Singular solutions of optimization and control problems

Ho, Yu-Chi

doi:10.1201/9781315137667-8

Cited by 106 publications

(221 citation statements)

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“…This backpropagation algorithm utilizes a partial derivative approach to refine the AI execution result, which is implemented in a propositional and symbolic way and is designed to improve the AI self-execution algorithm. [ 24 ] Through the concept of machine learning, AI moves from the stage that was used for the Turing test implementation and mathematical and logical verification to the upper level of real-life use.…”

Section: Deep Learning and Cognitive Sciencementioning

confidence: 99%

History and application of artificial neural networks in dentistry

Park

2018

Eur J Dent

132

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Artificial 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.

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Section: Deep Learning and Cognitive Sciencementioning

confidence: 99%

History and application of artificial neural networks in dentistry

Park

2018

Eur J Dent

132

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“…We used a feed-forward neural network with one hidden layer with ten nodes to impute those values. This graph of interconnected nodes (neurons) is capable of learning by adjusting the weights of the paths connecting its inputs to outputs ( Bryson and Ho 1969 ). In the datasets for patients 3 and 9 several observations are missing for both predictors, and since the neural network is trained using backward propagation of errors, we recode all the values to fall between

with missing values coded as 0, such that the weights for these inputs are also shrunk to 0 during back-propagation and effectively no learning is done on those branches.…”

Section: Methodsmentioning

confidence: 99%

Identifying predictors of time-inhomogeneous viral evolutionary processes

et al. 2016

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Various factors determine the rate at which mutations are generated and fixed in viral genomes. Viral evolutionary rates may vary over the course of a single persistent infection and can reflect changes in replication rates and selective dynamics. Dedicated statistical inference approaches are required to understand how the complex interplay of these processes shapes the genetic diversity and divergence in viral populations. Although evolutionary models accommodating a high degree of complexity can now be formalized, adequately informing these models by potentially sparse data, and assessing the association of the resulting estimates with external predictors, remains a major challenge. In this article, we present a novel Bayesian evolutionary inference method, which integrates multiple potential predictors and tests their association with variation in the absolute rates of synonymous and non-synonymous substitutions along the evolutionary history. We consider clinical and virological measures as predictors, but also changes in population size trajectories that are simultaneously inferred using coalescent modelling. We demonstrate the potential of our method in an application to within-host HIV-1 sequence data sampled throughout the infection of multiple patients. While analyses of individual patient populations lack statistical power, we detect significant evidence for an abrupt drop in non-synonymous rates in late stage infection and a more gradual increase in synonymous rates over the course of infection in a joint analysis across all patients. The former is predicted by the immune relaxation hypothesis while the latter may be in line with increasing replicative fitness during the asymptomatic stage.

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“…The most popular method for learning in multilayer networks is called backpropagation. It was first invented by Bryson and Ho [ 28 ]. But there are some drawbacks to backpropagation.…”

Section: Methodsmentioning

confidence: 99%

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

Falát

Marček

Ďurišová

2016

The Scientific World Journal

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This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

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Singular solutions of optimization and control problems

Cited by 106 publications

References 0 publications

History and application of artificial neural networks in dentistry

History and application of artificial neural networks in dentistry

Identifying predictors of time-inhomogeneous viral evolutionary processes

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

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