Influencing Factors Of Recruitment And Selection Process Through Artificial Intelligence- Multiple Regression Analysis

The utilization of artificial intelligence (AI) in the recruitment and selection processes within the Information Technology (IT) industrieshas become increasingly prevalent. This trend is driven by the recognition of the potential benefits that AI can offer in streamliningthese processes, making them more efficient, and enhancing decisionmaking. The researcher has found the influencing factors ofrecruitment and selection processes prevailed in the Information Technology (IT) industries in Chennai. The primary data collectedfrom 400 respondents based on simple random sampling method. The objective of this study is to find the factors influencing the artificial intelligence applications in recruitment and selection practices in IT Companies in Chennai. The authors have concluded that the AI applications in recruitment and selection practices in IT companies in Chennai are driven by the need for efficiency, objectivity, and inclusivity. Smart analysis and task automation enhance the recruitment process, while data-based decision-making and a focus on diversity and inclusion contribute to fair and effective candidate selection

Impact Of Artificial Intelligence In Recruitment And Selection Practices In Information Technology (It) Companies In Chennai – Principal Component Analysis

This study investigates the transformative impact of artificial intelligence (AI) on recruitment and selection practices within Information Technology (IT) companies in Chennai. Examining relevant literature, researchers, including Manthena, Choudhary,Sharma, Malik, Hemalatha, Vedapradha, Rajesh, and Soni, advocate for the strategic integration of AI as a valuable tool in optimizinghuman resource management. Utilizing Principal Component Analysis (PCA), the study identifies nuanced insights into AI's influence on recruitment and selection. In recruitment, smart analysis and task automation play a prominent role, emphasizing positive contributions from employee referrals and data aggregation. Selection practices reveal higher impacts in internal mobility, automating tasks, and databased decision-making. The sustained positivity across dimensions underscores the constructive roles of key variables, urging responsible AI adoption for continued enhancement in human resource practices

Genomic Approaches for Climate Resilience Breeding in Oats

Editors: Chittaranjan Kole.Oat (Avena sativa L.), ranking sixth in world cereal production, is primarily produced as a multipurpose crop for grain, pasture, and forage or as a rotation crop in many parts of the world. Recent research has elevated its potential dietary value for human nutrition and health care. Oats are well adapted to a wide range of soil types and can perform on acid soils. World oat production is concentrated between latitudes 35–65º N, and 20–46º S. Avena genomes are large and complex, in the range of 4.12–12.6 Gb. Oat productivity is affected by many diseases, although crown rust (Puccinia coronate f. sp. avenae) and stem rust (Puccinia graminis f. sp. avenae) are the key diseases worldwide. The focus of this chapter is to review the major developments and their impacts on oat breeding, especially on the challenges posed by climate or environmental changes (biotic and abiotic stresses mainly) for oat cultivation. Next-generation breeding tools will help to develop approaches to genetically improve and manipulate oat which would aid significantly in oat enhancement efforts. Although, oat biotechnology has been advanced at a similar pace as the rest of cereals, it lags still behind. More genomic tools, from genomic assisted breeding to genome editing tools are needed to improve the resources to improve oats under climate change in the next few decades