Optimizing Tax Compliance and Fraud Prevention through Intelligent Systems: The Role of Technology in Public Finance Innovation
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Abstract
Intelligent systems are the incorporation of self-adjusting and self-learning capabilities for automating a multitude of applications. Such systems have recently been actively explored in various domains and are proven to perform a given task in varying situations and environments more reliably and much faster than conventional ones, also complying with legal and normative constraints. Intelligent systems with a chance of ultimately winning a limited approach have already revolutionized a number of fields, such as facial recognition, automobile driving, and gaming.
Moreover, new approaches to quantum set-fuzzy theory, accessed by smaller units called qubits rather than bits, are considered a candidate for an intelligent system capable of very effectively analyzing the signal images in a broader range of data formats. The underlying principle of controllable wave superposition instead of exclusive allocation such as probabilistic assignment of one true value from either 0 or 1 with additional more powerful quantum gates providing the system with more scenario-changing opportunities significantly improved the performance, versatility, and capability of signal analysis approaches to machine learning tasks.
Tasks, signals, and domains of diverse classes of data formats are addressed. Mathematical ramifications of such data signal classes are thoroughly reviewed together with an analysis of their exploitation for specific applications. An overview of the available sources of qubit systems, signal generators, and data set families together with the respective advantages and disadvantages are compared. The set of existing quantum algorithms with an emphasis on quantum machine learning routines and their application for microscopy/hyper-spectral imaging data analysis, computation visualization/dynamic systems identification, control, and optimal design are summarized. The review summarizes the current state-of-the-art in this fast-grown area of research and points out promising research avenues still untackled.