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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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Regional feature learning using attribute structural analysis in bipartite attention framework for vehicle re-identification Cynthia Sherin; Kayalvizhi Jayavel
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5824-5832

Abstract

Vehicle re-identification identifies target vehicles using images obtained by numerous non-overlapping real-time surveillance cameras. The effectiveness of re-identification is further challenging because of illumination changes, pose differences of captured images, and resolution. Fine-grained appearance changes in vehicles are recognized in addition to the coarse-grained characteristics like color of the vehicle along with model, and other custom features like logo stickers, annual service signs, and hangings to overcome these challenges. To prove the efficiency of our proposed bipartite attention framework, a novel dataset called Attributes27 which has 27 labelled attributes for each class are created. Our framework contains three major sections: The first section where the overall and semantic characteristics of every individual vehicle image are extracted by a double branch convolutional neural network (CNN) layer. Secondly, to identify the region of interests (ROIs) each branch has a self-attention block linked to it. Lastly to extract the regional features from the obtained ROIs, a partition-alignment block is deployed. The results of our proposed system’s evaluation on the Attributes27 and VeRi-776 datasets has highlighted significant regional attributes of each vehicle and improved the accuracy. Attributes27 and VeRi-776 datasets exhibits 98.5% and 84.3% accuracy respectively which are comparatively higher than the existing methods with 78.6% accuracy.
Classification techniques using gray level co-occurrence matrix features for the detection of lung cancer using computed tomography imaging Shankara Chikkalingaiah; Subbarao Anantha Padmanabha Rao Hari Prasad; Latha Dabbegatta Uggregowda
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5135-5146

Abstract

Lung cancer, which causes the majority of fatalities worldwide each year, is one of the deadliest diseases. The survival rate of cancer patients could be improved with better cancer detection methods. Image processing and machine learning have both been used to aid in lung cancer detection, but a method that both increase accuracy and increases a patient’s survival rate has yet to be identified. In an effort to find the most effective method for the accurate lung cancer recognition, this paper analyses and compares several classification algorithms. Lung computed tomography (CT) images are enhanced by removing noise using a median filter. For filtered image, threshold segmentation is used to segment it into distinct parts. From the segmented image different features are extracted using the grey level co-occurrence matrix (GLCM). several classification strategies, including support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and decision tree (DT) methods, are used to classify lung images as malignant or normal based on the extracted features. Methods are evaluated based on a number of various performance measures, like accuracy, a precision, the recall, and the F1-Score. Based on the experimental outcomes, SVM outperforms other classification methods in accurately detecting lung cancer with an accuracy of 99.32%.
Image compression approach for improving deep learning applications Raed Altabeiri; Moath Alsafasfeh; Mohanad Alhasanat
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5607-5616

Abstract

In deep learning, dataset plays a main role in training and getting accurate results of detection and recognition objects in an image. Any training model needs a large size of dataset to be more accurate, where improving the dataset size is one of the most research problems that needs enhancement. In this paper, an image compression approach was developed to reduce the dataset size and improve classification accuracy for the trained model using a convolutional neural network (CNN), and speeds up the machine learning process, while maintaining image quality. The results revealed that the best scenario for deep learning models that provided good and acceptable classification accuracy was one that had the following parameters: 80×80 image size, 10 epochs, 64 batch size, 40 images dataset quality (images compressed 60%), and gray image mode. For this scenario a Dog vs Cat dataset is used, and the training time was 48 minutes, classification accuracy was 86%, and images dataset size was 317 MB on storage device. This size makes up 58% of the size of the original image’s dataset, saves 42% of the storage space and reduces the processing resources consumption.
Technical and market evaluation of thermal generation power plants in the Colombia power system Jorge Silva-Ortega; Jean Carlos Calvo Cervantes; Eliab de Jesus Rodriguez Acosta; Idi Amin Isaac Millán; Juan Rivera-Alvarado; Kelly Margarita Berdugo Sarmiento
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp4824-4834

Abstract

Thermal power plants are the widely conventional generation unit technology used to produce electricity being controllable and dispatchable. The location of thermal power plants depends on the energy availability conditions of the areas and the capacity to fuels access. Their location and geographical distribution define a high level of concentration in areas defined as thermal districts and its location define reliability, security, availability, and flexibility indices to avoid critical scenario or support system from contingencies. However, in many cases the electrical configuration does not correspond to requirements. This paper links the concentration by political distribution in Colombia and the configuration used in the generating substations to guarantee requirements. The Hirschman-Herfindahl index as a market tool is used to evaluate energy concentration facing representative participation in certain departments of Colombia. Results evidenced configurations and concentration in a study case, results and analysis could be used for planner to promote participation, reliability and promote. The paper’s contribution and conclusions are linked to guide planners towards market and technical tool to evaluate installed capacities, avoid market concentration, and reduce risky scenarios.
Trust based multi objective honey badger algorithm to secure routing in vehicular ad-hoc networks Pramod Mutalik; Venkangouda C. Patil
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5190-5197

Abstract

A vehicular ad-hoc network (VANET) is a set of intelligent vehicles that interact without any fixed infrastructure. Data transmission between each transmitter/receiver pair is accomplished using routing protocols. However, communication over the VANET is vulnerable to malicious attacks, because of the unavailability of fixed infrastructure and wireless communication. In this paper, the trust based multi objective honey badger algorithm (TMOHBA) is proposed to achieve secure routing over the VANET. The TMOHBA is optimized by incorporating different cost functions, namely, trust, end to end delay (EED), routing overhead, energy, and distance. The developed secure route discovery using the TMOHBA is used to improve the robustness against the malicious attacks, for increasing the data delivery. Moreover, the shortest path discovery is used to minimize the delay while improving the security of VANET. The TMOHBA method is evaluated using the packet delivery ratio (PDR), throughput and EED. Existing researches such as hybrid enhanced glowworm swarm optimization (HEGSO) and ad-hoc on-demand distance vector based secure protocol (AODV-SP) are used to evaluate the TMOHBA method. The PDR of the TMOHBA method for 10 malicious attacks is 90.6446% which is higher when compared to the HEGSO and AODV-SP.
An efficient asynchronous spatial division multiplexing router for network-on-chip on the hardware platform Renu siddagangappa; Nayana Dunthur Krishnagowda; Deepthi Tumkur Srinivas Murthy
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5388-5396

Abstract

The quasi-delay-insensitive (QDI) based asynchronous network-on-chip (ANoC) has several advantages over clock-based synchronous network-on-chips (NoCs). The asynchronous router uses a virtual channel (VC) as a primary flow-control mechanism however, the spatial division multiplexing (SDM) based mechanism performs better over input traffics over VC. This manuscript uses an asynchronous spatial division multiplexing (ASDM) based router for NoC architecture on a field-programmable gate array (FPGA) platform. The ASDM router is configurable to different bandwidths and VCs. The ASDM router mainly contains input-output (I/O) buffers, a switching allocator, and a crossbar unit. The 4-phase 1-of-4 dual-rail protocol is used to construct the I/O buffers. The performance of the ASDM router is analyzed in terms of lower urinary tract symptoms (LUTs) (chip area), delay, latency, and throughput parameters. The work is implemented using Verilog-HDL with Xilinx ISE 14.7 on artix-7 FPGA. The ASDM router achieves % chip area and obtains 0.8 ns of latency with a throughput of 800 Mfps. The proposed router is compared with existing asynchronous approaches with improved latency and throughput metrics.
Energy-efficient device-to-device communication in internet of things using hybrid optimization technique Yashoda M. Balakrishna; Vrinda Shivashetty
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5418-5430

Abstract

Device-to-device (D2D) communication has grown into notoriety as a critical component of the internet of things (IoT). One of the primary limitations of IoT devices is restricted battery source. D2D communication is the direct contact between the participating devices that improves the data rate and delivers the data quickly by consuming less battery. An energy-efficient communication method is required to enhance the communication lifetime of the network by reducing the node energy dissipation. The clustering-based D2D communication method is maximally acceptable to boom the durability of a network. The oscillating spider monkey optimization (OSMO) and oscillating particle swarm optimization (OPSO) algorithms are used in this study to improve the selection of cluster heads (CHs) and routing paths for D2D communication. The CHs and D2D communication paths are selected depending on the parameters such as energy consumption, distance, end-to-end delay, link quality and hop count. A simulation environment is designed to evaluate and test the performance of the OSMO-OPSO algorithm with existing D2D communication algorithms (such as the GAPSO-H algorithm, adaptive resource-aware split-learning (ARES), bio-inspired cluster-based routing scheme (Bi-CRS), and European society for medical oncology (ESMO) algorithm). The results proved that the proposed technique outperformed with respect to traditional routing strategies regarding latency, packet delivery, energy efficiency, and network lifetime.
Predicting active compounds for lung cancer based on quantitative structure-activity relationships Hamza Hanafi; Badr Dine Rossi Hassani; M’hamed Aït Kbir
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5755-5763

Abstract

Recently, advancements in computational and artificial intelligence (AI) methods have contributed in improving research results in the field of drug discovery. In fact, machine learning techniques have proven to be especially effective in this regard, aiding in the development of new drug variants and enabling more precise targeting of specific disease mechanisms. In this paper, we propose to use a quantitative structure-activity relationship-based approach for predicting active compounds related to non-small cell lung cancer. Our approach uses a neural network classifier that learns from sequential structures and chemical properties of molecules, as well as a gradient boosting tree classifier to conduct comparative analysis. To evaluate the contribution of each feature, we employ Shapley additive explanations (SHAP) summary plots to perform features selection. Our approach involves a dataset of active and non-active molecules collected from ChEMBL database. Our results show the effectiveness of the proposed approach when it comes to predicting accurately active compounds for lung cancer. Furthermore, our comparative analysis reveals important chemical structures that contribute to the effectiveness of the compounds. Thus, the proposed approach can greatly enhance the drug discovery pipeline and may lead to the development of new and effective treatments for lung cancer.
Improved feature selection using a hybrid side-blotched lizard algorithm and genetic algorithm approach Amr Abdel-aal; Ibrahim El-Henawy
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5737-5746

Abstract

Feature selection entails choosing the significant features among a wide collection of original features that are essential for predicting test data using a classifier. Feature selection is commonly used in various applications, such as bioinformatics, data mining, and the analysis of written texts, where the dataset contains tens or hundreds of thousands of features, making it difficult to analyze such a large feature set. Removing irrelevant features improves the predictor performance, making it more accurate and cost-effective. In this research, a novel hybrid technique is presented for feature selection that aims to enhance classification accuracy. A hybrid binary version of side-blotched lizard algorithm (SBLA) with genetic algorithm (GA), namely SBLAGA, which combines the strengths of both algorithms is proposed. We use a sigmoid function to adapt the continuous variables values into a binary one, and evaluate our proposed algorithm on twenty-three standard benchmark datasets. Average classification accuracy, average number of selected features and average fitness value were the evaluation criteria. According to the experimental results, SBLAGA demonstrated superior performance compared to SBLA and GA with regards to these criteria. We further compare SBLAGA with four wrapper feature selection methods that are widely used in the literature, and find it to be more efficient.
Breast cancer classification with histopathological image based on machine learning Jia Rong Leow; Wee How Khoh; Ying Han Pang; Hui Yen Yap
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5885-5897

Abstract

Breast cancer represents one of the most common reasons for death in the worldwide. It has a substantially higher death rate than other types of cancer. Early detection can enhance the chances of receiving proper treatment and survival. In order to address this problem, this work has provided a convolutional neural network (CNN) deep learning (DL) based model on the classification that may be used to differentiate breast cancer histopathology images as benign or malignant. Besides that, five different types of pre-trained CNN architectures have been used to investigate the performance of the model to solve this problem which are the residual neural network-50 (ResNet-50), visual geometry group-19 (VGG-19), Inception-V3, and AlexNet while the ResNet-50 is also functions as a feature extractor to retrieve information from images and passed them to machine learning algorithms, in this case, a random forest (RF) and k-nearest neighbors (KNN) are employed for classification. In this paper, experiments are done using the BreakHis public dataset. As a result, the ResNet-50 network has the highest test accuracy of 97% to classify breast cancer images.

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