Faculty Publications

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Author: search.filters.author.Anand Kumar, M.Subject: search.filters.subject.LSTM

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    Covid-19 Fake News Detector using Hybrid Convolutional and Bi-LSTM Model
    (Institute of Electrical and Electronics Engineers Inc., 2021) Surendran, P.; Balamuralidhar, B.; Kambham, H.; Anand Kumar, M.
    Fake news is essentially incorrect and deceiving information presented to the public as news with the motive of tarnishing the reputations of individuals and organizations. In today's world, where we are so closely connected due to the internet, we see a boom in the development of social networking platforms and, thus, the amount of news circulated over the internet. We must keep in mind that fake news circulated on social media and other platforms can cause problems and false alarms in society. In some cases, false information can cause panic and have a dangerous effect on society and the people who believe it to be true. Along with the virus, the Covid-19 pandemic has also brought on distribution and spreading of misinformation. Claims of fake cures, wrong interpretations of government policies, false statistics, etc., bring about a need for a fact-checking system that keeps the circulating news in control. This work examines multiple models and builds an Artificial Intelligence system to detect Covid-19 fake news using a deep neural network. © 2021 IEEE.
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    Long Short Term Memory Networks for Lexical Normalization of Tweets
    (Institute of Electrical and Electronics Engineers Inc., 2021) Nayak, P.; Praueeth, G.; Kulkarni, R.; Anand Kumar, M.
    Lexical normalization is converting a non-standard text into a standard text that is more readable and universal. Data obtained from social media sites and tweets often contain much noise and use non-canonical sentence structures such as non-standard abbrevlatlons, skipping of words, spelling errors, etc. Hence such data needs to be appropriately processed before it can be used. The processing can be done by lexical normalization, which reduces randomness and converts the sentence structure to a predefined standard. Hence. lexical normalization can help in improving the performance of systems that use user-generated text as inputs. There are several ways to perform lexical normalization, such as dictionary lookups, most frequent replacements, etc. However, VVe aim to explore the domain of deep learning to find approaches that can be used to normalize texts lexically. © 2021 IEEE.
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    LSTM-Attention Architecture for Online Bilingual Sexism Detection
    (CEUR-WS, 2023) Ravi, S.; Kelkar, S.; Anand Kumar, M.
    The paper describes the results submitted by ‘Team-SMS’ at EXIST 2023. A dataset of 6920 tweets for training, 1038 for validation, and 2076 tweets for testing was provided by the task organizers to train and test our models. Our models include LSTM models coupled with attention layers and without attention. For calculation of soft scores according to the task we tried to mimic human performance by taking an average of different machine learning model predictions using Multinomial Naive Bayes, Linear Support Vector Classifier, Multi Layer Perceptron, XGBoost, LSTM using GloVe embeddings, and LSTM using fastText embeddings. We discuss our approach to remove the ambiguity in the labeling process and detailed description of our work. © 2023 Copyright for this paper by its authors.
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    Hate Speech Detection Using Audio in Portuguese Language
    (Springer Science and Business Media Deutschland GmbH, 2024) Tembe, L.A.; Anand Kumar, M.
    This study focuses on hate speech in Portuguese language using audio and introduces a novel methodology that integrates audio-to-text and self-image technologies to effectively tackle this problem. We utilize Machine Learning and Deep Learning models to differentiate between hate speech and normal speech. The research utilized a total of 200 datasets, which were categorized into hate speech and normal speech. These datasets were collected by me personally for this project. Four distinct models are presented in the analysis: LSTM, SVM, CNN, and Random Forest. The findings highlight the superior performance of the CNN model when applied to spectrogram data, achieving an accuracy rate of 90%. Conversely, the Random Forest model outperforms others when dealing with text data, achieving an impressive accuracy rate of 73.1%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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    Long short-term memory network for learning sentences similarity using deep contextual embeddings
    (Springer Science and Business Media B.V., 2021) Meshram, S.; Anand Kumar, M.
    Semantic text similarity (STS) is a challenging issue for natural language processing due to linguistic expression variability and ambiguities. The degree of the likelihood between the two sentences is calculated by sentence similarity. It plays a prominent role in many applications like information retrieval (IR), plagiarism detection (PD), question answering platform and text paraphrasing, etc. Now, deep contextualised word representations became a better way for feature extraction in sentences. It has shown exciting experimental results from recent studies. In this paper, we propose a deep contextual long semantic textual similarity network. Deep contextual mechanisms for collecting high-level semantic knowledge is used in the LSTM network. Through implementing architecture in multiple datasets, we have demonstrated our model’s effectiveness. By applying architecture to various semantic similarity datasets, we showed the usefulness of our model’s on regression and classification dataset. Detailed experimentation and results show that the proposed deep contextual model performs better than the human annotation. © 2021, Bharati Vidyapeeth's Institute of Computer Applications and Management.
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    Video summarization and captioning using dynamic mode decomposition for surveillance
    (Springer Science and Business Media B.V., 2021) Radarapu, R.; Gopal, A.S.S.; Nh, M.; Anand Kumar, M.
    Video surveillance has become a major tool in security maintenance. But analyzing in a playback version to detect any motion or any sort of movements might be tedious work because only for a short length of the video there would be any motion. There would be a lot of time wasted in analyzing the video and also it is impossible to always find the accurate frame where the transition has occurred. So there is a need in obtaining a summary video that captures any changes/motion. With the advancements in image processing using OpenCV and deep learning, video summarization is no longer an impossible work. Captions are generated for the summarized videos using an encoder–decoder captioning model. With the help of large, well-labeled video data sets like common objects in context, Microsoft video description, video captioning is a feasible task. Encoder–decoder models are used extensively to extract text from visual features with the arrival of long short term memory (LSTM). Attention mechanism has been widely used on decoder for the work of video captioning. Keyframes are obtained from very long videos using methods like dynamic mode decomposition, an algorithm in fluid dynamics, OpenCV’s absdiff(). We propose these tools for motion detection and video/image captioning for very long videos which are common in video surveillance. © 2021, Bharati Vidyapeeth's Institute of Computer Applications and Management.