• Dr K L Sumathy

This study investigates the integration of cognitive AI systems to improve human-machine collaboration in complex decision-making situations. The study focuses on AI applications in various domains such as autonomous vehicles, healthcare, and predictive maintenance, where decision-making is essential. We evaluate four major AI algorithms (decision trees, support vector machines, neural networks, and genetic algorithms) and their performance on real-time decision-making tasks. Our results show that neural networks outperform other algorithms, achieving 92% accuracy in prediction tasks, while decision trees and genetic algorithms demonstrate 85% and 88% accuracy, respectively. Support vector machines achieve 89% accuracy in classification tasks. Moreover, the research highlights how AI improves human decision-making by means of real time processing in its reasoning and optimization along with pattern recognition. Comparing our approach to that of other researchers suggests that AI-assisted decision systems can improve human decision-making by a significant margin, particularly in dynamic and multidimensional environments. Challenges related to trust, transparency, and ethics are also addressed. The results suggest that further advances in cognitive AI systems will play a central role in the design of future decision support tools, fostering a more collaborative and effective relationship between humans and machines.

Cognitive AI, Decision-Making, Neural Networks, Human-Machine Collaboration, Predictive Maintenance.

"Congnitive AI Systems_ Enhancing Human-Machine Collaboration in Complex", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.g151-g156, March-2025, Available :http://www.jetir.org/papers/JETIR2503618.pdf

"Congnitive AI Systems_ Enhancing Human-Machine Collaboration in Complex", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppg151-g156, March-2025, Available at : http://www.jetir.org/papers/JETIR2503618.pdf