scholarly journals Design and Implementation of Web Based Risk Management System Based on Artificial Neural Networks for Software Projects: WEBRISKIT

2020 ◽  
Vol 26 (5) ◽  
pp. 993-1014
Author(s):  
M. Hanefi CALP ◽  
M. Ali AKCAYOL
Author(s):  
Mario Ibarra-manzano ◽  
Dora Almanza-ojeda ◽  
Andres Hernandez-Gutierrez ◽  
Juan Amezquita-sanchez ◽  
Luis Lopez-martinez

Author(s):  
Rafael Marti

The design and implementation of intelligent systems with human capabilities is the starting point to design Artificial Neural Networks (ANNs). The original idea takes after neuroscience theory on how neurons in the human brain cooperate to learn from a set of input signals to produce an answer. Because the power of the brain comes from the number of neurons and the multiple connections between them, the basic idea is that connecting a large number of simple elements in a specific way can form an intelligent system.


2022 ◽  
pp. 1031-1051
Author(s):  
Mriganka Mohan Chanda ◽  
Neelotpaul Banerjee ◽  
Gautam Bandyopadhyay

Agriculture is an important sector of the Indian economy. In the present paper an attempt has been made to theoretically explore the development of an agricultural knowledge management system (KMS) in respect of various micro irrigation techniques for agriculture, as well as relevant crop-/region-specific agricultural practices in different regions of the country, as the same has been observed to be very much necessary for the overall benefits of wider cross section of farmers, agricultural scientists, economists, and other stakeholders in the domain. It is further observed that artificial neural networks (ANNs), which are a part of soft computing techniques, can be used as a KMS tool for effective management of various sub sectors of agriculture. In this context, it has been shown that use of ANNs as a KMS tool can improve the effectiveness of applications of the above mentioned agricultural KMS by accurately forecasting the year-wise estimated yield of food grains of India with the help of past data of various relevant parameters.


1999 ◽  
Vol 19 (1) ◽  
pp. 3-31
Author(s):  
Annie R. Pearce ◽  
Rita A. Gregory ◽  
Laura Williams

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