BITS Faculty Publications

Permanent URI for this communityhttp://localhost:4000/handle/123456789/1867

Browse

Filters

2018 - 201912020 - 20233

Settings

Author: search.filters.author.Sharma, YashvardhanSubject: search.filters.subject.Machine learning (ML)

Search Results

Now showing 1 - 4 of 4
  • No Thumbnail Available
    Item
    Composite Sequential Modeling for Identifying Fake Reviews
    (De Gruyter, 2018-04) Sharma, Yashvardhan
    This paper presents a comprehensive analysis and comparison of various proposed sequential models based on different deep networks such as the convolutional neural network, long short-term memory, and recurrent neural network. The different sequential models are analyzed based on the number of layers, the number of output dimensions, order, and the combination of different deep network architectures. The proposed approach is compared to a baseline model based on traditional machine learning techniques.
  • No Thumbnail Available
    Item
    Hate Speech Detection in Marathi and Code-Mixed Languages using TF-IDF and Transformers-Based BERT-Variants
    (CEUR-WS, 2022) Sharma, Yashvardhan
    People now express their ideas on social media on a global scale. Online attacks against others can be made without fear of repercussions due to the increased sense of freedom provided by the anonymity feature, which eventually leads to the spread of hate speech. The current attempts to filter online information and stop the propagation of hatred are insufficient. Regional languages’ popularity on social media and the lack of hate speech detectors that can be used in multiple languages are two aspects that contribute to this. This paper discusses two aspects of fake news detection namely: Identification of Conversational Hate-Speech in Code-Mixed Languages like Hindi, English and German, while second part discusses about Offensive Language Identification in Marathi. Our approach uses TF-IDF word embedding combined with Machine Learning models and transformer based BERT models for the classification of hate speech in each of the two sub tasks. The MuRIL-BERT model produces the best results, with an accuracy of 73.1% and a Macro-F1 score of 0.727 for the code-mixed language and a macro F1-score of 0.8306 on Marathi data, which is 6% more from previous year.
  • No Thumbnail Available
    Item
    Comparative Analysis of Various Machine Learning Based Techniques for Predicting the Virality of Tweets
    (IEEE, 2022) Sharma, Yashvardhan
    Social media has become more popular, and people tend to read the news more often from it than traditional media. But all the information that is posted on the social media platform might not go viral. In this paper, we have analyzed the data from one of the social media platforms, Twitter, and established a few reasons for the virality of tweets. Along with it, given the tweet information and user details to the trained model, we could predict whether the tweets go viral or not. For this, we used multiple architectures from classical machine learning like Random Forest, XGBoost and Lightgbm and Convolutions from Deep Learning and got the highest accuracy using the Lightgbm model. The results show that using both text and image data combined provides better results when compared with using only text or images (unimodal data). The data used is from the competition with full user details, tweet information, and tweet text and image.
  • No Thumbnail Available
    Item
    Automatic Subjective Answer Evaluation
    (ICPRAM, 2023) Sharma, Yashvardhan
    The evaluation of answer scripts is vital for assessing a student’s performance. The manual evaluation of the answers can sometimes be biased. The assessment depends on various factors, including the evaluator’s mental state, their relationship with the student, and their level of expertise in the subject matter. These factors make evaluating descriptive answers a very tedious and time-consuming task. Automatic scoring approaches can be utilized to simplify the evaluation process. This paper presents an automated answer script evaluation model that intends to reduce the need for human intervention, minimize bias brought on by evaluator psychological changes, save time, maintain track of evaluations, and simplify extraction. The proposedmethod can automatically weigh the assessing element and produce results nearly identical to an instructor’s. We compared the model’s grades to the grades of the teacher, as well as the results of several keyword matching and similarity check techniques, in order to evaluate the developed model