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28,188 Article Results

Deepfake detection using convolutional neural networks: a deep learning approach for digital security

10.11591/ijeecs.v39.i2.pp1092-1099
Fenina Adline Twince Tobing , Adhi Kusnadi , Ivransa Zuhdi Pane , Rangga Winantyo
The development of artificial intelligence technology, especially deep learning, has facilitated the emergence of increasingly sophisticated deepfake technology. Deepfakes utilize generative adversarial networks (GANs) to manipulate images or videos, making it appear as if someone said or did things that never actually happened. As a result, deepfake detection has become a critical challenge, particularly in the context of the spread of false information and digital crime. The purpose of this research is to create a method for detecting deepfakes using a convolutional neural network (CNN) approach, which has been proven effective in visual pattern recognition. Through training with a dataset of original facial images and deepfakes, the CNN model achieved an accuracy of 81.3% in detecting deepfakes. The evaluation results for metrics such as precision, recall, and F1-score indicated good performance overall, although there is still room for improvement. This study is expected to make a significant contribution to enhancing digital security, especially in detecting visual manipulations based on deepfakes.
Volume: 39
Issue: 2
Page: 1092-1099
Publish at: 2025-08-01

Clustering technique for dense D2D communication in RIS-aided multicell cellular network

10.11591/ijeecs.v39.i2.pp927-940
Misfa Susanto , Soraida Sabella , Lukmanul Hakim , Rudi Kurnianto , Azrina Abd Aziz
Device-to-device (D2D) communication and reconfigurable intelligent surface (RIS) are well-known as two promising technologies for nextgeneration cellular communication networks. D2D users operate on the same spectrum as traditional cellular users, potentially leading to increased interference and reduced efficiency in frequency resource usage. RIS provides a remedy for clearing blocked signals from obstructions by reflecting the desired signals to the intended receiver. However, RIS elements reflect not only the desired signals but also the interference signals. This paper proposes a distance-based clustering method aimed at creating a grouping algorithm for neighboring D2D users using different channels, thereby reducing co-channel interference. The simulation indicates that the proposed clustering method for D2D users' equipment (DUEs) leads to a 0.72 dB increase in signal-to-interference-plus-noise ratio (SINR), enhances throughput to 11.25 Mbps, and reduces the bit error rate by up to 24×10⁻² compared to the baseline system. The study findings also indicate that cellular users' equipment (CUEs) experience satisfactory signal quality, even with the presence of DUEs on the cellular network. Our clustering algorithm is feasible to deploying D2D densely in RIS-aided cellular network without significantly affecting CUE performance.
Volume: 39
Issue: 2
Page: 927-940
Publish at: 2025-08-01

A hybrid machine learning approach for malicious website detection and accuracy enhancement

10.11591/ijeecs.v39.i2.pp1027-1034
Ahmed Abu-Khadrah , Shayma Alkhamis , Ali Mohd Ali , Muath Jarrah
Malicious URLs are web addresses purposely generated for a user’s detriment. Some examples include phishing scams in which the victim is fooled into logging into a fake site or portals for downloading malware where any click on a link invites a hostile program to the user’s device. The damage done to an individual’s finances, confidential information, and even reputation due to malicious URLs makes it crucial to devise means of countering these threats. This can be achieved by creating an intelligent model that identifies suspicious characteristics common to these websites. The objective of this research is to design a novel hybrid machine learning algorithm-based model for detecting malicious websites. A random forest, decision tree, and extreme gradient boosting (XGBoost) are the three hybrid classification algorithms proposed for the study. Accuracy in detection will help prevent and reduce the effects of such websites. The accuracy rate in this research is 98.7%, precision is at 98.9%, and recall at 98.5%. With these results, it follows that the hybrid model is more effective than training any individual algorithm with the given dataset.
Volume: 39
Issue: 2
Page: 1027-1034
Publish at: 2025-08-01

Recognizing AlMuezzin and his Maqam using deep learning approach

10.11591/ijeecs.v39.i2.pp1360-1372
Nahlah Mohammad Shatnawi , Khalid M. O. Nahar , Suhad Al-Issa , Enas Ahmad Alikhashashneh
Speech recognition is an important topic in deep learning, especially to Arabic language in an attempt to recognize Arabic speech, due to the difficulty of applying it because of the nature of the Arabic language, its frequent overlap, and the lack of available sources, and some other limitations related to the programming matters. This paper attempts to reduce the gap that exists between speech recognition and the Arabic language and attempts to address it through deep learning. In this paper, the focus is on Call for Prayer (Aladhan: ناذآلا ) as one of the most famous Arabic words, where its form is stable, but it differs in the notes and shape of its sound, which is known as the phonetic Maqam (Maqam: ماقملا  يتوصلا ). In this paper, a solution to identify the voice of AlMuezzin ( نذؤملا ), recognize AlMuezzin, and determine the form of the Maqam through VGG-16 model presented. The VGG-16 model examined with 4 extracted features: Chroma feature, LogFbank feature, MFCC feature, and spectral centroids. The best result obtained was with chroma features, where the accuracy of Aladhan recognition reached 96%. On the other hand, the classification of Maqam with the highest accuracy reached of 95% using spectral centroids feature.
Volume: 39
Issue: 2
Page: 1360-1372
Publish at: 2025-08-01

A multi-tier framework of decentralized computing environment for precision agriculture (DCEPA)

10.11591/ijeecs.v39.i2.pp1072-1080
Kiran Muniswamy Panduranga , Roopashree Hejjaji Ranganathasharma
Although collecting enormous volumes of heterogeneous data from many sensors and guaranteeing real-time decision-making are problems, precision agriculture (PA) has emerged as a promising approach to increase agricultural efficiency. The efficacy of current centralized solutions is limited in large-scale agricultural settings due to resource limitations and data saturation. In order to solve these problems, this paper suggests a decentralized computing environment for precision agriculture (DECPA), which divides resource management and data processing among several layers (end, edge, and cloud). DECPA optimizes task execution and resource allocation in the field by utilizing ensemble machine learning models (deep neural network (DNN), long short-term memory (LSTM), autoencoder (AE), and support vector machine (SVM)) and a multi-tier architecture. The findings demonstrate that DECPA combined with DNN performs better than alternative models, achieving a 20% decrease in energy usage, an 18% speedup in response time, a 5% improvement in accuracy, and a 51% reduction in latency. This illustrates the system’s capacity to manage massive amounts of data effectively while preserving peak performance. To sum up, DECPA uses decentralized resources and cutting-edge machine learning models to provide a scalable and affordable precision agriculture solution. To improve the system’s flexibility and real-time responsiveness, future research will investigate additional optimization and use in various agricultural contexts.
Volume: 39
Issue: 2
Page: 1072-1080
Publish at: 2025-08-01

Identification of chilli leaf disease using contrast limited histogram equalisation and k-means clustering

10.11591/ijeecs.v39.i2.pp1100-1108
Shiny Rajendrakumar , Rajashekarappa Rajashekarappa , Vasudev K. Parvati
Plant disease diagnosis is crucial for preventing productivity and quality losses in agricultural products. Because plants are continually attacked by insects, bacterial infections, and smaller scale organisms it is necessary for early diagnosis disease control is a vital part of profitable chilli crop production, hence early diagnosis of disease identification is an important aspect of crop management. This paper discusses strategies for detecting disease effectively in order to improve chilli plant product quality. An image processing technique based on identification of chilli leaf disease using contrast limited histogram equalisation and k-means clustering (KMC). The approach was carried out in five stages: acquiring the image, preprocessing, extracting features, classifying the diseases, and showing the outcome. This work offers a thorough implementation of CLAHE for preprocessing, k-means cluster for feature extraction and support vector machine (SVM) for classification of chilli leaf diseases. The accuracy was tested for standard chilli dataset for major 2 types of diseases including anthracnose and bacterial blight form kaggle dataset with varying samples of 70:30 and 60:40 respectively and it is observed that the average accuracy improved to 98% compared to existing techniques.
Volume: 39
Issue: 2
Page: 1100-1108
Publish at: 2025-08-01

Hierarchical enhanced deep encoder-decoder for intrusion detection and classification in cloud IoT networks

10.11591/ijeecs.v39.i2.pp1176-1188
Ramya K. M. , Rajashekhar C. Biradar
Securing cloud-based internet of things (IoT) networks against intrusions and attacks is a significant challenge due to their complexity, scale, and the diverse nature of connected devices. IoT networks consist of billions of devices, computer servers, data transmission networks, and application computers, all communicating vast amounts of data that must adhere to various protocols. This study introduces a novel approach, termed hierarchical enhanced deep encoder-decoder with adaptive frequency decomposition (HED-EDFD), and is designed to address these challenges within cloud-based IoT environments. The HED-EDFD methodology integrates adaptive frequency decomposition, specifically adaptive frequency decomposition, with a deep encoder-decoder model. This integration allows for the extraction and utilization of frequency domain features from time-sequence IoT data. By decomposing data into multiresolution wavelet coefficients, the model captures both high-frequency transient changes and low-frequency trends, essential for detecting potential intrusions. The deep encoder-decoder model, enhanced with deep contextual attention mechanisms, processes these features to identify complex patterns indicative of malicious activities. The hierarchical structure of the approach includes a hierarchical wavelet-based attention mechanism, which enhances the accuracy and robustness of feature extraction and classification. To address the issue of imbalanced intrusion data, a cosine-based SoftMax classifier is employed, ensuring effective recognition of minority class samples.
Volume: 39
Issue: 2
Page: 1176-1188
Publish at: 2025-08-01

Comparative analysis of Cohen-Coon and Ziegler-Nichols tuning methods for three-phase induction motor with speed sensorless control

10.11591/ijeecs.v39.i2.pp885-895
Christian Vieri Halim , Katherin Indriawati
The use of speed sensors in the speed controller of three-phase induction motors affects the reliability of the induction motors. In addition, the drive engine that is often used in industry is a three-phase induction motor. So, speed sensorless control is needed for induction motors to achieve the best performance. This study uses a discrete disturbance observer (D0) as feedback on the speed sensorless control. The controller used in this method is a discrete PI with the Cohen-Coon (CC) and Ziegler-Nichols (ZN) tuning method. The purpose of this study is to obtain a comparative analysis of the CC and ziegler nichols tuning method using a discrete PI on the speed sensorless control scheme with torque load variation. This study was carried out experimentally using an Alliance AY3A-90L4 induction motor. The results show that the CC tuning method is better under parameter efficiency and robustness against disturbance and ZN is better under parameter reliability.
Volume: 39
Issue: 2
Page: 885-895
Publish at: 2025-08-01

Efficiently tracking and recognition of human faces in real-time video stream with high accuracy and performance

10.11591/ijeecs.v39.i2.pp1261-1268
Imran Ulla Khan , D. R. Kumar Raja
Real time tracking and recognition of human faces in video streams is a critical challenge in computer vision. Existing systems often struggle to balance accuracy and performance, particularly in dynamic environments with varying lighting conditions, occlusions, and rapid movements. High computational overhead and latency further hinder their deployment in realworld applications. These limitations underscore the need for a robust solution capable of maintaining high accuracy and real-time efficiency under diverse conditions. This research addresses these challenges by developing a deep learning-based system that efficiently tracks and recognizes human faces in real-time video streams. Proposed system integrates advanced face detection models you only look once version 5 (YOLOv5) with state-of-theart tracking algorithms, such as deep simple online and real time tracking (SORT), to ensure consistency and robustness. By leveraging graphics processing unit (GPU) acceleration, the system achieves optimal performance while minimizing latency. Multi-frame analysis techniques are incorporated to enhance accuracy in detecting and recognizing faces, even under challenging conditions such as partial occlusions and motion blur. Developed system has broad applications across multiple domains, including surveillance and security, where it can enhance real-time monitoring in crowded environments for seamless face tracking in interactive systems. By focusing on efficiency, robustness, and adaptability this work offering a scalable and high-performance solution for real-time human face tracking and recognition.
Volume: 39
Issue: 2
Page: 1261-1268
Publish at: 2025-08-01

Performance evaluation of a photovoltaic system with phase change material in Guwahati

10.11591/ijeecs.v39.i2.pp737-746
Pallavi Roy , Bani Kanta Talukdar
Recently, there has been a lot of interest in solar photovoltaic (PV) technology as a clean and renewable energy source. The operating temperature of PV modules significantly impacts their performance; as the temperature rises, the modules perform worse. The phase change material (PCM) paraffin wax has been used to cool a PV system passively. The experiment was carried out during summer over three months, viz. April, May, and June when relative humidity was around 80.75% to 86.5% with two identical 20-watt PV panels in Guwahati, India (26.1332° North and 91.6214° East). One panel was coated with PCM, while the other panel functioned as a point of reference. The study reveals an impressive result: the output power produced by the system with PCM was 9.8%, 13.1%, and 10.3% greater than the reference PV, while the surface temperature had been lowered by 21.6%, 26.2%, and 30.6% in the three respective months. High humidity delays the release of latent heat of paraffin wax and hence improves its thermal conductivity. This study adds to the continuing efforts to promote sustainable energy solutions and creates new opportunities to enhance the performance of PV systems.
Volume: 39
Issue: 2
Page: 737-746
Publish at: 2025-08-01

Optimization of IoT-based monitoring system for automatic power factor correction using PZEM-004T sensor

10.11591/ijeecs.v39.i2.pp860-873
Maman Somantri , Mochamad Rizal Fauzan , Irgi Surya
Power factor correction (PFC) is crucial for improving energy efficiency and reducing excessive power consumption, especially in inductive loads commonly found in household and industrial environments. Conventional PFC methods often rely on manual capacitor switching, which is inefficient and impractical for real-time applications. This study proposes an IoT-based automatic power factor monitoring and correction system that dynamically adjusts the power factor using real-time data analysis. The system integrates NodeMCU ESP32 and the PZEM-004T sensor to monitor electrical parameters and automatically switch capacitors based on power factor conditions. The research follows the ADDIE approach (analysis, design, development, implementation, evaluation) to ensure a structured development process. Experimental results demonstrate an average power factor improvement of 48.77% and a reduction in current consumption by 39.90%, significantly enhancing energy efficiency. The system's web-based interface allows real-time monitoring with an average data transmission response time of 207.67 ms, ensuring efficient remote management. Compared to existing systems, the proposed approach eliminates manual intervention and optimizes PFC adaptively. Future research should focus on expanding system reliability, testing on larger-scale applications, and integrating artificial intelligence (AI) for predictive power factor adjustments.
Volume: 39
Issue: 2
Page: 860-873
Publish at: 2025-08-01

CriteriaChecker: a knowledge graph approach to enhance integrity and ethics in academic publication

10.11591/ijeecs.v39.i2.pp973-986
Garima Sharma , Vikas Tripathi , Vijay Singh
Academic writing is an integral part of scientific communities. This is a formal style of writing used by researchers and scholars to communicate critical analysis and evidence based arguments. This work showcased a graph-based approach for scraping, extracting, representing and evaluating the available academic writing forgery detection criteria and further enhancing the model by proposing a set of new age criteria. The proposed work is based on knowledge graphs and graph analytics capable of selecting subset of 16 criteria from the available superset of a cent of criterias provided by Bealls, Cabells, Shreshtha, and Think.Check.Submit, Scopus, and other relevant authors. The process for detecting the influencial parameters consists of 04 phases: dataset preparation, knowledge graph representation and making inferences through graph analytics and evaluation of results. The experimental results are then compared to the retraction database that consisting of information about retracted articles. The work enables the construction of an experiential knowledge graph that effectively identifies influential criteria, enhancing this list by incorporating new age criteria into current influential set and concluding in result by successfully detecting the academic predatory behavior.
Volume: 39
Issue: 2
Page: 973-986
Publish at: 2025-08-01

Optimization of hybrid PV-wind systems with MPPT and fuzzy logic-based control

10.11591/ijeecs.v39.i2.pp747-760
Ayoub Fenniche , Abdelkader Harrouz , Yassine Bellebna , Abdallah Laidi , Ismail Benlaria
The growing demand for sustainable and reliable energy solutions has driven the development of hybrid renewable energy systems (HRES) that combine multiple energy sources. This research explores the integration of solar energy and wind energy systems, utilizing permanent magnet synchronous generators (PMSG) for wind energy conversion. PMSGs are gaining popularity due to their high efficiency and ability to operate effectively in variable-speed wind conditions, making them ideal for hybrid systems. The study focuses on optimizing the energy extraction from both PV and wind systems using maximum power point tracking (MPPT) boost converters. The control for the MPPT boost converters is based on fuzzy logic (FL), a method that offers flexibility and adaptability in managing the non-linear and dynamic characteristics of renewable energy sources. A hybrid system consisting of PV, wind energy, and a battery storage system connected to a DC bus is simulated using MATLAB Simulink. The model demonstrates the effectiveness of integrating PV and wind energy with MPPT-controlled boost converters and fuzzy logic control, ensuring optimal energy utilization, stable system performance, and efficient energy storage. This research underscores the potential of hybrid renewable energy systems, showcasing how advanced control strategies can significantly improve the efficiency and reliability of energy generation and storage solutions.
Volume: 39
Issue: 2
Page: 747-760
Publish at: 2025-08-01

An optimized architecture for real-time fraud detection in big data systems, ecosystems, and environments

10.11591/ijeecs.v39.i2.pp1221-1235
Gaber Elsayed Abutaleb , Abdallah A. Alhabshy , Berihan R. Elemary , Ebeid Ali , Kamal Abdelraouf Eldahshan
The exponential growth of data in recent years has created significant challenges in fraud detection. Fraudulent activities are increasingly widespread across sectors, such as banking, web networks, health insurance, and telecommunications. This trend highlights a growing need for big data technologies such as Hadoop, Spark, Storm, and HBase to enable real-time detection and analysis of data fraud. This study aims to enhance understanding of the fraud classifications and their spread in various sectors. Fraud detection involves analyzing data and developing machine learning (ML) models or traditional rule-based systems to identify abnormal activities as they occur. The analysis in this paper examines both the advantages and limitations of these solutions, particularly regarding scalability and performance. This paper evaluates the methods and big data tools used in fraud detection and prevention through a comprehensive literature review, emphasizing the implementation challenges. This review discusses existing solutions, operational environments, and the ML algorithms and traditional rules employed. The main objective of this study is to address these challenges by proposing an innovative architecture that equips organizations with the latest knowledge and methodologies in big data technologies for real-time fraud detection and prevention.
Volume: 39
Issue: 2
Page: 1221-1235
Publish at: 2025-08-01

Binary white shark optimization algorithm with Z-shaped transfer function for feature selection problems

10.11591/ijeecs.v39.i2.pp1269-1279
Avinash Nagaraja Rao , Sitesh Kumar Sinha , Shivamurthaiah Mallaiah
Feature selection is critical for improving model performance and managing high-dimensional data, yet existing methods often face limitations such as inefficiency and suboptimal results. This study addresses these challenges by introducing a novel approach using the white shark optimization (WSO) algorithm and its binary variants to enhance feature selection. The proposed methods are evaluated on various datasets, including “Dorothea,” “Breast Cancer,” and “Arrhythmia,” focusing on classification accuracy, the number of features selected, and fitness values. Results demonstrate that the WSO algorithms significantly outperform traditional methods, offering notable improvements in accuracy and efficiency. Specifically, the WSO variants consistently achieve higher accuracy and better fitness values while effectively reducing the number of selected features. This research contributes to the field by providing a more effective optimization approach for feature selection, addressing existing inefficiencies, and suggesting future directions for further refinement and broader application. The findings highlight the potential of advanced optimization techniques in enhancing data analysis and model performance, offering valuable insights for practitioners and researchers.
Volume: 39
Issue: 2
Page: 1269-1279
Publish at: 2025-08-01
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