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Name: Shubham Jain

Type: User

Company: Blackland Research and Extension Center

Bio: Shubham is a Research Assistant at Texas A&M AgriLife Research and a PhD Candidate in the Water Management and Hydrological Science Department at Texas A&M

Location: Temple, Texas

Hi there! 👋

I'm Shubham Jain, a passionate water resources engineer and a PhD candidate at Texas A&M University. My research focuses on applying machine learning models to solve complex hydrology problems.

📚 PhD Candidate - Currently, I am pursuing my PhD at Texas A&M University, where I am developing interpretable machine learning models to enhance our understanding of hydrological processes.

💻 Machine Learning Enthusiast - I am deeply interested in the application of machine learning techniques to earth system science. I believe that AI can revolutionize how we manage and protect our precious water resources.

🔬 Research Focus - My research areas include rainfall-runoff modeling, flood prediction, and water quality assessment. I'm committed to advancing our knowledge in these critical areas.

🤝 Collaboration - I'm always open to collaborating on projects related to large-sample hydrological modeling. If you're working on similar topics or have ideas for potential collaborations, feel free to reach out! You can connect with me through GitHub or reach out via email at [email protected].

🌐 Connect with Me

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Shubham Jain's Projects

human-action-recognition-with-keras icon human-action-recognition-with-keras

Action/emotion detection is a very vital tool in smart home applications where the smart cameras can detect human actions/emotions using deep learning-based approaches and then address the user’s needs. In this study, a deep neural network for detecting the human action of “Eating” was developed using video clips that can be applied to smart cameras at homes. The widely available datasets from HMDB-51, UCF-101 and kinetics were used to train, test, and validate the models and then applied to YouTube videos recorded at home settings to examine the performance of the model in smart home applications. A combination of 3D CNN architecture for action detection and cv2 human face recognition resulted in an overall best accuracy for YouTube videos.

tx-select icon tx-select

R codes for data preparation and analysis for TXSELECT web-tool development

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