L V S K B KASYAP VARANASI

About

Dynamic and dedicated research student with extensive experience in developing progressive state-of-the-art deep learning techniques. Diligent in exploring novel methodologies of AI with strong analytical skills coupled with a creative mindset to solve complex problems.

Employment

Employment history is unavailable.

Education

• Vellore Institute of Technology University Student
  2020 - 2024

Projects & Funding

Projects & funding information is unavailable.

Publications (12)

• Deep Learning based techniques for Neuro-degenerative disorders detection
  Engineering Applications of Artificial Intelligence 2023 DOI: 10.1016/j.engappai.2023.106103
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• Certain integral representations of hypergeometric polynomials
  AIP Conference Proceedings 2023 DOI: 10.1063/5.0143628
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• DIVS: A Real-Time Video Stabilization Framework for Consumer Camera
  2023 DOI: 10.1007/978-981-99-4284-8_28
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• Early Detection of ColoRectal Cancer Using Patch-Based Hybrid Model and Transfer Learning
  Computational Vision and Bio-Inspired Computing 2023 DOI: 10.1007/978-981-19-9819-5_7
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• Gastric Cancer Detection using Hybrid-based Network and SHAP Analysis
  Explainable Artificial Intelligence for Biomedical Applications 2023
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• Neural architecture of 3D face modelling using generative adversarial networks
  AIP Conference Proceedings 2023 DOI: 10.1063/5.0143020
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• A Novel Deep Learning Framework for Diabetic Retinopathy Detection
  2022 IEEE 6th Conference on Information and Communication Technology (CICT) 2022 DOI: 10.1109/cict56698.2022.9997826
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• PsychNet: Explainable Deep Neural Networks for Psychiatric Disorders and Mental Illness
  2022 IEEE 6th Conference on Information and Communication Technology (CICT) 2022 DOI: 10.1109/cict56698.2022.9997832
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• Graph Neural Networks based Model for Aroma Prediction using Molecular Structures
  2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT) 2022 DOI: 10.1109/gcat55367.2022.9971823
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• RoadSDNet: A Robust Algorithm for Road Boundary Detection and Segmentation using Mixed Networks and Hough Transform
  2022 DOI: 10.1109/ACM57404.2022.00013
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