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30,735 Article Results

Evaluation of aerodynamic and structural design to enhance solar energy absorption for e-Cars

10.11591/ijeecs.v42.i3.pp649-665
Mohamed Abubakr Mahgoub Hassan , Belal Ahmed Hamida , El Sayed Soliman , Muhammed Zaharadeen Ahmed
The integration of photovoltaic (PV) systems into electric vehicles (EVs) offers a promising solution for extending driving range and reducing dependence on grid-based charging. However, vehicle-integrated PV systems are limited by aerodynamic drag, structural integration challenges, thermal losses, and inefficient energy management. This study presents a multidisciplinary simulation framework to evaluate aerodynamic and structural optimization strategies for enhancing solar energy absorption in EVs. Computational fluid dynamics (CFD) was used to analyze airflow and reduce aerodynamic drag, while finite element analysis (FEA) assessed structural integrity and weight optimization after PV integration. PV energy flow and thermal models were also developed to evaluate power generation, battery charging behavior, and temperature-dependent efficiency losses. The optimized design reduced the drag coefficient from 0.310 to 0.236, a 23.7% improvement, while maintaining a structural safety factor above 1.75 through lightweight composite materials. The optimized PV configuration increased solar conversion efficiency from 17.6% to 22.3% and daily energy generation from 2.91 kWh/day to 3.75 kWh/day, corresponding to a 28.9% increase in harvested energy. Thermal management strategies lowered average PV operating temperature by about 12 °C, improving efficiency by an additional 5%–7%. Unlike existing studies that examine aerodynamic, structural, or PV performance separately, this work provides a unified framework that evaluates their combined impact on solar energy harvesting in EVs. The proposed integrated design approach demonstrates that coordinated aerodynamic, structural, thermal, and energy-management optimization can substantially improve the practicality and energy contribution of solar-assisted EVs in high-irradiance environments.
Volume: 42
Issue: 3
Page: 649-665
Publish at: 2026-06-10

Sensor-based prediction of ALS progression: exploring PHI and feature engineering

10.11591/ijeecs.v42.i3.pp835-845
Chibuzor Chukwuemeka Okere , Edwin Thuma , Gontlafetse Mosweunyane
Amyotrophic lateral sclerosis (ALS) is a serious disease that affects nerve and muscle function, with no known cure. Early and accurate monitoring is essen tial to help physicians provide better care. Although machine learning has been applied to predict the progression of ALS, many models struggle with issues such as poor data quality and missing information, which affect accuracy. In this paper, our aim is to improve existing models by introducing better features to enhance prediction performance. A key contribution is the development of a new feature called the physical health index (PHI), which combines four im portant patient attributes: body mass index (BMI), weight, forced vital capacity (FVC), and basal calories. This feature provides a clearer view of the physical health of the patient, enabling the model to learn more effectively. We used the IDPP CLEF 2024 BTO dataset and performed three experiments: using 50 raw features, 29 engineered features, and 25 further engineered features including PHI. The results showed that the R-squared of the XGBoost model improved from 0.9573 to 0.9663 and finally 0.9828, while RMSE decreased from 0.2317 to 0.1801 and then 0.1182 with PHI. This study highlights how targeted feature engineering can improve the prediction of ALS using machine learning.
Volume: 42
Issue: 3
Page: 835-845
Publish at: 2026-06-10

Velocity hemodynamic patterns in aortic valve stenosis: a study of inlet velocity during systole phase

10.11591/ijeecs.v42.i3.pp884-891
Nur’Afifah Yousri , Nabilah Ibrahim , Ishkrizat Taib
This work highlighted a close-up version of the aortic valve that provide detail parameter and clearer graphics compared to the 3D version. Therefore, four simplified models are designed and simulated by using computational fluid dynamics (CFD) which are one healthy valve model (100%) and three stenotic models with varying valve opening (70%, 50%, and 30%). The model dimensions and setup parameters are determined by comparing the healthy aortic valve with the previous data. The analysis focused on two different views, which are the view on a targeting line velocity along the x-axis, and the view at the y-axis around the aortic valve. Results on the evaluation graph at the x-axis and y-axis show significant differences in flow patterns between healthy and aortic valve stenosis. The healthy model of 100% valve opening depicted a lower velocity (m/s) at 1.5m/s compared to the stenotic model of 70%, 50%, and 30% valve opening that showed higher velocities of 3.24 m/s, 6.09 m/s, and 14.57 m/s, respectively, due to the narrowing of the valve opening. Thus, the smallest orifice of the valve produced a higher velocity. This finding highlights the importance of hemodynamic assessment in aortic valve stenosis by providing valuable insight for clinicians in pre-surgical evaluation.
Volume: 42
Issue: 3
Page: 884-891
Publish at: 2026-06-10

Enhanced detection of chronic obstructive pulmonary disease via exhaled breath analysis: internet of things and electronic nose system

10.11591/ijeecs.v42.i3.pp875-883
Nur Hidayah Naimah Harahap , Budi Yanti , Muhammad Ilham , Muhammad Suhaili , Dzakiroh Mufidah Hasibuan , Farah Narizki
Chronic obstructive pulmonary disease (COPD) remains a major global health burden, highlighting the need for accessible, non-invasive screening tools. This study aims to develop a portable, real-time internet of things (IoT)-integrated electronic nose (e-nose) system for COPD detection using exhaled volatile organic compounds (VOCs). Breath samples from 44 participants (healthy, smokers, and COPD) were analyzed using a MOS based e-nose, and four machine-learning classifiers were evaluated. Data were processed through cloud-based pipelines enabling real-time acquisition and automated analysis. The random forest (RF) model achieved the highest performance (accuracy 86%) in distinguishing COPD-related VOC patterns. This approach overcomes limitations of earlier offline Tedlar-bag methods by enabling direct, real-time breath analysis. The prototype dashboard provides immediate visualization for potential remote monitoring. Key limitations include the small sample size and non-standardized breath sampling, which may affect VOC variability. Overall, this work contributes a cost-effective, portable, IoT-enabled framework demonstrating the feasibility of real-time VOC analysis for early COPD screening and future integration into telehealth and community-based diagnostics.
Volume: 42
Issue: 3
Page: 875-883
Publish at: 2026-06-10

Improved interactivity and automated response for visual question answering

10.11591/ijeecs.v42.i3.pp742-752
Nguyen Ha Manh Khang , Nguyen Tuan Anh , Nguyen Minh Hoang , Bui Thanh Hung
Visual question answering (VQA) systems have made substantial progress, yet they still face limitations in handling complex or ambiguous queries and supporting real-time interaction due to reliance on large, computationally expensive models that increase latency and restrict practical deployment, particularly in educational contexts. This study aims to develop an efficient and interactive VQA system that enhances answer accuracy while enabling natural two-way communication with users. To achieve this goal, we propose a lightweight multimodal framework based on pre-trained vision language models such as BLIP and fine-tuning T5, combined with prompt engineering to improve question understanding and answer generation. The system further incorporates conversational context memory and a feedback mechanism that generates clarification questions when user inputs are ambiguous, thereby strengthening interaction capabilities. Experiments are conducted on public benchmark dataset Flickr8k, using single-GPU computational settings to evaluate accuracy, response latency, and interaction effectiveness. The experimental results demonstrate that the proposed approach achieves competitive or superior accuracy compared to heavier baseline models, while significantly reducing inference time and enabling real-time interaction. The main contributions of this work include a lightweight, prompt-driven VQA architecture, an interactive strategy for resolving ambiguous queries, and empirical evidence that efficient models can support accurate and conversational VQA for education and other real world applications.
Volume: 42
Issue: 3
Page: 742-752
Publish at: 2026-06-10

Harmonization of regulations and innovation: VR/AR teacher readiness model in Indonesian education under public policy based on Pancasila

10.11591/ijeecs.v42.i3.pp767-773
Helga Charolina Antonia Silubun , Dadan Rosana , Samsul Hadi
The adoption of immersive technologies such as virtual reality (VR) and augmented reality (AR) faces the challenge of significant infrastructure disparities between developed countries (HIC) and developing countries (LMIC). In Indonesia, this implementation is hampered by an acute digital divide and the absence of an adaptive regulatory framework. This research proposes Pancasila-driven VR/AR educational architecture (PD-VAREA), a multidimensional framework that integrates principles of social justice into technical optimization through edge computing and adaptive rendering. The novelty of this research lies in the formalization of the teacher readiness index (Tready) using an integral calculus approach to predict systemic readiness. Numerical simulation results show that the PD-VAREA model produces a Tready value of 2.4 (High Readiness), far exceeding the conventional market-driven model, which reaches 0.6. These findings prove that the integration of cost-effective technology (frugal technology) with public policies based on the Fifth Principle of Pancasila is able to emphasize network latency below 20 ms while ensuring equitable access. This article provides a contribution in the form of a predictive model and strategic recommendations for policymakers in LMIC to mitigate the risks of digital inequality in the global education transformation.
Volume: 42
Issue: 3
Page: 767-773
Publish at: 2026-06-10

On exploring text mining approaches to sentiment analysis based on the combination of word-based and ontology-based approaches

10.11591/ijeecs.v42.i3.pp827-834
Suthira Plansangket , Supaporn Kansomkeat , Supasit Kajkamhaeng
Currently, sentiment analysis plays an important role in business. Entrepreneurs try to understand customer needs for products and services. If they know about the needs, they can create the marketing plans or strategy plans in their business that help improve products and services. Therefore, this study explores two novel approaches to improve the classification accuracy of sentiment analysis data using a combination of a word-based approach (TF-IDF or CSDF) and an ontology-based approach (ontoSen) to provide two new methods, called ontoTF IDF and ontoCSDF. The experimental results show that CSDF method had the best classification accuracy among all the methods in this study: ontoCSDF did not improve further the classification accuracy of sentiment analysis data. Furthermore, ontoTFIDF method improved the classification by IBk algorithm significantly (p
Volume: 42
Issue: 3
Page: 827-834
Publish at: 2026-06-10

Adaptive fractional-order PID-controlled DVR optimized by zebra algorithm for harmonic suppression

10.11591/ijeecs.v42.i3.pp786-796
Milind Paraye , Rajendra G. Sutar
Dynamic voltage restorers (DVRs) are widely employed to mitigate power quality disturbances in modern power grids. Existing DVR control strategies frequently struggle to adequately suppress harmonic distortions and voltage sags due to nonlinear grid behaviour, rapidly varying disturbances, and limited tuning flexibility. We suggest a grid-connected DVR with an adaptive fractional order proportional integral derivative (FOPID) controller whose parameters are improved using an improved zebra algorithm (IZA) in order to close this gap. The IZA algorithm is used to improve the FOPID controller parameters, ensuring rapid convergence and superior accuracy. The effectiveness of the proposed system is assessed under two different operating conditions. In case 1, the harmonic compensation is analyzed, in which the DVR reduces systemic harmonic disturbances. The results reveal that the proposed controller reduces the total harmonic distortion (THD) from 1.36% to 0.01% while maintaining a constant voltage amplitude of around 0.9986 V, demonstrating strong harmonic suppression capability. Voltage sag mitigation is assessed in Case 2. The load voltage is effectively restored from 0.722 V to 0.9986 V by the DVR, which also reduces THD from 32.97% to 1.6% by injecting the required compensatory current. Overall, the results confirm that the adaptive FOPID–IZA controlled DVR significantly improves power quality and voltage stability in grid-connected systems by effectively mitigating both harmonic distortion and voltage sags.
Volume: 42
Issue: 3
Page: 786-796
Publish at: 2026-06-10

Hybrid plugin for detecting illicit images on the internet using EfficientNet convolutional neural networks

10.11591/ijeecs.v42.i3.pp809-817
Christine Laure Mananga , Felix Paune , Léandre Nneme Nneme
The proliferation of illicit visual content on the internet, such as pornography and violent imagery, presents a growing societal concern. This paper proposes the design and implementation of a lightweight browser-integrated plugin that utilises a hybrid approach combining content based filtering with convolutional neural networks (CNNs), specifically the EfficientNetB7 architecture, to detect and block illicit images in real time. Developed using Python and TensorFlow, the plugin was trained on a curated dataset comprising NSFW, DeepNude, and safe-for-work (SFW) images. Experimental results on a dataset of 1,064 randomly selected images demonstrated a detection accuracy of 99%, with a processing time of 92 seconds and a 7% combined false positive and false negative rate. The plugin is compatible with Chrome browsers and contributes to safer online experiences, particularly for children, educators, and users in sensitive environments.
Volume: 42
Issue: 3
Page: 809-817
Publish at: 2026-06-10

Deep learning in cryptanalysis a comprehensive review of techniques, applications, challenges, and future trajectories

10.11591/ijeecs.v42.i3.pp846-855
Oussama Noui , Amine Barkat
The integration of deep learning (DL) into cryptanalysis represents a paradigm shift, challenging traditional mathematical approaches and unlocking novel attack vectors against cryptographic algorithms and implementations. This comprehensive review synthesizes findings from recent pivotal publications to provide an in-depth analysis of the current state-of-the-art, methodologies, empirical successes, fundamental limitations, and future potential of DL in cryptanalysis. We focus extensively on two primary domains DL-based side-channel analysis (DL-SCA) and DL-enhanced cryptanalysis of symmetric primitives. The review meticulously examines advancements in attack efficiency (reducing the number of traces/queries), robustness against sophisticated countermeasures, automated feature extraction, and the nascent exploration of theoretical foundations. While DL demonstrates remarkable capabilities in automating complex pattern recognition critical to cryptanalysis, significant challenges persist, including the “black-box” nature of models, data dependency, scalability to full cryptographic primitives, and the critical need for explainability and theoretical grounding. This review serves as a foundational resource for researchers and practitioners navigating this rapidly evolving intersection of artificial intelligence and cryptography.
Volume: 42
Issue: 3
Page: 846-855
Publish at: 2026-06-10

Smart contracts and a dual blockchain structure for collaborative tourism

10.11591/ijeecs.v42.i3.pp892-901
Zohra Temmar , Asmaa Boughrara
This article optimizes a decentralized system for collaborative tourism in Alge ria using blockchain, smart contracts, and proof of reputation (PoR) consensus. The system matches services into organized trips, manages reservations, and automates payments to ensure transparency and autonomy without centralized authority. This work opens the door to exploring dual-blockchain architectures. Building on a previous work, we enhanced node interactions, automated con tract execution, and introduced a dual-blockchain structure to reduce latency while improving scalability and security.
Volume: 42
Issue: 3
Page: 892-901
Publish at: 2026-06-10

Enhancing the performance of 3-phase induction motors by developing a 40-degree asymmetrical coil design for the stator coil

10.11591/ijeecs.v42.i3.pp678-687
Zuriman Anthony , Refdinal Nazir , Muhammad Imran Hamid
The use of three-phase induction motors in industry is very widespread due to their durability, simplicity, cost, and ease of use. These motors are continuously developed to improve performance. However, the increase in motor performance is directly proportional to the increase in the motor’s cost. Therefore, innovative solutions are needed to make three-phase induction motors more efficient without raising the motor’s price. The study’s goal is to create a motor coil design that won’t cost a lot more but will make 3-phase induction motors more efficient. This study developed a 2-layer coil design with a pair of poles for each layer. The second coil layer is located 40 electrical degrees away from the first layer. In the lab, the motor’s performance was assessed and compared to a conventional motor. This new motor uses identical materials as the conventional model; therefore, it does not incur additional costs. The results showed that this method could increase the rotor speed, output power, efficiency, and load torque of the motor by 0.21%, 16.29%, 15.90%, and 16.05%, respectively.
Volume: 42
Issue: 3
Page: 678-687
Publish at: 2026-06-10

Voice portraits: building faces through voice analysis

10.11591/ijeecs.v42.i3.pp902-912
Anandhu T. G. , John K. Joseph , Navneeth Krishnan J. , Richu Shibu , Elizabeth Isaac
Generation of a person’s appearance from their voice alone is an intriguing challenge. The proposed framework centers on recreating a person’s facial image based solely on a short audio recording of that person speaking. Using a deep neural network trained on millions of YouTube recordings where faces and voices appear together, the system learns voice-face relationships, enabling it to generate images that capture physical traits such as age, gender, and ethnicity. Operating in a self-supervised manner, this method takes advantage of the pairing of faces and voices in online videos, eliminating the need for explicit property modeling. The model achieved a classification accuracy of (95%) for gender, (83%) for age, and (65%) for race prediction from voice inputs, demonstrating an exceptional performance in demographic trait identification. The generated images are evaluated against real photographs of the speakers, assessing how closely these reconstructions resemble actual appearance. This framework has practical applications in forensic analysis, security systems, and privacy-conscious biometric identification, offering a non-invasive alternative to traditional facial recognition methods.
Volume: 42
Issue: 3
Page: 902-912
Publish at: 2026-06-10

Optimization of glioma segmentation using 3D U-Net++ in MRI surgical planning and patient safety outcomes

10.11591/ijeecs.v42.i3.pp798-808
Ahmed Bounegta , Mustapha Khelifi , Mohammed Beladgham
The main goal of this study is to develop and evaluate a novel 3D U-Net++ convolutional neural network for accurate segmentation of glioma sub regions in MRI scans, aiming to enhance surgical planning, targeted radiotherapy, and patient safety. Precise segmentation of glioma sub-regions is a persistent challenge in neuro-oncology due to substantial morphological variability across patients. To address this, we introduce an automatic segmentation model based on a 3D U-Net++ architecture with dense skip connections, which improves spatial feature extraction and the delineation of tumor boundaries. Utilizing volumetric data from the BraTS 2020 benchmark, the model automatically segments three clinically relevant substructures: tumor core, the enhancing tumor, and whole tumor. The integration of dense connections with 3D convolutional layers facilitates the detection of subtle tissue variations, including necrosis and edema. Quantitative evaluation demonstrates that the proposed 3D U-Net++ surpasses conventional architectures such as standard U-Net and DeepMedic in Dice coefficient, sensitivity, and specificity, yielding more homogeneous and continuous segmentations while reducing manual and semi-automatic annotation efforts. This approach supports advanced clinical decision making and workflow automation, and offers potential for application to other tumor types or integration into real-time clinical practice.
Volume: 42
Issue: 3
Page: 798-808
Publish at: 2026-06-10

Parameter identification and continuous friction modelling of a brushed DC motor

10.11591/ijeecs.v42.i3.pp699-707
Ahmad’Abdan Syakuro , Vani Virdyawan , Sri Raharno , Indrawanto Indrawanto , Tegoeh Tjahjowidodo
Designing high-performance control systems for brushed DC motors is often hindered by the lack of comprehensive dynamic parameters in manufacturer datasheets, particularly for low-cost DC motors. In addition to the parameter identification method, this study also introduces a continuously differentiable friction model incorporating Coulomb and viscous-like behaviors using a hyperbolic tangent function. The electrical and mechanical parameters of an RS-775 motor were identified using standard laboratory tools and the MATLAB system identification toolbox. The proposed model was validated against experimental data under square wave and sinusoidal inputs, achieving a position prediction error of less than 5% and capturing complex dynamic behaviors. The results demonstrate that this accessible identification approach provides a sufficiently accurate dynamic model for educational and industrial robotics applications, offering a superior alternative to trial-and-error tuning.
Volume: 42
Issue: 3
Page: 699-707
Publish at: 2026-06-10
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