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Energy-efficient AI-enhanced secure routing for protecting IoT networks from advanced attacks

10.11591/ijeecs.v41.i2.pp%p
Leelavathi R. , Vidya A.
This paper proposes artificial intelligence-enhanced secure routing (AIRS), a lightweight AI-enhanced secure routing protocol for internet of things (IoT) networks operating under advanced routing attacks. Unlike existing approaches that treat intrusion detection and routing separately, AIRS tightly integrates anomaly scoring into trust-aware routing decisions using a compact random forest model designed for constrained nodes. The anomaly detector is trained offline on simulated IoT traffic features and deployed for real-time inference during routing. Extensive Cooja simulations demonstrate that AIRS improves intrusion detection accuracy and packet delivery while reducing energy consumption compared to secure-RPL and trust-LEACH. The current validation is limited to simulation environments, and real-world testbed evaluation is left for future work.
Volume: 41
Issue: 2
Page: 731-739
Publish at: 2026-02-01

Control of multi-level NPC inverters in PV/grid systems using ADRC and MADRC

10.11591/ijeecs.v41.i2.pp456-469
Gherici Dinar , Ahmed Tahour
Grid-connected photovoltaic (PV) systems consist of solar panels that convert sunlight into electrical energy, interconnected directly with the utility grid. These systems comprise several key components: PV, multilevel, controllers, and grid interface equipment. In this context, fivelevel inverters are increasingly favoured over three-level inverters due to their ability to reduce total harmonic distortion (THD), improve efficiency, and ensure better power quality in grid-connected applications. This research presents a three-level enhanced control scheme aimed at optimizing the performance of a grid-connected photovoltaic system with a five-level inverter. A fractional-order proportional-integral (FOPI) controller is utilized for maximum power point tracking (MPPT) to ensure precise tracking under variable irradiance conditions. At the grid-interface stage, a modified active disturbance rejection controller (MADRC) is developed for grid-interface, featuring an inner loop for DC-link voltage regulation based on Lyapunov theory, leading to improved dynamic performance with lower THD of the grid current and enhanced efficiency. Simulation results highlight the effectiveness of the proposed system. Compared with the FOPI-ADRC, a three-level configuration (0.38% THD), the proposed FOPI-MADRC with a five-level inverter achieves superior performance, with only (0.22% THD). These results confirm the advantages of combining advanced control strategies with multilevel inverter technology in improving both power quality and system efficiency.
Volume: 41
Issue: 2
Page: 456-469
Publish at: 2026-02-01

A new hybrid model based on machine learning and fuzzy logic for QoS enhancing in IoT

10.11591/ijeecs.v41.i2.pp624-632
Oussama Lagnfdi , Marouane Myyara , Anouar Darif
The fast expansion of internet of things (IoT) devices presents a more complicated scenario for maintaining a stable quality of service (QoS), which would guarantee the network’s dependable operation. The emergence of increasingly complex applications that call for additional devices makes this even more crucial. Adaptive intelligence solutions that guarantee optimal network behavior are therefore required. This paper presents a hybrid optimized solution for a three-layer IoT network that models the application, network, and perception layers of an IoT network using machine learning and fuzzy logic (FL). This method guarantees optimal QoS prediction with improved network adaptability by using fuzzy membership parameters. When the number of devices increases from 100 to 1,500, FLGA maintains an average QoS of 95% to 87%, while FL maintains 84% and RANDOM maintains 79%. At the application level, genetic algorithm (GA) continues to outperform RANDOM by 15.57% and FL by 6.32%. The goal of this paper is to provide a solid network solution that could enhance the consistency of QoS performance in order to combat the increasingly complex scenario of an IoT network.
Volume: 41
Issue: 2
Page: 624-632
Publish at: 2026-02-01

Integrating contrastive and generative AI with RAG for responsible and fair CV classification

10.11591/ijeecs.v41.i2.pp710-719
Soumia Chafi , Mustapha Kabil , Abdessamad Kamouss
The automation of curriculum vitae (CV) classification raises major challenges related to accuracy, fairness, and the heterogeneity of candidate documents. Existing approaches often address these dimensions separately and struggle to reduce demographic bias while maintaining high predictive performance. This study addresses this gap by proposing a hybrid pipeline that combines contrastive learning for representation with a lightweight generative model within a retrieval-augmented generation (RAG) framework. The method is evaluated on a large dataset of 50,000 CVs, using standard classification metrics as well as fairness indicators based on reductions in demographic disparities and equality of opportunity. Experiments show that our approach achieves an accuracy of 95.6% and a fairness index of 0.94, reducing gender-related disparities from 4.8% to 0.3%. These results demonstrate that it is possible to simultaneously improve predictive performance and fairness through a multi-level fairness strategy. The proposed system thus represents a practical and responsible solution for integrating AI into recruitment processes.
Volume: 41
Issue: 2
Page: 710-719
Publish at: 2026-02-01

Classification of Cihateup duck egg fertility using convolutional neural network EfficientNet-B3

10.11591/ijai.v15.i1.pp798-809
Evi Dewi Sri Mulyani , Teuku Mufizar , Dani Rohpandi , Ayu Djuliani , Egi Rahmatulloh , Rinaldi Satia Aulia Rahmat
Accurate detection of egg fertility is crucial to improve hatching success in duck farming. Conventional candling methods rely heavily on human expertise, making them subjective and error-prone. This study proposes an automated classification system for Cihateup duck egg fertility using candling images and a convolutional neural network (CNN) based on the EfficientNet-B3 architecture. Image enhancement techniques, including contrast limited adaptive histogram equalization (CLAHE), unsharp masking, and adaptive thresholding, were applied to improve image quality and feature visibility. The dataset consisted of fertile and infertile egg images captured at two incubation stages: the first 24 hours and the 8th–15th days. Data were split into training, validation, and testing sets with a ratio of 70:15:15. Experimental results show that image enhancement significantly improves classification performance. Without enhancement, the model achieved an accuracy of 49% with an area under curve (AUC) of 0.4226, indicating poor discrimination capability. With image enhancement, the proposed method achieved accuracies of 77% for the first 24 hours dataset and 80% for the 8th–15th days dataset, with AUC values of 0.9962 and 0.9317, respectively. These results demonstrate that EfficientNet-B3 combined with image enhancement provides an effective and computationally efficient solution for automated fertility detection of Cihateup duck eggs.
Volume: 15
Issue: 1
Page: 798-809
Publish at: 2026-02-01

Hybrid AES-LEA encryption: a performance and security analysis

10.11591/ijeecs.v41.i2.pp532-545
Hala Shaker Mehdy , Mohd Ezanee Rusli , Haider Kadhim Hoomod
The advanced encryption standard-lightweight encryption algorithm (AESLEA) hybrid algorithm (ALESA) addresses a critical gap in cryptographic systems by solving the inherent trade-off between high security and computational efficiency. While the AES offers robust security, its complex operations result in high latency and energy costs, making it less suitable for resource-constrained environments. Conversely, lightweight alternatives like the LEA provide high speed but potentially weaker diffusion properties. This paper proposes a novel hybrid encryption model that strategically integrates AES and LEA by replacing AES’s computationally intensive MixColumns transformation with a streamlined LEA-based operation. This solution delivers the best of both paradigms: the security strength of AES and the operational efficiency of LEA, while also demonstrating superior statistical security by passing all NIST tests with higher p-values and maintaining near-optimal entropy. The hybrid ALESA algorithm thus presents an ideal, balanced solution for applications requiring both strong security guarantees and high performance, particularly in IoT and large-scale data encryption scenarios.
Volume: 41
Issue: 2
Page: 532-545
Publish at: 2026-02-01

Development of an educational SCADA training kit for electric railway system monitoring and control

10.11591/ijeecs.v41.i2.pp740-752
Krommavut Nongnuch , Saowalak Leelawongsarote , Tawan Khunarsa , Anucha Zahoh
The increasing dependence on supervisory control and data acquisition (SCADA) technology in electric railway systems underscores the need for practical and low-cost training platforms that reflect real supervisory control environments. Conventional educational tools often rely on software-only simulations or high-cost industrial equipment, resulting in a persistent gap between academic instruction and operational practice. This study presents an educational SCADA training kit designed specifically for railway power monitoring and control. The system replicates essential SCADA functions including real-time data acquisition, breaker operation, environmental monitoring, fault handling, and operator interface visualization through a modular hardware software architecture suitable for academic laboratories. Performance evaluation was conducted across multiple operational scenarios, including normal operation, induced faults, temperature variations, and emergency commands. Key performance indicators such as responsiveness, sensing accuracy, alarm reliability, and stability were measured over 50 repeated trials. Results show 98.7% responsiveness within a 200 ms threshold, sensor accuracy above 97.5%, and 100% alarm reliability across 25 fault events. Continuous testing confirmed stable operation without communication or actuation failures. These findings demonstrate that the proposed kit offers a reliable, scalable, and pedagogically valuable platform for teaching SCADA concepts in railway automation, while also supporting research and prototyping in supervisory control applications.
Volume: 41
Issue: 2
Page: 740-752
Publish at: 2026-02-01

Assessment of detection methods for back-end process defects in equipment and devices in semiconductor manufacturing

10.11591/ijeecs.v41.i2.pp494-503
Ameer Farhan Roslan , Masrullizam Mat Ibrahim , Nik Mohd Zarifie Hashim , Mohd Syahrin Amri Mohd Noh , Tole Sutikno
Defect detection plays a pivotal part in the manufacturing process of semiconductors. Defects can be rooted in the product on its own, as well as the tools used to process and make the product, particularly the equipment and machinery used. Defect detection is crucial in semiconductor manufacturing, where even minor flaws can compromise product performance. Defect detection in the backend process of semiconductor manufacturing, specifically in die attach and die bonding, is critical for ensuring product quality and reliability. Die attach involves securing semiconductor chips onto substrates, while die bonding involves connecting wires to the chip. Detecting defects during these processes is vital to prevent issues such as misalignment, inadequate bonding, or contamination, which can lead to malfunctioning chips or devices. Various techniques such as visual inspection, automated optical inspection (AOI), and X-ray imaging are utilized to identify defects like cracks, voids, or irregularities in bond formation. By employing rigorous defect detection measures, manufacturers can uphold stringent quality standards and produce reliable semiconductor devices for various applications.
Volume: 41
Issue: 2
Page: 494-503
Publish at: 2026-02-01

Machine learning models in the enhancement of PSE in high-dimensional socioeconomic data: a review

10.11591/ijeecs.v41.i2.pp645-654
Gene Marck B. Catedrilla , Joey Aviles
This study reviews the use of machine learning (ML) techniques to improve propensity score (PS) estimation in high-dimensional socioeconomic data. Traditional logistic regression (LR) often performs poorly under nonlinear and complex covariate structures, leading to bias and model misspecification. Across the reviewed studies, ensemble methods such as random forests (RF) and gradient boosting, and deep learning models consistently achieved better covariate balance, lower bias, and greater flexibility than conventional approaches, while classification-based methods improved performance in imbalanced datasets. The review also highlights practical considerations, including calibration, transparent reporting, and integration with doubly robust estimators to strengthen causal inference. The findings show that ML-based propensity score estimation (PSE) can substantially enhance the validity and reliability of socioeconomic evaluations, provided that its implementation is carefully guided by appropriate expertise and best-practice standards.
Volume: 41
Issue: 2
Page: 645-654
Publish at: 2026-02-01

Depth estimation in handheld augmented reality: a review

10.11591/ijeecs.v41.i2.pp589-600
Muhammad Anwar Ahmad , Norhaida Mohd Suaib , Ajune Wanis Ismail
Depth estimation involves capturing the depth information of a scene in the form of depth data. This depth information can be applied in computer vision tasks to enhance perception and comprehension. In handheld augmented reality (AR), depth estimation refers to the capability of a handheld device to estimate the depth or distance of objects in the real world based on input from its camera feed. Currently, there is a lack of work that reviews on this topic. Thus, this paper reviews and discusses the technologies regarding depth estimation on handheld devices and their applications in relation to AR. We employ partially the systematic review procedure to allow more specific focus for our, broken into three main focuses. First, we discuss the methods to obtain depth data on handheld devices. Next, we discuss on the existing frameworks that enable depth estimation for handheld AR. Then, we compile and discuss the applications of depth estimation for handheld AR based on the reviewed papers. Finally, we discuss the novelties and limitations of the current research to determine the gaps in this field of research.
Volume: 41
Issue: 2
Page: 589-600
Publish at: 2026-02-01

Predicting non-performing loans in Vietnam’s financial sector: a deep Q-learning approach

10.11591/ijeecs.v41.i2.pp700-709
Luyen Anh Do , Huong Thi Viet Pham , Thinh Duc Le , Oanh Thi Tran
Non-performing loans (NPLs) prediction is a very important task in risk management of financial institutions. NPLs often lead to substantial losses when loans are not paid back on time. While traditional machine learning (ML) models have been conventionally exploited for credit risk assessment, they frequently face challenges with handling imbalanced data. To deal with this problem, this paper introduces a novel approach using deep reinforcement learning (DRL), specifically deep Q-learning, to enhance the prediction of NPLs. To verify the effectiveness of the method, we introduce a new dataset comprising 83,732 customer records (each described with 22 key features) from one of Vietnam's largest financial entities. Our method is compared with standard ML techniques such as random forest, decision tree, logistic regression, support vector machine, LightGBM, and XGBoost. Experimental results on this dataset demonstrate that deep Q-learning outperforms these traditional models in handling imbalanced data and boosting prediction accuracy. This research highlights the potential of DRL as a robust risk management tool, helping financial institutions make credit assessments more efficiently and reducing decision-making costs.
Volume: 41
Issue: 2
Page: 700-709
Publish at: 2026-02-01

Fraud detection using TabNet* classifier: a machine learning approach

10.11591/ijeecs.v41.i2.pp601-613
G. Anish Mary , S. Sudha
Detecting fraudulent transactions is a big challenge in the digital financial world. Transaction volumes are growing quickly, and new attack methods often outstrip traditional detection systems. Current fraud-detection models usually lack clarity and do not perform reliably on unbalanced real-world datasets. This highlights the urgent need for clear and explainable deep-learning methods for tabular financial data. This paper presents an interpretable deep learning framework built on the TabNet classifier. It uses attention-driven feature selection, sparse representation learning, and sequential decision reasoning to model complex interactions among transactional, demographic, and geographical factors. The model was tested on a real-world credit card transaction dataset with 23 features. It achieved 99.69% accuracy, a 0.975 F1-score, and a 0.956 ROC-AUC. This performance outperforms benchmark models such as random forest, XGBoost, LightGBM, and logistic regression. In addition to outstanding predictive results. Furthermore, interpretability is enhanced by TabNet's attention-based feature attribution. This facilitates the clear understanding of model decisions, supporting its use in regulated financial environments where precision and responsibility are crucial.
Volume: 41
Issue: 2
Page: 601-613
Publish at: 2026-02-01

RAC: a reusable adaptive convolution for CNN layer

10.11591/ijeecs.v41.i2.pp753-763
Nguyen Viet Hung , Phi Dinh Huynh , Pham Hong Thinh , Phuc Hau Nguyen , Trong-Minh Hoang
This paper proposes reusable adaptive convolution (RAC), an efficient alternative to standard 3×3 convolutions for convolutional neural networks (CNNs). The main advantage of RAC lies in its simplicity and parameter efficiency, achieved by sharing horizontal and vertical 1×k/k×1 filter banks across blocks within a stage and recombining them through a lightweight 1×1 mixing layer. By operating at the operator design level, RAC avoids post-training compression steps and preserves the conventional Conv–BN–activation structure, enabling seamless integration into existing CNN backbones. To evaluate the effectiveness of the proposed method, extensive experiments are conducted on CIFAR-10 using several architectures, including ResNet-18/50/101, DenseNet, WideResNet, and EfficientNet. Experimental results demonstrate that RAC significantly reduces parameters and memory usage while maintaining competitive accuracy. These results indicate that RAC offers a reasonable balance between accuracy and compression, and is suitable for deploying CNN networks on resource-constrained platforms.
Volume: 41
Issue: 2
Page: 753-763
Publish at: 2026-02-01

Driving connectivity: a thorough review of networking protocols in electric mobility

10.11591/ijeecs.v41.i2.pp764-772
Ramandeep Sandhu , Harpreet Kaur Channi , Nimay Chandra Giri , Pulkit Kumar , Mohamed A. Elaskily , Mohamed A. Hebaishy
The rapid advancement of technology has transformed the automotive sector through intelligent systems for safety, control, and infotainment. This study reviews key networking protocols controller area network (CAN), local interconnect network (LIN), FlexRay, MOST, Ethernet, and Master-Slave used in electric vehicles (EVs) in India and worldwide, providing insights into their application trends across different regions. CAN provides reliable low-latency communication for safety-critical functions (1 Mbps), while CAN FD extends support up to 12 Mbps. LIN and Master-Slave topologies enable cost-effective low-speed operations (2–20 kbps). FlexRay ensures real-time communication (10–100 Mbps), and MOST supports 150 Mbps for multimedia applications. Ethernet offers superior bandwidth up to 10 Gbps for advanced driver assistance and autonomous systems, but it involves higher complexity and cost. The review identifies key challenges in interoperability, scalability, and cybersecurity and evaluates protocol suitability for next-generation EV architectures. It also integrates Industry 5.0 principles and SDGs 7, 9, and 13, emphasizing human-centric, sustainable, and resilient mobility.
Volume: 41
Issue: 2
Page: 764-772
Publish at: 2026-02-01

An automatic stock price movement prediction using circularly dilated convolutions with orthogonal gated recurrent unit

10.11591/ijeecs.v41.i2.pp823-832
Durga Meena Rajendran , Maharajan Kalianandi , Bhuvanesh Ananthan
Recently, stock trend analysis has played an integral role in gaining knowledge about trading policy and determining stock intrinsic patterns. Several conventional studies reported stock trend prediction analysis but failed to obtain better performance due to poor generalization capability and high gradient vanishing problems. In light of the need to forecast stock price trends using both textual and empirical price data, this research proposed a novel hybridized deep learning (DL) model. Preprocessing, feature extraction, and prediction are the three effective stages that the created research goes through in order to properly estimate the stock movements. Data cleaning, which helps improve data quality, is calculated in the preprocessing step. Next, we use the created CDConv-OGRU technique-hybridized circularly dilated convolutions with orthogonal gated recurrent units-to extract features and make predictions. Python serves as the platform for processing and analyzing the created approach. This research uses a publicly accessible StockNet database for testing and compares results using a number of performance metrics, including accuracy, recall, precision, Mathew’s correlation coefficient (MCC), and f-score. In the experimental part, the created approach obtains a total of 95.16% accuracy, 94.8% precision, 94.89% recall, 95% confidence interval, and 0.9 MCC, in that order.
Volume: 41
Issue: 2
Page: 823-832
Publish at: 2026-02-01
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