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31,456 Article Results

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

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

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

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

Prediction of permeability via nuclear magnetic resonance logging using convolutional neural networks

10.11591/ijeecs.v41.i1.pp168-179
Islamia Dasola Amusat , Ebenezer Leke Odekanle , Lanre Michael Toluhi , Sunday Adeola Ajagbe , Pragasen Mudali , Akeem Olatunde Arinkoola
Permeability is a critical parameter in subsurface fluid flow analysis, reservoir management, hydrocarbon recovery, and carbon dioxide sequestration. Traditional permeability measurement methods involve costly and time-consuming laboratory tests or well-related data. Machine learning (ML), specifically convolutional neural networks (CNN), is proposed as a cost-effective and rapid permeability prediction solution, harnessing interrelationships of input-output variables. In this study, empirical permeability correlation was developed using CNN. Forty nuclear magnetic resonance (NMR) T2 spectrums and 89 logarithmic mean NMR T2 distributions (T2lm) were preprocessed, screened and key spectra were identified using the principal component analysis (PCA). To develop the correlations, a custom-designed CNN architecture was employed to leverage the spatial patterns and intricate relationships embedded in the NMR data. The model was trained and validated rigorously using k-fold cross validation scheme to ensure robustness and generalization. Performance metrics like R-squared (R2), root mean squared error (RMSE), mean absolute error (MAE), standard deviation (SD), absolute deviation (AD), average absolute deviation (AAD), average absolute percentage relative error (AAPRE), and maximum error (Emax) were deployed to evaluate the model’s accuracy and ability to predict permeability values accurately. Among the folds considered, the fold 1 emerged as the best-performing model with the highest R2 value of 0.9544. This CNN-based correlation outperformed conventional and other AI-based models in terms of R2, Emax, AD, AAD, AAPRE, among other metrics. Overall, the study demonstrates the effectiveness of CNN in predicting permeability, offering a superior alternative to costly and limited traditional methods, with fold 1 showing the most promising results.
Volume: 41
Issue: 1
Page: 168-179
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

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

Design and construction of an Arduino-based baby incubator simulator using IoT

10.11591/ijeecs.v41.i1.pp99-108
Liza Rusdiyana , Joel Juanda Jamot Damanik , Bambang Sampurno , Suhariyanto Suhariyanto , Mahirul Mursid , Ika Silviana Widianti
This study aims to create a baby incubator simulator equipped with an internet of things (IoT)-based temperature control system using Arduino UNO. We use a DHT22 sensor to measure temperature and humidity, as well as fuzzy logic to ensure more accurate and responsive temperature control. The Thinger.io platform enables real-time monitoring and control of the incubator, providing flexibility and ease of supervision. With fuzzy logic, the temperature control system can handle changes and uncertainties in the incubator environment, providing a smoother response compared to traditional on-off methods. Testing shows that this system has a very low error rate, with an error value of only 0.97%, meaning that the measured temperature is almost identical to the actual conditions inside the incubator. Additionally, the authors used mice as a model for premature infants in the testing. The results showed that the mice's body temperature increased gradually and stably in line with the incubator conditions, reaching the desired temperature within 90 minutes. This demonstrates that our temperature control system is capable of maintaining optimal environmental conditions for premature infants.
Volume: 41
Issue: 1
Page: 99-108
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

Comparative analysis of fractional-order sliding mode and pole placement control for robotic manipulator

10.11591/ijeecs.v41.i1.pp90-98
Ahmed Bennaoui , Salah Benzian , Idrees Nasser Alsolbi , Aissa Ameur
Fractional-order sliding mode control (FOSMC) is benchmarked against pole placement control (PPC) on a nonlinear two-link manipulator subjected to identical trajectories and 10 N·m square disturbances. Quantitative head-to-head evidence against industrial PPC is scarce, leaving engineers uncertain when fractional designs justify their added complexity. We derive the plant via Lagrange dynamics, implement both controllers in Python, and evaluate tracking and torque effort using SciPy-based simulations. Under the adopted fractional derivative approximation, FOSMC attains RMSEs of 0.458 rad (q1) and 0.453 rad (q2) whereas PPC limits the errors to 0.365 rad and 0.337 rad. The fractional design, however, requires lower mean torques of 69.2/29.0 N·m compared to PPC’s 86.1/41.4 N·m, exposing a precision–energy trade-off that now favours PPC on accuracy and FOSMC on actuation effort. The benchmark delivers deployable evidence that fractional sliding surfaces shift torque demand even when their tracking performance lags, and it motivates hardware-in-the-loop validation to close the identified accuracy gap.
Volume: 41
Issue: 1
Page: 90-98
Publish at: 2026-01-01

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

Cyber hygiene awareness among Malaysian youth

10.11591/ijeecs.v41.i1.pp210-219
Amily Fikry , Azreen Joanna Abdul , Khairul Nazlin Kamaruzaman , Asnawati Asnawati
The study examined cyber hygiene awareness among Malaysian youth by analyzing the roles played by individual knowledge, awareness, attitudes, gender differences, and educational level. An online survey was conducted with 414 respondents in Peninsular Malaysia. The results showed no significant differences in cyber hygiene awareness based on gender and educational level. This suggests equal access to cybersecurity information and training across genders and education levels in Malaysia. This study also found significant relationships between individual characteristics (knowledge, rationality, and attitude) and cyber hygiene awareness. These findings indicate that individuals who are more knowledgeable, have positive attitudes, and make rational decisions tend to have higher cyber hygiene awareness. The results highlight the importance of fostering rationality and consistency in approaches to cybersecurity practices. The study contributes to the thoughtfully reflective decision-making (TRDM) theory, providing insights for developing targeted cybersecurity training programs and policies. Future research could explore additional factors influencing cyber hygiene awareness and examine how these findings translate to actual cybersecurity behaviors in professional settings.
Volume: 41
Issue: 1
Page: 210-219
Publish at: 2026-01-01

Enhancing wind energy prediction accuracy with a hybrid Weibull distribution and ANN model: a case study across ten locations in Java Island, Indonesia

10.11591/ijeecs.v41.i1.pp180-190
Silvy Rahmah Fithri , Nurry Widya Hesty , Rudi P. Wijayanto , Bono Pranoto , Prima Trie Wijaya , Akhmad Faqih , Wisnu Ananta Kusuma , Agus Nurrohim , Agus Sugiyono , Yudiartono Yudiartono
Accurate wind speed forecasting is essential for optimizing renewable energy (RE) systems, especially in coastal and island regions with high variability. This study proposes a hybrid predictive model that combines Weibull distribution parameters with artificial neural networks (ANN) to enhance forecasting accuracy. Using ten years of hourly NASA POWER data from 10 locations across Java Island, 24 scenarios were tested with varying combinations of Weibull and meteorological variables. Results demonstrate that incorporating both Weibull shape (k) and scale (c) parameters significantly improves performance, with the best configuration (Scenario 1) achieving a MAPE of 0.44% in Garut. Excluding one or both parameters sharply reduced accuracy, with errors rising up to 35.12%. Beyond technical accuracy, the findings emphasize the practical relevance of Weibull-informed ANN models for energy planning. Reliable forecasts support better wind resource assessment, grid integration, and investment decisions, reducing uncertainties that often hinder wind power deployment. By providing accurate and stable predictions across diverse locations, this approach offers policymakers and planners a robust tool to accelerate RE development and meet national energy targets.
Volume: 41
Issue: 1
Page: 180-190
Publish at: 2026-01-01

Enhancing cybersecurity in 5G networks systems through optical wireless communications

10.11591/ijeecs.v41.i1.pp250-257
Iyas Abdullah Alodat , Shadi Al-Khateeb
In this paper we will discuss with the recent global deployment of 5G networks, it has become imperative to ensure secure and reliable communications in addi tion to basic responsibility. Given that standard radio frequency (RF) communi cations have security flaws such as eavesdropping, signal jamming, and cyber attacks, wireless optical communications (WOC) offers a viable alternative. Us ing technologies such as visible light communications (VLC) and the free space optics (FSO) technologies, 5G networks can enhance the speed and efficiency of data transmission, while simultaneously enhancing cyber security. In addition to discussing the advantages of wireless on-chip communication technology com pared to RF solutions and the challenges that need to be addressed, this paper examines how WOC technology can enhance cyber security in 5G networks.
Volume: 41
Issue: 1
Page: 250-257
Publish at: 2026-01-01

Optimization of a hybrid forward chaining and certainty factor model for malaria diagnosis based on clinical and laboratory data

10.11591/ijeecs.v41.i1.pp419-429
Patmawati Hasan , Rahmat H. Kiswanto , Susi Lestari
Malaria remains a serious public health problem in Indonesia, particularly in Papua Province, which accounts for 89% of national malaria cases. The similarity of malaria symptoms with other infectious diseases and limited laboratory facilities often lead to delays and inaccuracies in diagnosis. The study proposes an optimized hybrid model that combines forward chaining and certainty factor (CF) by integrating clinical and laboratory data to improve the accuracy of malaria diagnosis. The research design includes acquiring knowledge from medical experts, developing a rule-based system using forward chaining, and applying CFs to overcome uncertainty in symptom interpretation. The system is implemented using Python with support from libraries such as NumPy and PyKnow. The test results showed that the integration of laboratory data significantly improved diagnostic performance, with accuracy increasing from 81% malaria-positive using clinical data alone to 98% malaria-positive after combining with laboratory data. Expert testing to validate the accuracy of clinical and laboratory data results compared to expert validation results in an accuracy score of 98%. These findings show that the optimization of the hybrid forward chaining model and CF for malaria diagnosis based on clinical and laboratory data as a recommendation tool for early diagnosis of malaria in endemic areas.
Volume: 41
Issue: 1
Page: 419-429
Publish at: 2026-01-01
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