scholarly journals A Secure Data Classification Model in Cloud Computing Using Machine Learning Approach

2016 ◽  
Vol 9 (8) ◽  
pp. 13-22
Author(s):  
Kulwinder Kaur ◽  
Vikas Zandu
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Data Classification ◽  
Classification Model ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Secure Data

scholarly journals A study secure multi authentication based data classification model in cloud based system

2020 ◽  
Vol 9 (3) ◽  
pp. 240
Author(s):  
Sakshi Kaushal ◽  
Bala Buksh
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Data Classification ◽  
Classification Model ◽  
Sensitive Data ◽  
Learning Technique ◽  
Mathematical Algorithms ◽  
Encryption Algorithms ◽  
Cloud Applications ◽  
Technology Resources

Cloud computing is the most popular term among enterprises and news. The concepts come true because of fast internet bandwidth and advanced cooperation technology. Resources on the cloud can be accessed through internet without self built infrastructure. Cloud computing is effectively manage the security in the cloud applications. Data classification is a machine learning technique used to predict the class of the unclassified data. Data mining uses different tools to know the unknown, valid patterns and relationships in the dataset. These tools are mathematical algorithms, statistical models and Machine Learning (ML) algorithms. In this paper author uses improved Bayesian technique to classify the data and encrypt the sensitive data using hybrid stagnography. The encrypted and non encrypted sensitive data is sent to cloud environment and evaluate the parameters with different encryption algorithms.

A hybrid machine learning approach for malicious behaviour detection and recognition in cloud computing

2020 ◽  
Vol 151 ◽  
pp. 102507 ◽  
Author(s):  
Mahdi Rabbani ◽  
Yong Li Wang ◽  
Reza Khoshkangini ◽  
Hamed Jelodar ◽  
Ruxin Zhao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Hybrid Machine ◽  
Detection And Recognition

scholarly journals Machine Learning Approach to Dysphonia Detection

2018 ◽  
Vol 8 (10) ◽  
pp. 1927 ◽  
Author(s):  
Zuzana Dankovičová ◽  
Dávid Sovák ◽  
Peter Drotár ◽  
Liberios Vokorokos
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Nearest Neighbors ◽  
Classification Model ◽  
Support Vector ◽  
Learning Approach ◽  
K Nearest Neighbors ◽  
Machine Learning Methods ◽  
Machine Learning Approach ◽  
Speech Features

This paper addresses the processing of speech data and their utilization in a decision support system. The main aim of this work is to utilize machine learning methods to recognize pathological speech, particularly dysphonia. We extracted 1560 speech features and used these to train the classification model. As classifiers, three state-of-the-art methods were used: K-nearest neighbors, random forests, and support vector machine. We analyzed the performance of classifiers with and without gender taken into account. The experimental results showed that it is possible to recognize pathological speech with as high as a 91.3% classification accuracy.

scholarly journals Handwritten digits recognition with decision tree classification: a machine learning approach

2019 ◽  
Vol 9 (5) ◽  
pp. 4446
Author(s):  
Tsehay Admassu Assegie ◽  
Pramod Sekharan Nair
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Classification Model ◽  
Learning Approach ◽  
Data Sets ◽  
The Past ◽  
Decision Tree Classification ◽  
Machine Learning Approach ◽  
Future Data ◽  
Class Labels

Handwritten digits recognition is an area of machine learning, in which a machine is trained to identify handwritten digits. One method of achieving this is with decision tree classification model. A decision tree classification is a machine learning approach that uses the predefined labels from the past known sets to determine or predict the classes of the future data sets where the class labels are unknown. In this paper we have used the standard kaggle digits dataset for recognition of handwritten digits using a decision tree classification approach. And we have evaluated the accuracy of the model against each digit from 0 to 9.

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