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30,376 Article Results

Design of miniaturized dual-band bandpass filter with enhanced selectivity for GPS and RFID applications

10.11591/ijict.v14i3.pp993-1001
Thupalli Shaik Mahammed Basha , Arun Raaza , Vishakha Bhujbal , Meena Mathivanan
This article presents a miniaturized interdigital coupled dual-band bandpass filter with multiple transmission zeros/poles. Stepped impedance resonators, interdigital coupled lines, and series coupled lines make up the proposed filter design. A circuit simulator is used to analyze a proposed filter, and the magnitude and bandwidth shifts have been investigated. To confirm the proposed filter design, equations for transmission zero frequencies have been constructed and verified based on even-odd mode analysis and lossless transmission line theory. A working prototype for 2.2 GHz (RFID) and 1.38 GHz (GPS) applications is made and tested. With λg representing the guided wavelength at the first band (1.38GHz), the finished prototype is compact, measuring 0.32 λg×0.27 λg. According to the experimental findings, there is strong selectivity in the first and second passbands, with roll-off rates of 190 and 168 dB/GHz, respectively. Good isolation between the two passbands is indicated by an insertion loss of less than 20 dB.
Volume: 14
Issue: 3
Page: 993-1001
Publish at: 2025-12-01

Numerical and experimental state of identification battery pack lithium-ion

10.11591/ijpeds.v16.i4.pp2623-2633
Dewi Anggraeni , Budi Sudiarto , Eriko Nasemudin Nasser , Wahyudi Hasbi , Yus Natali , Purnomo Sidi Priambodo
Two key indicators of a battery management system (BMS) are the state of charge (SoC) and the state of health (SoH). Accurately estimating SoC is important to prevent potential issues. Additionally, space, computing time, and cost are important factors in hardware development. To address these considerations, the first-order extended Kalman filter (EKF) and adaptive extended Kalman filter (AEKF) models were selected due to their simpler data pre-processing and better accuracy. The study recommends using the first-order equivalent circuit model (ECM) method in conjunction with the EKF and AEKF algorithms due to their straightforward setup and efficient computational process. Analysis of the charge-discharge cycles shows that the AEKF method consistently outperformed the EKF method regarding SoC accuracy. Moreover, when given different initial SoC values, the AEKF method displayed superior SoC estimation accuracy compared to the EKF method. Moreover, while the accuracy of the EKF is diminished, the error value remains below 2.5% for up to 500 cycles. Additionally, the shorter computing time of the EKF method is a consideration for practical real-world implementation. Furthermore, experiments conducted over 500 cycles revealed that SoH estimation declined from 99.97% to 76.1947%, suggesting that the battery has reached the end of life (EOL) stage.
Volume: 16
Issue: 4
Page: 2623-2633
Publish at: 2025-12-01

Smart wireless charging architecture for electric vehicles using resonant inductive coupling and low-component design

10.11591/ijape.v14.i4.pp859-869
Devarakonda Mahidhar , Burthi Loveswara Rao , K. V. Govardhan Rao , C. H. Rami Reddy
A wireless power transfer system designed for electro-vehicle recharge and low-power device charging is explained in this document through resonant inductive coupling technology. Once switched on the pulse generator and IRF540 MOSFETs from the IC CD4047 drive high-frequency signals through the transmitter coil. IR sensors function as operational safety tools by detecting valid receivers which activate a relay control system for transmitter power management and reduce unnecessary energy consumption. A full-wave rectifier along with the 7805-voltage regulator enables the receiver unit to deliver fully stable 5 V DC output. System status is displayed through a user interface equipped with an LCD and real-time billing information runs on ThingSpeak IoT platform for visualization. Tests show that the system reaches a maximum power transfer efficiency of 90% alongside successful relay operation lasting less than 150 ms. The system provides an inexpensive solution to build smart wireless charging infrastructure networks that remain energy-efficient and expandable through its built-in control and monitoring functions.
Volume: 14
Issue: 4
Page: 859-869
Publish at: 2025-12-01

A fuzzy logic approach to sustainable energy management in standalone microgrids

10.11591/ijape.v14.i4.pp999-1010
Suganthi Neelagiri , Srinivas Babu , Siddalingappagouda Biradar
The fast development of worldwide energy consumption, driven by industrial growth and increasing dependence on fossil fuels, has led to higher carbon emissions and degradation of the environment. In response, renewable energy sources, such as solar, wind, and hydroelectric power, offer cleaner and sustainable replacements with insignificant carbon emissions. This paper examines the role of artificial intelligence (AI)-based techniques, particularly fuzzy logic, in developing energy management system. A fuzzy logic-based energy management system is proposed for a renewable-powered microgrid that incorporates a hybrid energy storage system. Fuzzy logic-based energy management, due to its capability to manage uncertainty and complexity, offers viable solutions for improving the generation and distribution of energy within microgrid systems. This system is compared to a dynamic cascaded dual-loop proportional-integral (PI) controller-based energy management system in standalone mode. The comparative analysis emphasizes the ability of fuzzy logic-based energy management to improve the efficiency and sustainability of microgrids. The research aims to advance the creation of more intelligent and dependable energy solutions that integrate renewable resources and enhance energy management practices.
Volume: 14
Issue: 4
Page: 999-1010
Publish at: 2025-12-01

Deep learning approaches for Braille detection and classification: comparative analysis

10.11591/ijai.v14.i6.pp4652-4660
Surekha Janrao , Tavion Fernandes , Ojas Golatkar , Swaraj Dusane
This study proposes a hybrid approach to Braille translation leveraging the strengths of both YOLO for object detection and multitude of classification models such as ResNet, and ResNet for accurate Braille character classification from images. Upon comparing numerous models on various performance metrics, ResNet and DenseNet outperformed other models, exhibiting high accuracy (0.9487 and 0.9647 respectively) and F1-scores (0.9481 and 0.9666) due to their deep, densely connected architectures adept at capturing intricate Braille patterns. CNNs with pooling showed balanced results, while MobileNetV2's lightweight design limited complex classification. ResNeXt's multi-path learning achieved respectable performance but lagged behind ResNet and DenseNet. In the future the results from our study could be further explored on contracted Braille recognition, be adapted to various Braille codes, and optimized for mobile devices, for real time Braille detection and translation on smartphones.
Volume: 14
Issue: 6
Page: 4652-4660
Publish at: 2025-12-01

Enhanced cheetah optimizer for demand side management in smart grids with demand response and renewable energy

10.11591/ijape.v14.i4.pp912-922
Lakshmi Haveli , M. P. Flower Queen
For the effective operation of smart grids, it is critical to ensure that demand side management (DSM) includes strong two-way communication and addresses significant security and privacy issues. DSM success depends on the participation of customers who need a just system. The recent fairness studies in DSM have identified different definitions of fairness while this study presents an enhanced cheetah optimizer algorithm (ECOA) for solving complex dynamic economic dispatch (DED). The ECOA targets at minimizing operational costs as well as improving power system security. This research tests the ECOA performance by examining DED problem independently from DSM, and demonstrates its applicability on 10-unit and 20-unit test systems. These figures clearly show that ECOA decreases operational costs by about 0.24% and 0.43% respectively, once DSM is used. Thus, it is possible to conclude that DSM has the possibility of bringing down costs and enhancing economic efficiency. Considering the integration of renewable energy sources into microgrids with electric vehicles, ECOA’s adaptivity and dependability make it a potential approach to multi-objective energy management within such kind of networks.
Volume: 14
Issue: 4
Page: 912-922
Publish at: 2025-12-01

Hybrid AI framework for anomaly detection and root cause analysis in multi-agent systems

10.11591/ijai.v14.i6.pp5290-5302
Tahri Rachid , Ouammou Abdellah , Lasbahani Abdellatif , Abdessamad Jarrar , Balouki Youssef
Anomaly detection and root cause analysis (RCA) are critical for securing intelligent systems against evolving threats. Traditional models often suffer from high false alarms, weak adaptability to streaming contexts, and limited interpretability. This work proposes a hybrid artificial intelligence (AI) framework that integrates machine learning (ML) with prior knowledge, semantic rules, and bio-inspired modeling. The approach strengthens detection of diverse attacks, including DoS/DDoS, Probe, U2R, and R2L, while reducing human intervention. Experiments on the NSL-KDD dataset demonstrate that our method decreases spurious alerts by up to 90%, improves accuracy by 2–4%, and reduces false positives/negatives by about 4%. Beyond statistical gains, the framework ensures robustness in real-time environments, offering interpretable and scalable anomaly detection for heterogeneous systems. These results highlight the potential of hybrid symbolic–subsymbolic AI to enhance reliability in next-generation security infrastructures.
Volume: 14
Issue: 6
Page: 5290-5302
Publish at: 2025-12-01

Multi-objective energy management and environmental index optimization of a microgrid using swarm intelligence algorithm

10.11591/ijape.v14.i4.pp783-793
Ahmed Bahri , Nabil Mezhoud , Bilel Ayachi , Farouk Boukhenoufa , Lakhdar Bouras
Due to the need for better reliability, high energy quality, lower losses and cost, and clean environment, the application of renewable energy sources such as wind energy and solar energy in recent years has become more widespread mainly. In this work, one of the most general of all swarm intelligence algorithms, called particle swarm optimization (PSO) is applied to solve the optimal energy management (OEM) and environmental index optimization (EIO) problems of micro-grid (MG) operating by renewable and sustainable generation systems (RSGS). The PSO approach was examined and tested on standard MG composed of different types of RSGS, such as wind turbines (WT), photovoltaic systems (PV), fuel cells (FC), micro turbine (MT), and diesel electric generator (DEG) with energy storage systems (ESS). The results are promising and show the effectiveness and robustness of proposed approach to solve the OEM and the EIO. The results obtained were compared with some well-known references. The results show that the optimization process reduced the energy generation costs from 257283 ($/h), 263929 ($/h), and 263526 ($/h), respectively. While the environmental index further improved to 0.1548 (ton/h).
Volume: 14
Issue: 4
Page: 783-793
Publish at: 2025-12-01

Systematic literature review on cyber-attacks and cyber defense strategies in smart grids

10.11591/ijape.v14.i4.pp1044-1057
Anass Naqqad , Abdellah Boulal , Rachid Habachi
The smart grid is an advanced evolution of the traditional electrical power grid, developed to meet the increasing energy demands and requirements of the 21st century by incorporating digital technologies and data management systems to improve efficiency and reliability. Unlike conventional grids, the smart grid relies on a network of interconnected digital devices, sensors, and computerized controls that enable real-time monitoring and management of electricity distribution across vast geographic areas. However, the growing dependence on digital technologies also brings heightened cyber security concerns, since their integration can expose the grid to an increased risk of malicious intrusions. This systematic literature review investigates the nature and scope of cyber-attacks and cyber defense strategies in smart grids, which are critical to modern energy infrastructure. Following established research guidelines, this review rigorously examines existing studies by focusing on peer-reviewed articles and conference papers to understand the range of cyber security threats and defense mechanisms that smart grids face. The review uses a structured methodology to identify, evaluate, and synthesize key findings, revealing trends and gaps in current knowledge about smart grid security. The outcomes of this analysis offer valuable clarity on the specific weaknesses and operational challenges that affect smart grid infrastructures, contributing to the ongoing efforts to enhance cyber security measures and guide future research in this vital field.
Volume: 14
Issue: 4
Page: 1044-1057
Publish at: 2025-12-01

Inexpensive human audiometric system using Raspberry Pi and artificial intelligence

10.11591/ijai.v14.i6.pp4502-4510
Abdulrafa Hussain Maray , Muataz Akram Hassan , Taha Hussein Marai Al-Hassan
The most common and widespread disease in Iraq is hearing impairment for children and newborns. Also, in cities, people are exposed to high levels of noise, loud sounds at work, like factories, and machinery noise. In this paper, a system was designed and implemented to measure the level of hearing in the human ear, in order to reduce the cost of these devices. This system uses Raspberry Pi 3 microcontrollers, which are considered cheap and have high capabilities in open-source programming. Their abundant availability will lead to the provision of these systems in homes, health centers, and hospitals. In this proposed algorithm, two sine waves are generated by the microcontroller with different frequencies. It is transmitted by the MP3 audio transmission cable through the analog-to-digital (ADC) port. These audio signals are generated at a frequency of (0.5 to 12 kHz), these frequencies are the ones that humans can hear, and they can be represented by pulse width modulator (PWM) technology (x=255 samples). Convolutional neural network (CNN) is trained on the dataset acquired through deep learning algorithms.
Volume: 14
Issue: 6
Page: 4502-4510
Publish at: 2025-12-01

Improve the thermal performance of the combined water-paraffin hot storage tank in the absorption cooling cycle

10.11591/ijape.v14.i4.pp1011-1022
Maki Haj Zaidan , Thamir Khalil Ibrahim , Hussam S. Dheyab
This research investigates the thermal performance of storage materials in a hot tank designed to extend the operation time of a 1.5-ton water ammonia absorption cooling system. Thermal energy is supplied by concentric parabolic solar collectors, which heat the absorption cycle generator during periods of sufficient solar radiation. When the water temperature exceeds the system’s operating threshold, the additional heat accumulates in the hot tank. It is later used to drive the generator during periods of low solar availability, such as in the afternoon or after sunset. The system is designed to provide air conditioning for a room; its load was calculated hourly. The suitable size of the storage tank and the corresponding collector area were determined based on simulations of the absorption system to achieve an optimal coefficient of performance (COP). The collector area was increased after the addition of paraffin phase change material (PCM) to enhance system performance, and a temperature control strategy was implemented to prevent the water in the hot storage tank from reaching the boiling point. This was achieved by incorporating a specific percentage of paraffin, a PCM, with a melting point of 85 °C. The size of the hot storage tank containing both water and a specified proportion of paraffin, in addition to the solar collector area, was optimized to maximize the tank temperature. These parameters were entered into the energy balance model as input data to ensure the effective operation of the absorption system under optimal conditions. A comprehensive system simulation was conducted by deriving and simplifying the heat balance equations for the hybrid hot storage tank, the solar collector, and the absorption system. The simulation aimed to identify the optimal wax ratio of 5% to 20% to maximize system performance. The optimal paraffin ratio was found to be 10% of the tank volume, which enabled an additional 4 hours of operation and extended the system’s uptime to its maximum potential.
Volume: 14
Issue: 4
Page: 1011-1022
Publish at: 2025-12-01

Design and implementation of QUADRESCUE: A ROS-based quadruped robot for disaster response support

10.11591/ijra.v14i3.pp387-398
Sanjay Deshmukh , Ojas Chanakya , Om Gabani , Kashish Patni , Asmita Deshmukh
Search and rescue (SAR) operations in hazardous environments demand robotic systems capable of traversing complex terrains while ensuring responder safety. Traditional wheeled platforms often fail in debris-laden areas, and fully autonomous quadrupeds remain financially out of reach for many rescue agencies. This paper presents the design and development of QUADRESCUE, a modular operator-assisted quadruped robot built to bridge the gap between affordability and capability in disaster response. QUADRESCUE delivers core SAR functionalities including remote visual inspection, real-time terrain mapping via an RGB-D camera, payload transport, and GPS-based survivor localization. Built with a robust three degrees of freedom (3DoF) per leg design, the robot uses inverse kinematics algorithms to precisely control twelve servo motors for stable locomotion across uneven terrain. The system integrates the robot operating system (ROS) for seamless operation, real-time joystick control for easy navigation, an IMU for orientation sensing, and a GPS module with 3-meter accuracy. Field evaluations demonstrate 80–94% success rates on challenging surfaces, substantially outperforming wheeled counterparts 19% to 39% with a 200-meter control range and 45 minutes of runtime. QUADRESCUE offers a lightweight, cost-effective, and repairable solution that combines practical usability with advanced performance, making it well-suited for real-world deployment in emergency rescue situations.
Volume: 14
Issue: 3
Page: 387-398
Publish at: 2025-12-01

Enhancing communication and interaction in the movie industry based SparkMLlib's recommendation system

10.11591/ijai.v14.i6.pp4661-4674
Said Chakouk , Abdelkerim Zitouni , Nazif Tchagafo , Ahiod Belaid
In the ever-evolving landscape of streaming platforms, recommendation systems contribute significantly to enhancing the user experience. This article examines the significance of these systems in suggesting movies, analyzing their impact on user satisfaction and platform performance. Utilizing SparkMLlib, a powerful tool for large-scale data processing, we explore various recommendation techniques, including collaborative filtering and content-based filtering. We highlight the dimension of digital communication to further enhance the accuracy of recommendations and foster greater user engagement. Our study also addresses the challenges and future opportunities related to recommendation systems, emphasizing the need for transparency and ethical algorithms. This research highlights the potential for recommendation systems to revolutionize the digital entertainment landscape and shape the future of the movie industry.
Volume: 14
Issue: 6
Page: 4661-4674
Publish at: 2025-12-01

Arabic text classification using machine learning and deep learning algorithms

10.11591/ijai.v14.i6.pp5201-5217
Rawad Awad Alqahtani , Hoda A. Abdelhafez
The classification of Arabic textual content presents considerable challenges due to the language's rich morphological structure and the wide variation among its dialects. This study aims to enhance classification accuracy by leveraging ensemble learning techniques and a deep bidirectional transformer-based model, specifically the multilingual autoregressive BERT (MARBERT). To address linguistic variability, advanced preprocessing techniques were employed, including Farasa, Tashaphyne, and Assem stemming methods. The Al Khaleej dataset served as the basis for supervised learning, providing a representative sample of Arabic text. Furthermore, term frequency-inverse document frequency (TF-IDF) with bigram and trigram feature extraction was utilized to effectively capture contextual semantics. Experimental results indicate that the proposed approach, particularly with the integration of MARBERT, achieves a peak classification accuracy of 98.59%, outperforming existing models. This research underscores the efficacy of combining ensemble learning with deep transformer-based models for Arabic text classification and highlights the critical role of robust preprocessing techniques in managing linguistic complexity and improving model performance.
Volume: 14
Issue: 6
Page: 5201-5217
Publish at: 2025-12-01

Optimal battery sizing using modified spider monkey optimization in grid connected microgrids

10.11591/ijra.v14i3.pp356-365
Meraj Fatima , Manne Rama Subbamma
Microgrids (MGs) must have optimally sized storage and renewable energy sources to operate efficiently, economically, and reliably. MG may benefit from optimization techniques in their scheduling and sizing since they have a variety of energy sources with varying availability conditions and necessary costs. In this research, a novel modified spider monkey-based energy management system (MSM-EMS) has been proposed by increasing the photovoltaic (PV) or battery energy storage system (BESS) module capacity while minimizing grid connectivity dependency. The fundamental idea behind the proposed approach is greater dependability at the lowest feasible cost. By taking into account the BESS utilization factor and PV forced outage rates in a MG, the method becomes more realistic. Despite the absence of renewable energy sources and the grid, the proposed strategy provided critical loads according to schedule while maintaining reserve margins. Experimental findings demonstrate that the modified spider monkey optimization (MSMO)-based algorithm can determine the best BESS size and PV depending on cost. In comparison to particle swarm optimization (PSO) of $2756.1 and ABC of $2912.65, the ideal cost for EMS-MSMO is $2215.77 which is relatively low compared to the existing technique. As a result, the suggested MSMO algorithm and innovative energy management system has been optimized along with PV and battery dimensions.
Volume: 14
Issue: 3
Page: 356-365
Publish at: 2025-12-01
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