Microwave breast tumor localization using wavelet feature extraction and genetic algorithm‐neural network

2021 ◽  
Author(s):  
Min Lu ◽  
Xia Xiao ◽  
Guancong Liu ◽  
Hong Lu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Feature Extraction ◽  
Breast Tumor ◽  
Tumor Localization

scholarly journals Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings

2020 ◽  
Vol 131 ◽  
pp. 109980 ◽  
Author(s):  
X.J. Luo ◽  
Lukumon O. Oyedele ◽  
Anuoluwapo O. Ajayi ◽  
Olugbenga O. Akinade ◽  
Hakeem A. Owolabi ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Feature Extraction ◽  
Energy Consumption ◽  
Deep Neural Network ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

Image Recognition Technology Based on Artificial Intelligence

2013 ◽  
Vol 347-350 ◽  
pp. 3537-3540
Author(s):  
Hai Yun Lin ◽  
Yu Jiao Wang ◽  
Jian Chun Cai
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Semantic Feature ◽  
Current Image ◽  
Semantic Feature Extraction

In respect of the classification of current image retrieval technology and the existing issues, the paper put forward a method designed for image semantic feature extraction based on artificial intelligence. The new method has solved the tough problem of image semantic feature extraction, by fusing fuzzy logic, genetic algorithm and artificial neural network altogether, which greatly improved the efficiency and accuracy of image retrieval.

scholarly journals FACE RECOGNITION IN EIGEN DOMAIN WITH NEURO-FUZZY CLASSIFIER AND EVOLUTIONARY OPTIMIZATION

2012 ◽  
pp. 129-133
Author(s):  
PRASANTH.R. S ◽  
SARITHA. R
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Feature Extraction ◽  
Face Recognition ◽  
Optimization Technique ◽  
Recognition Rate ◽  
Fuzzy Classifier ◽  
Neuro Fuzzy ◽  
Extraction Test ◽  
Almost All

Face Recognition is a nascent field of research with many challenges. The proposed system focuses on recognizing faces in a faster and more accurate way using eigenface approach and genetic algorithm by considering the entire problem as an optimization problem. It consists of two stages: Eigenface approach is used for feature extraction and genetic algorithm based feed forward Neuro-Fuzzy System is used for face recognition. Classification of face images to a particular class is done using an artificial neural network. The training of neural network is done using genetic algorithm, a machine learning approach which optimizes the weights used in the neural network. This is an efficient optimization technique and an evolutionary classification method. The algorithm has been tested on 200 images (20 classes). A recognition score for test lot is calculated by considering almost all the variants of feature extraction. Test results gave a recognition rate of 97.01%.

Speaker and Speech Recognition using Deep Neural Network

2018 ◽  
Vol 6 (8) ◽  
pp. 118 ◽  
Author(s):  
Gurpreet Kaur ◽  
Mohit Srivastava ◽  
Amod Kumar
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Feature Extraction ◽  
Speech Recognition ◽  
Deep Neural Network ◽  
Rate Sensitivity ◽  
Extraction Process ◽  
Recall Rate ◽  
Feature Extraction Method ◽  
Work Model

In command and control applications, feature extraction process is very important for good accuracy and less learning time. In order to deal with these metrics, we have proposed an automated combined speaker and speech recognition technique. In this paper five isolated words are recorded with four speakers, two males and two females. We have used the Mel Frequency Cepstral Coefficient (MFCC)  feature extraction method with Genetic Algorithm to optimize the extracted features and generate an appropriate feature set. In first phase, feature extraction using MFCC is executed following the feature optimization using Genetic Algorithm and in last & third phase, training is conducted using the Deep Neural Network. In the end, evaluation and validation of the proposed work model is done by setting real environment. To check the efficiency of the proposed work, we have calculated the parameters like accuracy, precision rate, recall rate, sensitivity and specificity..

INTELLIGENT BREAST TUMOR DETECTION SYSTEM WITH TEXTURE AND CONTRAST FEATURES

2013 ◽  
Vol 25 (03) ◽  
pp. 1350008 ◽  
Author(s):  
Szu-Yin Wu ◽  
Chiun-Li Chin ◽  
Yu-Shun Cho ◽  
Yen-Ching Chang ◽  
Li-Pin Hsu
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Feature Extraction ◽  
Mortality Rate ◽  
Breast Tumor ◽  
Learning Algorithm ◽  
Detection System ◽  
Tumor Detection ◽  
Experimental Results ◽  
World Health

According to a research report by the World Health Organization (WHO), breast cancer is the most common type of cancer in women, while the mortality rate of breast cancer of females over 40 years old is extremely high. If detected early, it can be treated early, and the mortality rate of breast cancer can be reduced. Meanwhile, the image processing and pattern recognition technology has been adopted to select suspicious regions, provides alerts to assist in doctors' diagnosis, and reduces misdiagnosis rates due to fatigue of doctors, and improves diagnostic accuracy. Hence, this paper proposed an intelligent breast tumor detection system with texture and contrast features. This system consists of three parts: preprocessing, feature extraction, and learning algorithm. The goal of preprocessing is to obtain a good image quality and a real breast area. In the feature extraction, we extract the two features to describe the breast tumor. These features include Laws' Mask features which are the representation of the texture and modification average distance (MAD) feature which is the representation of the contrast. Each region of interest (ROI) image block will be extracted by these two features. And we will extract useful feature from all extracted features. We hope that a small quantity of feature can be used in our proposed system. Next, we use neural network as learning algorithm to detect the tumor with extracted features. Finally, in the experimental results, we use three databases to verify our proposed system, and two radiologists participated in that process and designed final verification study. Thus, we understand from the experimental results that a detection rate as high as 98% can be achieved by using only a few features and the simplest artificial neural network rather than a large number of features and a complex classifier. The success of the system will improve the accuracy of the existing detection methods, assist medical diagnosis, and decrease the time of the judgment effective by doctors.

scholarly journals Labeling Confidence Values for Wafer-Handling Robot Arm Performance Using a Feature-Based General Regression Neural Network and Genetic Algorithm

2019 ◽  
Vol 9 (20) ◽  
pp. 4241
Author(s):  
Yi-Cheng Huang ◽  
Zi-Sheng Yang ◽  
Hsien-Shu Liao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Feature Extraction ◽  
Ccd Camera ◽  
Remaining Useful Life ◽  
General Regression Neural Network ◽  
Robot Arm ◽  
Feature Extraction Method ◽  
A Charge ◽  
General Regression

The prognosis and management of machine health statuses are emerging research topics. In this study, the performance degradation of a wafer-handling robot arm (WHRA) was predicted using the proposed machine-learning approach. This method considers the eccentric vertical and planar position deviations from a wafer mark using a charge-coupled device (CCD) camera. Synthesized position signals were defined using the square root of x- and y-axes deviations in the horizontal view and the square of the wafer mark diameter in the vertical view. A feature extraction method was used to determine the position status on the basis of these displacements and the area of a wafer mark in a CCD image. The root mean square error and mean, maximum, and minimum of the synthesized position signals were extracted through feature extraction and used for data mining by a general regression neural network (GRNN) and logistic regression (LR) models. The lifetime assessment by confidence value of the WHRA’s remaining useful life (RUL) by the genetic algorithm/GRNN exhibited nearly the same trend as that predicted through a run-to-failure LR model. The experimental results indicated that the proposed methodology can be used for proactive assessments of the RUL of WHRAs.

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