Responsible Data Science [electronic resource] : Select Proceedings of ICDSE 2021 /

End-to-end Hierarchical Approach for Emotion Detection in short texts -- Towards an Enhanced Understanding of Bias in Pre-trained Neural Language Models: A Survey with Special Emphasis on Affective Bias -- Exploring Rawlsian Fairness for K-Means Clustering -- Hybrid Explainable Educational Recommender using Self Attention and Knowledge Based Systems for E-Learning in MOOC Platforms -- An Improved Recommendation System with Aspect-Based Sentiment Analysis -- Exploring Biomarker Identification and Mortality Prediction of COVID-19 Patients using ML Algorithms -- COVID-19 cases prediction based on LSTM and SIR model using social media -- Joint Geometrical and Statistical Alignment using Triplet loss for Deep Domain Adaptation -- Virtual Try-On Using Style Transfer -- Attention Mechanism in Convolutional Recurrent Neural Network for Improving Recognition Accuracy in Printed Devanagari Text.

This book comprises select proceedings of the 7th International Conference on Data Science and Engineering (ICDSE 2021). The contents of this book focus on responsible data science. This book tries to integrate research across diverse topics related to data science, such as fairness, trust, ethics, confidentiality, transparency, and accuracy. The chapters in this book represent research from different perspectives that offer novel theoretical implications that span multiple disciplines. The book will serve as a reference resource for researchers and practitioners in academia and industry.