Geometric Deep Learning is a Deep Learning technique which aims to generalize deep neural models to non-Euclidean domains such as graphs and manifolds. Its goal is geometric unification of a broad class of machine learning problems from the perspectives of symmetry and invariance. To determine the performance of geometric deep learning models, various mathematical and statistical models need to be investigated. Once the learning process is completed, then the model can then be used to make an assumption, to classify and to test data.
2. Role of Geometric Deep learning in agriculture
3. Component of Geometric Deep Learning in basic predictions
4. Role of Geometric Deep learning in soil monitoring and management
5. Geometric Deep Learning used for crop quality prediction
6. Geometric Deep Learning used for yield prediction
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Dr Mohammad Shabaz has completed his B.Tech in Information Technology and Telecommunication Engineering from Baba Ghulam Shah Badshah University, J&K, M.E and Ph.D in Computer Science Engineering from Chandigarh University, Mohali. He is working as C.E.O and Chief Researcher at JESM Consultancy Poonch Office, India, Visiting Faculty at Arba Minch University, Ethiopia and Assistant Professor at MIET, Jammu, India. He is Currently holding the positions of Managing Editor and Publisher at Journal of Engineering, Science and Mathematics (JESM), Managing Guest Editor at Informatics in Medicine Unlocked (Elsevier) and Editor at Neuroscience Informatics (Elsevier). His area of interest is application of computer science in interdisciplinary domains. He has Published over 100+ research papers in various journals indexed in Scopus/Web of Science, 4 Indian Patents and 3 Australian Patents.
DOI: PihzIk2ocTu6d2nvG5MK_prepost_3
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