Review on Heart Studies

2014 ◽  
pp. 113-128
Keyword(s):  
Feature Extraction ◽  
Noise Removal ◽  
Heart Disorder ◽  
Total Process

For many years, researchers have studied various aspects of heart disorder detecting. Noise removal, feature extraction and optimized approaches for classifications of heart signals are some of the main areas of their studies. In this chapter, some of the previous research on the mentioned areas is collected so that the readers may form a view from the total process of heart disorder detecting.

scholarly journals Deep Learning Based Depthwise Separable Model For Effective Diagnosis And Classification of Lung Ct Images

2019 ◽  
Vol 9 (1) ◽  
pp. 1808-1819 ◽  
Keyword(s):  
Lung Cancer ◽  
Feature Extraction ◽  
Deep Learning ◽  
Noise Removal ◽  
Ct Images ◽  
Classification Model ◽  
Diagnosis And Classification ◽  
Effective Diagnosis ◽  
Lung Ct

Lung cancer is a serious illness which leads to increased mortality rate globally. The identification of lung cancer at the beginning stage is the probable method of improving the survival rate of the patients. Generally, Computed Tomography (CT) scan is applied for finding the location of the tumor and determines the stage of cancer. Existing works has presented an effective diagnosis classification model for CT lung images. This paper designs an effective diagnosis and classification model for CT lung images. The presented model involves different stages namely pre-processing, segmentation, feature extraction and classification. The initial stage includes an adaptive histogram based equalization (AHE) model for image enhancement and bilateral filtering (BF) model for noise removal. The pre-processed images are fed into the second stage of watershed segmentation model for effectively segment the images. Then, a deep learning based Xception model is applied for prominent feature extraction and the classification takes place by the use of logistic regression (LR) classifier. A comprehensive simulation is carried out to ensure the effective classification of the lung CT images using a benchmark dataset. The outcome implied the outstanding performance of the presented model on the applied test images.

scholarly journals Noise Removal and Feature Extraction in Airborne Radar Sounding Data of Ice Sheets

2022 ◽  
Vol 14 (2) ◽  
pp. 399
Author(s):  
Xueyuan Tang ◽  
Sheng Dong ◽  
Kun Luo ◽  
Jingxue Guo ◽  
Lin Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Ice Sheets ◽  
Ice Sheet ◽  
Noise Removal ◽  
Airborne Radar ◽  
Learning Methods ◽  
Ice Flow ◽  
Machine Learning Methods ◽  
Internal Structures

The airborne ice-penetrating radar (IPR) is an effective method used for ice sheet exploration and is widely applied for detecting the internal structures of ice sheets and for understanding the mechanism of ice flow and the characteristics of the bottom of ice sheets. However, because of the ambient influence and the limitations of the instruments, IPR data are frequently overlaid with noise and interference, which further impedes the extraction of layer features and the interpretation of the physical characteristics of the ice sheet. In this paper, we first applied conventional filtering methods to remove the feature noise and interference in IPR data. Furthermore, machine learning methods were introduced in IPR data processing for noise removal and feature extraction. Inspired by a comparison of the filtering methods and machine learning methods, we propose a fusion method combining both filtering methods and machine-learning-based methods to optimize the feature extraction in IPR data. Field data tests indicated that, under different conditions of IPR data, the application of different methods and strategies can improve the layer feature extraction.

Automated Noise Removal and Feature Extraction in ECGs

2013 ◽  
Vol 39 (3) ◽  
pp. 1937-1948 ◽  
Author(s):  
Rabya B. K. Hoti ◽  
Shahid Khattak
Keyword(s):  
Feature Extraction ◽  
Noise Removal

scholarly journals Automatic Handwritten Devanagari Text Generation in Marathi Styles using Ant Miner Algorithm

2019 ◽  
Vol 9 (2) ◽  
pp. 2541-2551
Keyword(s):  
Feature Extraction ◽  
Noise Removal ◽  
Text Generation ◽  
Indian Languages ◽  
Scale Invariant ◽  
Linear Discriminant ◽  
Document Recognition ◽  
Feature Extraction Method ◽  
Two Phases ◽  
Artificial Neural Network Ann

The Devanagari scripts forms the backbone of the writing system of several Indian languages includes Hindi, Sanskrit and Marathi. With the increased demand, exploration and globalization of digital Devanagari documents, different printed and handwritten document recognition techniques have involved since last two decades. In literature many methods of Devanagari script recognition have been used but it is not able to attain the best results in recognition. Hence, in this paper is proposed Ant Miner Algorithm (AMA) for recognition and text generation of handwritten Devanagari Marathi Scripts. The proposed method recognition process is working with the four different stages such as pre-processing, segmentation, feature extraction and recognition with text generation. The first stage pre-processing is consists of skew correction, noise removal and binarization. The second stage is segmentation that contains the line segmentation, word segmentation and character segmentation. The third stage is feature extraction method it contains four methods such as Scale Invariant Feature Transform (SIFT), Linear Discriminant Analysis (LDA), Discrete Cosine Transform (DCT) and Local Binary Pattern (LBP). The final stage is recognition and text generation with attain with the help of AMA algorithm. It works based on the two phases such as training and testing phase. The proposed method is implemented in the python platform and it compared with the Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN). The performance of the proposed method is analysed with statistical measurements of accuracy, precision and recall.

Literature Review

2018 ◽  
pp. 67-80
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Literature Review ◽  
Noise Removal ◽  
Swarm Optimization ◽  
Artificial Neural Network Ann ◽  
Intelligent Methods ◽  
Short Explanation

This chapter introduces different resources about noise in heart signals. It also provides a short explanation about artificial neural network (ANN), particle swarm optimization (PSO), and presents some of the previous studies related to heart signal noise removal, intelligent methods for detection of disorders, and feature extraction.

scholarly journals Novel BSSSO-Based Deep Convolutional Neural Network for Face Recognition with Multiple Disturbing Environments

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 626
Author(s):  
Neha Soni ◽  
Enakshi Khular Sharma ◽  
Amita Kapoor
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Optimization Algorithm ◽  
Cuckoo Search ◽  
Noise Removal ◽  
Search Optimization ◽  
Heuristic Technique ◽  
Cuckoo Search Optimization ◽  
Experimental Findings ◽  
Pose Variation

Face recognition technology is presenting exciting opportunities, but its performance gets degraded because of several factors, like pose variation, partial occlusion, expression, illumination, biased data, etc. This paper proposes a novel bird search-based shuffled shepherd optimization algorithm (BSSSO), a meta-heuristic technique motivated by the intuition of animals and the social behavior of birds, for improving the performance of face recognition. The main intention behind the research is to establish an optimization-driven deep learning approach for recognizing face images with multiple disturbing environments. The developed model undergoes three main steps, namely, (a) Noise Removal, (b) Feature Extraction, and (c) Recognition. For the removal of noise, a type II fuzzy system and cuckoo search optimization algorithm (T2FCS) is used. The feature extraction is carried out using the CNN, and landmark enabled 3D morphable model (L3DMM) is utilized to efficiently fit a 3D face from a single uncontrolled image. The obtained features are subjected to Deep CNN for face recognition, wherein the training is performed using novel BSSSO. The experimental findings on standard datasets (LFW, UMB-DB, Extended Yale B database) prove the ability of the proposed model over the existing face recognition approaches.

Discrete-wavelet-transform-based noise removal and feature extraction for ECG signals

IRBM ◽  
2014 ◽  
Vol 35 (6) ◽  
pp. 351-361 ◽  
Author(s):  
H.-Y. Lin ◽  
S.-Y. Liang ◽  
Y.-L. Ho ◽  
Y.-H. Lin ◽  
H.-P. Ma
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Noise Removal ◽  
Discrete Wavelet ◽  
Ecg Signals

scholarly journals Offline Signature Verification System Using SVM Classifier with Image Pre-processing Steps and SURF Algorithm

2021 ◽  
Vol 2107 (1) ◽  
pp. 012069
Author(s):  
Li Wen Goon ◽  
Swee Kheng Eng
Keyword(s):  
Feature Extraction ◽  
Noise Removal ◽  
Signature Verification ◽  
Support Vector ◽  
Svm Classifier ◽  
Speeded Up Robust Features ◽  
Verification System ◽  
Offline Signature Verification ◽  
Average Accuracy ◽  
Processing Steps

Abstract A signature is a mark or name that represents the identity of the people and the Signature Verification System (SVS) is used to validate the identity of people. The signature verification system is mostly used for bank cheques, vouchers, intelligence agencies and others. There are two types of SVS which are online and offline signature verification systems. The paper deals with an offline signature verification system. The proposed system consists of four main stages, (i) image acquisition, (ii) image pre-processing, (iii) feature extraction and (iv) classification. The image pre-processing steps involved binarization, noise removal using Gaussian filter and image resizing and thinning. In the feature extraction stage, Bag-of-Features with the Speeded Up Robust Features (SURF) extractor was utilized. In the third stage, the Support Vector Machine (SVM) classifier is used. Lastly, the confusion matrix and the verification rate were used to evaluate the performance of the classifier. In this paper, we implement and compare the performance of the signature verification system without entering the user ID and the signature verification system entering the user ID. For the ratio of 75% and 25% of the training and testing, respectively, the average accuracy for the signature verification system without entering the user ID is 71.36%, whereas the average accuracy for the signature verification system entering the user ID is 79.55%.

A Review of Fingerprint Image Pre-processing

2014 ◽  
Vol 69 (2) ◽  
Author(s):  
Alaa Ahmed Abbood ◽  
Ghazali Sulong ◽  
Sabine U. Peters
Keyword(s):  
Feature Extraction ◽  
State Of The Art ◽  
Noise Removal ◽  
Extraction Methods ◽  
Poor Quality ◽  
The Other ◽  
Gray Level ◽  
Fingerprint Image ◽  
False Acceptance

Fingerprints are the most widely used form of human identification and verification due to their uniqueness and permanence. For that reason, many Automatic Fingerprint Identification Systems (AFIS) have been commercially produced and accepted by the international community. Though their performance is good, there is still room for improvement. One of the main concerns is poor fingerprint images that are caused by capturing devices. Thus, to improve the efficiency of AFIS, both image enhancement and feature extraction methods are required to be implemented. An effective feature extraction depends on the quality of its image whereby high image quality would normally produce genuine features. On the other hand, poor quality would lead to fake features that will result in false acceptance. This paper reviews several state-of-the-art methods of fingerprint image pre-processing including gray level normalization, noise removal and segmentation.

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