Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.Optimisation

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    Optimization of Declarative Graphics by parallel programming
    (Institute of Electrical and Electronics Engineers Inc., 2017) Balachandran, M.; Nagori, K.; Rajan, A.; Koolagudi, S.G.; Afroz, F.
    Declarative Graphics is a new model built using the concepts of declarative programming in graphics. This paper presents an optimization of declarative graphics by parallelization. The optimization tries to reduce the backend processing of declarative graphics which induces an overhead. The parallelization is achieved by manipulating the inbuilt structure of declarative graphics. By parallelizing the backend computation a significant reduction in computation time has been achieved. © 2016 IEEE.
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    Multiple response optimisation of process parameters during drilling of GFRP composite with a solid carbide twist drill
    (Elsevier Ltd, 2020) Bhat, R.; Mohan, N.; Sharma, S.; Dayananda Pai, D.; Kulkarni, S.M.
    The article focuses on investigating the effect of operational parameters like feed and speed along with the composite material thickness on the damages caused in the glass fibre reinforced polymer (GFRP) composites during the drilling process. The GFRP composite studied in the presented work comprises E-glass fibre as the reinforcing material and the marine-grade isophthalic polyester as the binding matrix. Multiple responses considered in work comprises Peel-up delamination, push-down delamination and surface roughness. The technique for order of preference by similarity to ideal solution (TOPSIS) is used to develop the performance index and optimise the multiple response problem. Stepwise analysis of variance (S-ANOVA) is used to investigate the significance of each input parameter. The interaction effects of the variables are investigated using the response surface plots. The results indicate that the composite thickness contributes maximum towards the variance in the overall performance index (21.30%) and the optimum combination obtained using TOPSIS approach within the experimental limits for the selected GFRP is N3f1t1 with the maximum value of Pi (0.888). The regression model developed proves to have high goodness of fit with just 6.01% average error between predicted and experimental values. © 2019 Elsevier Ltd.
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    Machine Learning Models with Optimization for Clothing Recommendation from Personal Wardrobe
    (Institute of Electrical and Electronics Engineers Inc., 2020) Jain, M.; Singh, S.; Chandrasekaran, K.; Rathnamma, M.V.; Ramana, V.
    In the present-day scenario, several clothing recommender systems have been developed for the online e-commerce industry. However, when it comes to recommending clothes that a person already possesses, i.e, from their personal wardrobe, there are very few systems that have been proposed to perform the task. In this paper, we tackle the latter issue, and perform experimental analysis of the various Machine Learning techniques that can be used for carrying out the task. Since the recommendations must be made from a user's personal wardrobe, the recommender system doesn't follow a traditional approach. This is explained in detail in the following sections. Further, the paper contains a complete description of the results obtained from the experiments conducted, and the best approach is specified, with appropriate justification for the same. © 2020 IEEE.