Question Answering System (QAS) for Online Services

1. Improving the accuracy of the Text-Visual Question Answering System (Text-VQA) that can read text from the daily scene images and document images-to help the visually impaired people and for a better navigation system. Use the individual and multimodal attention mechanism for effectively joining all the modalities in a common space to provide the exact answer.
2. Developing a Visual Dialogue System (Visual Chatbot) by identifying the Intent in the question, Image, and dialogue history.
3. Developing a Visual Question Answering System (VQA) that can answer a natural language question by extracting the visual features from the given Image. Improving the accuracy of the Multi-lingual VQA system for the Indian languages.
4. Developing an Ontology-based Question Answering System with the LCQUAD dataset and improving the problem of Entity/Relation resolution using the knowledge graph.
5. Improving the accuracy of the FAQ-based Question Answering System, which recommends a list of similar questions to the user.

Fake News Detection in Social Media (FND) 

1. Cross-Lingual Fake News Detection using the Fact-checked given by the high-resource languages to be used in the Low-resource languages.
2. Fake News Detection by leveraging the latest Few shot and Zero shot learning methods for the Multilingual using the Low-resource languages such as Indian Languages: Hindi, Bengali etc.
3. Design a Multimodal and Multilingual framework for the Weibo dataset (Chinese) using Transfer Learning.

Machine Translation

1. Using Large Language Models to improve Machine Translation for Indic languages

 Human Action Recognition 

1. Visual feature determination and system development for Alzheimer's.
2. Visual feature determination and system development for dementia.
3. Visual cues determination and system development Post Stroke.

Designing Optimal and Targeted Advertisement Campaign Strategy for Online E-Commerce Platforms using AI and Machine Learning 

1. Analyzing and predicting factors affecting the overall profitability of an advertisement campaign.
2. Identifying and explaining the impact of changing input variables [Bid, Budget] on the output metrics [ %impression share, ROAS - return on adspend] across such campaigns.

Conversational AI to understand customer behavior :

• Developing a Conversational Chatbot for Omni-Channel Customer Experiences.
• Build a Conversational Model Based on Reinforcement Learning Which Generates Responses in Native-Language.
• Improving Conversational System of Unscripted, Unplanned Responses using Machine Learning to get Accurate Responses.
• Developing a Multi-Lingual Embodied Conversational Question- Answering agents.
• Implementing a Rasa-X: Simultaneous Conversational Chatbot for Multi-intent.
• Implementing a Neural Conversational Model for Multilingual Question-Answering System.

 For Students

Please fill in your academic details by 25^th April 2024 for the SOP/ LOP /Thesis

Please fill your interest  https://forms.gle/WN4GpfqWzuoiM3BMA