Conference Papers

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

Browse

Author: search.filters.author.Bhat, S.S.Has files: No

Search Results

Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    A study of performance scalability by parallelizing loop iterations on multi-core SMPs
    (2010) Raghavendra, P.S.; Behki, A.K.; Hariprasad, K.; Mohan, M.; Jain, P.; Bhat, S.S.; Thejus, V.M.; Prabhu, V.
    Today, the challenge is to exploit the parallelism available in the way of multi-core architectures by the software. This could be done by re-writing the application, by exploiting the hardware capabilities or expect the compiler/software runtime tools to do the job for us. With the advent of multi-core architectures ([1] [2]), this problem is becoming more and more relevant. Even today, there are not many run-time tools to analyze the behavioral pattern of such performance critical applications, and to re-compile them. So, techniques like OpenMP for shared memory programs are still useful in exploiting parallelism in the machine. This work tries to study if the loop parallelization (both with and without applying transformations) can be a good case for running scientific programs efficiently on such multi-core architectures. We have found the results to be encouraging and we strongly feel that this could lead to some good results if implemented fully in a production compiler for multi-core architectures. © Springer-Verlag Berlin Heidelberg 2010.
  • No Thumbnail Available
    Item
    A TFD Approach to Stock Price Prediction
    (Springer, 2020) Chanduka, B.; Bhat, S.S.; Rajput, N.; Mohan, B.R.
    Accurate stock price predictions can help investors take correct decisions about the selling/purchase of stocks. With improvements in data analysis and deep learning algorithms, a variety of approaches has been tried for predicting stock prices. In this paper, we deal with the prediction of stock prices for automobile companies using a novel TFD—Time Series, Financial Ratios, and Deep Learning approach. We then study the results over multiple activation functions for multiple companies and reinforce the viability of the proposed algorithm. © 2020, Springer Nature Singapore Pte Ltd.
  • No Thumbnail Available
    Item
    Comparative Performance Evaluation of Web-Based Book Recommender Systems
    (Institute of Electrical and Electronics Engineers Inc., 2022) Bhat, S.S.; Pranav, P.; Shashank, K.V.; Raghunandan, A.; Mohan, B.R.
    In today's world, recommendation algorithms are popularly utilised for personalization. To improve their business, e-commerce behemoths rely heavily on their recommendation algorithms. As a result, the quality of suggestions can have a big impact on how much money they make. As a result, effective evaluation of recommender systems is critical. Traditional evaluation measures are limited to error-based and accuracy-based metrics, and do not account for characteristics such as novelty, informedness, markedness, and so on. This research study aims to compare the effectiveness of two web-based book recommendation systems by using the measures like diversity, informedness, and markedness, which are less well-known but equally essential. © 2022 IEEE.