scholarly journals Credit Card Fraud Detection

2021 ◽  
Vol 7 (05) ◽  
pp. 60-62
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Credit Card ◽  
Learning Algorithm ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Nearest Neighbour ◽  
Decision Tree Algorithm ◽  
Random Forest Algorithm ◽  
Real World Problem

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.

scholarly journals Comparison of Machine Learning algorithm for COVID-19 Death Risk Prediction

2021 ◽  
Author(s):  
Praveeen Anandhanathan ◽  
Priyanka Gopalan
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Nearest Neighbour ◽  
Decision Tree Algorithm ◽  
The Past ◽  
Random Forest Method ◽  
Learning Techniques ◽  
The World

Abstract Coronavirus disease (COVID-19) is spreading across the world. Since at first it has appeared in Wuhan, China in December 2019, it has become a serious issue across the globe. There are no accurate resources to predict and find the disease. So, by knowing the past patients’ records, it could guide the clinicians to fight against the pandemic. Therefore, for the prediction of healthiness from symptoms Machine learning techniques can be implemented. From this we are going to analyse only the symptoms which occurs in every patient. These predictions can help clinicians in the easier manner to cure the patients. Already for prediction of many of the diseases, techniques like SVM (Support vector Machine), Fuzzy k-Means Clustering, Decision Tree algorithm, Random Forest Method, ANN (Artificial Neural Network), KNN (k-Nearest Neighbour), Naïve Bayes, Linear Regression model are used. As we haven’t faced this disease before, we can’t say which technique will give the maximum accuracy. So, we are going to provide an efficient result by comparing all the such algorithms in RStudio.

scholarly journals Post hoc support vector machine learning for impedimetric biosensors based on weak protein–ligand interactions

The Analyst ◽  
2018 ◽  
Vol 143 (9) ◽  
pp. 2066-2075 ◽  
Author(s):  
Y. Rong ◽  
A. V. Padron ◽  
K. J. Hagerty ◽  
N. Nelson ◽  
S. Chi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Mobile Phone ◽  
Learning Algorithm ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Impedimetric Biosensor ◽  
Post Hoc

We develop a simple, open source machine learning algorithm for analyzing impedimetric biosensor data using a mobile phone.

scholarly journals Heart Disease Prediction Using Machine Learning Algorithm

2019 ◽  
Vol 8 (10) ◽  
pp. 2603-2606
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Decision Tree ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Tree Classifier

Heart disease is a common problem which can be very severe in old ages and also in people not having a healthy lifestyle. With regular check-up and diagnosis in addition to maintaining a decent eating habit can prevent it to some extent. In this paper we have tried to implement the most sought after and important machine learning algorithm to predict the heart disease in a patient. The decision tree classifier is implemented based on the symptoms which are specifically the attributes required for the purpose of prediction. Using the decision tree algorithm, we will be able to identify those attributes which are the best one that will lead us to a better prediction of the datasets. The decision tree algorithm works in a way where it tries to solve the problem by the help of tree representation. Here each internal node of the tree represents an attribute, and each leaf node corresponds to a class label. The support vector machine algorithm helps us to classify the datasets on the basis of kernel and it also groups the dataset using hyperplane. The main objective of this project is to try and reduce the number of occurrences of the heart diseases in patients

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

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.

Classification of P2P Traffic Based on a Heteromorphic Ensemble Learning Model

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.

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