Performance Evaluation of Supervised Machine Learning Classifiers for Analyzing Agricultural Big Data

2019 ◽  
pp. 135-150
Author(s):  
R. Anusuya ◽  
S. Krishnaveni
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Performance Evaluation ◽  
Supervised Machine Learning ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Supervised Machine Learning Classifiers

scholarly journals Defending Malicious Script Attacks Using Machine Learning Classifiers

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Nayeem Khan ◽  
Johari Abdullah ◽  
Adnan Shahid Khan
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Malicious Code ◽  
Supervised Machine Learning ◽  
Feature Subset ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Wrapper Method ◽  
Supervised Machine Learning Classifiers ◽  
Client Side

The web application has become a primary target for cyber criminals by injecting malware especially JavaScript to perform malicious activities for impersonation. Thus, it becomes an imperative to detect such malicious code in real time before any malicious activity is performed. This study proposes an efficient method of detecting previously unknown malicious java scripts using an interceptor at the client side by classifying the key features of the malicious code. Feature subset was obtained by using wrapper method for dimensionality reduction. Supervised machine learning classifiers were used on the dataset for achieving high accuracy. Experimental results show that our method can efficiently classify malicious code from benign code with promising results.

Port-Scanning Attack Detection Using Supervised Machine Learning Classifiers

2021 ◽  
Author(s):  
Akram Q. M. Algaolahi ◽  
Abdullah A. Hasan ◽  
Amer Sallam ◽  
Abdullah M. Sharaf ◽  
Aseel A. Abdu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Attack Detection ◽  
Supervised Machine Learning ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Supervised Machine Learning Classifiers

scholarly journals Using supervised machine learning classifiers to estimate likelihood of participating in clinical trials of a de-identified version of ResearchMatch

2020 ◽  
pp. 1-7
Author(s):  
Janette Vazquez ◽  
Samir Abdelrahman ◽  
Loretta M. Byrne ◽  
Michael Russell ◽  
Paul Harris ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Deep Learning ◽  
Supervised Machine Learning ◽  
Outcome Variable ◽  
Medical Conditions ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Supervised Machine Learning Classifiers

Abstract Introduction: Lack of participation in clinical trials (CTs) is a major barrier for the evaluation of new pharmaceuticals and devices. Here we report the results of the analysis of a dataset from ResearchMatch, an online clinical registry, using supervised machine learning approaches and a deep learning approach to discover characteristics of individuals more likely to show an interest in participating in CTs. Methods: We trained six supervised machine learning classifiers (Logistic Regression (LR), Decision Tree (DT), Gaussian Naïve Bayes (GNB), K-Nearest Neighbor Classifier (KNC), Adaboost Classifier (ABC) and a Random Forest Classifier (RFC)), as well as a deep learning method, Convolutional Neural Network (CNN), using a dataset of 841,377 instances and 20 features, including demographic data, geographic constraints, medical conditions and ResearchMatch visit history. Our outcome variable consisted of responses showing specific participant interest when presented with specific clinical trial opportunity invitations (‘yes’ or ‘no’). Furthermore, we created four subsets from this dataset based on top self-reported medical conditions and gender, which were separately analysed. Results: The deep learning model outperformed the machine learning classifiers, achieving an area under the curve (AUC) of 0.8105. Conclusions: The results show sufficient evidence that there are meaningful correlations amongst predictor variables and outcome variable in the datasets analysed using the supervised machine learning classifiers. These approaches show promise in identifying individuals who may be more likely to participate when offered an opportunity for a clinical trial.

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