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

28,188 Article Results

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

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

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

Analyzing and clustering students admission data in Yala Rajabhat University Thailand

10.11591/ijeecs.v39.i2.pp1310-1325
Thanakorn Pamutha , Wanchana Promthong , Sofwan Pahlawan
This research explores the use of clustering techniques to analyze student admission data at Yala Rajabhat University, Thailand, aiming to enhance recruitment strategies and understand student profiles. Employing K-means, Hierarchical Clustering, and Density-based spatial clustering of applications with noise (DBSCAN), the study groups admission data based on factors like educational institution, geographic location, and program chosen. The methodology incorporates normalization and principal component analysis (PCA) to ensure data quality, while the Elbow Method determines the optimal number of clusters for effective data segmentation. The davies-bouldin index (DBI) evaluates the clustering configurations, ensuring that clusters are well-separated and cohesive. The results reveal distinct student profiles that can inform targeted marketing and improve recruitment strategies. This study not only provides strategic insights into student recruitment but also contributes to the literature on the use of data science in educational settings, highlighting the transformative impact of advanced analytics on institutional effectiveness. The research emphasizes the importance of data-driven approaches in adapting to the changing dynamics of student admissions and the competitive landscape of higher education.
Volume: 39
Issue: 2
Page: 1310-1325
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

Machine learning framework and tools in precision farming

10.11591/ijeecs.v39.i2.pp1063-1071
Patil Sagar Baburao , R. B. Kulkarni , Suchita S. Patil
Farming using machine learning (ML) techniques has a role to play in the current globalization scenario due to the advantages it offers for costeffective harvesting of the crop. The areas such as crop disease detection, soil nutrient detection, fertilizer analysis and optimization, weather and irrigation schedule prediction, are investigated utilizing a range of deep learning and ML techniques, such as K-nearest neighbors (KNNs), convolutional neural networks (CNNs), and support vector machines (SVMs). The article concentrates on preparing the recommendation system for the farmer to take a quick and timely decision for crop disease, use of optimal fertilizer for crop growth, and water requirement prediction to overcome water wastage. A massive amount of data, including image data from publicly accessible sources, such as PlantVillage, Kaggle is used to train the model. Sensor data is fed into the ML model for the nutrients analysis and water requirement analysis. An Android application is developed, which can be used from any handheld device by the farmers to take advantage of the proposed recommendation system. The result shows the promising future with better accuracy than previously available models in the same area. Parameters including recall, accuracy, precision, and F1-score are considered to gauge performance.
Volume: 39
Issue: 2
Page: 1063-1071
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

Analytical study of a single slope solar still: experimental evaluation

10.11591/ijeecs.v39.i2.pp850-859
M. Bhanu Prakash Sharma , D. Arumuga Perumal , M. S. Sivagama Sundari , Ilango Karuppasamy
Even though water covers the surface of the Earth in three quarters, many nations face shortages of drinkable water due to rapid global population and industrial growth. Solar power emerges as an efficient solution, particularly in hot climates with water and energy scarcity. This research focuses on a practical solar solution known as a solar still, a basic apparatus designed to convert available salty water into potable water. In this study, a single-slope solar still using acrylic material is experimentally analysed, predicting daily distillate production under varying climatic conditions. Using heat and solar radiation, solar distillation offers a simple, affordable, and small-scale approach to clean water production. The solar still, utilizing acrylic sheets as a basin material, minimizes heat losses and enhances water evaporation rates, making it a promising technology for addressing water scarcity issues. The experimental analysis results revealed a distillate output of 420 ml per 0.49 m² per day.
Volume: 39
Issue: 2
Page: 850-859
Publish at: 2025-08-01

Devising the m-learning framework for enhancing students' confidence through expert consensus

10.11591/ijeecs.v39.i2.pp1035-1052
Teik Heng Sun , Muhammad Modi Lakulu , Noor Anida Zaria Mohd Noor
Past research has shown the relationship between self-regulated learning (SRL) and academic success. Self-regulated learners will monitor their learning, reflect on what they have learnt, adjust their learning strategies accordingly, and repeat this entire process throughout their learning. The ability to perform SRL will require the individual to have the belief and confidence in his/her capacity to succeed and accomplish the tasks. Therefore, this study aims to devise a mobile learning (m-learning) framework for enhancing the students’ confidence. To achieve this, the Fuzzy Delphi method was used to validate the proposed framework where the survey questionnaire was distributed to 21 experts who are the experts in their respective fields for their consensus to be obtained. Consensus showed that “assessment data” can indicate the students’ confidence when they attempt the assessment. Experts opined that “goal expectation,” and “viewed lessons, chapters, or syllabus” exert the most influence on the students’ confidence when they attempt their assessment. There was strong consensus from experts that “data security” is the most important element in the system infrastructure, and the “text mining technique” element can be used to evaluate the students’ confidence.
Volume: 39
Issue: 2
Page: 1035-1052
Publish at: 2025-08-01

Wirelength estimation for VLSI cell placement using hybrid statistical learning

10.11591/ijeecs.v39.i2.pp840-849
Joyce Ng Ting Ming , Ab Al-Hadi Ab Rahman , Nuzhat Khan , Muhammed Paend Bakht , Shahidatul Sadiah , Mohd Shahrizal Rusli , Muhammad Nadzir Marsono
Optimizing wirelength involves predicting the total length of wires needed to connect different components within a chip during cell placement. It is a fundamental challenge in very-large-scale integration (VLSI) of integrated circuit (IC) design, as it directly impacts the overall performance and manufacturability of chips. Accurate wire-length estimation in the early stages of the design process is critical for guiding subsequent optimization tasks. This paper proposes a novel hybrid linear regression wirelength (hybrid-LRWL) method that combines the strengths of existing methods rectilinear Steiner minimal tree (RSMT) for low-degree nets and a statistical learning-based approach for high-degree nets. Additionally, it compares the performance of three well-established wirelength estimation techniques: half-perimeter wirelength (HPWL), rectilinear minimum spanning tree (RMST), and RSMT. The methods were evaluated using the International Symposium on Physical Design (ISPD) 2011 benchmark suite, considering accuracy and computational efficiency. The experimental results demonstrated that the proposed hybrid method achieves superior accuracy, with a mean error of less than 0.05% in total wirelength, closely approximating RSMT results. The proposed method reduces computational time up to 3.6 times faster than traditional RSM-based methods. The results establish a strong framework for accurate and efficient wirelength estimation in VLSI design for modern, high-performance ICs.
Volume: 39
Issue: 2
Page: 840-849
Publish at: 2025-08-01

Advancements and challenges in deep learning techniques for lung disease diagnosis

10.11591/ijeecs.v39.i2.pp1053-1062
Laxmi Bagalkot , Kelapati Kelapati
This study explores the application of deep learning (DL) techniques in diagnosing lung diseases using screening methods such as Chest X-Rays (CXRs) and computed-tomography (CT) scans. The motivation for this research stems from the need for advanced diagnostic tools in healthcare, with DL showing significant potential in medical image analysis. Despite advancements, challenges such as high costs of CT scans, processing time constraints, image noise, and variability persist. To address these issues, the study conducts a thorough literature survey to identify diverse preprocessing techniques, detection algorithms, and classification models designed for CXR analysis. In conclusion, this work contributes to the advancement of medical imaging technologies by offering innovative solutions, acknowledging existing limitations, and addressing the challenges in lung disease diagnosis. Future research should focus on further refining these techniques and exploring their application in broader clinical settings.
Volume: 39
Issue: 2
Page: 1053-1062
Publish at: 2025-08-01

Creating inclusive UX: uncovering gender-bugs in higher education website through GenderMag’ing

10.11591/ijeecs.v39.i2.pp996-1004
Maria Isabel Milagroso Santos , Thelma Domingo Palaoag , Anazel Patricio Gamilla
Higher education websites serve as service-providing and information-disseminating platforms which may contain gender-related usability issues that affect how male and female users interact with digital platforms. This study applied the gender inclusiveness magnifier (GenderMag) method to identify and assess these gender-specific usability barriers. Researchers conducted cognitive walkthrough sessions using gendered personas, Abi (female) and Tim (male), uncovering key inclusivity bugs aligned to specific cognitive facets-motivation, information processing style, computer self-efficacy, risk aversion, and learning style. Insights from these walkthroughs guided the creation of a structured usability survey, administered to 200 respondents equally divided between males and females, comprising faculty and upper-year BS information technology students. Statistical analysis revealed significant gender differences specifically in information processing style (p=0.0003), emphasizing distinct preferences for content organization and navigation between genders. The integration of usability factors with GenderMag’s cognitive facets effectively pinpointed areas requiring inclusive design adjustments, guiding future efforts to enhance equitable digital interactions in educational environments.
Volume: 39
Issue: 2
Page: 996-1004
Publish at: 2025-08-01
1 2 3
...
1879 1880
Show 28 of 1880

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