Predicting The Effectiveness Of It Employee Training And Development Programs Using Convolutional Neural Networks
DOI:
https://doi.org/10.64252/vw0mcm57Keywords:
Employee Training,IT Industry,Convolutional Neural Networks,Effectiveness,Skill Gap and Diversity and Inclusion, Reskilling Upskilling, Employee Motivation, Innovation.Abstract
The primary concern centers on evaluating emerging training technologies like virtual reality, augmented reality, and AI-powered learning platforms. Our goal is to assess their impact on elements such as the retention of knowledge, the acquisition of skills, and the overall performance of employees. Additionally, we will analyze the cost-effectiveness of implementing these technologies. The second focal point addresses the significant skills gap evident in emerging IT fields like artificial intelligence, block chain, and cyber security. To rectify this gap, our focus is on identifying specific skill deficiencies and devising precise training and development strategies. Convolutional Neural Networks (CNNs) will be deployed to predict the efficacy of these strategies. Lastly, we explore the consequences of diversity and inclusion initiatives embedded in training programs. Our objective is to quantify how these efforts influence employee performance, innovation, and retention. Furthermore, we delve into the formulation and execution of training programs that promote diversity and inclusion, scrutinizing their impact on the culture and productivity of organizations.Through the utilization of CNN models for predictive analysis, this research seeks to offer data-driven insights into the efficiency of IT employee training and development programs. These insights aim to contribute to the reinforcement of the IT workforce's competitive edge and their adaptability to the perpetually evolving technological landscape.