scholarly journals Hydrodinamic Processes Control System for Wastewater Using Artificial Intelligence Techniques

2020 ◽  
Vol 22 (1) ◽  
pp. 7-12
Author(s):  
Anamaria-Cătălina Nițu
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Control System ◽  
Modelling And Simulation ◽  
Artificial Intelligence Techniques ◽  
Hydrodynamic Processes ◽  
Management Techniques ◽  
Positive Effect

Abstract In this study, it is presented a multicriterial approach of the automatic management techniques of industrial installations using wastewater. This has an extremely positive effect on the economical results of the entire irrigation and wastewater equipment. Choosing the automatic management by a neural networks system is the result of several stages of analysis, modelling and simulation of hydrodynamic processes that take place within the wastewater installation on each equipment and in its integrity. The study exposes an analysis and offers a solution of automatic management of the installation of irrigation using wastewater.

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 Visual control system for grip of glasses oriented to assistance robotics

2020 ◽  
Vol 10 (6) ◽  
pp. 6330
Author(s):  
Robinson Jimenez-Moreno ◽  
Astrid Rubiano ◽  
Jose L. Ramirez
Keyword(s):  
Quality Of Life ◽  
Artificial Intelligence ◽  
Control System ◽  
Fuzzy System ◽  
People With Disabilities ◽  
Visual Control ◽  
Liquid Level ◽  
Robotic Arm ◽  
Artificial Intelligence Techniques

Assistance robotics is presented as a means of improving the quality of life of people with disabilities, an application case is presented in assisted feeding. This paper presents the development of a system based on artificial intelligence techniques, for the grip of a glass, so that it does not slip during its manipulation by means of a robotic arm, as the liquid level varies. A faster R-CNN is used for the detection of the glass and the arm's gripper, and from the data obtained by the network, the mass of the beverage is estimated, and a delta of distance between the gripper and the liquid. These estimated values are used as inputs for a fuzzy system which has as output the torque that the motor that drives the gripper must exert. It was possible to obtain a 97.3% accuracy in the detection of the elements of interest in the environment with the faster R-CNN, and a 76% performance in the grips of the glass through the fuzzy algorithm.

Facial Expression Recognition with Gender Identification

2020 ◽  
pp. 57-63
Author(s):  
admin admin ◽  
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Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Human Face ◽  
Gender Identification ◽  
Artificial Intelligence Techniques ◽  
The Mind

The human facial emotions recognition has attracted interest in the field of Artificial Intelligence. The emotions on a human face depicts what’s going on inside the mind. Facial expression recognition is the part of Facial recognition which is gaining more importance and need for it increases tremendously. Though there are methods to identify expressions using machine learning and Artificial Intelligence techniques, this work attempts to use convolution neural networks to recognize expressions and classify the expressions into 6 emotions categories. Various datasets are investigated and explored for training expression recognition models are explained in this paper and the models which are used in this paper are VGG 19 and RESSNET 18. We included facial emotional recognition with gender identification also. In this project we have used fer2013 and ck+ dataset and ultimately achieved 73% and 94% around accuracies respectively.

Watermarking Using Artificial Intelligence Techniques

2014 ◽  
pp. 2665-2674
Author(s):  
Channapragada R. S. G. Rao ◽  
Vadlamani Ravi ◽  
Munaga. V. N. K. Prasad ◽  
E. V. Gopal
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Image Watermarking ◽  
Color Images ◽  
Digital Image Watermarking ◽  
Future Directions ◽  
Artificial Intelligence Techniques ◽  
Work Done

This Chapter presents a brief review of the work done during 1990-2013, in the application of intelligent techniques to digital image watermarking. The review discusses many papers of the gray-scale and color images than other multimedia. The review is structured by considering the type of technique applied to solve the problem as an important dimension. Consequently the papers are grouped into the following two families, (i) Neural networks, (ii) Fuzzy logic. Comparative analysis of different techniques is also presented. Finally, the review is concluded with future directions.

Bio-Inspired Algorithms in Bioinformatics II

2011 ◽  
pp. 241-247
Author(s):  
José Antonio Seoane Fernández ◽  
Mónica Miguélez Rico
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Immune System ◽  
Evolutionary Computation ◽  
Computational Models ◽  
Previous Article ◽  
Artificial Intelligence Techniques ◽  
Common Problems ◽  
Bioinspired Algorithms

Our previous article presented several computational models inspired on biological models, such as neural networks, evolutionary computation, swarm intelligence, and the artificial immune system. It also explained the most common problems in bioinformatics to which these models can be applied. The present article presents a series of approaches to bioinformatics tasks that were developed by means of artificial intelligence techniques and focus on bioinspired algorithms such as artificial neural networks and evolutionary computation.

A Survey on Artificial Intelligence Techniques to Prevent Cyber Crime

2018 ◽  
Vol 8 (5) ◽  
pp. 80
Author(s):  
Juveriya Afreen
Keyword(s):  
Artificial Intelligence ◽  
Cyber Security ◽  
Data Security ◽  
Human Reasoning ◽  
Cyber Crime ◽  
Artificial Intelligence Techniques

Abstract-- With increase in complexity of data, security, it is difficult for the individuals to prevent the offence. Thus, by using any automation or software it’s not possible by only using huge fixed algorithms to overcome this. Thus, we need to look for something which is robust and feasible enough. Hence AI plays an epitome role to defense such violations. In this paper we basically look how human reasoning along with AI can be applied to uplift cyber security.

Modeling of Self-Induced Vibrations that Occur During the Machining Process of Casting Patterns with the Use of The Fuzzy-Neural Networks Method

2013 ◽  
Vol 58 (3) ◽  
pp. 871-875
Author(s):  
A. Herberg
Keyword(s):  
Neural Networks ◽  
Control System ◽  
Network Structure ◽  
Fuzzy Systems ◽  
Fuzzy Neural Networks ◽  
Cnc Machining ◽  
Machining Process ◽  
Modeling Process ◽  
Fuzzy Neural ◽  
Takagi Sugeno

Abstract This article outlines a methodology of modeling self-induced vibrations that occur in the course of machining of metal objects, i.e. when shaping casting patterns on CNC machining centers. The modeling process presented here is based on an algorithm that makes use of local model fuzzy-neural networks. The algorithm falls back on the advantages of fuzzy systems with Takagi-Sugeno-Kanga (TSK) consequences and neural networks with auxiliary modules that help optimize and shorten the time needed to identify the best possible network structure. The modeling of self-induced vibrations allows analyzing how the vibrations come into being. This in turn makes it possible to develop effective ways of eliminating these vibrations and, ultimately, designing a practical control system that would dispose of the vibrations altogether.

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