[hpc-announce] ISF SI: Novel Machine Learning, AI and Big Data Methods and Findings for COVID-19 extended til 31 Jan 2021

Victor Chang vic1e09 at soton.ac.uk
Fri Jan 22 22:41:20 CST 2021

Dear colleagues,

Due to numerous requests, deadline is only extended till 31 Jan, 16 days more. Please consider submission. Details are as follows. Website: https://resource-cms.springernature.com/springer-cms/rest/v1/content/18246802/data/v3 . Thanks.

Thanks and regards,


Brief description/Scope

COVID-19 has become the most significant challenge the human beings have encountered since World War 2 (WW2). It is reported to have more deaths in the US than the combination of the Pearl Harbor War and the September 11 terror attacks. COVID-19 itself is highly infectious and speed in which it can mutate is rapid and in different varieties, with reported six strands of active coronaviruses widely spread worldwide. It has infected more than 17 million of the population worldwide in late July 2020. In early March 2020, the total infected cases were still not reaching 100,000 (WHO, 2020). This global challenge is causing rapidly increased numbers of infected cases, death, and the way we live, such as social distancing. This has caused a lack of medical resources and healthcare crisis to fight against the infection before the development of vaccines and drugs. Other economic and social problems are common, such as job loss, insecurity, lack of movements, increases in crimes, improvements in fighting limited resources and has been seen (Ecke, 2020). In addition to this, the computing services for the identification and development of drugs are also challenging. In such cases, the quality and the quantity of the collected data plays a major role which uses cloud computing architectures (Chang, 2014; Sicari et al., 2016; Hosseinian-Far et al., 2018). The technology of the Internet of Things combined with Artificial Intelligence techniques may provide good solutions to this health-oriented problem (Vaishya et al., 2017).

Solutions for those urgent needs are required globally to understand how to tackle this challenge. Scientists have a crucial role, not only in research and development, but also provide positive impacts to the society. In terms of Machine Learning, AI and Big Data research, scientists can offer recommendations, new discoveries and pioneering methods (Gupta et al. 2018), which may provide positive impacts and findings to the causes, cure and analysis of treatment. The recent diagnosis of COVID-19 is based on real-time reverse-transcriptase polymerase chain reaction (RT-PCR) and used widely for confirmation of infection (Kim et al., 2020). Moreover, secure transmission of messages among medical professionals is also a challenging task during COVID-19 diagnosis and the subsequent treatment (Wang et al., 2020). It has already been widely recognized that novel Machine Learning, AI and Big Data methods can potentially have significant substantial roles in streamlining and accelerating the diagnosis of COVID-19 patients, offering high-quality research outputs and accurate predictive modeling (Oksavik et al., 2020; Tuli et al., 2020). Therefore, this requires novel methods such as blended LSTM, hybrid reinforcement learning, advanced deep learning, modern artificial intelligence and computational data intelligence since they are crucial for research findings. Together with pioneering methods, innovative Machine Learning, AI and Big Data for COVID-19 can provide added values for scientists. In this special issue, we seek unpublished and high quality work based on unique Machine Learning, AI and Big Data methods and findings. Best paper winners and top authors from IIoTBDSC 2020 will also be invited.

Topics of interest include, but are not limited to:

  *   Novel Machine Learning, AI and Big Data methods based COVID-19 diagnostic systems
  *   Novel AI and Data Science Techniques for lung and infection segmentation
  *   Accurate prediction of COVID-19 based on advanced Pioneering AI and Data Science methods
  *   Novel Machine Learning, AI and Big Data methods for tracking and detecting COVID-19
  *   Novel Machine Learning, AI and Big Data methods for data mining and analytics in COVID-19
  *   Novel Machine Learning, AI and Big Data methods for computational analysis of COVID-19
  *   Novel Machine Learning, AI and Big Data methods for predicting the long-term risk of COVID-19
  *   Novel Predictive Modeling for Viruses & in the Era of post-COVID-19
  *   Novel Recommendation System for treatment of COVID-19 patients based on psychological factors

ISF is a high-ranking research journal. The journal is abstracted or indexed in Science Citation Index Expanded, Current Contents/Engineering and other major sources.

Submission Instruction

Manuscripts must be submitted in PDF format to the ISF-Springer online submission system at https://www.editorialmanager.com/isfi and the authors need to select "Special Issue:

“Novel Machine Learning, AI and Big Data Methods and Findings for COVID-19" during the submission process. Paper submissions must conform to the format guidelines of Information Systems Frontiers available at https://www.springer.com/journal/10796/submissionguidelines. Submissions should be approximately 32 pages double spaced, including references.

Important Dates

Submissions Due: January 31, 2021 (final)

Pre-screening: As soon as possible

First Round Review Completion: March 31, 2021

Second Round Review Completion: June 15, 2021

Final/revised manuscript due: August 15, 2021

Guest Editors

Prof. Victor Chang, Teesside University, UK (Lead guest editor; email for queries). Email: victorchang.research at gmail.com

Prof. Carole Goble, University of Manchester, UK. Email: carole.goble at manchester.ac.uk

Dr. Muthu Ramachandran, Leeds Beckett University, UK. Email: M.Ramachandran at leedsbeckett.ac.uk

Dr. Lazarus Jegatha Deboarh, Anna University, India. Email: blessedjeny at gmail.com<mailto:blessedjeny at gmail.com>

Prof. Reinhold Behringer, Knorr-Bremse GmbH, Germany. Email: reinhold.behringer at gmail.com

Guest Editors’ Biography

Prof. Victor Chang is currently a Full Professor of Data Science and Information Systems at the School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK. He co-lead Computational Biology and Data Analytics Research Group and he leads the Beneficial Artificial Intelligence Research Group at Teesside University, UK. He was a Senior Associate Professor, Director of Ph.D. (June 2016- May 2018), Director of MRes (Sep 2017 - Feb 2019) and Interim Director of BSc IMIS (Aug 2018-Feb 2019) at Xi’an Jiaotong-Liverpool University (XJTLU), Suzhou, China, between June 2016 and August 2019. He was also a very active and contributing key member at the Research Institute of Big Data Analytics (RIBDA), XJTLU. He was an Honorary Associate Professor at the University of Liverpool. Previously he was a Senior Lecturer at Leeds Beckett University, UK, between Sep 2012 and May 2016. Within four years, he completed Ph.D. (CS, Southampton) and PGCert (Higher Education, Fellow, Greenwich) while working for several projects at the same time. Prof Chang has been involved in funding, with a total of £13 million in Europe and Asia. Before becoming an academic, he has achieved 97% on average in 27 IT certifications. He won a European Award on Cloud Migration in 2011, IEEE Outstanding Service Award in 2015, best papers in 2012, 2015 and 2018, the 2016 European award, Outstanding Young Scientist 2017, Data Science special Award 2017, 4 INSTICC Service Awards 2017-2020, Outstanding Reviewer Awards 2018 and 2019, etc. He is a visiting scholar/Ph.D. examiner at several universities, an Editor-in-Chief of IJOCI & OJBD journals, Editor of FGCS (Oct 2014- Feb 2020), Associate Editor of TII & Information Fusion, founding chair of two international workshops and founding Conference Chair of IoTBDS and COMPLEXIS since the Year 2015-2016. He is the founding Conference Chair for FEMIB since the Year 2018-2019 and a founding Conference Chair for IIoTBDSC since 2019-2020. He published three books as sole authors and the editor of 2 books on Cloud Computing and related technologies. He has received Outstanding Reviewer Awards from several top journals and the Outstanding Editor Award from FGCS. He gave 18 keynotes at international conferences. He has pioneering work for this research and has been invited for several keynotes. He is widely regarded as one of the most active and influential young scientists and experts in IoT/Data Science/Cloud/Security/AI/IS, as he has the experience to develop ten different services for multiple disciplines.

Prof Carole Goble, CBE FREng, is a Professor of Computer Science at the University of Manchester. She is the PI of the myGrid, BioCatalogue and myExperiment projects and co-leads the Information Management Group (IMG) with Norman Paton. She has successfully secured funding from the European Union, the Defense Advanced Research Projects Agency (DARPA) in the United States and UK funding agencies including the Engineering and Physical Sciences Research Council (EPSRC), Biotechnology and Biological Sciences Research Council (BBSRC), Economic and Social Research Council (ESRC), Medical Research Council (United Kingdom) (MRC), the Department of Health, The Open Middleware Infrastructure Institute and the Department of Trade and Industry. Prof Goble has successfully secured funding from the European Union, the Defense Advanced Research Projects Agency (DARPA) in the United States and UK funding agencies including the Engineering and Physical Sciences Research Council (EPSRC), Biotechnology and Biological Sciences Research Council (BBSRC), Economic and Social Research Council (ESRC), Medical Research Council (United Kingdom) (MRC), the Department of Health, The Open Middleware Infrastructure Institute and the Department of Trade and Industry. Prof Goble was a recipient of the first Jim Gray e-Science Award in December 2008. Tony Hey, corporate vice-president of Microsoft External Research who sponsored the award, said Goble was chosen for the award because of her work to help scientists do data-intensive science through the Taverna workbench. Her work has won the best paper awards at the 3rd IEEE International Conference on e-Science and Grid Computing (2007) and the 11th ACM International Conference on Hypertext. In 2002 she was honoured by Sun Microsystems for her significant achievements in advancing Life Science Computing. She has given keynotes in many forums, including international conferences on Digital curation, e-Social Science, Grid Computing, Intelligent Systems for Molecular Biology, Pacific Symposium on Biocomputing, Hypertext and Hypermedia, Bioinformatics Open Source Conference (BOSC), Artificial intelligence, Systems Biology, Discovery Science, the Semantic Web, International World Wide Web Conference and Medical Informatics. Prof Goble was appointed Commander of the Order of the British Empire (CBE) in the 2014 New Year Honours for services to science. She was elected a Fellow of the Royal Academy of Engineering (FREng) in 2010. In January 2018, Goble was awarded the degree of Doctorem (Honoris Causa) by Maastricht University.

Dr. Muthu Ramachandran is currently a Principal Lecturer (Associate Professor) in the School of Computing, Creative Technologies, and Engineering at Leeds Beckett University in the UK. Prior to this, he spent eight years in industrial research at Philips Research Labs and subsequently at Volantis Systems Ltd, Surrey, the UK where Muthu has worked on various research projects for Software Engineering Applications for Consumer Electronics including software architecture design and reuse for large scale telecommunication, multi-media and car navigation systems. Currently, Muthu is leading research in the areas of Cloud Software Engineering, Big Data Software Engineering, IoT Software Engineering, Software Security Engineering, SOA, Cloud Computing, and in the main areas of Software Engineering on RE, CBSE, software architecture, reuse, quality and testing. Muthu’s first career started as a research scientist, where Muthu has worked on large scale high integrity real-time systems for Aerospace industrial applications. Muthu is the author of books: Software Components: Guidelines and Applications (Nova Publishers, NY, USA, 2008) and Software Security Engineering: Design and Applications (Nova Publishers, NY, USA, 2011). He has also widely authored and published over ten books, 100s of journal articles, over 50 book chapters and 200 conference papers on various advanced topics in software engineering, software security, cloud computing and education. Muthu has been leading conferences as chairs and as keynote speakers on global safety, security and sustainability, emerging services, IoTBDS, COMPLEXIS, Big Data, and Software Engineering for Service and Cloud Computing (SE-CLOUD 2018). Muthu is a member of various professional organizations and computer societies: IEEE, ACM, Fellow of BCS, and a Senior Fellow of HEA. Muthu’s had worked on several research projects, including all aspects of software engineering, SPI for SMEs (known as a Prism model), emergency and disaster management systems, software components and architectures, good practice guidelines on software developments, software security engineering, and service and cloud computing. Project details can be accessed at Leeds Beckett: http://www.leedsbeckett.ac.uk/staff/dr-muthu-ramachandran/ Scopus profile: https://www.scopus.com/authid/detail.uri?authorId=8676632200 Google Scholar: https://scholar.google.co.uk/citations?user=KDXE-G8AAAAJ&hl=en.

Dr. Lazarus Jegatha Deboarh completed her Ph.D. in Computer Science and Engineering in Anna University Chennai in the year 2013 and completed her Master of Engineering in the field of Computer Science and Engineering in Karunya Institute of Technology in the year 2005. She completed her Bachelor of Engineering under Madurai Kamarajar University, Madurai in the year 2002. She is presently working as an Assistant Professor at Anna University Chennai (University College of Engineering, Tindivanam), Chennai, India. She is also acting as Head (in charge) of the CSE Department at University College of Engineering, Tindivanam since 2008. She has published many quality papers in reputed journals. She had travelled to many countries like the United States of America and Malaysia to present her research works in reputed conferences. She had also visited China to deliver a guest lecture to the research students working in Artificial Intelligence in the School of Cyber Engineering, Xidian University, Xidian, China. She is a life member of ISTE. She is a doctoral committee member for many research scholars in various universities like VIT, Sathyabama University. Her key areas of interest include Data Mining, Machine Learning, Natural Language Processing, Deep learning for e-learning applications. She completed a minor research project funded by Anna University Chennai to develop an e-learning platform as a learning and management system. She had been acting as the Guest Editor during the past years for special issues in various reputed journals like Elsevier, The Online Journal of Distance Education and E-Learning, International Journal of Internet Technology and Secured Transactions. She is an active researcher and had taken initiatives in conducting international conferences, faculty development programs and symposiums at the student level. She is currently working in developing Recommendation Systems using deep learning architectures based on psychological perspectives. Her other work includes reinforcement-based learning concepts used in vehicular ad-hoc networks.

Prof. Reinhold Behringer is currently a Visiting Professor at Leeds Beckett University, where he was Professor of Creative Technology from 2005 to 2017. Since April 2019, Reinhold Behringer is working at Knorr Bremse GmbH on the development of autonomous truck systems. He previously (7/2017-3/2019) has been employed by Daimler-Protics to develop the Augmented Reality system for passenger vehicles. His R&D focus in this position was on eLearning, location-based application development and mobile devices. He has two degrees in Physics (1988: MA, SUNY Buffalo, USA. 1990: Diplom in Physics, University Würzburg, Germany) and a Ph.D. in Engineering (1996: Dr.Ing., UniBwM München, Germany). His main professional expertise is in autonomous road vehicles and real-time computer vision systems. He participated in the first US DARPA Grand Challenge (2004) and earlier in the development of the very first autonomous road vehicle, which was driving on public roads in Germany (1995). For this vehicle, he has developed a real-time computer vision system for road/lane detection and following. During his following work at Rockwell Scientific (Thousand Oaks, USA) (1996-2005), he has developed prototype systems for Augmented Reality demonstrators and multi-modal Human-Computer Interaction demonstrations, which did employ real-time computer vision approaches for scene detection and motion/orientation tracking. In his current role at Knorr-Bremse GmbH, he is involved in the development of neural network concepts for utilizing data from various sensors to enable autonomously driving trucks in SAE level 3 and higher.


Chang, V. (2014). Cloud Bioinformatics in a private cloud deployment. In Advancing Medical Practice through Technology: Applications for Healthcare Delivery, Management, and Quality (pp. 205-220). IGI Global.

Ecke, J. (2020). Labor Issues, Social Movement Studies, Social Economics, Politics & Government, Anarchist Studies. Anarchist Studies.

Gupta, A., Deokar, A., Iyer, L., Sharda, R., & Schrader, D. (2018). Big data & analytics for societal impact: Recent research and trends. Information Systems Frontiers, 20(2), 185-194.

Hosseinian-Far, A., Ramachandran, M., & Slack, C. L. (2018). Emerging trends in cloud computing, big data, fog computing, IoT and smart living. In Technology for Smart Futures (pp. 29-40). Springer, Cham.

Kim, H., Hong, H., & Yoon, S. H. (2020). Diagnostic performance of CT and reverse transcriptase-polymerase chain reaction for coronavirus disease 2019: a meta-analysis. Radiology, 201343.

Oksavik, A., Hildre, H. P., Pan, Y., Jenkinson, I., Kelly, B., Paraskevadakis, D., & Pyne, R. (2020). Future skill and competence needs.

Ranney, M. L., Griffeth, V., & Jha, A. K. (2020). Critical supply shortages—the need for ventilators and personal protective equipment during the Covid-19 pandemic. New England Journal of Medicine.

Sicari, S., Cappiello, C., De Pellegrini, F., Miorandi, D., & Coen-Porisini, A. (2016). A security-and quality-aware system architecture for Internet of Things. Information Systems Frontiers, 18(4), 665-677.

Tuli, S., Tuli, S., Tuli, R., & Gill, S. S. (2020). Predicting the Growth and Trend of COVID-19 Pandemic using Machine Learning and Cloud Computing. Internet of Things, 100222.

Vaishya, R., Javaid, M., Khan, I. H., & Haleem, A. (2020). Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews.

Wang, C. J., Ng, C. Y., & Brook, R. H. (2020). Response to COVID-19 in Taiwan: big data analytics, new technology, and proactive testing. Jama, 323(14), 1341-1342.

World Health Organization, Coronavirus disease (COVID-19) pandemic, https://www.who.int/emergencies/diseases/novel-coronavirus-2019, accessed on May 19, 2020.

More information about the hpc-announce mailing list