Research Publications

Abstract: Deep learning has revolutionized the field of artificial intelligence and machine learning by enabling the development of complex neural network architectures capable of solving a wide range of real-world problems. Weight initialization in deep learning is a critical phase in the training process that involves assigning initial values to neural network parameters. The challenge at hand is finding the correct balance in initializing weights - not setting them too high or too low - to promote effective training. This paper compares various weight initialization techniques to determine their impact on model performance.

Abstract: Customer segmentation is a crucial task in marketing and business strategy, enabling organizations to better understand their customer's behavior and needs. In today's environment, it is critical for businesses to segment their clients to remain competitive. The aim of this research is to predict customer segmentation using the Random Forest algorithm in RapidMiner studio based on various customer attributes. This research illustrates the potential of customer segmentation to promote customer engagement, loyalty, and overall business success, demonstrating that Random Forest effectively integrates predictions from multiple decision trees to improve accuracy and generalization.