Year: 2025
Type: article
Abstract: : The development of cognitive algorithms that emulate human-like reasoning represents a transformative advancement in artificial intelligence (AI). This paper investigates the principles, models, and evaluation of cognitive algorithms within machine learning systems. It emphasizes algorithmic structures that mimic cognitive processes such as inference, abstraction, generalization, and decision-making. By combining symbolic reasoning with neural learning architectures, Researcher demonstrated enhanced performance in tasks requiring interpretability and adaptability. Using regression and predictive modeling techniques, the paper evaluates the accuracy, transparency, and flexibility of selected cognitive algorithmic models. Empirical data, visualizations, and comparative performance tables substantiate the findings, offering new insights into creating more explainable and effective AI systems. Keywords: Cognitive algorithms, human-like reasoning, machine learning, artificial intelligence, predictive modeling, regression analysis, algorithmic cognition, interpretability, deep learning, decision-making
Author: Murali Krishna Pasupuleti
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4410635040