Manual prediction of brain tumors is a time-consuming and subjective task, reliant on radiologists' expertise, leading to potential inaccuracies. In response, this study proposes an automated solution utilizing a Convolutional Neural Network (CNN) for brain tumor classification, achieving an impressive accuracy of 98.89%. Following classification, a hybrid approach, integrating graph-based and threshold segmentation techniques, accurately locates the tumor region in magnetic resonance (MR) brain images across sagittal, coronal, and axial views. Comparative analysis with existing research papers validates the effectiveness of the proposed method, and similarity coefficients, including a Bfscore of 1 and a Jaccard similarity of 93.86%, attest to the high concordance between segmented images and ground truth.
{"title":"Classification and Segmentation of MRI Images of Brain Tumors Using Deep Learning and Hybrid Approach","authors":"Sugandha Singh, Vipin Saxena","doi":"10.32985/ijeces.15.2.5","DOIUrl":"https://doi.org/10.32985/ijeces.15.2.5","url":null,"abstract":"Manual prediction of brain tumors is a time-consuming and subjective task, reliant on radiologists' expertise, leading to potential inaccuracies. In response, this study proposes an automated solution utilizing a Convolutional Neural Network (CNN) for brain tumor classification, achieving an impressive accuracy of 98.89%. Following classification, a hybrid approach, integrating graph-based and threshold segmentation techniques, accurately locates the tumor region in magnetic resonance (MR) brain images across sagittal, coronal, and axial views. Comparative analysis with existing research papers validates the effectiveness of the proposed method, and similarity coefficients, including a Bfscore of 1 and a Jaccard similarity of 93.86%, attest to the high concordance between segmented images and ground truth.","PeriodicalId":507791,"journal":{"name":"International journal of electrical and computer engineering systems","volume":"57 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140436971","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}
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.
{"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}
Baydaa M. Merzah, Muayad S. Croock, Ahmed N. Rashid
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.
机器学习（ML）方法的显著效果使其在各个学术领域的应用大幅增加，尤其是在不同的体育领域。在过去十年中，学者们将机器学习（ML）算法应用于足球领域，以实现各种目标，包括分析足球运动员的表现、预测伤病、预测市场价值和识别动作。然而，针对足球运动员表现评估的研究却很少，而这正是教练们值得关注的问题。因此，这项工作的目标是根据活动特征将足球运动员的表现分为活跃、正常和乏力。这将通过使用性能评估机器学习模型（PEMLM）来实现，该模型采用了两个涵盖训练和比赛的新型数据集。为实现这一目标，采用了七种机器学习方法，即随机森林、决策树、逻辑回归、支持向量机、高斯奈夫贝叶、多层感知器和 K-近邻。研究结果表明，在与匹配会话相对应的数据集中，决策树分类器的准确率最高（100%），测试时间最短。相比之下，K-近邻分类器的准确率最高（96%），测试时间也较短。所报告的这些指标强调了所提出的评估方法在评估足球运动员在网络游戏中的表现方面的可靠性和有效性。通过 k 倍交叉验证过程对结果进行了验证，并对模型的过拟合情况进行了评估。
{"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}
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.
{"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}
Amirul Asyraf Zhahir, Siti Munirah Mohd, Mohd Ilias M Shuhud, B. Idrus, Hishamuddin Zainuddin, Nurhidaya Mohamad Jan, Mohamed Ridza Wahiddin
Quantum computing was proposed to simulate processes that surpass the capabilities of its counterpart, classical computing. Utilizing the principles of quantum mechanics, it improves the computing power of quantum computing. Top developers namely IBM, Rigetti, D-Wave, Qutech and Google have invested greatly in the technology. Nowadays, users can access the quantum computing system publicly over the network in a cloud environment, this system architecture is known as cloud-based quantum computing. However, different developers deliver different architecture and functionality of the system on their platforms. This has indirectly spawned a question of which cloud-based quantum computing platform is a better option based on certain specific requirements by an individual or group. The main objective of this study is to provide a proposed framework using the existing cloud-based service of quantum computing based on previous studies for users with their specific demands.
{"title":"Quantum Computing in The Cloud - A Systematic Literature Review","authors":"Amirul Asyraf Zhahir, Siti Munirah Mohd, Mohd Ilias M Shuhud, B. Idrus, Hishamuddin Zainuddin, Nurhidaya Mohamad Jan, Mohamed Ridza Wahiddin","doi":"10.32985/ijeces.15.2.7","DOIUrl":"https://doi.org/10.32985/ijeces.15.2.7","url":null,"abstract":"Quantum computing was proposed to simulate processes that surpass the capabilities of its counterpart, classical computing. Utilizing the principles of quantum mechanics, it improves the computing power of quantum computing. Top developers namely IBM, Rigetti, D-Wave, Qutech and Google have invested greatly in the technology. Nowadays, users can access the quantum computing system publicly over the network in a cloud environment, this system architecture is known as cloud-based quantum computing. However, different developers deliver different architecture and functionality of the system on their platforms. This has indirectly spawned a question of which cloud-based quantum computing platform is a better option based on certain specific requirements by an individual or group. The main objective of this study is to provide a proposed framework using the existing cloud-based service of quantum computing based on previous studies for users with their specific demands.","PeriodicalId":507791,"journal":{"name":"International journal of electrical and computer engineering systems","volume":"21 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140435338","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}
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.
{"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}
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