Editorial Comment

Abstract

The Journal of Computers, Mechanical and Management is pleased to introduce the first issue of the second volume of our esteemed journal. This issue encompasses a diverse collection of scholarly works, comprising six original full-length research articles and two insightful review articles that explore various compelling subjects.The first article, "Comprehensive Study of Machine learning Algorithms for Stock Market Prediction during " by Saxena et al. [1], presents an extensive study of machine learning algorithms for stock market prediction amid the COVID-19 pandemic. The authors analyze the performance of various machine learning algorithms and identify the most effective algorithm for stock market prediction. This study's outcomes will provide valuable guidance for investors and traders in making informed decisions during times of uncertainty, such as the COVID-19 pandemic. The second article, "Sentiment Analysis using Latent Dirichlet Allocation for Aspect Term Extraction" by Rajput et al. [2], introduces a novel approach for sentiment analysis utilizing latent Dirichlet allocation for aspect term extraction. The authors propose a new method that effectively extracts aspect terms from text data, contributing to applications such as opinion mining and customer feedback analysis.In the third article, conducted by Sikandar and Sikandar [3], an investigation into the Quality of Work Life (QWL) of women officers working in publicly funded Higher Educational Institutions (HEIs) in India is presented. The study reveals that the respondents experience equitable treatment in the workplace, possess autonomy in their roles, foster good reporting relationships, and maintain a healthy work-life balance. Examining the impact of COVID-19 on the well-being of teachers in higher education institutions, the fourth article by Pavitra et al. [4] emphasizes the significance of supporting teacher well-being during the pandemic. The study highlights the importance of prioritizing physical health, fostering relationships, and engaging in meaningful activities to enhance the well-being of teachers. The fifth article, authored by Verma [5], proposes an enhanced algorithm for the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol in wireless sensor networks. This proposed method significantly enhances the protocol's performance, leading to improved network lifetime, throughput, and the number of living nodes. As a result, it holds great promise for efficient routing in wireless sensor networks. Presenting a numerical analysis of p-type CdTe and n-type TiO 2 heterojunction solar cells, the sixth article by Chougale et al. [6] conducts simulations to investigate the effects of varying parameters such as the