scholarly journals Classification of Phishing Email Using Random Forest Machine Learning Technique

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Andronicus A. Akinyelu ◽  
Aderemi O. Adewumi
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Algorithm ◽  
False Negative ◽  
Machine Learning Algorithm ◽  
Detection Techniques ◽  
Phishing Attacks ◽  
Learning Technique ◽  
Phishing Detection

Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars have been lost by many companies and individuals. In 2012, an online report put the loss due to phishing attack at about $1.5 billion. This global impact of phishing attacks will continue to be on the increase and thus requires more efficient phishing detection techniques to curb the menace. This paper investigates and reports the use of random forest machine learning algorithm in classification of phishing attacks, with the major objective of developing an improved phishing email classifier with better prediction accuracy and fewer numbers of features. From a dataset consisting of 2000 phishing and ham emails, a set of prominent phishing email features (identified from the literature) were extracted and used by the machine learning algorithm with a resulting classification accuracy of 99.7% and low false negative (FN) and false positive (FP) rates.

scholarly journals Classification of Diabetes using Random Forest with Feature Selection Algorithm

2019 ◽  
Vol 9 (1) ◽  
pp. 1295-1300 ◽  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Electronic Health Records ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Health Records

Diabetes has become a serious problem now a day. So there is a need to take serious precautions to eradicate this. To eradicate, we should know the level of occurrence. In this project we predict the level of occurrence of diabetes. We predict the level of occurrence of diabetes using Random Forest, a Machine Learning Algorithm. Using the patient’s Electronic Health Records (EHR) we can build accurate models that predict the presence of diabetes.

scholarly journals Prediction of the Minimum Film Boiling Temperature of Quenching Vertical Rods in Water Using Random Forest Machine Learning Algorithm

2021 ◽  
Vol 9 ◽  
Author(s):  
Sorour Alotaibi ◽  
Shikha Ebrahim ◽  
Ayed Salman
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Algorithm ◽  
Substrate Material ◽  
Linear Process ◽  
Boiling Temperature ◽  
Film Boiling ◽  
Machine Learning Algorithm ◽  
Learning Technique ◽  
Theoretical And Numerical Analysis

A great amount of research is focused, nowadays, on experimental, theoretical, and numerical analysis of transient pool boiling. Knowing the minimum film boiling temperature (Tmin) for rods with different substrate materials that are quenched in distilled water pools at various system pressures is known to be a complex and highly non-linear process. This work aims to develop a new correlation to predict the Tmin in the above process: Random forest machine learning technique is applied to predict the Tmin. The approach trains a machine learning algorithm using a set of experimental data collected from the literature. Several parameters such as liquid subcooling temperature (Tsub), fluid to the substrate material thermophysical properties (βf/βw), and system saturated pressure (Psat) are collected and used as inputs, whereas Tmin is measured and used as the output. Computational results show that the algorithm achieves superior results compared to other correlations reported in the literature.

scholarly journals Multi-Class Assessment Based on Random Forests

2021 ◽  
Vol 11 (3) ◽  
pp. 92
Author(s):  
Mehdi Berriri ◽  
Sofiane Djema ◽  
Gaëtan Rey ◽  
Christel Dartigues-Pallez
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Random Forests ◽  
Learning Algorithm ◽  
Teaching Staff ◽  
Machine Learning Algorithm ◽  
Process Data ◽  
Training Courses ◽  
Education Courses

Today, many students are moving towards higher education courses that do not suit them and end up failing. The purpose of this study is to help provide counselors with better knowledge so that they can offer future students courses corresponding to their profile. The second objective is to allow the teaching staff to propose training courses adapted to students by anticipating their possible difficulties. This is possible thanks to a machine learning algorithm called Random Forest, allowing for the classification of the students depending on their results. We had to process data, generate models using our algorithm, and cross the results obtained to have a better final prediction. We tested our method on different use cases, from two classes to five classes. These sets of classes represent the different intervals with an average ranging from 0 to 20. Thus, an accuracy of 75% was achieved with a set of five classes and up to 85% for sets of two and three classes.

Detecting Cognitive Distraction Using Random Forest by Considering Eye Movement Type

2018 ◽  
pp. 1587-1599
Author(s):  
Hiroaki Koma ◽  
Taku Harada ◽  
Akira Yoshizawa ◽  
Hirotoshi Iwasaki
Keyword(s):  
Machine Learning ◽  
Eye Movements ◽  
Random Forest ◽  
Decision Trees ◽  
Eye Movement ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Still Images ◽  
Cognitive Distraction ◽  
Movement Type

Detecting distracted states can be applied to various problems such as danger prevention when driving a car. A cognitive distracted state is one example of a distracted state. It is known that eye movements express cognitive distraction. Eye movements can be classified into several types. In this paper, the authors detect a cognitive distraction using classified eye movement types when applying the Random Forest machine learning algorithm, which uses decision trees. They show the effectiveness of considering eye movement types for detecting cognitive distraction when applying Random Forest. The authors use visual experiments with still images for the detection.

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