scholarly journals A diagnostic genomic signal processing (GSP)-based system for automatic feature analysis and detection of COVID-19

2020 ◽  
Author(s):  
Safaa M Naeem ◽  
Mai S Mabrouk ◽  
Samir Y Marzouk ◽  
Mohamed A Eldosoky
Keyword(s):  
Nearest Neighbor ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Automated System ◽  
K Nearest Neighbor ◽  
Effective Parameters ◽  
Wuhan City ◽  
Genomic Signal Processing ◽  
Chinese Province ◽  
Genomic Signal

Abstract Coronavirus Disease 2019 (COVID-19) is a sudden viral contagion that appeared at the end of last year in Wuhan city, the Chinese province of Hubei, China. The fast spread of COVID-19 has led to a dangerous threat to worldwide health. Also in the last two decades, several viral epidemics have been listed like the severe acute respiratory syndrome coronavirus (SARS-CoV) in 2002/2003, the influenza H1N1 in 2009 and recently the Middle East respiratory syndrome coronavirus (MERS-CoV) which appeared in Saudi Arabia in 2012. In this research, an automated system is created to differentiate between the COVID-19, SARS-CoV and MERS-CoV epidemics by using their genomic sequences recorded in the NCBI GenBank in order to facilitate the diagnosis process and increase the accuracy of disease detection in less time. The selected database contains 76 genes for each epidemic. Then, some features are extracted like a discrete Fourier transform (DFT), discrete cosine transform (DCT) and the seven moment invariants to two different classifiers. These classifiers are the k-nearest neighbor (KNN) algorithm and the trainable cascade-forward back propagation neural network where they give satisfying results to compare. To evaluate the performance of classifiers, there are some effective parameters calculated. They are accuracy (ACC), F1 score, error rate and Matthews correlation coefficient (MCC) that are 100%, 100%, 0 and 1, respectively, for the KNN algorithm and 98.89%, 98.34%, 0.0111 and 0.9754, respectively, for the cascade-forward network.

scholarly journals Rapid and Nondestructive On-Site Classification Method for Consumer-Grade Plastics Based on Portable NIR Spectrometer and Machine Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yinglin Yang ◽  
Xin Zhang ◽  
Jianwei Yin ◽  
Xiangyang Yu
Keyword(s):  
Near Infrared ◽  
Nearest Neighbor ◽  
Diffuse Reflectance Spectrum ◽  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Site Classification ◽  
Classification Models ◽  
K Nearest Neighbor

The classification of plastic waste before recycling is of great significance to achieve effective recycling. In order to achieve rapid, nondestructive, and on-site detection, a portable near-infrared spectrometer was used in this study to obtain the diffuse reflectance spectrum for both standard and commercial plastics made by ABS, PC, PE, PET, PP, PS, and PVC. After applying a series of pretreatments, the principal component analysis (PCA) was used to analyze the cluster trend. K-nearest neighbor (KNN), support vector machine (SVM), and back propagation neural network (BPNN) classification models were developed and evaluated, respectively. The result showed that different plastics could be well separated in top three principal components space after pretreatment, and the classification models performed excellent classification results and high generalization capability. This study indicated that the portable NIR spectrometer, integrated with chemometrics, could achieve excellent performance and has great potential in the field of commercial plastic identification.

scholarly journals Automatic Detection and Staging of Lung Tumors using Locational Features and Double-Staged Classifications

2019 ◽  
Vol 9 (11) ◽  
pp. 2329 ◽  
Author(s):  
May Phu Paing ◽  
Kazuhiko Hamamoto ◽  
Supan Tungjitkusolmun ◽  
Chuchart Pintavirooj
Keyword(s):  
Lung Cancer ◽  
Nearest Neighbor ◽  
Treatment Options ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Classification Performance ◽  
Clinical Staging ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Experimental Findings

Lung cancer is a life-threatening disease with the highest morbidity and mortality rates of any cancer worldwide. Clinical staging of lung cancer can significantly reduce the mortality rate, because effective treatment options strongly depend on the specific stage of cancer. Unfortunately, manual staging remains a challenge due to the intensive effort required. This paper presents a computer-aided diagnosis (CAD) method for detecting and staging lung cancer from computed tomography (CT) images. This CAD works in three fundamental phases: segmentation, detection, and staging. In the first phase, lung anatomical structures from the input tomography scans are segmented using gray-level thresholding. In the second, the tumor nodules inside the lungs are detected using some extracted features from the segmented tumor candidates. In the last phase, the clinical stages of the detected tumors are defined by extracting locational features. For accurate and robust predictions, our CAD applies a double-staged classification: the first is for the detection of tumors and the second is for staging. In both classification stages, five alternative classifiers, namely the Decision Tree (DT), K-nearest neighbor (KNN), Support Vector Machine (SVM), Ensemble Tree (ET), and Back Propagation Neural Network (BPNN), are applied and compared to ensure high classification performance. The average accuracy levels of 92.8% for detection and 90.6% for staging are achieved using BPNN. Experimental findings reveal that the proposed CAD method provides preferable results compared to previous methods; thus, it is applicable as a clinical diagnostic tool for lung cancer.

Lubrication Leakage Alarm of Wind Power Gearbox Based on K-nearest Neighbor and Back Propagation Neural Network

2013 ◽  
Vol 12 (15) ◽  
pp. 3152-3157
Author(s):  
Chun-Yao Lee ◽  
Chih-Ju Chou ◽  
Chun-Chi Chen ◽  
Ryan Liu ◽  
I. Hsiang Tse ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Nearest Neighbor ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
K Nearest Neighbor

Development of Automated Algorithms for Pavement Condition Survey

1996 ◽  
Vol 1536 (1) ◽  
pp. 103-109
Author(s):  
Chia-pei J. Chou ◽  
Tz-kai Liau
Keyword(s):  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Automated System ◽  
Pavement Condition ◽  
Pavement Distress ◽  
Transverse Cracking ◽  
Automated Evaluation ◽  
Surface Distress ◽  
Automated Algorithms ◽  
Analytical System

Image processing and analysis techniques have been successfully applied to the evaluation of some types of pavement surface distress, including longitudinal, transverse, and alligator cracking. Many efforts have been made to develop automated evaluation for other types of distress patterns, but, due to the complexities of these distress types, the results were not as positive as for those distresses mentioned above. The main objectives are to develop image segmentation and classification methods to isolate distress features, and to develop the back-propagation neural network model to recognize block cracking and potholes, in addition to alligator, longitudinal, and transverse cracking. It was observed that longitudinal, transverse, alligator, and block cracking were accurately recognized, and the analytical system has a success rate of 93 percent for potholes. Preliminary results indicate that the proposed approach is very positive and has great potential for integration into an automated system for pavement distress evaluation.

scholarly journals Health Monitoring for Balancing Tail Ropes of a Hoisting System Using a Convolutional Neural Network

2018 ◽  
Vol 8 (8) ◽  
pp. 1346 ◽  
Author(s):  
Ping Zhou ◽  
Gongbo Zhou ◽  
Zhencai Zhu ◽  
Chaoquan Tang ◽  
Zhenzhi He ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Health Monitoring ◽  
Nearest Neighbor ◽  
Back Propagation ◽  
Feature Space ◽  
Batch Size ◽  
K Nearest Neighbor ◽  
Hoisting System

With the arrival of the big data era, it has become possible to apply deep learning to the health monitoring of mine production. In this paper, a convolutional neural network (CNN)-based method is proposed to monitor the health condition of the balancing tail ropes (BTRs) of the hoisting system, in which the feature of the BTR image is adaptively extracted using a CNN. This method can automatically detect various BTR faults in real-time, including disproportional spacing, twisted rope, broken strand and broken rope faults. Firstly, a CNN structure is proposed, and regularization technology is adopted to prevent overfitting. Then, a method of image dataset description and establishment that can cover the entire feature space of overhanging BTRs is put forward. Finally, the CNN and two traditional data mining algorithms, namely, k-nearest neighbor (KNN) and an artificial neural network with back propagation (ANN-BP), are adopted to train and test the established dataset, and the influence of hyperparameters on the network diagnostic accuracy is investigated experimentally. The experimental results showed that the CNN could effectively avoid complex steps such as manual feature extraction, that the learning rate and batch-size strongly affected the accuracy and training efficiency, and that the fault diagnosis accuracy of CNN was 100%, which was higher than that of KNN and ANN-BP. Therefore, the proposed CNN with high accuracy, real-time functioning and generalization performance is suitable for application in the health monitoring of hoisting system BTRs.

Stationary wavelet singular entropy based electronic tongue for classification of milk

2020 ◽  
Vol 42 (4) ◽  
pp. 870-879 ◽  
Author(s):  
Pauroosh Kaushal ◽  
Rohini Mudhalwadkar
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Back Propagation ◽  
Electronic Tongue ◽  
Cow Milk ◽  
Model Parameters ◽  
K Nearest Neighbor ◽  
Feature Extraction Method

Electronic tongue mimics human gustatory sensation and is used to characterize and discriminate beverages and foods. Feature extraction plays a key role in improving the classification accuracy by preserving the distinct characteristics while reducing high dimensionality of data generated from electronic tongue. This paper presents a new feature extraction method based on stationary wavelet singular entropy for a developed electronic tongue system to classify pasteurized cow milk. The electronic tongue consists of an array of five working electrodes along with a reference and a counter electrode to characterize milk sample. The feature extraction of acquired data is done by computing stationary wavelet transform to obtain detail and approximate coefficients at different level of decomposition. These coefficients are processed using singular value decomposition followed by calculation of entropy to obtain stationary wavelet singular entropy values. These values form the feature set and feed to two classifiers, k-nearest neighbor and back propagation artificial neural network, and their classification accuracy is evaluated with variation in their model parameters. The proposed method is compared with other wavelet transform-entropy methods in terms of classification accuracy, which indicates that the proposed method is more effective in discriminating milk samples.

scholarly journals Novel Mathematical Model of Breast Cancer Diagnostics Using an Associative Pattern Classification

Diagnostics ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 136 ◽  
Author(s):  
Raúl Santiago-Montero ◽  
Humberto Sossa ◽  
David A. Gutiérrez-Hernández ◽  
Víctor Zamudio ◽  
Ignacio Hernández-Bautista ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Nearest Neighbor ◽  
Early Stage ◽  
Back Propagation ◽  
Cancer Diagnostics ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Breast Cancer Death ◽  
Positron Emission ◽  
The Government

Breast cancer is a disease that has emerged as the second leading cause of cancer deaths in women worldwide. The annual mortality rate is estimated to continue growing. Cancer detection at an early stage could significantly reduce breast cancer death rates long-term. Many investigators have studied different breast diagnostic approaches, such as mammography, magnetic resonance imaging, ultrasound, computerized tomography, positron emission tomography and biopsy. However, these techniques have limitations, such as being expensive, time consuming and not suitable for women of all ages. Proposing techniques that support the effective medical diagnosis of this disease has undoubtedly become a priority for the government, for health institutions and for civil society in general. In this paper, an associative pattern classifier (APC) was used for the diagnosis of breast cancer. The rate of efficiency obtained on the Wisconsin breast cancer database was 97.31%. The APC’s performance was compared with the performance of a support vector machine (SVM) model, back-propagation neural networks, C4.5, naive Bayes, k-nearest neighbor (k-NN) and minimum distance classifiers. According to our results, the APC performed best. The algorithm of the APC was written and executed in a JAVA platform, as well as the experimental and comparativeness between algorithms.

Comparison of the Hybrid Credit Scoring Models Based on Various Classifiers

2012 ◽  
pp. 220-239
Author(s):  
Fei-Long Chen ◽  
Feng-Chia Li
Keyword(s):  
Nearest Neighbor ◽  
Credit Scoring ◽  
Back Propagation ◽  
Support Vector ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
Hybrid Features ◽  
Credit Data ◽  
Learning Machine ◽  
Propagation Network

Credit scoring is an important topic for businesses and socio-economic establishments collecting huge amounts of data, with the intention of making the wrong decision obsolete. In this paper, the authors propose four approaches that combine four well-known classifiers, such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Back-Propagation Network (BPN) and Extreme Learning Machine (ELM). These classifiers are used to find a suitable hybrid classifier combination featuring selection that retains sufficient information for classification purposes. In this regard, different credit scoring combinations are constructed by selecting features with four approaches and classifiers than would otherwise be chosen. Two credit data sets from the University of California, Irvine (UCI), are chosen to evaluate the accuracy of the various hybrid features selection models. In this paper, the procedures that are part of the proposed approaches are described and then evaluated for their performances.

Detection of Structural Defects in Fabric Parts Using a Novel Edge Detection Method

2018 ◽  
Vol 62 (7) ◽  
pp. 1036-1043
Author(s):  
M Dhivya ◽  
M Renuka Devi
Keyword(s):  
Nearest Neighbor ◽  
Automated System ◽  
Structural Defects ◽  
K Nearest Neighbor ◽  
Detection Scheme ◽  
Detection Techniques ◽  
Production Pipeline ◽  
Edge Detection Method ◽  
Fabric Production ◽  
Performance Results

Abstract A fabric fault detection scheme is implemented in this work using frame harmonizing-based approach. Many recent research works deliver the fabric defect detection techniques. But in the textiles industry, the fast process of fabric cutting and sewing, lots of auxiliary imperfections have emerged. Particularly these defects cannot be identified easily by experts as well as an automated system. In our system, a novel frame extraction technique is used to find the defects in the fabric production pipeline. The input fabric image frame is pre-processed by transformation and filtering techniques. In this work,a novel multi-directional and multi-scale outline extraction method is proposed to extract the edge map. Contour-based features are extracted and classified by K-nearest neighbor classifier. The experimentation with real-time dataset produced the outstanding performance results when compared with state of the art methods.

Comparison of the Hybrid Credit Scoring Models Based on Various Classifiers

2010 ◽  
Vol 6 (3) ◽  
pp. 56-74 ◽  
Author(s):  
Fei-Long Chen ◽  
Feng-Chia Li
Keyword(s):  
Nearest Neighbor ◽  
Credit Scoring ◽  
Back Propagation ◽  
Support Vector ◽  
Sufficient Information ◽  
Features Selection ◽  
K Nearest Neighbor ◽  
Hybrid Features ◽  
Wrong Decision ◽  
Propagation Network

Credit scoring is an important topic for businesses and socio-economic establishments collecting huge amounts of data, with the intention of making the wrong decision obsolete. In this paper, the authors propose four approaches that combine four well-known classifiers, such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Back-Propagation Network (BPN) and Extreme Learning Machine (ELM). These classifiers are used to find a suitable hybrid classifier combination featuring selection that retains sufficient information for classification purposes. In this regard, different credit scoring combinations are constructed by selecting features with four approaches and classifiers than would otherwise be chosen. Two credit data sets from the University of California, Irvine (UCI), are chosen to evaluate the accuracy of the various hybrid features selection models. In this paper, the procedures that are part of the proposed approaches are described and then evaluated for their performances.

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