Special Issue "IoT and Artificial Intelligence Approaches to Defeat COVID-19 Outbreak"
Deadline for manuscript submissions: 15 December 2020.
Interests: Different aspects of security, privacy, and trust practices to address emergency events like COVID-19 outbreak and other e-Health measures; context-aware access control; data sharing and privacy; security and AI; ransomware detection and defense; IoT security; and cloud/fog security.
Interests: Examining the links between film piracy and the proliferation of child abuse material online; AI and penetration testing; cybercrime and cyber terrorism; online threats and social harms; malware and ransomware; API security; identity thefts; scams and phishing.
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Interests: The intersection of computer security and machine learning, with interests in using machine learning to improve software security and in improving the security and reliability of the machine learning models themselves; insider, intrusion, and misuse detection; adaptive and resilience systems; data science and advance analytics.
Interests: Internet of Things; Cyber Security; Intelligent Computing and Applications; and HCI.
Interests: Database usability; skyline queries; parallel and distributed processing of queries; spatial data management; advanced data analytics; machine learning; natural language processing and artificial intelligence.
Interests: Internet of Things (IoT); Cyber Security; Machine Learning; Privacy-Preserving Machine Learning; Blockchain; Mobile Computing; Location-Based Applications; Smart City; Security Privacy, and Trust issues with emergency events like COVID-19 pandemics.
Sensors provide valuable data about physical devices and the associated environment. The unprecedented increase in data volumes related to different sensor applications and networks is powering big data analytics through a range of artificial intelligence (AI) techniques. In the context of COVID-19, big data refers to patient healthcare data such as lists of physicians and patients, medical images, physician notes, case history, chest X-ray reports, information about outbreak areas, and so on. These data are generated from a number of sources, ranging from Internet of Things (IoT) sensors (e.g., smartphone data) to online social platforms (e.g., public reactions). The traditional data analytic tools and mechanisms are not adequate for meeting the requirements during the COVID-19 pandemic. For example, two of the new research directions with COVID-19 involve using AI techniques for medical image processing and sentiment analysis toward social distancing. The translation of these big data into concrete actions (e.g., deriving valuable information from people’s opinions toward social distancing measures) requires processing the inputs acquired from sensors and social networks. Such transformation and processing can benefit from the new insights provided by branches of AI, like the use of machine learning and deep learning to improve the COVID-19 pandemic situation and drive further mitigation of the COVID-19 outbreak.
Authors of selected high-qualified papers from the International Workshop on Security, Privacy, and Trust for Emergency Events (EmergencyComm 2020) will be invited to submit extended versions of their original papers (50% extensions of the contents of the conference paper) and contributions.
Topics of interest include but are not limited to:
- COVID-19 crisis management and communication strategies;
- Security, privacy, and trust practices to address events like the COVID-19 outbreak through data from social and IoT networks;
- Sentiment analysis toward social distancing against COVID-19;
- AI to process COVID-19 data from IoT sensor networks;
- AI techniques for medical image processing for COVID-19;
- Automated messaging to deliver timely and relevant prevention messages against COVID-19;
- Identifying and blocking scams and other cybercrime tactics involving COVID-19;
- Measuring community acceptance of social distancing against COVID-19;
- The role of messaging and chatbots in engaging concerned users;
- Privacy-preserving data mining and machine learning for emergency events through IoT ;
- Modelling and protection of the disease spread and other hazardous consequences;
- Understanding risks associated with coronavirus infections through AI-based sensor applications; and
- Identifying social distancing parameters through deep learning architectures along with data from IoT sensor networks
Prof. Dr. Paul Watters
Dr. Ebrima Ceesay
Dr. Man Qi
Dr. Md. Saiful Islam
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.