scholarly journals Performance Evaluation of CNN-Based End-Point Detection Using In-Situ Plasma Etching Data

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 49
Author(s):  
Bobae Kim ◽  
Sungbin Im ◽  
Geonwook Yoo
Keyword(s):  
Plasma Etching ◽  
Optical Emission ◽  
Process Variations ◽  
Ensemble Classifier ◽  
Support Vector ◽  
Svm Classifier ◽  
Endpoint Detection ◽  
Complex Architectures ◽  
And Shifts ◽  
Point Detection

As the technology node shrinks and shifts towards complex architectures, accurate control of automated semiconductor manufacturing processes, particularly plasma etching, is crucial in yield, cost, and semiconductor performance. However, current endpoint detection (EPD) methods relying on the experience of skilled engineers result in process variations and even errors. This paper proposes an enhanced optimal EPD in the plasma etching process based on a convolutional neural network (CNN). The proposed approach performs feature extraction on the spectral data obtained by optical emission spectroscopy (OES) and successfully predicts optimal EPD time. For the purpose of comparison, the support vector machine (SVM) classifier and the Adaboost Ensemble classifier are also investigated; the CNN-based model demonstrates better performance than the two models.

Deep-learning and radiomics ensemble classifier for false positive reduction in brain metastases segmentation

2021 ◽  
Author(s):  
Zi Yang ◽  
Mingli Chen ◽  
Mahdieh Kazemimoghadam ◽  
Lin Ma ◽  
Strahinja Stojadinovic ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Metastases ◽  
False Positive ◽  
False Positive Rate ◽  
Ensemble Classifier ◽  
Support Vector ◽  
Svm Classifier ◽  
Siamese Network ◽  
Proposed Model ◽  
Positive Rate

Abstract Stereotactic radiosurgery (SRS) is now the standard of care for brain metastases (BMs) patients. The SRS treatment planning process requires precise target delineation, which in clinical workflow for patients with multiple (>4) BMs (mBMs) could become a pronounced time bottleneck. Our group has developed an automated BMs segmentation platform to assist in this process. The accuracy of the auto-segmentation, however, is influenced by the presence of false-positive segmentations, mainly caused by the injected contrast during MRI acquisition. To address this problem and further improve the segmentation performance, a deep-learning and radiomics ensemble classifier was developed to reduce the false-positive rate in segmentations. The proposed model consists of a Siamese network and a radiomic-based support vector machine (SVM) classifier. The 2D-based Siamese network contains a pair of parallel feature extractors with shared weights followed by a single classifier. This architecture is designed to identify the inter-class difference. On the other hand, the SVM model takes the radiomic features extracted from 3D segmentation volumes as the input for twofold classification, either a false-positive segmentation or a true BM. Lastly, the outputs from both models create an ensemble to generate the final label. The performance of the proposed model in the segmented mBMs testing dataset reached the accuracy (ACC), sensitivity (SEN), specificity (SPE) and area under the curve (AUC) of 0.91, 0.96, 0.90 and 0.93, respectively. After integrating the proposed model into the original segmentation platform, the average segmentation false negative rate (FNR) and the false positive over the union (FPoU) were 0.13 and 0.09, respectively, which preserved the initial FNR (0.07) and significantly improved the FPoU (0.55). The proposed method effectively reduced the false-positive rate in the BMs raw segmentations indicating that the integration of the proposed ensemble classifier into the BMs segmentation platform provides a beneficial tool for mBMs SRS management.

Gesture Classification of Surface Electromyography Signals Using Machine Learning Algorithms for Hand Prosthetics

2021 ◽  
Vol 11 (12) ◽  
pp. 3141-3152
Author(s):  
N. Subhashini ◽  
A. Kandaswamy
Keyword(s):  
Machine Learning ◽  
Surface Electromyography ◽  
Learning Algorithms ◽  
Ensemble Classifier ◽  
Correlation Factor ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
Movement Classification

The actions of humans executed by their hands play a remarkable part in controlling and handling variety of objects in their daily life activities. The effect of losing or degradation in the functioning of one hand has a greater influence in bringing down the regular activity. Hence the design of prosthetic hands which assists the individuals to enhance their regular activity seems a better remedy in this new era. This paper puts forward a classification framework using machine learning algorithms for classifying hand gesture signals. The surface electromyography (sEMG) dataset acquired for 9 wrist movements of publicly available database are utilized to identify the potential biomarkers for classification and in evaluating the efficacy of the proposed algorithm. The statistical and time domain features of the sEMG signals from 27 intact subjects and 11 trans-radial amputated subjects are extracted and the optimal features are determined implementing the feature selection approach based on correlation factor. The classifiers performance of machine learning algorithms namely support vector machine (SVM), Naïve bayes (NB) and Ensemble classifier are evaluated. The experimental results highlight that the SVM classifier can yield the maximum accuracy movement classification of 99.6% for intact and 97.56% for trans-amputee subjects. The proposed approach offers better accuracy and sensitivity compared to other approaches that have used the sEMG dataset for movement classification.

Binary Spectrum Feature for Improved Classifier Performance

2020 ◽  
Author(s):  
Nalika Ulapane ◽  
Karthick Thiyagarajan ◽  
sarath kodagoda
Keyword(s):  
Machine Learning ◽  
Classification Performance ◽  
Feature Reduction ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Monitoring Task ◽  
Classifier Performance ◽  
Spectrum Feature

<div>Classification has become a vital task in modern machine learning and Artificial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classification. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classifier performance. In this paper, we consider the case of a given supervised learning classification task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classification performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classification accuracy of a Support Vector Machine (SVM) classifier increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>

Identification of Chronic Hypersensitivity Pneumonitis Biomarkers with Machine Learning and Differential Co-expression Analysis

2020 ◽  
Vol 20 ◽  
Author(s):  
Hongwei Zhang ◽  
Steven Wang ◽  
Tao Huang
Keyword(s):  
Feature Selection ◽  
Expression Analysis ◽  
Hypersensitivity Pneumonitis ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Great Promise ◽  
Support Vector ◽  
Svm Classifier ◽  
Clinical Tool ◽  
Chronic Hypersensitivity Pneumonitis

Aims: We would like to identify the biomarkers for chronic hypersensitivity pneumonitis (CHP) and facilitate the precise gene therapy of CHP. Background: Chronic hypersensitivity pneumonitis (CHP) is an interstitial lung disease caused by hypersensitive reactions to inhaled antigens. Clinically, the tasks of differentiating between CHP and other interstitial lungs diseases, especially idiopathic pulmonary fibrosis (IPF), were challenging. Objective: In this study, we analyzed the public available gene expression profile of 82 CHP patients, 103 IPF patients, and 103 control samples to identify the CHP biomarkers. Method: The CHP biomarkers were selected with advanced feature selection methods: Monte Carlo Feature Selection (MCFS) and Incremental Feature Selection (IFS). A Support Vector Machine (SVM) classifier was built. Then, we analyzed these CHP biomarkers through functional enrichment analysis and differential co-expression analysis. Result: There were 674 identified CHP biomarkers. The co-expression network of these biomarkers in CHP included more negative regulations and the network structure of CHP was quite different from the network of IPF and control. Conclusion: The SVM classifier may serve as an important clinical tool to address the challenging task of differentiating between CHP and IPF. Many of the biomarker genes on the differential co-expression network showed great promise in revealing the underlying mechanisms of CHP.

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