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

Enhanced Antimicrobial activities of Hybrid ZnMgAlO nanocomposite by soft chemical method

In the present investigation, ZnMgAlO nanoparticles were prepared by soft chemical method.  The synthesized NPs were analyzed by XRD and SEM EDAX. ZnMgAlO crystal structure was confirmed through powder XRD technique as hexagonal wurtzite structure. The surface morphology was analyzed from SEM images. Finally, antimicrobial activity of all the synthesized samples was tested against Bacillus subtilis and Chlamydia trachomatis bacteria and Xylaria hypoxylon, Fistulina hepatica fungus. The observed results showed good anti-bacterial and anti-fungal activities. Â

Qualifying System F-sub

Type qualifiers offer a lightweight mechanism for enriching existing type systems to enforce additional, desirable, program invariants. They do so by offering a restricted but effective form of subtyping. While the theory of type qualifiers is well understood and present in many programming languages today, polymorphism over type qualifiers is an area that is less examined. We explore how such a polymorphic system could arise by constructing a calculus System F<:Q which combines the higher-rank bounded polymorphism of System F<: with the theory of type qualifiers. We explore how the ideas used to construct System F<:Q can be reused in situations where type qualifiers naturally arise -- in reference immutability, function colouring, and capture checking. Finally, we re-examine other qualifier systems in the literature in light of the observations presented while developing System F<:Q.Comment: 24 page

Experimental and Analytical Study of Masonry Subjected to Uniaxial Cyclic Compression

Structural evaluation of masonry against dynamic seismic actions invariably requires appropriate cyclic compression constitutive models. However, not many research studies have been dedicated to date to investigate the cyclic compression behaviour of masonry. Therefore, series of experimental investigation followed by analytical model verification were employed in this research to better understand the cyclic compression characteristics of masonry. Twelve masonry wallettes were experimentally tested under cyclic compression loading with different unit-to-mortar assemblies, which are commonly found in masonry structures. The experimental results indicated that the cyclic compression behaviour is greatly influenced by the masonry compressive strength and deformation properties. Thereafter, the ability of five literature analytical models to predict the masonry structural response under cyclic compression loading was investigated. The advantages and limitations of these models are presented and discussed, and the most appropriate analytical model to define the cyclic compression characteristics of masonry has been evaluated and reported. The suggested analytical model is shown to predict the cyclic compression characteristics of different masonry assemblies such as the envelop response, the stiffness degradation, the plastic strain history of the unloading and reloading stages