Artificial Intelligence in Human Resource Management: Advancements, Implications and Future Prospects

The present condition, challenges, and potential applications of artificial intelligence (AI) in human resource management (HRM) are all explored in this survey article. As an innovation, artificial intelligence (AI) has the potential to completely revolutionize several facets of human resource management (HRM). Examining the usage of AI-powered tools and systems in different HR processes, the present situation with AI in HRM is examined. These encompass learning and development, performance management, employee engagement, and recruiting. The use of AI algorithms and machine learning approaches to automate regular HR operations, analyze vast amounts of employee data, and provide insightful data to aid decision-making is addressed in this article. However, integrating AI into HRM also poses a number of difficulties that must be resolved. Bias, privacy issues, and transparency are just a few of the ethical and legal ramifications of using AI in decision-making processes that are discussed in this survey. The study emphasizes how accountability and fairness must be maintained in AI systems by responsible design, oversight, and periodic evaluation. With an emphasis on job displacement and workforce reorganization, the possible influence of AI on the human workforce is also explored. To effectively traverse this change, strategies including work role redefinition, employee up skilling, and establishing a collaborative atmosphere between humans and AI are suggested. The possible advantages and breakthroughs that AI might bring to HRM practices are highlighted as the future perspectives of AI in HRM are examined. As new applications for AI in HRM, sentiment analysis, predictive analytics, intelligent decision support, and personalized employee experiences are all highlighted. In order to fully realize the promise of AI in HRM, the study underlines the significance of data infrastructure, data governance frameworks, and a data-driven culture. Overall, this survey study offers an in-depth review of the existing situation, difficulties, and prospects for AI in HRM. It aggregates current information, identifies research gaps, and gives practitioners and scholars new perspectives on how AI will fundamentally alter the way HRM activities are carried out in the future

Heterogeneous Ensemble Variable Selection To Improve Customer Prediction Using Naive Bayes Model

The analysis of customer patterns and behaviours is essential for all businesses, as the customer is the sole source of revenue. Understanding customer patterns and behavior enables businesses to enhance their business processes and customer happiness. The availability of voluminous client datasets within organizations facilitates efficient customer analysis. Yet, the inclusion of interrelated, irrelevant, as well as missing factors leads to a poor forecast of the dataset. Feature selection techniques are investigated in order to handle the problem. Objective of feature selection is to pick the pertinent variables from a complete set of associated, irrelevant, and missing variables. In general, FS is classified into 3 types: filter, wrapper, & hybrid method. The filter method is a quick one, but the variables used are ineffective. Similarly, a wrapper method is effective yet computationally inefficient. In this study, an ensemble feature selection strategy is presented and tested to circumvent the issue with these feature selections. There are two techniques to ensemble FS: one is homogenous and the other is heterogeneous. This study employs a heterogeneous ensemble feature selection method. In the suggested method, the learning dataset is applied to five distinct filter FS approaches, and the ranked attributes that result are aggregated using two distinct methods: the mean method and the min method. Relevant variables are chosen to further build the final sorted qualities using the cut off value as a guide. As the HEVS technique's filter approach simply ranks the variables, it is necessary to select the variable subset cut off value. The experimental technique is conducted from two distinct vantage points: Heterogeneous ensemble variable selection with Naive Bayes and Naive Bayes without variable selection. In the end, the outcomes that were obtained via the use of the two different approaches are compared using different factors. The experimental results demonstrate that the suggested HEVS method outperforms the usual Naive Bayes model. As relevant variables are included when modeling using NB, the computational complexity of this proposed methodology is also minimized