Journal Article
Permanent URI for this collectionhttps://ir.daiict.ac.in/handle/123456789/37
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Publication Metadata only Exploring Topic Trends in COVID-19 Research Literature using Non-Negative Matrix Factorization(IEEE, 12-06-2025) Patel, Divya; Parikh, Vansh; Patel, Om; Shah, Agam; Chaudhury, Bhaskar; DA-IICT, GandhinagarPublication Metadata only Understanding and Attaining an Investment Grade Rating in the Age of Explainable AI(Springer, 18-08-2024) Makwana, Ravi; Bhatt, Dhruvil; Delwadia, Kirtan; Shah, Agam; Chaudhury, Bhaskar; DA-IICT, Gandhinagar; Makwana, Ravi (201801461); Bhatt, Dhruvil (201801056); Delwadia, Kirtan (201801020)Specialized agencies issue corporate credit ratings to evaluate the creditworthiness of a company, serving as a crucial financial indicator for potential investors. These ratings offer a tangible understanding of the risks associated with the credit investment returns of a company. Every company aims to achieve a favorable credit rating, as it enables them to attract more investments and reduce their cost of capital. Credit rating agencies typically employ unique rating scales that are broadly categorized into investment-grade or non-investment-grade (junk) classes. Given the extensive assessment conducted by credit rating agencies, it becomes a challenge for companies to formulate a straightforward and all-encompassing set of rules which may help to understand and improve their credit rating. This paper employs explainable AI, specifically decision trees, using historical data to establish an empirical rule on financial ratios. The rule obtained using the proposed approach can be effectively utilized to understand as well as plan and attain an investment-grade rating. Additionally, the study investigates the temporal aspect by identifying the optimal time window for training data. As the availability of structured data for temporal analysis is currently limited, this study addresses this challenge by creating a large and high-quality curated dataset. This dataset serves as a valuable resource for conducting comprehensive temporal analysis. Our analysis demonstrates that the empirical rule derived from historical data, yields a high precision value, and therefore highlights the effectiveness of our proposed approach as a valuable guideline and a feasible decision support system.Publication Metadata only Deep learning assisted microwave-plasma interaction based technique for plasma density estimation(IOP Science, 01-08-2024) Ghosh, Pratik; Chaudhury, Bhaskar; Purohit, Shishir; Joshi, Vishv; Kothari, Ashray; Shetranjiwala, Devdeep; Chaudhury, Bhaskar; Chaudhury, Bhaskar; Chaudhury, Bhaskar; Chaudhury, Bhaskar; Chaudhury, Bhaskar; DA-IICT, Gandhinagar; Ghosh, Pratik (201721010); Joshi, Vishv (201901453); Kothari, Ashray (201901457); Shetranjiwala, Devdeep (202001150)The electron density is a key parameter to characterize any plasma. Most of the plasma applications and research in the area of low-temperature plasmas (LTPs) are based on the accurate estimations of plasma density and plasma temperature. The conventional methods for electron density measurements offer axial and radial profiles for any given linear LTP device. These methods have major disadvantages of operational range (not very wide), cumbersome instrumentation, and complicated data analysis procedures. The article proposes a deep learning (DL) assisted microwave-plasma interaction-based non-invasive strategy, which can be used as a new alternative approach to address some of the challenges associated with existing plasma density measurement techniques. The electric field pattern due to microwave scattering from plasma is utilized to estimate the density profile. The proof of concept is tested for a simulated training data set comprising a low-temperature, unmagnetized, collisional plasma. Different types of symmetric (Gaussian-shaped) and asymmetrical density profiles, in the range 1016�1019 m?3, addressing a range of experimental configurations have been considered in our study. Real-life experimental issues such as the presence of noise and the amount of measured data (dense vs sparse) have been taken into consideration while preparing the synthetic training data-sets. The DL-based technique has the capability to determine the electron density profile within the plasma. The performance of the proposed DL-based approach has been evaluated using three metrics- structural similarity index, root mean square logarithmic error, and mean absolute percentage error. The obtained results show promising performance in estimating the 2D radial profile of the density for the given linear plasma device and affirms the potential of the proposed machine learning-based approach in plasma diagnostics.Publication Metadata only Automated labelling and correlation analysis of diagnostic signals from ADITYA tokamak for developing AI-based disruption mitigation systems(Taylor and Francis, 09-08-2024) Agarwal, J; Chaudhury, Bhaskar; Jakhar, S; Shah, N; Arora, S; Katrodia, D; Sharma, M; DA-IICT, GandhinagarAI/ML-based data-driven methodologies are becoming increasingly effective in understanding and predicting plasma disruption in tokamaks by identifying critical signatures present in various diagnostic signals obtained from tokamaks. A high-performance ML-based disruption predictor requires large accurately labelled data. Until now, plasma shots from the ADITYA tokamak have primarily been classified (labelled) as disruptive or non-disruptive manually. Here, we present three computational techniques, namely the Sorted-array approach, the Interval comparison approach and the Threshold-Straight line method for automatic labelling of the ADITYA shots as disruptive or non-disruptive based on the plasma current dropdown time. Statistical analysis and comparison between automatic labelling and manual labelling indicate the promising potential of the proposed techniques. A correlation analysis is also conducted by incorporating plasma diagnostics such as Plasma current, Loop voltage, Bolometer, Mirnov, Hard X-ray, Soft�X-ray, Radiation from Hydrogen-alpha, ionised oxygen and ionised carbon. This comprehensive study offers valuable insights into diverse physical phenomena associated with disruptions. Furthermore, correlation analysis based on current quench time highlights the significance of different diagnostics in providing distinct signatures related to plasma disruption. The insights obtained from this work can play a pivotal role in advancing the development of data-driven disruption prediction systems for ADITYA tokamak.Publication Metadata only CONCORD: Enhancing COVID-19 Research with Weak-Supervision based Numerical Claim Extraction(Research Square, 18-03-2024) Shah, Dhwanil; Shah, Krish; Jagani, Manan; Shah, Agam; Chaudhury, Bhaskar; Chaudhury, Bhaskar; Chaudhury, Bhaskar; Chaudhury, Bhaskar; Chaudhury, Bhaskar; Chaudhury, Bhaskar; DA-IICT, Gandhinagar; Shah, Dhwanil (201901450); Shah, Krish (201901465); Jagani, Manan (201901295)The COVID-19 Numerical Claims Open Research Dataset (CONCORD) is a comprehensive, open-source dataset that extracts numerical claims from academic papers on COVID-19 research. To extract numerical claims, a weak-supervision based model is employed, leveraging its white-box, explainable nature and advantages over transformer-based models in terms of computational and manual annotation costs. Labelling functions are used to programmatically generate labels, incorporating techniques like pattern matching, external knowledge bases, phrase matching, and third-party models. An aggregator function reconciles overlapping or contradictory labels. The weak-supervision model is evaluated against established baselines and transformer based models, achieving a weighted F1-score of 0.932 and micro F1-score of 0.930 in extracting numerical claims.While the weak-supervision model showcases superior performance compared to baseline models, it is observed that transformer-based models achieve comparable results.CONCORD, comprising around 200,000 numerical claims extracted from over 57,000 COVID-19 research articles, serves as a valuable tool for knowledge discovery and understanding the chronological developments in various research areas associated with COVID-19. In conclusion, CONCORD, alongside the weak-supervision methodology, offers researchers a valuable resource, enhancing advancements in COVID-19 research while highlighting the significant potential of weak-supervision models within the broader biomedical domain.