Opening the black box of neural networks: methods for interpreting neural network models in clinical applications

Recently, deep learning has been successfully applied to robotic grasp detection. Based on convolutional neural networks (CNNs), there have been lots of end-to-end detection approaches. But end-to-end approaches have strict requirements for the dataset used for training the neural network models and it’s hard to achieve in practical use. Therefore, we proposed a two-stage approach using particle swarm optimizer (PSO) candidate estimator and CNN to detect the most likely grasp. Our approach achieved an accuracy of 92.8% on the Cornell Grasp Dataset, which leaped into the front ranks of the existing approaches and is able to run at real-time speeds. After a small change of the approach, we can predict multiple grasps per object in the meantime so that an object can be grasped in a variety of ways.

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Demand Modelling in Telecommunications

Acta Polytechnica ◽

10.14311/1121 ◽

2009 ◽

Vol 49 (2) ◽

Author(s):

M. Chvalina

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Neural Networks ◽

Network Models ◽

Short Term ◽

Neural Network Models ◽

Term Forecast ◽

Artificial Intelligence Methods ◽

Demand Modelling

This article analyses the existing possibilities for using Standard Statistical Methods and Artificial Intelligence Methods for a short-term forecast and simulation of demand in the field of telecommunications. The most widespread methods are based on Time Series Analysis. Nowadays, approaches based on Artificial Intelligence Methods, including Neural Networks, are booming. Separate approaches will be used in the study of Demand Modelling in Telecommunications, and the results of these models will be compared with actual guaranteed values. Then we will examine the quality of Neural Network models.

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Artificial Polynomial and Trigonometric Higher Order Neural Network Group Models

Artificial Higher Order Neural Networks for Modeling and Simulation ◽

10.4018/978-1-4666-2175-6.ch005 ◽

2013 ◽

pp. 78-102 ◽

Cited By ~ 2

Author(s):

Ming Zhang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Continuous Function ◽

Trigonometric Polynomial ◽

Network Models ◽

Higher Order ◽

Financial Data ◽

Neural Network Models ◽

Piecewise Continuous ◽

Discontinuous Data

Real world financial data is often discontinuous and non-smooth. Accuracy will be a problem, if we attempt to use neural networks to simulate such functions. Neural network group models can perform this function with more accuracy. Both Polynomial Higher Order Neural Network Group (PHONNG) and Trigonometric polynomial Higher Order Neural Network Group (THONNG) models are studied in this chapter. These PHONNG and THONNG models are open box, convergent models capable of approximating any kind of piecewise continuous function to any degree of accuracy. Moreover, they are capable of handling higher frequency, higher order nonlinear, and discontinuous data. Results obtained using Polynomial Higher Order Neural Network Group and Trigonometric polynomial Higher Order Neural Network Group financial simulators are presented, which confirm that PHONNG and THONNG group models converge without difficulty, and are considerably more accurate (0.7542% - 1.0715%) than neural network models such as using Polynomial Higher Order Neural Network (PHONN) and Trigonometric polynomial Higher Order Neural Network (THONN) models.

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Artificial Neural Networks

Intelligent Information Technologies ◽

10.4018/978-1-59904-941-0.ch010 ◽

2011 ◽

pp. 222-243

Author(s):

Joarder Kamruzzaman ◽

Ruhul Sarker

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Network Models ◽

Neural Network Models ◽

Network Applications ◽

The Neural Network ◽

Artificial Neural ◽

Neural Network Applications

The primary aim of this chapter is to present an overview of the artificial neural network basics and operation, architectures, and the major algorithms used for training the neural network models. As can be seen in subsequent chapters, neural networks have made many useful contributions to solve theoretical and practical problems in finance and manufacturing areas. The secondary aim here is therefore to provide a brief review of artificial neural network applications in finance and manufacturing areas.

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Financial Data Prediction by Artificial Sine and Cosine Trigonometric Higher Order Neural Networks

Emerging Capabilities and Applications of Artificial Higher Order Neural Networks - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-3563-9.ch006 ◽

2021 ◽

pp. 262-301

Keyword(s):

Neural Network ◽

Neural Networks ◽

Network Models ◽

Higher Order ◽

Financial Data ◽

Data Simulation ◽

Neural Network Models ◽

Data Prediction ◽

Higher Order Neural Networks

This chapter develops two new nonlinear artificial higher order neural network models. They are sine and sine higher order neural networks (SIN-HONN) and cosine and cosine higher order neural networks (COS-HONN). Financial data prediction using SIN-HONN and COS-HONN models are tested. Results show that SIN-HONN and COS-HONN models are good models for some sine feature only or cosine feature only financial data simulation and prediction compared with polynomial higher order neural network (PHONN) and trigonometric higher order neural network (THONN) models.

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Neural Networks and Adaptive Control: Neural Network Models

The Cerebellum and Adaptive Control ◽

10.1017/cbo9780511529771.015 ◽

2002 ◽

pp. 204-222

Keyword(s):

Neural Network ◽

Neural Networks ◽

Adaptive Control ◽

Network Models ◽

Neural Network Models

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Multiple Ensemble Neural Network Models with Fuzzy Response Aggregation for Predicting COVID-19 Time Series: The Case of Mexico

Healthcare ◽

10.3390/healthcare8020181 ◽

2020 ◽

Vol 8 (2) ◽

pp. 181 ◽

Cited By ~ 5

Author(s):

Patricia Melin ◽

Julio Cesar Monica ◽

Daniela Sanchez ◽

Oscar Castillo

Keyword(s):

Neural Network ◽

Neural Networks ◽

Time Series ◽

Fuzzy Logic ◽

Network Models ◽

Neural Network Models ◽

Validation Data ◽

Data Set ◽

Ensemble Neural Networks ◽

Ensemble Neural Network

In this paper, a multiple ensemble neural network model with fuzzy response aggregation for the COVID-19 time series is presented. Ensemble neural networks are composed of a set of modules, which are used to produce several predictions under different conditions. The modules are simple neural networks. Fuzzy logic is then used to aggregate the responses of several predictor modules, in this way, improving the final prediction by combining the outputs of the modules in an intelligent way. Fuzzy logic handles the uncertainty in the process of making a final decision about the prediction. The complete model was tested for the case of predicting the COVID-19 time series in Mexico, at the level of the states and the whole country. The simulation results of the multiple ensemble neural network models with fuzzy response integration show very good predicted values in the validation data set. In fact, the prediction errors of the multiple ensemble neural networks are significantly lower than using traditional monolithic neural networks, in this way showing the advantages of the proposed approach.

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Real-Time Guitar Amplifier Emulation with Deep Learning

Applied Sciences ◽

10.3390/app10030766 ◽

2020 ◽

Vol 10 (3) ◽

pp. 766 ◽

Cited By ~ 2

Author(s):

Alec Wright ◽

Eero-Pekka Damskägg ◽

Lauri Juvela ◽

Vesa Välimäki

Keyword(s):

Neural Network ◽

Neural Networks ◽

Real Time ◽

Reference Signal ◽

Network Models ◽

Neural Network Models ◽

Desktop Computer ◽

The Neural Network ◽

Highly Nonlinear ◽

The Difference

This article investigates the use of deep neural networks for black-box modelling of audio distortion circuits, such as guitar amplifiers and distortion pedals. Both a feedforward network, based on the WaveNet model, and a recurrent neural network model are compared. To determine a suitable hyperparameter configuration for the WaveNet, models of three popular audio distortion pedals were created: the Ibanez Tube Screamer, the Boss DS-1, and the Electro-Harmonix Big Muff Pi. It is also shown that three minutes of audio data is sufficient for training the neural network models. Real-time implementations of the neural networks were used to measure their computational load. To further validate the results, models of two valve amplifiers, the Blackstar HT-5 Metal and the Mesa Boogie 5:50 Plus, were created, and subjective tests were conducted. The listening test results show that the models of the first amplifier could be identified as different from the reference, but the sound quality of the best models was judged to be excellent. In the case of the second guitar amplifier, many listeners were unable to hear the difference between the reference signal and the signals produced with the two largest neural network models. This study demonstrates that the neural network models can convincingly emulate highly nonlinear audio distortion circuits, whilst running in real-time, with some models requiring only a relatively small amount of processing power to run on a modern desktop computer.

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Missing Data Approaches to Classification

Computational Intelligence for Missing Data Imputation, Estimation, and Management ◽

10.4018/978-1-60566-336-4.ch009 ◽

2010 ◽

pp. 187-209

Author(s):

Tshilidzi Marwala

Keyword(s):

Neural Network ◽

Neural Networks ◽

Missing Data ◽

Network Model ◽

Network Models ◽

Feed Forward Neural Network ◽

Neural Network Models ◽

Feed Forward ◽

Missing Data Problem ◽

Feed Forward Neural Networks

In this chapter, a classifier technique that is based on a missing data estimation framework that uses autoassociative multi-layer perceptron neural networks and genetic algorithms is proposed. The proposed method is tested on a set of demographic properties of individuals obtained from the South African antenatal survey and compared to conventional feed-forward neural networks. The missing data approach based on the autoassociative network model proposed gives an accuracy of 92%, when compared to the accuracy of 84% obtained from the conventional feed-forward neural network models. The area under the receiver operating characteristics curve for the proposed autoassociative network model is 0.86 compared to 0.80 for the conventional feed-forward neural network model. The autoassociative network model proposed in this chapter, therefore, outperforms the conventional feed-forward neural network models and is an improved classifier. The reasons for this are: (1) the propagation of errors in the autoassociative network model is more distributed while for a conventional feed-forward network is more concentrated; and (2) there is no causality between the demographic properties and the HIV and, therefore, the HIV status does change the demographic properties and vice versa. Therefore, it is better to treat the problem as a missing data problem rather than a feed-forward problem.

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