Artificial intelligence (AI) has the potential to transform the human resource (HR) industry by automating routine tasks, improving decision-making, and enhancing employee engagement and retention. In this paper, we explore the use of machine learning and deep learning techniques to boost employee retention in the HR industry. We review the current state of the art in AI for HR, including the use of predictive analytics, natural language processing, and chatbots for talent management and employee development. We also discuss the challenges and ethical considerations of using AI in HR, including issues of bias and the need for transparent and explainable algorithms. Finally, we present case studies of successful AI-powered HR initiatives that have demonstrated improvements in employee retention and engagement. Our findings suggest that AI has the potential to significantly enhance employee retention in the HR industry, but its implementation requires careful planning and consideration of potential risks and ethical issues.

In computer music, instrument recognition is a critical part of sound modeling. Pitch, timbre, loudness, duration, and spatialization are all components of musical sounds. All of these components play a significant part in determining the quality of the tonal sound. It is possible to alter the first four parameters, but timbre always poses a challenge [6]. It was inevitable that timbre would take center stage. Musical instruments are distinguished from one other by their distinct sound quality, independent of their pitch or volume. To distinguish between monophonic and polyphonic music recordings, this method might be used. In Musical Information Retrieval, classification plays one of the critical role. Monophonic instrument classification can be found in literature with quiet a substantial combinations of features and classifiers. Polyphonic instrument classification witnessed less references in the literature and is still an area to be explored specifically when it comes to Indian Classical domain. The present paper exactly focusses on this experimentation.  Several Indian instruments were used to produce training data sets for the proposed approach’s evaluation purposes. Among the instruments utilized are the flute, harmonium, and sitar. Statistical and spectral factors are used to classify Indian musical instruments along with the Artificial Intelligence-based methods. Hybrid features from multiple domains that extract essential musical properties are extracted. Accuracy is demonstrated through an Indian Musical Instrument SVM and GMM classification. With monophonic sounds, SVM and Polyphonic produce an average accuracy of 89% and 91%. GMM outperforms SVM in monophonic recordings by a factor of 96.33 and polyphonic recordings by a factor of 93.33, according to the results of the studies. The future scope of this recognition framework can be an Artificial Intelligence System with a system linked with the Industrial Internet of Things (IIOT) framework to develop a standalone system or application which can be used for real- time classification of instruments.