Pixel-Based Artificial Neural Networks in Computer-Aided Diagnosis

Suzuki, Kenji

doi:10.5772/16084

Cited by 34 publications

(49 citation statements)

References 70 publications

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“…As the weights for irrelevant variables add noise to the prediction results, they negatively affect the prediction accuracy [51]. Moreover, the inclusion of more irrelevant variables increases the number of local optima in the error function, because there are more combinations of weights that can yield locally optimal values of the error function [52]. This can become a serious problem as more hidden units are used in the prediction model, which increases model complexity.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Effect of Irrelevant Variables on Faulty Wafer Detection in Semiconductor Manufacturing

Kim

Kang

2019

Energies

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Machine learning has been applied successfully for faulty wafer detection tasks in semiconductor manufacturing. For the tasks, prediction models are built with prior data to predict the quality of future wafers as a function of their precedent process parameters and measurements. In real-world problems, it is common for the data to have a portion of input variables that are irrelevant to the prediction of an output variable. The inclusion of many irrelevant variables negatively affects the performance of prediction models. Typically, prediction models learned by different learning algorithms exhibit different sensitivities with regard to irrelevant variables. Algorithms with low sensitivities are preferred as a first trial for building prediction models, whereas a variable selection procedure is necessarily considered for highly sensitive algorithms. In this study, we investigate the effect of irrelevant variables on three well-known representative learning algorithms that can be applied to both classification and regression tasks: artificial neural network, decision tree (DT), and k-nearest neighbors (k-NN). We analyze the characteristics of these learning algorithms in the presence of irrelevant variables with different model complexity settings. An empirical analysis is performed using real-world datasets collected from a semiconductor manufacturer to examine how the number of irrelevant variables affects the behavior of prediction models trained with different learning algorithms and model complexity settings. The results indicate that the prediction accuracy of k-NN is highly degraded, whereas DT demonstrates the highest robustness in the presence of many irrelevant variables. In addition, a higher model complexity of learning algorithms leads to a higher sensitivity to irrelevant variables.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Effect of Irrelevant Variables on Faulty Wafer Detection in Semiconductor Manufacturing

Kim

Kang

2019

Energies

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“…When using ANN for solving a problem, the following steps should be chosen carefully to make ANN works in an effective way: Design of ANN topology, choosing suitable learning way, and setting the inputs. There are many ANN topologies such as: [12] • Feed-forward ANNs • Recurrent ANNs • Hopfield ANN There are three generations of neuron models [13]. The first generation of ANNs also called perceptrons, which are composed each of two sections: Sum and threshold.…”

Section: A Annsmentioning

confidence: 99%

Intelligent Techniques in Cryptanalysis: Review and Future Directions

Al-Janabi

Al‐Khateeb

Ahmed

2017

UHDJST

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“…The neural networks are able to handle large input data sets and based on appropriate learning, they are able to find complex nonlinear relationships among the data in order to make accurate predictions. Significant efforts have been made in order to develop a prediction framework for different cases based on ANNs [9][10][11][12]. Based on these, ANNs offer high prediction capabilities.…”

Section: Ann and Prediction Related Workmentioning

confidence: 99%

“…According to Shamishi et al [16], it is explained how MATLAB tools can be used in writing scripts, which will help the development of ANN models in order to predict global solar radiation in United Arab Emirates. Added to this, Jahirul et al [9,11] provide advances of ANN applications on different situations. They present the methodology and the biomedical applications of ANNs as well as applications of ANNs in industry and engineering.…”

Section: Ann and Prediction Related Workmentioning

confidence: 99%

A Design Space Exploration Framework for ANN-Based Fault Detection in Hardware Systems

Savva

Theocharides

Nicopoulos

2017

Journal of Electrical and Computer Engineering

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This work presents a design exploration framework for developing a high level Artificial Neural Network (ANN) for fault detection in hardware systems. ANNs can be used for fault detection purposes since they have excellent characteristics such as generalization capability, robustness, and fault tolerance. Designing an ANN in order to be used for fault detection purposes includes different parameters. Through this work, those parameters are presented and analyzed based on simulations. Moreover, after the development of the ANN, in order to evaluate it, a case study scenario based on Networks on Chip is used for detection of interrouter link faults. Simulation results with various synthetic traffic models show that the proposed work can detect up to 96-99% of interrouter link faults with a delay less than 60 cycles. Added to this, the size of the ANN is kept relatively small and they can be implemented in hardware easily. Synthesis results indicate an estimated amount of 0.0523 mW power consumption per neuron for the implemented ANN when computing a complete cycle.

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Pixel-Based Artificial Neural Networks in Computer-Aided Diagnosis

Cited by 34 publications

References 70 publications

Effect of Irrelevant Variables on Faulty Wafer Detection in Semiconductor Manufacturing

Effect of Irrelevant Variables on Faulty Wafer Detection in Semiconductor Manufacturing

Intelligent Techniques in Cryptanalysis: Review and Future Directions

A Design Space Exploration Framework for ANN-Based Fault Detection in Hardware Systems

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