a classification accuracy of 99.48% and a precision of 100%, demonstrating its effectiveness in enhancing fault detection. Moreover, the interpretability of the generated classification rules contributes to the generation of outcomes that are both actionable and comprehensible to decision-makers.
Volume: 15
Issue: 6
Page: 5286-5296
Publish at: 2025-12-01

Intelligent control for distributed smart grid: comprehensive system integrating wave, fuel cell, and photovoltaic power generation

10.11591/ijece.v15i6.pp5119-5129
Manohar B S , Basavaraja Banakara
The intermittent supply from renewable energy sources reckons integration of different renewable sources that can provide robust and uninterrupted energy supply to the grid. This paper applies an intelligent control method to such hybrid power generation involving a wave generator, fuel cell, and solar power generator integrated into the distribution power grid. A common DC link that supplies the voltage source converter (VSC) is powered by the output from the hybridized wave, fuel cell and photovoltaic (PV) output. Wave generator uses the rectifier DC-DC converter, PV uses a maximum power point tracking (MPPT)-controlled DC-DC converter and fuel cell uses a DC-DC converter. All DC sources converge at the DC link, connecting to an inverter featuring another voltage source controller for controlled AC voltage. In instances of power unavailability from renewable resources, the fuel cell seamlessly provides power. The inverter controls the integration of power from these sources to the grid and maintains stable DC link voltage due to the dynamic nature of the DQ controller. MATLAB-based simulation is developed for the proposed controller and a comparison between both proportional integral and adaptive neuro-fuzzy inference system (ANFIS) controller in the DC link voltage regulation loop is observed. An ANFIS controller is employed as an alternative to the proportional integral (PI) controller and found that the ANFIS controller outperformed the PI controller in voltage regulation at the DC link.
Volume: 15
Issue: 6
Page: 5119-5129
Publish at: 2025-12-01

Development of a hydraulic jack system bending tool for improved manufacturing efficiency

10.11591/ijaas.v14.i4.pp1072-1082
Muhammad Arsyad Suyuti , Rusdi Nur , Ahmad Nurul Muttaqin , Arminas Arminas , Zainal Sudirman
This article presents the design, fabrication, and testing of a hydraulic sheet metal bending tool. The main objective was to create a tool capable of bending sheets of various thicknesses, ranging from 2 to 4 mm, with high precision and minimal operator effort. The design incorporates a hydraulic ram for easy operation, allowing multiple plates to be bent in a short period of time. Key calculations, including bending force, spring load, and hydraulic force, are performed to ensure the efficiency and safety of the tool. Experimental results show that the tool is able to achieve the desired bending angles, with minimal spring return, and can handle up to three 10 cm wide sheets in approximately 10 minutes. The performance of the tool has been proven by tests, and the results confirm that it can meet the requirements of industrial sheet metal bending. Based on these results, the tool demonstrates its effectiveness in small and medium-scale operations, providing a cost-effective solution for sheet metal production.
Volume: 14
Issue: 4
Page: 1072-1082
Publish at: 2025-12-01

Advancements in electric vehicle safety and charging infrastructure

10.11591/ijaas.v14.i4.pp1332-1339
Debani Prasad Mishra , Rudranarayan Senapati , Nisha Kedia , Sanchita Sahay , Raj Alpha Swain , Surender Reddy Salkuti
In electric vehicles (EVs), safety measures must be taken to prevent dangerous accidents. Safety regulations must be in place for two important things: electric or EV batteries and EV equipment. Operating an electric vehicle charging stations (EVCS) is a challenging task. This holistic approach is used to evaluate when renewable energy is produced. It's best to focus on the popularity of EVs as more and more people choose this mode of transportation. It is important to know that power plants can be risky. Therefore, safety issues related to EV charging must be addressed quickly and appropriately. Potential safety issues with EVs include overcurrent, ground faults, and overheating. If the charging system does not work, the electric car's battery may heat up and catch fire, and overcharging may cause other problems. To avoid security risks, you must comply with security regulations, use payment devices that meet security requirements, and follow the manufacturer's instructions.
Volume: 14
Issue: 4
Page: 1332-1339
Publish at: 2025-12-01

Optimizing radial basis function networks for harmful algal bloom prediction: a hybrid machine learning approach

10.11591/ijece.v15i6.pp5647-5654
Nik Nor Muhammad Saifudin Nik Mohd Kamal , Ahmad Anwar Zainuddin , Amir ‘Aatieff Amir Hussin , Ammar Haziq Annas , Normawaty Mohammad-Noor , Roziawati Mohd Razali
The deployment of artificial intelligence in environmental monitoring demands models balancing efficiency, interpretability, and computational cost. This study proposes a hybrid radial basis function network (RBFN) framework integrated with fuzzy c-means (FCM) clustering for predicting harmful algal blooms (HABs) using water quality parameters. Unlike conventional approaches, our model leverages localized activation functions to capture non-linear relationships while maintaining computational efficiency. Experimental results demonstrate that the RBFN-FCM hybrid achieved high accuracy (F1-score: 1.00) on test data and identified Chlorophyll-a as the strongest predictor (r = 0.94). However, real-world validation revealed critical limitations: the model failed to generalize datasets with incomplete features or distribution shifts, predicting zero HAB outbreaks in an unlabeled 11,701-record dataset. Comparative analysis with Random Forests confirmed the RBFN-FCM's advantages in training speed and interpretability but highlighted its sensitivity to input completeness. This work underscores the potential of RBFNs as lightweight, explainable tools for environmental forecasting while emphasizing the need for robustness against data variability. The framework offers a foundation for real-time decision support in ecological conservation, pending further refinement for field deployment.
Volume: 15
Issue: 6
Page: 5647-5654
Publish at: 2025-12-01

Image-based assessment of cattle manure-induced soil erosion in grazing systems

10.11591/ijece.v15i6.pp5360-5370
Cristian Gómez-Guzmán , Yeison Alberto Garcés-Gómez
Extensive livestock farming significantly impacts soil erosion, necessitating accurate monitoring and assessment to mitigate environmental damage and enhance sustainable pasture management. This study employs unsupervised classification of high-resolution drone imagery to detect and quantify soil erosion associated with cattle manure in pastures, focusing on evaluating classification algorithms, identifying relevant spectral and textural features, and quantifying the extent and severity of erosion. The results demonstrate the effectiveness of unsupervised classification in identifying erosion zones and their impact on soil health and water quality. Field validation confirms the accuracy of the analysis, emphasizing the need for sustainable management practices such as controlled manure redistribution and soil conservation to mitigate erosion and protect natural resources. This approach offers practical tools for mitigating the environmental impacts of semi-extensive livestock farming and promoting more sustainable management. The findings provide practical recommendations for sustainable pasture management, contributing to environmental conservation and the long-term health of live-stock systems.
Volume: 15
Issue: 6
Page: 5360-5370
Publish at: 2025-12-01

Optimizing parameter selection in bidirectional encoder portrayal for transformers algorithm using particle swarm optimization for artificial intelligence generate essay detection

10.11591/ijece.v15i6.pp5543-5554
Tegar Arifin Prasetyo , Rudy Chandra , Wesly Mailander Siagian , Horas Marolop Amsal Siregar , Samuel Jefri Saputra Siahaan
This research proposes a novel method for detecting artificial intelligence (AI)-generated essays by integrating the bidirectional encoder representations from transformers (BERT) model with particle swarm optimization (PSO). Unlike traditional approaches that rely on manual hyperparameter tuning, this study introduces a systematic optimization technique using PSO to improve BERT’s performance in identifying AI-generated content. The key problem addressed is the lack of effective, real-time detection systems that preserve academic integrity amidst rapid AI advancements. This optimization enhances the model’s detection accuracy and operational efficiency. The research dataset consisted of 46,246 essays, which, after data cleaning, were refined to 44,868. The model was then tested on 9,250 essays. Initial evaluations showed BERT's accuracy ranging from 83% to 94%. After being optimized with PSO, the model achieved an accuracy of 98%, an F1-score of 98.31%, precision of 97.75%, and recall of 98.87%. The model was deployed using a FastAPI-based web interface, enabling real-time detection and providing users with an efficient way to quickly verify text authenticity. This research contributes a scalable, automated solution for AI-generated text detection and offers promising implications for its application in various academic and digital content verification contexts.
Volume: 15
Issue: 6
Page: 5543-5554
Publish at: 2025-12-01

6G internet of things networks for remote location surgery also a review on resource optimization strategies, challenges, and future directions

10.11591/ijece.v15i6.pp5968-5977
Md Asif , Tan Kaun Tak , Pravin R. Kshirsagar
Remote location surgery presents stringent requirements for wireless communication, particularly in terms of reliability, speed, and low latency. The emergence of sixth-generation (6G) wireless networks is expected to address these challenges effectively. With the rapid expansion of internet of things (IoT) applications in healthcare, maintaining real-time connectivity has become essential. Ensuring such performance in 6G-enabled IoT networks relies heavily on the implementation of advanced resource optimization techniques. Recent studies have focused on improving key performance metrics, including latency, reliability, energy efficiency, spectral efficiency, data rate, and bandwidth usage. Comprehensive reviews of these techniques reveal a growing emphasis on multi-objective optimization strategies to balance conflicting requirements. Research has also highlighted limitations in existing approaches, suggesting the need for further innovation, particularly for mission-critical applications like remote surgery. Within this context, 6G IoT systems have demonstrated the potential to maintain high data rates and stable throughput, both of which are essential for safe and responsive surgical operations conducted over long distances. These findings underscore the importance of continued development in resource management to fully enable remote healthcare delivery through advanced wireless technologies.
Volume: 15
Issue: 6
Page: 5968-5977
Publish at: 2025-12-01

Design and experimental validation of a microstrip Vivaldi antenna-based system for breast tumor detection

10.11591/ijece.v15i6.pp5497-5505
Samiya Qanoune , Hassan Ammor , Zakaria Er-Reguig , Zouhair Guennoun
Breast cancer remains one of the leading causes of death among women worldwide, highlighting the critical need for accurate, non-invasive, and cost-effective diagnostic solutions. In light of this, microwave imaging has surfaced as a promising alternative to conventional diagnostic methods. This approach leverages its capability to differentiate between healthy and cancerous tissues by examining their dielectric properties. This study presents the design, implementation, and experimental assessment of a Vivaldi antenna-based system aimed at breast cancer detection. The antenna is designed to operate within the ultra-wideband frequency range, which facilitates high-resolution imaging and effective deep tissue penetration. Data collected from tissue-mimicking phantoms reveals the system’s proficiency in identifying anomalies, showcasing a significant contrast between malignant and normal tissue regions. We analyze various performance metrics, including signal reflection, penetration depth, and imaging resolution to substantiate the system's efficacy. The results underline the significant potential of Vivaldi antennas in improving early- stage breast cancer detection, thus contributing to advancements in microwave imaging technology.
Volume: 15
Issue: 6
Page: 5497-5505
Publish at: 2025-12-01

Artificial intelligence-based multi-key security for protected and transparent medical cloud storage

10.11591/ijaas.v14.i4.pp1241-1250
Ravi Kiran Bagadi , Neelima Santoshi Koraganji , Bandreddi Venkata Seshukumari , Kavya Ramya Sree Karuturi , Sireesha Abotula , Bodapati Venkata Rajanna , Mahalakshmi Annavarapu , Nitalaksheswara Rao Kolukula , Jayasree Pinajala , James Stephen Meka
Ensuring the security and privacy for the patient medical records and medical reports data is a crucial challenge as cloud-based healthcare technologies become more prevalent. For cloud-hosted medical data, internet of things (IoT) and artificial intelligence (AI) technologies shows best solutions for the challenges in the medical domain. This study suggests a Secure and Transparent Multi-Key Authentication Framework that makes use of AI. Using Z-score normalization, the framework first preprocesses the data before clustering to create a multi-level multi-key security structure. The physics-informed triangulation aggregation neural network (PITANN) model in the study reduces computation costs by minimizing overhead, ensuring secure handling of location-based and medical data for enhanced data classification and encryption effectiveness. A multi-key derivation of an elliptic curve, the ElGamal cryptography scheme is presented, which allows for safe multi-key encryption with little increase in the length of the ciphertext. This method guarantees safe, confidential access to cloud-hosted encrypted health information. An envisioned amalgamation improves flexibility by enhancing performance metrics such as speed of computation while safeguarding patient information through enhanced security measures and ensuring precise medical record integrity within virtual healthcare systems.
Volume: 14
Issue: 4
Page: 1241-1250
Publish at: 2025-12-01

Integrity verification of medical images in internet of medical things for smart cities using data hiding scheme

10.11591/ijece.v15i6.pp5770-5781
Kilari Jyothsna Devi , Ravuri Daniel , Bode Prasad , Mohamad Khairi Ishak , Dorababu Sudarsa , Pasam Prudhvi Kiran
As technology has advanced, the internet of medical things (IoMT) has become incredibly useful. It is used to transmit a wide variety of medical images. Sensitive patient data may be altered during transmission or subject to illegal access. To overcome all of these challenges and preserve the integrity of medical images while transmission over IoMT, a blind region-based data concealing approach called medical image watermarking (MIW) is suggested. The region of interest (ROI) and region of non-interest (RONI) are the two sections that make up the medical image. The aim of the suggested MIW technique is to prevent transmission-related manipulation of medical image ROI. To provide high imperceptibility and resilience, confined integrity verification and recovery bits (CIVRB) bits are embedded in the RONI using hybrid integer wavelet transform–singular value decomposition (IWT-SVD). According to the experimental results, the suggested system is highly imperceptible (average peak signal-to-noise ratio (PSNR)=56dB), robust (average NC=0.99), and exhibits integrity verification accuracy of over 98% against a variety of image processing attacks. In terms of several watermarking properties, the proposed technique performs over state-of-the-art schemes. This method offers a dependable framework for protecting medical images in real-time IoMT applications and is suitable for smart healthcare environments.
Volume: 15
Issue: 6
Page: 5770-5781
Publish at: 2025-12-01

Optimizing smart grids with blockchain-driven automation and demand response

10.11591/ijaas.v14.i4.pp985-998
B. Jyothi , Bhavana Pabbuleti , Ravi Ponnala , Kambhampati Venkata Govardhan Rao , S. Sai Srilakshmi , Putta Dhanush Narasimha , Mareboyina Karthik Yadav , Malligunta Kiran Kumar , Ch. Rami Reddy
To increase resilience, efficiency, and engagement in the network, it shall develop and test its smart grid system integrating blockchain-based authentication and automated demand response management. Simulations are made on the dynamic behavior of the grid in energy generation, consumption, and management through demand responses through MATLAB/Simulink assessment of performance and stability. Ethereum is used in implementing and managing smart contracts that automate and secure events of demand response and consumer interactions for transparency in transactions. It uses Python with Pandas to process, analyze, and visualize simulation data that gives insight into the effectiveness of demand response strategies; PostgreSQL supports the structured storage and querying of data with comprehensive data management. Proper integration of such tools can result in the proper robust simulation of the smart grid system that is highly reliable, efficient usage of energy, and can empower consumers through secure, efficient demand response mechanisms. These immediate issues about managing the grid can thus solve the way toward the future development of such smart grid technologies and their possible integration with the blockchain.
Volume: 14
Issue: 4
Page: 985-998
Publish at: 2025-12-01
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