Deep study on autonomous learning techniques for complex pattern recognition in interconnected information systems

Artificial Intelligence (AI) and Machine Learning (ML) are being used more and more to handle complex tasks in many different areas. As a result, interconnected information systems are growing, which means that autonomous systems are needed to help them adapt, find complex patterns, and make better decisions in areas like cybersecurity, finance, healthcare, authentication, marketing, and supply chain optimization. Even though there have been improvements in self-learning methods for complex pattern recognition in linked information systems, these studies still do not have a complete taxonomy that sorts these methods by how they can be used in different areas. It is hard to fully understand important factors and do the comparisons that are needed to drive the growth and use of autonomous learning in linked systems because of this gap. Because these methods are becoming more important, new study is looking into how they can be used in different areas. Still, recent study shows that we do not fully understand the environment of other uses for independent learning methods, which encourages us to keep looking into it. We come up with a new classification system that puts applications into six groups: finding cybersecurity threats, finding fraud in finance, diagnosing and monitoring healthcare, biometric authentication, personalized marketing, and optimizing the supply chain in systems that are all connected. The latest developments in this area can be seen by carefully looking at basic factors like pros and cons, modeling setting, and datasets. In particular, the data show that Elsevier and Springer both put out a lot of important papers (26.5 % and 11.8 %, respectively). With rates of 12.9 %, 11 %, and 8 %, respectively, the study shows that accuracy, mobility, and privacy are the most important factors. Tools like Python and MATLAB are now the most popular ways to test possible answers in this growing field.