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UNSUPERVISED PATTERN LEARNING, ANALYTICS & PREDICTION OF COVID-19 GLOBAL PANDEMIC (PR-COVID)

UNSUPERVISED PATTERN LEARNING, ANALYTICS & PREDICTION OF COVID-19 GLOBAL PANDEMIC (PR-COVID)

Journal
Impact Score 6.76

OFFICIAL WEBSITE

Special Issue Information

Submission Deadline: 20-06-2021
Journal Impact Score: 6.76
Journal Name: Pattern Recognition Letters
Publisher: Pattern Recognition Letters

Special Issue Call for Papers


DESCRIPTION



COVID-19 disease, caused by the SARS- virus, was detected in December 2019 and declared a global pandemic on 11 March 2020 by the WHO. Artificial Intelligence (AI) is a highly effective method for fighting the pandemic COVID-19. AI can be described as Machine Learning (ML), Natural Language Processing (NLP), and Computer Vision applications for present purposes to teach computers to use large data-based models for pattern recognition, description, and prediction. Such functions can help identify (diagnosing), forecasting, and describing (treating) COVID-19 infections and aiding in controlling socioeconomic impacts. After the pandemic epidemic, for these reasons, there has been a rush to use and test AI and other data analytics devices. Such tasks can be useful for identifying (diagnosing), forecasting, and describing (treating) COVID-19 infections and helping to control socioeconomic impacts. Since the onset of the pandemic, there has been a rush to use and test AI and other data mining techniques for these purposes. The risk of the epidemic in terms of life and economic loss would be terrible; much confusion engulfed predictions of how bad and how effective non-pharmaceutical and pharmaceutical solutions would be. A worthy goal is to strengthen AI, one of the most popular data analytics tools that have been developed in the past decade or reduce these uncertainties. Data scientists have been willing to take up the opportunity.



In AI, machine learning and its subset(Deep Learning) methods are employed in various applications to solve multiple problems that occur due to uncertainty. But these problems were solved with the help of data collected from the history of occurrences of the event. Most of the machine learning and deep learning algorithms are trained to address the supervised learning problem, where the algorithms know the prediction requirement. On the other hand, the potential measure of the unsupervised learning method is quite high. The ability to explore new possibilities of the outcome is high. In general, supervised learning methods are bounded with biases, in which the set of rules are determined with the DOs and DONTs, which prohibit the thinking of other possibilities. Also, a high effort, manual work, and time are required to label the data for the supervised learning process, in case the labeling is not available. The primary objective of this special issue is to enhance the ability of unsupervised learning into the deep learning methodologies to find a solution to the COVID-19. To improve the behavior and nature of the deep learning method with the quality of the clustering algorithm. So, the unsupervised learning methodology can be implemented in the deep learning algorithms for efficient data classification.



The focus of this special issue is to provide a platform and opportunity for the researchers to find the solution for the current pandemic and future hazards like this that humanity has to face using the AI that involves self-learning methodologies.



SUBJECT COVERAGE



TOPICS MAY INCLUDE, BUT NOT LIMITED TO THE FOLLOWING:



· intelligent signal computing based on Deep Embedded clustering



· An evolutionary approach to Process the signals and its application



· Architectures for Real-time sensing and intelligent processing



· Auto-Encoders, Restricted Boltzmann Machines for signal classification



· Real-time Signal processing based on DEC



· Parallel and distributed algorithm design and implementation in signal sensing



· Analytics for multi-dimension data



· Intelligent computing on signal for data analysis



· Real-time remote sensing signals, such as hyperspectral signal classification, content-based signal indexing, and retrieval, monitoring of natural.



· the selection of suitable unsupervised learning methodologies.



· the selection of suitable and efficient deep learning methodology.



· the selection of diverse datasets and problems to test and validate the research outcomes.



· the exploration of the optimal deep learning methodology for data classifications.



IMPORTANT DATES



Submission of manuscripts: 01 APR 2021



Submission Deadline: 20 JUN 2021



Acceptance Deadline: 20 OCT 2021



GUEST EDITOR(S)



Prof. Dr. B.Nagaraj M.E., Ph.D., MIEEE



Dean - Innovation Centre



Rathinam Group of Institutions



Coimbatore, Tamilnadu, India



dean.sa@rathinam.in



Prof. Dr. Danilo Pelusi,



University of Teramo, Italy



Dept. of Communication Engineering



[email protected]



Prof. Valentina E. Balas



Professor-Automation and Applied Informatics,



Aurel Vlaicu University of Arad, Romania.



[email protected]



Prof. B. Nagaraj is working as a Professor and Dean in Rathinam Group of Institutions, Coimbatore, India. He received his M.E. and PhD degrees from Anna University, and Karpagam University in 2004 and 2010, respectively. In 2005 he joined a Lecturer in Kamaraj College of Engineering, India and he worked there for 12 years (till May 15th, 2013) in various positions. His technical expertise and research interests include a control system, Automation, soft computing, and high-speed signal processing. He received Best Researcher Award from Karunya University for the best research paper in the year 2010. He is the author or co-author of more than 48-refereed publications in journals and conferences. He applied for five patents and is published in Indian Patent Journal. He is a member of various professional bodies like IEEE, MAENG, IACSIT, ISTE, and IETE. He is a reviewer for different reputed journals like Elsevier, Wiley, Inderscience, etc., and he has been the Guest Editor for a few special issues in Hindawi, Elsevier, Inderscience, Springer, etc.



Prof. Danilo Pelusi, Teacher in the Faculties of Communication Sciences and Bioscience and Agro-Food and Environmental Technology, his research is on coding theory and artificial intelligence. Moreover, he is interested in signal processing, patterns recognition, fuzzy logic, neural networks, and genetic algorithms. Assistant Professor of Computer Science from 2009 to 2012 at the University of Teramo, he has developed research activity on control systems optimisation and database management to the Astronomic Observatory Collurania "V. Cerulli" of Teramo. Reviewer of international journals and conferences, he is Editorial board member of journals "Journal of Universal Computer Science," "International Journal of Advances in Telecommunications, Electrotechnics, Signals, and Systems," "Engineering, Technology & Applied Science Research," "Science Journal of Circuits, Systems, and Signal Processing." Member of the FUTURE GENERATION COMPUTER SYSTEMS PhD board in Epistemology of Informatics and Social Changes (University of Teramo), Administrator of the e-learning platform e-RID of the University of Teramo from 2009 to 2012, PhD in Computational Astrophysics, he obtained the degree in Physics from the University of Bologna. Guest editor for Inderscience, Springer.



Prof. Valentina E. Balas is currently Full Professor in the Department of Automatics and Applied Software at the Faculty of Engineering, “Aurel Vlaicu” University of Arad, Romania. She holds a Ph.D. in Applied Electronics and Telecommunications from Polytechnic University of Timisoara. Dr. Balas is author of more than 280 research papers in refereed journals and International Conferences. Her research interests are in Intelligent Systems, Fuzzy Control, Soft Computing, Smart Sensors, Information Fusion, Modeling and Simulation. She is the Editor-in Chief to International Journal of Advanced Intelligence Paradigms (IJAIP) and to International Journal of Computational Systems Engineering (IJCSysE), member in Editorial Board member of several national and international journals and is evaluator expert for national, international projects and PhD Thesis. Dr. Balas is the director of Intelligent Systems Research Centre in Aurel Vlaicu University of Arad and Director of the Department of International Relations, Programs and Projects in the same university. She served as General Chair of the International Workshop Soft Computing and Applications (SOFA) in eight editions 2005-2018 held in Romania and Hungary. Dr. Balas participated in many international conferences as Organizer, Honorary Chair, Session Chair and member in Steering, Advisory or International Program Committees. She is a member of EUSFLAT, SIAM and a Senior Member IEEE, member in TC – Fuzzy Systems (IEEE CIS), member in TC - Emergent Technologies (IEEE CIS), member in TC – Soft Computing (IEEE SMCS). Dr. Balas was past Vice-president (Awards) of IFSA International Fuzzy Systems Association Council (2013-2015) and is a Joint Secretary of the Governing Council of Forum for Interdisciplinary Mathematics (FIM), - A Multidisciplinary Academic Body, India.




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