Sargam Yadav

I am a PhD candidate in artificial intelligence at the Regulated Software Research Centre at Dundalk Institute of Technology. I did my masters in data analytics from Dublin business school. My research topic is "Developing Interpretable Artificial Intelligence Tool Kit for Detecting Misogynistic Mixed-code Online Content in South Asian Countries". The objective of my research topic is to assemble a dataset in mix-code Hinglish that can be used to train machine learning and deep learning models for detection of misogynistic hate speech comments. The study also explores explainability of the classifiers through XAI and attempts to mitigate bias.

My other research interests include sentiment analysis, fairness and bias, explainable artificial intelligence, smart farming technologies, dialogue systems, and more. My previous research work has included review papers on artificial intelligence technologies in the healthcare sector, evaluation of dialogue systems, sentiment analysis in code-mixed Hinglish using traditional machine learning classifiers and state-of-the-art Transformer models such as BERT, XLM-RoBERTa, IndicBERT, and more. I was also part of the group project that created and evaluated the ‘Atreya chatbot’ for chemical scientists. I also authored a survey paper for comparing Cloud-IoT systems that were used to track Covid-19 cases during the pandemic.

 

Bibliography:

1)      Yadav, S., Kaushik, A., Sharma, M., & Sharma, S. (2022). Disruptive technologies in smart farming: an expanded view with sentiment analysis. AgriEngineering, 4(2), 424-460.

2)      Yadav, S., Kaushik, A., & Sharma, S. (2021). Simplify the difficult: artificial intelligence and cloud computing in healthcare. IoT and Cloud Computing for Societal Good, 101-124.

3)      Yadav, S., & Kaushik, A. (2022). Do You Ever Get Off Track in a Conversation? The Conversational System’s Anatomy and Evaluation Metrics. Knowledge, 2(1), 55-87.

4)      Sharma, M., Kaushik, A., Kumar, R., Rai, S. K., Desai, H. H., & Yadav, S. (2021). Communication is the universal solvent: atreya bot--an interactive bot for chemical scientists. arXiv preprint arXiv:2106.07257.

5)      Yadav, S., Kaushik, A., & Sharma, S. (2021, December). Cooking well, with love, is an art: Transformers on youtube hinglish data. In 2021 International Conference on Computational Performance Evaluation (ComPE) (pp. 836-841). IEEE.

6)      Mahak, S., Abhishek, K., Shubham, S., & Sargam, Y. (2022, February). Communication is the Universal Solvent: Usability Study on Atreya Bot–An Interactive Bot for Chemical Scientists. In Proceedings of the International Conference on Best Innovative Teaching Strategies (ICON-BITS 2021).

7)      Sharma, M., Yadav, S., Kaushik, A., & Sharma, S. (2021, December). Examining Usability on Atreya Bot: A Chatbot Designed for Chemical Scientists. In 2021 International Conference on Computational Performance Evaluation (ComPE) (pp. 729-733). IEEE.

8)      Yadav, S., & Kaushik, A. (2023). Comparative Study of Pre-trained Language Models for Text Classification in Smart Agriculture Domain. In Advances in Data-driven Computing and Intelligent Systems: Selected Papers from ADCIS 2022, Volume 2 (pp. 267-279). Singapore: Springer Nature Singapore.

9)      Yadav, S., Kaushik, A., & Sharma, M. (2022). A Comparison of Cloud-IoT-Based Frameworks of Identification and Monitoring of Covid-19 Cases. In Cloud IoT (pp. 125-142). Chapman and Hall/CRC.

10)   Yadav, S., & Kaushik, A. (2022, November). Contextualized Embeddings from Transformers for Sentiment Analysis on Code-Mixed Hinglish Data: An Expanded Approach with Explainable Artificial Intelligence. In International Conference on Speech and Language Technologies for Low-resource Languages (pp. 99-119). Cham: Springer International Publishing.

11)   Kovvuri, R. R., Kaushik, A., & Yadav, S. Disruptive technologies for smart farming in developing countries: Tomato leaf disease recognition systems based on machine learning. The Electronic Journal of Information Systems in Developing Countries, e12276.