“Big Data Affective and Intelligent Decision-Making Support”
Professor A. Kaklauskas, Vilnius Gediminas Technical University, Lithuania
The most important elements of intelligent decision-making are exhaustive big data, information and knowledge collection, extraction, analytics and rational decision making and its adaptation to the changing micro-, mezzo- and macro- environments. However, it is sufficiently difficult to put into practice lately. Decision makers end up having to examine scrupulously somewhat more big data, information and knowledge in the world of today than they ever had to previously or falling into ill-defined situations, which interfere with their compiling an array of alternatives and making appropriate decisions. For example, a Covid-19 pandemics is much like a raging hurricane, and there is no way to hide from it. Efforts of many decision makers to change the direction of the storm are futile. A top notch decision maker will not waste time for no reason or make unnecessary moves. Such a professional will not get excited about something he/she cannot change—for example, the critical mezzo- and macro- economic situations.
PhD DrSc A. Kaklauskas is a Professor at Vilnius Gediminas Technical University, in Lithuania; Member of the Research Council of Lithuania; Member the European Open Science Cloud Steering Board; Member of the Science Europe working group on Data Sharing and Supporting Infrastructures; Head of the Department of Construction Management and Property; laureate of the Lithuanian Science Prize; member of the Lithuanian Academy of Sciences, editor in chief of Journal of Civil Engineering and Management, editor of Engineering Applications of Artificial Intelligence, an international journal and associate editor of journal “Ecological Indicators”. He contributed to nine Framework and five Horizon 2020 programmes projects and participated in over 30 other projects in the EU, US, Africa and Asia. The Belarusian State Technological University (Minsk, Belarus) awarded him an Honorary Doctorate in 2014. His publications include nine books and 162 papers in Web of Science Journals. Fifteen PhD students successfully defended their theses under his supervision. The Web of Science H-Index of Prof. A. Kaklauskas is 31. Web of Science Journals have cited him 3032 times and average citations per article – 18.72. His areas of interest include affective computing, Big Data, analytics, data mining and computational intelligence, AI ethics, neuromarketing, intelligent tutoring systems; affective intelligent tutoring systems; Massive open online courses (MOOCS); affective Internet of Things; smart built environment; intelligent event prediction, opinion mining, intelligent decision support systems, life cycle analyses of built environments, energy, climate change, resilience management, healthy houses, sustainable built environments, text analytics, intelligent library, internet of things, etc.