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Evaluating test case minimization with DB K-means

10.11591/ijeecs.v41.i2.pp555-563
Sanjay Sharma , Jitendra Choudhary
This paper evaluates a new method for test case minimization using clustering methods. Clustering is a method used on data sets to generate clusters of the same behavior; thus, unnecessary and redundant data sets are removed. Hence, minimized data sets are generated that represent the same coverage as the original data sets. This is achieved by a new method based on clustering that separates data sets into two sets, outlier and non-outlier, after reducing redundant test cases, combines minimized data sets named DB K-means. The methods individually worked on outlier and non-outlier data sets and removed redundant data sets to minimize test cases. The result of the proposed method is better than the simple clustering method used for test case minimization. The software development would only be complete with software testing. Enhancing software quality requires testing numerous test cases, a laborious and time-consuming process, testing a program using a set of inputs known as test cases. Test case minimization approaches are critical in software testing, as they optimize testing resources and provide comprehensive coverage. Minimization is the process of choosing a subset of test cases that accurately captures the behavior of the entire test suite to minimize duplicacy and increase efficiency.
Volume: 41
Issue: 2
Page: 555-563
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

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

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

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

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

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

Quantitative evaluation of a virtual tour navigation system using satisfaction modeling: a case study in Thai cultural tourism

10.11591/ijeecs.v41.i2.pp690-699
Ekapong Nopawong , Rawinan Praditsangthong
This research aims to develop and evaluate the Lak Hok virtual tour navigation system to promote sustainable cultural tourism by showcasing Thai wisdom through immersive digital experiences. The system utilized 360-degree panoramic images hosted on a web server and supported accessibility via laptops, smartphones, and virtual reality (VR) headsets. Both subjective evaluations and objective performance metrics were employed to assess the system’s usability, aesthetic appeal, and content quality (CQ). User satisfaction, measured through a survey of 87 participants, demonstrated consistently high ratings (mean scores: 3.59-3.77 for ease of use (EU), 3.32-3.95 for design aesthetics, and 3.62-3.70 for content knowledge). Objective tests revealed an average system response time of 1.45 seconds, a false interaction rate of 4.2%, and a navigation accuracy of 98.5%. Statistical analysis showed no significant differences in user satisfaction across gender, age, or region, highlighting the system’s broad accessibility and usability. Unlike prior systems, this study formalizes satisfaction modeling via equation-based analysis. This virtual tour system provides a scalable and engaging platform for preserving and promoting cultural heritage, offering a sustainable solution for modern tourism development.
Volume: 41
Issue: 2
Page: 690-699
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

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

IoT-enabled connected incubator with redundant communication for real-time neonatal monitoring

10.11591/ijeecs.v41.i2.pp633-644
Naçima Mellal , Soumia Hadj Maatallah , Ammar Merazga , Rachida Bouchouareb , Souad Nacer
Premature birth remains a major challenge in neonatal care, especially in resource-constrained settings, where continuous monitoring and timely intervention are limited. Most existing neonatal incubators offer limited real-time monitoring, unreliable alerting, and lack communication redundancy, potentially delaying critical responses. This paper presents a comprehensive internet of thing (IoT) enabled connected incubator with redundant communication (Wi-Fi and GSM) for real-time monitoring of physiological and environmental parameters. The system integrates sensing, processing, cloud connectivity, a mobile application, and multi-channel alerts (App notifications, SMS, voice calls, and local alarms). It was experimentally evaluated under controlled laboratory conditions. Quantitative evaluation shows a cloud transmission success rate of 99.1%, end-to-end communication latency below 1 second via Wi-Fi and 2.2 seconds via GSM, with 98% of alerts successfully delivered within 6 seconds. The proposed system provides a low-cost, reliable platform that enhances neonatal safety, supports timely clinical decisions, and is scalable for resource-constrained healthcare environments.
Volume: 41
Issue: 2
Page: 633-644
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

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

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
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