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Author: search.filters.author.Gupta, RajivSubject: search.filters.subject.Machine learning (ML)

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    Comparative assessment of LSTM approaches for enhanced prediction of rainfall climatology with minimum uncertainty
    (Inder Science, 2025) Gupta, Rajiv
    Forecasting precipitation is highly challenging for scientific modellers due to the complexity and uncertainty of atmospheric data and weather prediction models. To investigate the hydrological alternations such as rising sea levels, increasing floods and evaporation, and changes in snowpack caused by climate change, it is essential to accurately predict precipitation, a function of several interrelated climatic variables. This study presents a unique approach to predicting precipitation with minimum uncertainty by performing a comparative assessment of long-short-term memory (LSTM) approaches. The LSTM prediction models were run using quarterly, semi-annual, annual, and biannual precipitation data and other data such as temperature, vapour pressure, cloud cover, rainy days, and potential evaporation. Bivariate models using potential evaporation and temperature produced equivalent results to the multivariate model as the mean absolute error (MAE) was found to be 23.89% and 26.35%, respectively, compared to the univariate model (MAE 76.29%).
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    Inception SN: An Inception based Convolutional Neural Network for Hyperspectral Image Classification
    (IEEE, 2021-10) Gupta, Rajiv
    Hyperspectral satellite imagery provides a wealth of spatial and spectral information about a given scene of interest. Therefore it is widely used in several applications like pixel-wise classification, vegetation mapping, ocean color monitoring and so on. Many pixel-wise classification algorithms like support vector machine, random forest, parallelopiped classifier, and neural networks are used for this purpose. The advent of convolutional neural networks (CNN) has brought about great development in this field, owing to their unique property of automatic feature extraction. Plain CNN architectures perform only one of pooling/convolution at each stage for feature extraction. This paper describes a new CNN architecture, the Inception SN, which makes use of both pooling and convolution at each stage to effectively extract features. It also makes use of spatial and spectral information in order to carry out classification. The outcome of this is a robust algorithm which performs well even with lower training data.
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    Recent Advances in Material, Manufacturing, and Machine Learning
    (CRC Press, 2023) Gupta, Rajiv
    The role of manufacturing in a country’s economy and societal development has long been established through their wealth generating capabilities. To enhance and widen our knowledge of materials and to increase innovation and responsiveness to ever-increasing international needs, more in-depth studies of functionally graded materials/tailor-made materials, recent advancements in manufacturing processes and new design philosophies are needed at present. The objective of this volume is to bring together experts from academic institutions, industries and research organizations and professional engineers for sharing of knowledge, expertise and experience in the emerging trends related to design, advanced materials processing and characterization, and advanced manufacturing processes.