P
Prayag Tiwari
Researcher at Aalto University
Publications - 126
Citations - 3190
Prayag Tiwari is an academic researcher from Aalto University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 15, co-authored 84 publications receiving 1120 citations. Previous affiliations of Prayag Tiwari include National University of Science and Technology & University of Padua.
Papers
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Journal ArticleDOI
Internet of Things is a revolutionary approach for future technology enhancement: a review
TL;DR: The article discusses different challenges and key issues of IoT, architecture and important application domains, and the importance of big data and its analysis with respect to IoT has been discussed.
Journal ArticleDOI
A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images
Vikash Chouhan,Sanjay Kumar Singh,Aditya Khamparia,Deepak Gupta,Prayag Tiwari,Catarina Moreira,Robertas Damaševičius,Victor Hugo C. de Albuquerque +7 more
TL;DR: A novel deep learning framework for the detection of pneumonia using the concept of transfer learning, where features from images are extracted using different neural network models pretrained on ImageNet, which then are fed into a classifier for prediction.
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Identifying pneumonia in chest X-rays: A deep learning approach
Amit Kumar Jaiswal,Prayag Tiwari,Sachin Kumar,Deepak Gupta,Ashish Khanna,Joel J. P. C. Rodrigues,Joel J. P. C. Rodrigues +6 more
TL;DR: The proposed identification model is based on Mask-RCNN, a deep neural network which incorporates global and local features for pixel-wise segmentation which achieves robustness through critical modifications of the training process and a novel post-processing step which merges bounding boxes from multiple models.
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Brain MRI Image Classification for Cancer Detection Using Deep Wavelet Autoencoder-Based Deep Neural Network
Pradeep Kumar Mallick,Seuc-Ho Ryu,Sandeep Kumar Satapathy,Shruti Mishra,Gia Nhu Nguyen,Prayag Tiwari +5 more
TL;DR: A technique for image compression using a deep wavelet autoencoder (DWA), which blends the basic feature reduction property of autoen coder along with the image decomposition property of wavelet transform is proposed and it is noted that the proposed method outshines the existing methods.
Journal ArticleDOI
Sound Classification Using Convolutional Neural Network and Tensor Deep Stacking Network
TL;DR: It is concluded that the proposed approach for sound classification using the spectrogram images of sounds can be efficiently used to develop the sound classification and recognition systems.