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Sustainable e-mobility with controlled charging scheme based on grid energy using machine learning

10.11591/ijape.v15.i3.pp1036-1050
Archana Kadam , Ramesh Mali , Reena Gunjan , Virendra Shete , Pradeep Mane
The electric vehicle (EV) popularity has taken off among consumers, which has in turn led to efforts to create an efficient EV charging infrastructure. This paper addresses this challenge by proposing a scheduled charging scheme that uses real-time data from a grid-connected charging station at Baner, Pune, operated by Pune Mahanagar Parivahan Mahamandal Ltd (PMPML). The proposed system makes use of advanced machine learning techniques such as the Stochastic dual coordinate ascent (SDCA) and Fast Forest (FF) algorithm, both of which allow for precise and efficient computations to predict charging finish times and make optimal scheduling decisions. The use of these algorithms in conjunction with ToU tariffs is cost effective when compared to flat rate tariffs. Grid load analysis shows that scheduling according to time lowers peak demand, equalizes load distribution, and lowers operating costs. A quantitative comparison has demonstrated both grid stability and economic efficiency gains over uncontrolled charging. The result is an extremely flexible framework for different charging events or stations which will be a viable way of managing energy in the fast-growing EV charging networks.
Volume: 15
Issue: 3
Page: 1036-1050
Publish at: 2026-09-01

Sliding-mode assisted direct torque control for reliable wind turbine operation

10.11591/ijape.v15.i3.pp1180-1189
Nehal Ouassila , Dib Djalel , Billel Meghni , Dib Nour Elhouda
This paper investigates and compares the performance of two advanced control strategies, direct torque control (DTC) and sliding mode control (SMC), applied to a permanent magnet synchronous generator (PMSG) used in wind energy conversion systems. The control schemes aim to ensure efficient energy conversion and stable operation under variable wind conditions. The comparison is carried out through detailed simulations considering electromagnetic torque response, stator flux behavior, speed regulation, and robustness to disturbances and parameter variations. The results show that DTC provides a fast dynamic response with a relatively simple control structure, but suffers from torque and flux ripples and sensitivity to parameter variations. In contrast, SMC demonstrates higher robustness against uncertainties and disturbances, with smoother torque characteristics and improved speed regulation. Overall, the study indicates that SMC is a promising approach for enhancing the stability and performance of PMSG-based wind energy systems. Future work may explore hybrid strategies combining the fast response of DTC with the robustness of SMC, as well as intelligent control techniques to further optimize energy extraction.
Volume: 15
Issue: 3
Page: 1180-1189
Publish at: 2026-09-01

Environmental footprint assessment of lithium-ion (Li-ion) batteries in electric scooters: a case study of PT Motor Listrik Indonesia

10.11591/ijape.v15.i3.pp955-964
Rafi Juniar Saputra , Aufar Fikri Dimyati , Silvi Istiqomah , Dwi Heru Siswantoro
Indonesia ranks as the world's sixth-largest contributor to CO2 emissions, with motorcycles alone responsible for 56,788 tons of CO2 in 2020. While electric motorbikes are considered a cleaner alternative due to their lack of exhaust emissions, their overall environmental impact must be evaluated throughout the entire battery life cycle. This study conducts a gate-to-grave life cycle assessment (LCA) on the lithium-ion (Li-ion) batteries used in MOLINDO electric motorbikes, covering the supplier, production, usage, and disposal stages. The total environmental impact was found to be 920 points (pt), with the usage phase being the largest contributor at 550 pt, followed by production at 185.5 pt, supplier at 179 pt, and disposal at 5.54 pt. Two improvement strategies were explored: reusing external battery cases, which slightly reduced the impact to 918.86 pt, and repurposing second-life batteries, which extended battery use but increased the impact to 998.04 pt due to additional energy requirements. Compared to conventional motorbikes, electric motorbikes still offer a major environmental advantage in the usage phase (550 pt vs. 3,397 pt). These findings suggest that, alongside the shift to electric mobility, targeted actions such as better battery reuse, more efficient recycling, and cleaner electricity sources are essential to fully unlock the environmental benefits of electric motorbikes.
Volume: 15
Issue: 3
Page: 955-964
Publish at: 2026-09-01

Multi-objective planning of distributed resources (PV and SVC) with NSGA-II for radial networks: application to the IEEE 33-bus test system

10.11591/ijape.v15.i3.pp1243-1252
Hassane Ousseyni Ibrahim , Abdoul Malik Maman Issaka , Moussa Gonda , Arouna Oloulade , François-Xavier Fifatin
The quality of electricity supply in distribution networks is critically dependent on minimizing active power losses and ensuring voltage stability. This study proposes a unified multi-objective optimization approach for the simultaneous placement and sizing of a photovoltaic (PV) source and a static var compensator (SVC) in radial networks. The non-dominated sorting genetic algorithm II (NSGA-II) is employed as the robust methodology to generate the Pareto optimal front, effectively exploring the trade-offs between two conflicting objectives: active loss minimization and voltage profile improvement. Unlike sequential or single-unit optimization strategies, this joint optimization framework is the key novelty, leveraging the specific physical interaction between PV active power injection and SVC-based dynamic reactive support to maximize overall network efficiency. Simulations are performed on the standard IEEE 33-bus test system. The results demonstrate that the optimal and coordinated integration of a 0.97 MW PV system at bus 14 and a 1.32 MVAr SVC at bus 30 yields superior electrical performance. Specifically, the system achieves a substantial active power loss reduction of 62.53% and decreases the voltage deviation index from 0.117 p.u. to a minimum of 0.0169 p.u., confirming the effectiveness of the proposed NSGA-II approach for comprehensive distributed resource planning.
Volume: 15
Issue: 3
Page: 1243-1252
Publish at: 2026-09-01

Condition assessment of medium voltage cable insulation using leakage current and phase-resolved partial discharge

10.11591/ijape.v15.i3.pp1340-1350
Kamrai Janprom , Sittadach Morkmechai , Natchanun Prainetr , Supachai Prainetr
Reliable operation of medium-voltage distribution networks critically depends on the integrity of cross-linked polyethylene (XLPE) insulated cables. This paper proposes a diagnostic methodology that integrates leakage current (LC) measurement with phase-resolved partial discharge (PRPD) analysis to assess cable insulation condition. A MATLAB R2025 simulation model is first developed to emulate partial discharge (PD) signals superimposed on leakage current, providing preliminary validation of the proposed approach. The method is then experimentally verified using a 70 mm² XLPE cable rated at 16/20 (24) kV and tested in accordance with IEC 60502. Under applied high-voltage stress, leakage current and PD activity are measured, and insulation degradation is characterized using PRPD patterns. Both simulation and experimental results confirm that the proposed method reliably detects insulation defects and provides accurate condition assessment of XLPE cables. These findings demonstrate the potential of the method as a practical tool for supporting condition-based maintenance in medium-voltage power distribution systems.
Volume: 15
Issue: 3
Page: 1340-1350
Publish at: 2026-09-01

Predicting student academic success using entry test, language, and spiritual formation data with ensemble learning

10.11591/ijict.v15i3.pp1322-1330
Evander Banjarnahor , Budi Wibawanta , Ronald Belferik , Rijanto Purbojo
Student academic success is influenced by various factors, both academic and non-academic. This study aims to examine the correlation between key student attributes and final grade point average (GPA), as well as to develop a machine learning model to predict academic success. The correlation analysis involved academic variables such as admission scores (Mathematics, English, Indonesian, and academic aptitude test/TPA), English ability test (EAT), spiritual formation (SF), and first-year GPA (GPA_1). The results indicate that GPA_1 has the highest correlation with final GPA (0.63), followed by SF (0.44), while other variables exhibit lower correlations. To enhance prediction accuracy, a machine learning approach using three primary models was employed: Naïve Bayes, support vector machine (SVM), and an ensemble learning method based on a stacking classifier that combines SVM and Naïve Bayes. The evaluation used five train-test split ratios and performance metrics, including accuracy, precision, recall, and F1-score. Experimental results reveal that the SVM model achieves the highest accuracy at 88.40%, followed by the ensemble model combining SVM and Naïve Bayes (88.00%) and the Naïve Bayes model (87.10%). These findings confirm that the machine learning approaches, could effectively predict student academic success, providing a foundation for academic decision-making and educational intervention strategies.
Volume: 15
Issue: 3
Page: 1322-1330
Publish at: 2026-09-01

Improving the performance of leaf disease detection and classification using beetle swarm optimization technique

10.11591/ijict.v15i3.pp967-974
Penugonda Seetha Rama Krishna , S. Nagarajan
The timely identification and diagnosis of leaf diseases is crucial for crop productivity and health. This study proposes a robust approach to this issue by combining beetle swarm optimization (BSO) with other ML models. Four different datasets were used to train our model: apple leaf, grape leaf, plant village leaf, and tomato leaf for disease detection. The process begins with preparing the leaf images, involving contrast enhancement and noise reduction. Through color-based segmentation, we can distinguish healthy regions from diseased ones, aiding in the classification process. Our research demonstrates the effectiveness of the BSO-convolutional neural networks (CNN) method in recognizing and categorizing plant diseases with high accuracy rates. Leveraging the power of BSO to adjust the model’s parameters and incorporating color-based segmentation enhances the model’s robustness and accuracy. The results of this study highlight the potential of automated disease management systems for agriculture, providing agronomists and farmers with the necessary tools to address and monitor emerging threats to their crops effectively.
Volume: 15
Issue: 3
Page: 967-974
Publish at: 2026-09-01

Impact of power cable modelling on switching transient overvoltage analysis in medium voltage motors

10.11591/ijape.v15.i3.pp1233-1242
A. Nisar Basha , N. Mahiban Lindsay
Precise electrical cable representation is crucial for examining surge transient overvoltages in medium voltage (MV) motor systems. These brief overvoltages, initiated by swift switching actions, may induce substantial insulation strain, equipment degradation, and potential system failures. This study explores the influence of different power cable modeling techniques on transient overvoltage characteristics in MV motors during switching operations. Various modeling techniques, such as aggregated parameter, spread parameter, and frequency-dependent models, are evaluated for their effectiveness in inward capturing transient phenomena. Simulation studies using industry-standard electromagnetic temporary (EMT) assessment instruments evaluate the effect of these simulation methods on voltage spikes, shape distortions, and rise times. The discoveries indicate that exact electrical wire depiction is pivotal in molding, fleeting reactions, emphasizing the significance of choosing suitable simulation methods for efficient insulation coordination and system protection. This research provides valuable guidance for power system engineers, helping them mitigate transient overvoltage risks and improve the reliability of MV motor applications. Also, this study demonstrates electrical cable simulation that can impact the advice and verdicts obtained from moderate voltage motor.
Volume: 15
Issue: 3
Page: 1233-1242
Publish at: 2026-09-01

Intelligent engineering framework for managing hospital cardiac arrest resources

10.11591/ijict.v15i3.pp1290-1302
Chams Eddine Fathoun , Mohamed Ridda Laouar , Safa Abid , Sean B. Eom
In-hospital cardiac arrest in intensive care remains frequent (often cited incidence roughly 0.5%-7.8% of admissions), while causes differ in what staff and equipment must be ready. We ask whether vital-sign trajectories from a standard EHR can classify which of three cardiac-related mechanisms is most salient arrhythmia, acute myocardial infarction (AMI), or respiratory failure or hypoxia so ICU resources can be aligned with risk. Using MIMIC-IV, we extracted diagnoses and charted vitals in the 12 hours before the index event, applied cleaning, aggregation, label encoding, sequence padding, and class balancing (3,000 cases per class), then trained and compared eXtreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and logistic regression (LR) with 5-fold cross-validation on an 80/20 split. XGBoost performed best (about 93% accuracy; sensitivity 89.15%; specificity 90.43%; AUC-ROC 0.94). Feature importance highlighted heart rate, oxygen saturation, and blood pressure patterns consistent with bedside monitoring practice. The study supports mechanism-oriented triage labels derived from widely recorded vitals, as a complement to generic early warning scores, for prioritizing telemetry, respiratory support, and cardiology pathways. External validation and prospective evaluation are needed before deployment.
Volume: 15
Issue: 3
Page: 1290-1302
Publish at: 2026-09-01

Design and implementation of an AI, IoT, and blockchain-based system for circular economy transition in landfill management: a case study of Quilmaná, Peru

10.11591/ijict.v15i3.pp1254-1262
Brandon Perez Flores , Juan Villantoy Peralta , Jimmy Acosta García , Jesús Zamora Mondragon , Cesar Patricio-Peralta , Luis Segura Terrones , Héctor Odín Delgado-Enríquez , Walter Patricio Peralta , Richard Aguilar Paredes
This study presents the design and implementation of an integrated system based on artificial intelligence (AI), internet of things (IoT), and blockchain to support circular economy practices in landfill management. The system addresses the lack of integrated and validated digital solutions for environmental monitoring, resource optimization, and social inclusion in resource-constrained contexts. Developed under a design science research (DSR) approach, the system combines IoT sensors for real-time monitoring, machine learning models (LSTM for methane prediction, CNN for waste classification, and reinforcement learning (RL) for biogas optimization), and a blockchain-based platform for transparent transactions and recycler formalization. The system was implemented and evaluated over 12 months using operational data. The LSTM model achieved 95% prediction accuracy, while the CNN model demonstrated high classification performance. Results indicate a 35% reduction in landfill waste, a 40% decrease in CH₄ emissions, and a 30% increase in recycler income, with 60% of informal workers formalized. These findings demonstrate that integrating AI, IoT, and blockchain enables the transformation of landfill systems into scalable circular economy platforms for sustainable waste management.
Volume: 15
Issue: 3
Page: 1254-1262
Publish at: 2026-09-01

Probabilistic inventory modeling for chlorine gas using minitab and python: a comparative study of demand distributions

10.11591/ijict.v15i3.pp1026-1037
Oki Dwipurwani , Fitri Maya Puspita , Siti Suzlin Supadi , Evi Yuliza
The availability of chlorine gas (Cl2) is a critical component in the drinking water disinfection process at the regional drinking water company (PDAM), as it plays a vital role in ensuring microbiological safety. Disruptions in the chlorine gas supply may lead to interruptions in water distribution and pose significant public health risks. This study investigates the application of a probabilistic (Q, r) inventory model for managing chlorine gas stock, incorporating several probability distributions that satisfy the underlying model assumptions. The resulting optimal inventory policies derived from each distribution are then compared. Chlorine gas demand forecasting is also performed using the seasonal autoregressive integrated moving average (SARIMA) model. The objective of this research is to generate an optimal inventory policy and accurate demand forecasts, with the entire implementation carried out in Python software. The results show that the best model obtainis the SARIMA (0,1,0)(0,1,1)12 model, with a MAPE value of 5.48%, and that the chlorine gas demand data follow normal, gamma, exponential, and erlang probability distributions. The comparison results show that the optimal policy of the gamma probabilistic model provides the best results, as well as being better than Normal and exponential policies in previous studies.
Volume: 15
Issue: 3
Page: 1026-1037
Publish at: 2026-09-01

Enhanced thermal management in 3D integrated circuits coupling

10.11591/ijict.v15i3.pp1208-1216
Vempalle Rafi , Shaik Hussain Vali , Pradyumna Kumar Dhal , Sadhu Radha Krishna , Murkur Rajesh , Malagonda Siva Kumar
3D IC integration, which comprises vertically stacking several IC layers, is one of the new technologies that works well with complementary metal-oxide-semiconductor (CMOS) implementations. The layers of a three-dimensional integrated circuit (3D IC) are physically and electrically connected via copper-silicon bonding and through silicon vias (TSVs). Limitations in 3D IC designs, such as layer-to-layer thermal difficulties and TSV-to-substrate and TSV-to-TSV noise coupling, significantly impact system performance as a whole. Integrating 3D ICs relies heavily on heat spreaders and thermal through silicon vias (TTSVs). Overheating is a common cause of IC failure; however, heat spreaders and FIN to TTSV have been suggested as potential remedies for this problem in the last few years. A 3D IC might melt under the stress of an applied voltage because it becomes hotter inside. Engineers have added fins to the TTSV in a number of ways, each of which maximizes heat dissipation in a different way, in order to reduce this danger. The exceptional thermal cooling characteristics of graphene and carbon nanotubes (CNTs) have led to their widespread dissemination. This research shows that a FIN may efficiently transport thermal energy to a heat sink by using heat spreaders and optimum orientations to distribute heat in all directions. Additionally, we demonstrated the many scenarios in which the IC's potential distribution is impacted by various thermal cooling effects. We found that when it comes to transferring heat away from heat sources and TSVs, CNTs outperform Graphene. We included Al2o3, Si3N4, and SiO2 as examples to examine the consequences of modifying the model's dielectric characteristics.
Volume: 15
Issue: 3
Page: 1208-1216
Publish at: 2026-09-01

Neural network-based diagnosis of type 2 diabetes using an iridology approach

10.11591/ijict.v15i3.pp1226-1237
Alaa Abdulkareem Ahmed , Mohammad Tariq Yaseen
The growing global occurrence of type 2 diabetes requires the development of non-invasive and effective diagnostic methods. This work proposes a novel approach to detecting type 2 diabetes using iridology and machine learning (ML) techniques. By analyzing the iris of the right eye, a single region of interest (ROI) corresponding to the head of the pancreas is recognized for feature extraction. A total of 112 statistical and texture features are extracted using gray-level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT) algorithms. Five neural network (NN) models, narrow, medium, wide, bi-layered, and tri-layered are deployed to classify healthy and diabetic people. The models are trained and assessed using a range of k-fold values (2 to 20) to optimize performance. The highest classification accuracy of 83.2% was reached using the narrow neural network (NNN) model at 7-fold cross-validation. This work exhibts the potential of iridology-based ML approaches for non-invasive diabetes diagnosis, providing a promising substitute to traditional blood tests.
Volume: 15
Issue: 3
Page: 1226-1237
Publish at: 2026-09-01

A multi-expert approach to content-based image retrieval using feature fusion and late re-ranking

10.11591/ijict.v15i3.pp1376-1384
Ali Abdulazeez Mohammed Baqer Qazzaz , Yousif Samer Mudhafar
As digital data rapidly grows, content-based image retrieval (CBIR) has become important for optimizing collections of visual data. This work proposes a retrieval framework which operates in two stages and improves accuracy by using systematic fusion of features. In the first stage, first-stage wide-scope descriptors called bag-of-visual-words (BoVW), scattering wavelet transform (SWT), discrete cosine transform (DCT), and principal component analysis (PCA) retrieve initial candidate images. The second stage undertakes detailed re-ordering of candidate images by implementing the local binary pattern (LBP), histogram of oriented gradients (HOG), and singular value decomposition (SVD) descriptors to re-evaluate similarity scores. Each individual descriptor returned results for mean average precision for the top 10 retrieved images (mAP, top-10) of between 0.63 and 0.79 and the fused framework achieved 0.88, which is evidence of the viability of complementary feature integration. These findings support the hypothesis that while multiple descriptors performed well and delivered high retrieval accuracy, hierarchical fusion of multiple handcrafted descriptors does not involve the computational costs associated with deep learning methods.
Volume: 15
Issue: 3
Page: 1376-1384
Publish at: 2026-09-01

Multi-objective optimization and multi-criteria decision analysis of passive power filters for power quality improvement in arc furnace applications

10.11591/ijape.v15.i3.pp1200-1211
Alvaro Yassif Marca Yucra , Gastón Orlando Suvire , John Armando Morales
This article presents a multi-objective optimization methodology for the optimal tuning of passive power filters in steelmaking facilities that operate with electric arc furnaces (EAFs). These industrial loads are well-known for introducing severe harmonic distortion, voltage unbalance, and flicker into the electrical network, significantly degrading power quality and equipment performance. To address these challenges, a multi-objective optimization problem is solved using the non-dominated sorting genetic algorithm II (NSGA-II), which simultaneously minimizes three key power quality indices: total harmonic distortion (THD), total demand distortion (TDD), and voltage unbalance factor (VUF). In addition, a multi-criteria decision analysis (MCDA) technique is applied to rank and select the most balanced and robust solution in different EAF operating scenarios. Unlike conventional filter design methods that prioritize a single performance criterion or rely on static harmonic assumptions, the proposed approach accounts for the nonlinear and time-varying behavior of EAFs, ensuring robust performance under diverse operating conditions. A comprehensive case study based on a Bolivian steel plant illustrates the effectiveness of the optimization strategy. Results indicate reductions of 47.6% in THD, 33.5% in TDD, and 63.6% in VUF, clearly outperforming conventional design approaches and significantly improving overall power quality. This work highlights the potential of evolutionary multi-objective algorithms for enhancing passive filter performance in complex industrial environments with highly distorted and unbalanced power conditions.
Volume: 15
Issue: 3
Page: 1200-1211
Publish at: 2026-09-01
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