scholarly journals Recognition of Traffic Signs Applying Neural Network

2019 ◽  
Vol 11 (1) ◽  
pp. 145-148
Author(s):  
Zsolt Barnabás Neurohr ◽  
Edit Tóthné Laufer
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Calculation Methods ◽  
Traffic Signs ◽  
Artificial Intelligence Research ◽  
The Neural Network

Abstract Artificial intelligence is one of the most dynamically developing areas of science today. Although it is not yet an integral part of our lives to use artificial intelligence solutions, it can be seen in terms of development, that it will become available to everyone in the coming decades, and not be exclusive for the richest. An important part of artificial intelligence research are the so-called soft calculation methods, the most important of which are fuzzy logic, genetic algorithms and neural networks. In this article, the authors present a method of identifying certain traffic signs with the help of the neural network.

scholarly journals Artificial Intelligence Techniques for Phishing Detection

2019 ◽  
Vol 8 (11) ◽  
pp. 2330-2335
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Neural Systems ◽  
Element Determination ◽  
Artificial Intelligence Techniques ◽  
The Neural Network ◽  
Proper Number ◽  
Phishing Detection

The objective of this undertaking is to apply neural systems to phishing email recognition and assess the adequacy of this methodology. We structure the list of capabilities, process the phishing dataset, and execute the Neural Network frameworks. we analyze its exhibition against that of other real Artificial Intelligence Techniques – DT , K-nearest , NB and SVM machine.. The equivalent dataset and list of capabilities are utilized in the correlation. From the factual examination, we infer that Neural Networks with a proper number of concealed units can accomplish acceptable precision notwithstanding when the preparation models are rare. Additionally, our element determination is compelling in catching the qualities of phishing messages, as most AI calculations can yield sensible outcomes with it.

scholarly journals NEAT Snake: a both evolutionary and neural network adaptation approach

2020 ◽  
Author(s):  
Alisson Steffens Henrique ◽  
Vinicius Almeida dos Santos ◽  
Rodrigo Lyra
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Decision Making ◽  
Genetic Algorithms ◽  
Real Time ◽  
Relative Position ◽  
Network Adaptation ◽  
The Neural Network

There are several challenges when modeling artificial intelligencemethods for autonomous players on games (bots). NEAT is one ofthe models that, combining genetic algorithms and neural networks,seek to describe a bot behavior more intelligently. In NEAT, a neuralnetwork is used for decision making, taking relevant inputs fromthe environment and giving real-time decisions. In a more abstractway, a genetic algorithm is applied for the learning step of the neuralnetworks’ weights, layers, and parameters. This paper proposes theuse of relative position as the input of the neural network, basedon the hypothesis that the bot profit will be improved.

scholarly journals An Automatic Logistics System using Artificial Intelligence

2019 ◽  
Vol 8 (2S4) ◽  
pp. 924-926
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Optimal Route ◽  
Logistics System ◽  
The Neural Network ◽  
Artificial Intelligence Technology ◽  
Artificial Neural ◽  
Set Up

In this paper, we propose a method to utilize machine learning to automate the system of classifying and transporting large quantities of logistics. First, establish an environment similar to the task of transferring logistics to the desired destination, and set up basic rules for classification and transfer. Next, each of the logistics that need sorting and transportation is defined as one entity, and artificial intelligence is introduced so that each individual can go to an optimal route without collision between the objects to the destination. Artificial intelligence technology uses artificial neural networks and uses genetic algorithms to learn neural networks. The artificial neural network is generated by each chromosome, and it is evolved based on the most suitable artificial neural network, and a score is given to each operation to evaluate the fitness of the neural network. In conclusion, the validity of this algorithm is evaluated through the simulation of the implemented system.

scholarly journals Neural Network Effectiveness Evaluation While the Intersection of Images in the Receptor Field

2021 ◽  
Vol 17 ◽  
pp. 172-176
Author(s):  
Siranush Sargsyan ◽  
Anna Hovakimyan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Probabilistic Approach ◽  
Effectiveness Evaluation ◽  
Training Set ◽  
Receptor Field ◽  
Network Effectiveness ◽  
The Neural Network

The study and application of neural networks is one of the main areas in the field of artificial intelligence. The effectiveness of the neural network depends significantly on both its architecture and the structure of the training set. This paper proposes a probabilistic approach to evaluate the effectiveness of the neural network if the images intersect in the receptor field. A theorem and its corollaries are proved, which are consistent with the results obtained by a different path for a perceptron-type neural network.

scholarly journals The control to aggregates of pumping stations using a regulator based on a neural network with fuzzy logic

2019 ◽  
Vol 102 ◽  
pp. 03007
Author(s):  
Vladlen Kuznetsov ◽  
Sergey Dyadun ◽  
Valentin Esilevsky
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Simulation Modeling ◽  
Neural Controller ◽  
The Neural Network ◽  
Pumping Stations ◽  
Fuzzy Neural ◽  
Pumping Units ◽  
Theory Of Fuzzy Sets

A pumping station control system is considered using a controller based on a fuzzy logic neural network. The simulation of the classical and fuzzy regulators. The possibility of the implementation of the controller in the form of an adaptive multilayer neural network is shown. The use of the theory of fuzzy sets in combination with the theory of neural networks to create a fuzzy-neural regulator to control pumping units provides a promising approach. Simulation modeling and real operation have shown that fuzzy-logic regulators have a number of advantages over classical regulators, which allow the use of form and limitations. Using the neural network model allows you to add the properties of adaptability and learning. The fuzzy-neural controller for controlling pumping units is promising in terms of efficiency and safety by controlling pumping stations.

Neural Networks for Content-Based Image Retrieval

2011 ◽  
pp. 252-272 ◽  
Author(s):  
Brijesh Verma ◽  
Siddhivinayak Kulkarni
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
The Neural Network ◽  
Benchmark Database ◽  
Overall Performance ◽  
Critical Literature

This chapter introduces neural networks for Content-Based Image Retrieval (CBIR) systems. It presents a critical literature review of both the traditional and neural network based techniques that are used in retrieving the images based on their content. It shows how neural networks and fuzzy logic can be used in interpretation of queries, feature extraction and classification of features by describing a detailed research methodology. It investigates a neural network based technique in conjunction with fuzzy logic to improve the overall performance of the CBIR systems. The results of the investigation on a benchmark database with a comparative analysis are presented in this chapter. The methodologies and results presented in this chapter will allow researchers to improve and compare their methods and it will also allow system developers to understand and implement the neural network and fuzzy logic based techniques for content based image retrieval.

scholarly journals A SURVEY OF ARTIFICIAL INTELLIGENCE METHODS IN INTRUSION DETECTION TASKS

InterConf ◽  
2021 ◽  
pp. 443-449
Author(s):  
Oleksandr Shmatko ◽  
German Zviertsev
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Intrusion Detection ◽  
Continuous Administration ◽  
Network Attacks ◽  
Artificial Intelligence Methods ◽  
The Neural Network

There are many methods for detecting network attacks, but since attacks are constantly changing, special databases of rules or signatures to detect attacks require continuous administration, it becomes necessary to add new rules. One of the ways to eliminate this problem is to use neural networks as a mechanism to detect network attacks. In contrast to the signature-based approach, the neural network analyzes information and provides information about the attacks that it is trained to recognize. In addition, neural networks have the advantage of being able to adapt to previously unknown attacks and detect them. That is why the development of software based on neural networks is relevant.

scholarly journals Cost Forecasting of Public Construction Projects Using Multilayer Perceptron Artificial Neural Networks: A Case Study

2021 ◽  
Vol 41 (3) ◽  
pp. e87737
Author(s):  
Alcineide Pessoa ◽  
Gean Sousa ◽  
Luiz Maués ◽  
Felipe Alvarenga ◽  
Débora Santos
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Public Sector ◽  
Multilayer Perceptron ◽  
Construction Projects ◽  
Gradient Descent Method ◽  
The Neural Network ◽  
Artificial Neural

The execution of public sector construction projects often requires the use of financial resources not foreseen during the tendering phase, which causes management problems. This study aims to present a computational model based on artificial intelligence, specifically on artificial neural networks, capable of forecasting the execution cost of construction projects for Brazilian educational public buildings. The database used in the training and testing of the neural model was obtained from the online system of the Ministry of Education. The neural network used was a multilayer perceptron as a backpropagation algorithm optimized through the gradient descent method. To evaluate the obtained results, the mean absolute percentage errors and the Pearson correlation coefficients were calculated. Some hypothesis tests were also carried out in order to verify the existence of significant differences between real values and those obtained by the neural network. The average percentage errors between predicted and actual values varied between 5% and 9%, and the correlation values reached 0,99. The results demonstrated that it is possible to use artificial intelligence as an auxiliary mechanism to plan construction projects, especially in the public sector.

A Review of Artificial Intelligence Technologies to Achieve Machining Objectives

2019 ◽  
pp. 138-159 ◽  
Author(s):  
Deivanathan R.
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Optimization Techniques ◽  
Computer Integrated Manufacturing ◽  
Potential Applications ◽  
Continuous Interaction ◽  
User Friendly

Bridging the design, planning and manufacturing departments of a production enterprise is not a conclusive effort for the implementation of computer integrated manufacturing. Continuous interaction and seamless exchange of information among these functions is needed and requires the maintenance of a large database and user-friendly search and optimization techniques. Among several artificial intelligence techniques capable of the above task, four important and popular ones are, expert systems, artificial neural networks, fuzzy logic and genetic algorithms. In this chapter, these four techniques have been conceptually studied in detail and exemplified by reviewing an application in the manufacturing domain. Successful implementations of artificial intelligence that are recently reported in machining domain are also reviewed, suggesting potential applications in the future.

scholarly journals Insights into Artificial Neural Network techniques, and its Application in Steganography

2021 ◽  
Vol 2089 (1) ◽  
pp. 012043
Author(s):  
R Gurunath ◽  
Debabrata Samanta
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Short Term Memory ◽  
Feed Forward Neural Network ◽  
Automated Generation ◽  
The Neural Network ◽  
Learning Techniques ◽  
Hide Data ◽  
Network Approaches

Abstract Deep Steganography is a data concealment technology that uses artificial intelligence (AI) to automate the process of hiding and extracting information through layers of training. It enables for the automated generation of a cover depending on the concealed message. Previously, the technique depended on the existing cover to hide data, which limited the number of Steganographic characteristics available. Artificial intelligence and deep learning techniques have been used to steganography recently and the results are satisfactory. Although neural networks have demonstrated their ability to imitate human talents, it is still too early to draw comparisons between people and them. To improve their capabilities, neural networks are being employed in a number of disciplines, including steganography. Recurrent Neural Networks (RNN) is a widely used technology that automatically creates Stego-text regardless of payload volume. The features are extracted using a convolution neural network (CNN) based on the image. Perceptron, Multi-Layer Perceptron (MLP), Feed Forward Neural Network, Long Short Term Memory (LSTM) networks, and others are examples of this. In this research, we looked at all of the neural network approaches for Steganographic purposes in depth. This article also discusses the problems that each technology faces, as well as potential solutions.

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