Harsh Sharma

Stuti Sehgal, Harsh Sharma, Akshat Anand

People have the ability to make sensible assumptions about other people's emotional states by being sympathetic, and because of our common sense of knowledge and the ability to think visually. Over the years, much research has been done on providing machines with the ability to detect human emotions and to develop automated emotional intelligence systems. The computer's ability to detect human emotions is gaining popularity in creating sensitive systems such as learning environments, health care systems and real-world. Improving people's health has been the subject of much research. This paper describes the formation as conceptual evidence of emotional acquisition and control in intelligent health settings. The authors of this paper aim for an unconventional approach with a friendly look to get emotional scenarios from the system to establish a functional, non-intrusive and emotionally-sensitive environment where users can do their normal activities naturally and see the program only when pleasant mood activating services are received. The context-sensitive system interacts with users to detect and differentiate emotions through facial expressions or speech recognition, to make music recommendations and mood color treatments with the services installed on their IoT devices.

Debaditya Pal, Harsh Sharma, Kaustubh Chaudhari

Relational databases are among the most widely used architectures to store massive amounts of data in the modern world. However, there is a barrier between these databases and the average user. The user often lacks the knowledge of a query language such as SQL required to interact with the database. The NL2SQL task aims at finding deep learning approaches to solve this problem by converting natural language questions into valid SQL queries. Given the sensitive nature of some databases and the growing need for data privacy, we have presented an approach with data privacy at its core. We have passed RoBERTa embeddings and data-agnostic knowledge vectors into LSTM based submodels to predict the final query. Although we have not achieved state of the art results, we have eliminated the need for the table data, right from the training of the model, and have achieved a test set execution accuracy of 76.7%. By eliminating the table data dependency while training we have created a model capable of zero shot learning based on the natural language question and table schema alone.