Bøger af Jovan Pehcevski

The generative AI is especially powerful in several areas, such as text generation (product description, article writing), image and video generation (AI-generated pictures and videos for marketing industry), and voice and sound generation (for film industry). This book edition covers different topics of generative AI models, including: image generation techniques, video generation techniques, speech / voice generation techniques, and societal and ethical issues of these models.

This book covers different topics from data analysis and information processing, including data analytics methods, big data methods, data mining methods, and information processing methods. Section 1 focuses on data analytics methods, describing data analytics in mental healthcare, a case study on data analytics and machine learning accuracy, a survey from a big data perspective on data modeling and data analytics, big data analytics for business intelligence in accounting and audit, and a knowledge-based approach on big data analytics in immunology. Section 2 focuses on big data methods, describing an integrated real-time big data stream sentiment analysis service, the influence of big data analytics in the industry, big data usage in the marketing information systems, a review of big data for organizations, and an application research of big data technology in audit field. Section 3 focuses on data mining methods, describing a short review of classification algorithms accuracy for data prediction in data mining applications, different data mining approaches based on medical text data, the benefits and challenges of data mining in electronic commerce, and a research study on realization of petrophysical data mining based on big data technology. Section 4 focuses on information processing methods, describing application of spatial digital information fusion technology in information processing of national traditional sports, effects of quality and quantity of information processing on design coordination performance, a neural network optimization method and its application in information processing, and information processing features that can detect behavioral regimes of dynamical systems.

This book covers topics from numerical and symbolic computing, including the main numerical and symbolic methods, schemes and applications. Section 1 focuses on numerical computing methods, describing matrix differential equations for solving systems of linear algebraic equations, a numerical problem encryption for high-performance computing applications, a numerical verification method of solutions for elliptic variational inequalities, a trigonometric numerical integrator for solving first order ordinary differential equation, and augmented Lagrangian methods for numerical solutions to higher order differential equations. Section 2 focuses on symbolic computing methods, describing a learning network assisted by means of symbolic computation, multiple factorial analysis of symbolic data, combining symbolic tools with interval analysis, and application to solve robust control problems, symbolic and graphical computations of a class of slightly perturbed equations. Section 3 focuses on numerical computing applications, describing non-negativity preserving numerical algorithms for problems in mathematical finance, a continuum approach using numerical simulations of microscale gas flows, numerical methods in electro-cardiology, numerical simulation of the blood flow through a brain vascular aneurysm with an artificial stent using the SPH method, and numerical modeling of soil water flow and nitrogen dynamics in a tomato field irrigated with municipal wastewater. Section 4 focuses on symbolic computing applications, describing symbolic time series analysis and its application in social sciences, application of symbolic computation in nonlinear differential-difference equations, symbolic modelling of dynamic human motions, and a framework for bridging the gap between symbolic and non-symbolic AI.