Chatbot for Mental health support using NLP Gupta, Vanshika; Joshi, Varun; Jain, Akshat ...
2023 4th International Conference for Emerging Technology (INCET),
2023-May-26
Conference Proceeding
Mental health issues are a growing concern worldwide, and seeking support for these issues can be difficult due to various reasons. Chatbots have emerged as a promising solution to provide accessible ...and confidential support to individuals facing mental health issues. With recent advances in technology, digital interventions designed to supplement or replace in-person mental health services have proliferated, including the emergence of mental health chatbots that claim to provide assistance through automated natural language processing (NLP) therapeutic approaches. A chatbot can be described as a computer program capable of providing intelligent answers to user input by understanding natural language using one or more NLP techniques. In this study, we discuss the use of NLP in psychotherapy and compare the responses provided by chatbots to a set of predefined user inputs related to well-being and mental health queries and compare existing systems. A general analysis was performed. The general approach to building such chatbots includes basic NLP techniques such as word embedding, sentiment analysis, sequence-by-sequence models, and attention mechanisms. We also looked at Mental Ease, a mobile app that uses NLP technology not only to provide conversational assistance but also to tool up useful features for maintaining mental health. Incorporating mental health assessment tools into the chatbot interface, it can help patients cope with mild anxiety and depression alongside conventional therapy. It can also overcome some barriers to mental health, such as waiting lists and geographical barriers to face-to-face consultations.
Legal systems worldwide are inundated with exponential growth in cases and documents. There is an imminent need to develop NLP and ML techniques for automatically processing and understanding legal ...documents to streamline the legal system. However, evaluating and comparing various NLP models designed specifically for the legal domain is challenging. This paper addresses this challenge by proposing IL-TUR: Benchmark for Indian Legal Text Understanding and Reasoning. IL-TUR contains monolingual (English, Hindi) and multi-lingual (9 Indian languages) domain-specific tasks that address different aspects of the legal system from the point of view of understanding and reasoning over Indian legal documents. We present baseline models (including LLM-based) for each task, outlining the gap between models and the ground truth. To foster further research in the legal domain, we create a leaderboard (available at: https://exploration-lab.github.io/IL-TUR/) where the research community can upload and compare legal text understanding systems.
Interactive Learning Through the Metaverse and its Impact on Primary Education Sharma, Cheilsi; Agarwal, Basant; Wuttisittikulkij, Lunchakorn ...
2024 21st International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON),
2024-May-27
Conference Proceeding
The rise of the Metaverse, an immersive, three-dimensional digital environment, has been gradually transforming human-computer interaction. This paper focuses on exploring the potential of the ...Metaverse to improve learning through interactive games. The Metaverse serves as a dynamic platform for students to collaborate, explore and engage with. It enables educators to create interactive learning environments that amalgamate both physical and virtual spaces through technologies such as virtual reality, augmented reality, and artificial intelligence. These interactive games within the Metaverse foster critical thinking and problem-solving on top of making learning enjoyable. This paper discusses the development of educational environments within the metaverse, the integration of interactive games as well as their impact on learning outcomes and student engagement. The Metaverse poses a novel route towards effective education with this gamified approach. This study highlights the potential for the Metaverse to prepare students for the challenges inherent in this digital age by transforming traditional educational methods.
The task of Prior Case Retrieval (PCR) in the legal domain is about automatically citing relevant (based on facts and precedence) prior legal cases in a given query case. To further promote research ...in PCR, in this paper, we propose a new large benchmark (in English) for the PCR task: IL-PCR (Indian Legal Prior Case Retrieval) corpus. Given the complex nature of case relevance and the long size of legal documents, BM25 remains a strong baseline for ranking the cited prior documents. In this work, we explore the role of events in legal case retrieval and propose an unsupervised retrieval method-based pipeline U-CREAT (Unsupervised Case Retrieval using Events Extraction). We find that the proposed unsupervised retrieval method significantly increases performance compared to BM25 and makes retrieval faster by a considerable margin, making it applicable to real-time case retrieval systems. Our proposed system is generic, we show that it generalizes across two different legal systems (Indian and Canadian), and it shows state-of-the-art performance on the benchmarks for both the legal systems (IL-PCR and COLIEE corpora).
Reliability of News Harjule, Priyanka; Sharma, Akshat; Chouhan, Sachin ...
2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE),
2020-Feb.
Conference Proceeding
In modern times, because of the the advancement of social media platforms, fake news relating to different purposes has been increasing day by day. Fake News on the internet is defined as a ...fabricated article with the intention to mislead, usually for profiting. Fake news and hoaxes have been there since before the advent of the Internet. Hoaxes have existed for a long time, since the "Great moon hoax" published in 1835. Along with the increase in the use of social media platforms like Facebook, Twitter etc. news spreads rapidly among millions of users within a very short span of time. This paper's purpose is to investigate the concepts, approaches and algorithms for identifying fake news articles and their creators from online social media platforms and assessing their performance. This paper introduces two models for detection of fake news. First by text classification where different classifier models were applied and it was found that RNN(LSTM) gave the best accuracy of 93 %. Second by crowd analysis where Parameter tuning method gave the best accuracy of 80 %.
Neural Dense Captioning with Visual Attention Jadhav, Akshat Arvind; Kundale, Jyoti; Joshi, Ankita Yashwant ...
2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT),
2021-June-18
Conference Proceeding
Image captioning is the task of how to represent image information more effectively and efficiently. It uses both Natural Language Processing and Computer Vision for generating the caption. In the ...existing research, many methods for caption generation were proposed, but the information in the description is not so related to the image and also most of them uses the global information of the image. In order to solve this problem, in this paper we have described three image captioning model namely ResNet, Glove and VGG16.These are the pretrained models which uses Convolutional Neural Network (CNN) and Deep Recurrent Neural Network (RNN) based on Long Short Term Memory (LSTM). CNN has one or more convolutional layers and are used for image classification and features extraction. Only the meaningful area's features were extracted through CNN. For generating the caption LSTM uses over RNN because it has many layers so the predictions for generating the captions are more accurate in LSTM. The dataset use in our project is Flicker8k dataset. It contains total 8k images where 2k images are test dataset and 6k images are trained dataset. The Flicker8k dataset is used to demonstrate the proposed methodology using python as a language.
The GPU usually handles the homogenous data parallel work, by taking advantage of its massive number of cores. In most of the applications, we use CUDA programming for utilizing the power of GPU. In ...data intensive high computational applications like neural networks, utilizing the GPU on a single machine is time consuming. Instead if multiple GPUs are used in a network the amount of time required will be significantly reduced. Traditionally to enable a set of program to be run in a distributed environment, programmer has to accordingly design components to make his system dynamic and resilient to changes in number of systems in cluster, which is a daunting task. This work distribution can be a poor solution as it may underutilize the GPUsIn our approach, we developed a framework which transparently distributes data parallel kernels across multiple GPUs in a distributed network. The programmer is responsible for developing a single data parallel kernel in CUDA while the framework automatically distributes the workload across an arbitrary set of CUDA enabled GPUs. Depending on current workload on GPUs and the amount of data to be processed optimal distribution is done. The goal is to maximally utilize the available resources with minimal programming complexity. The systems not compatible with CUDA can also utilize our solution. We expect our framework to reduce the processing time along with simplifying the task of programmers.
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