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Enhancing Adaptive Learning and Decision-making Systems Using Swarm Intelligence and Deep Learning for Advanced AI Applications


Affiliations
1 Department of Information Technology, Siddhant College of Engineering, India
2 Department of Computer Engineering, Shri Vile Parle Kelavani Mandal's Narsee Monjee Institute of Management Studies, India
3 Department of Information Technology, Universal College of Engineering, India

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The rapid development of autonomous vehicles (AVs) demands robust and adaptive AI systems capable of handling complex real-world environments. Traditional optimization and learning algorithms often struggle with dynamic and uncertain conditions, leading to suboptimal decision-making. Swarm intelligence, particularly Hawk Fire Optimization (HFO), offers a promising solution by simulating cooperative behaviors seen in nature, like hawks in hunting, to optimize decision-making processes. Coupled with advanced deep learning techniques like Federated Dropout Learning (FDL), this hybrid approach can enhance the adaptability, scalability, and efficiency of AI systems. This paper addresses the challenge of improving decisionmaking and learning in autonomous vehicles by integrating HFO with FDL. HFO optimizes parameters in real-time, allowing AVs to adapt rapidly to changing environments. Federated Dropout Learning, a variant of federated learning, further improves system resilience by sharing learning across distributed nodes while minimizing communication overhead and enhancing privacy. By combining these methods, the proposed system ensures robust performance in unpredictable scenarios. Experimental results show that the hybrid model outperforms traditional methods in terms of decision accuracy, response time, and energy efficiency. Specifically, the system achieved a 12% improvement in decision accuracy, reduced processing time by 18%, and cut energy consumption by 22%, compared to standard algorithms. These findings suggest that the combination of HFO and FDL can significantly improve the performance of autonomous vehicles, providing safer and more efficient AI-driven navigation.

Keywords

Swarm Intelligence, Hawk Fire Optimization, Federated Dropout Learning, Autonomous Vehicles, Adaptive Decision-Making
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Abstract Views: 36




  • Enhancing Adaptive Learning and Decision-making Systems Using Swarm Intelligence and Deep Learning for Advanced AI Applications

Abstract Views: 36  | 

Authors

Brijendra Gupta
Department of Information Technology, Siddhant College of Engineering, India
Atul Dusane
Department of Computer Engineering, Shri Vile Parle Kelavani Mandal's Narsee Monjee Institute of Management Studies, India
Neeta P. Patil
Department of Information Technology, Universal College of Engineering, India
Yogita Deepak Mane
Department of Information Technology, Universal College of Engineering, India
Sanketi Raut
Department of Information Technology, Universal College of Engineering, India
Akshay Agrawal
Department of Information Technology, Universal College of Engineering, India

Abstract


The rapid development of autonomous vehicles (AVs) demands robust and adaptive AI systems capable of handling complex real-world environments. Traditional optimization and learning algorithms often struggle with dynamic and uncertain conditions, leading to suboptimal decision-making. Swarm intelligence, particularly Hawk Fire Optimization (HFO), offers a promising solution by simulating cooperative behaviors seen in nature, like hawks in hunting, to optimize decision-making processes. Coupled with advanced deep learning techniques like Federated Dropout Learning (FDL), this hybrid approach can enhance the adaptability, scalability, and efficiency of AI systems. This paper addresses the challenge of improving decisionmaking and learning in autonomous vehicles by integrating HFO with FDL. HFO optimizes parameters in real-time, allowing AVs to adapt rapidly to changing environments. Federated Dropout Learning, a variant of federated learning, further improves system resilience by sharing learning across distributed nodes while minimizing communication overhead and enhancing privacy. By combining these methods, the proposed system ensures robust performance in unpredictable scenarios. Experimental results show that the hybrid model outperforms traditional methods in terms of decision accuracy, response time, and energy efficiency. Specifically, the system achieved a 12% improvement in decision accuracy, reduced processing time by 18%, and cut energy consumption by 22%, compared to standard algorithms. These findings suggest that the combination of HFO and FDL can significantly improve the performance of autonomous vehicles, providing safer and more efficient AI-driven navigation.

Keywords


Swarm Intelligence, Hawk Fire Optimization, Federated Dropout Learning, Autonomous Vehicles, Adaptive Decision-Making