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30,185 Article Results

Hydrothermal synthesis and defect-driven optical characterization of CdS nanoparticles for semiconductor and solar applications

10.11591/ijape.v15.i1.pp440-448
Deepti Bhargava , R. K. N. R. Manepalli , M. C. Rao , P. Venkata Ramana Rao , N. S. Subba Rao , A. Narendra Babu , P. Sree Brahmanandam
Nanoparticles (NPs) play a crucial role in advancing technology, particularly by enhancing the performance of energy storage in semiconductor applications. The synthesis of NPs with reduced particle size and increased surface area, along with a higher number of active sites, facilitates improved ion diffusion, making them highly suitable for such applications. Various methods have been employed to reduce the size of NPs, depending on factors such as purity and controlled composition. The present study focuses on controlling both the size and composition of cadmium sulfide (CdS) NPs, aiming to achieve a high surface-to-volume ratio. These NPs were synthesized using a hydrothermal method in a high-pressure autoclave. The evaluation of the synthesized inorganic CdS-NPs for technological applications requires experimental validation of their characteristics, including particle size, energy band gap, thermal stability, temperature response, as well as optical and electronic properties. The results obtained using the proposed methods reveal a bandgap of 2.28 eV, a hexagonal wurtzite structure with an average crystallite size of 10.26 nm, reduced effective mass, and an intense absorption peak at a higher wavelength. These characteristics indicate that the synthesized CdS nanoparticles are suitable for various applications, including high-power semiconductors, solar energy harvesting, optoelectronic devices, and materials for energy and electrical engineering.
Volume: 15
Issue: 1
Page: 440-448
Publish at: 2026-03-01

Modeling and optimization of angular misalignment effects in resonant inductive wireless power transfer for electric vehicle charging

10.11591/ijpeds.v17.i1.pp394-404
Samshul Munir Muhamad , Wan Muhamad Hakimi Wan Bunyamin , Rahimi Baharom
This paper presents an enhanced electromagnetic modeling and optimization study on the effects of angular misalignment in resonant inductive wireless power transfer (RIWPT) systems for electric vehicle (EV) charging. A detailed 3D model of a double-layer circular coil was developed in CST Studio Suite to investigate coupling degradation, energy loss, and efficiency behavior under angular deviations ranging from 0° to 25°, at a fixed air gap of 30 mm. Performance metrics including mutual inductance, magnetic field distribution, power transfer efficiency (PTE), and loss characteristics were analyzed to establish quantitative misalignment correlations. Results indicate a steady reduction in PTE from 99.979% at 0° to 88.441% at 25°, accompanied by corresponding increases in field asymmetry and energy dissipation. To mitigate these losses, an impedance-tuning strategy was applied by jointly optimizing transmitter-side series and parallel compensation capacitors, which improved PTE at 5° misalignment from 98.777% to 99.801%, restoring near-resonant operation. Additional analyses evaluated thermal impact, material robustness, and dynamic misalignment effects, providing a more holistic understanding of real-world charging scenarios. The study further discusses real-time tuning feasibility using embedded controllers and aligns performance with SAE J2954 and IEC standards for EV wireless charging. The findings establish validated design guidelines and adaptive tuning frameworks for achieving high-efficiency, misalignment-tolerant RIWPT systems, contributing toward robust and energy-efficient EV charging infrastructure.
Volume: 17
Issue: 1
Page: 394-404
Publish at: 2026-03-01

473 kV lightning impulse test of an insulator embedded in pressurized and heated liquid nitrogen

10.11591/ijape.v15.i1.pp352-360
Stefan Fink , Sven Lautensack , Volker Zwecker
Liquid nitrogen is the most common fluid for cooling superconducting power engineering devices. The dielectric strength of an insulator rod embedded in liquid nitrogen at a pressure of 0.3 MPa was investigated with lightning impulse voltage series of 20 impulses of ±473 kV for gap lengths up to 50 mm between a grounded plane and a high voltage electrode in the shape of a bell. The influence of boiling due to quenching of the superconductor was simulated by heating impulses with a duration of 10.1 s. Before triggering the heater impulse, the liquid nitrogen was in the subcooled state i.e., a pure liquid. Transient bubble generation due to the heater impulse was confirmed by video recording through an observation window of the cryostat. The voltage of 473 kV was kept by a gap length of 18 mm in case of impulses of positive polarity. A gap of 30 mm was necessary in case of negative polarity. Hence, a strong polarity effect was found. Calculated field values based on the experimental results do not exceed limits used for the high voltage design study for a support insulator of a superconducting fault current limiter.
Volume: 15
Issue: 1
Page: 352-360
Publish at: 2026-03-01

Modeling of solar and wind energy using MATLAB/Simulink: a review

10.11591/ijaas.v15.i1.pp107-122
Nicholas Pranata , Fahmy Rinanda Saputri
This paper presents a concise review of solar (photovoltaic (PV)) and wind (horizontal axis) energy systems, focusing on their modeling and simulation using MATLAB)/Simulink. The advantages, disadvantages, strengths, and weaknesses of each system are discussed, providing a comprehensive overview of their characteristics. The review explores the mathematical modeling approaches for PV cells and modules specific for single diode model, as well as horizontal-axis wind turbine systems, highlighting the key equations and parameters involved. Furthermore, the paper discusses the emerging trend of hybrid solar-wind energy systems and their potential for optimizing power output, efficiency, and reliability. The review emphasizes the importance of accurate modeling based on fundamental knowledge, which serves as a practical implication for readers to understand the mechanism. Future research directions and challenges in the field of renewable energy modeling and simulation are also outlined. This review serves as a valuable resource for researchers, engineers, and decision-makers involved in the development and implementation of solar and wind energy systems.
Volume: 15
Issue: 1
Page: 107-122
Publish at: 2026-03-01

Crop prediction in Tamil Nadu according to environmental and soil factors using hybrid machine learning architecture

10.11591/ijaas.v15.i1.pp405-415
Sundaraj Kannan Susee , Shenbagaramasubramanian Shenbaga Vadivu , Murugesan Senthil Kumar
Mathuranthagam, Tamil Nadu, India is the site of this research initiative that employs state-of-the-art hybrid machine learning (ML) architectures to forecast crop suitability in relation to environmental and soil characteristics. The model takes advantage of the strengths of linear support vector machine (SVM) classifier, bidirectional long short-term memory (BiLSTM), and convolutional LSTM (ConvLSTM) networks, and the data to capture complicated temporal and spatial correlations. To prepare the dataset for model training, it is normalized using min-max scaling and then feature selected using a Jaya optimization technique. The dataset contains variables such as humidity, rainfall, temperature, and pH. Both the BiLSTM and the ConvLSTM improve the model's comprehension of context from both previous and subsequent time steps. The ConvLSTM also records spatial dependencies. A powerful decision-making tool for differentiating across crop varieties is the linear SVM classifier. Comparing the hybrid model's performance to that of traditional LSTM approaches using measures such as recall, accuracy, precision, and F1-score shows that it performs much better. Using this approach can see how deep learning (DL) can supplement more conventional ML methods and see how important local environmental data is for agricultural policy and planning.
Volume: 15
Issue: 1
Page: 405-415
Publish at: 2026-03-01

DCNNVA: a deep convolutional neural network for volcanic activity classification using satellite imagery

10.11591/ijaas.v15.i1.pp281-292
Yasir Hussein Shakir , Reem Ali Mutlag , Eshaq Aziz Awadh AL Mandhari , Mohamed Shabbir Abdulnabi
Monitoring and classifying volcanic activity are a critical task for disaster risk reduction and hazard management. Recent discoveries in machine learning and deep learning have proved excellent satellite image classification and volcanic anomaly identification capabilities, yet the majority of existing methods suffer from small datasets, particularly on solitary data modalities or particular cases, merely as examples. In this research work, we put forward develop deep convolutional neural network for volcanic activity (DCNNVA) classification specifically designed for satellite imagery on volcanic activity. We rigorously benchmarked DCNNVA model's strength against a total of eight state-of-the-art transfer learning models: ResNet50, NASNetLarge, DenseNet121, MobileNet, InceptionV3, Xception, VGG19, and VGG16. Comparative experimental results show that proposed DCNNVA framework's overall performance significantly surpasses its competitors with an accuracy of 99.33%, precision of 100%, recall of 98.67%, and F1-score of 99.33%, significantly beating existing state-of-the-art methods. Also, we create a deployable graphical user interface (GUI) system that is capable of real-time monitoring on volcanic activity and generates multi-modal alert processing that can make this research directly applicable for practical use on disaster management as well as in early warning systems. This research contributes a scalable, strong, as well as practical solution towards volcanic hazard identification as well as a baseline system toward developing future multi-modal as well as real-time geohazard tracking system frameworks.
Volume: 15
Issue: 1
Page: 281-292
Publish at: 2026-03-01

Effect of fasteners variations on the performance of one-phase induction motors in bio-pellet production process

10.11591/ijaas.v15.i1.pp253-260
Ediwan Ediwan , Arnawan Hasibuan , Abubakar Dabet , Muhammad Daud , Fajar Syahbakti Lukman , Gandi Supriadi
Indonesia has many oil palm plantation areas. One of the negative impacts is the large amount of empty fruit bunch (EFB) waste. Utilizing EFB as a bio pellet as a renewable energy source is one of the solutions to reduce waste while supporting the green energy transition. EFB bio-pellets have the potential to replace fossil fuels, but face challenges in setting good quality standards. The production process of EFB bio-pellets uses a variety of binder contents. This study aims to analyze the influence of different levels of binder content on the quality of bio-pellet products. Statistical analysis of linear regression was performed to measure energy consumption and motor performance in the production process of EFB bio-pellets. This study provides recommendations to help maximize the quality and efficiency of the bio-pellet production process from palm oil EFB waste.
Volume: 15
Issue: 1
Page: 253-260
Publish at: 2026-03-01

Temperature and pH effects on bioethanol production from wild cassava (Manihot glaziovii Muell. Arg) using simultaneous co-fermentation

10.11591/ijaas.v15.i1.pp227-235
Ida Ayu Pridari Tantri , Ida Bagus Wayan Gunam , Anak Agung Made Dewi Anggreni , I Gede Arya Sujana
Bioethanol is a clean alternative energy source, with wild cassava (Manihot glaziovii Muell. Arg) as a potential feedstock. Fermentation converts glucose from hydrolysis into ethanol. This study examines the effects of pH and fermentation temperature on bioethanol characteristics using a simultaneous saccharification and co-fermentation (SSCF) technique. A factorial randomized block design (RBD) was used with two factors: pH (4.5, 5.0, and 5.5) and fermentation temperature (30, 32.5, and 35 °C). Data were analyzed using variance and Duncan’s test. Results showed that pH and temperature significantly affected pH value, total soluble solids, reducing sugar, and ethanol content. The optimal conditions for bioethanol production were pH 4.5 and temperature 32.5 °C, yielding a pH of 3.55±0.07, total soluble solids of 9.3±0.57 °Brix, reducing sugar of 3.038±0.10 mg/mL, and ethanol content of 3.48±0.37 (%w/v). Based on the results of this study, wild cassava can be utilized as bioethanol by considering the effect of fermentation conditions.
Volume: 15
Issue: 1
Page: 227-235
Publish at: 2026-03-01

A three isolated port DC/DC converter for an energy storage system for renewable energy applications

10.11591/ijpeds.v17.i1.pp533-552
Faruk Ahmeti , Dimitar Arnaudov , Sabrije Osmanaj
The use of renewable energy sources like solar photovoltaic, wind, and fuel cells is gaining popularity due to growing environmental awareness, technological advancements, and declining production costs. Power electronic converters are usually used to convert the power from renewable sources to match the load demand and grid requirements. Among these, DC–DC converters are essential for improving system functionality and power density, especially in low-voltage renewable systems that require high voltage gain. This paper presents a systematic evaluation of five advanced DC-DC converter topologies: multi-port DC, boost multiport interleaved step-up, isolated bidirectional, voltage/current fed, and general resonant focusing on their structural complexity, component count, and potential application scenarios. In addition, a novel high-gain three-port resonant A DC-DC converter is proposed, incorporating galvanic isolation via a three-winding high-frequency transformer. The converter adopts a half-bridge resonant inverter and rectifier-based load port, resulting in a compact and cost-effective solution. A detailed analysis of the converter's operation, design considerations, and control strategy is conducted using PLECS simulation. Furthermore, an experimental setup is developed to validate the converter’s practical feasibility. The setup schematic and comprehensive comparative tables are included to support the evaluation and highlight the proposed design’s capabilities.
Volume: 17
Issue: 1
Page: 533-552
Publish at: 2026-03-01

Miniaturized circular fractal patch antenna with defected ground structure for high-selectivity dual-band X-band applications

10.11591/ijaas.v15.i1.pp372-383
Raju Thommandru , Rengaraj Saravanakumar
Microstrip patch antennas are easily fabricated and have a low profile, making them widely used in radar, satellite, and defence applications. Achieving high selectivity and miniaturization in X-band dual-band operation remains a challenge. Conventional designs using simple patch geometries and defected ground structures (DGS) often exhibit limited bandwidth, poor impedance matching, and reduced gain. To address these limitations, this work presents a miniaturized circular fractal patch antenna with an optimized DGS to enhance frequency selectivity, improve impedance matching, and maintain compact size. Circular fractal slots are introduced in the radiating patch to extend the effective current path while preserving the footprint. A centrally placed diamond-shaped slot provides capacitive loading that aids impedance tuning. Electromagnetic simulations were conducted in Ansys HFSS 2023 R2, and a prototype was fabricated on an FR-4 substrate with εr = 4.4, loss tangent = 0.02, and thickness 1.6mm. Measurements verify two passbands: 8.637–9.173GHz (center 8.8025GHz, return loss −22.0267dB, voltage standing wave ratio (VSWR) 1.1720, gain 4.82dB, efficiency 63.51%) and 10.121–10.956GHz (center 10.3700GHz, return loss −25.2864dB, VSWR 1.1199, gain 3.42dB, efficiency 72.58%). The antenna shows steady radiation and improved matching across both bands, supporting use in compact X-band front ends.
Volume: 15
Issue: 1
Page: 372-383
Publish at: 2026-03-01

Comparative analysis of YOLO variants and EfficientNet for detecting bone fractures in X-ray images

10.11591/ijaas.v15.i1.pp155-167
Shatabdi Sarker , Avizit Roy , Shaila Sharmin , Shakila Rahman , Jia Uddin
A bone fracture is a serious medical problem, and accurate and prompt diagnosis is crucial for optimal treatment. This study highlights the progress of automatic bone fracture detection using deep learning (DL) models. A dataset containing 17 different fracture classes was used to train and evaluate the models. The dataset had class imbalance and minor fracture detection challenges. Extensive preprocessing, including data augmentation and resizing, has been applied to solve these problems, which has helped to increase the robustness of the model. Seven state-of-the-art models—you only look once (YOLO)v8, YOLOv9, YOLOv10, YOLOv11, EfficientNetB0, DenseNet169 and ResNet50—are trained and evaluated. Precision, recall, F1-score, and mean average precision (mAP) were used to evaluate the performance of the models. Among all models, YOLOv11 leads the others by achieving the highest precision, mAP, and precision-recall balance. YOLOv11 adds architectural improvements such as a deep backbone network and hybrid feature fusion, which make the model more reliable in different types of fracture detection. It is capable of reducing false detections and maintaining stable memory usage consistency even under different imaging conditions. Overall, YOLOv11 showed promising results and highlighted the potential of AI-powered diagnostic tools to improve clinical processes and patient care. As future work, the application field of the model can be extended to larger medical imaging tasks, and it can be further refined for effective use in resource-limited environments.
Volume: 15
Issue: 1
Page: 155-167
Publish at: 2026-03-01

Application of fuzzy logic for the evaluation of student academic performance in biomedical subjects

10.11591/ijaas.v15.i1.pp236-244
Elda Maraj , Anila Peposhi , Aida Bendo
Conventional educational systems primarily use rigid assessment models that narrowly define student achievement through examination scores, categorizing outcomes into success or failure. Fuzzy logic, a mathematical approach derived from set theory, provides a more flexible framework capable of capturing uncertainty and gradations in performance. Initially applied in engineering and artificial intelligence, fuzzy logic has shown significant promise in educational contexts where nuanced evaluation is essential. This study applies a fuzzy logic-based methodology to the evaluation of biomedical course performance at the Sports University of Tirana, Faculty of Rehabilitation Sciences. Data were collected from fifty students enrolled in biomedical subjects and analyzed through both classical examination grading and fuzzy logic evaluation. Comparative analysis revealed that while classical assessment remains constrained by static calculations, fuzzy logic introduces dynamic adaptability. The findings highlight the superiority of fuzzy logic over traditional methods in providing a multidimensional picture of academic achievement. This approach not only refines evaluation accuracy but also supports fairer and more individualized assessment practices. Consequently, fuzzy logic emerges as a powerful tool for modernizing educational evaluation systems, particularly in biomedical disciplines where learning outcomes often extend beyond conventional metrics.
Volume: 15
Issue: 1
Page: 236-244
Publish at: 2026-03-01

Artificial intelligence-powered image recognition retail checkout systems

10.11591/ijaas.v15.i1.pp187-197
Malyssa Alias , Dhaifina Saidi , Lim Jia Huey , Lee Qing Fang , Durghaashini S. Ragunathan , JosephNg Poh Soon , Phan Koo Yuen , Lim Jit Theam , Wong See Wan
The integration of artificial intelligence (AI) with big data analytics leads to substantial transformations in the retail sector. This research explores the impact of AI-powered image recognition checkout systems on the retail industry, focusing on operational efficiency, customer experience, and resource waste. Employing a mixed-methods approach, this study combines usability testing and data analytics to assess the viability of this technology in attaining automation and accuracy in retail operations. The study focuses on the creation of robust, resource-efficient systems that foster long-term industrial growth. The findings show that AI-powered solutions not only speed the checkout process but also contribute to sustainable infrastructure by reducing resource consumption and increasing energy efficiency. This report offers significant information, like the impact of AI-powered image recognition checkout systems on operational efficiency, customer experience, and the role of AI in promoting sustainable infrastructure for retailers and governments looking to advance the digitalization of the retail industry.
Volume: 15
Issue: 1
Page: 187-197
Publish at: 2026-03-01

Soft fuzzy partial metric and some results on fixed point theory under soft set

10.11591/ijaas.v15.i1.pp427-436
Rohini R. Gore , Renu P. Pathak
This research paper establishes a new concept of soft fuzzy partial metric spaces, combining soft sets, partial metric spaces, and fuzzy sets to handle uncertainty and imprecision. This paper's primary goal is to use soft fuzzy partial metric spaces to examine various fixed-point theory conclusions. A few fixed-point results are defined under the 𝛹 −contraction mapping on soft fuzzy partial metric space and the soft fuzzy contraction mapping. Also, illustrate the related example of fixed-point theorem. Soft fuzzy partial metric spaces have applications in various fields, including image processing, decision-making analysis.
Volume: 15
Issue: 1
Page: 427-436
Publish at: 2026-03-01

Ensemble machine learning based model to estimate irrigation water requirement for wheat crop

10.11591/ijaas.v15.i1.pp142-154
Satendra Kumar Jain , Anil Kumar Gupta
India faces a serious water shortage issue, as its population grows faster than the percentage of fresh water available, with only 4% of the world's fresh water available to 18% of the world's population. Agriculture sector is more water-consuming sector in India. India's irrigation system still faces two significant problems: low irrigation efficiency and a lack of optimization during irrigation. To address these problems, agriculturists ought to be aware of the water requirements for crops beforehand. Innovative fields like machine learning, a branch of artificial intelligence, have a big potential to improve irrigation. Verifying the suitability of the gradient boosting regressor machine learning algorithm-based model for estimating irrigation water requirements (IWR) is the aim of this research. The experiment is conducted in Ludhiana, a city in Central Punjab, India, with a hot, semi-arid climate that features scorching summers and chilly winters. The results demonstrate the remarkably high accuracy rate with coefficient of determination (R2) =0.98 for predicting IWR. The suggested model, which is based on a gradient boosting regression, allows the stakeholders to accurately estimate the amount of water needed for irrigation, the number of irrigation applications for the growing season of wheat crops, and the interval between irrigations.
Volume: 15
Issue: 1
Page: 142-154
Publish at: 2026-03-01
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