International journal of electrical and computer engineering systems最新文献_第2页

Classification and Segmentation of MRI Images of Brain Tumors Using Deep Learning and Hybrid Approach 利用深度学习和混合方法对脑肿瘤 MRI 图像进行分类和分割

International journal of electrical and computer engineering systems Pub Date : 2024-02-23 DOI: 10.32985/ijeces.15.2.5

Sugandha Singh, Vipin Saxena

引用次数: 0

Exploring the Satisfaction and Continuance Intention to Use E-Learning Systems 探索电子学习系统的满意度和持续使用意向

International journal of electrical and computer engineering systems Pub Date : 2024-02-23 DOI: 10.32985/ijeces.15.2.8

Ahmad AL-Hawamleh

{"title":"Exploring the Satisfaction and Continuance Intention to Use E-Learning Systems","authors":"Ahmad AL-Hawamleh","doi":"10.32985/ijeces.15.2.8","DOIUrl":"https://doi.org/10.32985/ijeces.15.2.8","url":null,"abstract":"In view of the global crisis that has increased the use of online learning, it is imperative to comprehend the factors that affect users' perceptions and behaviors when utilizing e-learning systems. In order to examine the impact of quality factors on user satisfaction and continuance intention using e-learning systems, this study integrates the Information Systems Success Model (ISSM) with the Technology Acceptance Model (TAM). The aim of this research is to shed light on the relationships between the e-learning systems' quality, perceived usefulness, perceived ease of use, user satisfaction, and intention to continue using them. This research employed partial least squares structural equation modeling (PLS-SEM) to assess the research model. The analysis was grounded in survey data collected from a randomly selected sample of 372 students at Arab Open University in Saudi Arabia. The study's results confirm that information quality for platforms and courses positively influences perceived usefulness, system quality, and perceived ease of use. Additionally, perceived usefulness and ease of use are significantly linked to user satisfaction, supporting the notion that enhancing information quality contributes to higher user satisfaction and encourages continued engagement. The developers of e-learning systems and educational institutions may use these findings to enhance the design, content, and usability of their platforms.","PeriodicalId":507791,"journal":{"name":"International journal of electrical and computer engineering systems","volume":"55 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140436777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Intelligent Classifiers for Football Player Performance Based on Machine Learning Models 基于机器学习模型的足球运动员表现智能分类器

International journal of electrical and computer engineering systems Pub Date : 2024-02-23 DOI: 10.32985/ijeces.15.2.6

Baydaa M. Merzah, Muayad S. Croock, Ahmed N. Rashid

{"title":"Intelligent Classifiers for Football Player Performance Based on Machine Learning Models","authors":"Baydaa M. Merzah, Muayad S. Croock, Ahmed N. Rashid","doi":"10.32985/ijeces.15.2.6","DOIUrl":"https://doi.org/10.32985/ijeces.15.2.6","url":null,"abstract":"The remarkable effectiveness of Machine Learning (ML) methodologies has led to a significant increase in their application across various academic domains, particularly in diverse sports sectors. Over the past decade, scholars have utilized Machine Learning (ML) algorithms in football for varied objectives, encompassing the analysis of football players' performances, injury prediction, market value forecasting, and action recognition. Nevertheless, there has been a scarcity of research addressing the evaluation of football players' performance, which is a noteworthy concern for coaches. Hence, the objective of this work is to categorize the performance of football players into active, normal, or weak based on activity features. This will be achieved through the utilization of the Performance Evaluation Machine Learning Model (PEMLM), employing two novel datasets that cover both training and match sessions. To attain this goal, seven machine learning methods are applied, namely Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, Gaussian Naïve Bayes, Multi-Layer Perceptron, and K-Nearest Neighbor. The findings indicate that in the dataset corresponding to match sessions, the Decision Tree classifier attains the highest accuracy (100%) and the shortest test time. In contrast, the K-Nearest Neighbor demonstrates the best accuracy (96%) and a reasonable test time for the training dataset. These reported metrics underscore the reliability and validity of the proposed assessment approach in evaluating the performance of football players in online games. The results are verified and the models are assessed for overfitting through a k-fold cross-validation process.","PeriodicalId":507791,"journal":{"name":"International journal of electrical and computer engineering systems","volume":"7 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140435877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intrusion Detection System based on Chaotic Opposition for IoT Network 基于混沌对立的物联网网络入侵检测系统

International journal of electrical and computer engineering systems Pub Date : 2024-02-23 DOI: 10.32985/ijeces.15.2.1

Richa Singh, R.L. Ujjwal

{"title":"Intrusion Detection System based on Chaotic Opposition for IoT Network","authors":"Richa Singh, R.L. Ujjwal","doi":"10.32985/ijeces.15.2.1","DOIUrl":"https://doi.org/10.32985/ijeces.15.2.1","url":null,"abstract":"The rapid advancement of network technologies and protocols has fueled the widespread endorsement of the Internet of Things (IoT) in numerous domains, including everyday life, healthcare, industries, agriculture, and more. However, this rapid growth has also given rise to numerous security concerns within IoT systems. Consequently, privacy and security have become paramount issues in the IoT framework. Due to the heterogeneous data produced by smart IoT devices, traditional intrusion detection system doesn't work well with IoT system. The massive volume of heterogeneous data has several irrelevant, redundant, and unnecessary features which lead to high computation time and low accuracy of IDS. Therefore, to tackle these challenges, this paper presents a novel metaheuristic-based IDS model for the IoT systems. The chaotic opposition-based Harris Hawk optimization (CO-IHHO) algorithm is used to perform the feature selection of data traffic. The chosen features are subsequently inputted into a machine learning (ML) classifier to detect network traffic intrusions. The performance of the CO-IHHO based IDS model is verified against the BoT-IoT dataset. Experimental findings reveal that CO-IHHO-DT achieves the maximal accuracy of 99.65% for multiclass classification and 100% for binary classification, and minimal computation time of 31.34 sec for multiclass classification and 133.54 sec for binary classification.","PeriodicalId":507791,"journal":{"name":"International journal of electrical and computer engineering systems","volume":"23 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140437877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantum Computing in The Cloud - A Systematic Literature Review 云中的量子计算--系统性文献综述

International journal of electrical and computer engineering systems Pub Date : 2024-02-23 DOI: 10.32985/ijeces.15.2.7

Amirul Asyraf Zhahir, Siti Munirah Mohd, Mohd Ilias M Shuhud, B. Idrus, Hishamuddin Zainuddin, Nurhidaya Mohamad Jan, Mohamed Ridza Wahiddin

引用次数: 0

PrioriNet PrioriNet

International journal of electrical and computer engineering systems Pub Date : 2024-02-23 DOI: 10.32985/ijeces.15.2.4

K. M, Angeline Prasanna G.

{"title":"PrioriNet","authors":"K. M, Angeline Prasanna G.","doi":"10.32985/ijeces.15.2.4","DOIUrl":"https://doi.org/10.32985/ijeces.15.2.4","url":null,"abstract":"During disaster scenarios, effective communication systems are essential for coordinating emergency response efforts and ensuring the safety of affected individuals. However, existing communication protocols often face challenges in providing reliable and efficient communication in these highly dynamic and resource-constrained environments. To overcome these challenges a novel energy-efficient emergency priority protocol namely PrioriNet technique which specifically tailored for urban earthquake scenarios. The protocol focuses on prioritizing the transmission of emergency data packets to ensure their prompt and reliable delivery, while appropriately managing normal data packets. The PrioriNet prioritizes the emergency messages as high and low priority messages and allocate them to energy efficient nodes efficiently. The experimental results indicates that the suggested protocol performs better than the existing LEACH technique in terms of energy consumption, network coverage, packet delivery ratio, and throughput. In emergency data scenarios, the LEACH protocol demonstrates throughputs between 0.3 Mbps and 1.2 Mbps, whereas the proposed method consistently outperforms the LEACH protocol with throughputs ranging from 0.7 Mbps to 1.8 Mbps respectively.","PeriodicalId":507791,"journal":{"name":"International journal of electrical and computer engineering systems","volume":"20 S12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140437702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0