Indian Institutes of Information Technology
About: Indian Institutes of Information Technology is a based out in . It is known for research contribution in the topics: Cloud computing & Deep learning. The organization has 3228 authors who have published 4513 publications receiving 30709 citations. The organization is also known as: IIiTS.
Topics: Cloud computing, Deep learning, Convolutional neural network, Feature extraction, Authentication
Papers published on a yearly basis
TL;DR: A hybrid spectral CNN (HybridSN) for HSI classification is proposed that reduces the complexity of the model compared to the use of 3-D-CNN alone and is compared with the state-of-the-art hand-crafted as well as end-to-end deep learning-based methods.
Abstract: Hyperspectral image (HSI) classification is widely used for the analysis of remotely sensed images. Hyperspectral imagery includes varying bands of images. Convolutional neural network (CNN) is one of the most frequently used deep learning-based methods for visual data processing. The use of CNN for HSI classification is also visible in recent works. These approaches are mostly based on 2-D CNN. On the other hand, the HSI classification performance is highly dependent on both spatial and spectral information. Very few methods have used the 3-D-CNN because of increased computational complexity. This letter proposes a hybrid spectral CNN (HybridSN) for HSI classification. In general, the HybridSN is a spectral–spatial 3-D-CNN followed by spatial 2-D-CNN. The 3-D-CNN facilitates the joint spatial–spectral feature representation from a stack of spectral bands. The 2-D-CNN on top of the 3-D-CNN further learns more abstract-level spatial representation. Moreover, the use of hybrid CNNs reduces the complexity of the model compared to the use of 3-D-CNN alone. To test the performance of this hybrid approach, very rigorous HSI classification experiments are performed over Indian Pines, University of Pavia, and Salinas Scene remote sensing data sets. The results are compared with the state-of-the-art hand-crafted as well as end-to-end deep learning-based methods. A very satisfactory performance is obtained using the proposed HybridSN for HSI classification. The source code can be found at https://github.com/gokriznastic/HybridSN .
TL;DR: The proposed swarm intelligence approach is named as Spider Monkey Optimization (SMO) algorithm and can broadly be classified as an algorithm inspired by intelligent foraging behavior of fission–fusion social structure based animals.
Abstract: Swarm intelligence is one of the most promising area for the researchers in the field of numerical optimization. Researchers have developed many algorithms by simulating the swarming behavior of various creatures like ants, honey bees, fish, birds and the findings are very motivating. In this paper, a new approach for numerical optimization is proposed by modeling the foraging behavior of spider monkeys. Spider monkeys have been categorized as fission–fusion social structure based animals. The animals which follow fission–fusion social systems, split themselves from large to smaller groups and vice-versa based on the scarcity or availability of food. The proposed swarm intelligence approach is named as Spider Monkey Optimization (SMO) algorithm and can broadly be classified as an algorithm inspired by intelligent foraging behavior of fission–fusion social structure based animals.
TL;DR: This paper provides an overview of existing security and privacy concerns, particularly for the fog computing, and highlights ongoing research effort, open challenges, and research trends in privacy and security issues for fog computing.
Abstract: Fog computing paradigm extends the storage, networking, and computing facilities of the cloud computing toward the edge of the networks while offloading the cloud data centers and reducing service latency to the end users. However, the characteristics of fog computing arise new security and privacy challenges. The existing security and privacy measurements for cloud computing cannot be directly applied to the fog computing due to its features, such as mobility, heterogeneity, and large-scale geo-distribution. This paper provides an overview of existing security and privacy concerns, particularly for the fog computing. Afterward, this survey highlights ongoing research effort, open challenges, and research trends in privacy and security issues for fog computing.
••10 Jul 2018
TL;DR: The various components and working principle of smart contract are explained, the various use cases ofSmart contract are identified and analysed along with the advantage of using smart contract in blockchain application and challenges lie in implementing smart contract the future real-life scenario.
Abstract: In the last decade blockchain technology become mainstream research topic because of its decentralized, peer to peer transaction, distributed consensus, and anonymity properties. The blockchain technology overshadows regulatory problem and technical challenges. A smart contract is a set of programs which are self-verifying, self-executing and tamper resistant. Smart contract with the integration of blockchain technology capable of doing a task in real time with low cost and provide a greater degree of security. This paper firstly, explains the various components and working principle of smart contract. Secondly, identify and analyse the various use cases of smart contract along with the advantage of using smart contract in blockchain application. Lastly, the paper concludes with challenges lie in implementing smart contract the future real-life scenario.
Showing all 3228 results
|Mohammad S. Obaidat||50||847||11247|
|Ravindra P. Joshi||45||280||7242|
|C. V. Jawahar||45||479||9582|
|Satish K. Singh||43||278||7182|
|D. Amaranatha Reddy||41||108||3930|
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