Dr. Dariusz Jacek Jakbczak
Biography
Dr. Dariusz Jacek Jakbczak was born in Koszalin, Poland, on December 30, 1965. He completed his M.A. in Mathematics (numerical methods and programming) at the University of Gdansk in 1990 and earned his Ph.D. in Computer Science from the PolishJapanese Institute of Information Technology in Warsaw in 2007.
From 1991 to 1994, he served as a civilian programmer at the High Military School in Koszalin. Between 1995 and 1999, he taught mathematics and computer science at the Private Economic School in Koszalin. Since 1998, he has been associated with the Department of Electronics and Computer Science at Koszalin University of Technology, and from 2007 onwards, he has served as an Assistant Professor in the Chair of Computer Science and Management. His interdisciplinary research bridges mathematics and computer science.
Research Interest
Artificial Intelligence
Computer Vision
Shape Representation
Curve Interpolation
Contour Reconstruction and Geometric Modeling
Numerical Methods
Probabilistic Methods
Game Theory
Operations Research
Discrete Mathematics
Abstract
"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. The proposed method, called Two-Points Smooth Interpolation (TPSI), is used for 2D curve interpolation and extrapolation using key data points (nodes). These nodes serve as characteristic points for modeling and analysis. The method builds models based on chosen probability distribution functions and node combinations. TPSI modeling, combined with parameter ? as the distribution function, facilitates value prediction, risk analysis, and decision-making. The interpolation functions can include polynomial, trigonometric (sine, cosine, tangent, etc.), logarithmic, exponential, and other continuous probability distributions.
