Conference Papers

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

Browse

Subject: search.filters.subject.AdaBoost

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Estimation of Tyre Pressure from the Characteristics of the Wheel: An Image Processing Approach
    (Springer, 2020) Vineeth Reddy, V.B.; Ananda Rao, H.; Yeshwanth, A.; Ramteke, P.B.; Koolagudi, S.G.
    Improper tyre pressure is a safety issue that falls prey to ignorance of users. But a drop in tyre pressure can result in the reduction of mileage, tyre life, vehicle safety and performance. In this paper, an approach is proposed to measure the tyre pressure from the image of the wheel. The tyre pressure is classified into under pressure and normal pressure using load index, tyre type, tyre position and ratio of compressed and uncompressed tyre radius. The efficiency of the feature is evaluated using three classifiers namely Random Forest, AdaBoost and Artificial Neural Networks. It is observed that the ratio of radii plays a major role in classifying the tyres. The proposed system can be used to obtain a rough idea on whether the tyre should be refilled or not. © 2020, Springer Nature Singapore Pte Ltd.
  • No Thumbnail Available
    Item
    NBA MVP Prediction and Historical Analysis Using Cross-Era Comparison Approaches
    (Institute of Electrical and Electronics Engineers Inc., 2024) Godbole, I.; Murali, S.S.; Sowmya Kamath, S.
    In order to understand the crucial player statistics that decide the Most Valuable Player (MVP) Trophy, this research study dives into a substantial 32-year dataset of the National Basketball Association (NBA). We build a predictive framework trained on historical player statistics and MVP voting results using a sophisticated ensemble of machine learning models, including Support Vector Machines (SVM), ElasticNet, AdaBoost, Random Forest and Back-propagation Neural Network (BP). We determine the key elements influencing this renowned award by evaluating connections between player stats and MVP picks. Our research provides insights into the MVP selection process by utilising the models' ability to capture complex patterns and nonlinear interactions, providing stakeholders with a reliable tool for assessing player performances.This work advances the discourse surrounding the NBA MVP Trophy and enriches our comprehension of player value assessment. Also, the prediction models are used to conduct various historical analysis experiments, by finding an objective method to compare performances of players from different eras. © 2024 IEEE.