Prin. L. N. Welingkar Institute of Management Development and Research

About: Prin. L. N. Welingkar Institute of Management Development and Research is a education organization based out in Mumbai, Maharashtra, India. It is known for research contribution in the topics: Empirical research & Context (language use). The organization has 59 authors who have published 55 publications receiving 710 citations. The organization is also known as: Welingkar Institute of Management Development and Research & WeSchool.

An Investigation of Customer Awareness with Reference to Green Marketing of Automobiles: An Empirical Study Conducted at Indore, Madhya Pradesh

Abstract: The first part of this article focuses on detailed literature review in the area of green marketing, as it was felt by the authors that little attempt has been made to understand this area in the Indian context. The second part of the work deals with assessment of awareness for the green automobiles among the customers of various segments in Indore district of Madhya Pradesh. Looking to the adopted methodology, the study is termed as an empirical study. The work includes testing of three hypotheses related to the above objective. Few questions of the questionnaire used in the study have been analyzed and presented in detail to bring out the attitude of customers towards the green automobile. A factor analysis was also carried out to get more insight into the obtained responses to the variables used in the study.The results of this survey are encouraging in the sense, that they reveal significant awareness about green marketing among the customers, and more so in higher age group. They also appear to be ready, to pay a marginal extra price for obtaining a green automobile. It can be concluded that, the product appears to be welcome in rural as well as urban segments, with owners of four wheelers, favoring it more.

Diagnosing the major contributing factors in the classification of the fetal health status using cardiotocography measurements: An AutoML and XAI approach

Abstract: The universal criticality of mother’s and child’s health can scarcely be overstated. Deterioration in an expectant mother’s health may lead to several complications in the antepartum and intrapartum period that which may be fatal. Hence, simultaneous tracking of prenatal parameters such as uterine contraction and Fetal Heart Rate (FHR) through Cardiotocography (CTG) is of critical importance. This preponderantly remains a manual procedure in developing nations as the inclusion of machine learning (ML) technology is still not widespread. Numerous studies have pointed to the fact that electronic monitoring of FHR has not had any noteworthy benefit in lowering the incidence of perinatal mortality and morbidity. Further, FHR monitoring is beset with other problems such as high disparities in intra-and inter-observations and enhanced rate of caesarean vis-a-vis normal delivery. These drawbacks of FHR notwithstanding, the fact remains that FHR is regarded as a vital obstetric procedure and CTG as equipment is deployed the most in perinatal diagnostics. With advancements in medical technology, techniques viz. computerized analysis of FHR and ECG are now being utilized as an accompaniment to CTG. These sophisticated computerized methods enable an analysis of variations in heart rate and assessing the patterns of short term heart rate. Further studies about multiple parameters causing FHR variability is needed to better our understanding of the primacy and salience of various parameters of raw fetal heartbeat data. Our experimental results using AutoML approach gave an accuracy rate of 0.9561, Recall 0.9056, Kappa 0.8792, Precision 0.9552, AUC 0.9864, F1 0.9550, MCC 0.8805 with model handling time of 2 minutes.

An Exploration of Impact of COVID 19 on mental health -Analysis of tweets using Natural Language Processing techniques

Abstract: Twitter is one of the world’s biggest social media platforms for hosting abundant number of user-generated posts. It is considered as a gold mine of data. Majority of the tweets are public and thereby pullable unlike other social media platforms. In this paper we are analyzing the topics related to mental health that are recently (June, 2020) been discussed on Twitter. Also amidst the on-going pandemic, we are going to find out if covid-19 emerges as one of the factors impacting mental health. Further we are going to do an overall sentiment analysis to better understand the emotions of users. Executive Summery Novel Corona virus’s spread and its impact on various aspects of national and individual’s well-being has been at the center of lot of discussions across micro-blogging sites and various social media platforms ever since it commenced in December 2019. Users are voicing their opinions on several topics related to covid-19. Social distancing as prescribed by Government and Local Administration We all are aware that the Novel Corona virus has significantly affected our physical health; however the current social distancing norms are taking a toll on the psychological well-being of individuals. The research paper presents a two-phased analysis of most recent 2000 tweets related to mental health pulled out twice over a span of one month on 28 June 2020 and 28 July2020 respectively, thereby analyzing 4000 tweets in total. The second phase analysis was conducted exactly after a gap of one month to validate the results generated by the first analysis. The intention is to analyze to what extent people have discussed about mental health in the past few months based on the information disseminated on Twitter. Data was extracted using Twitter’s search application programming interface (API) and Python’s tweepy library. A predefined keyword like ‘mental health’ was given to find out if Covid-19 emerges as a reason for the same. Several natural language processing (NLP) techniques like tokenization, removing URL and stop words, stemming and lemmatization were used to pre-process the text data and make it ready for analysis. These collected tweets were analyzed using word frequencies of single and double words (unigram, bigram). A very unique feature of this analysis includes a network diagram that shows interconnections between the set of most common words used in to its and the connections (if any) are represented through links. Topic modeling technique in NLP visualizes the top concerns of tweeters through a word cloud. At present we have many methods to do topic modeling. In this paper we are using the Latent Dirichlet Allocation (LDA) method which is a probabilistic approach of modeling given by Prof David H.B in 2003. This model deals with distribution of topics to tweets and allocation of those topics to documents and words to topics. Finally a sentiment analysis is done using text mining techniques to analyze the sentiment of the tweets in the form of positive, negative and neutral.

Credit constraints and Rural Migration: Evidence from Six Villages in Uttar Pradesh

Abstract: Rural economies in developing countries are often characterized by credit constraints. Although few attempts have been made to understand the trends and patterns of male out-migration from Uttar Pradesh (UP), there is dearth of literature on the linkage between credit accessibility and male migration in rural Uttar Pradesh. The present study tries to fill this gap. The objective of this study is to assess the role of credit accessibility in determining rural male migration. A primary survey of 370 households was conducted in six villages of Jaunpur district in Uttar Pradesh. Simple statistical tools and a binary logistic regression model were used for analyzing the data. The result of the empirical analysis shows that various sources of credit and accessibility to them play a very important role in male migration in rural Uttar Pradesh. The study also found that the relationship between credit constraints and migration varies across various social groups in UP.

Analysing Retraction Notices of Scholarly Journals: A Study

Abstract: Information and communication technology (ICT) is not an unalloyed advantage when talking about propagation and expansion of scholarly knowledge. The same ICT which acts as an enabler to research in the comfort of one’s study and preferred environment makes the researchers with weak conscience vulnerable to the temptation of research misconduct. Surprisingly, the same technology acts as a sentinel, helping academe nail such transgressions and withdrawing them or taking the contextual corrective recourse. Of late, there has been a substantial increase in the invalidation and withdrawal of research articles based on invalid data and findings. One analysed the retraction notices of 249 annulled articles, indexed in Scopus, during the period, 2000-2017. The study has highlighted that the majority of the retracted notices do not have explicit reasons for revoking the findings of research articles. It has stressed upon the immensely pivotal role of libraries in spreading awareness and sensitising researchers with regard to adherence to norms, ethics and policies of scholarly communication.