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Author: search.filters.author.Anand Kumar, M.A.Has files: No

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    Extraction of named entities from social media text in tamil language using N-gram embedding for disaster management
    (Springer Verlag service@springer.de, 2020) Remmiya Devi, G.R.; Anand Kumar, M.A.; Padannayil, K.P.
    In the present era, data in any form is considered with greater importance. More specifically, text data has rich and brief information than any other form of data. Extraction and analysis of these data can result in various new findings through text analytics. This has led to applications such as search engines, extraction of product names, sentiment analysis, document classification and few more. Companies are much focused on sentimental analysis to review the positive, negative and neutral comments for their products. Summarization of text is a notable application of Natural Language Processing that reveals the gist of brief documents. Apart from these, on concerning welfare of the society, application based on information extraction can be developed. Handling an emergency situation requires collection of vast information. Extraction of such data can be supportive during disaster management. In order to perceive such task, system must learn the meaning of human languages. To ease the accessibility of text data across language barriers is the primary motive of Natural Language Processing (NLP) systems. The proposed systems has utilized word embedding model, specifically skip gram model to implement the most fundamental task of NLP—entity extraction in social media text. Implementation of N-gram embedding methods paved way for creation of rich context knowledge for the system to handle social media text. Classification of named entities using the proposed system has been carried out using machine learning classifier Support Vector Machine (SVM). © Springer Nature Switzerland AG 2020.
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    Image Manipulation Detection Using Augmentation and Convolutional Neural Networks
    (Springer Science and Business Media Deutschland GmbH, 2024) Maheshwari, A.; Jain, R.; Mahapatra, R.; Palakuru, S.; Anand Kumar, M.A.
    Image tampering is now simpler than ever, thanks to the explosion of digital photos and the creation of easy image modification tools. As a result, if the situation is not handled properly, the major problems may arise. Many computer vision and deep learning strategies have been put out over the years to address the problem. Having said that, people can easily recognize the photographs that were used in that research. This begs the key question of how CNNs might do on more difficult samples. In this chapter, we build a complex CNN network and use various machine learning algorithms to classify the images and compare the accuracies obtained by them. Its performance is also compared on two different datasets. Additionally, we assess the impact of various hyperparameters and a data augmentation strategy on classification performance. This leads to a conclusion that performance can be considerably impacted by dataset difficulty. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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    Generating Synthetic Text Data for Improving Class Balance in Personality Prediction
    (Springer Science and Business Media Deutschland GmbH, 2024) Lakhtaria, D.; L, D.H.; Chhabra, R.; Taparia, R.; Anand Kumar, M.A.
    The growing popularity of social media as a means of self-expression and self-discovery has sparked a heightened curiosity in utilizing the Myers–Briggs Type Indicator (MBTI) to investigate human personalities. Despite the increasing use of word-embedding techniques, machine learning algorithms, and imbalanced data-handling techniques to predict MBTI personality types, further research is needed to explore how these approaches can enhance the accuracy of the results. Our research aimed to use the GPT model to address the problem of class imbalance. We have implemented several machine learning models such as RCNN, LSTM, XGBoost, and Random Forest. We have also tried using two-word embedding including Word2Vec and GloVe Embedding. According to our findings, the approach we used can attain a considerably high F1-score, which is dependent on the selected model for the prediction and classification of MBTI personality. The ability to accurately predict and classify MBTI personality through our approach has the potential to improve our comprehension of MBTI. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.