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Book ChapterDOI

Providing Data Security in Deep Learning by Using Genomic Procedure

01 Jan 2020-pp 247-258
TL;DR: Routine besides of execution intended for key creation grounded on DNA algorithm with Needleman–Wunsch (NW) algorithm is introduced that ensures hiding an information by use of DNA terms and deep learning.
Abstract: Cryptanalysis is the technique for putting a difficult arithmetic and rationality to give a heavy-duty policy procedure to pelt information named by way of enciphering and then also reclaim novel information again named as deciphering. The main determination of cryptanalysis is to communicate information among one hub to another and also avoid the listener problems, and mainly we want a strong procedures and strategy and also need good encipherment techniques. For this, introduce a new concept of DNA deep learning cryptography that ensures hiding an information by use of DNA terms and deep learning. In this method, every ABCs are transformed to various mixtures of the four bases. This is, respectively, A, C, G and T. These base pairs are making a humanoid deoxyribonucleic acid (DNA). In this paper to begin with, routine besides of execution intended for key creation grounded on DNA algorithm with Needleman–Wunsch (NW) algorithm. And furthermore, the genetic procedures transcription, translation, DNA sequencing and deep learning are used to encrypt and decrypt the information.
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Journal ArticleDOI
TL;DR: The biological background of DNA cryptography and the principle of DNA computing is introduced, the progress of DNA cryptographic research and several key problems are summarized, and the status, security and application fields ofDNA cryptography with those of traditional cryptography and quantum cryptography are compared.
Abstract: DNA cryptography is a new born cryp- tographic field emerged with the research of DNA computing, in which DNA is used as information car- rier and the modern biological technology is used as implementation tool. The vast parallelism and ex- traordinary information density inherent in DNA molecules are explored for cryptographic purposes such as encryption, authentication, signature, and so on. In this paper, we briefly introduce the biological background of DNA cryptography and the principle of DNA computing, summarize the progress of DNA cryptographic research and several key problems, discuss the trend of DNA cryptography, and compare the status, security and application fields of DNA cryptography with those of traditional cryptography and quantum cryptography. It is pointed out that all the three kinds of cryptography have their own ad- vantages and disadvantages and complement each other in future practical application. The current main difficulties of DNA cryptography are the absence of effective secure theory and simple realizable method. The main goal of the research of DNA cryptography is exploring characteristics of DNA molecule and reac- tion, establishing corresponding theories, discovering possible development directions, searching for sim- ple methods of realizing DNA cryptography, and lay- ing the basis for future development.

168 citations

Journal ArticleDOI
01 Oct 2015
TL;DR: This paper will help researchers in selecting appropriate crossover operator for better results and contains description about classical standard crossover operators, binary crossover operator, and application dependant crossover operators.
Abstract: The performance of Genetic Algorithm (GA) depends on various operators. Crossover operator is one of them. Crossover operators are mainly classified as application dependent crossover operators and application independent crossover operators. Effect of crossover operators in GA is application as well as encoding dependent. This paper will help researchers in selecting appropriate crossover operator for better results. The paper contains description about classical standard crossover operators, binary crossover operators, and application dependant crossover operators. Each crossover operator has its own advantages and disadvantages under various circumstances. This paper reviews the crossover operators proposed and experimented by various researchers.

165 citations

Posted ContentDOI
05 Jan 2017
TL;DR: The superior and beneficial of the deep learning methodology and its hierarchy in layers and nonlinear operations are presented and compared with the more conventional algorithms in the common applica- tions.
Abstract: Deep learning is an emerging area of machine learning (ML) research. It comprises multiple hidden layers of artificial neural networks. The deep learning methodology applies nonlinear transformations and model abstractions of high level in large databases. The recent advancements in deep learning architectures within numerous fields have already provided significant contributions in artificial intelligence. This article presents a state of the art survey on the contributions and the novel applications of deep learning. The following review chronologically presents how and in what major applications deep learning algorithms have been utilized. Furthermore, the superior and beneficial of the deep learning methodology and its hierarchy in layers and nonlinear operations are presented and compared with the more conventional algorithms in the common applications. The state of the art survey further provides a general overview on the novel concept and the ever-increasing advantages and popularity of deep learning.

101 citations

Journal ArticleDOI
TL;DR: Simulation results demonstrate that the proposed collision risk evaluation framework could offer rationale estimations to the possible collision risk in car-following scenarios for the next discrete monitoring interval.

95 citations

Journal ArticleDOI
TL;DR: An Artificial Neural Network (ANN) as discussed by the authors is an information processing paradigm that is inspired by the way biological nervous systems such as the brain, process information, and it is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems.
Abstract: An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, s uch as pattern recognition o r data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of ANNs as well. This paper gives overview of Artificial Ne ural Network, working & training of ANN. It also explain the application and advantages of ANN.

88 citations