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30,938 Article Results

A novel approach for detecting diabetic retinopathy using two-stream CNNs model

10.11591/ijeecs.v41.i1.pp200-209
Pham Thi Viet Huong , Le Duc Thinh , Tran Thi Oanh , Tran Xuan Bach , Hoang Quang Huy , Tran Anh Vu
Major causes of visual impairment, particularly diabetic retinopathy (DR) and aged-related macular degeneration (AMD), has posed significant challenges for clinical diagnosis and treatment. Early detection and prompt intervention can help prevent severe consequences for patients. The study presents a novel approach for detecting eye diseases using a two-stream convolutional neural network (CNN) model. The first stream processes preprocessed fundus images, while the second stream analyzes high-pass filtered fundus images in the spatial frequency domain. To assess the model’s performance, we use the APTOS 2019 dataset, which was originally compiled for the Asia Pacific Tele-Ophthalmology Society 2019 Blindness Detection competition and is publicly available on Kaggle. Our method shows promise as an early screening tool for DR detection with an accuracy of 0.986.
Volume: 41
Issue: 1
Page: 200-209
Publish at: 2026-01-01

YOLOv8m enhancement using α-scaled gradient-normalized sigmoid activation for intelligent vehicle classification

10.11591/ijeecs.v41.i1.pp153-167
Renz Raniel V. Serrano , Jen Aldwayne B. Delmo , Cristina Amor M. Rosales
Vehicle classification plays a vital part in the development of intelligent transportation systems (ITS) and modern traffic management, where the ability to detect and identify vehicles accurately in real time is essential for maintaining road efficiency and safety. This paper presents an enhancement to the YOLOv8m model by refining its activation function to achieve higher accuracy and faster response in diverse traffic and environmental situations. In this study, two alternative activation functions—Mish and Swish—were integrated into the YOLOv8m structure and tested against the model’s default sigmoid linear unit (SiLU). Training and evaluation were carried out using a comprehensive dataset of vehicles captured under different lighting and weather conditions. The experimental findings show that the modified activation design leads to better model convergence, improved generalization, and a noticeable boost in detection performance, recording up to 5.4% higher accuracy and 6.6% better mAP scores than the standard YOLOv8m. Overall, the results confirm that fine-tuning activation behavior can make deep learning models more adaptive and reliable for vehicle classification tasks in real-world intelligent transportation environments.
Volume: 41
Issue: 1
Page: 153-167
Publish at: 2026-01-01

Cryptojacking detection using model-agnostic explainability

10.11591/ijeecs.v41.i1.pp394-408
Elodie Ngoie Mutombo , Mike Wa Nkongolo , Mahmut Tokmak
Cryptojacking is the illicit use of computing resources for cryptocurrency mining. It has emerged as a serious cybersecurity threat that degrades critical system performance and increases operational costs. This paper proposes an advanced machine learning (ML) framework that integrates transformer based language models with post hoc explainable artificial intelligence (XAI) to detect cryptojacking using complementary network traffic and process memory data. Numerical and categorical features are discretized and tokenized to enable semantic modelling and contextual learning. Experimental results show that transformer models effectively capture cryptojacking-related behavioral patterns, with decoding-enhanced BERT with disentangled attention (DeBERTa) achieving high detection performance and recall exceeding 80%. bidirectional encoder representations from transformers (BERT) attains comparable recall with lower computational overhead, making it well suited for real-time environments, while robustly optimized BERT approach (RoBERTa) and DeBERTa are more appropriate for offline or batch-based analysis. Model performance is evaluated using standard classification metrics, and XAI techniques provide interpretable insights into feature relevance, supporting transparent and reliable detection. In general, the proposed framework delivers an effective and deployment-ready solution for cryptojacking detection.
Volume: 41
Issue: 1
Page: 394-408
Publish at: 2026-01-01

Comparative analysis of linear regression, random forest, and LightGBM for hepatitis disease prediction

10.11591/ijeecs.v41.i1.pp430-438
Hennie Tuhuteru , Goldy Valendria Nivaan , Marvelous Marvel Rijoly , Joselina Tuhuteru
In bioinformatics research, computational pattern-analysis techniques are frequently employed to assist in disease prediction and diagnostic modeling, including applications for hepatitis prognosis. Hepatitis is a type of serious disease with various types that have the potential to threaten the life of the sufferer without showing significant symptoms and signs, so many sufferers do not realize that they are affected by the disease. Various methods are used to predict diseases in the hope of providing the best results from the learning model used. The objective of this study is to implement linear regression, random forest, and light gradient boosting machine (LightGBM) to estimate mortality risk among hepatitis patients. In addition, a performance comparison of the results of hepatitis disease prediction using the three algorithms was also carried out to find out which model gave the most accurate and optimal results. The results of this study show that the application of learning models from the linear regression, random forest and Light-GBM algorithms has been successfully carried out to predict the survival status of patients with hepatitis. The findings reveal that random forest achieved the highest predictive performance with an accuracy of 84%, followed by LightGBM at 77% and linear regression at 32%.
Volume: 41
Issue: 1
Page: 430-438
Publish at: 2026-01-01

Detection of COVID-19 using chest X-rays enhanced by histogram equalization and convolutional neural networks

10.11591/ijeecs.v41.i1.pp387-393
Nazif Tchagafo , Abderrahmane Ez-Zahout , Ahiod Belaid
The persistent global health crisis initiated by the COVID-19 pandemic continues to demand robust and high-throughput diagnostic solutions. While gold-standard methods, such as polymerase chain reaction (PCR) testing, are accurate, their scalability and turnaround time remain limitations in high volume settings. This paper introduces a novel deep learning framework designed for rapid and accurate detection of COVID-19 from chest X-ray (CXR) imagery. Our methodology leverages a convolutional neural network (CNN) architecture, augmented by a crucial pre-processing stage: histogram equalization. This step is vital for enhancing the subtle contrast features inherent in CXR scans, there by significantly improving the quality of the input data and facilitating superior feature extraction by the CNN. The model was trained and rigorously validated on a dedicated dataset. Performance was systematically quantified using a comprehensive confusion matrix, yielding key metrics such as precision and specificity, alongside the receiver operating characteristic (ROC) curve. The achieved results are highly encouraging, demonstrating a classification accuracy of 98.45%. This innovative approach offers a substantial acceleration of the diagnostic process, providing a non-invasive and highly effective complementary tool for clinicians. Ultimately, this advancement has the potential to streamline patient management protocols and alleviate diagnostic pressures on global healthcare infrastructures.
Volume: 41
Issue: 1
Page: 387-393
Publish at: 2026-01-01

Relationship between voltage and resistance in hybrid nanoconductive ink on different substrates in wet and dry conditions

10.11591/ijeecs.v41.i1.pp18-32
Norashikin Shari , Nurfaizey Abd Hamid , Chonlatee Photong , Alan J. Watson , Mohd Azli Salim
Hybrid graphene nanoplatelet/silver (GNP/Ag/SA) conductive inks are increasingly used in flexible electronics, yet there is limited understanding of how substrate type, solvent composition, and moisture exposure jointly control the electrical performance on metal and polymer substrates. This work aims to clarify how terpinol content (5T, 10T, 15T) and substrate properties of copper (Cu), polyethylene terephthalate (PET), and thermoplastic polyurethane (TPU) influence voltage, resistance, and resistivity of screen-printed GNP/Ag/SA tracks under dry and postimmersion wet conditions. GNP/Ag/SA inks were formulated with fixed butanol and varied terpinol contents, printed on Cu, PET, and TPU, and characterized using electrical measurements, adhesion evaluation, and microstructural observations to relate resistivity trends to morphology, surface energy, and hygroscopic behavior. The Cu substrate showed the best performance, with Cu 10T achieving the lowest dry resistivity of approximately 1.2×10-5 Ω.m and Cu 15T the lowest wet resistivity of approximately 2.0×10-5 Ω.m, supported by dense, well-adhered microstructures. The PET exhibited higher resistivity values up to about 10-3 Ω.m and clear degradation after water immersion, while TPU showed very high or unmeasurable resistivity in wet conditions caused by severe ink loss and hygroscopic swelling, highlighting the important role of substrate surface energy and moisture response in determining the reliability of GNP/Ag/SA inks for applications in humid or wet environments.
Volume: 41
Issue: 1
Page: 18-32
Publish at: 2026-01-01

Tool support for LoRaWAN development: a comparative perspective on simulation and emulation

10.11591/ijeecs.v41.i1.pp233-249
Ntshabele Koketso , Bassey Isong
This paper explores the use of various long range wireless area network (LoRaWAN) simulation and emulation tools when designing and evaluating IoT networks. Simulation tools are often popular with researchers because they are less costly and can easily simulate large-scale networks, allowing for easy and faster tests of the scalability of various protocols and behaviors. However, they often lack the unpredictable nature of real deployments. Emulation and cloud-based tools fill this gap, but with their flexibility they provide a more realistic approximation of real-world performance and allow easier interfacing with actual network hardware infrastructure, although they generally incur a higher cost which is often controlled by technical skill level use. 
Volume: 41
Issue: 1
Page: 233-249
Publish at: 2026-01-01

Development of unified college admission system for Philippine state universities and colleges: a data-driven approach to equity and access

10.11591/ijeecs.v41.i1.pp61-72
Abegail G. Bordios , Sherrie Ann Cananua-Labid , Ariel B. Mabansag , Mae V. Cañal , Jake Boy D. Carboquillo , Ma. Andrea C. Del Rosario
This paper presents the development and pilot evaluation of the unified college admission system (UCAS), a centralized and equity-oriented digital platform designed to streamline admissions across Philippine state universities and colleges (SUCs). Anchored on Republic Act No. 10931, UCAS functions as a unified application repository that standardizes admissions data, consolidates applicant records, and enables real-time monitoring of equity target students (ETS) to support fair and transparent access to higher education. The system integrates student-facing and administrative portals that facilitate application submission, institutional coordination, and equity-focused analytics. A pilot evaluation involving student applicants and administrators assessed usability, efficiency, and reliability, yielding consistently positive results across user groups. Findings indicate that UCAS is technically robust, user-centered, and suitable for multi-level admissions governance. Overall, the study demonstrates the potential of a centralized, data-driven admissions platform to complement tuition-free education policies by addressing inequities at the admissions stage.
Volume: 41
Issue: 1
Page: 61-72
Publish at: 2026-01-01

A hybrid divisive K-means framework for big data–driven poverty analysis in Central Java Province

10.11591/ijeecs.v41.i1.pp258-269
Bowo Winarno , Budi Warsito , Bayu Surarso
Clustering is essential in big data analytics, especially for partitioning high dimensional socioeconomic datasets to support interpretation and policy decisions. While K-Means is widely used for its simplicity and scalability, its strong sensitivity to initial centroid selection often leads to unstable results and slower convergence. Previous hybrid approaches, such as Agglomerative–K-Means, attempted to address this issue by using hierarchical clustering for centroid initialization; however, these methods rely on bottom-up merging, which can produce suboptimal initial partitions and increase computational overhead for larger datasets. To overcome these limitations, this study proposes a hybrid divisive–K-Means (DHC) model that employs top-down hierarchical splitting to generate more coherent initial centroids before refinement with K-Means. Using a multidimensional poverty dataset from Central Java Province provided by the Indonesian Central Bureau of Statistics (BPS), the performance of DHC was evaluated against standard K-Means and Agglomerative–K-Means. The assessment included execution time, convergence iterations, and cluster validity indices (Silhouette, Davies–Bouldin, and Calinski–Harabasz). Experimental results demonstrate that DHC reduces execution time by up to 97% and requires 40% fewer iterations than standard K-Means, while achieving comparable or improved cluster quality (e.g., CH Index increasing from 14.3 to 15.8). These findings indicate that the DHC model offers a more efficient and stable clustering solution, addressing the shortcomings of previous standard K-Means methods and improving performance for large-scale socioeconomic data analysis.
Volume: 41
Issue: 1
Page: 258-269
Publish at: 2026-01-01

Lung cancer segmentation and classification using hybrid CNN-LSTM model

10.11591/ijeecs.v41.i1.pp309-319
Manaswini Pradhan , Ahmed Alkhayyat
A collection of genetic disorders and various types of abnormalities in the metabolism lead to cancer, a fatal disease. Lung and colon cancer are found to be main causes of death and infirmity in people. When choosing the best course of treatment, the diagnosis of these tumors is usually the most important consideration. This study's main objectives are to classify lung cancer and its severity, as well as to recognize malignant lung nodules. The suggested approach additionally classifies the stages of lung cancer in order to recognize lung nodules. The convolutional neural network (CNN) is used to detect lung nodules, identifying a nodule which is accurately segmented and classified. The suggested method is separated into dual parts: primarily, it classifies normal and abnormal behavior, and the subsequent one classifies the different stages of lung cancer. Texture and intensity-based features are extracted during the classification stage. When compared to other methods such as nested long short-term memory (LSTM)+ CNN, the hybrid CNN LSTM obtains superior outcomes in terms of accuracy (99.35%), specificity (99.30%), sensitivity (99.32%), and F1-score (99.29%).
Volume: 41
Issue: 1
Page: 309-319
Publish at: 2026-01-01

Advancing intelligent, sustainable, and secure engineering systems for future technologies

10.11591/ijeecs.v41.i1.pp1-2
Tole Sutikno
This editorial introduces Volume 41, Number 1, January 2026, of the Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), highlighting pivotal research trajectories expected to influence future progress in electrical engineering and computer science. Instead of covering all aspects of the field, this issue is structured around three strategic macroclusters: intelligent and sustainable engineering systems, AI-driven healthcare and human-centered technologies, and secure, comprehensible, and interconnected intelligent infrastructure. These themes show how artificial intelligence, sustainability, and security are coming together more and more in modern engineering applications. The editorial talks about how important intelligent energy systems, advanced control and hardware solutions, data-driven healthcare innovations, and reliable digital infrastructures are for solving global technological problems. This issue's contributions demonstrate IJEECS's dedication to publishing significant, cross-disciplinary research that bridges theory and practice. This issue of the journal makes it clear that it is a progressive platform that wants to promote smart, long-lasting, and safe technologies for the engineering systems of the future.
Volume: 41
Issue: 1
Page: 1-2
Publish at: 2026-01-01

Intelligent dust monitoring and cleaning optimization on photovoltaic panels

10.11591/ijeecs.v41.i1.pp409-418
Ali Kourtiche , Souaad Belhia , Santiago Felici-Castell , Mohammed El Amine Said , Rania Bouanani
Dust deposition on photovoltaic (PV) panels is a significant operational issue, often leading to power losses exceeding 15–30% in regions with high airborne particle concentrations. Although numerous studies have investigated either visual detection of dust or analytical estimation of performance loss, most approaches focus on a single task and provide limited practical insight for real-time maintenance. This work introduces a dual-task deep learning framework that simultaneously classifies dust severity and predicts the corresponding power loss from panel images. Five recent architectures vision transformer (ViT), swin transformer, GhostNet, DenseNet, and MobileNetV2 are employed as backbone feature extractors, with extracted embeddings processed by a multi-head multi-layer perceptron (MLP) combining shared representation learning with separate classification and regression outputs. The system is trained and evaluated on a real-world dataset of PV panels, and performance is assessed using accuracy and mean absolute error. DenseNet achieves the highest accuracy (94%) and lowest prediction error, while lightweight convolutional neural network (CNN) backbones demonstrate the best balance between precision and computational efficiency. By integrating hybrid processing and dual predictive capability, the proposed method offers a more comprehensive and deployable solution for automated PV monitoring compared to existing single-output approaches.
Volume: 41
Issue: 1
Page: 409-418
Publish at: 2026-01-01

Optimal thermo-QoS-aware routing protocol for WBAN communication

10.11591/ijeecs.v41.i1.pp270-282
Pardeep Bedi , Sanjoy Das , S. B. Goyal , Manoj Kumar , Sunil Gupta
Wireless body area network (WBAN) has emerged as a promising solution to address problems such as population aging, a lack of medical facilities, and different chronic ailments. WBANs have real-time applications, and there is an increasing demand for them. However, due to changing network structure, power supply limitations, and constrained computing capacity, energy constraints, it is difficult task to achieve quality of service (QoS). To mitigate these limitations, the paper proposed an optimal thermo-QoS aware routing protocol (OTQRP) for WBAN communication. The result was investigated in terms of temperature rise, energy consumption and delay. The paper shows better energy efficiency with respect to existing works. Finally, OTQRP feature comparison is also presented with recent research in terms of features such as complexity, latency, and energy economy and observed that OTQRP shows best performance as compared to others.
Volume: 41
Issue: 1
Page: 270-282
Publish at: 2026-01-01

An energy-efficient hardware module for edge detection using XNOR-Popcount in resource-constrained devices

10.11591/ijeecs.v41.i1.pp73-82
Van-Khoa Pham , Lai Le
Edge detection is a fundamental building block in many embedded vision tasks, including drone navigation, IoT cameras, and wearable devices. However, traditional edge detectors based on multiply–accumulate (MAC) operations are poorly suited to the tight power and area budgets of such resource-constrained hardware. This work introduces a fully synthesizable Prewitt edge detector that replaces MAC operations with 1-bit XNOR– Popcount logic. Incoming 8-bit pixels and ±1 kernel coefficients are binarized, processed by parallel XNOR gates, and tallied by a lightweight Popcount adder tree, eliminating all multipliers and DSP slices. Prototyped on a Xilinx Zynq-7020 FPGA, the proposed design reduces lookup-table usage by 55% and flip-flop count by 26%, cuts dynamic power by about 60%, and supports clock frequencies up to five times higher than a MACbased core. Frame-level evaluations on the MNIST and ORL datasets show near-lossless edge fidelity, with per-image dissimilarity scores below 0.08 and throughput gains approaching four times. These results demonstrate that hardware-aware binary approximations can enable real-time, energyefficient edge detection for embedded AI systems without sacrificing functional accuracy.
Volume: 41
Issue: 1
Page: 73-82
Publish at: 2026-01-01

Dynamic behavior of induction machines in ATP-EMTP with space harmonics

10.11591/ijeecs.v41.i1.pp3-17
Jose Manuel Aller , Ruben Nicolas Guevara , Bryam Steven Pulla
This work develops a space-vector model of a squirrel-cage induction machine that incorporates the effects of spatial harmonics arising from the winding distribution. The modeling approach includes the first, fifth, and seventh spatial harmonics, which are the components with the greatest influence on the machine’s magnetic field. Simulation results highlight the impact of these harmonics on the stator and rotor currents, the electromagnetic torque, and the machine’s speed. To build the model, the voltage behind reactances (VBR) technique is employed, enabling a hybrid strategy that combines circuit-based modeling tools—such as ATP-EMTP—with computational programming in models to complement the solution of the differential equations governing the behavior of the electromechanical system. This methodology effectively transforms the induction machine into a dynamic Thevenin-equivalent circuit for each phase of the converter. ` This study provides a useful framework for evaluating how space harmonics affect the performance and operating characteristics of induction machines. The models were implemented using the ATP-EMTP software and its graphical interface, ATPDraw.
Volume: 41
Issue: 1
Page: 3-17
Publish at: 2026-01-01
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