Browsing by Author "Anand Kumar, A.M."

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Early detection of depression using BERT and DeBERTa
(CEUR-WS, 2022) Devaguptam, S.; Kogatam, T.; Kotian, N.; Anand Kumar, A.M.
In todayâ€™s world, social media usage has become one of the most fundamental human activities. On the report of Oberlo, at present, 3.2 billion people are on social media, which comprises 42% of the Worldâ€™s population. People usually post about their daily life style, special occasions, views about on-going issues and their networks on the social media platforms. People also share things on social media which otherwise would not have shared with other people. Social media helps us to stay connected, keep informed, mobilise on social issues. Due to the surge of suicide attempts, social media can act as a life saver in detecting and tracing users who are on the verge of depression and self-harm. Natural language processing methods with the help of deep learning are aiding in solving language/text related real world problems like sentiment analysis, translation of text into different languages, depression detection. Many transformer based models like BERT (Bidirectional Encoders Representations from Transformers) are put to use to solve NLP problems, which voluntarily learns to attend to different features differently (Weighing). In this paper, a supervised machine learning algorithm with transfer learning approach is used to detect self-harm tendency in the social media users at the earliest. Â© 2022 Copyright for this paper by its authors.
Fake News Detection for Hindi Language
(CEUR-WS, 2022) Madathil, K.T.; Mirji, N.; Charan, R.; Anand Kumar, A.M.
The understanding of the term â€œFake newsâ€ varies from one individual to the other. If we look into the basic meaning of â€œFake newsâ€ , it refers to inappropriate and made up news. In most cases, the news is made up of baseless sources and facts. These news generally mislead the reader and are generally published for oneâ€™s own benefit or to defame others. In recent years, a large population is active on various social media platforms and hence they have become the major medium through which fake news is circulated. A lot of fake news is been circulated in local languages as well. Also most of the existing work is based on the English language and only very little work is done using resource scare language for fake news identification like Indic Languages. So this paper focuses to define false news and suggest an effective method for detecting fake news in Hindi using standard machine learning algorithms like Multi-layer Perceptron and Naive Bayes and deep learning techniques like transforms - mainly mBERT. Â© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Identifying Similar Questions in the Medical Domain Using a Fine-tuned Siamese-BERT Model
(Institute of Electrical and Electronics Engineers Inc., 2022) Merchant, A.; Shenoy, N.; Bharali, A.; Anand Kumar, A.M.
A large number of people search about their health related problems on the web. However, the number of sites with qualified and verified people answering their queries is quite low in comparison to the number of questions being put up. The rate of queries being searched on such sites has further increased due to the COVID-19 pandemic. The main reason people find it difficult to find solutions to their queries is due to ineffective identification of semantically similar questions in the medical domain. For most cases, answers to the queries people ask would be present, the only caveat being the question may be present in a different form than the one asked by the particular user. In this research, we propose a Siamese-based BERT model to detect similar questions using a fine-tuning approach. The network is fine-tuned with medical question-answer pairs and then with question-question pairs to get a better question similarity prediction. Â© 2022 IEEE.
Legal Text Analysis Using Pre-trained Transformers
(Springer Science and Business Media Deutschland GmbH, 2022) Prajwal, M.P.; Anand Kumar, A.M.
In this paper, we investigate the application of pre-trained transformers for text classification and similarity identification in the legal domain. We do several experiments applying various pre-trained transformer models to predict the descriptor of law or case based on text and identify similar cases. We consider an Indian Supreme Court judicial cases dataset containing cases and statutes and the EURLEX dataset containing approximately 57,000 documents and 4000 labels. EURLEX is a collection of treaties and laws related to the European Union. We preprocess the texts in the dataset and obtain embeddings from pre-trained transformers. Then, we use these embeddings as input to LSTM/BiLSTM layer to classify or predict similarity. Our results show that pre-trained transformers are sufficiently good when the length of the text to be classified or similarity predicted is small rather than large texts. Â© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Multiple Choice Question Answering Using Attention Based Ranking and Transfer Learning
(Institute of Electrical and Electronics Engineers Inc., 2022) Kadam, N.; Anand Kumar, A.M.
The multiple choice question answering is still considered as an challenging task in Natural Language Processing. In this paper, we have tried to solve the problem of answering multiple choice questions where supporting documents corresponding to each question are not explicitly provided. Context retrieval is the strategy, which focuses on both reasoning and retrieving better supporting contexts. We present a improvised version of attention based deep neural network that eventually learns to order documents according to their relevance in relation to a given topic, all while achieving the goal of predicting the correct response. The top documents retrieved are considered more relevant context for given question answer pair. To achieve more accurate results transformer based pre-trained models are used in the implementation. We have used the concept of transfer learning which is related to learning and adapting knowledge by fine tuning model on other datasets. The reasoning challenge dataset by Allen institute is used to test the approach and SQuAD 2.0 and RACE datasets are used to fine tune the transformer based models. The accuracy of proposed model on ARC easy dataset is 89.51% and on ARC challenge dataset is 62.53%. Â© 2022 IEEE.
NITK-IT NLP at CheckThat! 2022: Window based approach for Fake News Detection using transformers
(CEUR-WS, 2022) LekshmiAmmal, H.R.; Anand Kumar, A.M.
Misinformation is a severe threat to society which mainly spreads through online social media. The amount of misinformation generated and propagated is much more than authentic news. In this paper, we have proposed a model for the shared task on Fake News Classification by CLEF2022 CheckThat! Lab1, which had mono-lingual Multi-class Fake News Detection in English and cross-lingual task for English and German. We employed a transformer-based model with overlapping window strides, which helped us to achieve 7th and 2nd positions out of 25 and 8 participants on the final leaderboard of the two tasks respectively. We got an F1 score of 0.2980 and 0.2245 against the top score of 0.3391 and 0.2898 for the two tasks. Â© 2022 Copyright for this paper by its authors.
Overview of the Shared Task on Machine Translation in Dravidian Languages
(Association for Computational Linguistics (ACL), 2022) Anand Kumar, A.M.; Hegde, A.; Banerjee, S.; Chakravarthi, B.R.; Priyadarshini, R.; Shashirekha, H.L.; Mccrae, J.P.
This paper presents an outline of the shared task on translation of under-resourced Dravidian languages at DravidianLangTech-2022 workshop to be held jointly with ACL 2022. A description of the datasets used, approach taken for analysis of submissions and the results have been illustrated in this paper. Five sub-tasks organized as a part of the shared task include the following translation pairs: Kannada to Tamil, Kannada to Telugu, Kannada to Sanskrit, Kannada to Malayalam and Kannada to Tulu. Training, development and test datasets were provided to all participants and results were evaluated on the gold standard datasets. A total of 16 research groups participated in the shared task and a total of 12 submission runs were made for evaluation. Bilingual Evaluation Understudy (BLEU) score was used for evaluation of the translations. Â© 2022 Association for Computational Linguistics.
Predicting Survival of People with Heart Failure Using Oversampling, Feature Selections and Dimensionality Reduction
(Institute of Electrical and Electronics Engineers Inc., 2022) Niharika, G.; Lekha, A.I.; Leela Akshaya, T.; Anand Kumar, A.M.
Cardiovascular diseases are deadly and kill millions of people around the world every year. Heart failure is one of the unfortunate consequence where the heart is unable to pump enough blood for the body. A medical checkup of these patients with attributes including creatinine phosphokinase, ejection fraction, serum creatinine and serum sodium can be used for analysis. In this paper, we have analysed this clinical data and built machine learning models that can predict the survival rate of heart failure of a person. We have used various dimensionality reduction techniques to analyse the data with the aim of reducing the dimensions of the dataset. Finally, we reduced the overfitting of data using Synthetic Minority Oversampling Technique(SMOTE) and Adaptive Synthetic(ADASYN). Â© 2022 IEEE.
Price Prediction of Agricultural Products Using Deep Learning
(Springer Science and Business Media Deutschland GmbH, 2022) Kankar, M.; Anand Kumar, A.M.
Every field in the world is undergoing a significant change because of the influence of technology. The agricultural sector of the Indian economy needs more technological support for its development and growth in India. Price prediction of agricultural products helps ensure that the farmers either get good returns or recover their investments. Hence, the characteristics of deep neural networks such as CNN and deep learning models can be used in predicting prices. A convolution neural network-based model can indirectly predict fruits and vegetable prices by classifying images to their variety. Deep learning models such as long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) can also help predict the market price of agricultural products. Fruits and vegetable prices mainly depend on a few things, variety, quality, and market rate. We use the CNN model to deal with variety and quality, different varieties of a single fruit or vegetable having different prices, followed by prediction using LSTM and bidirectional LSTM to deal with market price prediction in a volatile market. Â© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Sentiment Analysis and Homophobia Detection of YouTube Comments
(CEUR-WS, 2022) Ugursandi, S.; Anand Kumar, A.M.
Sentiment analysis identifies a graded scale of opinions or emotional responses to a particular subject. Many industries and organisations have been actively researching this area for more than 20 years. The key to understand a userâ€™s behaviour while responding on a social media site is to understand their feelings. In contemporary research, a sentenceâ€™s content is evaluated, the emotion predicted, that helps researchers gain an insight on the reaction of an individual towards a social media topic. Here, a sentenceâ€™s text data is analysed using several Natural Language Processing techniques before being utilised to categorise this multi-class issue. The detection of homophobia and transphobia in comments on YouTube or other social media sites is second objective of this work. Anger, discomfort, or suspicion against Lesbian, Gay, Bisexual and Transgender people is known as homophobia. It can incite individuals to feel panic, dislike, disrespect, aggression, or wrath. By identifying such occurrences on social media, we can better understand how society works and how people behave. The goal of this work is to analyze social media texts such as comments from YouTube and detect homophobic sentiments using deep learning or machine learning models. In this work 6-layer classification model is used, the F1-Score for sentiment identification using the proposed model in this study was 0.5 on multi-class classification and 0.97 on homophobic/transphobic classification and achieved 1st rank on Homophobic detection in Malayalam language and 4th rank for sentiment analysis in Kannada language. Â© 2022 Copyright for this paper by its authors.
Sequential Memory Modelling for Video Captioning
(Institute of Electrical and Electronics Engineers Inc., 2022) Puttaraja, P.; Nayaka, C.; Manikesh, M.; Sharma, N.; Anand Kumar, A.M.
In recent years, the automatic generation of natural language descriptions of video has focused on deep learning research and natural voice processing. Video understanding has multiple applications such as video search and indexing, but video subtitles are a correct sophisticated topic for complex and diverse types of video content. However, the understanding between video and natural language sets remains an open issue to better understand the video and create multiple methods to create a set automatically. The deep learning method has a major focus on the direction of video processing with performance and high-speed computing capabilities. This polling discusses an encoder-decoder network end-in-frame based on a deep learning approach to generate caption. In this paper we will describe the model, dataset and parameters used to evaluate the model. Â© 2022 IEEE.
Speaker Identification and Verification using Deep Learning
(Institute of Electrical and Electronics Engineers Inc., 2022) Recharla, R.; Jeevan Reddy, C.; Tanguturu, R.; Anand Kumar, A.M.
Many voice assistants gained importance across globe in the recent times, for example, Cortana, Siri, Ok Google. These assistants are part of everyone's life these days. The main motive behind the proposed system is to improve recognition assistant system. The speaker prediction model is trained using features MFCC, Chroma, Tonnetz, Mel spectrogram, and Spectral contrast extracted from audio samples. The proposed system has numerous real-world applications, such as meeting transcription, unlocking smart devices using voice, and online viva voice verification. It can replace the existing biometric system for faculty attendance and traditional fingerprint recognition. A Dense Neural Network was created for each audio feature and finally concatenated using a concatenation layer which fetched the best performance output compared to LSTM. Dense Neural Network successfully predicted the speaker with an accuracy of more than 95% most of the times. In the case of LSTM, due to fewer samples, the accuracy of speaker prediction is around 79%. In the case of CNN, the accuracy of speaker prediction is around 86%; this behavior can be attributed to the noise environment. When an unknown speaker tries to speak, the Dense Neural network can manage the task by placing them in an anonymous class. Â© 2022 IEEE.
Task Scheduling Using Deep Q-Learning
(Springer Science and Business Media Deutschland GmbH, 2022) Velingkar, G.; Kumar, J.K.; Varadarajan, R.; Lanka, S.; Anand Kumar, A.M.
Process scheduling is a very crucial task of operating systems. Effective scheduling ensures system efficiency and minimizes wastage of resources and cost overall, enhancing productivity. Most commonly, it is an exhaustive task to select the most accurate resources in executing these tasks. The solution for this effective job scheduling and resource management would preferably be dependent on the nature of the workload and adapt to any given environment compared to an algorithmic one. Thus, to meet this rising demand for an automated, self-assigning system, a deep Q-learning (Reinforcement learning technique)-based implementation has been done, which schedules tasks to maximize CPU utilization and memory utilization. Â© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Unsupervised Abstractive Text Summarization with Length Controlled Autoencoder
(Institute of Electrical and Electronics Engineers Inc., 2022) Dugar, A.; Singh, G.; Balamuralidhar, B.; Anand Kumar, A.M.
This work deals with taking an unsupervised approach to abstractive text summarization where a large set of sentences is converted into a concise summary highlighting the essential details. This is achieved with the use of an adversarial autoencoder model. The model encodes the input to a smaller latent vector and the decoder decodes this latent code to generate the higher dimensional output with some loss. Unlike variational autoencoders, AAE's use discriminators to learn using adversarial loss. K-Means clustering and language models are used to get the final summary. This model has been tested with different datasets like the Amazon, Rotten Tomatoes and Yelp reviews dataset to essentially do an opinion summarization task and this is finally evaluated using ROGUE-1, ROGUE-2,ROGUE-L and BLEU scores. The same task is also conducted on a dataset in Hindi. We obtain a ROGUE-1 score of around 24% for Amazon, Yelp and CNN/Daily Mail dataset and a score of 12% for Rotten Tomatoes while the score obtained for the Hindi news articles dataset is only 8%. Â© 2022 IEEE.