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

Combined harmonic reduction and DC voltage regulation of a single DC source five-level multilevel inverter for wind electric system

Wind power generation has increased in the past twenty years due to the development of power electronic converters. Power generation through wind has advantages over other renewable sources, such as having more efficiency, being pollution-free, and its abundant availability. Power electronic converters play a vital role in the wind energy conversion system. This paper presents a wind-electric system with a permanent magnet synchronous generator, diode rectifier, DC-DC converter (buck-Boost or Cuk converter), and a three-phase five-level inverter. The five-level inverter is a modified form of a cascaded H-bridge inverter that uses a single DC source as an input irrespective of several levels and phases. As the wind speed changes, the Permanent Magnet Synchronous Generator (PMSG) voltage and frequency changes, but for practical applications, these changes should not be allowed; hence, a voltage controller is used that maintains the output voltage of a DC converter, andthus a constant AC output is obtained. The DClink voltage is maintained at the desired voltage by a Proportional plus Integral (PI)-based voltage controller. The DC link voltage fed to the multilevel inverter (MLI) is converted to AC to feed the load. The MLI is controlled with a new Selected Harmonic Elimination (SHE), which decreases the total harmonic distortion (THD). The system is simulated with an Resistive plus Inductive (RL) load and is tested experimentally with the same load;the results prove that the Cuk converter has a better efficiency compared to the Buck-Boost converter, and the system has less THD when compared with the conventional SHE Pulse Width Modulation (PWM) technique