Pub Date : 2021-11-01DOI: 10.1109/ITSS-IoE53029.2021.9615289
M. Mohammed, A. Romli, R. Mohamed
Smart manufacturing is widely focused on sustainable development at the industrial level. The lack of knowledge about using smart manufacturing limits the ability to assess, share, and reuse knowledge by decision makers. The goal is to enable decision-makers to use sustainable information relevant to life cycle sustainability assessment techniques based on ontology at the design stage by facilitating the assessment, sharing, and reusing of knowledge. In this paper, we present the materials and process selection tools by illustrating their application to promoting reusability in manufacturing. It is expected that this study will contribute to solving the problem of the lack of information sharing and providing high quality and comprehensive recommendations for supporting the processes of smart manufacturing.
{"title":"Using Ontology to Enhance Decision-Making for Product Sustainability in Smart Manufacturing","authors":"M. Mohammed, A. Romli, R. Mohamed","doi":"10.1109/ITSS-IoE53029.2021.9615289","DOIUrl":"https://doi.org/10.1109/ITSS-IoE53029.2021.9615289","url":null,"abstract":"Smart manufacturing is widely focused on sustainable development at the industrial level. The lack of knowledge about using smart manufacturing limits the ability to assess, share, and reuse knowledge by decision makers. The goal is to enable decision-makers to use sustainable information relevant to life cycle sustainability assessment techniques based on ontology at the design stage by facilitating the assessment, sharing, and reusing of knowledge. In this paper, we present the materials and process selection tools by illustrating their application to promoting reusability in manufacturing. It is expected that this study will contribute to solving the problem of the lack of information sharing and providing high quality and comprehensive recommendations for supporting the processes of smart manufacturing.","PeriodicalId":230566,"journal":{"name":"2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125948998","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}
Pub Date : 2021-11-01DOI: 10.1109/ITSS-IoE53029.2021.9615257
Liwa H. Al-Farhani
Typically, in the smart city concept, a wireless sensor network contains many power-constrained sensors. The sensors sensed data from the environment and transmitted them towards the base station in a cooperative way. Therefore, an efficient energy consumption strategy leads to extend the lifetime of wireless sensor networks. Furthermore, the clustering structure pattern regulates the data transmission and reduces the total consumed energy. In this paper, we propose a new routing protocol that represents an improvement on Energy Efficient Sleep Awake Aware Sensor Network Routing Protocol (EESAA) called Improved –EESAA (I-EESAA) for heterogeneous wireless sensor networks (WSNs). I-EESAA protocol consists of many algorithms for clustering, cluster head selection, grouping, sensor mode scheduling, and data transmission. The main idea of I-EESAA is the grouping concept that aims to form groups of sensors with the same application type and located in the same communication range. After groups forming, one sensor in each group will still be in active mode while the others enter sleep mode. Simulation results show that the I-EESAA protocol performs better than the DEEC, DEV-DEEC, and EESAA in network lifetime, the first node dies, cluster head selection process, and throughput. Three system models are present to test I-EESAA with different environments.
{"title":"Improved Energy Efficient Sleep Awake Aware Sensor Network Routing Protocol","authors":"Liwa H. Al-Farhani","doi":"10.1109/ITSS-IoE53029.2021.9615257","DOIUrl":"https://doi.org/10.1109/ITSS-IoE53029.2021.9615257","url":null,"abstract":"Typically, in the smart city concept, a wireless sensor network contains many power-constrained sensors. The sensors sensed data from the environment and transmitted them towards the base station in a cooperative way. Therefore, an efficient energy consumption strategy leads to extend the lifetime of wireless sensor networks. Furthermore, the clustering structure pattern regulates the data transmission and reduces the total consumed energy. In this paper, we propose a new routing protocol that represents an improvement on Energy Efficient Sleep Awake Aware Sensor Network Routing Protocol (EESAA) called Improved –EESAA (I-EESAA) for heterogeneous wireless sensor networks (WSNs). I-EESAA protocol consists of many algorithms for clustering, cluster head selection, grouping, sensor mode scheduling, and data transmission. The main idea of I-EESAA is the grouping concept that aims to form groups of sensors with the same application type and located in the same communication range. After groups forming, one sensor in each group will still be in active mode while the others enter sleep mode. Simulation results show that the I-EESAA protocol performs better than the DEEC, DEV-DEEC, and EESAA in network lifetime, the first node dies, cluster head selection process, and throughput. Three system models are present to test I-EESAA with different environments.","PeriodicalId":230566,"journal":{"name":"2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123443593","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}
Pub Date : 2021-11-01DOI: 10.1109/ITSS-IoE53029.2021.9615349
D. Mitra, Shikha Gupta, Pawandeep Kaur
Fraud using credit cards is still rife today, and the modes are increasingly varied. To avoid scams with various ways of credit cards, we must identify and find out what methods are often used by fraudsters. The comparative analysis depicts that the parameters, i.e., Precision/Recall and F1-Score the K-Nearest Neighbor, are better for detecting fraudulent transactions than the Logistic Regression and Naïve Bayes. However, the accuracy is marginal high of Logistic Regression, but the False Positive parameters cannot identify the imbalanced data; therefore, they disguise the results and accuracy of Logistic Regression and K--Nearest Neighbor deems fit for such cases. Kaggle Dataset for fraud detection has been used to experiment. Therefore, under the scheme, we used various models of machine learning models based on classification and Regression. The results show that the K--Nearest Neighbor is the better approach for detecting fraudulent transactions than the Logistic Regression and Naïve Bayes.
如今，使用信用卡进行诈骗仍然很普遍，而且诈骗方式也越来越多样化。为了避免各种信用卡诈骗，我们必须识别和找出骗子经常使用的方法。对比分析表明，与Logistic回归和Naïve贝叶斯相比，Precision/Recall和F1-Score The K-Nearest Neighbor这两个参数更适合检测欺诈交易。然而，逻辑回归的准确率较高，但假阳性参数不能识别不平衡数据;因此，它们掩盖了逻辑回归和K-最近邻认为适合这种情况的结果和准确性。用于欺诈检测的Kaggle数据集已被用于实验。因此，在该方案下，我们使用了基于分类和回归的各种机器学习模型。结果表明，K-最近邻是比逻辑回归和Naïve贝叶斯更好的检测欺诈交易的方法。
{"title":"An Algorithmic Approach to Machine Learning Techniques for Fraud detection: A Comparative Analysis","authors":"D. Mitra, Shikha Gupta, Pawandeep Kaur","doi":"10.1109/ITSS-IoE53029.2021.9615349","DOIUrl":"https://doi.org/10.1109/ITSS-IoE53029.2021.9615349","url":null,"abstract":"Fraud using credit cards is still rife today, and the modes are increasingly varied. To avoid scams with various ways of credit cards, we must identify and find out what methods are often used by fraudsters. The comparative analysis depicts that the parameters, i.e., Precision/Recall and F1-Score the K-Nearest Neighbor, are better for detecting fraudulent transactions than the Logistic Regression and Naïve Bayes. However, the accuracy is marginal high of Logistic Regression, but the False Positive parameters cannot identify the imbalanced data; therefore, they disguise the results and accuracy of Logistic Regression and K--Nearest Neighbor deems fit for such cases. Kaggle Dataset for fraud detection has been used to experiment. Therefore, under the scheme, we used various models of machine learning models based on classification and Regression. The results show that the K--Nearest Neighbor is the better approach for detecting fraudulent transactions than the Logistic Regression and Naïve Bayes.","PeriodicalId":230566,"journal":{"name":"2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128276119","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}
Pub Date : 2021-11-01DOI: 10.1109/ITSS-IoE53029.2021.9615269
Jatin Aditya
Breast cancer-associated to females has been reckoned as one of the most prevalent cancers. For better medical treatments premature detection of breast cancer is an essential step. This study focuses on automated breast cancer prediction using the Ensemble Machine learning paradigm. Supervised machine learning models are trained using labelled data to perceive a hypothesis that will give good predictions for a particular problem domain. Although the hypothesis space contains hypotheses that are very well-suited for a particular problem, it may be very difficult to find a good one. Ensemble learning combines multiple learnings to form a better hypothesis. The expression Ensemble is usually reserved for methods that generate predictions from various hypotheses using homogeneous or non-homogeneous base learners. Additional computation is typically required in assessing such types of ensemble models than evaluating the prediction from a single model. Unlike bagging or boosting, we are using non-homogeneous classifiers to predict whether the breast cancer is cancerous or not that is, malignant or benign using GaussianNB as meta classifier in stacking classifier of sci-kit learn in python and we are using breast cancer dataset from Wisconsin, maintained by the University of California. The recorded prediction was achieved to be 99.41% which outperforms the performance of the single algorithm.
与女性有关的乳腺癌被认为是最常见的癌症之一。为了更好的医疗，乳腺癌的早期检测是必不可少的一步。本研究的重点是使用集成机器学习范式进行乳腺癌的自动预测。有监督的机器学习模型使用标记数据进行训练，以感知一个假设，该假设将为特定问题领域提供良好的预测。尽管假设空间包含了非常适合某个特定问题的假设，但要找到一个好的假设可能非常困难。集成学习将多种学习结合起来，形成更好的假设。表达式Ensemble通常用于使用齐次或非齐次基础学习器从各种假设生成预测的方法。在评估这类集成模型时，通常需要额外的计算，而不是评估单一模型的预测。与bagging或boosting不同，我们使用非同质分类器来预测乳腺癌是否是癌性的，即恶性的还是良性的，使用GaussianNB作为scikit learn in python的堆叠分类器中的元分类器，我们使用来自威斯康星州的乳腺癌数据集，由加州大学维护。记录的预测率达到99.41%，优于单一算法的预测率。
{"title":"Optimized Ensemble Prediction Model for Breast Cancer","authors":"Jatin Aditya","doi":"10.1109/ITSS-IoE53029.2021.9615269","DOIUrl":"https://doi.org/10.1109/ITSS-IoE53029.2021.9615269","url":null,"abstract":"Breast cancer-associated to females has been reckoned as one of the most prevalent cancers. For better medical treatments premature detection of breast cancer is an essential step. This study focuses on automated breast cancer prediction using the Ensemble Machine learning paradigm. Supervised machine learning models are trained using labelled data to perceive a hypothesis that will give good predictions for a particular problem domain. Although the hypothesis space contains hypotheses that are very well-suited for a particular problem, it may be very difficult to find a good one. Ensemble learning combines multiple learnings to form a better hypothesis. The expression Ensemble is usually reserved for methods that generate predictions from various hypotheses using homogeneous or non-homogeneous base learners. Additional computation is typically required in assessing such types of ensemble models than evaluating the prediction from a single model. Unlike bagging or boosting, we are using non-homogeneous classifiers to predict whether the breast cancer is cancerous or not that is, malignant or benign using GaussianNB as meta classifier in stacking classifier of sci-kit learn in python and we are using breast cancer dataset from Wisconsin, maintained by the University of California. The recorded prediction was achieved to be 99.41% which outperforms the performance of the single algorithm.","PeriodicalId":230566,"journal":{"name":"2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129963968","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}
Pub Date : 2021-11-01DOI: 10.1109/ITSS-IoE53029.2021.9615323
Israa A. Aljabry, G. Al-Suhail, W. Jabbar
Over recent years, a new technology named VANET (Vehicular Ad-hoc Networks) is highly recommended in smart cities and especially in Intelligent Transportation Systems (ITS). The VANET technology relies on the nodes acting like cars without the necessity for any controller or central base station by creating a wireless link among them. It enables cars to send and receive information between themselves and their environment. most VANETs utilize position-based routing protocols because they contain a GPS device. To deal with VANET problems, one solution is Geographic Perimeter Stateless Routing (GPSR) which has been broadly implemented. This paper suggests an effective intelligent fuzzy logic control system; called the FL-QN GPSR routing protocol. The proposed routing protocol incorporates two metrics link quality, and neighbor node to detect the best next-hop node for packet forwarding also updates the format of the Hello message by adding the direction field to be more suitable to our simulation. The OMNeT++ and SUMO simulation tools are both used in parallel to examine the VANET environment. The obtained results of the four simulation experiments in urban environments indicate substantial improvements in the network performance compared to the traditional GPSR and AODV concerning the QoS parameters.
{"title":"A Fuzzy GPSR Route Selection Based on Link Quality and Neighbor Node in VANET","authors":"Israa A. Aljabry, G. Al-Suhail, W. Jabbar","doi":"10.1109/ITSS-IoE53029.2021.9615323","DOIUrl":"https://doi.org/10.1109/ITSS-IoE53029.2021.9615323","url":null,"abstract":"Over recent years, a new technology named VANET (Vehicular Ad-hoc Networks) is highly recommended in smart cities and especially in Intelligent Transportation Systems (ITS). The VANET technology relies on the nodes acting like cars without the necessity for any controller or central base station by creating a wireless link among them. It enables cars to send and receive information between themselves and their environment. most VANETs utilize position-based routing protocols because they contain a GPS device. To deal with VANET problems, one solution is Geographic Perimeter Stateless Routing (GPSR) which has been broadly implemented. This paper suggests an effective intelligent fuzzy logic control system; called the FL-QN GPSR routing protocol. The proposed routing protocol incorporates two metrics link quality, and neighbor node to detect the best next-hop node for packet forwarding also updates the format of the Hello message by adding the direction field to be more suitable to our simulation. The OMNeT++ and SUMO simulation tools are both used in parallel to examine the VANET environment. The obtained results of the four simulation experiments in urban environments indicate substantial improvements in the network performance compared to the traditional GPSR and AODV concerning the QoS parameters.","PeriodicalId":230566,"journal":{"name":"2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115195019","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}
Pub Date : 2021-11-01DOI: 10.1109/ITSS-IoE53029.2021.9615254
Abdullatif Ghallab, Ali Almuzaiqer, A. Al-Hashedi, A. Mohsen, K. Bechkoum, Wajdi Aljedaani
Small and medium-sized enterprises (SMEs) are significant contributors to countries' economic activities. SMEs need to use enterprise resource planning (ERP) systems to increase revenue and productivity. Due to the high licensing costs of these systems, open source ERP (OSERP) could be an alternative solution to this problem. This study investigates the factors affecting the intention to adopt the OSERP system by SMEs in Yemen using the Technology-Organization-Environment (TOE) Framework and The Diffusion of Innovation (DOI) Theory. Using a questionnaire, data were collected from a sample of 600 subjects. The model was validated empirically using Structural Equation Modeling (SEM). The results show that relative advantage, compatibility, trialability, observability, ICT infrastructure, IT skills, top management support, cost-saving, competitive pressure, vendor support, and regulatory support positively influence the intention to adopt OSERP. In contrast, complexity has a negative impact on the intention to adopt. However, security and organizational culture have no significant influence on SMEs' intention to adopt OSERP in Yemen.
{"title":"Factors Affecting Intention to Adopt Open Source ERP Systems by SMEs in Yemen","authors":"Abdullatif Ghallab, Ali Almuzaiqer, A. Al-Hashedi, A. Mohsen, K. Bechkoum, Wajdi Aljedaani","doi":"10.1109/ITSS-IoE53029.2021.9615254","DOIUrl":"https://doi.org/10.1109/ITSS-IoE53029.2021.9615254","url":null,"abstract":"Small and medium-sized enterprises (SMEs) are significant contributors to countries' economic activities. SMEs need to use enterprise resource planning (ERP) systems to increase revenue and productivity. Due to the high licensing costs of these systems, open source ERP (OSERP) could be an alternative solution to this problem. This study investigates the factors affecting the intention to adopt the OSERP system by SMEs in Yemen using the Technology-Organization-Environment (TOE) Framework and The Diffusion of Innovation (DOI) Theory. Using a questionnaire, data were collected from a sample of 600 subjects. The model was validated empirically using Structural Equation Modeling (SEM). The results show that relative advantage, compatibility, trialability, observability, ICT infrastructure, IT skills, top management support, cost-saving, competitive pressure, vendor support, and regulatory support positively influence the intention to adopt OSERP. In contrast, complexity has a negative impact on the intention to adopt. However, security and organizational culture have no significant influence on SMEs' intention to adopt OSERP in Yemen.","PeriodicalId":230566,"journal":{"name":"2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE)","volume":"298 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128617093","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}
Pub Date : 2021-11-01DOI: 10.1109/ITSS-IoE53029.2021.9615256
Wedad Al-Sorori, A. Mohsen, Yousefvand Ali, Naseebah Maqtary, Asma M. Altabeeb, Belal A. Al-fuhaidi, Abdullah Alhashedi, Hasan Ali Gamal Al-Kaf
The need to study and analyze public opinions about the Corona virus (COVID-19) pandemic or about those preventive measures that are imposed, led to the emergence of many studies. These conducted studies have concerned the analysis of public feelings and opinions, known as sentiment analysis (SA). Taking a benefit of social media platforms such as Twitter a dataset of Arab people feelings, especially fear and anxiety, towards Covid-19 was built through surveying the Arabic content in this platform. A machine learning (ML) model was applied to analyze and categorize the tweets related to fear and anxiety regarding Covid-19 outbreak. In this model, the word2vec was employed for word embedding to form the vector of features with two CBOW pre-trained models CC.AR.300 and Arabic.news. Moreover, the effect of the sampling technique that is called Synthetic Minority Over-sampling Technique and Edited Nearest Neighbors (SMOTENN) was investigated in this study. In addition, the performance of several single-based and ensemble classifiers were evaluated and discussed. The experimental results show that applying word embedding and SMOTENN with both single and ensemble classifiers achieve a good improvement in terms of F1 average score compared to the baseline, single and ensemble classifiers without SMOTENN.
{"title":"Arabic Sentiment Analysis towards Feelings among Covid-19 Outbreak Using Single and Ensemble Classifiers","authors":"Wedad Al-Sorori, A. Mohsen, Yousefvand Ali, Naseebah Maqtary, Asma M. Altabeeb, Belal A. Al-fuhaidi, Abdullah Alhashedi, Hasan Ali Gamal Al-Kaf","doi":"10.1109/ITSS-IoE53029.2021.9615256","DOIUrl":"https://doi.org/10.1109/ITSS-IoE53029.2021.9615256","url":null,"abstract":"The need to study and analyze public opinions about the Corona virus (COVID-19) pandemic or about those preventive measures that are imposed, led to the emergence of many studies. These conducted studies have concerned the analysis of public feelings and opinions, known as sentiment analysis (SA). Taking a benefit of social media platforms such as Twitter a dataset of Arab people feelings, especially fear and anxiety, towards Covid-19 was built through surveying the Arabic content in this platform. A machine learning (ML) model was applied to analyze and categorize the tweets related to fear and anxiety regarding Covid-19 outbreak. In this model, the word2vec was employed for word embedding to form the vector of features with two CBOW pre-trained models CC.AR.300 and Arabic.news. Moreover, the effect of the sampling technique that is called Synthetic Minority Over-sampling Technique and Edited Nearest Neighbors (SMOTENN) was investigated in this study. In addition, the performance of several single-based and ensemble classifiers were evaluated and discussed. The experimental results show that applying word embedding and SMOTENN with both single and ensemble classifiers achieve a good improvement in terms of F1 average score compared to the baseline, single and ensemble classifiers without SMOTENN.","PeriodicalId":230566,"journal":{"name":"2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127084685","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}
Pub Date : 2021-11-01DOI: 10.1109/ITSS-IoE53029.2021.9615292
Maged Rfeqallah, R. Kasim, Mohammed A. Al-Sharafi
Social media has attracted considerable attention from students at higher level of educational pursuit and has become an important communication tool that enables rapid information exchange, connects with friends, and instructs and influences their academic performance. Students are prone to the effect of social media as they usually spend more time using social sites without proper monitoring from their parents, which affects their academic endeavors. This goal of this study is to propose a theoretical model for investigating the impact of social media usage on students’ academic performance. The proposed model has been developed by extending the Technology Acceptance Model theory with communication theory factors (motivation and perceived ease of communication) that consider the real motivation factors to accept and use new technologies. In addition, this study explores the effect of self-regulation as the moderating variable in the relationship between social media use and academic performance. This study provides comprehensive findings and insights of social media use among universities, researchers, and students and the extent to which academic performance is influenced by the use of social media. Furthermore, the proposed model must be tested in future studies.
{"title":"Conceptualizing a Model for Using Social Media as a Learning Tool and Its Effect on Academic Performance: The Moderating Effect of Self-Regulation","authors":"Maged Rfeqallah, R. Kasim, Mohammed A. Al-Sharafi","doi":"10.1109/ITSS-IoE53029.2021.9615292","DOIUrl":"https://doi.org/10.1109/ITSS-IoE53029.2021.9615292","url":null,"abstract":"Social media has attracted considerable attention from students at higher level of educational pursuit and has become an important communication tool that enables rapid information exchange, connects with friends, and instructs and influences their academic performance. Students are prone to the effect of social media as they usually spend more time using social sites without proper monitoring from their parents, which affects their academic endeavors. This goal of this study is to propose a theoretical model for investigating the impact of social media usage on students’ academic performance. The proposed model has been developed by extending the Technology Acceptance Model theory with communication theory factors (motivation and perceived ease of communication) that consider the real motivation factors to accept and use new technologies. In addition, this study explores the effect of self-regulation as the moderating variable in the relationship between social media use and academic performance. This study provides comprehensive findings and insights of social media use among universities, researchers, and students and the extent to which academic performance is influenced by the use of social media. Furthermore, the proposed model must be tested in future studies.","PeriodicalId":230566,"journal":{"name":"2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131004148","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}
Pub Date : 2021-11-01DOI: 10.1109/ITSS-IoE53029.2021.9615337
Afshin Balal, Miguel Herrera, Yao Lung Chuang, Shahab Balali
Rectifier circuits made up of diodes are used in quite a few applications, including Uninterrupted Power Supply (UPS), Switch Mode Power Supply (SMPS), and battery energy storage to convert the AC voltage to the DC voltage. However, the low power factor issue, which causes high current harmonics, is the major disadvantage of this diode rectifier. The need for high-power components with low total harmonic distortion (THD) and high PF is growing. This article aims to investigate three techniques for achieving a PF close to one utilizing buck, boost, and flyback topologies as active power factor correction APFC methods.
{"title":"Analysis of Buck, Boost, and Flyback Topologies Using for Active Power Factor Correction","authors":"Afshin Balal, Miguel Herrera, Yao Lung Chuang, Shahab Balali","doi":"10.1109/ITSS-IoE53029.2021.9615337","DOIUrl":"https://doi.org/10.1109/ITSS-IoE53029.2021.9615337","url":null,"abstract":"Rectifier circuits made up of diodes are used in quite a few applications, including Uninterrupted Power Supply (UPS), Switch Mode Power Supply (SMPS), and battery energy storage to convert the AC voltage to the DC voltage. However, the low power factor issue, which causes high current harmonics, is the major disadvantage of this diode rectifier. The need for high-power components with low total harmonic distortion (THD) and high PF is growing. This article aims to investigate three techniques for achieving a PF close to one utilizing buck, boost, and flyback topologies as active power factor correction APFC methods.","PeriodicalId":230566,"journal":{"name":"2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125046910","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}
Pub Date : 2021-11-01DOI: 10.1109/ITSS-IoE53029.2021.9615252
Azhar Kassem Flayeh, Azmi Shawkat Abdulbaqi, I. Y. Panessai
Electroencephalogram (EEG) Simulator or often called EEG Specter in principle is a signal generator in the form of an "EEG-like" signal or EEG signal that has been recorded. The purpose of this manuscript is to design an EEG Simulator tool. The design through the stages as follows: circuit design and circuit testing. This design is based on Arduino UNO and uses 12-bit Digital to Analog Converter to convert Digital data which is the output of Arduino UNO into Analog data in the form of EEG signals. Based on the measurement results obtained an error rate (ER) of 0.420% sensitivity of 0.5mV, 0.22% sensitivity of 1.0mV, and 0.22% sensitivity of 2.0mV in the BPM setting 30, obtained an ER value of 0.342% sensitivity of 0.5mV, 0.460% sensitivity of 1.0mV, and 0.432 % sensitivity of 2.0mV at BPM setting 60, obtained an error rate value of 0.121% sensitivity of 0.5mV, 0.1% sensitivity of 1.0mV, and 0.1% sensitivity of 2.0mV at setting BPM 120, obtained an error rate value of 0.423% sensitivity of 0.5mV, 0.310% 1.0mV sensitivity, and 0.520% 2.0mV sensitivity at 180 BPM settings and 0.246% 0.5mV sensitivity, 0.230% 1.0mV sensitivity and 0.246% 2.0mV sensitivity at 240 BPM settings.
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