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Deepak Kumar Jain

Bio: Deepak Kumar Jain is an academic researcher from Chongqing University of Posts and Telecommunications. The author has contributed to research in topics: Computer science & Artificial intelligence. The author has an hindex of 19, co-authored 59 publications receiving 905 citations. Previous affiliations of Deepak Kumar Jain include Jaypee University of Engineering and Technology & Chinese Academy of Sciences.

Papers published on a yearly basis

Papers
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Journal ArticleDOI
TL;DR: The aim of this work is to classify each image into one of six facial emotion classes, based on single Deep Convolutional Neural Networks (DNNs), which contain convolution layers and deep residual blocks.

231 citations

Journal ArticleDOI
TL;DR: Analysis and evaluations manifest that the proposed BlockTDM scheme provides a general, flexible, and configurable blockchain-based paradigm for trusted data management with tamper-resistance, which is suitable for edge computing with high-level security and creditability.
Abstract: With rapid development of computing technologies, large amount of data are gathered from edge terminals or Internet of Things (IoT) devices, however data trust and security in edge computing environment are very important issues to be considered, especially when the gathered data are fraud or dishonest, or the data are misused or spread without any authorization, which may lead to serious problems. In this article, a blockchain-based trusted data management scheme (called BlockTDM) in edge computing is proposed to solve the above problems, in which we proposed a flexible and configurable blockchain architecture that includes mutual authentication protocol, flexible consensus, smart contract, block and transaction data management, blockchain nodes management, and deployment. The BlockTDM scheme can support matrix-based multichannel data segment and isolation for sensitive or privacy data protection, and moreover, we have designed user-defined sensitive data encryption before the transaction payload stores in blockchain system, and have implemented conditional access and decryption query of the protected blockchain data and transactions through smart contract. Finally, we have evaluated the proposed BlockTDM scheme security, availability, and efficiency with large amount of experiments. Analysis and evaluations manifest that the proposed BlockTDM scheme provides a general, flexible, and configurable blockchain-based paradigm for trusted data management with tamper-resistance, which is suitable for edge computing with high-level security and creditability.

131 citations

Journal ArticleDOI
TL;DR: This research proposes sarcasm detection using deep learning in code-switch tweets, specifically the mash-up of English with Indian native language, Hindi, with a hybrid of bidirectional long short-term memory with a softmax attention layer and convolution neural network for real-time sarcasm Detection.

94 citations

Journal ArticleDOI
TL;DR: This paper presents a hybrid system where a supervised deep belief network is trained to select generic features, and a kernel-based SVM is trained from the features that learned by the DBN, and substituted linear kernel for nonlinear ones without loss of accuracy.

73 citations

Journal ArticleDOI
TL;DR: The novel Multi-Angle Optimal Pattern-based Deep Learning (MAOP-DL) method is presented to rectify the problem from sudden illumination changes, find the proper alignment of a feature set by using multi-angle-based optimal configurations and the facial alignment.

69 citations


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Reference EntryDOI
15 Oct 2004

2,118 citations

Journal ArticleDOI
TL;DR: A comprehensive survey on deep facial expression recognition (FER) can be found in this article, including datasets and algorithms that provide insights into the intrinsic problems of deep FER, including overfitting caused by lack of sufficient training data and expression-unrelated variations, such as illumination, head pose and identity bias.
Abstract: With the transition of facial expression recognition (FER) from laboratory-controlled to challenging in-the-wild conditions and the recent success of deep learning techniques in various fields, deep neural networks have increasingly been leveraged to learn discriminative representations for automatic FER. Recent deep FER systems generally focus on two important issues: overfitting caused by a lack of sufficient training data and expression-unrelated variations, such as illumination, head pose and identity bias. In this paper, we provide a comprehensive survey on deep FER, including datasets and algorithms that provide insights into these intrinsic problems. First, we describe the standard pipeline of a deep FER system with the related background knowledge and suggestions of applicable implementations for each stage. We then introduce the available datasets that are widely used in the literature and provide accepted data selection and evaluation principles for these datasets. For the state of the art in deep FER, we review existing novel deep neural networks and related training strategies that are designed for FER based on both static images and dynamic image sequences, and discuss their advantages and limitations. Competitive performances on widely used benchmarks are also summarized in this section. We then extend our survey to additional related issues and application scenarios. Finally, we review the remaining challenges and corresponding opportunities in this field as well as future directions for the design of robust deep FER systems.

712 citations

01 Dec 1996

452 citations