Performance evaluation of 2D face recognition techniques under image processing attacks

2018 ◽  
Vol 32 (19) ◽  
pp. 1850212 ◽  
Author(s):  
Sahil Sharma ◽  
Vijay Kumar
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Face Recognition ◽  
Recognition System ◽  
Two Dimensions ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Ensemble Techniques ◽  
Learning Techniques ◽  
Face Recognition System

Face recognition is a vastly researched topic in the field of computer vision. A lot of work have been done for facial recognition in two dimensions and three dimensions. The amount of work done with face recognition invariant of image processing attacks is very limited. This paper presents a total of three classes of image processing attacks on face recognition system, namely image enhancement attacks, geometric attacks and the image noise attacks. The well-known machine learning techniques have been used to train and test the face recognition system using two different databases namely Bosphorus Database and University of Milano Bicocca three-dimensional (3D) Face Database (UMBDB). Three classes of classification models, namely discriminant analysis, support vector machine and k-nearest neighbor along with ensemble techniques have been implemented. The significance of machine learning techniques has been mentioned. The visual verification has been done with multiple image processing attacks.

scholarly journals Cardiac Disease Prediction using Supervised Machine Learning Techniques.

2022 ◽  
Vol 2161 (1) ◽  
pp. 012013
Author(s):  
Chiradeep Gupta ◽  
Athina Saha ◽  
N V Subba Reddy ◽  
U Dinesh Acharya
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Cardiac Disease ◽  
Performance Metrics ◽  
Confusion Matrix ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Ensemble Techniques ◽  
Learning Techniques

Abstract Diagnosis of cardiac disease requires being more accurate, precise, and reliable. The number of death cases due to cardiac attacks is increasing exponentially day by day. Thus, practical approaches for earlier diagnosis of cardiac or heart disease are done to achieve prompt management of the disease. Various supervised machine learning techniques like K-Nearest Neighbour, Decision Tree, Logistic Regression, Naïve Bayes, and Support Vector Machine (SVM) model are used for predicting cardiac disease using a dataset that was collected from the repository of the University of California, Irvine (UCI). The results depict that Logistic Regression was better than all other supervised classifiers in terms of the performance metrics. The model is also less risky since the number of false negatives is low as compared to other models as per the confusion matrix of all the models. In addition, ensemble techniques can be approached for the accuracy improvement of the classifier. Jupyter notebook is the best tool, for the implementation of Python Programming having many types of libraries, header files, for accurate and precise work.

scholarly journals Comparative Study of Artificial Intelligence Techniques for Image Classification

2019 ◽  
pp. 904-910
Author(s):  
Prashant Udawant ◽  
Atul Patidar ◽  
Abhijeet Singh ◽  
Atyant Yadav
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Image Classification ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Specific Class ◽  
Detection Techniques ◽  
Learning Techniques ◽  
Segmentation Models

There are various applications of image processing in the field of engineering, agriculture, graphic design, commerce, historical research and architecture. This paper studies and compares most of the research works done in the field of image processing and machine learning for the purpose of image classification based on the features extracted from the image through different feature extraction techniques. The machine learning techniques studied in this paper are Convolution Neural Network (CNN), Support Vector Machine (SVM) and Fuzzy logic. The paper studies and compares these methods for their implementation in classification of digital images. Color based segmentation models are used to segment the specific features from image and categories them into different classes. First image preprocessing is done on the image to reduce the noise from the image. Then image segmentation and edge detection techniques are used to identify the objects in the image and extract the features through which the image can be labeled with a specific class.

scholarly journals Machine Learning Applications based on SVM Classification A Review

2021 ◽  
Vol 1 (2) ◽  
pp. 81-90
Author(s):  
Dakhaz Mustafa Abdullah ◽  
Adnan Mohsin Abdulazeez
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Sets ◽  
Learning Techniques ◽  
Svm Algorithm ◽  
Machine Learning Applications

Extending technologies and data development culminated in the need for quicker and more reliable processing of massive data sets. Machine Learning techniques are used excessively. This paper, therefore, attempts to deal with data processing, using a support vector machine (SVM) algorithm in different fields since it is a reliable, efficient classification method in the area of machine learning. Accordingly, many works have been explored in this paper to cover the use of SVM classifier. Classification based on SVM has been used in many fields like face recognition, diseases diagnostics, text recognition, sentiment analysis, plant disease identification and intrusion detection system for network security application. Based on this study, it can be concluded that SVM classifier has obtained high accuracy results in most of the applications, specifically, for face recognition and diseases identification applications.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

scholarly journals Predictive modeling for peri-implantitis by using machine learning techniques

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tomoaki Mameno ◽  
Masahiro Wada ◽  
Kazunori Nozaki ◽  
Toshihito Takahashi ◽  
Yoshitaka Tsujioka ◽  
...  
Keyword(s):  
Machine Learning ◽  
Demographic Data ◽  
Risk Indicators ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Machine Learning Methods ◽  
Complex Interactions ◽  
Learning Techniques ◽  
Increased Risk ◽  
Vector Machines

AbstractThe purpose of this retrospective cohort study was to create a model for predicting the onset of peri-implantitis by using machine learning methods and to clarify interactions between risk indicators. This study evaluated 254 implants, 127 with and 127 without peri-implantitis, from among 1408 implants with at least 4 years in function. Demographic data and parameters known to be risk factors for the development of peri-implantitis were analyzed with three models: logistic regression, support vector machines, and random forests (RF). As the results, RF had the highest performance in predicting the onset of peri-implantitis (AUC: 0.71, accuracy: 0.70, precision: 0.72, recall: 0.66, and f1-score: 0.69). The factor that had the most influence on prediction was implant functional time, followed by oral hygiene. In addition, PCR of more than 50% to 60%, smoking more than 3 cigarettes/day, KMW less than 2 mm, and the presence of less than two occlusal supports tended to be associated with an increased risk of peri-implantitis. Moreover, these risk indicators were not independent and had complex effects on each other. The results of this study suggest that peri-implantitis onset was predicted in 70% of cases, by RF which allows consideration of nonlinear relational data with complex interactions.

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