2023 Invited Speakers
Dr. Dariusz Jacek Jakóbczak
Koszalin University of Technology, Poland
Biography: Dariusz Jacek Jakóbczak was born in Koszalin, Poland, on December 30, 1965. He graduated in mathematics (numerical methods and programming) from the University of Gdansk, Poland in 1990. He received the Ph.D. degree in 2007 in computer science from the Polish – Japanese Institute of Information Technology, Warsaw, Poland. From 1991 to 1994 he was a civilian programmer in the High Military School in Koszalin. He was a teacher of mathematics and computer science in the Private Economic School in Koszalin from 1995 to 1999. Since March 1998 he has worked in the Department of Electronics and Computer Science, Koszalin University of Technology, Poland and since October 2007 he has been an Assistant Professor in the Chair of Computer Science and Management in this department. His research interests connect mathematics with computer science and include computer vision, artificial intelligence, shape representation, curve interpolation, contour reconstruction and geometric modeling, numerical methods, probabilistic methods, game theory, operational research and discrete mathematics.
Speech Title: Reconstruction of Multidimensional Data on Intelligent Technology and Artificial Intelligence
Artificial Intelligence is applied for prediction and calculations of unknown values of data or coordinates. Decision makers, academicians, researchers, advanced-level students, technology developers, and government officials will find this text useful in furthering their research exposure to pertinent topics in AI, computer science, numerical analysis or operations research and assisting in furthering their own research efforts in these fields. Proposed method, called Two-Points Smooth Interpolation (TPSI), is the method of 2D curve interpolation and extrapolation using the set of key points (knots or nodes). Nodes can be treated as characteristic points of data for modeling and analyzing. The model of data can be built by choice of probability distribution function and nodes combination. TPSI modeling via nodes combination and parameter γ as probability distribution function enables value anticipation in AI, risk analysis and decision making. Two-dimensional curve is extrapolated and interpolated via nodes combination and different functions as continuous probability distribution functions: polynomial, sine, cosine, tangent, cotangent, logarithm, exponent, arc sin, arc cos, arc tan, arc cot or power function.