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Prashant Gidde
Researcher at Central Electronics Engineering Research Institute
Publications - 7
Citations - 57
Prashant Gidde is an academic researcher from Central Electronics Engineering Research Institute. The author has contributed to research in topics: Convolutional neural network & Deep learning. The author has an hindex of 2, co-authored 5 publications receiving 8 citations. Previous affiliations of Prashant Gidde include Council of Scientific and Industrial Research.
Papers
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Journal ArticleDOI
Dual integrated convolutional neural network for real-time facial expression recognition in the wild
TL;DR: This paper presents an efficient dual integrated convolution neural network (DICNN) model for the recognition of facial expressions in the wild in real-time, running on an embedded platform and optimized the designed DICNN model using TensorRT SDK and deployed it on an Nvidia Xavier embedded platform.
Book ChapterDOI
Power Line Segmentation in Aerial Images Using Convolutional Neural Networks.
TL;DR: Experimental results show that out of the four deep learning-based segmentation architectures used in the experiments the Nested U-Net architecture out-performed others in terms of line segmentation accuracy in various background scenarios.
Journal ArticleDOI
Real-time eye state recognition using dual convolutional neural network ensemble
Journal ArticleDOI
Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays.
Prashant Gidde,Shyam Sunder Prasad,Ajay Singh,Nitin Bhatheja,Satyartha Prakash,Prateek Singh,Aakash Saboo,Rohit Takhar,Salil Gupta,Sumeet Saurav,Muthukurussi Varieth Raghunandanan,Amritpal Singh,Viren Sardana,Harsh Mahajan,Arjun Kalyanpur,Atanendu Shekhar Mandal,Vidur Mahajan,Anurag Agrawal,Anjali Agrawal,Vasantha Kumar Venugopal,Sanjay Singh,Debasis Dash +21 more
TL;DR: CovBaseAI as mentioned in this paper uses an ensemble of three deep learning models and an expert decision system (EDS) for COVID-Pneumonia diagnosis, trained entirely on pre-COVID-19 datasets.
Book ChapterDOI
Real-Time Vehicle Detection in Aerial Images Using Skip-Connected Convolution Network with Region Proposal Networks
TL;DR: This paper aims to provide a solution to the problem faced in real-time vehicle detection in aerial images and videos by using hyper maps generated by skip connected Convolutional network to generate object like proposals accurately.