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Artificial Intelligence – based Multiple Regression Algorithm to Indicate Human Development in the Workplace
Muthuvelayutham C, Nilofer Fathima.S.M. Associate Professor, Department of Management Studies, Regional Campus Anna University, Madurai.Research Scholar, Department of Management Sciences, Regional Campus Anna University, Madurai.
Pages: 1-11 | First Published: 05 Dec 2023
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Abstract

There are particular difficulties in integrating human growth into the human workplace. Potential, poverty, and productivity are the three fundamental obstacles to human development. Maintaining employee skill development and training is a problem that human resource development frequently tackles. Employees must broaden their knowledge and acquire new skills as demands and trends change. In this study, an Artificial Intelligence-based Multiple Regression Algorithm (MRA) is employed to indicate human development in the workplace and is termed as AI-MRA. This MRA is compared with the existing models such as Random Forest, Naïve Bayes, and a hybrid model of Convolutional Neural Network withVariable-Length Markov Modelling (CNN-VMM) based on the parameters such as accuracy, sensitivity, specificity, precision, and recall. It is observed from the results that the proposed model has outperformed other existing algorithms.
Keywords: Human Development, Artificial Intelligence-based Multiple Regression Algorithm (AI-MRA), Random Forest, Naïve Bayes, Convolutional Neural Network withVariable-Length Markov Modelling (CNN-VMM).

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Effect of Malnutrition on Children’s Growth, Development, and Health in India: A Home Science-Based Empirical Analysis
Dr Belinda Lopez, Assistant Professor, Dept. of Home Science, Smt. VHD Central Institute of Home Science (DCE), Maharani Cluster University, Bangalore.
Pages: 12-21 | First Published: 02 Dec 2023
Full text | Abstract | PDF | References | Request permissions

Malnutrition remains a persistent public health challenge affecting children’s physical growth, cognitive development, and health outcomes in India. This empirical study examines the relationship between nutritional status and developmental indicators among children, with a Home Science perspective emphasizing household nutrition practices. Using a modelled dataset supported by secondary data sources, statistical analysis including correlation and chi-square tests was conducted to evaluate associations between socio-economic status, dietary patterns, and child health outcomes. The findings indicate a significant relationship between malnutrition and impaired growth, reduced immunity, and increased morbidity. The study highlights the importance of nutrition education, household-level interventions, and policy support in addressing malnutrition.

Keywords: Malnutrition, children, Home Science, growth, nutrition, empirical study, India

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