Kostas Nikolopoulos, Professor in Business Information Systems, Durham University, U.K.
Prof. Konstantinos Nikolopoulos is the Professor in Business Information Systems and Analytics at Durham University Business School. He is an Associate Editor of Oxford IMA “Journal of Management Mathematics” and the “Supply Chain Forum, an International Journal” (Taylor & Francis); he is also the Section Editor-In-Chief for the “Forecasting in Economics and Management” section in the MDPI open access journal “Forecasting”. Konstantinos’ work has been consistently appearing in the International Journal of Forecasting (29 outputs) but also in journals for broader audiences including the Journal of Operations Management, the European Journal of Operational Research, and the Journal of Computer Information Systems.
TOPIC: Long-Term Forecasting for Policymaking with Structured Analogies
We provide forecasts on how the Kingdom of Saudi Arabia can reduce its dependency on the oil sector. This is a very timely quest, given the negative prices of oil in the peak of the COVID-19 pandemic. The forecasting task involves estimating the contribution of the top-5 sectors of the GDP in 20-years-time. The study involves 4 sequential experiments and 110 participants with increasing levels of expertise: novices (19), semi-experts (73), and experts (18). The first two experiments involved forecasting individually with Unaided Judgment and Structured Analogies; the third formed Interaction Groups. In these experiments, participants did not know for which country they produced forecasts; in the final experiment, we revealed the country name and rerun the third experiment. We demonstrate that the proposed forecasting framework can comprehensively identify areas of long-term GDP diversification, and inform policymaking; furthermore, it can quantify the contribution of each GDP sector to the economy.
Yu-Dong Zhang, Professor in Knowledge Discovery and Machine Learning, University of Leicester, U.K.
Prof. Yu-Dong Zhang serves as Professor with Department of Informatics, University of Leicester, UK. His research interests include deep learning and medical image analysis. Prof. Zhang is the Fellow of IET (FIET), and Senior Members of IEEE and ACM. He was included in “Most Cited Chinese researchers (Computer Science)” by Elsevier from 2014 to 2018. He was the 2019 recipient of “Highly Cited Researcher” by Web of Science. He won “Emerald Citation of Excellence 2017” and “MDPI Top 10 Most Cited Papers 2015”. He was included in “Top Scientist” in Guide2Research. He is the author of over 200 peer-reviewed articles, including more than 30 “ESI Highly Cited Papers”, and 2 “ESI Hot Papers”. His citation reached 17,158 in Google Scholar, and 10,183 in Web of Science. He has conducted many successful industrial projects and academic grants from NSFC, NIH, Royal Society, EPSRC, MRC, and British Council.
TOPIC: Artificial Intelligence and Big Data in COVID-19 Diagnosis
COVID-19 is a pandemic disease that has already caused more than 631 million confirmed cases and more than 4.97 million deaths until 29/Oct/2021. CT scans are a medical imaging technique used in radiology to get detailed images of the body noninvasively for diagnostic purposes. CT is now one of the main techniques to diagnose COVID-19 patients. This presentation will discuss the clinical biomarkers, vaccines, and diagnosis procedures of CT-based COVID-19 cases. Also, this presentation will provide the recent progress of different big data, artificial intelligence, and deep learning techniques (transfer learning, graph neural network, attention network, explainable deep learning, ensemble learning, etc.) in chest CT-based COVID-19 diagnosis. Fusion techniques of chest X-ray and chest CT will also be introduced. Two cloud-based web apps are shown to elucidate how to develop remote intelligent diagnoses on chest CT images. Two other chest-related diseases: secondary pulmonary tuberculosis and community-acquired pneumonia, will be covered in this presentation.