Dinesh Kumar Vishwakarma

TL;DR: This paper provides a detailed survey of popular deep learning models that are increasingly applied in sentiment analysis and presents a taxonomy of sentiment analysis, which highlights the power of deep learning architectures for solving sentiment analysis problems.

TL;DR: A holistic view of how the information is being weaponized to fulfil the malicious motives and forcefully making a biased user perception about a person, event or firm is put forward.

TL;DR: In this article, the authors conducted a survey of a total of 1182 individuals of different age groups from various educational institutes in Delhi-NCR, India and identified the following as the impact of COVID-19 on the students: time spent on online classes and self-study, medium used for learning, sleeping habits, daily fitness routine and subsequent effects on weight, social life, and mental health.

TL;DR: The proposed literature provides feature designs of abnormal human activity recognition in a video with respect to the context or application such as fall detection, Ambient Assistive Living, homeland security, surveillance or crowd analysis using RGB, depth and skeletal evidence.

TL;DR: This survey presents detailed attributes of CNN with special emphasis on optimization methods that have been utilized in CNN-based methods, and introduces a taxonomy that summarizes important aspects of the CNN for approaching crowd behaviour analysis.