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30,376 Article Results

Spth-FCM: decision support tool for speech therapist based on fuzzy cognitive mapping

10.11591/ijict.v14i3.pp845-859
Maziz Asma , Taouche Cherif
The development and integration of medical information systems into a unified information space is a significant focus in the field of information technologies. It is essential to develop decision support systems (DSS) to enhance the effectiveness of medical and diagnostic procedures. This article presents a novel decision support tool for speech therapists, which is based on fuzzy cognitive maps (FCM). The latter is a method of modeling complex systems using knowledge of human existence and experience. The proposed tool is composed of three phases. The first phase focuses on entering patient information into the graphical interface developed in JAVA based on the most precise observations. An FCM will be automatically constructed, describing the type of disorder and the patient’s case during the second phase. Finally, in the third phase, FCM-based scenarios were built during the execution of the inference process under FCM expert. The system is presented and demonstrated using a real cases study for eight weeks. The results show that the tool makes it possible to display, guide, assist, and confirm the medical decision of the speech therapist for an appropriate diagnosis and treatment.
Volume: 14
Issue: 3
Page: 845-859
Publish at: 2025-12-01

Transmission line fault detection using empirical mode decomposition in presence of wind intermittency

10.11591/ijape.v14.i4.pp960-969
Venkata Krishna Bokka , E. R. Biju , Sai Veerraju Mortha , Majahar Hussain Mahammad , Shaik Mohammad Irshad
The regular fault detection approaches are failed to detect the faults in wind integrated transmission networks due to intermittency nature of the wind energy. More reliable schemes are required to accomplish the detection of faults in presence wind. This article proposed empirical mode decomposition (EMD) based fault detection scheme to detect various faults in wind integrated transmission lines during the normal and stressed conditions of the system. The instantaneous current measurements available at either sending or receiving end are processed through EMD to decompose it into a series of intrinsic mode functions (IMFs) and IMF2 is identified as a dominated IMF with numerous case wise investigations. 1/4th cycle moving window is used to calculate the absolute sum of the IMF2 coefficients to detect the faults with the support of a predefined threshold. The efficacy of the method is tested on different types of faults during the normal condition in presence of wind and later extended to stressed conditions such as power swing. The method is reliable during the typical cases and includes remote end and high resistance faults. All the experiments are carried out in Simulink to generate the measurement data and programs are executed in MATLAB.
Volume: 14
Issue: 4
Page: 960-969
Publish at: 2025-12-01

Speed control of 3-phase induction motor with modified DTC using HTAF-ANN

10.11591/ijpeds.v16.i4.pp2197-2211
Arpita Banik , Raja Gandhi , Chandan Kumar , Achyuta Nand Mishra , Rakesh Roy
In this research paper, an artificial neural network (ANN) algorithm is implemented with modifications to enhance the performance of a direct torque controlled (DTC) induction motor drive. Since the main challenge in the conventional DTC technique is to tune the PI controller appropriately therefore in this work, an ANN technique is incorporated in place of the conventional PI controller. Sudden changes in speed and loading in induction motor drives lead to sharp fluctuations and disturb the motor performance. In order to overcome these issues, a trained ANN controller is initially used here to enhance motor drive performance. Subsequently, the performance is further improved by modifying the activation function in the ANN controller. Here, motor parameters at rated and variable speed with various loading conditions have been analyzed and compared for the DTC with a conventional PI controller with ANN, and a proposed ANN controller. Simulation of the complete model with the conventional and proposed controllers is done using MATLAB/Simulink platform to observe the various speed responses for different conditions, and the experimental setup is used to demonstrate the effectiveness and performance of the proposed system.
Volume: 16
Issue: 4
Page: 2197-2211
Publish at: 2025-12-01

A novel WSSA technique for multi-objective optimal capacitors placement and rating in radial distribution networks

10.11591/ijape.v14.i4.pp934-950
Omar Muhammed Neda
Minimizing power loss while keeping the voltage profile within acceptable limits is a great challenge for the distribution system operators. Properly sized and optimally placed shunt capacitors (SCs) in radial distribution networks (RDNs) can enhance system efficiency and offer both technical and economic benefits. This paper presents a novel meta-heuristic technique, the weight salp swarm algorithm (WSSA) as a modified version of the original SSA algorithm by incorporating an inertia weight parameter to improve precision, speed, and consistency in solving the optimal capacitor placement (OCP) problem. The proposed method minimizes power loss, annual total costs, and improves the voltage profile of RDNs, ensuring practical applicability. Two RDNs, IEEE 33-bus and a real Iraqi 65-bus in Sadat Al-Hindiya, Babel Governorate, Iraq, were used to evaluate WSSA's performance. Comparative analysis with recently published approaches demonstrates WSSA’s superiority in reducing power loss, lowering costs, and improving voltage profiles. For the IEEE 33-bus, power loss is decreased by 34.81%, and the total cost is lessened by 29.08% (savings of $30,965.33). For the Iraqi 65-bus, WSSA reduces power loss by 32.03% and decreases the total cost by 29.51% (savings of $69,201.57). These results confirm WSSA’s effectiveness in achieving OCP with enhanced technical and economic benefits.
Volume: 14
Issue: 4
Page: 934-950
Publish at: 2025-12-01

Advancements in brain tumor classification: a survey of transfer learning techniques

10.11591/ijict.v14i3.pp1002-1014
Snehal Jadhav , Smita Bharne , Vaibhav Narawade
This survey article presents a critical review of the state-of-the-art transfer learning (TL) methodologies applied in the field of brain tumor classification, with a special emphasis on their various contributions and associated performance metrics. We will discuss various pre-processing approaches, the underlying fine-tuning strategies, whether used purely or in an end-to-end training manner, and multi-modal applications. The current study specifically highlights the application of VGG16 and residual network (ResNet) methods for feature extraction, demonstrating that leveraging highorder features in magnetic resonance imaging (MRI) images can enhance accuracy while reducing training. We further analyze fine-tuning methods in relation to their role in optimizing model layers for small, domain-specific datasets, finding them particularly effective in enhancing performance on the small brain tumor dataset. It will look into end-to-end training, which means fine-tuning models that have already been trained on large datasets to make them better. It will also present multimodal TL as a way to use both MRI and computed tomography (CT) scan data to get better classification results. Comparing different pre-trained models can provide a better understanding of the strengths and weaknesses associated with the particular brain tumor classification task. This review aims to analyze the advancements in TL for medical image analysis and explore potential avenues for future research and development in this crucial field of medical diagnostics.
Volume: 14
Issue: 3
Page: 1002-1014
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

Navigating predictive landscapes of cloud burst prediction approaches: insights from comparative research

10.11591/ijict.v14i3.pp1146-1155
Anil Hingmire , Sunayana Jadhav , Megha Trivedi , Karan Sankhe , Omkar Khanolkar , Yukta Patil
Cloud burst forecasting remains an evolving field that grapples with the complexities of atmospheric phenomena and their impact on local environments. Cloud bursts in hilly regions demand robust predictive models to mitigate risks. This study addresses the challenge of imbalanced cloud burst occurrences, emphasizing the need for accurate predictions to minimize damage. It develops and evaluates a machine learning-based forecasting approach that includes several weather factors such as temperature, humidity, wind speed, and atmospheric pressure. The study also tackles the imbalance in cloud burst data. A dual-axis chart visually merges cloud burst occurrences with weather parameters, providing insights into their relationships over time. The model’s overall accuracy is 0.68, with precision and recall for cloud burst events at 0.25 and 0.07, respectively, and an F1-score of 0.11. However, when it comes to forecasting non-cloud burst occurrences, it shows a high precision of 0.72. This study evaluates machine learning models for cloud burst prediction, highlighting random forest as the top performer with an accuracy of 85.43%, effectively balancing true positives and true negatives while minimizing misclassifications. This research contributes to cloud burst prediction, offering performance insights and suggesting avenues for future exploration.
Volume: 14
Issue: 3
Page: 1146-1155
Publish at: 2025-12-01

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

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

Comparative study of traditional edge detection methods and phase congruency based method

10.11591/ijict.v14i3.pp868-880
Rajendra Vasantrao Patil , Vinodpuri Rampuri Gosavi , Govind Mohanlal Poddar , Suman Kumar Swarnkar
Finding relevant and crucial details from images and effectively interpreting what they represent are two of image processing's main goals. An edge is the line that separates an object from its backdrop and shows where two things meet. Mining the picture's borders for extracting useful data remains one of the trickiest steps in understanding of an image. The borders of the objects may be used to build the image's edges, which are its basic characteristics. There are different types of traditional edge retrieval techniques that are conventionally categorized as first order and second gradient based methods such as Roberts, Prwitt, Kirsch, Robinson, canny, Laplacian and Laplacian of gaussian. The majority of research and review work on edge detection algorithms focuses on conventional algorithms and soft computing based methods, neglecting illumination invariant phase congruency based edge detector. This study aims to compare traditional derivative based edge detection algorithms with log Gabor wavelet based edge detector phase congruency. This work does a thorough examination of various edgedetecting approaches, including traditional boundary detection methods and log Gabor wavelet based method. To test effectiveness of edge detection algorithms, experimental results are obtained on images from DRIVE, STARE, and BSDS500 dataset.
Volume: 14
Issue: 3
Page: 868-880
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

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

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

Digital control of plant development through sensors and microcontrollers in Kosova

10.11591/ijict.v14i3.pp1072-1084
Ragmi M. Mustafa , Kujtim R. Mustafa , Refik Ramadani
The plant monitoring system aims to develop an automated solution for optimizing plant growth. Using the Arduino Uno ATMEGA328P microcontroller module and various sensors, this system regulates environmental conditions to promote optimal plant development. It requires adequate software to operate effectively, enabling the microcontroller to monitor and regulate climatic conditions. The primary goal of this paper is to present a comprehensive system that continuously measures parameters such as light intensity, air humidity, and soil moisture in real time within a vegetable greenhouse or a plastic-covered plant environment. This scientific paper provides an in-depth description of the hardware components used, their electronic connections, and the implementation of program code written in C++. Based on the measured physical parameters, the plant monitoring system performs specific actions, such as watering the plants and regulating the ambient temperature. In conclusion, this system effectively supports healthy plant growth and enhances the quality and yield of plant products. The paper serves as a practical example for improving plant cultivation in the agricultural sector in the Republic of Kosova.
Volume: 14
Issue: 3
Page: 1072-1084
Publish at: 2025-12-01
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