Enhancing Human Resources Management With AI-Powered Talent Acquisition Strategy Using Deep RESNET

In human resource management (HRM), artificial intelligence (AI) is becoming an increasingly popular tool for assisting firms in speeding up and improving the processes involved in talent acquisition. Finding and hiring exceptional people quickly and effectively is difficult using the standard HRM approach. We propose integrating Deep ResNet, a cutting-edge deep learning architecture that possesses exceptional feature extraction capabilities, with the systems that are already in place as a solution to this problem. While research into how Deep ResNet might be used to the talent acquisition process is lagging behind, the use of artificial intelligence in human resource management is becoming an increasingly popular trend. This research presents a novel approach to talent acquisition in HRM by utilising Deep Residual Networks (ResNet). The research employs a stringent research approach that incorporates data analysis, machine learning techniques, and real-world case studies. The purpose of this methodology is to establish whether or not the proposed AI-powered talent acquisition strategy is successful. Deep ResNet will be trained on a variety of datasets to increase its ability to locate and grade potential candidates. As a direct result of this, both the accuracy and the efficiency of the process of obtaining fresh talent are significantly improved. The purpose of this study is to provide empirical evidence confirming the efficacy of Deep ResNet in strengthening human resource management practises.