2. Conference Papers
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Item Evolution of Tribological Properties of Cast Al�10Zn�2Mg Alloy Subjected to Severe Plastic Deformation(2020) Manjunath, G.K.; Preetham, Kumar, G.V.; Udaya, Bhat, K.In the current investigation, tribological behaviour of the cast Al�10Zn�2Mg alloy processed by severe plastic deformation (SPD) technique was studied. In this work, one of the SPD techniques, equal channel angular pressing (ECAP) was adopted as a processing tool. ECAP was carried out in route BC and processing was attempted at the lowest temperature. After ECAP, grain structure of the material was refined and considerable improvement in the microhardness of the alloy was perceived. Mainly, wear resistance of the alloy material was enhanced with successive ECAP passes. Coefficient of friction of the alloy material was decreased with successive ECAP passes. Wear resistance of the alloy was decreased with a rise in the applied load and the sliding speed. Both at low and high load condition, abrasive wear was noticed in as-cast and homogenized specimens. Whereas in ECAPed specimens, in addition to abrasive wear, oxidation wear and adhesive wear were observed in low load and it changes to abrasive wear at high load. In the ECAPed specimens, at low load transfer of iron particles from the steel disc surface to the specimen surface was identified. � 2020, Springer Nature Singapore Pte Ltd.Item Improving convergence in Irgan with PPO(2020) Jain, M.; Sowmya, Kamath S.Information retrieval modeling aims to optimise generative and discriminative retrieval strategies, where, generative retrieval focuses on predicting query-specific relevant documents and discriminative retrieval tries to predict relevancy given a query-document pair. IRGAN unifies the generative and discriminative retrieval approaches through a minimax game. However, training IRGAN is unstable and varies largely with the random initialization of parameters. In this work, we propose improvements to IRGAN training through a novel optimization objective based on proximal policy optimisation and gumbel-softmax based sampling for the generator, along with a modified training algorithm which performs the gradient update on both the models simultaneously for each training iteration. We benchmark our proposed approach against IRGAN on three different information retrieval tasks and present empirical evidence of improved convergence. � 2020 Copyright held by the owner/author(s). Publication rights licensed to ACM.Item Deep neural learning for automated diagnostic code group prediction using unstructured nursing notes(2020) Jayasimha, A.; Gangavarapu, T.; Sowmya, Kamath S.; Krishnan, G.S.Disease prediction, a central problem in clinical care and management, has gained much significance over the last decade. Nursing notes documented by caregivers contain valuable information concerning a patient's state, which can aid in the development of intelligent clinical prediction systems. Moreover, due to the limited adaptation of structured electronic health records in developing countries, the need for disease prediction from such clinical text has garnered substantial interest from the research community. The availability of large, publicly available databases such as MIMIC-III, and advancements in machine and deep learning models with high predictive capabilities have further facilitated research in this direction. In this work, we model the latent knowledge embedded in the unstructured clinical nursing notes, to address the clinical task of disease prediction as a multi-label classification of ICD-9 code groups. We present EnTAGS, which facilitates aggregation of the data in the clinical nursing notes of a patient, by modeling them independent of one another. To handle the sparsity and high dimensionality of clinical nursing notes effectively, our proposed EnTAGS is built on the topics extracted using Non-negative matrix factorization. Furthermore, we explore the applicability of deep learning models for the clinical task of disease prediction, and assess the reliability of the proposed models using standard evaluation metrics. Our experimental evaluation revealed that the proposed approach consistently exceeded the state-of-the-art prediction model by 1.87% in accuracy, 12.68% in AUPRC, and 11.64% in MCC score. � 2020 Association for Computing Machinery.Item Tensile toughness characteristics of cast Al-Zn-Mg alloys processed by equal channel angular pressing(2020) Manjunath, G.K.; Bhat, K.U.; Preetham, Kumar, G.V.In the current study, consequence of ECAP on the toughness characteristics of the Al-Zn-Mg alloys was studied. Three set of Al-Zn-Mg alloys (5, 10 and 15% Zn and 2% Mg) were selected and ECAPed. Also, consequence of zinc on the toughness characteristics of the alloy, before and after ECAP was studied. After ECAP, grain size of the alloys decreased and significant rise in the strength and ductility of the alloys were noticed. Mainly, modulus of toughness of the alloys increased with successive ECAP passes. But, the modulus of toughness of the alloys decreased with rise in the zinc in the material. � 2020 Trans Tech Publications Ltd, Switzerland.