SG
Syam Machinathu Parambil Gangadharan
Home Depot, Liverpool John Moores University, University of Madras, Sambhram Institute of Technology
Research areas: Computer Science · Computer Vision and Pattern Recognition · AI in cancer detection · Advanced Neural Network Applications · Brain Tumor Detection and Classification
0000-0002-7470-4479
About
Syam Gangadharan is a researcher in the field of machine learning and Artificial Intelligence. He obtained his Masters from Liverpool John Moores University, in 2022. His research is focused on the application of machine learning techniques, IOT & AI in the field of Neuroscience, Computer Vision & Human Computer Interaction
Employment
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Home Depot2022 - Present
Education
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Liverpool John Moores University MS in Machine Learning and Artificial Intelligence2020 - Present
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University of Madras MBA in Financial Management2011 - 2012
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Sambhram Institute of Technology BE in Electronics and Communication2004 - 2008
Projects & Funding
Projects & funding information is unavailable.
Publications (10)
- Glaucoma Detection and Classification Using Improved U-Net Deep Learning Model Save
- Multi-Class Pixel Certainty Active Learning Model for Classification of Land Cover Classes Using Hyperspectral Imagery Save
- Breast Cancer Detection on Histopathological Images Using a Composite Dilated Backbone Network Save
- Detection of Adulteration in Food Using Recurrent Neural Network with Internet of Things Save
- Improved Machine Learning Method for Intracranial Tumor Detection with Accelerated Particle Swarm Optimization Save
- Sentiment Analysis of Statements on Social Media and Electronic Media Using Machine and Deep Learning Classifiers Save
- Change Detection in Remote Sensing Image Data Comparing Algebraic and Machine Learning Methods Save
- Apple Sweetness Measurement and Fruit Disease Prediction Using Image Processing Techniques Based on Human‐Computer Interaction for Industry 4.0 Save
- Internet of Things‐Based Data Hiding Scheme for Wireless Communication Save
- Skin cancer multiclass classification through different types of Convolutional Neural Networks Save