A review on artificial intelligence techniques in electrical drives: Neural networks, fuzzy logic, and genetic algorithm

2017 ◽  
Author(s):  
S. Sakunthala ◽  
R. Kiranmayi ◽  
P. Nagaraju Mandadi
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Artificial Intelligence Techniques ◽  
Electrical Drives

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.

The Application of Artificial Intelligence in Power System State Estimation with Intelligent Materials

2012 ◽  
Vol 485 ◽  
pp. 131-135 ◽  
Author(s):  
Yun Jing Liu ◽  
Feng Wen Wang
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Power Systems ◽  
Power System ◽  
State Estimation ◽  
Real Time ◽  
System State ◽  
Power System State Estimation ◽  
Artificial Intelligence Techniques

With the development of power systems, the problem of security, stability and economics has become increasingly important. Reliable real-time data base is the foundation of analysis of the systems security and stability. Power system state estimation is used to build reliable real-time model of the power network. It has the on-line security analysis function. Power systems are large, complex systems containing highly nonlinear components. Therefore, traditional approaches often have difficulties in finding the optimal solution efficiently. Artificial intelligence techniques are being applied to a wide range of practical problems in power system. With their ability to some laws of nature and mimic human reasoning, AI techniques such as fuzzy logic and genetic algorithm seem to be more efficient in dealing with large systems and complex problems. Artificial intelligence techniques have been applied in power system applications. This paper presents a method of adaptive genetic algorithm and fuzzy logic applied in phasor measurement placement and bad data identification. And simulation is evaluated on IEEE 22-bus power system.

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.

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.

Artificial Intelligence Review

2019 ◽  
pp. 23-39
Author(s):  
Amal Kilani ◽  
Ahmed Ben Hamida ◽  
Habib Hamam
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Intelligent System ◽  
Route Planning ◽  
Turing Test ◽  
Future Research ◽  
Interaction Data ◽  
Research Directions ◽  
Future Research Directions

In this chapter, the authors present a profound literature review of artificial intelligence (AI). After defining it, they briefly cover its history and enumerate its principal fields of application. They name, for example, information system, commerce, image processing, human-computer interaction, data compression, robotics, route planning, etc. Moreover, the test that defines an artificially intelligent system, called the Turing test, is also defined and detailed. Afterwards, the authors describe some AI tools such as fuzzy logic, genetic algorithms, and swarm intelligence. Special attention will be given to neural networks and fuzzy logic. The authors also present the future research directions and ethics.

scholarly journals The Interplay of Knowledge Representation with Various Fields of Artificial Intelligence in Medicine

2019 ◽  
Vol 28 (01) ◽  
pp. 027-034 ◽  
Author(s):  
Laszlo Balkanyi ◽  
Ronald Cornet
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Knowledge Representation ◽  
Expert Systems ◽  
Frequency Analysis ◽  
Medical Subject Headings ◽  
Subject Headings

Introduction: Artificial intelligence (AI) is widespread in many areas, including medicine. However, it is unclear what exactly AI encompasses. This paper aims to provide an improved understanding of medical AI and its constituent fields, and their interplay with knowledge representation (KR). Methods: We followed a Wittgensteinian approach (“meaning by usage”) applied to content metadata labels, using the Medical Subject Headings (MeSH) thesaurus to classify the field. To understand and characterize medical AI and the role of KR, we analyzed: (1) the proportion of papers in MEDLINE related to KR and various AI fields; (2) the interplay among KR and AI fields and overlaps among the AI fields; (3) interconnectedness of fields; and (4) phrase frequency and collocation based on a corpus of abstracts. Results: Data from over eighty thousand papers showed a steep, six-fold surge in the last 30 years. This growth happened in an escalating and cascading way. A corpus of 246,308 total words containing 21,842 unique words showed several hundred occurrences of notions such as robotics, fuzzy logic, neural networks, machine learning and expert systems in the phrase frequency analysis. Collocation analysis shows that fuzzy logic seems to be the most often collocated notion. Neural networks and machine learning are also used in the conceptual neighborhood of KR. Robotics is more isolated. Conclusions: Authors note an escalation of published AI studies in medicine. Knowledge representation is one of the smaller areas, but also the most interconnected, and provides a common cognitive layer for other areas.

scholarly journals ARTIFICIAL INTELLIGENCE IN POWER INDUSTRY

2021 ◽  
Vol 1 (1) ◽  
pp. 225-226
Author(s):  
Yuriy Konovalov ◽  
Anton Vaygachev
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Systematic Approach ◽  
Power Industry ◽  
Artificial Neural ◽  
Uncertainty Of Parameters

Trends in the development of artificial intelligence and the use of neural networks as applied to the power industry are considered. It is revealed that the well-known forecasting systems based on artificial neural networks are difficult to formalize and get an unambiguous solution. There fore, this problem must be solved using a systematic approach that combines the capabilities of artifi cial neural networks and fuzzy logic under conditions of partial uncertainty of parameters

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