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Robust palmprint biometric solution for secure mobile authentication

10.11591/ijeecs.v41.i2.pp680-689
Son Nguyen , Arthorn Luangsodsai , Pattarasinee Bhattarakosol
Smartphones increasingly rely on biometric authentication for access to financial and personal services, creating a need for palmprint recognition that is accurate, fast, and deployable on device. This paper proposes an end-to-end smartphone palmprint authentication framework that integrates guided mobile image capture, landmark-based region-of-interest (ROI) extraction, and compact embedding inference. A ResNet-18 teacher is first trained with self-supervised contrastive learning to reduce dependence on labeled biometric data, then distilled into a lightweight MobileNetV3 student for efficient mobile deployment. The learned embeddings support both on device verification and large-scale identification using an approximate nearest neighbor index (FAISS). Experiments on a public Kaggle palm dataset achieve 99.2% accuracy with a 0.15% equal error rate (EER). On an iPhone 13, the end-to-end pipeline runs in 87.0 ms with a 12.4 MB student model. For a 1 million-entry gallery, FAISS provides 32 ms query latency while maintaining 99.5% Recall@1. Limitations include evaluation under mostly controlled capture conditions and the absence of an explicit liveness or presentation attack detection (PAD) module; future work will address unconstrained testing and anti-spoofing integration.
Volume: 41
Issue: 2
Page: 680-689
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

Engineering intelligence for sustainable and secure digital futures

10.11591/ijeecs.v41.i2.pp453-455
Tole Sutikno
This editorial introduces Volume 41, Number 2 (February 2026) of the Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), which presents a diverse collection of peer-reviewed articles reflecting recent advances in electrical engineering, electronics, and computer science. The issue highlights the convergence of power and energy systems, artificial intelligence, cybersecurity, the Internet of Things (IoT), and datadriven engineering methodologies in addressing contemporary technological and societal challenges, with key contributions focusing on renewable energy integration, intelligent control strategies, secure and trusted digital infrastructures, smart IoT-based systems, and AI-driven applications in healthcare, finance, industrial automation, and human-centered computing. Particular emphasis is placed on energy efficiency, system resilience, explainable and trustworthy artificial intelligence, and sustainable engineering practices. Collectively, the published works demonstrate how interdisciplinary research can bridge theory and real-world implementation while supporting the United Nations Sustainable Development Goals, including affordable and clean energy, good health and well-being, sustainable cities, responsible consumption, and strong digital institutions. By fostering innovation, cross-domain collaboration, and responsible technology development, this issue of IJEECS aims to advance secure, intelligent, and sustainable engineering solutions that respond to both current demands and future global challenges. This issue further reinforces the journal’s commitment to advancing engineering intelligence that is ethically grounded, environmentally responsible, and resilient by design.
Volume: 41
Issue: 2
Page: 453-455
Publish at: 2026-02-01

ETV: efficient text vision for text localization in natural scene images

10.11591/ijeecs.v41.i2.pp812-822
Suman Suman , Champa H. N.
In the current digital era, the extraction and comprehension of textual information from images have emerged as pivotal tasks. With the exponential growth of text documents, efficient processing and analysis have become imperative. However, text localization in images remains challenging due to complex backgrounds, uneven illumination, diverse text styles, and perspective distortions, rendering traditional optical character recognition (OCR) techniques inadequate. To address these challenges, this paper proposes an integrated method named efficient text vision (ETV). ETV combines the OCR capabilities of Tesseract with the efficient and accurate scene text detector (EAST) algorithm, supplemented by nonmaximum suppression (NMS). The Tesseract OCR component facilitates the extraction and identification of individual characters, while EAST excels in the efficient detection and localization of complete text sections. The incorporation of NMS enhances localization accuracy by eliminating redundant or overlapping bounding boxes.
Volume: 41
Issue: 2
Page: 812-822
Publish at: 2026-02-01

Smart home automation using internet of things

10.11591/ijeecs.v41.i2.pp579-588
Roopa R. , Pallavi B. , Lakshmi Neelima , Parikshith J. , Kashish Agarwal
This research paper delves into the development and implementation of an advanced home automation system utilizing internet of things (IoT) technology to bolster safety and comfort within residential environments. The proposed system architecture revolves around an ESP8266 microcontroller board interfaced with a diverse array of sensors, including motion detectors, temperature and humidity sensors, and air quality sensors specifically designed to detect gas leaks. Additionally, the system incorporates a servo motor for stove control and relays for fan activation. The described system adds novel safety-focused features, including servo-controlled stoves and fan-gas leak integration, making it applicable for critical home safety scenarios. However, it shares common weaknesses with existing systems, such as inadequate attention to security, energy efficiency, and scalability. By addressing these gaps, this system could set itself apart as a comprehensive IoT solution for home automation.
Volume: 41
Issue: 2
Page: 579-588
Publish at: 2026-02-01

Parkinson's disease diagnosis using voice biomarkers: a machine learning approach

10.11591/ijeecs.v41.i2.pp800-811
Amit Kumar , Neha Sharma , Shubham Mahajan , Seifedine Kadry
Parkinson's disease (PD) is a degenerative neurological disease, and at present there are no reliable laboratory tests for it. So how does this happen when people go to identify PD? vocal biomarkers, combined with machine learning (ML), seem to be an option for noninvasive diagnostics. In our work, we used a voice recording dataset which consisted of 26 different feature sets mined by various techniques. When using the extreme gradient boosting (XGBoost) method, out of all these models tested, an accuracy of 91.79% was achieved. As can be seen from its high precision, recall and F1- score, XGBoost performed very well in differentiating PD cases from non-cases. The study concludes that the application of ML, particularly XGBoost, to the diagnostic process can establish a valuable tool for early screening of PD, which will facilitate more speedy and correspondingly cost-effective clinical evaluations. This paper represents an important contribution to the rapidly developing fields of artificial intelligence-based on diagnosis of neurological diseases and digital health.
Volume: 41
Issue: 2
Page: 800-811
Publish at: 2026-02-01

Intelligent cybersecurity framework for real-time threat detection and data protection

10.11591/ijeecs.v41.i2.pp504-514
Gunti Viswanath , Kurapati Srinivasa Rao
Organizations operating across cloud, mobile, and enterprise environments are increasingly exposed to sophisticated cyberattacks that traditional rule-based security systems struggle to detect in real time. These legacy approaches lack adaptability, making it difficult to continuously monitor distributed networks, identify anomalies, and prevent zero-day threats before sensitive data is compromised. To address these challenges, this paper proposes an intelligent cybersecurity framework that integrates real-time network monitoring with AI/ML-based anomaly detection models. The framework utilizes structured preprocessing, feature engineering, and supervised learning on the UNSW-NB15 dataset (version 2015, Cyber Range Lab) to enhance detection accuracy and reduce response time. The experimental setup evaluates multiple ML classifiers using stratified train- test splitting and 5-fold cross-validation, ensuring robust performance validation. Experimental results show that the random forest (RF) model achieves 94.28% accuracy, a 2.93% false-positive rate, and an average detection time of 0.41 seconds, outperforming other baseline models. In addition to the detection layer, the framework incorporates mobile device management (MDM) controls and cloud-storage policy enforcement to strengthen organizational security posture. The main contributions of this work include: i) a unified AI/ML-driven anomaly detection model, ii) integration of MDM and cloud policy enforcement for end-to-end protection, and iii) improved empirical performance validated using a benchmark cybersecurity dataset. This combined architecture significantly enhances real-time threat identification and reduces alert latency, supporting a more security-aware and resilient enterprise environment.
Volume: 41
Issue: 2
Page: 504-514
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 survey on leveraging artificial intelligence tools for enhancing advanced mathematical education and problem-solving

10.11591/ijai.v15.i1.pp76-85
Hadeel N. Abosaooda , Syaiba Balqish Ariffin , Osamah Mohammed Alyasiri
Artificial intelligence (AI) has increasingly shaped education, with ChatGPT developed by OpenAI, emerging as a prominent tool due to its ability to generate contextually relevant language and support learning. This survey investigates the integration of ChatGPT into mathematics education, focusing on three dimensions. First, it explores innovative strategies for creating interactive and personalized learning environments that adapt to individual student needs. Second, it evaluates ChatGPT’s specific advantages in mathematics instruction, including providing tailored feedback, assisting with problem-solving, and deepening conceptual understanding. Third, it addresses the challenges of adopting ChatGPT in advanced mathematics education, such as risks of over-reliance, the necessity of balancing AI with traditional pedagogy, and the importance of ongoing professional development for educators. Recent studies highlight ChatGPT’s potential to solve complex mathematical problems, such as those in linear algebra and word problems, while also noting limitations related to accuracy and the preservation of critical thinking skills. The findings demonstrate that ChatGPT can significantly enhance mathematics education by supporting personalized learning and complex problem-solving. Therefore, this study will contribute to the discourse on AI in education by identifying opportunities, challenges, and implications for equity, pedagogy, and the responsible integration of ChatGPT in future classrooms.
Volume: 15
Issue: 1
Page: 76-85
Publish at: 2026-02-01

A new approach for distance vector-Hop localization algorithm improvement in wireless sensor networks

10.11591/ijeecs.v41.i2.pp515-531
Omar Arroub , Anouar Darif , Rachid Saadane , My Driss Rahmani , Zineb Aarab
This article shows a new range-free localization technique based on a metaheuristic algorithm (MA) dedicated to wireless sensor network (WSN), named sequential online-grey wolf optimization-distance vector-Hop (SOGWO-DVHOP). Indeed, we use the improved GWO based on selective opposite learning to improve GWO in order to enhance the traditional DVHOP localization algorithm. In reality, we choose GWO due to its better outcomes compared to other meta-heuristics, which leads us to improve this algorithm further. In the literature, the improvement works of GWO try to reconstruct the hierarchy of GWO or improve specifically the role of omega individuals. In our contribution, we opt for opposition-based learning (OBL) to ameliorate GWO, aiming to further enhance the quality of localization made by DVHOP. On the other hand, we make an empirical comparison of DVHOP and its improved versions in terms of accuracy. The results of the simulation demonstrate that SO-GWO-DVHOP gives the best performance when we vary the anchor ratio and the density of nodes.
Volume: 41
Issue: 2
Page: 515-531
Publish at: 2026-02-01

An artificial intelligence technology for promoting hom-thong banana agriculture system

10.11591/ijai.v15.i1.pp568-579
Ratsames Tanveenukool , Suwit Somsuphaprungyos , Boonyarit Nokkurth , Likit Chamuthai , Patumwadee Bonguleaum , Parinya Natho
The hom-thong banana, being a high-value Thai export variety, is facing significant risk from disease outbreaks affecting crop yield and quality. Traditional visual inspection methods in detection of diseases are labor consuming, error-prone. This research addresses these limitations by developing a new artificial intelligence (AI)-based automatic disease detection system for the hom-thong banana industry on top of cutting-edge computer vision technology. The study employed deep learning object detection models, contrasting Roboflow, you only look once (YOLO)v11, and YOLOv12 architectures, which were trained on a large dataset of 2,576 images of Thai banana plantations. With systematic data augmentation techniques, the dataset was augmented to 6,184 images of seven types of disease under varied environmental conditions. The method entailed extensive preprocessing and evaluation of performance through precision, recall, and mean average precision (mAP) metrics. Outcomes indicated that YOLOv12 outperformed with 93.3% accuracy, 83.3% sensitivity, and 86.3% mAP@50 compared to standard inspection schemes. This research is applicable to Thailand's smart agriculture initiative by providing farmers with low-cost, accurate, and effective disease monitoring equipment. The application of this AI system has the ability to enhance the yield of crops, reduce losses, and enhance the competitiveness of Thai banana exports in the global market, in support of sustainable agricultural development.
Volume: 15
Issue: 1
Page: 568-579
Publish at: 2026-02-01

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

Enhancing industrial cybersecurity via IoT device-trusted remote attestation framework with zero trust architecture in brewery operations

10.11591/ijeecs.v41.i2.pp720-730
Muhammad Salman , Alan Budiyanto
The rapid expansion of industrial internet of things (IIoT) adoption in Industry 4.0 has improved automation and real-time control yet simultaneously increased security risks in operational technology (OT) environments, where device integrity and system reliability are critical. Existing attestation approaches such as SAFEHIVE, SEDA, CRA, and ERASMUS provide scalable verification capabilities but still lack continuous hardware-rooted validation and adaptive access control required for real-time industrial systems. To address this gap, this study proposes a hybrid cybersecurity framework that integrates IoT device-trusted remote attestation (ID-TRA) based on trusted platform module (TPM) with zero trust architecture (ZTA) to ensure continuous device trustworthiness in brewery operations. The framework was implemented on an industrial testbed with programmable logic controllers (PLCs), edge devices, and industrial switches, and it was evaluated through measurements of attestation latency, false positive rate, communication overhead, and TPM resource utilization. Experimental results show that the framework achieves an average attestation latency of 250 ms, a false positive rate below 2%, and a communication overhead of only 1.1%, while TPM resource usage remains within acceptable bounds (62% CPU and 48 MB RAM). These outcomes demonstrate that the proposed solution can reliably detect unauthorized firmware modifications, prevent compromised devices from accessing critical network zones, and maintain compatibility with real-time control processes. Overall, the integration of ID-TRA and ZTA enhances device-level assurance and strengthens industrial cybersecurity resilience against firmware tampering, replay attacks, and unauthorized lateral movement.
Volume: 41
Issue: 2
Page: 720-730
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
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