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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6,301 Documents
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Model of semiconductor converters for the simulation of an asymmetric loads in an autonomous power supply system Tavarov, Saidjon; Senyuk, Mihail; Safaraliev, Murodbek; Kokin, Sergey; Tavlintsev, Alexander; Svyatykh, Andrey
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1332-1347

Abstract

This article is devoted to the development of computer model with semiconductor converters for the simulation of asymmetric loads allowing to solve the voltage symmetry problems under asymmetric loads (active and active-inductive) for isolated electric networks with renewable energy sources (mini hydroelectric power plants). A model of a symmetry device has been developed in the MATLAB/Simulink environment based on a proportional-integral controller and a relay controller - P. The effectiveness of their use depends on the load's nature. The implementation of a voltage converter is presented considering a three-phase inverter with discrete key switching at 120, 150, and 180 degrees with a purely active load. Based on the harmonic analysis of the three-phase voltage at discrete conversion, the value of the first harmonic is determined. Voltage transformations under active-inductive load at 120, 150, and 180 degrees are mathematically described. To determine the harmonic spectrum, an analysis of the fast Fourier transform for the three-phase voltage of a MATLAB/Simulink semiconductor converter was carried out. It is established that the alternating current output voltage is generated on the output side of the inverter of a three-phase voltage source through a three-phase load connected by a star with a harmonic suppression method.
Plant disease detection using vision transformers Ali, Mhaned; Salma, Mouatassim; Haji, Mounia El; Jamal, Benhra
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2334-2344

Abstract

Plant diseases present a major risk to worldwide food security and the sustainability of agriculture, leading to substantial economic losses and hindering rural livelihoods. Conventional methods for disease detection, including visual inspection and laboratory-based techniques, are limited in their scalability, efficiency, and accuracy. This paper addresses the critical problem of accurately detecting and diagnosing plant diseases using advanced machine learning techniques, specifically vision transformers (ViTs), to overcome these limitations. ViTs leverage self-attention mechanisms to capture intricate patterns in plant images, enabling accurate and efficient disease classification. This paper reviews the literature on deep learning techniques in agriculture, emphasizing the growing interest in ViTs for plant disease detection. Additionally, it presents a comprehensive methodology for training and evaluating ViT models for plant disease classification tasks. Experimental results demonstrate the effectiveness of ViTs in accurately identifying various plant diseases across a balanced 55 classes dataset, highlighting their potential to revolutionize precision agriculture and promote sustainable farming practices.
Developing an algorithm for the adaptive neural network for direct online speed control of the three-phase induction motor Al-Mahasneh, Ahmad J.; Salah, Samer Z.; Ghaeb, Jasim A.; Baniyounis, Mohammed
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1499-1510

Abstract

In this paper, an online adaptive general regression neural network (OAGRNN) is presented as a direct online speed controller for a three-phase induction motor. To keep the induction motor running at its rated speed in real-time and under a variety of load conditions, the speed error and its derivative are continuously measured and fed back to the OAGRNN controller. The OAGRNN controller provides the inverter with the control signal it needs to produce the proper frequency and voltage for the induction motor instantly. Notably, the OAGRNN controller demonstrated remarkable performance without the need for a learning mode; it was able to track the desired motor speed, starting its operation from scratch. A setup utilizing a three-phase induction motor has been developed to show the high capacity of OAGRNN for tracking the desired speed of the motor while subjected to the varied load torque. The performance of OAGRNN is examined in two phases: the MATLAB simulation and the experimental setup. Furthermore, when the OAGRNN performance is compared with that of the proportional integral (PI) controller, it demonstrates its outstanding ability and superiority for online adjustments related to the three-phase induction motor's speed control.
Integrating deep learning and optimization algorithms to forecast real-time stock prices for intraday traders Korade, Nilesh B.; Salunke, Mahendra B.; Bhosle, Amol; Joshi, Dhanashri; Patil, Kavita; Sangve, Sunil M.; Deshmukh, Rushali A.; Patil, Aparna S.
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2254-2263

Abstract

The number of stock investors is steadily increasing due to factors such as the availability of high-speed internet, smart trading platforms, lower trading commissions, and the perception that trading is an effective way of earning extra income to enhance financial stability. Accurate forecasting is crucial to earning profits in the stock market, as it allows traders to anticipate price changes and make strategic investments. The traders must skillfully negotiate short-term market changes to maximize gains and minimize losses, as intraday profit mostly depends on the timing of buy and sell decisions. In the presented work, we provide minute-by-minute forecasts that assist intraday traders in making the best decisions on when to buy and sell, consequently maximizing profits on each trade they make. We have implemented a one-dimensional convolutional neural network and bidirectional long-short-term memory (1DCNN-BiLSTM) optimized with particle swarm optimizer (PSO) to forecast the value of stocks for each minute using real-time data extracted from Yahoo Finance. The proposed method is evaluated against state-of-the-art technology, and the results demonstrate its strong potential to accurately forecast the opening price, stock movement, and price for the next timeframe. This provides valuable insights for intraday traders to make informed buy or sell decisions.
OCNet-23: a fine-tuned transfer learning approach for oral cancer detection from histopathological images Akhi, Amatul Bushra; Noman, Abdullah Al; Shaha, Sonjoy Prosad; Akter, Farzana; Lata, Munira Akter; Sheikh, Rubel
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1826-1833

Abstract

Oral squamous cell carcinoma (OSCC) is emerging as a significant global health concern, underscoring the need for prompt detection and treatment. Our study introduces an innovative diagnostic method for OSCC, leveraging the capabilities of artificial intelligence (AI) and histopathological images (HIs). Our primary objective is to expedite the identification process for medical professionals. To achieve this, we employ transfer learning and incorporate renowned models such as VGG16, VGG19, MobileNet_v1, MobileNet_v2, DenseNet, and InceptionV3. A key feature of our approach is the meticulous optimization of the VGG19 architecture, paired with advanced image preprocessing techniques such as contrast limited adaptive histogram equalization (CLAHE) and median blur. We conducted an ablation study with optimized hyperparameters, culminating in an impressive 95.32% accuracy. This groundbreaking research ensures accurate and timely diagnoses, leading to improved patient outcomes, and represents a significant advancement in the application of AI for oral cancer diagnostics. Utilizing a substantial dataset of 5,192 meticulously categorized images into OSCC and normal categories, our work pioneers the field of OSCC detection. By providing medical professionals with a robust tool to enhance their diagnostic capabilities, our method has the potential to revolutionize the sector and usher in a new era of more effective and efficient oral cancer treatment.
A novel hybrid statistical model for camouflaged target detection K, Karthiga; A, Asuntha
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1612-1619

Abstract

Camouflage is a defense mechanism employed as a concealing technique to reduce the possibility of the target being identified. In order to complete military tasks accurately, the detection of camouflaged targets in different scenarios plays a crucial role. Traditional target detection systems have the problem of detecting camouflaged targets in complex environmental conditions. There are various challenges in camouflage target detection system development, such as size, random texture, and recognition accuracy. To resolve these issues, a hybrid statistical model is proposed to identify the target from camouflage images. In this work, hybrid statistical model consists of two modules. A feature extraction module is utilized to extract the low-level features of the image based on texture and scale. A segmentation module is utilized to extract the target region and enhance the boundaries based on seed point selection and detection of edges. Further, the use of morphological processes to highlight the target region. Experiments demonstrate that the proposed hybrid statistical model performs well in camouflage detection in different environments.
A novel competitive cuckoo search algorithm for node placement in wireless sensor networks Gunjal, Monica Shivaji; Kulkarni, Pramodkumar H.
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1735-1744

Abstract

Wireless sensor networks (WSNs) are essential for many different types of applications, including industrial, commercial, and agricultural. Inadequate node location, dynamic nodes, network longevity, increased packet drop rates, scaling problems, limited adaptability, and changing climatic conditions pose challenges to the WSN's efficacy. Numerous bio-inspired algorithms have been previously introduced for node placement, demonstrating a significant improvement in data aggregation performance. However, because of the low variability in the solution, weak convergence, and poor balance between exploitation and exploration, the results of WSNs are challenging to interpret. With a unique competitive Cuckoo search algorithm (CCSA), this research presents a connectivity-aware, energy- efficient, and node placement method. Using the elite population to increase the variety of the answer, the suggested competitive strategy aims to enhance convergence. It additionally employs the fuzzy C-mean clustering algorithm. The clustering optimization based on cluster head energy, density, location, Gini coefficients, and other comparable factors uses an artificial bee colony optimization (ABC) algorithm to enhance the clustering and cluster head position. A comparison between the recommended scheme and the traditional state-of-the-art reveals that the suggested system performs better regarding network throughput, residual energy, and network lifetime.
Trust factor validation for distributed denial of service attack detection using machine learning Jayakumar Raghvin, Manju; Bharamagoudra, Manjula R.; Dash, Ritesh
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1455-1462

Abstract

Distributed denial of service (DDoS) attacks, predicted to be 100 Gbps and greater, are expected to begin in the first quarter of 2019, with 77% of all attacks concentrated on at least two vectors. According to a Neustar Research Agency assessment, DDoS attacks are becoming more powerful and common. Among many other issues, distributed denial of service is a notable security issue. A large number of research projects have been conducted to address this issue, but their methodologies are either inaccurate or computationally expensive, making developing an effective DDoS assault detection method a critical demand of current research. A DDoS attack employs a huge number of agents or resources to carry out the attack, resulting in a large-scale attack power. The attackers use their intelligence to discover the weak system, which is then coordinated and managed remotely. The suggested detection framework uses a frequent time interval balancing module with node trust factor validation (FTIBM-NTFV) that is used to identify the DDoS attacks in the system for improving the security levels of the network. The proposed model is compared with the traditional methods and the results are analyzed that represents the proposed model is achieving better outcomes.
Amharic event text classification from social media using hybrid deep learning Ayalew, Amogne Andualem; Tegegne, Melaku Lake; Manivannan, Bommy; Suresh, Tamilarasi; Kumar, Napa Komal; Prasad, Battula Krishna; Assegie, Tsehay Admassu; Salau, Ayodeji Olalekan
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2264-2270

Abstract

This study aims to develop a hybrid deep-learning model for detecting and classifying Amharic text. Various natural language applications, such as information extraction, event extraction, conversation, text summarization, and require an automatic event classification. However, existing studies focused on classification, giving little attention to the preprocessing and feature extraction techniques. To address this problem, this work proposed a hybridized deep learning-based Amharic social media text event classification model. The model consists of word-to-vector (Word2vecv) word embedding techniques to capture the semantic and syntactic representation. Convolutional neural network (CNN) is used to extract short-length text features. Additionally, bidirectional long-short memory (Bi-LSTM) is used to extract features from long Amharic sentences and classify those events based on their classes. The dataset used for training and testing consists of 6,740 labeled Amharic text sentences, collected from social media. The result shows an accuracy of 94.8% in detecting and classifying Amharic text events.
A novel multi-objective economic load dispatch solution using bee colony optimization method Khamsen, Wanchai; Takeang, Chiraphon; Janthawang, Thawinan; Aurasopon, Apinan
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1385-1395

Abstract

This article presents a novel multi-objective economic load dispatch solution with the bee colony optimization method. The purposes of this research are to find the lowest total power generation cost and the lowest total power loss at the transmission line. A swarm optimization method was used to consider the non-smooth fuel cost function characteristics of the generator. The constraints of economic load dispatch include the cost function, the limitations of generator operation, power losses, and load demand. The suggested approach evaluates an IEEE 5, 26, and 118 bus system with 3, 6, and 15 generating units at 300, 1,263, and 2,630 megawatt (MW) and uses a simulation running on the MATLAB software to confirm its effectiveness. The outcomes of the simulation are compared with those of the exchange market algorithm, the cuckoo search algorithm, the bat algorithm, the hybrid bee colony optimization, the multi-bee colony optimization, the decentralized approach, the differential evolution, the social spider optimization, and the grey wolf optimization. It demonstrates that the suggested approach may provide a better-quality result faster than the traditional approach.

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