Artificial Neural Networks for Prediction

Intelligent Systems ◽

Intelligent System ◽

Human Capabilities ◽

Design And Implementation ◽

Starting Point ◽

Input Signals ◽

Artificial Neural ◽

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.

Intelligent Traffic Sign Classifiers

Encyclopedia of Artificial Intelligence ◽

10.4018/978-1-59904-849-9.ch141 ◽

2011 ◽

pp. 956-962

Author(s):

Raúl Vicen Bueno ◽

Elena Torijano Gordo ◽

Antonio García González ◽

Manuel Rosa Zurera ◽

Roberto Gil Pita

Keyword(s):

Neural Networks ◽

Intelligent Systems ◽

Main Part ◽

Point Of View ◽

Traffic Sign ◽

Traffic Signs ◽

Artificial Neural ◽

The Artificial Neural Networks (ANNs) are based on the behavior of the brain. So, they can be considered as intelligent systems. In this way, the ANNs are constructed according to a brain, including its main part: the neurons. Moreover, they are connected in order to interact each other to acquire the followed intelligence. And finally, as any brain, it needs having memory, which is achieved in this model with their weights. So, starting from this point of view of the ANNs, we can affirm that these systems are able to learn difficult tasks. In this article, the task to learn is to distinguish between different kinds of traffic signs. Moreover, this ANN learning must be done for traffic signs that are not in perfect conditions. So, the learning must be robust against several problems like rotation, translation or even vandalism. In order to achieve this objective, an intelligent extraction of information from the images is done. This stage is very important because it improves the performance of the ANN in this task.

Intelligent Radar Detectors

Encyclopedia of Artificial Intelligence ◽

10.4018/978-1-59904-849-9.ch137 ◽

2011 ◽

pp. 933-939

Author(s):

Raúl Vicen Bueno ◽

Manuel Rosa Zurera ◽

María Pilar Jarabo Amores ◽

Roberto Gil Pita ◽

David de la Mata Moya

Keyword(s):

Neural Networks ◽

Intelligent Systems ◽

Main Part ◽

Point Of View ◽

Communications Systems ◽

Reflected Signal ◽

Artificial Neural ◽

The Artificial Neural Networks (ANNs) are based on the behaviour of the brain. So, they can be considered as intelligent systems. In this way, the ANNs are constructed according to a brain, including its main part: the neurons. Moreover, they are connected in order to interact each other to acquire the followed intelligence. And finally, as any brain, it needs having memory, which is achieved in this model with their weights. So, starting from this point of view of the ANNs, we can affirm that these systems are able to learn difficult tasks. In this article, the task to learn is to distinguish between the presence or not of a reflected signal called target in a Radar environment dominated by clutter. The clutter involves all the signals reflected from other objects in a Radar environment that are not the desired target. Moreover, the noise is considered in this environment because it always exists in all the communications systems we can work with.

THE PROBLEMS OF INFORMATION DEFENCE IN DIAGNOSIS INTELLIGENT SYSTEMS OF MICROPROCESSOR DEVICES

International Journal of Computing ◽

10.47839/ijc.4.2.336 ◽

2014 ◽

pp. 46-53

Author(s):

Viktor Lokazyuk ◽

Oksana Pomorova

Keyword(s):

Neural Networks ◽

Intelligent Systems ◽

Intelligent System ◽

User Authentication ◽

Keystroke Dynamics ◽

Artificial Neural

The method for protection of diagnosis intelligent system of microprocessor devices is represented in the paper. This method based on background authentication of the user in the process of keyboarding. The user’s keystroke dynamics characteristics are the means of authentication. For realization of the user authentication method uses the artificial neural networks of ART2 architecture.

Artificial Neural Networks and Their Applications in Business

Encyclopedia of Information Science and Technology, Fourth Edition ◽

10.4018/978-1-5225-2255-3.ch576 ◽

2018 ◽

pp. 6642-6657

Author(s):

Trevor J. Bihl ◽

William A. Young II ◽

Gary R. Weckman

Keyword(s):

Neural Networks ◽

Pattern Recognition ◽

Big Data ◽

Model Complexity ◽

Ann Model ◽

Processing Methods ◽

Starting Point ◽

Clustering And Classification ◽

Artificial Neural

Despite the natural advantage humans have for recognizing and interpreting patterns, large and complex datasets, as in Big Data, preclude efficient human analysis. Artificial neural networks (ANNs) provide a family of pattern recognition approaches for prediction, clustering and classification applicable to KDD with ANN model complexity ranging from simple (for small problems) highly complex (for large issues). To provide a starting point for readers, this chapter first describes foundational concepts that relate to ANNs. A listing of commonly used ANN methods, heuristics, and criteria for initializing ANNs is then discussed. Common pre- and post- data processing methods for dimensionality reduction and data quality issues are then described. The authors then provide a tutorial example of ANN analysis. Finally, the authors list and describe applications of ANNs to specific business related endeavors for further reading.

Advanced Methodologies and Technologies in Artificial Intelligence, Computer Simulation, and Human-Computer Interaction - Advances in Computer and Electrical Engineering ◽

Artificial Neural Networks and Their Applications in Business

10.4018/978-1-5225-7368-5.ch074 ◽

2019 ◽

pp. 1009-1027

Author(s):

Trevor J. Bihl ◽

William A. Young II ◽

Gary R. Weckman

Keyword(s):

Neural Networks ◽

Pattern Recognition ◽

Big Data ◽

Model Complexity ◽

Ann Model ◽

Processing Methods ◽

Starting Point ◽

Clustering And Classification ◽

Artificial Neural

Despite the natural advantage humans have for recognizing and interpreting patterns, large and complex datasets, as in big data, preclude efficient human analysis. Artificial neural networks (ANNs) provide a family of pattern recognition approaches for prediction, clustering, and classification applicable to KDD with ANN model complexity ranging from simple (for small problems) to highly complex (for large issues). To provide a starting point for readers, this chapter first describes foundational concepts that relate to ANNs. A listing of commonly used ANN methods, heuristics, and criteria for initializing ANNs are then discussed. Common pre- and post-data processing methods for dimensionality reduction and data quality issues are then described. The authors then provide a tutorial example of ANN analysis. Finally, the authors list and describe applications of ANNs to specific business-related endeavors for further reading.

Advances in Environmental Engineering and Green Technologies - Smart Agricultural Services Using Deep Learning, Big Data, and IoT ◽

A Neural Network-Based Approach for Pest Detection and Control in Modern Agriculture Using Internet of Things

10.4018/978-1-7998-5003-8.ch001 ◽

2021 ◽

pp. 1-31

Author(s):

Pankaj Dadheech ◽

Ankit Kumar ◽

Vijander Singh ◽

Linesh Raja ◽

Ramesh C. Poonia

Keyword(s):

Neural Networks ◽

Neural Structure ◽

Modern Agriculture ◽

Different Types ◽

Pest Detection ◽

Real Time Applications ◽

Artificial Neural ◽

And Control ◽

The networks acquire an altered move towards the difficulty solving skills rather than that of conventional computers. Artificial neural networks are comparatively crude electronic designs based on the neural structure of the brain. The chapter describes two different types of approaches to training, supervised and unsupervised, as well as the real-time applications of artificial neural networks. Based on the character of the application and the power of the internal data patterns we can normally foresee a network to train quite well. ANNs offers an analytical solution to conventional techniques that are often restricted by severe presumptions of normality, linearity, variable independence, etc. The chapter describes the necessities of items required for pest management through pheromones such as different types of pest are explained and also focused on use of pest control pheromones.

Use of Multiple Astrocytic Configurations within an Artificial Neuro-Astrocytic Network

Proceedings ◽

10.3390/proceedings2019021046 ◽

2019 ◽

Vol 21 (1) ◽

pp. 46

Author(s):

Francisco Cedron ◽

Sara Alvarez-Gonzalez ◽

Alejandro Pazos ◽

Ana B. Porto-Pazos

Keyword(s):

Neural Networks ◽

Glial Cell ◽

Process Information ◽

Connectionist System ◽

Artificial Neural ◽

The artificial neural networks used in a multitude of fields are achieving good results. However, these systems are inspired in the vision of classical neuroscience where neurons are the only elements that process information in the brain. Advances in neuroscience have shown that there is a type of glial cell called astrocytes that collaborate with neurons to process information. In this work, a connectionist system formed by neurons and artificial astrocytes is presented. The astrocytes can have different configurations to achieve a biologically more realistic behaviour. This work indicates that the use of different artificial astrocytes behaviours is beneficial.