Machine Mentality and its Progression Through the Complexity of Mathematical Modules
M. Preetha, Mukul Mishra, Sujayaraj Samuel Jayakumar, Deepika Dongre, Nainan Thangarasu, Noopor Pandey · 2024
This work examines how machine awareness has changed over time by examining mathematical module complexity using a new technique that combines historical study, real-world testing, and sophisticated machine learning algorithms. We can completely comprehend how mathematical complexity influences machine intelligence with the provided strategy. Our method begins with a detailed examination of AI's mathematical underpinnings' evolution. This historical perspective shows how arithmetic has affected robot thinking. Second, real-world trials evaluate machine learning and mathematical models. These approaches are evaluated on accuracy, precision, memory, F1 score, mean squared error, and computing efficiency. The comparison shows that the recommended strategy outperforms six well-known strategies that employ many criteria. Results show the proposed strategy works. Traditional approaches can't match its 95% accuracy. The approach is more beneficial than normal methods since it has superior accuracy, memory, and F1 scores. A smaller mean squared error indicates that the recommended strategy predicts better than current methods. Its 7.2 millisecond processing time makes it the most computing-efficient option. This unique methodology lets us understand how machine intelligence has evolved and choose the optimal solutions for particular occupations. The recommended technique is effective for studying the complex link between mathematical modules and AI progress since it incorporates historical background and cuttingedge algorithms.