Pub Date : 2021-04-06DOI: 10.1109/CAIDA51941.2021.9425268
Enas S. Alhazmi, R. Alraddadi, Liyakathunisa Syed
In Hajj and Umrah, a large number of people are gathered every year to perform this ritual. Therefore, maintaining and organizing these rituals are a significant job to prevent crowding. In this paper a spatial crowdsourcing web-based application is developed to prevent crowds during hajj and Umrah ritual being performed by pilgrims specially during COVID 19 pandemic and to facilitate the movement of pilgrims. The application will monitor the pilgrim’s movements by obtaining the location of the users and sorting the data within the boundaries in a map.
{"title":"Spatial Crowdsourcing for Social and Government Applications for Hajj-Umarah","authors":"Enas S. Alhazmi, R. Alraddadi, Liyakathunisa Syed","doi":"10.1109/CAIDA51941.2021.9425268","DOIUrl":"https://doi.org/10.1109/CAIDA51941.2021.9425268","url":null,"abstract":"In Hajj and Umrah, a large number of people are gathered every year to perform this ritual. Therefore, maintaining and organizing these rituals are a significant job to prevent crowding. In this paper a spatial crowdsourcing web-based application is developed to prevent crowds during hajj and Umrah ritual being performed by pilgrims specially during COVID 19 pandemic and to facilitate the movement of pilgrims. The application will monitor the pilgrim’s movements by obtaining the location of the users and sorting the data within the boundaries in a map.","PeriodicalId":272573,"journal":{"name":"2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131800917","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 : 2021-04-06DOI: 10.1109/CAIDA51941.2021.9425063
Hadil Shaiba, Maya John
Saudi Arabia has recorded the highest Middle East Respiratory Syndrome Coronavirus (MERS-CoV) infections globally. Nearly 2,000 cases have been recorded in Saudi Arabia, with a high mortality rate, since the outbreak in 2012. The source of the disease remains unclear, and it has been evident that MERS-CoV can spread through communicating, directly or indirectly, with humans or animals. In our study, we evaluated different machine learning models that can accurately predict the probability of a patient's recovery from MERS-CoV. The data was from the Saudi Ministry of Health’s website corresponding to the years from 2015 to April 2018. A stacking-based ensemble learning has been built to increase the performance of individual models. In our study, we examined the following individual classifiers: naïve Bayes, support vector machine, logistic regression, k-nearest neighbour, Bayesian networks, J48, and random forest along with the proposed stacking-based model. The results show that, in most cases, simple machine learning techniques perform well when predicting recovery unlike predicting death cases. The proposed stacking-based ensemble learning method has shown improvement in the prediction of death cases while maintaining a good predictive power for recovery cases. The proposed technique, which is an ensemble learning method, performed best with 0.751 balanced accuracy and 0.750 G-Mean. Predicting the survivability of patients can help in decision-making on prevention and recovery of MERS-CoV.
{"title":"Enhancing the Prediction of MERS-CoV Survivability Using Stacking-Based Method","authors":"Hadil Shaiba, Maya John","doi":"10.1109/CAIDA51941.2021.9425063","DOIUrl":"https://doi.org/10.1109/CAIDA51941.2021.9425063","url":null,"abstract":"Saudi Arabia has recorded the highest Middle East Respiratory Syndrome Coronavirus (MERS-CoV) infections globally. Nearly 2,000 cases have been recorded in Saudi Arabia, with a high mortality rate, since the outbreak in 2012. The source of the disease remains unclear, and it has been evident that MERS-CoV can spread through communicating, directly or indirectly, with humans or animals. In our study, we evaluated different machine learning models that can accurately predict the probability of a patient's recovery from MERS-CoV. The data was from the Saudi Ministry of Health’s website corresponding to the years from 2015 to April 2018. A stacking-based ensemble learning has been built to increase the performance of individual models. In our study, we examined the following individual classifiers: naïve Bayes, support vector machine, logistic regression, k-nearest neighbour, Bayesian networks, J48, and random forest along with the proposed stacking-based model. The results show that, in most cases, simple machine learning techniques perform well when predicting recovery unlike predicting death cases. The proposed stacking-based ensemble learning method has shown improvement in the prediction of death cases while maintaining a good predictive power for recovery cases. The proposed technique, which is an ensemble learning method, performed best with 0.751 balanced accuracy and 0.750 G-Mean. Predicting the survivability of patients can help in decision-making on prevention and recovery of MERS-CoV.","PeriodicalId":272573,"journal":{"name":"2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121822365","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 : 2021-04-06DOI: 10.1109/CAIDA51941.2021.9425196
M. Shafiullah, Md. Ershadul Haque, F. Al-Ismail, Asif Islam, Md. Shafiul Alam, Amjad Ali, S. Rahman
The equivalent electric circuit models reflect the electrical characteristics of the photovoltaic (PV) modules. Estimation of PV module parameters is considered as one of the challenging tasks while evaluating the performance. This article presents a new and useful approach to estimate the five-parameter PV module electrical circuit model. It translates the PV module parameter estimation process into an optimization problem using the information provided by the manufacturer on the rear side of the PV modules. It then employs an efficient metaheuristic technique, namely the backtracking search algorithm, to solve the developed optimization problem. The efficacy of the proposed approach is investigated by predicting the parameters of three PV module technologies: monocrystalline, poly-crystalline, and thin film. Finally, to check the feasibility of the proposed technique, this paper compares the approximate parameters of modeled I-V curves with experimental curves. The findings confirm the reliability of the estimated model parameters in simulating the near realistic characteristics of the PV modules.
{"title":"Backtracking Search Algorithm for PV Module Electrical Parameter Estimation","authors":"M. Shafiullah, Md. Ershadul Haque, F. Al-Ismail, Asif Islam, Md. Shafiul Alam, Amjad Ali, S. Rahman","doi":"10.1109/CAIDA51941.2021.9425196","DOIUrl":"https://doi.org/10.1109/CAIDA51941.2021.9425196","url":null,"abstract":"The equivalent electric circuit models reflect the electrical characteristics of the photovoltaic (PV) modules. Estimation of PV module parameters is considered as one of the challenging tasks while evaluating the performance. This article presents a new and useful approach to estimate the five-parameter PV module electrical circuit model. It translates the PV module parameter estimation process into an optimization problem using the information provided by the manufacturer on the rear side of the PV modules. It then employs an efficient metaheuristic technique, namely the backtracking search algorithm, to solve the developed optimization problem. The efficacy of the proposed approach is investigated by predicting the parameters of three PV module technologies: monocrystalline, poly-crystalline, and thin film. Finally, to check the feasibility of the proposed technique, this paper compares the approximate parameters of modeled I-V curves with experimental curves. The findings confirm the reliability of the estimated model parameters in simulating the near realistic characteristics of the PV modules.","PeriodicalId":272573,"journal":{"name":"2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133140525","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 : 2021-04-06DOI: 10.1109/CAIDA51941.2021.9425085
Hilalah F. Al-Turkistani, Hanan Ali
During COVID-19 pandemic, a lot of organizations and average users were victims of wireless network security breaches that caused huge impacts on their businesses and privacy of their data. This research reviews the most common wireless network attacks and explores the incidents of security breaches reported during the pandemic. The study conducts a survey among 56 sample of users to identify their basic knowledge that qualifies to withstand against wireless network attacks. The outcome of the survey shows that most wireless network based cyber-attacks that happens during pandemic are: phishing emails, DoS (denial of service) and social engineering, whereas the majority of the respondents did nothing to counter or search online on resolving the issues. Therefore, this study proposes a few simple and free cost solutions to increase knowledge among technical and basic users to enhance their security and privacy concerns while using wireless network.
{"title":"Enhancing Users’ Wireless Network Cyber Security and Privacy Concerns during COVID-19","authors":"Hilalah F. Al-Turkistani, Hanan Ali","doi":"10.1109/CAIDA51941.2021.9425085","DOIUrl":"https://doi.org/10.1109/CAIDA51941.2021.9425085","url":null,"abstract":"During COVID-19 pandemic, a lot of organizations and average users were victims of wireless network security breaches that caused huge impacts on their businesses and privacy of their data. This research reviews the most common wireless network attacks and explores the incidents of security breaches reported during the pandemic. The study conducts a survey among 56 sample of users to identify their basic knowledge that qualifies to withstand against wireless network attacks. The outcome of the survey shows that most wireless network based cyber-attacks that happens during pandemic are: phishing emails, DoS (denial of service) and social engineering, whereas the majority of the respondents did nothing to counter or search online on resolving the issues. Therefore, this study proposes a few simple and free cost solutions to increase knowledge among technical and basic users to enhance their security and privacy concerns while using wireless network.","PeriodicalId":272573,"journal":{"name":"2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA)","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133177426","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 : 2021-02-20DOI: 10.1109/CAIDA51941.2021.9425301
F. Alanazi
The infectious coronavirus disease 2019 (COVID-19) has become a serious global pandemic. Different studies have shown that increasing temperature can play a crucial role in the spread of the virus. Most of these studies were limited to winter or moderate temperature levels and were conducted using conventional models. However, traditional models are too simplistic to investigate complex, non-linear relationships and suffer from some restrictions. Therefore, we employed copula models to examine the impact of high temperatures on virus transmission. The findings from the copula models showed that there was a weak to moderate effect of temperature on the number of infections and the effect almost vanished under a lockdown policy. Therefore, this study provides new insight into the relationship between COVID-19 and temperature, both with and without social isolation practices. Such results can lead to improvements in our understanding of this new virus. In particular, the results derived from the copula models examined here, unlike existing traditional models, provide evidence that there is no substantial influence of high temperatures on the active COVID-19 outbreak situation. In addition, the results indicate that the transmission of COVID-19 is strongly influenced by social isolation practices.
{"title":"The spread of COVID-19 at Hot-Temperature Places With Different Curfew Situations Using Copula Models","authors":"F. Alanazi","doi":"10.1109/CAIDA51941.2021.9425301","DOIUrl":"https://doi.org/10.1109/CAIDA51941.2021.9425301","url":null,"abstract":"The infectious coronavirus disease 2019 (COVID-19) has become a serious global pandemic. Different studies have shown that increasing temperature can play a crucial role in the spread of the virus. Most of these studies were limited to winter or moderate temperature levels and were conducted using conventional models. However, traditional models are too simplistic to investigate complex, non-linear relationships and suffer from some restrictions. Therefore, we employed copula models to examine the impact of high temperatures on virus transmission. The findings from the copula models showed that there was a weak to moderate effect of temperature on the number of infections and the effect almost vanished under a lockdown policy. Therefore, this study provides new insight into the relationship between COVID-19 and temperature, both with and without social isolation practices. Such results can lead to improvements in our understanding of this new virus. In particular, the results derived from the copula models examined here, unlike existing traditional models, provide evidence that there is no substantial influence of high temperatures on the active COVID-19 outbreak situation. In addition, the results indicate that the transmission of COVID-19 is strongly influenced by social isolation practices.","PeriodicalId":272573,"journal":{"name":"2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114602739","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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