Dr.Punamkumar Hinge
@lavasa.christuniversity.in
Assistant Professor, School of business management
christ university,banglore
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
C.Balarama Krishna, Meeta Joshi, K. Sathesh Kumar, Nalla Bala Kalyan, Shivani Bhardwaj, and Punamkumar Hinge
IEEE
The strategic alignment between organisational objectives and human resource management is stronger in contemporary organizations. As deep learning methods and machine learning solutions play a larger role in managing human resource management operations, organizations are focusing on more applicable sets of solutions. Models based on machine learning are now making progress in a variety of HRM-related fields. Machine learning is being used in human resource management to anticipate who will remain and who will depart the company, as well as to gauge workers' interest in their specific organisation.Data scraping methods are used to extract the data, which is then saved in CSV format. With the aid of ML algorithms, the many characteristics in the data acquired using this method may be used to make predictions. The management may develop a strategy to keep a deserving person in the organization by using the analysis to draw conclusions about who will remain or depart the company.We used a variety of methods in our investigation, including feature scaling and SMOTE. The recommended techniques, such as random forest and XG boost classifier, are supported by the findings. We'll arrive to a judgment based on the accuracy rate (%) numbers for the results generated by the offered approaches.
Geetha Manoharan, Vinay Kumar Sharma, Melanie Lourens, Akshay Kumar, Bijaya Bijeta Nayak, and Punamkumar Hinge
IEEE
Artificial intelligence (AI), deep learning (DL), and automated processes have been quickly advancing, considerably boosting the significance of information technology (IT) within corporate procedures. Rising AI-based responses in human resource management (HRM) have been rapidly being used to handle time-consuming and difficult activities within HRM capabilities.Workers in most businesses are currently experiencing high work stress, which has an adverse impact on efficiency, security, and wellness. To cope with personal stress, it is critical for the HR sector to handle stress efficiently, connecting the barrier between administration and stressed personal. This research creates 2 stress prediction frameworks and also 2 neural network designs. This research use data from personal to train these 2 stress prediction systems. Investigations on 2 real-world databases, indicate that the suggested DL-driven method can accurately predict personal’ stress condition with 71.2 percent accuracy in the classification method model and 11.1 prediction decline in the regression framework. The HRM of businesses can be enhanced by precisely forecasting personal’ stress levels using this approach.
Manikandan Rajagopal, BaigMuntajeeb Ali, S.Sharon Priya, W.Aisha Banu, Madhavi G. M, and Punamkumar
IEEE
Online education has become a popular choice for learners of all ages and backgrounds due to its accessibility and flexibility. However, providing personalized learning experiences for a diverse range of students in online education can be challenging. Machine learning methods can be used to provide personalized learning experiences and improve student engagement in online education. In this case study, We're going to do some research on machine learning. methods in an online education platform. The platform provides courses in various subjects and is designed to be accessible to students from all over the world. The platform collects data on student behavior, such as the courses they enroll in, the time they spend on each course, and their performance on assignments and quizzes. We will explore several machine learning methods that can be applied to this data, including clustering, classification, and recommendation systems. Clustering algorithms can be used to group students based on their learning behavior and preferences, allowing instructors to provide personalized feedback and course recommendations. Classification algorithms can be used to predict student success in a particular course, allowing instructors to intervene and provide additional support if needed. Recommendation systems can be used to suggest courses to students based on their interests and past behavior. We will also discuss the potential benefits and challenges of using machine learning methods in online education. Benefits include increased student engagement, improved learning outcomes, and more efficient use of resources. Challenges include ensuring data privacy and security, preventing algorithmic bias, and maintaining transparency and fairness in the decision-making process. Overall, machine learning methods have the potential to transform online education by providing personalized learning experiences and improving student outcomes. By leveraging the vast amounts of data generated by online education platforms, we can create more effective and efficient learning experiences that meet the needs of students from diverse backgrounds and learning styles.
Manikandan Rajagopal, Punamkumar Hinge, Kolachina Srinivas, Manesh R. Palav, P. Balaji, and Iskandar Muda
IEEE
High-quality data is utilized to make informed decisions that effectively help to successfully safeguard our environment. When there is an abundance of information that is both heterogeneous in nature (coming from a wide variety of fields or sources) and of unknown quality, various problems may occur. Furthermore, the problem’s dynamic nature also imposes some other complications. In order to deal with such complications, the central role played by supercomputers in the modern environment is to promote protection initiatives like monitoring, data analysis, communication, and information storage and retrieval. In current days, the higher dependency on the data management process forced the developers to integrate and enhance all these initiatives with Artificial Intelligence knowledge-based techniques so that smart systems can be utilized by a vast number of people. In this context, this study has illustrated how Artificial Intelligence methods have changed the nature of Environmental Decision Support Systems (EDSS) over the course of the last two decades. The strengths that an EDSS should exhibit have been emphasized in this review. In the final section, we look at some of the more innovative solutions used for various environmental issues.