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30,376 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

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

Survey on plant disease detection via combination of deep learning and optimization algorithms with IoT sensors

10.11591/ijeecs.v41.i1.pp357-366
Santhiya Govindapillai , Radhakrishnan A
Crop diseases are one of the main problems facing the farming sector. Detecting plant diseases using some automatic techniques is advantageous because it recognizes problems early and eliminates a significant amount of monitoring effort on massive farms. Numerous investigators have created various metaheuristic optimizing and an innovative technique for deep learning to recognize and classify plant illnesses. This research analyzes many IoT-based methods for automated plant disease identification and detection. The automatic module for detecting plant diseases provides data to a sink node that the system maintains to facilitate IoT-based monitoring. Numerous methods based on plant disease and computer vision exist. Thirty three papers in all are examined here. This research also offers a thorough understanding of how to enhance IoT-integrated plant disease detection and identification capabilities. In addition to this, various problems and research gaps are noted along with potential research.
Volume: 41
Issue: 1
Page: 357-366
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

Cyber physical systems maintenance with explainable unsupervised machine learning

10.11591/ijeecs.v41.i1.pp300-308
V. Durga Prasad Jasti , Koudegai Ashok , Ramarao Gude , Prabhakar Kandukuri , Surendra Nadh Benarji Bejjam , Anusha B.
As cyber-physical systems (CPS) continue to play a pivotal role in modern technological landscapes, the need for robust and transparent machine learning (ML) models becomes imperative. This research paper explores the integration of explainable artificial intelligence (XAI) principles into unsupervised machine learning (UML) techniques for enhancing the interpretability and understanding of complex relationships within CPS. The key focus areas include the application of self-organizing maps (SOMs) as a representative unsupervised learning algorithm and the incorporation of interpretable ML methodologies. The study delves into the challenges posed by the inherently intricate nature of CPS data, characterized by the fusion of physical processes and digital components. Traditional black-box approaches in unsupervised learning often hinder the comprehension of model-generated insights, making them less suitable for critical CPS applications. In response, this research introduces a novel framework that leverages SOMs, a powerful unsupervised technique, while concurrently ensuring interpretability through XAI techniques. The paper provides a comprehensive overview of existing XAI methods and their adaptation to unsupervised learning paradigms. Special emphasis is placed on developing transparent representations of learned patterns within the CPS domain. The proposed approach aims to enhance model interpretability through the generation of human-understandable visualizations and explanations, bridging the gap between advanced ML models and domain experts.
Volume: 41
Issue: 1
Page: 300-308
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

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

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

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

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

Convolutional neural network DenseNet in classifying dyslexic handwriting images

10.11591/ijeecs.v41.i1.pp220-232
Chelsea Zaomi Pondayu , Widodo Widodo , Murien Nugraheni
Dyslexia is a specific learning disability (SLD) associated with word-level reading difficulties and often manifests in childhood handwriting through irregular spacing and inconsistent letter sizing, due to shared phonological and orthographic processing. Early identification is critical; however, traditional diagnostic procedures are time-consuming and unsuitable for large-scale screening. This study aimed to develop a handwriting analysis at the paragraph-level using a DenseNet121 convolutional neural network (CNN) model as a low-cost dyslexia screening tool for resource-constrained educational settings. One hundred English handwriting images were preprocessed and standardized into two hundred samples, with 70% of the dataset evaluated using 4-fold cross-validation and the remaining 30% used for testing. The model achieved 90% test accuracy and 92.86% training accuracy, significantly outperforming a random forest baseline that reached 83.57% train accuracy and 63.33% test accuracy, with statistical significance confirmed by McNemar’s test. The main contribution of this study is the demonstration that a lightweight, single-architecture DenseNet121 using paragraph-level analysis can achieve competitive performance compared to prior studies that relied on more complex hybrid models and character-level analysis, while requiring substantially lower computational resources and simplified pipeline. These findings indicate that DenseNet121 provides a robust and low-cost solution for preliminary dyslexia screening in resource-limited educational environments.
Volume: 41
Issue: 1
Page: 220-232
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

Neural-network based representation framework for adversary identification in internet of things

10.11591/ijece.v15i6.pp%p
Thanuja Narasimhamurthy , Gunavathi Hosahalli Swamy
Machine learning is one of the potential solutions towards optimizing the security strength towards identifying complex forms of threats in internet of things (IoT). However, a review of existing machine learning-based approaches showcases their sub-optimal performance when exposed to dynamic forms of unseen threats without any a priori information during the training stage. Hence, this manuscript presents a novel machine-learning framework towards potential threat detection capable of identifying the underlying patterns of rapidly evolving threats. The proposed system uses a neural network-based learning model emphasizing representation learning where an explicit masked indexing mechanism is presented for high-level security against unknown and dynamic adversaries. The benchmarked outcome of the study shows to accomplish 11% maximized threat detection accuracy and 33% minimized algorithm processing time.
Volume: 15
Issue: 6
Page: 6043-6052
Publish at: 2025-12-18

Challenges in radar-based non-supercell tornado detection using machine learning approaches

10.12928/telkomnika.v24i1.27451
Kiki; IPB University Indonesian Agency for Meteorology, Climatology and Geophysics (BMKG) Kiki , Yonny; IPB University Koesmaryono , Rahmat; IPB University Hidayat , Donaldi; Indonesian Agency for Meteorology, Climatology and Geophysics (BMKG) Sukma Permana , Perdinan; IPB University Perdinan , Abdullah; Indonesian Agency for Meteorology, Climatology and Geophysics (BMKG) Ali
Tornado detection in Indonesia remains challenging as most areas are monitored by single-polarization weather radar, while dual-polarization systems offer superior detection capabilities. This study presents a novel approach by applying random forest (RF) and XGBoost machine learning algorithms to detect tornadoes using single-polarization radar data, addressing a critical gap in tropical tornado monitoring where dual-pol infrastructure is limited. Four tornado cases in Surabaya during 2024 were analyzed. Radar features including reflectivity, radial velocity, vorticity, and angular momentum were extracted through a multi-elevation sliding window technique. Spatial labels were assigned based on reports from the Indonesian National Meteorological Services (BMKG) with a 7.5 km radius from the event center. The dataset was balanced using synthetic minority over sampling technique (SMOTE). Evaluation was performed using the leave one-case-out (LOCO) scheme. Within-case evaluation showed strong performance with area under the curve (AUC) >0.94 for both models. XGBoost achieved higher probability of detection (POD 0.67-0.72) but with elevated false alarm rates (FAR up to 70%). RF demonstrated more balanced performance (POD 0.61-0.65, FAR 0.34-0.35). LOCO evaluation revealed significant POD reduction and FAR increase when tested on new cases. This indicates generalization challenges due to variability in tornado characteristics. This study demonstrates the potential of machine learning for tropical tornado early detection using readily available single-polarization radar.
Volume: 24
Issue: 1
Page: 162-174
Publish at: 2025-12-08
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