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Ayan Seal
Researcher at Indian Institute of Information Technology, Design and Manufacturing, Jabalpur
Publications - 82
Citations - 1663
Ayan Seal is an academic researcher from Indian Institute of Information Technology, Design and Manufacturing, Jabalpur. The author has contributed to research in topics: Facial recognition system & Computer science. The author has an hindex of 16, co-authored 66 publications receiving 693 citations. Previous affiliations of Ayan Seal include Jadavpur University & University of Hradec Králové.
Papers
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DeprNet: A Deep Convolution Neural Network Framework for Detecting Depression Using EEG
Ayan Seal,Rishabh Bajpai,Jagriti Agnihotri,Anis Yazidi,Enrique Herrera-Viedma,Ondrej Krejcar +5 more
TL;DR: In this paper, a DL-based convolutional neural network (CNN) called DeprNet was proposed for classifying the EEG data of depressed and normal subjects, where the Patient Health Questionnaire 9 score was used for quantifying the level of depression.
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A FPGA based implementation of Sobel edge detection
TL;DR: An architecture for Sobel edge detection on Field Programmable Gate Array (FPGA) board, which is inexpensive in terms of computation and reduces the time and space complexity compare to two existing architectures.
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Facial Expression Recognition Using Local Gravitational Force Descriptor-Based Deep Convolution Neural Networks
TL;DR: In this article, a deep learning-based scheme is proposed for identifying the facial expression of a person, which consists of two parts: the former one finds out local features from face images using a local gravitational force descriptor, while, in the latter part, the descriptor is fed into a novel deep convolution neural network (DCNN) model.
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MRI and SPECT Image Fusion Using a Weighted Parameter Adaptive Dual Channel PCNN
TL;DR: Experimental results demonstrate that the proposed method outperforms some of the state-of-the-art methods in terms of both visual quality and objective assessment.
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SoyNet: Soybean leaf diseases classification
Aditya Karlekar,Ayan Seal +1 more
TL;DR: A computer vision approach for plant diseases classification using deep learning convolution neural network, SoyNet, for soybean plant diseases recognition using segmented leaf images that outperforms nine state-of-the-art methods/models.