Conference Papers

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

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Author: search.filters.author.Maheshwari, A.

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    Hadoop task scheduling - Improving algorithms using tabular approach
    (Institute of Electrical and Electronics Engineers Inc., 2015) Maheshwari, A.; Bhardwaj, A.; Chandrasekaran, K.
    Map Reduce is a widely adopted implementation in many fields like that of scientific analysis for data processing, processing data on web as well as areas like high performance computing.Computing systems with heavy data handling requirements should provide an effective scheduling method so that utilization is enhanced.The major problems encountered in scheduling MapReduce jobs are mostly caused by locality and overhead of synchronization.Various other factors like fairness constraints and distribution of workload have been discussed further in the paper and are the highlight of the paper.The paper describes the Hadoop and working of MapReduce in brief.Our paper compares different scheduling methods for handling the mentioned issues in MapReduce and they are compared on the basis of their strength, weakness and features.Through this paper, we aim to consider three different factors along with introducing a small modification to enhance the scheduling by using tabular approach.The purpose is to provide researchers further with a direction in which they can proceed and come up with a more generic algorithm for task scheduling in Hadoop MapReduce. © 2015 IEEE.
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    Computationally efficient fault tolerant ANTS
    (Association for Computing Machinery acmhelp@acm.org, 2016) Tripathi, A.; Maheshwari, A.; Chandrasekaran, K.
    In this paper, we formulate a method to utilize n mobile agents to solve a variant of Ants Nearby Treasure Search problem (ANTS), where an adversary can place treasure at any cell at a distance D from the origin. We devise a method which finds the treasure with the time complex-ity of O(D + D2=n + Df) where D is the Manhattan dis-tance of the treasure from the source and f is the maximum number of failures such that f 2 o(n). The algorithm is specially designed to reduce computation complexity of the distributed system as a whole by efficiently handling fail-ures and also, introducing the elements of parallelism with respect to handling failures. Using our algorithm, we bring down the computation cost/complexity of the system by an order of n, when failures occur, where n is the total number of ants. ANTS problem utilizes the multi-Agent system with self-organization and steering based on a control mechanism which is analogous to the problem of discovering resources that are available to the distributed system. © 2016 ACM.
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    Drought Detection in India using Spatio-Temporal Graph Neural Networks
    (Institute of Electrical and Electronics Engineers Inc., 2025) Sudhakara, B.; Maheshwari, A.; Palakuru, S.; Bhattacharjee, S.
    Droughts, which are defined by extended periods of water scarcity, offer significant difficulties to agriculture, ecosystems, and human populations. Drought detection that requires timely and precise assessment is critical for effective mitigation and resource planning. This work proposes a novel technique for drought detection using satellite imagery with the capabilities of Graph Neural Networks (GNNs). The proposed GNN-based model captures spatio-temporal dependencies by representing 671 districts across India as nodes, connected based on geographical proximity. The spatio-temporal model achieved its best performance with an RMSE of 6.849, MAE of 4.367, and R2 of 0.903 for the Normalized Vegetation Supply Water Index (NVSWI). This work is one of the initial attempts to predict the drought over the Indian region using graph neural networks. © 2025 IEEE.
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    High-resolution Soil Moisture Prediction from SMOS using Machine Learning Models
    (Institute of Electrical and Electronics Engineers Inc., 2025) Sudhakara, B.; Maheshwari, A.; Periasamy, M.; Bhattacharjee, S.
    Soil moisture is essential for the land carbon cycle, surface and groundwater circulation, heat transport, energy exchange between these systems and other processes. SMOS's (Soil Moisture and Ocean Salinity) 36-kilometer spatial resolution and 3-day temporal resolution offer valuable insights into soil moisture dynamics. This research paper introduces an innovative approach to enhance our understanding and prediction of SMOS values by applying advanced machine learning models. Our research focuses on developing and implementing advanced downscaling techniques, leveraging advanced machine learning algorithms. The primary objective is to establish a robust framework for estimating soil moisture levels at multiple geographic locations within the study region of Oklahoma, USA. To achieve this, three years of SMOS (Soil Moisture and Ocean Salinity) data was integrated with remotely captured images spanning the full range of the electromagnetic spectrum, from visible to infrared wavelengths. The LSTM model performed significantly better in predicting soil moisture values with 0.041 RMSE (m3/m3) and 0.869 (R2) than the other models. © 2025 IEEE.