The optical reservoir computer: a new approach to a programmable integrated optics system based on an artificial neural network

2020 ◽  
pp. 361-380
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Integrated Optics ◽  
New Approach ◽  
Artificial Neural

New Approach for 3D Mesh Retrieval Using Artificial Neural Network and Histogram of Features

2018 ◽  
Vol 10 (2) ◽  
pp. 84-94 ◽  
Author(s):  
M. Pershina ◽  
V.S. Bouksim ◽  
K. Arhid ◽  
F.R. Zakani ◽  
M. Aboulfatah ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
New Approach ◽  
3D Mesh ◽  
Artificial Neural

On the usage of acoustic properties combined with an artificial neural network – A new approach of determining presence of dairy fouling

2011 ◽  
Vol 103 (4) ◽  
pp. 449-456 ◽  
Author(s):  
Eva Wallhäußer ◽  
Walid B. Hussein ◽  
Mohamed A. Hussein ◽  
Jörg Hinrichs ◽  
Thomas M. Becker
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Properties ◽  
New Approach ◽  
Artificial Neural

Artificial Neural Network and Falls in Community-Dwellers: A New Approach to Identify the Risk of Recurrent Falling?

2015 ◽  
Vol 16 (4) ◽  
pp. 277-281 ◽  
Author(s):  
Anastasiia Kabeshova ◽  
Cyrille P. Launay ◽  
Vasilii A. Gromov ◽  
Cédric Annweiler ◽  
Bruno Fantino ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
New Approach ◽  
Artificial Neural ◽  
Community Dwellers

Risk-based liquefaction potential evaluation using standard penetration tests

2000 ◽  
Vol 37 (6) ◽  
pp. 1195-1208 ◽  
Author(s):  
C Hsein Juang ◽  
Caroline J Chen ◽  
Tao Jiang ◽  
Ronald D Andrus
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Limit State ◽  
State Function ◽  
Limit State Function ◽  
Liquefaction Potential ◽  
New Approach ◽  
Mapping Functions ◽  
Artificial Neural ◽  
Penetration Tests

In this paper, a new approach is presented for developing a liquefaction limit state function, which defines a boundary that separates liquefaction from no-liquefaction occurrence. The new approach is developed using a database consisting of 243 field liquefaction performance cases at sites where standard penetration tests (SPT) had been conducted. This database is first used to train and test an artificial neural network for predicting the occurrence of liquefaction or no liquefaction. The successfully trained neural network is then used to establish a liquefaction limit state function. Based on the developed limit state function, mapping functions that relate calculated factors of safety to probability of liquefaction are established. The established mapping functions form a basis for the development of a risk-based chart for liquefaction potential evaluation.Key words: probability, risk-based design, liquefaction potential, SPT, artificial neural network.

scholarly journals Autoencoder Artificial Neural Network Public Key Cryptography in Unsecure Public channel Communication

2019 ◽  
Vol 8 (11) ◽  
pp. 4023-4032
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Communication ◽  
Public Key Cryptography ◽  
Experimental Result ◽  
Main Concern ◽  
New Approach ◽  
Digital World ◽  
Cryptographic Algorithm ◽  
Artificial Neural

As we all known that cryptography is a procedure to hide data so that it can’t be access or modified by any unauthorized entity. At the present digital world security is a main concern. To maintain this security there are many cryptographic algorithm exist. But the world technology grew each and every day so we have to find some new algorithms to maintain the security at higher level. In the proposed and implemented work used artificial neural network to increase the security during data communication in digital world. Autoencoder Neural Network is a new approach in the era of digital world so that used here in cryptographic algorithm to increase the strength of the security. There are three basic aims of cryptography availability, privacy and integrity easily achieved by this new approach. This work examine that the attacker can’t get access the data however he/she exist in the same network or not. Neural Network’s uncertainty property make this possible. This approach also examined on different data size and key size. Proposed work used the autoencoder for encryption and decryption. The final experimental result show our purposed algorithm efficient and accurate and also show how this approach perform better. Proposed and implemented algorithm can be easily used for secure data communication with more efficiently

scholarly journals Prediction of soil water characteristic curve using artificial neural network: a new approach

2018 ◽  
Vol 162 ◽  
pp. 01014
Author(s):  
Abdul-Kareem Esmat Zainal ◽  
Shaimaa Hasan Fadhil
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Properties ◽  
Soil Water ◽  
Unsaturated Soil ◽  
Characteristic Curve ◽  
New Approach ◽  
Soil Water Characteristic Curve ◽  
Important Relationship ◽  
Artificial Neural

Soil-Water Characteristic Curve (SWCC) is an important relationship between matric suction and volumetric water content of soils especially when dealing with unsaturated soil problems, these problems may include seepage, bearing capacity, volume change, etc. where the matric or total suction may have a considerable effect on unsaturated soil properties. Obtaining an accurate SWCC for a soil could be cumbersome and sometimes it is time consuming and needs effort for some soils, either through laboratory tests or through field tests. Accurate prediction of this curve can give more precise expectations in design or analysis that include some unsaturated soil properties, which can save more effort and time. This work will concentrate on proposing a new approach for determining the SWCC using Artificial Neural Network (ANN) depending on some soil properties (air-entry point and residual degree of saturation) through computer software MatLab as a tool for ANN. The new approach is to plot the SWCC curve points instead of obtaining the parameters used in Brooks and Corey (BC) Model (1964), van Genuchten (VG) Model (1980), or Fredlund and Xing (FX) Model (1994). Results showed close agreement in determination of the SWCC by verification of the ANN results with an additional curve sample.

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