scholarly journals Support Vector Machine Based Feature Extraction For Gender Recognition From Objects Using Lasso Classifier

2020 ◽  
Author(s):  
Damodara Krishna Kishore Galla ◽  
BabuReddy Mukamalla ◽  
Rama Prakasha Reddy Chegireddy
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Face Recognition ◽  
Selection Process ◽  
Recognition System ◽  
Classification Model ◽  
Support Vector ◽  
Gender Classification ◽  
Accuracy Score ◽  
Lasso Regression

Abstract The blind people has their difficulty to identify the object moving around them, therefore with a high accuracy score object detection and human face recognition system will helps them in identifying the things around them with ease. Facial record images are immobile an difficult assignment for biometric authentication systems due to various types of characteristics are dimensions, pose, expressions, illustrations and age etc. In facial and other united images includes different objects classifications. In this research article, a minimum distance trainer for feature selection by accessing SVM feature optimization process. For feature selection process SVM (support vector machine) was considered for improving its feature interpretability and computational efficiency., then LASSO classifier applied to perform object recognition and gender classification. Original face image database used for the gender classification. This approach was implemented with dual classification model (1) Recognizing or classifying human faces from various objects and (2) Classifying gender through face recognition] is made possible with the help of combining modified SIFT feature in combination with ridge regression (RR), elastic net (EN), lasso regression(LR) and lasso regression with Gaussian Support Vector Machines (LRGS) based classification.

scholarly journals Support Vector Machine Based Feature Extraction for Gender Recognition From Objects Using Lasso Classifier

2020 ◽  
Author(s):  
Damodara Krishna Kishore Galla ◽  
BabuReddy Mukamalla ◽  
Rama Prakasha Reddy Chegireddy
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Classification Model ◽  
Support Vector ◽  
Gender Classification ◽  
Accuracy Score ◽  
Lasso Regression ◽  
Vehicle Recognition ◽  
And Gender ◽  
Human Faces

Abstract The blind people has their difficulty to identify the object moving around them, therefore with a high accuracy score object detection and human face recognition system will helps them in identifying the things around them with ease. In this research article,a minimum distance trainer for feature selection by accessing SVM feature optimization process, then LASSO classifier applied to perform object recognition and gender classification. Database of 100 images (50 male and 50 female face images considered from 5 different databases) and 10 categories of vehicle types are used for gender and vehicle recognition and classification. Original face image database used for the gender classification. This approach was implemented with dual classification model [(1) Recognizing or classifying human faces from various objects and (2) Classifying gender through face recognition] is made possible with the help of combining modified SIFT feature in combination with ridge regression (RR), elastic net (EN), lasso regression(LR) and lasso regression with Gaussian Support Vector Machines (LRGS) based classificatioins. The final classification results accurate are as follows RR- 89.6%, EN- 93.5%, LR-93.2% and the proposed approach is LRGS with 98.4% accurate detection rate with rediction names.

scholarly journals Discrimination of Gentiana and Its Related Species Using IR Spectroscopy Combined with Feature Selection and Stacked Generalization

Molecules ◽  
2020 ◽  
Vol 25 (6) ◽  
pp. 1442 ◽  
Author(s):  
Tao Shen ◽  
Hong Yu ◽  
Yuan-Zhong Wang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Related Species ◽  
Predictive Accuracy ◽  
Classification Model ◽  
Venn Diagram ◽  
Support Vector ◽  
Stacked Generalization ◽  
Svm Model

Gentiana, which is one of the largest genera of Gentianoideae, most of which had potential pharmaceutical value, and applied to local traditional medical treatment. Because of the phytochemical diversity and difference of bioactive compounds among species, which makes it crucial to accurately identify authentic Gentiana species. In this paper, the feasibility of using the infrared spectroscopy technique combined with chemometrics analysis to identify Gentiana and its related species was studied. A total of 180 batches of raw spectral fingerprints were obtained from 18 species of Gentiana and Tripterospermum by near-infrared (NIR: 10,000–4000 cm−1) and Fourier transform mid-infrared (MIR: 4000–600 cm−1) spectrum. Firstly, principal component analysis (PCA) was utilized to explore the natural grouping of the 180 samples. Secondly, random forests (RF), support vector machine (SVM), and K-nearest neighbors (KNN) models were built while using full spectra (including 1487 NIR variables and 1214 FT-MIR variables, respectively). The MIR-SVM model had a higher classification accuracy rate than the other models that were based on the results of the calibration sets and prediction sets. The five feature selection strategies, VIP (variable importance in the projection), Boruta, GARF (genetic algorithm combined with random forest), GASVM (genetic algorithm combined with support vector machine), and Venn diagram calculation, were used to reduce the dimensions of the data variable in order to further reduce numbers of variables for modeling. Finally, 101 NIR and 73 FT-MIR bands were selected as the feature variables, respectively. Thirdly, stacking models were built based on the optimal spectral dataset. Most of the stacking models performed better than the full spectra-based models. RF and SVM (as base learners), combined with the SVM meta-classifier, was the optimal stacked generalization strategy. For the SG-Ven-MIR-SVM model, the accuracy (ACC) of the calibration set and validation set were both 100%. Sensitivity (SE), specificity (SP), efficiency (EFF), Matthews correlation coefficient (MCC), and Cohen’s kappa coefficient (K) were all 1, which showed that the model had the optimal authenticity identification performance. Those parameters indicated that stacked generalization combined with feature selection is probably an important technique for improving the classification model predictive accuracy and avoid overfitting. The study result can provide a valuable reference for the safety and effectiveness of the clinical application of medicinal Gentiana.

scholarly journals Support Vector Machine And K-Nearest Neighbor Based Liver Disease Classification Model

2021 ◽  
Vol 3 (1) ◽  
pp. 9-14
Author(s):  
Tsehay Admassu Assegie
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Liver Disease ◽  
Classification Model ◽  
Support Vector ◽  
Disease Prediction ◽  
Accuracy Score ◽  
Learning Models ◽  
Accuracy And Precision ◽  
Machine Learning Models

Machine-learning approaches have become greatly applicable in disease diagnosis and prediction process. This is because of the accuracy and better precision of the machine learning models in disease prediction. However, different machine learning models have different accuracy and precision on disease prediction. Selecting the better model that would result in better disease prediction accuracy and precision is an open research problem. In this study, we have proposed machine learning model for liver disease prediction using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) learning algorithms and we have evaluated the accuracy and precision of the models on liver disease prediction using the Indian liver disease data repository. The analysis of result showed 82.90% accuracy for SVM and 72.64% accuracy for the KNN algorithm. Based on the accuracy score of SVM and KNN on experimental test results, the SVM is better in performance on the liver disease prediction than the KNN algorithm.  

scholarly journals Feature Selection Based on L1-Norm Support Vector Machine and Effective Recognition System for Parkinson’s Disease Using Voice Recordings

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 37718-37734 ◽  
Author(s):  
Amin Ul Haq ◽  
Jian Ping Li ◽  
Muhammad Hammad Memon ◽  
Jalaluddin khan ◽  
Asad Malik ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Support Vector Machine ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Recognition System ◽  
Support Vector ◽  
L1 Norm

Ensemble Learning based Age Invariant Fea-ture Recognition Using Soft Computing

2021 ◽  
pp. 7278-7290
Author(s):  
Divyanshu Sinha, Dr J. P. Pandey, Dr. Bhavesh Chauhan
Keyword(s):  
Support Vector Machine ◽  
Face Recognition ◽  
Recognition System ◽  
Support Vector ◽  
Matlab Simulation ◽  
Automated Recognition ◽  
Face Recognition System ◽  
Proposed Model ◽  
Overall Performance ◽  
Vision Application

Face recognition system is a state-of-the-art computer vision application within the artificial intelligence arena. Face recognition is the automated recognition of humans for their names/unique ID. The age invariant face recognition is a challenge task in the field of face recog-nition. In this work, we have introduced a stacked support vector machine where kernel activation of prototype examples is combined in nonlinear ways. The proposed work integrates soft compu-ting-based support vector machine (SVM) with deep SVM. The proposed model uses the implied relation between the variables described above in order to optimize their overall performance. Specifically, our method uses three different stages of complex convolution neural networks that detect and analyze the location of faces position and landmarks. This work has introduced cross-age celebrity dataset (CACD) for both single as well as cross-database enabling the transition of age. The proposed work has been implemented in the MATLAB simulation tool considering CACD dataset. Experimental results indicate that our techniques significantly outperform other strategies across a range of challenging metrics.

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