Articles

Energy loss prediction using least absolute shrinkage and selection operator regression and SHAP explainability

10.11591/ijai.v15.i4.pp3103-3119

Nur Diana Izzani Masdzarif , Siti Azirah Asmai , Yogan Jaya Kumar , Muhammad Hafidz Fazli Md Fauadi

Technical energy loss estimation in power distribution systems is essential for improving operational efficiency and cost-effectiveness. However, distribution-level datasets are often failed to cope with the nonlinear behavior and sparse, low-resolution data typical in modern grid environments. This study proposes an interpretable artificial intelligence (AI) framework based on least absolute shrinkage and selection operator (LASSO) regression integrated with Shapley additive explanations (SHAP) to estimate and explain technical energy losses at the feeder level. An exploratory multicollinearity assessment using correlation analysis and variance inflation factor (VIF) revealed severe redundancy among operational variables, justifying the adoption of L1-regularized regression. Hyperparameter tuning via LassoCV identified an optimal regularization parameter, resulting in strong predictive performance. Comparative evaluation with nonlinear models, including random forest and gradient boosting, demonstrated that LASSO achieves competitive or superior generalization performance while preserving interpretability. Feature importance analysis and SHAP-based explanations confirmed that operational loading variables particularly infeed energy, load factor, and maximum demand are the dominant drivers of technical losses. SHAP dependence and interaction analyses further revealed context-dependent behavior among correlated predictors, enriching interpretability beyond coefficient-based rankings. The results demonstrate that regularized linear modeling, when combined with explainable AI techniques, provides a robust, transparent, and practically deployable solution for technical loss estimation in distribution networks.

Volume: 15

Issue: 4

Page: 3103-3119

Publish at: 2026-08-01

Utilizing Poisson modeling and advanced machine learning techniques for enhanced detection of slowloris attacks

10.11591/ijai.v15.i4.pp3154-3163

Prathima Mabel John , Mamatha Ram , Vani Kurugod Ashwathanarayana Reddy , Santhosh Babu , Rama Mohan Babu Kasturi Nagappasetty

This paper presents a secure framework for detecting and mitigating slowloris attacks in software defined networks (SDN) using Poisson arrival modeling to generate attack traffic. Slowloris attacks imitate legitimate traffic patterns, making them difficult to identify. The framework includes three modules: a data generation module, a feature selection module based on the blended statistical and information gain (BSIG) approach, and a traffic classification module using machine learning and deep learning algorithms. Poisson arrival modeling captures the stochastic nature of attack traffic by introducing variability in packet inter-arrival times, thereby simulating realistic network conditions. Monte Carlo simulations establish anomaly thresholds, while the BSIG method identifies key features, such as inter-arrival variability and CPU utilization. Support vector machine (SVM), k-nearest neighbors (KNN), and random forest (RF) classifiers achieve high precision in distinguishing legitimate traffic from attack traffic. The integration of Poisson modeling with advanced analytical methods improves detection capability while maintaining scalability for similar stealth attacks. The proposed methodology provides high detection accuracy and adapts to dynamic network environments, enhancing web server defenses against slowloris attacks in real-world scenarios. The framework also reduces false positives, supports efficient resource utilization, and strengthens proactive security management in modern SDN infrastructures under varying conditions.

Volume: 15

Issue: 4

Page: 3154-3163

Publish at: 2026-08-01

Deep learning for categorizing microsatellite stability in colorectal cancer

10.11591/ijai.v15.i4.pp3761-3769

Sofyan El Idrissi , Yassine Drider , Ikram Ben Abdel Ouahab , Mohammed Bouhorma , Fatiha El Ouaai

Cancer remains a significant global health challenge, with its incidence rising steadily in recent decades. In colorectal cancer (CRC), microsatellite instability (MSI), and microsatellite stability (MSS) are important biomarkers that influence treatment decisions and patient outcomes. Accurate MSI classification is critical but traditional methods can be costly and time-consuming. This study explores the potential of deep learning to classify MSI and MSS in CRC. A large dataset of CRC patients with confirmed MSI and MSS status was utilized, obtained through standard testing images. Deep learning models were applied to histopathological images, analyzing tissue features from digital slides. Convolutional neural network (CNN) and residual network (ResNet)-18 models demonstrated high accuracy in distinguishing between MSI and MSS CRCs. The best-performing model, which integrated genomic and histopathological data, achieved an area under the curve (AUC) receiver operating characteristic (ROC) of 0.85, indicating strong discrimination capability. The findings suggest that deep learning could be a valuable tool for clinical decision-making and personalized medicine in CRC.

Volume: 15

Issue: 4

Page: 3761-3769

Publish at: 2026-08-01

Biometric authentication using dual-modal deep learning based-on electrocardiogram and ear features

10.11591/ijai.v15.i4.pp3252-3268

Mohamed S. Khalaf , Said Fathy Al-Zoghdy , Mariana Barsoum , Ibrahim Omara

Biometric authentication systems are essential for secure access control; however unimodal systems are vulnerable to spoofing and environmental variations. This study proposes a dual-modal biometric system combining electrocardiogram (ECG) signals and ear features to enhance security and accuracy. Deep learning architectures including VGG-verydeep16 and convolutional neural network 5 (CNN5) are evaluated for feature extraction and fusion. Experimental results show that hybrid model (VGG-verydeep16 + CNN5) achieves around 98% accuracy, outperforming individual models. The system adapts to dataset size, using CNN5 for large datasets and VGG-verydeep16 for smaller ones. This approach offers a robust, efficient, and scalable solution for real-world biometric authentication.

Volume: 15

Issue: 4

Page: 3252-3268

Publish at: 2026-08-01

Multi-stage hybrid YOLO-driven and MobileNetV2-CNN variants for robust fish freshness classification

10.11591/ijai.v15.i4.pp3660-3671

Raseeda Hamzah , Rosniza Roslan , Amni Munira Khidir , Lala Septem Riza

This study presents a multi-stage hybrid representation learning framework for robust fish freshness classification to address the critical challenge of reliable quality assessment in unpreserved food supply chains. The proposed pipeline operates in two stages: stage-1 employs you only look once (YOLO)v8n as feature-gated detector to validate inputs and eliminate non-fish images, while stage-2 leverages transfer-learned MobileNetV2 variants enhanced with convolutional neural network (CNN) layers, fine-tuning, adaptive learning rate schedulers, and expanded fully connected layers for hierarchical classification into three freshness classes i.e., highly fresh, fresh, and not fresh. The framework has been trained and evaluated on two curated datasets comprising 4,500 fish and non-fish images and 11,111 freshness-labeled including augmented dataset to increase diversity. The experimental results showed significant improvements. The best performance model transfer learning (TL)-MobileNetV2 + CNN + fine-tuning achieved 98.07% training accuracy and 67.72% validation accuracy on 80:20 split, and training accuracy of 97.57%, validation accuracy of 97.21% through 10-fold cross-validation. The comparative benchmarking confirmed that dual-stage design outperformed baseline MobileNetV2 and YOLOv5s models across precision, recall, and F1-score. The findings highlighted significant value of integrating detection-driven validation with transfer-learning classification, and propose new benchmark for intelligent freshness monitoring. For future work, this study aims to explore attention-based models, data integration, and species diversity.

Volume: 15

Issue: 4

Page: 3660-3671

Publish at: 2026-08-01

An overview of SSP structures in dragon graphs and domination and independent domination fuzzy SSP graphs

10.11591/ijeecs.v43.i2.pp485-494

R. Mary Jeya Jothi

Social network modeling is one of the many applications of dominant sets in graph theory. Social networks are made up of personalities or sets of individuals linked by relationships; graph theory provides a useful framework for examining and simulating these structures. We have already covered the idea of domination and how to calculate the domination numeral for various fuzzy graphs. Unlike anything found in the literature to date, we presented the idea of domination of fuzzy SSP graphs in this study. It is also spoken about the several super strongly perfect (SSP) structural parameters in fuzzy SSP graphs. The study appears to contribute to the ground of theory of graphs by extending the notion of domination to fuzzy SSP graphs. The paper also covers how to acquire specific dominant sets, coloring numbers, and cliques (maximal) in the context of fuzzy SSP graphs. In these kinds of scenarios, researchers can employ the following broad techniques to identify dominating sets.

Volume: 43

Issue: 2

Page: 485-494

Publish at: 2026-08-01

Advanced state machine-sliding mode current control energy management for multi-source DC microgrid

10.11591/ijeecs.v43.i1.pp63-77

Hamza Rezigue , Mabrouk Khemliche , Samia Latreche , Badreddine Kanouni , Hamza Khemliche

This work addresses effective power management in a multi-source DC microgrid. An innovative energy management technique utilizing a state machine control (SMC) integrated with a sliding mode current control (SMCC) has been developed. This approach offers advantages through its equitable distribution of power among sources, storage devices, and demand loads, thereby optimizing the flow rate, discharge, and charge cycles of energy storage devices; additionally, it improves the response time of PEMFC power across various states in comparison to a conventional PI controller. The SMC-SMCC has proposed nine scenarios, categorized into three state of charge (SOC) situations, to fulfill a predetermined set of parameters for the operation of the DC microgrid. Simulation studies conducted with a precise model in MATLAB/Simulink have demonstrated that the proposed SMC-SMCC is proficient in achieving effective power sharing, rapid DC link voltage control in terms of stability, and maintaining the SOC within its constraints; additionally, the SMC-SMCC exhibits a quicker response time compared to conventional SMC-PI.

Volume: 43

Issue: 1

Page: 63-77

Publish at: 2026-07-01

A hierarchical scholar expertise information system based on publication profiles and research records: a case in Universitas Diponegoro

10.11591/ijeecs.v43.i1.pp250-258

Eko Didik Widianto , Hadiyanto Hadiyanto , Teddy Mantoro , Raka Sindu Wardoyo , Muhamad Irham Maulana

One factor that boosts the reputation of higher education institutions (HEIs) in gaining competitive advantage is the research productivity of their scholars, including at Diponegoro University (Undip). However, existing studies focus on metrics and their measurement but lack discussion of systems for leveraging these metrics to promote these scholars’ expertise. This study proposes a scholars’ expertise information system at Undip based on their publications and research track records to bridge information between the campus and outside parties, namely industry, government, and society. It provides expertise searching and browsing facilities in Dewey’s hierarchy of subjects and the institutional structure hierarchy. Scholar publications and research data are retrieved using the science and technology index application program interface (SINTA API). The collected data included lecturer profiles, study programs, SINTA subjects, articles, research, community service, intellectual property rights (IPR), and books. It has been fully deployed and adopted online, presenting the expertise of Undip scholars based on their publications and research records. The finding shows that all pages perform well, with an average GTmetrix B grade and a performance score of 91%, a structure score of 88%, and a 1.8s loading time. With all the functionalities and performances of the system, this study contributes to developing an information system model to promote the expertise of university scholars, primarily based on their publications and research.

Volume: 43

Issue: 1

Page: 250-258

Publish at: 2026-07-01

Insights on routing and scheduling approaches in the IoT from the perspective of energy, QoS, and security–a systematic review

10.11591/ijeecs.v43.i1.pp335-344

Rajeshwari Kenchammana Hosakote Nanjappa , Manuvinakurike Narasimha Sastry Suma

The internet of things (IoT) has been conceptualized to bring more efficient and seamless connectivity to a large number of low-power and low-cost embedded devices. In the context of IoT, limited radio resources create new challenges, such as collisions and access conflicts. Another challenge arises in dealing with energy constraints, as battery-powered IoT sensors have limited energy capacity in the sensing layer. Consequently, routing mechanisms play a fundamental role in dealing with route optimization and reliable data transmission problems in the transport layer, whereas broadcast and link scheduling are also considered as appealing solutions for fulfilling the energy efficiency, collision-free transmissions and latency requirements in the IoT. Also, security vulnerabilities are hard to identify in the IoT perception and transport layer due to its ad-hoc network topological factors. Thus, deploying efficient routing schemes in IoT demands effective collaborative solutions for cost-effective and secure route formation with energy-aware scheduling performance, which were not, explored much in the past. This investigation thereby analyses the strengths and limitations of existing efficient routing and scheduling solutions in IoT and extracts the critical findings which could provide quick survey to many researchers in this application area.

Volume: 43

Issue: 1

Page: 335-344

Publish at: 2026-07-01

Analysis of OFDM and filter bank multicarrier with offset quadrature amplitude modulation for 5G communication: a comparative study

10.11591/ijeecs.v43.i1.pp127-138

Magda Yousef Mοhаmеd , Esraa M. Eid , Mohammed Abo-Zahhad , Ahmed Hassan Еldеib

Emerging applications for 5G and beyond require wireless communication systems with high spectral efficiency, low latency, reliable synchronization, and robust channel estimation techniques. This paper presents a comparative analysis between orthogonal frequency division multiplexing (OFDM) and filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) under identical simulation conditions. The comparison is performed in terms of spectral efficiency, power spectral density (PSD), bit error rate (BER), peak-to-average power ratio (PAPR), and channel estimation performance. Simulation results show that FBMC/OQAM has higher spectral efficiency and significantly reduced out-of-band (OOB) emissions than OFDM due to its superior spectral containment. Moreover, FBMC/OQAM provides better channel estimation performance in the frequency-selective multipath fading environment. On the other hand, OFDM has lower computational complexity and better PAPR performance. The obtained results highlight the performance differences between OFDM and FBMC/OQAM systems and demonstrate the potential of FBMC/OQAM as a promising waveform candidate for future wireless communication systems.

Volume: 43

Issue: 1

Page: 127-138

Publish at: 2026-07-01

FPGA based forest fires prediction system

10.11591/ijeecs.v43.i1.pp78-92

Faroudja Abid , Nouma Izeboudjen , Fatiha Louiz

This paper describes the idea of designing and implementing an field programmable gate array (FPGA) based system on chip (SoC) for forest fires prediction (FFP). The FFP in the proposed system is based on the decision tree (DT) algorithm implemented as an intellectual property (IP) in the FFP Zynq-SoC architecture that constitutes the processing part of a smart sensor node. This latter processes the collected meteorological data and takes decision locally at sensor node level; without having to send massive data to the base station for decision. The performance of the decision-tree software classifier in terms of accuracy and recall are about 75% and 0.88, respectively. The hardware implementation results of the DT -based forest fires prediction SoC show that the developed DT IP core is area and power efficient.

Volume: 43

Issue: 1

Page: 78-92

Publish at: 2026-07-01

Automated drone-assisted detection system for rice leaf pathologies a deep learning approach

10.11591/ijeecs.v43.i1.pp148-156

Erfan Rohadi , Cahya Rahmad , Septian Enggar Sukmana , Aida Sartimbul , Kismet Anak Hong Ping , Dimas Rosiawan , Ahmad Afifuddin Zakki

Early and accurate detection of plant diseases is vital for maintaining agricultural productivity. This study investigates an automated disease identification system specifically designed for the IR64 rice cultivar. By combining drone-captured aerial imagery (UAV) taken during the plant's vegetative stage with public datasets, we established a comprehensive training dataset. The study evaluates and compares four convolutional neural network (CNN) architectures, InceptionV3, ResNet50, EfficientNetV2S, and MobileNetV2, assessing their predictive accuracy and real-world computational efficiency. Our 10-fold cross-validation results indicate varying levels of inference speed and accuracy among the models. InceptionV3 and MobileNetV2 displayed the highest stability and minimal misclassification rates across multiple disease types. In contrast, the performance of ResNet50 and EfficientNetV2S fluctuated significantly depending on the detected pathogen. In conclusion, coupling UAV imagery with fine-tuned deep learning models provides a fast, scalable solution for continuous crop monitoring and precision agriculture.

Volume: 43

Issue: 1

Page: 148-156

Publish at: 2026-07-01

User-centered requirements elicitation for explainable AI transparency in recommendation and advertising systems

10.11591/ijeecs.v43.i1.pp271-280

Osama Dakhel Alsuhaimy , M. Rizwan Jameel Qureshi

Modern recommendation and advertising systems increasingly rely on complex artificial intelligence (AI) models whose opaque decision-making processes limit user understanding and trust. While explainable artificial intelligence (XAI) techniques aim to improve transparency, excessive disclosure can increase privacy concerns and psychological discomfort. This study addresses the lack of structured approaches for regulating transparency in user-facing AI systems. We propose the optimal transparency and psychological safety (OTPS) framework, which regulates explanation depth, timing, and user control to balance interpretability with psychological safety. The framework is implemented through a modular architecture consisting of an explanation generation module, transparency controller, and user interface layer. A user survey involving 35 participants was conducted to evaluate perceptions of transparency, trust, and psychological comfort. Statistical analysis, including reliability testing and response distribution evaluation, indicates strong user preference for adaptive transparency mechanisms. The results demonstrate that regulated transparency improves user trust and usability without introducing significant system overhead, providing practical design guidance for explainable AI systems in recommendation and advertising platforms.

Volume: 43

Issue: 1

Page: 271-280

Publish at: 2026-07-01

Electrical engineering in the era of autonomous intelligence: building sustainable, resilient, and self-evolving energy systems

10.11591/ijeecs.v43.i1.pp1-6

Tole Sutikno

Electrical engineering is entering a transformative era in which autonomous intelligence is becoming an integral component of modern energy infrastructures rather than merely an auxiliary computational tool. The convergence of advanced power electronics, renewable energy technologies, intelligent sensing, edge computing, artificial intelligence, and high-speed communications is enabling electrical systems to evolve from passive and centrally controlled networks into adaptive, resilient, and self-evolving ecosystems. This editorial discusses the emerging paradigm of autonomous electrical engineering, where future power systems are expected to perceive operating conditions, learn from historical and real-time data, predict disturbances, and autonomously optimize their performance while maintaining reliability, security, and sustainability. Beyond conventional objectives such as efficiency and stability, next-generation electrical systems must address increasing renewable penetration, distributed energy resources, electrified transportation, cyber-physical security, and climate resilience. The editorial also highlights several promising research directions, including AI native power system operation, autonomous microgrids, digital twins, physics-informed intelligence, trustworthy and explainable AI, intelligent power electronic converters, and coordinated human–AI decision-making. These developments position electrical engineering as a foundational discipline for achieving sustainable development and future energy transition, while emphasizing that autonomous intelligence should augment engineering expertise to create safer, more reliable, and environmentally responsible electrical infrastructures.

Volume: 43

Issue: 1

Page: 1-6

Publish at: 2026-07-01

Deep Q learning algorithm for detecting DDoS attacks on IoT devices

10.11591/ijeecs.v43.i1.pp299-313

Lana Kamla Ahmed , Kayhan Zrar Ghafoor

The rapid expansion of internet of things (IoT) networks has heightened security risks, particularly regarding distributed denial of service (DDoS) attacks against devices with limited computing capacity. High detection accuracy is crucial for these resource-constrained environments, where false positives can disrupt legitimate traffic and false negatives allow attacks to persist. However, modern reinforcement learning (RL) and machine learning (ML) intrusion detection solutions often exhibit poor generalization due to static state representations. To address this, this paper proposes a deep Q-learning (DQL) framework that integrates K-means clustering directly into the RL action space. Unlike prior RL-based IDS models, our approach dynamically integrates clustering into the learning process, enabling adaptive state representation and improved generalization to unseen traffic patterns. The system is formulated as a Markov decision process where the agent optimizes a composite reward function based on accuracy, precision, recall, and F1-score. Evaluated on the N-BaIoT dataset using 10-fold cross-validation, the proposed method achieves a classification accuracy of 98.95% and a weighted F1-score of 98.73%, significantly outperforming traditional ML and RL baselines. These results demonstrate the framework's effectiveness as a scalable, adaptive solution for intelligent IoT DDoS detection.

Volume: 43

Issue: 1

Page: 299-313

Publish at: 2026-07-01

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31,377 Article Results

Energy loss prediction using least absolute shrinkage and selection operator regression and SHAP explainability

Utilizing Poisson modeling and advanced machine learning techniques for enhanced detection of slowloris attacks

Deep learning for categorizing microsatellite stability in colorectal cancer

Biometric authentication using dual-modal deep learning based-on electrocardiogram and ear features

Multi-stage hybrid YOLO-driven and MobileNetV2-CNN variants for robust fish freshness classification

An overview of SSP structures in dragon graphs and domination and independent domination fuzzy SSP graphs

Advanced state machine-sliding mode current control energy management for multi-source DC microgrid

A hierarchical scholar expertise information system based on publication profiles and research records: a case in Universitas Diponegoro

Insights on routing and scheduling approaches in the IoT from the perspective of energy, QoS, and security–a systematic review

Analysis of OFDM and filter bank multicarrier with offset quadrature amplitude modulation for 5G communication: a comparative study

FPGA based forest fires prediction system

Automated drone-assisted detection system for rice leaf pathologies a deep learning approach

User-centered requirements elicitation for explainable AI transparency in recommendation and advertising systems

Electrical engineering in the era of autonomous intelligence: building sustainable, resilient, and self-evolving energy systems

Deep Q learning algorithm for detecting DDoS attacks on IoT devices

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