The objective of this project is to uncover textual patterns using association rules derived from the parallel FP Growth Algorithm. Within the scope of this endeavor, we have extensively delved into revered ancient texts such as Bhagavad Gita, Ramayana, Vedas, Puranas, and various other pivotal literary works. The invaluable guidance of Dr. Animesh Chaturvedi, Dr. Vivekraj, and Dr. Radika BS.

It is established that about 1.8 percent of indian population which measures upto 12 million people have physical disabilities of one or many forms. It is important. There should be awareness both in government and society about the need to reach out to the disabled people to enable them to become self sufficient and independent. Thus we contribute a step towards helping those people in achieving their goal to be independent to increase their confidence in living a normal life in this vast diversified society

Indian government is providing quotas under pwd in many government sectors to boost the chances of pwd people to get a job and be independent, but many people lack guidance in acquiring those jobs .so we build this website to help them achieve their goals

Ukrainian Resilience is a project that detects the posts of the innocent people who are in need of help. The project aims in identifying help seeking posts such that it can help governments and NGOs for target helping. We propose a dataset of the human gold annotated standards by collecting posts from social media. It is a binary classification dataset of require help vs does not. Our dataset contains various posts who are in need of jobs, shelter, food etc.

Large Language Models (LLMs) are continuously evolving and revealing new possibilities. These advanced LLMs excel at understanding and generating human-like text based on contextual prompts, but there's a concern about their potential to produce gossip-like content in certain situations. To address this issue, we introduce the GossipPrompts dataset for detecting prompts that lead to gossip generation. This binary classification dataset contains 9946 prompts, with labels indicating whether they produce gossip or not. We have developed baseline models, achieving an accuracy of 88.20% in the detection process.

Halbert L. Dunn’s concept of wellness is a multidimensional aspect encompassing social and mental well-being. Neglecting these dimensions over time can have a negative impact on an individual’s mental health. The manual efforts employed in in-person therapy sessions reveal that underlying factors of mental disturbance if triggered, may lead to severe mental health disorders. In our research, we introduce a fine-grained task focused on identifying indicators of wellness dimensions and mark their presence in self-narrated human-writings on Reddit social media platform. Our work serves as a valuable ground work for the early detection of chronic mental disturbances. We present the MULTIWD dataset, a curated collection comprising 3281 instances, as a specifically designed and annotated dataset that facilitates the identification of multiple wellness dimensions in Reddit posts. In our study, we utilize existing classifiers to solve this multi-label classification task and introduce them as baselines. We highlight practical implications and our findings indicate the potential for further enhancements to develop more comprehensive and contextually-aware AI models in this domain.