Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM...Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance.Since,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by SM.This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic outbreaks.DL has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation results.In recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM analysis.This paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM analysis.Finally,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.展开更多
In Wuhan,China,a novel Corona Virus(COVID-19)was detected in December 2019;it has changed the entire world and to date,the number of diagnosed cases is 38,756,2891 and 1,095,2161 people have died.This happened because...In Wuhan,China,a novel Corona Virus(COVID-19)was detected in December 2019;it has changed the entire world and to date,the number of diagnosed cases is 38,756,2891 and 1,095,2161 people have died.This happened because a large number of people got affected and there is a lack of hospitals for COVID-19 patients.One of the precautionary measures for COVID-19 patients is isolation.To support this,there is an urgent need for a platform that makes treatment possible from a distance.Telemedicine systems have been drastically increasing in number and size over recent years.This increasing number intensies the extensive need for telemedicine for the national healthcare system.In this paper,we present Tele-COVID which is a telemedicine application to treat COVID-19 patients from a distance.Tele-COVID is uniquely designed and implemented in Service-Oriented Architecture(SOA)to avoid the problem of interoperability,vendor lock-in,and data interchange.With the help of Tele-COVID,the treatment of patients at a distance is possible without the need for them to visit hospitals;in case of emergency,necessary services can also be provided.展开更多
In the age of smartphones, people do most of their daily work using their smartphones due to significant improvement in smartphone technology. When comparing different platforms such as Windows, iOS, Android, and Blac...In the age of smartphones, people do most of their daily work using their smartphones due to significant improvement in smartphone technology. When comparing different platforms such as Windows, iOS, Android, and Blackberry, Android has captured the highest percentage of total market share [1]. Due to this tremendous growth, cybercriminals are encouraged to penetrate various mobile marketplaces with malicious applications. Most of these applications require device information permissions aiming to collect sensitive data without user’s consent. This paper investigates each element of system information permissions and illustrates how cybercriminals can harm users’ privacy. It presents some attack scenarios using READ_PHONE_STATE permission and the risks behind it. In addition, this paper refers to possible attacks that can be performed when additional permissions are combined with READ_PHONE_STATE permission. It also discusses a proposed solution to defeat these types of attacks.展开更多
Most of the millions of Android users worldwide use applications from the official Android market (Google Play store) and unregulated alternative markets to get more functionality from their devices. Many of these app...Most of the millions of Android users worldwide use applications from the official Android market (Google Play store) and unregulated alternative markets to get more functionality from their devices. Many of these applications transmit sensitive data stored on the device, either maliciously or accidentally, to outside networks. In this paper, we will study the ways that Android applications transmit data to outside servers and propose a user-friendly application, DroidData, to inform and protect the user from these security risks. We will use tools such as TaintDroid, AppIntent, and Securacy to propose an application that reveals what types of data are being transmitted from apps, the location to which the data is being transmitted, whether the data is being transmitted through a secure channel (such as HTTPS) and whether the user is aware that the information is being transmitted. The application will generate a report that allows the user to block the application that leaks sensitive information. In doing so, we will examine the importance, relevance, and prevalence of these Android Data security issues.展开更多
基金authors are thankful to the Deanship of Scientific Research at Najran University for funding this work,under the Research Groups Funding Program Grant Code(NU/RG/SERC/12/27).
文摘Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance.Since,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by SM.This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic outbreaks.DL has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation results.In recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM analysis.This paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM analysis.Finally,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.
文摘In Wuhan,China,a novel Corona Virus(COVID-19)was detected in December 2019;it has changed the entire world and to date,the number of diagnosed cases is 38,756,2891 and 1,095,2161 people have died.This happened because a large number of people got affected and there is a lack of hospitals for COVID-19 patients.One of the precautionary measures for COVID-19 patients is isolation.To support this,there is an urgent need for a platform that makes treatment possible from a distance.Telemedicine systems have been drastically increasing in number and size over recent years.This increasing number intensies the extensive need for telemedicine for the national healthcare system.In this paper,we present Tele-COVID which is a telemedicine application to treat COVID-19 patients from a distance.Tele-COVID is uniquely designed and implemented in Service-Oriented Architecture(SOA)to avoid the problem of interoperability,vendor lock-in,and data interchange.With the help of Tele-COVID,the treatment of patients at a distance is possible without the need for them to visit hospitals;in case of emergency,necessary services can also be provided.
文摘In the age of smartphones, people do most of their daily work using their smartphones due to significant improvement in smartphone technology. When comparing different platforms such as Windows, iOS, Android, and Blackberry, Android has captured the highest percentage of total market share [1]. Due to this tremendous growth, cybercriminals are encouraged to penetrate various mobile marketplaces with malicious applications. Most of these applications require device information permissions aiming to collect sensitive data without user’s consent. This paper investigates each element of system information permissions and illustrates how cybercriminals can harm users’ privacy. It presents some attack scenarios using READ_PHONE_STATE permission and the risks behind it. In addition, this paper refers to possible attacks that can be performed when additional permissions are combined with READ_PHONE_STATE permission. It also discusses a proposed solution to defeat these types of attacks.
文摘Most of the millions of Android users worldwide use applications from the official Android market (Google Play store) and unregulated alternative markets to get more functionality from their devices. Many of these applications transmit sensitive data stored on the device, either maliciously or accidentally, to outside networks. In this paper, we will study the ways that Android applications transmit data to outside servers and propose a user-friendly application, DroidData, to inform and protect the user from these security risks. We will use tools such as TaintDroid, AppIntent, and Securacy to propose an application that reveals what types of data are being transmitted from apps, the location to which the data is being transmitted, whether the data is being transmitted through a secure channel (such as HTTPS) and whether the user is aware that the information is being transmitted. The application will generate a report that allows the user to block the application that leaks sensitive information. In doing so, we will examine the importance, relevance, and prevalence of these Android Data security issues.
