Conference Papers

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

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Author: search.filters.author.Harsha, S.S.

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    Hybridization Approach to Optimize the Expected Loss of Demand in Drone Delivery Scheduling
    (Institute of Electrical and Electronics Engineers Inc., 2024) Harsha, S.S.; Muddi, K.S.; Jindrali, S.S.; Reji, S.; Das, M.; Mohan, B.R.
    This paper explores a hybrid-optimization approach for reducing the expected loss of delivery in drone delivery.This paper aims to give a deep knowledge about drone scheduling using machine learning and bio-optimized approaches. Using hybridization of K-Mean Clustering algorithms and Genetic algorithms, the paper makes a comparison between the performance of the above algorithm with the hybridization of hierarchical agglomerative clustering algorithms and ant colony optimization algorithms, resulting in valuable insights into drone delivery efficiency and reliability. © 2024 IEEE.
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    Neural Pooling for Graph Neural Networks
    (Springer Science and Business Media Deutschland GmbH, 2024) Harsha, S.S.; Mishra, D.
    Tasks such as graph classification, require graph pooling to learn graph-level representations from constituent node representations. In this work, we propose two novel methods using fully connected neural network layers for graph pooling, namely Neural Pooling Method 1 and 2. Our proposed methods have the ability to handle variable number of nodes in different graphs, and are also invariant to the isomorphic structures of graphs. In addition, compared to existing graph pooling methods, our proposed methods are able to capture information from all nodes, collect second-order statistics, and leverage the ability of neural networks to learn relationships among node representations, making them more powerful. We perform experiments on graph classification tasks in the bioinformatics and social network domains to determine the effectiveness of our proposed methods. Experimental results show that our methods lead to an increase in graph classification accuracy over previous works and a general decrease in standard deviation across multiple runs indicating greater reliability. Experimental results also indicate that this improvement in performance is consistent across several datasets. © Springer Nature Switzerland AG 2024.