Conference Papers
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Item A Probabilistic Precision Information Retrieval Model for Personalized Clinical Trial Recommendation based on Heterogeneous Data(Institute of Electrical and Electronics Engineers Inc., 2021) Kamath S․, S.; Veena Mayya; Priyadarshini, R.In modern healthcare practices, diagnosis and treatment for certain complex illnesses require specific information on the. patients' background, genealogy, heredity, demographic data etc. Even with a similar diagnosis, treatments may need to designed specifically to adapt well to the patients' genetic, cultural, and lifestyle aspects. Precision medicine mainly deals with enabling personalized care based on a given patient's conditions in a scientifically rigorous way. Because this entails recommending personalized therapies to patients and has the potential to affect the health of other people, the performance of a designed system must be accurate and exact. In this paper, a precision information retrieval system is proposed that leverages structured and unstructured data to retrieve. relevant knowledge for enabling personalized recommendations, The. proposed pipeline is validated with the cllnlcal trial dataset of the Precision medicine track of TREe 2017. A set of relevant ranked clinical trials for a given condition/disease that could not be cured using any of the traditional treatments suggested are retrieved using structured and unstructured patient data. 'We employ multiple IR techniques like Best Match 25, query reformulation and rearanking facilitated through deep neural networks, focusing on extracting highly accurate and relevant trials. The proposed pipeline achieved a high score of 0.58 in terms of Normalized Discounted Cumulative Gain (NDCG) score for ranking the relevant clinical trials, outperforming the state-of-the-art approaches. © 2021 IEEE.Item Overview of the Shared Task on Machine Translation in Dravidian Languages(Association for Computational Linguistics (ACL), 2022) Anand Kumar, A.M.; Hegde, A.; Banerjee, S.; Chakravarthi, B.R.; Priyadarshini, R.; Shashirekha, H.L.; Mccrae, J.P.This paper presents an outline of the shared task on translation of under-resourced Dravidian languages at DravidianLangTech-2022 workshop to be held jointly with ACL 2022. A description of the datasets used, approach taken for analysis of submissions and the results have been illustrated in this paper. Five sub-tasks organized as a part of the shared task include the following translation pairs: Kannada to Tamil, Kannada to Telugu, Kannada to Sanskrit, Kannada to Malayalam and Kannada to Tulu. Training, development and test datasets were provided to all participants and results were evaluated on the gold standard datasets. A total of 16 research groups participated in the shared task and a total of 12 submission runs were made for evaluation. Bilingual Evaluation Understudy (BLEU) score was used for evaluation of the translations. © 2022 Association for Computational Linguistics.Item Spatio-temporal Analysis and Modeling of Coastal areas for Water Salinity Prediction(Institute of Electrical and Electronics Engineers Inc., 2023) Sudhakara, B.; Priyadarshini, R.; Bhattacharjee, S.; Kamath S․, S.; Umesh, P.; Gangadharan, K.V.; Ghosh, S.K.Salinity is an important parameter affecting the quality of water, and excessive amounts adversely affect vege-tation growth and aquatic organism populations. Natural factors like tidal waves, natural calamities etc., and man-made factors like unchecked disposal of industrial wastes, domestic/ urban sewage, and fish hatchery activities can cause significant increases in water salinity. In this article, an approach that utilizes multimodal data like Landsat 8 optical observations and the SMAP salinity data product for predicting water salinity indices in the coastal region is proposed. Machine Learning models such as K-nearest neighbor (KNN), Gradient Boost (GB), Extremely Randomized Tree (ERT), Random Forest Regression (RFR), Decision Tree (DT), Multiple Linear Regression (MLR), Lasso Regression (LR), and Ridge Regression (RR) are used for salinity prediction. Empirical experiments revealed that the ERT model outperformed other ML models, with a R2 of 0.92 and RMSE of 0.25 psu. © 2023 IEEE.Item Water Salinity Assessment Using Remotely Sensed Images—A Comprehensive Survey(Springer Science and Business Media Deutschland GmbH, 2023) Priyadarshini, R.; Sudhakara, B.; Kamath S․, S.; Bhattacharjee, S.; Umesh, P.; Gangadharan, K.V.In the past few years, the problem of growing salinity in river estuaries has directly impacted living and health conditions, as well as agricultural activities globally, especially for those rivers which are the sources of daily water consumption for the surrounding community. Key contributing factors include hazardous industrial wastes, residential and urban wastewater, fish hatchery, hospital sewage, and high tidal levels. Conventional survey and sampling-based approaches for water quality assessment are often difficult to undertake on a large-scale basis and are also labor and cost-intensive. On the other hand, remote sensing-based techniques can be a good alternative to cost-prohibitive traditional practices. In this article, an attempt is made to comprehensively assess various approaches, datasets, and models for determining water salinity using remote sensing-based approaches and in situ observations. Our work revealed that remote sensing techniques coupled with other techniques for estimating the salinity of water offer a clear advantage over traditional practices and also is very cost-effective. We also highlight several observations and gaps that can be beneficial for the research community to contribute further in this significant research domain. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Automated Marine Debris Detection from Sentinel-2 Satellite Imagery(Institute of Electrical and Electronics Engineers Inc., 2024) Priyadarshini, R.; Arya, V.; Sowmya Kamath, S.Marine debris present a severe, escalating threat to oceans and coastal ecosystems, requiring effective monitoring and detection. This work proposes an automated marine debris detection system utilizing satellite imagery data from the MARIDA dataset, sourced from Sentinel-2. Advanced AI techniques are leveraged to analyze high-resolution satellite imagery, and the models are trained to facilitate the identification/tracking of marine debris across various water bodies. Experiments reveal that the machine learning models form a robust baseline, while the UNet model achieves improved precision. The proposed Attention-activated UNet model achieved the best performance, particularly in challenging conditions. © 2024 IEEE.