Mathematics and Computer Science: Contemporary Developments Vol. 7

This book covers key areas of mathematics and computer science. The contributions by the authors include exponential generalized autoregression conditional heteroscdastiicity, combine white noise model, minimum forecast errors, Levene’s test, itô integral, Brownian motion, probabilistic functional analysis, Banach space, Cauchy problem, Darboux problem, propagation domain, nonlinear oscillations, machine learning, reinforcement learning, SHapley Additive exPlanations, support vector machines, Galois ring, algebraic cryptography, zero divisor graphs, nontrivial automorphism, kamal transform, dirac delta function, initial value problem, integrable Boehmians, classical fourier integral, α- Cut Diminish Fuzzy Graph, α- Cut strong arc dominates, α- Cut strong global domination, fractional fourier transform, discrete fractional fourier transform, multiple-parameter discrete fractional fourier transform, peak signal to noise ratio, compression ratio, GeoGebra Applets, ChatGPT, first-order linear differential equations, effective learning experience, Vapnik’s quadratic programming, linear programming, support vector machine, highly efficient SVM, machine accuracy cost. This book contains various materials suitable for students, researchers, and academicians in the fields of mathematics and computer science.