Discriminant Analysis and Naïve Bayes Classifier-Based Biometric Identification Using Finger Veins

2019 ◽  
Vol 9 (4) ◽  
pp. 15-27
Author(s):  
Insha Qayoom ◽  
Sameena Naaz
Keyword(s):  
Discriminant Analysis ◽  
Naive Bayes ◽  
Gabor Filter ◽  
Research Work ◽  
Region Of Interest ◽  
Threshold Value ◽  
Naïve Bayes ◽  
Biometric Technology ◽  
Finger Vein ◽  
Secure Environment

Finger vein identification is a dominating method of biometric technology used for authentication in a highly secure environment. Vein patterns are unique for each individual and it is underneath skin so there is less chance for forgery. In the current research work, finger vein features are extracted and verified for the purpose of authentication. The first step in this work is to pre-process the image obtained from the database. In order to get the region of interest (ROI) the threshold value is calculated using a standard deviation method followed by morphology-based functions available in the MATLAB software. After pre -processing a Gabor filter, fast filter, and freak descriptors are used. The features calculated at the freak descriptor processing are trained on classifiers namely discriminant and Naïve Bayes. The features trained to the classifiers are then fed again into the classifiers and cross verified to update the results of accuracy. The accuracy calculated using discriminant analysis is 94.46% and by using Naïve Bayes is 98.38%.

scholarly journals Voting Based Classification Method for Diabetes Prediction

2019 ◽  
Vol 8 (2S6) ◽  
pp. 913-918
Keyword(s):  
Support Vector Machine ◽  
Naive Bayes ◽  
Research Work ◽  
Prediction Method ◽  
Naïve Bayes ◽  
Support Vector ◽  
Decision Tree Classifier ◽  
Prediction Analysis ◽  
Tree Classifier ◽  
Diabetes Prediction

This research work is based on the diabetes prediction analysis. The prediction analysis technique has the three steps which are dataset input, feature extraction and classification. In this previous system, the Support Vector Machine and naïve bayes are applied for the diabetes prediction. In this research work, voting based method is applied for the diabetes prediction. The voting based method is the ensemble based which is applied for the diabetes prediction method. In the voting method, three classifiers are applied which are Support Vector Machine, naïve bayes and decision tree classifier. The existing and proposed methods are implemented in python and results in terms of accuracy, precision-recall and execution time. It is analyzed that voting based method give high performance as compared to other classifiers.

scholarly journals Tinjauan Algoritma RoI (Region of Interest) Dengan Metode Pengambangan Otsu Dan Klasterisasi K-Mean; Hasil Dan Tantangannya

2020 ◽  
Vol 16 (2) ◽  
pp. 75
Author(s):  
Didit Widiyanto
Keyword(s):  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Region Of Interest ◽  
Naïve Bayes ◽  
Support Vector ◽  
Gray Level

Akurasi sebuah klasifikasi citra ditentukan oleh pengklasifikasi.  Meskipun RoI (Region of Interest) tidak menentukan secara langsung akurasi, namun RoI menentukan lingkup klasifikasi citra.   Terdapat tiga algoritma yang dapat digunakan sebagai algoritma RoI yaitu; Balanced Histogram Thresholding (BHT), algoritma Otsu, dan algoritma klasterisasi K-Means.  Paper ini meninjau algoritma Otsu dan algoritma klasterisasi K-Means yang digunakan oleh lima peneliti.  Dari ke lima peneliti; tiga peneliti menerapkan algoritma Otsu dan dua peneliti menerapkan algoritma K-Means sebagai algoritma RoI. Setelah operasi RoI, ke lima peneliti menerapkan algoritma GLCM (Gray Level Co-occurance Matrix) sebagai pengekstraksi ciri tekstur.  Hasil ekstraksi ciri diklasifikasi dengan menggunakan berbagai pengklasifikasi antara lain SVM (Support Vector Machine), Naive Bayes, dan Decision Tree. Akhirnya dengan membandingkan hasil dari ke lima peneliti, akurasi tertinggi diperoleh sebesar 100% dengan pengklasifikasi SVM menggunakan algoritma Otsu sebagai algoritma RoI, dan akurasi terendah adalah sebesar52% yang menggunakan algoritma Otsu pada kanal S dari citra HSV (Hue, Saturation Value).

Research on Filtration System of Network Negative Information on the Basis of Naive Bayes

2011 ◽  
Vol 271-273 ◽  
pp. 911-916
Author(s):  
Xi Ai Yan ◽  
Jin Min Yang
Keyword(s):  
Pattern Classification ◽  
Naive Bayes ◽  
Threshold Value ◽  
Negative Information ◽  
Naïve Bayes ◽  
Classification Method ◽  
Filtration System ◽  
Strong Robustness

The difficulty of filtrating network negative information lies in how to classify information correctly. As one of the classification method with the advantage of strong robustness and good understandability in the field of pattern classification, Naïve Bayes has been used widely. A method for filtrating network negative information on the basis of Naïve Bayes, improvement proposals aiming at the disadvantages of Naïve Bayes and amelioration of erroneous judgment of negative information by setting threshold value k have been put forward in this article. The experiment shows that by adjusting threshold value k can the integrity of the system can be optimum and can favorable application effects be achieved.

scholarly journals Diagnosis of Diabetes Mellitus in Women of Reproductive Age using the Prediction Methods of Naive Bayes, Discriminant Analysis, and Logistic Regression

2021 ◽  
Vol 6 (2) ◽  
pp. 96-104
Author(s):  
Yulia Resti ◽  
Endang Sri Kresnawati ◽  
Novi Rustiana Dewi ◽  
Des Alwine Zayanti ◽  
Ning Eliyati
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Model Validation ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Reproductive Age ◽  
Training Data ◽  
Diabetes Diagnosis ◽  
Women Of Reproductive Age ◽  
Diabetes Status

Diabetes is a chronic disease that can cause serious illness. Women are four times more likely to develop heart problems caused by diabetes. Women are also more prone to experience complications due to diabetes, such as kidney problems, depression, and decreased vision quality. Nearly 200 million women worldwide are affected by diabetes, with two out of five affected by the disease being women of reproductive age. This paper aims to predict women with at least 21 years of age having diabetes based on eight diagnostic measurements using the statistical learning methods; Multinomial Naive Bayes, Fisher Discriminant Analysis, and Logistic Regression. Model validation is built based on dividing the data into training data and test data based on 5-fold cross-validation. The model validation performance shows that the Gaussian Naïve Bayes is the best method in predicting diabetes diagnosis. This paper’s contribution is that all performance measures of the Multinomial Naïve Bayes method have a value greater than 93 %. These results are beneficial in predicting diabetes status with the same explanatory variables.

scholarly journals Klasifikasi rempah rimpang berdasarkan ciri warna rgb dan tekstur glcm menggunakan algoritma naive bayes

2020 ◽  
Vol 16 (3) ◽  
pp. 156
Author(s):  
Nadya P. Batubara ◽  
Didit Widiyanto ◽  
Nurul Chamidah
Keyword(s):  
Image Enhancement ◽  
Cross Validation ◽  
Naive Bayes ◽  
Region Of Interest ◽  
Naïve Bayes ◽  
Grey Level ◽  
Occurrence Matrix ◽  
Fold Cross Validation

Abstrak. Pada penelitian ini akan membahas bagaimana cara mengklasifikasikan beberapa jenis rempah berdasarkan algoritma Naïve Bayes dengan menggunakan ekstraksi ciri warna RGB dan tekstur GLCM. Tahapan dalam proses klasifikasi citra digital pada penelitian ini yaitu praproses citra, segmentasi, ekstraksi ciri, klasifikasi dan uji performa Proses yang dilakukan pada penelitian ini adalah mengubah RGB to Grayscale untuk mendapatkan citra abunya, setelah mengubah citra menjadi Grayscale. Setelah melakukan image enhancement, citra di segmentasi dengan thresholding menggunakan metode Otsu. Setelah mendapatkan hasil dari segmentasi dilakukan RoI (Region of Interest) yang menghasilkan perkalian pixel. Setelah itu dilakukan ekstraksi ciri dengan menggunakan GLCM (Grey Level Co-occurrence Matrix) dan ekstraksi fitur RGB (Red, green, blue) yang di ekstrak ke dalam GLCM. Setelah mendapatkan hasil dari ekstraksi ciri maka dilakukan klasifikasi menggunakan algoritma Naïve Bayes. Tahapan terakhir pada penelitian ini adalah uji performa menggunakan K-fold cross validation dengan K=10 dan mendapatkan hasil akurasi sebesar 52%. Kata Kunci: Rempah-rempah, Naïve Bsayes, RGB, GLCM.

scholarly journals Alifta Ainurrochmah, Perbandinga

2020 ◽  
Vol 11 (2) ◽  
pp. 37
Author(s):  
Alifta Ainurrochmah ◽  
Memi Nor Hayati ◽  
Andi M. Ade Satriya
Keyword(s):  
Discriminant Analysis ◽  
Naive Bayes ◽  
Insurance Premium ◽  
Naïve Bayes ◽  
Fisher Discriminant Analysis ◽  
Bayes Method ◽  
Premium Payment ◽  
Multivariate Technique ◽  
Fisher’S Discriminant Analysis ◽  
Naive Bayes Method

Classification is a technique to form a model of data that is already known to its classification group. The model was formed will be used to classify new objects. Fisher discriminant analysis is multivariate technique to separate objects in different groups. Naive Bayes is a classification technique based on probability and Bayes theorem with assumption of independence. This research has a goal to compare the level of classification accuracy between Fisher's discriminant analysis and Naive Bayes method on the insurance premium payment status customer. The data used four independent variables that is income, age, premium payment period and premium payment amount. The results of misclassification using the APER (Apparent Rate Error) indicate that the naive Bayes method has a higher level of accuracy is 15,38% than Fisher’s discriminant analysis is 46,15% on the insurance premium payment status customer.

scholarly journals An Ingenious Methodology for the Collation of Existing Algorithms for the Prognosis of Student Performance

2020 ◽  
Vol 9 (5) ◽  
pp. 1749-1752
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Random Forest ◽  
Student Performance ◽  
Naive Bayes ◽  
Research Work ◽  
Large Data ◽  
Naïve Bayes ◽  
Impact Factors ◽  
Data Mining Technique

In this proposed research work we use a profound Data mining technique which is an automated procedure of discovering interesting patterns by means of comprehensible predictive models from large data sets by grouping them. Predicting a student's academic performance is very crucial especially for universities. Educational Data Mining (EDM) is an approach for extricating useful data that could possibly affect a firm. Nowadays student’s performance is swayed by a lot of aspects. These aspects might involve the academic performance of a student. This subject evaluates numerous factors probably suspected to alter a student’s empirical performance in scholastic, and discover a subjective design which classifies and forecast the student’s learning outcomes. The intention of this research is to conduct a case study on factors swayed by the student’s academic achievements and to dictate greater impact factors. In this paper we focus on the academic achievement evaluation on the basis of correct instances and incorrect instances by means of Naive Bayes and Random Forest algorithms. This paper intends to make a metaphorical assessment of Naive Bayes and random Forest classifier on student data and dictate the best algorithm.

scholarly journals A Diabetic Prediction System Based on Mean Shift Clustering

2021 ◽  
Vol 26 (2) ◽  
pp. 231-235
Author(s):  
Satyanarayana Murthy Teki ◽  
Kuncham Venkata Sriharsha ◽  
Mohan Krishna Varma Nandimandalam
Keyword(s):  
Naive Bayes ◽  
Color Change ◽  
Serum Insulin ◽  
Mean Shift ◽  
Region Of Interest ◽  
Naïve Bayes ◽  
Integrated System ◽  
Chemical Agent ◽  
Mean Shift Clustering ◽  
Bayes Algorithm

An abnormal rise in glucose levels may lead to diabetes. Around 30 million people are diagnosed with this disease in our country. In this perspective Indian Council of Medical Research funded by Registry of People with diabetes in India have taken an initiative and come up with numerous solutions but unfortunately neither of them has taken shape. Initially, the behavior of chemical reaction between glucose with chemical agent is estimated and tracked in the region of interest via mean shift algorithm using spatial and range information. This color change is related to plasma glucose concentration (plas), diastolic blood pressure, (pres.) Triceps skin fold thickness(skin), 2_hour serum insulin(insu), Body mass index and age. These features obtained from these 768 instances are classified using Naïve Bayes Algorithm. The results are compared with our previous work, an integrated system of K means and Naïve Bayes approach in terms of sensitivity, specificity, precision, and F-measure. It is worth noticing that our integration of mean-shift clustering and classification gives promising results with an utmost accuracy rate of 99.42% even after removing nearby duplicates in predefined clusters.

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