Identification of patients with and without minimal hepatic encephalopathy based on gray matter volumetry using a support vector machine learning algorithm

This research paper proposes a solution that should be deployed to identify whether the transaction is fraud or not. Although we know that most of the transaction takes place online meaning that this transaction can be theft on the go and will create problem to user therefore this paper focus on some particular machine learning algorithm for example Random forest Algorithm, Decision Tree Algorithm, Logistic Regression, Support Vector Machine, K Nearest Neighbour, XGBoost .Which aims at solving such kind of real-world problem.

Download Full-text

A Practical Fault Tolerance Approach in Cloud Computing Using Support Vector Machine

10.54646/bijscit.010 ◽

2020 ◽

pp. 45-49

Author(s):

Gajendra Sharma ◽

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Cloud Computing ◽

Fault Tolerance ◽

Learning Algorithm ◽

Supervised Machine Learning ◽

Support Vector ◽

Machine Learning Algorithm ◽

Tolerance Approach ◽

Systematic Solution

Fault tolerance is an important issue in the field of cloud computing which is concerned with the techniques or mechanism needed to enable a system to tolerate the faults that may encounter during its functioning. Fault tolerance policy can be categorized into three categories viz. proactive, reactive and adaptive. Providing a systematic solution the loss can be minimized and guarantee the availability and reliability of the critical services. The purpose and scope of this study is to recommend Support Vector Machine, a supervised machine learning algorithm to proactively monitor the fault so as to increase the availability and reliability by combining the strength of machine learning algorithm with cloud computing.

Download Full-text

Classification of P2P Traffic Based on a Heteromorphic Ensemble Learning Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.687-691.2693 ◽

2014 ◽

Vol 687-691 ◽

pp. 2693-2697

Author(s):

Li Ding ◽

Li Mao ◽

Xiao Feng Wang

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Ensemble Learning ◽

Classification Accuracy ◽

Learning Algorithm ◽

Learning Model ◽

Support Vector ◽

Machine Learning Algorithm ◽

Data Environment

One single machine learning algorithm presents shortcomings when the data environment changes in the process of application. This article puts forward a heteromorphic ensemble learning model made up of bayes, support vector machine (SVM) and decision tree which classifies P2P traffic by voting principle. The experiment shows that the model can significantly improve the classification accuracy, and has a good stability.

Download Full-text

A new semi-supervised support vector machine learning algorithm based on active learning

2010 2nd International Conference on Future Computer and Communication ◽

10.1109/icfcc.2010.5497471 ◽

2010 ◽

Cited By ~ 2

Author(s):

Li Cunhe ◽

Wu Chenggang

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Active Learning ◽

Learning Algorithm ◽

Support Vector ◽

Machine Learning Algorithm

Download Full-text

Discrimination of SARS-Cov 2 and arboviruses (DENV, ZIKV and CHIKV) clinical features using machine learning techniques: a fast and inexpensive clinical screening for countries simultaneously affected by both diseases

10.1101/2021.01.28.21250714 ◽

2021 ◽

Author(s):

João Daniel S. Castro

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Learning Algorithm ◽

Machine Learning Techniques ◽

Support Vector ◽

Machine Learning Algorithm ◽

Clinical Screening ◽

Learning Techniques ◽

The World ◽

Area Under Roc Curve

AbstractSARS-Cov-2 (Covid-19) has spread rapidly throughout the world, and especially in tropical countries already affected by outbreaks of arboviruses, such as Dengue, Zika and Chikungunya, and may lead these locations to a collapse of health systems. Thus, the present work aims to develop a methodology using a machine learning algorithm (Support Vector Machine) for the prediction and discrimination of patients affected by Covid-19 and arboviruses (DENV, ZIKV and CHIKV). Clinical data from 204 patients with both Covid-19 and arboviruses obtained from 23 scientific articles and 1 dataset were used. The developed model was able to predict 93.1% of Covid-19 cases and 82.1% of arbovirus cases, with an accuracy of 89.1% and Area under Roc Curve of 95.6%, proving to be effective in prediction and possible screening of these patients, especially those affected by Covid-19, allowing early isolation.

Download Full-text