scholarly journals History and application of artificial neural networks in dentistry

2018 ◽  
Vol 12 (04) ◽  
pp. 594-601 ◽  
Author(s):  
Wook Joo Park ◽  
Jun-Beom Park
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Dental Treatment ◽  
Daily Life ◽  
Human Life ◽  
Modern Human ◽  
Medical Decision ◽  
Dental Practice ◽  
Current Application ◽  
Artificial Neural

ABSTRACTArtificial intelligence (AI) is a commonly used term in daily life, and there are now two subconcepts that divide the entire range of meanings currently encompassed by the term. The coexistence of the concepts of strong and weak AI can be seen as a result of the recognition of the limits of mathematical and engineering concepts that have dominated the definition. This presentation reviewed the concept, history, and the current application of AI in daily life. Applications of AI are becoming a reality that is commonplace in all areas of modern human life. Efforts to develop robots controlled by AI have been continuously carried out to maximize human convenience. AI has also been applied in the medical decision-making process, and these AI systems can help nonspecialists to obtain expert-level information. Artificial neural networks are highly interconnected networks of computer processors inspired by biological nervous systems. These systems may help connect dental professionals all over the world. Currently, the use of AI is rapidly advancing beyond text-based, image-based dental practice. This presentation reviewed the history of artificial neural networks in the medical and dental fields, as well as current application in dentistry. As the use of AI in the entire medical field increases, the role of AI in dentistry will be greatly expanded. Currently, the use of AI is rapidly advancing beyond text-based, image-based dental practice. In addition to diagnosis of visually confirmed dental caries and impacted teeth, studies applying machine learning based on artificial neural networks to dental treatment through analysis of dental magnetic resonance imaging, computed tomography, and cephalometric radiography are actively underway, and some visible results are emerging at a rapid pace for commercialization.

scholarly journals RESULTS AND CHALLENGES OF ARTIFICIAL NEURAL NETWORKS USED FOR DECISION-MAKING AND CONTROL IN MEDICAL APPLICATIONS

2019 ◽  
Vol 17 (3) ◽  
pp. 285 ◽  
Author(s):  
Adriana Albu ◽  
Radu-Emil Precup ◽  
Teodor-Adrian Teban
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Artificial Neural Networks ◽  
Medical Problem ◽  
Medical Decision ◽  
Control Group ◽  
Medical Systems ◽  
Modeling Tools ◽  
Artificial Neural ◽  
Intelligent Models

The aim of this paper is to present several approaches by which technology can assist medical decision-making. This is an essential, but also a difficult activity, which implies a large number of medical and technical aspects. But, more important, it involves humans: on the one hand, the patient, who has a medical problem and who requires the best solution; on the other hand, the physician, who should be able to provide, in any circumstances, a decision or a prediction regarding the current and the future medical status of the patient. The technology, in general, and particularly the Artificial Intelligence (AI) tools could help both of them, and it is assisted by appropriate theory regarding modeling tools. One of the most powerful mechanisms that can be used in this field is the Artificial Neural Networks (ANNs). This paper presents some of the results obtained by the Process Control group of the Politehnica University Timisoara, Romania, in the field of ANNs applied to modeling, prediction and decision-making related to medical systems. An Iterative Learning Control-based approach to batch training a feedforward ANN architecture is given. The paper includes authors’ concerns in this domain and emphasizes that these intelligent models, even if they are artificial, are able to make decisions, being useful tools for prevention, early detection and personalized healthcare.

scholarly journals APPLICATION OF BACKPROPAGATION NEURAL NETWORKS IN PREDICTING RAINFALL DATA IN AMBON CITY

2018 ◽  
Vol 2 (2) ◽  
Author(s):  
Yopi Andry Lesnussa ◽  
C. G. Mustamu ◽  
F. Kondo Lembang ◽  
M. W. Talakua
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Human Life ◽  
Prediction System ◽  
Air Velocity ◽  
Rainfall Prediction ◽  
Accuracy Level ◽  
Backpropagation Method ◽  
Iteration Number ◽  
Artificial Neural

The Artificial Neural Networks is a process of information system on certain traits which as representatives of the human neural networks. The Artificial Neural Networks can be applied in every area of human life, one of them is environment especially about prediction of climate or weather. In this research, the artificial neural network is used to predict the rainfall with Backpropagation method and using MATLAB software. The other meteorology parameters used to predict the rainfall are air temperature, air velocity and air pressure. The result showed less accuracy level is 80% by using alpha 0,7, iteration number (epoch) 10000 and MSE value = 0,0218. Therefore, the result of rainfall prediction system is accurate.

scholarly journals Development of a method for assessment and forecasting of the radio electronic environment

2021 ◽  
pp. 30-40
Author(s):  
Oleg Sova ◽  
Andrii Shyshatskyi ◽  
Olha Salnikova ◽  
Oleksandr Zhuk ◽  
Oleksandr Trotsko ◽  
...  
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Artificial Neural Networks ◽  
Information Processing ◽  
System Analysis ◽  
Human Life ◽  
Cognitive Model ◽  
Additional Information ◽  
Electronic Environment ◽  
Artificial Neural

Decision making support systems (DSS) are actively used in all spheres of human life. The system of the electronic environment analysis is not an exception. However, there are a number of problems in the analysis of the electronic environment, for example: the signals are analyzed in a complex electronic environment against the background of intentional and natural interference. Input signals do not match the standards, and their interpretation depends on the experience of the operator (expert), the completeness of additional information on a particular task (uncertainty condition). The best solution in this situation is found in the integration with the data of the information system analysis of the electronic environment, artificial neural networks and fuzzy cognitive models. Their advantages are also the ability to work in real time and quick adaptation to specific situations. The article develops a method for assessing and forecasting the electronic environment. Improving the efficiency of evaluation information processing is achieved through the use of evolving neuro-fuzzy artificial neural networks; learning not only the synaptic weights of the artificial neural network, the type and parameters of the membership function. The efficiency of information processing is also achieved through training in the architecture of artificial neural networks; taking into account the type of uncertainty of the information that has to be assessed; synthesis of rational structure of fuzzy cognitive model. It reduces the computational complexity of decision-making; has no accumulation of learning error of artificial neural networks as a result of processing the information coming to the input of artificial neural networks. The example of assessing the state of the electronic environment showed an increase in the efficiency of assessment at the level of 15–25 % on the efficiency of information processing

scholarly journals ARTIFICIAL NEURAL NETWORKS IN FOREX CURRENCY MARKETS

2021 ◽  
Vol 10 (5) ◽  
pp. 89-92
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Daily Life ◽  
Currency Markets ◽  
Artificial Neurons ◽  
The Neural Networks ◽  
Artificial Neural

The Artificial Intelligence is growing and covering various aspects of our daily life. The idea seems to be very complex. It seems that a program cannot be developed using our home PC. But believe me, it's not that difficult. Let us try to understand what the neural networks are and how they can be applied in trading. Artificial Neural Networks can be used in forex Currency Trading, for finding or predicting the next possible movements. We know that Artificial Neural Networks involves study of neurons in the human brain, sometimes called as biological network.ANN is based on connections of nodes, units called as artificial nodes or neurons. Neural network is an entity consisting of artificial neurons, among which there is an organized relationship. These relations are similar to a biological brain.

scholarly journals Artificial Intelligence: Learning and Limitations

2020 ◽  
Vol 17 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Human Life ◽  
New Technology ◽  
Development History ◽  
Adversarial Examples ◽  
Real Value ◽  
Artificial Neural

Artificial Intelligence, IA, is a new technology with enormous potential to change the world forever as we know it. It finds applications in many fields of human activity, including services, industry, education, social networks, transportation, among others. However, there is little discussion about the accuracy and reliability of such technology, which has been used in situations where human life depends on its decision-making process, which is the result of its training, one of the stages of development. It is known that the learning process of an Artificial Intelligence, which can use the Artificial Neural Networks technology, presents an error of the predicted value in relation to the real value, which can compromise its application, being more critical in situations where the user's security is a major issue. In this article, we discuss the main technologies used in AI, their development history, considerations about Artificial Neural Networks and the failures arising from the training and hardware processes used. Three types of errors are discussed: The Adversarial Examples, the Soft Errors and the Errors due the lack of Appropriate Training. A case study associated with the third type of error is discussed and actions based on Design of Experiments are proposed. The objective is to change the way the AI models are trained, to add some rare conditions, and to improve their ability to forecast with greater accuracy in any situation

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