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29,922 Article Results

ARX based cipher with S-box augmentation: statistical and differential evaluation

10.11591/ijeecs.v41.i3.pp946-953
Manita Rajput , Pranali Chaudhari
With the growth of internet of medical things (IoMT), the continuous transfer of vital biomedical data requires lightweight encryption with strong resistance to statistical and differential attacks. The Speck cipher is a suitable candidate because of its low memory and execution time. However, its vulnerability to differential cryptanalysis limits wider use in healthcare environments. In this work, a hybrid lightweight algorithm is proposed by integrating the PRESENT substitution box within the Speck64/96 round structure. The substitution layer was evaluated at three different positions in the round function. Statistical and differential analyses were performed on four sets of plaintext data, each containing 1,000 test pairs. Index of coincidence (IoC), entropy, and avalanche effect were used as the primary statistical metrics. Differential trail strength was assessed using ciphertext differences and round-wise differential probability (DP). The experimental results show that the proposed version, named Speckpres_S, achieves a 6.02% reduction in IoC, a 3.8% improvement in entropy, and a 1.7% rise in avalanche effect when compared with Speck64/96. The differential trail becomes weaker, with a 46% reduction in trail probability and a 12–15% increase in trail weight across all datasets. The execution time remained within IoMT limits. This indicates stronger resistance to differential attacks with predictable diffusion. The study demonstrates that Speckpres_S improves security while maintaining practical latency and throughput for IoMT applications. Although execution time increases marginally, the gain in differential resistance and statistical performance makes the proposed algorithm a more robust option for transmitting sensitive biomedical parameters.
Volume: 41
Issue: 3
Page: 946-953
Publish at: 2026-03-10

Design and implementation of novel encryption architecture using mix column with novel adder

10.11591/ijeecs.v41.i3.pp1134-1140
Radha Appisetty , Munuswamy Siva Kumar
Digital information is extremely simple to process these days, but it can be accessed by unauthorized people. Cryptography is one of the most effective and widely used methods for data security, to protect this information. The cryptography techniques are becoming popular and widely adopted due to the security threats during data transmission. An essential part of a cryptographic system, cryptography algorithms are developed and implemented to increase data security. The developers of these cryptographic algorithms took into consideration additional parameters, including speed, resource consumption, reliability, usage type, and flexibility, even if their primary goals are confidentiality, integrity, and authenticity. It’s important to understand that each component affects the way that a cryptographic technique is designed. Hence, this analysis presents the design and implementation of a novel encryption architecture using mix column with a novel adder. The novel encryption algorithm is designed for an encryption architecture (EA) with mix column using novel adder. This novel encryption algorithm will attain better security and performance.
Volume: 41
Issue: 3
Page: 1134-1140
Publish at: 2026-03-10

Leveraging CNN to analyze facial expressions for academic engagement monitoring with insights from the multi-source academic affective engagement dataset

10.11591/ijeecs.v41.i3.pp977-999
Noora C. T. , P. Tamil Selvan
The dynamics of student engagement and emotional states significantly influence learning outcomes. Positive emotions, stemming from successful task completion, contrast with negative emotions arising from learning struggles or failures. Effective transitions to engagement occur upon problem resolution, while unresolved issues lead to frustration and subsequent boredom. Facial engagement monitoring is crucial for assessing students’ attention, interest, and emotional responses during learning. Recent advancements in convolutional neural networks (CNNs) show promise in automatically analyzing facial expressions to infer engagement levels. This study proposes a CNN-based approach utilizing the multi-source academic affective engagement dataset (MAAED) to categorize facial expressions into boredom, confusion, frustration, and yawning. By extracting features from facial images, this method offers an efficient and objective means to gauge student engagement. Recognizing and addressing negative affective states, such as confusion and boredom, is fundamental in creating supportive learning environments. Through automated frame extraction and model comparison, this study demonstrates reduced loss values with improving accuracy, showcasing the effectiveness of this method in objectively evaluating student engagement. Facial engagement monitoring with CNNs, using the MAAED dataset, is pivotal in understanding human behavior and enhancing educational experiences. The CNN model, trained on MAAED annotated facial expressions, accurately classifies engagement categories. Experimental results underscore the CNN-based approach’s efficacy in monitoring facial engagement, highlighting its potential to enrich educational environments and personalized learning experiences.
Volume: 41
Issue: 3
Page: 977-999
Publish at: 2026-03-10

A smart-contract framework for patient identity management in digital health platforms

10.11591/ijeecs.v41.i3.pp1025-1039
Cahyo Prihantoro , Dany Candra Febrianto , Maie Istighosah , Ahmad Uffi Lestrasi Ma’ruf , Angger Taufiqur Rohman Winarno , Yudha Islami Sulistya
Asmart-contract framework for patient identity management in digital health platforms. A major gap in current digital health ecosystems is the absence of a portable and verifiable patient identity layer across fragmented electronic health record (EHR) systems. The problem addressed is the lack of a portable, verifiable, and patient-centric identity layer across fragmented electronic health record systems, which weakens access accountability and privacy. The proposed solution couples fast healthcare interoperability resources (FHIR) with self-sovereign identity (SSI), storing FHIR payloads off-chain in the InterPlanetary file system (IPFS) and committing only encrypted pointers and policies on Polygon smart contracts. Patient identifiers and content addresses are protected with AES-256 GCMauthenticated encryption and elliptic-curve key wrapping (ECIES) for both the healthcare administrator and the patient. A web implementation in Next.js using thirdweb automates wallet creation, keystore handling, encryption, and on-chain commits. In evaluation with 50 synthetic registrations, success reached 100 percent, median end-to-end latency was 5.86 seconds, mean on-chain latency 3.77 seconds, average transaction fee 0.0401 POL/MATIC, encryption time 13.9 milliseconds, and all decryptions validated. The results indicate practical feasibility for portable identity and auditable access, with on-chain latency as the main bottleneck to be reduced through batching, cheaper layers, and broader field trials. However, this study is limited because the evaluation uses only synthetic data and singleprovider testing, without real-world patients or multi-institutional environments. Zero-knowledge proofs (ZKP) are discussed conceptually as future integration and are not implemented or benchmarked in this work.
Volume: 41
Issue: 3
Page: 1025-1039
Publish at: 2026-03-10

Study of performance the 3-phase induction motor that drives by using scalar and vector control with variable speed loading

10.11591/ijeecs.v41.i3.pp894-911
Omran Alabedalkhamis , Baran Karahan , İbrahim İdiz , Hüseyin Alptekin , Enver Ediz Erol
Induction motor performance and efficiency greatly depend on the applied control technique, particularly in variable- and fixed-speed industrial applications. This paper aims to comparatively assess scalar control and vector control strategies for three-phase squirrel-cage induction motors. Using a simulation-based approach in MATLAB/Simulink, scalar control with sinusoidal pulse width modulation (SPWM) and vector control with space vector modulation (SVM) are built and analyzed under constant, variable, and bidirectional speed loading situations characteristic of a drive system. The results demonstrate that vector control provides greater speed regulation (about 93% compared to scalar control), reduced torque ripple (about 97% compared to scalar control), lower current stress (about 94% compared to scalar control), and improved dynamic responsiveness compared to scalar control, especially during transient operation. The paper is limited to numerical simulations. This paper’s biggest contribution is a clear, practical comparison which provides performance- and cost-oriented guidelines for selecting appropriate induction motor control strategies in severel applications.
Volume: 41
Issue: 3
Page: 894-911
Publish at: 2026-03-10

An innovative deep learning based approach for anomaly detection in intelligent video surveillance

10.11591/ijeecs.v41.i3.pp1105-1116
Megha G. Pallewar , Vijaya R. Pawar
Nowadays, anomaly detection has gained vital importance as security is a major concern everywhere. This work focuses on developing an intelligent video surveillance system capable of detecting anomalous activities in videos, utilizing the UCF Crime dataset as the primary source. The proposed model employed a multistage method uniting the convolutional neural networks (CNN) and long short-term memory (LSTM) networks. In the proposed approach, video frames serve as input to the CNN, which processes them to extract key features. These features are then passed to an LSTM network to capture temporal dependencies and identify anomalous events over time. This CNN-LSTM architecture successfully detects twelve distinct types of anomalous activities: abuse, arrest, arson, assault, burglary, explosion, fight, road accident, robbery, stealing, shoplifting, and vandalism. The dataset is divided into portions for training, testing, and validation, along with cross-validation to ensure model generalization. The system achieves an accuracy of 98.6%, reflecting a significant improvement of 4-5% over existing systems. This demonstrates the robustness of the proposed method in detecting anomalous behavior in video data.
Volume: 41
Issue: 3
Page: 1105-1116
Publish at: 2026-03-10

Fuzzy logic-based load balancing for voltage symmetry in distribution networks

10.11591/ijeecs.v41.i3.pp873-884
Adeel Saleem , Kholiddinov Ilkhombek Khosiljonovich , Kholiddinova Mashkhurakhon Mutalibjon Qizi , Begmatova Mukhlisakhon Mutalibjon Qizi , Sharobiddinov Mirzokhid
This paper introduces a load balancing approach based on fuzzy logic to enhance the efficiency of power distribution networks. The unbalance of voltages and an unequal load of the phases continue to be the problematic situation of the low-voltage distribution networks, particularly as the percentage of photovoltaic (PV) systems is growing. The results of such conditions include a deviation of voltage, higher losses of power, faster equipment aging, and lower power quality. This paper proposes a fuzzy logic–based phase load balancing approach that explicitly integrates voltage symmetry requirements defined by the GOST 13109-97 power quality standard. Unlike optimization-based and heuristic methods, the proposed fuzzy logic controller (FLC) redistributes phase currents using linguistic rules derived from voltage unbalance coefficients and phase current conditions, without iterative optimization procedures. Simulation results obtained in MATLAB/Simulink demonstrate a reduction of the voltage unbalance factor (VUF) by approximately 25–30% and a decrease in active power losses by 12–15% compared to the initial unbalanced operating state. The proposed method offers low computational complexity, fast response, and high interpret-ability, making it suitable for real-time implementation in smart distribution networks with distributed PV generation.
Volume: 41
Issue: 3
Page: 873-884
Publish at: 2026-03-10

FGMPSO: a hybrid firefly-gradient-MOPSO framework for high-dimensional feature selection

10.11591/ijeecs.v41.i3.pp1082-1094
Alwatben Batoul Rashed
When working with high-dimensional datasets, selecting the most relevant features is essential for improving both model clarity and processing efficiency, all while keeping predictive accuracy intact. In response to this challenge, the study introduces firefly-gradient-multi-objective particle swarm optimization (FGMPSO), an advanced hybrid technique that blends the firefly algorithm, gradient descent (GD), and multi-objective particle swarm optimization (MOPSO). This approach is specifically designed to identify an optimal subset of features that balances dimensionality reduction with strong classification performance. The method was evaluated on eight benchmark datasets and compared against multiple PSO-based feature selection techniques. The empirical results demonstrated that FGMPSO consistently achieved superior or competitive classification accuracy while selecting significantly fewer features. Notably, in several datasets, FGMPSO not only reduced dimensionality but also outperformed other methods in terms of classification accuracy. This efficiency is attributed to the intelligent exploration of the search space by the firefly algorithm, refinement via GD, and effective trade-off optimization enabled by MOPSO. The findings suggest that FGMPSO is a robust and scalable solution for feature selection, particularly suitable for complex and high-dimensional datasets. Its adaptability, convergence speed, and balance between dimensionality reduction and accuracy position it as a valuable tool in modern machine learning pipelines.
Volume: 41
Issue: 3
Page: 1082-1094
Publish at: 2026-03-10

Behavioral analysis across multiple domains using machine learning and deep learning models

10.11591/ijeecs.v41.i3.pp1124-1133
Suryakant Suryakant , Kumar P K
Behavioral analysis.using machine learning (ML) and deep learning (DL) has become critical across healthcare, finance, cybersecurity, education, and marketing. This systematic review synthesizes advancements in ML- and DL-driven behavioral analysis (2019-2025) across five key domains. Our findings reveal that Deep Learning techniques achieve superior predictive accuracy (85-97% in healthcare imaging anomaly detection), while Machine Learning remains preferred for interpretability in finance (accuracy: 78-92%, with explainability advantage). A major trade-off emerges: DL models demonstrate higher accuracy but require substantial labeled data and computational resources, whereas ML models offer transparency but limited scalability. This review contributes by: (1) systematically analyzing domain-specific performance metrics and model evolution; (2) providing comparative synthesis of ML vs. DL approaches with quantitative benchmarking; (3) identifying critical challenges (data quality, privacy, algorithmic bias, interpretability); and (4) proposing actionable future directions, including Explainable AI, Federated Learning, and multimodal fusion. We adopt PRISMA-guided methodology examining 100+ peer-reviewed studies, revealing that hybrid ML-DL architectures represent the emerging best practice for balancing accuracy with interpretability.
Volume: 41
Issue: 3
Page: 1124-1133
Publish at: 2026-03-10

Enhanced prediction of chronic kidney disease onset through machine learning techniques

10.11591/ijeecs.v41.i3.pp966-976
Samuel John Parreño , Maria Cristine Joy Anter
Chronic kidney disease (CKD) is a global health concern that often progresses silently to severe complications. This study aims to enhance CKD prediction using machine learning models: support vector machines (SVM), extreme gradient boosting (XGBoost), k-nearest neighbors (k-NN), and a stacking model. The dataset, sourced from the UCI machine learning repository, includes clinical and demographic attributes from 200 patients. After preprocessing, the final dataset comprised 161 samples and 143 features. SVM achieved perfect classification performance with 100% accuracy, precision, and recall. XGBoost followed closely with an accuracy of 97.44% and a kappa statistic of 0.9451. The k-NN model delivered strong performance, achieving 92.31% accuracy. The stacking model outperformed all individual models, achieving perfect accuracy. The models demonstrated high sensitivity and specificity, indicating their effectiveness in distinguishing CKD from non-CKD cases. These findings emphasize the potential of machine learning in CKD diagnosis. Early detection can lead to improved clinical outcomes by enabling timely interventions and personalized treatment strategies. Future research should emphasize comprehensive feature engineering and larger, more diverse datasets to improve predictive accuracy and generalizability. Incorporating machine learning models in nephrology could significantly advance CKD detection and management.
Volume: 41
Issue: 3
Page: 966-976
Publish at: 2026-03-10

Classification of DoS/distributed DoS threats in software defined networks using advanced deep belief network-long short term memory architecture

10.11591/ijeecs.v41.i3.pp1000-1016
Manjula Maraiah , Venkatesh Venkatesh
With the evolution of telecommunication core and access networks, the next generation networks leverages software defined networks (SDN) to provide flexi bility, scalability and centralized control. Denial of service (DoS)/distributed DoS (DDoS) attacks have been a major threat to next generation networks especially to the centralized architecture of SDNs. The ever-changing and dynamic nature of the DoS/DDoS attacks makes it challenging to detect and resolve them. The existing models to handle DoS/DDoS attacks often suffer from false positive rates and adaptability. In order to solve these problems, this study aims to create and apply sophisticated deep learning framework namely adversarial DBN-LSTM to accurately detect and classify various DoS/DDoS attack types. The proposed adversarial DBN-LSTM model is based on the generative adversarial networks. The proposed model uses generator to generate the adversarial attack and discrim inator to detect the attacks. The adversarial DBN-LSTM model is evaluated using a dataset specifically generated in a Mininet-based SDN controller environment to ensure relevance and practical applicability. The performance of the adver sarial DBN-LSTM is compared with other prevalent models. The adversarial DBN-LSTMmodelachieves accuracy about 99.4%. The proposed work achieves a breakthrough in identifying and preventing DoS/DDoS threats in relation to SDNenvironment.
Volume: 41
Issue: 3
Page: 1000-1016
Publish at: 2026-03-10

Thermal effects of curing parameters on the natural frequency of GNP/Ag ink composites

10.11591/ijeecs.v41.i3.pp845-858
Khirwizam Md Hkhir , Nor Azmmi Masripan , Cholatee Photong , Alan Watson , Mohd Azli Salim
This research examines how curing temperature and duration influence the electrical and mechanical behavior of hybrid graphene nanoplatelet and silver (GNP/Ag) conductive ink. The ink was formulated from GNPs, silver flakes and silver acetate printed on copper substrates, then cured 240 °C, 250 °C, and 260 °C for one to three hours. Electrical resistance was measured using a Two-Point probe, while natural frequency was obtained from experimental modal analysis (EMA) on stainless-steel (SUS304) cantilever beams laminated with printed ink. The results show that the higher curing temperature and longer curing time reduce resistivity and increase natural frequency, with the best performance observed at 260 °C for 3 hours (8.4×10⁻⁶ Ω.m and a 4.2 Hz increase). These findings confirm that a direct relationship between conductivity and stiffness, where conditions that promote stronger particle bonding also raise structural rigidity. The main contribution of this research is the joint evaluation of curing effects on both electrical and vibrational responses, offering a combined electro-mechanical perspective that is not often explored in GNP/Ag ink research. The results provide practical guidance for selecting curing conditions based on the required balance between conductivity and mechanical stability in flexible and stretchable electronic applications.
Volume: 41
Issue: 3
Page: 845-858
Publish at: 2026-03-10

Mobility-aware adaptive tag selection strategy in ambient backscatter systems

10.11591/ijeecs.v41.i3.pp924-934
Mengistu Abera Mulatu , Thembelihle Dlamini , Wiseman Nkosingiphile Nyembe , Zenzo Police Ncube , Asrat Mulatu Beyene
Ambient backscatter communication (AmBC) has emerged as a promising solution to enable ultra-low-power connectivity in large-scale internet of things (IoTs) and future 6G mobile networks. In this paper, we consider a mobility-aware AmBC system, where a mobile user equipped with a reader in teracts with multiple passive tags deployed in the coverage area of a base station (BS). To achieve high decoding reliability, an adaptive tag selection scheme is proposed based on received signal strength (RSS) and interference constraints. Here, we derive a closed-form expression of the outage probabilities of both the mobile user and tags taking into account the Rayleigh double-fading nature of backscatter links. Performance evaluation carried out through simulations vali dates the theoretical analysis based on various selected system parameters. The results obtained show that the proposed adaptive scheme significantly improves system reliability compared to fixed tag selection strategies, thus emphasizing the importance of mobility-aware and context-driven adaptation in mobile IoT scenarios such as smart transportation and aerial data collection.
Volume: 41
Issue: 3
Page: 924-934
Publish at: 2026-03-10

Ontology-based semantic link prediction for enhancing academic collaboration through knowledge management

10.11591/ijeecs.v41.i3.pp1040-1048
Pham Thi Thu Thuy , Thinh Thi Thuy
This paper introduces a novel ontology-based semantic link prediction framework that unifies structural, temporal, and semantic signals from heterogeneous scholarly sources to enhance academic collaboration forecasting. By integrating AMiner, DBLP, and Mendeley datasets into a unified SKOS- and Dublin Core-aligned ontology, the framework enables semantic enrichment, cross-source reasoning, and contextualized link prediction. Unlike previous studies that focus solely on structural features or basic content similarity, our approach leverages ontology-based semantic feature engineering and graph-based learning for robust and interpretable predictions. Experimental results show that random forest and graph neural networks significantly outperform traditional models, achieving high accuracy and ranking precision. This work contributes to knowledge management by enabling expert recommendation, trend identification, and semantic integration for strategic academic planning.
Volume: 41
Issue: 3
Page: 1040-1048
Publish at: 2026-03-10

A microservice-oriented machine learning framework for cold chain management in perishable fish logistics

10.11591/ijeecs.v41.i3.pp1070-1081
Maun Jamaludin , Arief Ginanjar , Leni Herdiani , Toto Ramadhan , Muhammad Alif Naufal , R. Ismet Rohimat
This study proposes a microservice-oriented machine learning framework to enhance intelligence and scalability in perishable fish cold chain logistics. Unlike conventional monitoring-centric systems, the framework integrates edge–cloud computing with multimodal machine learning models, including random forest for anomaly detection, long short-term memory (LSTM) for spoilage risk prediction, and convolutional neural network (CNN) for visual fish quality classification. The research adopts a design science approach combining literature analysis, field observations at cold storage facilities in Indramayu, Indonesia, and simulation-based validation. Experimental results demonstrate the feasibility of distributed analytics, modular deployment, and real-time inference within heterogeneous logistics environments. The proposed framework provides a deployable architectural reference for intelligent fisheries cold chain management and supports future large-scale, multi-stakeholder implementation.
Volume: 41
Issue: 3
Page: 1070-1081
Publish at: 2026-03-10
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