2835 research outputs found
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NIAS-DST Training Programme on Transformational Leadership in Science: Re-imagining Science in Indian Society. 5-9 February 2024
No full textLeading From Within, NIAS Leadership Programme on Personal Mastery and Role Excellence (23-27 September 2024)
No full textUnderstanding Maoism in India with Socio-Economic Discriminations and Rebel Capabilities
No full textWay Forward for the Viability of Power Distribution Sector in India a case Study in Karnataka
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Crafting new service workers: skill training, migration and employment in Bengaluru, India
Get PDFThe paper documents the role of skill training centres in Bengaluru, India, in the production of a peripatetic and precarious workforce for India’s new service economy. It describes how semi-educated youth from disadvantaged rural backgrounds are recruited for short-term training courses, which are promoted as a route to economic mobility, but are then placed in undesirable low-end and low-paid urban service jobs. Because the employment offered rarely matches their expectations or aspirations, graduates of training programmes often quit within a few weeks, returning to their home villages or searching for other job opportunities. The findings of the study suggest that skill training centres, rather than fulfilling their expressed goals of lifting rural youth out of poverty, contribute to the creation of a footloose and insecure workforce – thus catering to the requirements of organised service industries rather than the needs of unemployed youth. The paper contributes to current debates on youth unemployment, skill development, and labour precarity in the Global Sout
Evaluating the Determinants of Mode Choice Using Statistical and Machine Learning Techniques in the Indian Megacity of Bengaluru
No full textThe decision making involved behind the mode choice is critical for transportation planning. While statistical learning techniques like discrete choice models have been used traditionally, machine learning (ML) models have gained traction recently among the transportation planners due to their higher predictive performance. However, the black box nature of ML models pose significant interpretability challenges, limiting their practical application in decision and policy making. This study utilised a dataset of 1350 households belonging to low and low-middle income bracket in the city of Bengaluru to investigate mode choice decision making behaviour using Multinomial logit model and ML classifiers like decision trees, random forests, extreme gradient boosting and support vector machines. In terms of accuracy, random forest model performed the best (0.788 on training data and 0.605 on testing data) compared to all the other models. This research has adopted modern interpretability techniques like feature importance and individual conditional expectation plots to explain the decision making behaviour using ML models. A higher travel costs significantly reduce the predicted probability of bus usage compared to other modes (a 0.66% and 0.34% reduction using Random Forests and XGBoost model for 10% increase in travel cost). However, reducing travel time by 10% increases the preference for the metro (0.16% in Random Forests and 0.42% in XGBoost). This research augments the ongoing research on mode choice analysis using machine learning techniques, which would help in improving the understanding of the performance of these models with real-world data in terms of both accuracy and interpretability