This ground breaking volume presents a unified exploration of hybrid and quantum-inspired approaches to time series forecasting, digital image classification and optimization. Bridging fuzzy logic, neutrosophic theory, granular computing, and quantum computation, the book introduces novel models that enhance predictive accuracy and optimization efficiency under uncertainty and complexity.
From a hybrid Neutrosophic-PSO model for uncertain time series (Chapter 1) to quantum-inspired forecasting frameworks (Chapter 3), the work addresses indeterminacy with cutting-edge methodologies. Evolutionary techniques and granular computing further refine forecasting accuracy in vague data contexts (Chapter 2). The book also pioneers the fast forward quantum optimization algorithm, analyzing its convergence properties and showcasing its efficacy across diverse domains, from unconstrained optimization problems (Chapter 4) and solving the Traveling Salesman Problem using quantum wavefunction optimization algorithm (Chapter 5) to the tuning of convolutional neural networks for digital image classification using the fast forward quantum optimization algorithm (Chapter 6).
A vital resource for researchers and practitioners in data science, artificial intelligence, and quantum optimization, this book opens new avenues in modeling, forecasting, and problem-solving under uncertainty.
