Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

31,456 Article Results

Optimal battery selection for electric vehicles: a comparative ranking approach

10.11591/ijape.v15.i1.pp319-327
Ramana Pilla , Rebba Sasidhar , Malleti Sreedhar , Tentu Papi Naidu , Shaik Rafi Kiran , Vasupalli Manoj , Kalyana Kiran Kumar
Electric vehicles (EVs) have emerged as an eco-friendly alternative to traditional internal combustion engines, with battery technology playing a pivotal role in their success. Key factors like energy density, power output, charging speed, durability, cost, safety, and environmental impact hinge on the choice of battery. Various technologies in lithium-ion batteries are assessed for their suitability in EVs. The right battery is essential for optimal performance, extended range, and sustainability. This paper offers an in-depth look at battery selection in EVs, examining different types in lithium-ion and their pros and cons. Additionally, it explores into three prominent decision-making methods: fuzzy analytic hierarchy process (FAHP), evaluation based on distance from average solution (EDAS), and preference ranking organization method for enrichment evaluation-II (PROMETHEE II). FAHP ranks batteries based on their relevance to specific EV requirements, while EDAS and PROMETHEE II provide a multi-criteria framework. These methods offer valuable insights into choosing the most suitable lithium-ion battery for EVs. The study underscores the importance of meticulous battery selection and highlights the efficacy of decision-making approaches like FAHP, EDAS, and PROMETHEE II. As battery tech advances, future research on alternatives like solid-state and sodium-ion batteries could revolutionize the EV industry.
Volume: 15
Issue: 1
Page: 319-327
Publish at: 2026-03-01

Design and development of an enhanced U-shaped microstrip antenna for super wideband applications in next-generation wireless systems

10.11591/ijres.v15.i1.pp204-213
Mani Periyasamy , Shankar Sharma Karthikeyan Jayalakshmi
The proposed enhanced U-shaped microstrip antenna is conceived with the aim of meeting the emerging needs of super wideband (SWB) applications in contemporary wireless communication systems. An efficient upgraded U-shaped patch design, in combination with substrate enhancements and impedance matching methods, is introduced in this work to remarkably increase the operational bandwidth, gain, and radiation efficiency of antenna. The antenna aims SWB achievement with the help of optimized dimensions and it is designed in such a way that it minimizes ground wave losses. It maximizes the impedance matching over a frequency range of 2 MHz to 20 GHz. Through various simulation outputs and experimental verifications, the antenna designed demonstrates excellent performance with a broad impedance bandwidth greater than 100% and the radiation patterns that are stable beyond entire frequency band. This work illustrates that the enhanced U-shaped microstrip antenna can attain the needs of next-generation communication technologies with specific criteria, and it establishes an efficient solution to SWB systems without sacrificing performance, cost, or size issues.
Volume: 15
Issue: 1
Page: 204-213
Publish at: 2026-03-01

High voltage asymmetric converter for electrostatic particle accelerators

10.11591/ijpeds.v17.i1.pp553-571
Diego Alberto Fanego , Orlando Silvio Sandini , Hernan Emilio Tacca , Andres Juan Kreiner
This work presents several topologies of asymmetric high voltage converters for electrostatic particle accelerators. The options are compared on the basis of their transfer functions and the magnetic components required, and the most suitable for the intended purpose is selected. Simulations and measurement results of the prototype, which has symmetrical voltage output and soft switching in the main transistor, are presented. The prototype built features output voltages of 10 kVand-10 kV, the converter uses a single common command ground for the transistors simplifying its drivers, and also by means of the presented snubber circuit it recovers energy during soft switching.
Volume: 17
Issue: 1
Page: 553-571
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

Modeling and simulation of an active quarter-car suspension system using a synergetic controller

10.11591/ijra.v15i1.pp210-221
Dao Trong Dung , Trong Nghia Le , Alexandr D. Lukyanov , Nguyen Xuan Chiem
This paper presents the modeling and simulation of an active quarter-car suspension system (AQCSS) designed to enhance operational performance and ride comfort across various road conditions. First, a dynamic quarter-car model was developed, incorporating all the components of AQCSS and road-induced stimuli, based on the Euler–Lagrange method. Subsequently, a synergetic controller is designed by selecting a manifold that meets the system’s technical requirements. The proposed controller ensures a balance between ride comfort and road-holding performance by leveraging this manifold design. This control framework enables flexible adjustment of the damping force in real time according to the system states and external excitations. The stability of the closed-loop system is rigorously established through Lyapunov analysis. Numerical simulations are carried out in MATLAB to assess the proposed control law by benchmarking it against a passive suspension configuration and a sliding mode control approach, thereby demonstrating its effectiveness.
Volume: 15
Issue: 1
Page: 210-221
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

Reliability-constrained optimal scheduling of PV-based microgrids using deterministic time-series forecasting and load prioritization strategies

10.11591/ijpeds.v17.i1.pp250-266
Dunya Sh. Wais , Huda A. Abbood
This paper presents an advanced MPC-based energy scheduling framework for islanded microgrids operating under uncertain and dynamic conditions where photovoltaic (PV) generation and energy storage systems (ESS) are integrated, and load management is hierarchically prioritized. The framework employs a hybrid ARIMA and random forest forecasting model to improve day-ahead and intra-day predictions of PV generation and load demand, enabling intelligent demand response, prioritized load shedding, and adaptive storage operation. Moreover, the proposed framework incorporates time-of-use (TOU) pricing and load importance weighting to minimize operational costs while ensuring a reliable power supply for critical loads. Simulation results across four operational scenarios demonstrate that the proposed method achieves approximately 32% improvement in critical load protection, 30% reduction in total operating cost, and 33.3% decrease in total load shedding compared to conventional MPC-based approaches. The proposed approach, therefore, provides a comprehensive, dynamic, and cost-efficient solution for microgrid scheduling and can be extended to multi-microgrid cluster applications in future research.
Volume: 17
Issue: 1
Page: 250-266
Publish at: 2026-03-01

A novel BERT-long short-term memory hybrid model for effective credit card fraud detection

10.11591/ijai.v15.i1.pp788-797
Oussama Ndama , Safae Ndama , Ismail Bensassi , El Mokhtar En-Naimi
In the rapidly evolving landscape of financial transactions, the detection of fraudulent activities remains a critical challenge for financial institutions worldwide. This study introduces a novel bidirectional encoder representation from transformers (BERT)–long short-term memory (LSTM) hybrid model that integrates both textual and numerical data to enhance credit card fraud detection. Leveraging BERT for deep contextual embeddings and LSTM for sequence analysis, the model provides a comprehensive approach that surpasses traditional fraud detection systems primarily based on numerical analysis. On the validation set, the model achieved a recall of 100% and an accuracy of 99.11%, highlighting strong effectiveness in identifying fraudulent transactions under class imbalance. Through rigorous evaluation, the model demonstrated exceptional accuracy and reliability, promising improvements in fraud detection and mitigation. This paper details the development and validation of the hybrid model, emphasizing its use of mixed data types to capture complex patterns in transaction data. The results indicate a new frontier in fraud detection by combining natural language processing (NLP) and sequential data analysis to create a robust solution for real-world applications, supporting the security and integrity of financial systems globally.
Volume: 15
Issue: 1
Page: 788-797
Publish at: 2026-02-01

Energy-efficient AI-enhanced secure routing for protecting IoT networks from advanced attacks

10.11591/ijeecs.v41.i2.pp731-739
Leelavathi R. , Vidya A.
This paper proposes artificial intelligence-enhanced secure routing (AIRS), a lightweight AI-enhanced secure routing protocol for internet of things (IoT) networks operating under advanced routing attacks. Unlike existing approaches that treat intrusion detection and routing separately, AIRS tightly integrates anomaly scoring into trust-aware routing decisions using a compact random forest model designed for constrained nodes. The anomaly detector is trained offline on simulated IoT traffic features and deployed for real-time inference during routing. Extensive Cooja simulations demonstrate that AIRS improves intrusion detection accuracy and packet delivery while reducing energy consumption compared to secure-RPL and trust-LEACH. The current validation is limited to simulation environments, and real-world testbed evaluation is left for future work.
Volume: 41
Issue: 2
Page: 731-739
Publish at: 2026-02-01

Robust UAV localization of ground sensors in urban environments via path loss refinement and geometric selection

10.11591/ijai.v15.i1.pp412-428
Ahmed M. A. A. Elngar , Heng Siong Lim , Yee Kit Chan , Yaser Awadh Bakhuraisa , Ida Wahidah
Localizing ground sensors with unmanned aerial vehicles (UAVs) in dense urban environments is challenging because multipath and non-line-of-sight (NLoS) propagation distorts path loss (PL) measurements. This paper proposes a two-stage UAV localization framework that refines PL data and selects geometrically stable waypoint subsets before position estimation. In stage 1, PL samples are spatially smoothed by averaging measurements at neighboring UAV waypoints to reduce localized fluctuations. In stage 2, waypoint subsets are filtered using non-collinearity and non-adjacency constraints, and sensor positions are estimated using weighted least squares (WLS) and particle swarm optimization (PSO), with final estimates averaged across valid subsets. Wireless InSite ray-tracing simulations show that the framework reduces mean absolute error (MAE) from over 150 m to approximately 8.5 m. The proposed approach improves the practicality of UAV-assisted localization for urban internet of things (IoT) sensor deployments.
Volume: 15
Issue: 1
Page: 412-428
Publish at: 2026-02-01

Text summarization: BART, RF, and hybrid BART-RF algorithm comparison

10.11591/ijai.v15.i1.pp929-940
Muhammad Adib Zamzam , Agus Buono , Toto Haryanto
Data and information accumulate quantitatively and qualitatively. Abundant text data are posted on the internet. The number correlates to the complexity of the summarization. Automatic text summarization (ATS) is one of the most challenging tasks in natural language processing (NLP). ATS approached in three ways: extractive, abstractive, and hybrid. Hybrid approach combines both extractive and abstractive. This research tests and compares performance of bidirectional auto-regressive transformer (BART) and random forest (RF) individually and the performance combination of hybrid BART and RF in ATS. The research shows that individually, BART and RF recall-oriented understudy for gisting evaluation (ROUGE) scores are having quite differences. Consecutively, ROUGE RF scores in R1, R2, and RL are 51.45, 45.52, and 54.58 respectively. Meanwhile, ROUGE BART scores are 32.78, 16.17, and 32.19. Consecutively, average ROUGE RF, BART, and RF×BART F-measure are 45.73, 21.38, and 31.31. RF has the highest average score. ATS hybrid RF×BART is shown to be performed better than the default BART. The average ROUGE F-measures for RF×BART obtain moderate score at 31.31. This score is better than the default BART’s ROUGE score. RF×BART can be an alternative to the effective hybrid approach.
Volume: 15
Issue: 1
Page: 929-940
Publish at: 2026-02-01

Neuro-DANet: dual attention deep neural network long short term memory for autism spectrum disorder detection

10.11591/ijai.v15.i1.pp810-823
Sujatha Hanumantharayappa , Manjula Rudragouda Bharamagoudra
Autism spectrum disorder (ASD) is neurological illness affects ability of individuals to communicate and interact socially, and it is diagnosed in any time. Early detection of ASD is especially significant due to its subtle characteristics and high costs associated with the detection process. Traditional deep learning (DL) models struggle to capture intricate spatiotemporal dependencies in functional magnetic resonance imaging (fMRI) data, resulting in minimized detection performance and poor generalization. To address these drawbacks, the proposed Neuro-DANet combines a dual-attention deep neural network (DA-DNN) with long short term memory (LSTM) to efficiently learn spatial and temporal features from fMRI scans. The continuous wavelet transform (CWT) is used to extract multi-scale features and the principal component analysis (PCA) is utilized to dimensionality reduction, which enhances robustness and efficacy. The dual self-attention mechanism improves the interpretability of the model by focusing on critical brain regions and time steps that are most relevant to ASD severity. The developed Neuro-DANet obtains the highest accuracy of 98.51% on autism brain imaging data exchange (ABIDE)-I and 98.81% on ABIDE-II datasets when compared with traditional algorithms.
Volume: 15
Issue: 1
Page: 810-823
Publish at: 2026-02-01

Artificial intelligence in orthodontics: modeling decision support systems for treatment planning

10.11591/ijai.v15.i1.pp97-105
Sowmya Lakshmi Belur Subramanya , Advaith Vijaya Mohan , Achala Varsha Vishlavath Premalatha , Manchikanti Varunsai
Orthodontic treatment planning involves complex clinical decision-making that can benefit from artificial intelligence (AI). This study evaluates machine learning and deep learning models—including random forest, AdaBoost, gradient boosting, and artificial neural networks (ANNs)—for predicting orthodontic treatment strategies using a dataset of 612 anonymized patient records with 66 clinically validated features across four categories (extraction, non-extraction, functional appliance, and orthopedic case). Preprocessing included imputation, normalization, and the synthetic minority oversampling technique (SMOTE) for class imbalance, while performance was assessed via 10-fold cross-validation. Results showed that ANNs achieved the highest balanced accuracy (0.83), F1-score (0.84), and receiver operating characteristic area under the curve (ROC-AUC) (0.90), outperforming ensemble and baseline models. Shapley additive explanations (SHAP) analysis confirmed clinically meaningful predictors such as vertical face proportions and mandibular plane angle, enhancing interpretability. Although promising, the study is limited by its single-institution dataset and lack of external validation. Future research should incorporate multicenter, multimodal datasets and interpretable-by-design frameworks to enable clinically trusted AI decision-support systems in orthodontics.
Volume: 15
Issue: 1
Page: 97-105
Publish at: 2026-02-01

Brain tumor segmentation and classification using artificial hummingbird optimization algorithm

10.11591/ijai.v15.i1.pp429-442
Radhakrishnan Karthikeyan , Arappaleeswaran Muruganandham
The time and medical personnel experience are the only factors that determine whether brain tumors can be manually identified from numerous magnetic resonance imaging (MRI) pictures in medical practice. Many frameworks based on brain tumors are diagnosed using both deep learning and machine learning. This study proposes a Wasserstein deep convolutional generative adversarial network (WDCGAN) optimized using the artificial hummingbird optimization algorithm (AHBOA) for brain tumor segmentation and classification (SCBT). First, the BraTS dataset is used to gather the input data. Then it is pre-processed consuming adaptive self guided filtering (ASGF) and the result is segmented using fuzzy possibilistic C-ordered mean clustering (FPCOMC). After that, features are extracted using the dual tree complex discrete wavelet transform (DT-CDWT). The characteristics of feature extracted are fed to WDCGAN for effectively categorize the various parameters. Then the proposed MATLAB is used to implement the technique, and the performance measurements like F1-score, accuracy, error rate, precision, sensitivity, mean square error, receiver operating characteristic (ROC), and computational time are analyzed. The WDCGAN-AHBOA-SCBT method significantly improves precision in SCBT by integrating adaptive optimization strategies, resulting in 32.18, 32.75, and 32.90% higher precision in contrast to current techniques. This demonstrates that the approach is more accurate and effective, making it a reliable tool for medical diagnosis.
Volume: 15
Issue: 1
Page: 429-442
Publish at: 2026-02-01

Centrality-optimized coalition formation: a genetic algorithm approach with leadership attributes

10.11591/ijai.v15.i1.pp383-398
Anon Sukstrienwong , Sorapak Pukdesree
In graph theory, centrality is often assessed using traditional methods such as closeness centrality, which measures the average shortest path length between nodes in a network. In this study, we primarily focus on developing the proposed approach and demonstrating its effectiveness through initial experimental results. A novel genetic algorithm (GA)–based method named centrality–optimized leadership coalition formation (COLCF) has been designed. It emphasizes actual agent distances according to closeness centrality and leadership attributes in group formation. We detail the COLCF algorithm, present empirical case studies, and provide efficiency comparisons. In accordance with our simulation results, the proposed algorithm is capable of capitalizing on the ideal coalition structure for achieving high closeness centrality when incorporated with leadership attributes. The experimental results demonstrate the algorithm’s robustness and effectiveness in addressing complex coalition formation challenges.
Volume: 15
Issue: 1
Page: 383-398
Publish at: 2026-02-01
1 2 3
...
2097 2098
Show 137 of 2098

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration