Real and artificial neurons

2018 ◽  
pp. 13-23
Keyword(s):  
Artificial Neurons

scholarly journals LOAD SEDIMENTS QUANTIFICATION IN ALGERIAN NORTH-WEST BASINS BY ANN (ARTIFICIAL NEURONS NETWORK) METHOD

2019 ◽  
Vol 65 (3) ◽  
pp. 1-17
Author(s):  
Boualem REMINI ◽  
Yacine HAFIED ◽  
Nadir MAROUF ◽  
Mohamed BOUZIANE ◽  
Shiynkaruk LUBOMIR
Keyword(s):  
North West ◽  
Artificial Neurons ◽  
Network Method

Artificial Intelligence in Business Processes

2022 ◽  
pp. 205-230
Author(s):  
S. Asif Basit
Keyword(s):  
Neural Networks ◽  
Process Management ◽  
Business Processes ◽  
Control Process ◽  
Shop Floor ◽  
Business Environments ◽  
Novel Approach ◽  
Governing Factor ◽  
Artificial Neurons ◽  
Process Interaction

The aim of this chapter is to establish that the principles used by neural networks can be applied to business process management. The similarity between artificial neurons and business processes, and hence between neural networks and process landscapes, will be demonstrated. This novel approach leads to an emphasis on process interactions and their effect on actions as a major governing factor in controlling process outputs. Stigmergic interaction in biological systems is explored in the context of business processes, and its potential to understand process interaction is investigated. In order to verify the use of stigmergy in business environments, a pilot study is described in which shop floor business processes in a retailing environment are observed and described using a stigmergic framework. Establishing the viability of using stigmergic interaction to control process actions and outputs is the first step towards designing neural process networks.

Neural networks and Keras

2019 ◽  
pp. 66-90
Author(s):  
Thomas P. Trappenberg
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
Basic Operation ◽  
Neuron Models ◽  
Recent Success ◽  
Artificial Neurons ◽  
Artificial Neural ◽  
The Brain

This chapter discusses the basic operation of an artificial neural network which is the major paradigm of deep learning. The name derives from an analogy to a biological brain. The discussion begins by outlining the basic operations of neurons in the brain and how these operations are abstracted by simple neuron models. It then builds networks of artificial neurons that constitute much of the recent success of AI. The focus of this chapter is on using such techniques, with subsequent consideration of their theoretical embedding.

scholarly journals Sparse evolutionary deep learning with over one million artificial neurons on commodity hardware

2020 ◽  
Author(s):  
Shiwei Liu ◽  
Decebal Constantin Mocanu ◽  
Amarsagar Reddy Ramapuram Matavalam ◽  
Yulong Pei ◽  
Mykola Pechenizkiy
Keyword(s):  
Deep Learning ◽  
Commodity Hardware ◽  
Artificial Neurons

scholarly journals Neuron Blockchain Algorithm for Legal Problems in Inheritance of Legacy

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1595
Author(s):  
Seong-Kyu Kim ◽  
Jun-Ho Huh
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Non Fiction ◽  
Legal Problems ◽  
Artificial Neurons ◽  
The Will ◽  
Artificial Neural ◽  
And Performance ◽  
Artificial Neural Network Ann ◽  
Artificial Network

This paper discusses the worldwide trend of aging as the lifespan of humans increases. Nonetheless, most people do not write wills, which results in many legal problems after their death. There are many reasons for this including the problem of the validity of their heritage possibly not being legally certified. Wills can be divided into two categories, i.e., testimony and documents. A lawyer in the middle should notarize them, however, instead of providing these notarized services, we propose more transparent algorithms, blockchain shading, and smart country functions. Architectures are designed based on a neural network, the blockchain deep neural network (DNN), and deep neural network-based units are built with a necessary artificial neural network (ANN) base. A heritage inherited blockchain architecture is designed to communicate between nodes based on the minimum distance algorithm and multichannel protocol. In addition, neurons refer to the nerve cells that make up the nervous system of an organism, and artificial neurons are an abstraction of the functions of dendrite, soma, and axon that constitute the neurons of an organism. Similar to the neurons in organisms, artificial neural algorithms such as the depth-first search (DFS) algorithm are expressed in pseudocode. In addition, all blockchain nodes are equipped with verified nodes. A research model is proposed for an artificial network blockchain that is needed for this purpose. The experimental environment builds the server and network environments based on deep neural networks that require verification. Weights are also set for the required verification and performance. This paper verifies the blockchain algorithm equipped with this non-fiction preprocessor function. We also study the blockchain neuron engine that can safely construct a block node for a suicide blockchain. After empirical testing of the will system with artificial intelligence and blockchain, the values are close to 2 and 10 and the distribution is good. The blockchain node also tested 50 nodes more than 150 times, and we concluded that it was suitable for actual testing by completing a demonstration test with 4500 TPS.

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