scholarly journals Phishing Scam Detection using Machine Learning

2019 ◽  
Vol 9 (1S) ◽  
pp. 114-118
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Learning Algorithm ◽  
The Internet ◽  
Machine Learning Algorithm ◽  
Learning Approaches ◽  
Decision Tree Algorithm ◽  
Sensitive Data ◽  
Tree Algorithm ◽  
Testing Issue

As a wrongdoing of utilizing specialized intends to take sensitive data of clients and users in the internet, phishing is as of now an advanced risk confronting the Internet, and misfortunes due to phishing are developing consistently. Recognition of these phishing scams is a very testing issue on the grounds that phishing is predominantly a semantics based assault, which particularly manhandles human vulnerabilities, anyway not system or framework vulnerabilities. Phishing costs. As a product discovery plot, two primary methodologies are generally utilized: blacklists/whitelists and machine learning approaches. Every phishing technique has different parameters and type of attack. Using decision tree algorithm we find out whether the attack is legitimate or a scam. We measure this by grouping them with diverse parameters and features, thereby assisting the machine learning algorithm to edify.

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

scholarly journals Predicting fertilizer treatment of maize using decision tree algorithm

2020 ◽  
Vol 20 (3) ◽  
pp. 1427
Author(s):  
Nusrat Jahan ◽  
Rezvi Shahariar
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Ground Cover ◽  
Fertilizer Treatment ◽  
Learning Approaches ◽  
Decision Tree Algorithm ◽  
Agricultural Field ◽  
Tree Algorithm ◽  
Maize Field ◽  
Ground Coverage

<span>Machine learning approaches are progressively successful in image based analysis such as different diseases prediction as well as level of risk assessment etc. In this paper, image based data analysis with machine learning technique were used for fertilizer treatment of maize. We address this issue as our country depend on agricultural field rather than others. Maize has a bright future. To predict fertilizer treatment of maize dataset were comprised of ground coverage region which highlights the green pixels of a maize image. For calculating green pixels from an image we used “Can Eye” tool.  The achievement of machine learning approaches is highly dependent on quality and quantity of the dataset which is used for training the machine for better classification result. For this perseverance, we collected images from the maize field directly. Then processed those images and classified the data into four classes (Less Nitrogen=-N, Less Phosphorus=-P, Less Potassium=-K and NPK) to train our machine using decision tree algorithm to predict fertilizer treatment. We got 93% classification accuracy for decision tree. Finally, the outcome of this paper is the fertilizer treatment of a maize field based on the ground cover percentage, and we implemented this whole work using an android platform because of the availability of android mobile phone throughout the world.</span>

scholarly journals Machine Learning for Internet of Things

2019 ◽  
Vol 8 (07) ◽  
pp. 24680-24782
Author(s):  
Manisha Bagri ◽  
Neha Aggarwal
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Learning Algorithm ◽  
Weather Prediction ◽  
Support Vector ◽  
The Internet ◽  
Machine Learning Algorithm ◽  
Learning Patterns ◽  
New Development ◽  
Share Data

By 2020 around 25-50 billion devices are likely to be connected to the internet. Due to this new development, it gives rise to something called Internet of Things (IoT). The interconnected devices can generate and share data over a network. Machine Learning plays a key role in IoT to handle the vast amount of data. It gives IoT and devices a brain to think, which is often called as intelligence. The data can be feed to machines for learning patterns, based on training the machines can identify to predict for the future. This paper gives a brief explanation of IoT. This paper gives a crisp explanation of machine learning algorithm and its types. However, Support Vector Machine (SVM) is explained in details along with its merits and demerits. An algorithm is also proposed for weather prediction using SVM for IoT.

scholarly journals Predicting Bank Operational Efficiency Using Machine Learning Algorithm: Comparative Study of Decision Tree, Random Forest, and Neural Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Peter Appiahene ◽  
Yaw Marfo Missah ◽  
Ussiph Najim
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Banking Sector ◽  
Banking Industry ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
And Performance

The financial crisis that hit Ghana from 2015 to 2018 has raised various issues with respect to the efficiency of banks and the safety of depositors’ in the banking industry. As part of measures to improve the banking sector and also restore customers’ confidence, efficiency and performance analysis in the banking industry has become a hot issue. This is because stakeholders have to detect the underlying causes of inefficiencies within the banking industry. Nonparametric methods such as Data Envelopment Analysis (DEA) have been suggested in the literature as a good measure of banks’ efficiency and performance. Machine learning algorithms have also been viewed as a good tool to estimate various nonparametric and nonlinear problems. This paper presents a combined DEA with three machine learning approaches in evaluating bank efficiency and performance using 444 Ghanaian bank branches, Decision Making Units (DMUs). The results were compared with the corresponding efficiency ratings obtained from the DEA. Finally, the prediction accuracies of the three machine learning algorithm models were compared. The results suggested that the decision tree (DT) and its C5.0 algorithm provided the best predictive model. It had 100% accuracy in predicting the 134 holdout sample dataset (30% banks) and a P value of 0.00. The DT was followed closely by random forest algorithm with a predictive accuracy of 98.5% and a P value of 0.00 and finally the neural network (86.6% accuracy) with a P value 0.66. The study concluded that banks in Ghana can use the result of this study to predict their respective efficiencies. All experiments were performed within a simulation environment and conducted in R studio using R codes.

scholarly journals Decision Tree Algorithm for Mining "If Then Else" Rule in Single Slope Basin Solar Still plant

2020 ◽  
Vol 9 (3) ◽  
pp. 405-410
Keyword(s):  
Decision Making ◽  
Decision Tree ◽  
Information Gain ◽  
Learning Approaches ◽  
Decision Tree Algorithm ◽  
Rule Mining ◽  
Rule Based ◽  
Tree Algorithm ◽  
C4.5 Decision Tree ◽  
Learning Concept

Soft computing dedicatedly works for decision making. In this domain a number of techniques are used for prediction, classification, categorization, optimization, and information extraction. Among rule mining is one of the essential methodologies. “IF Then Else” can work as rules, to classify, or predict an event in real world. Basically, that is rule based learning concept, additionally it is frequently used in various data mining applications during decision making and machine learning. There are some supervised learning approaches are available which can be used for rule mining. In this context decision tree is a helpful algorithm. The algorithm works on data splitting strategy using entropy and information gain. The data information is mapped in a tree structure for developing “IF Then Else” rules. In this work an application of rule based learning is presented for recycling of water in a distillation unit. By using the designed experimental still plant different attributes are collected with the observed distillated yield and instantaneous efficiency. This observed data is learned with the C4.5 decision tree algorithm and also predict the distillated yield and instantaneous efficiency. Finally to classify and predict the required parameters “IF Then Else” rules are prepared. The experimental results demonstrate, the proposed C4.5 algorithm provides higher accuracy as compared to similar state of art techniques. The proposed technique offers up to 5-9% improved outcome in terms of accuracy.

scholarly journals Booster in High Dimensional Data Classification using CNN and Decision Tree Algorithm

2019 ◽  
Vol 8 (2S5) ◽  
pp. 148-152
Keyword(s):  
Decision Tree ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Decision Tree Algorithm ◽  
Classification Problems ◽  
Medical Field ◽  
Sensitive Data ◽  
Tree Algorithm ◽  
Evaluation Measure ◽  
The Stability

Classification problems in high dimensional data with small number of observations are becoming more common especially in microarray data. The performance in terms of accuracy is essential while handling sensitive data particularly in medical field. For this the stability of the selected features must be evaluated. Therefore, this paper proposes a new evaluation measure that incorporates the stability of the selected feature subsets and accuracy of the prediction. Booster in feature selection algorithm helps to achieve the same. The proposed work resolves both structured and unstructured data using convolution neural network based multimodal disease prediction and decision tree algorithm respectively. The algorithm is tested on heart disease dataset retrieved from UCI repository and the analysis shows the improved prediction accuracy.

scholarly journals Anticipation of Living Status of Hepatitis B Patient by using Machine Learning

2021 ◽  
Vol 9 (11) ◽  
pp. 924-930
Author(s):  
Mr. G Ragu
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Hepatitis B ◽  
Decision Tree ◽  
Age Groups ◽  
Public Health Care ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Visualization Process ◽  
Exploratory Data

Abstract: Recently the methods of Data mining and machine learning are widely used in medical field. These methods/techniques have given better results in the prediction of respective diseases. Hepatitis B is a Liver inflammation; it can affect people of all age groups. Lakhs of people across the globe are thought to be affected by Hepatitis B. Early prediction of Hepatitis B with accurate results can save many people. Hepatitis B is a tough challenge for public health care system because of limited clinical diagnosis in the early stages of disease. This paper presents the decision tree algorithm to diagnose the Hepatitis B. The proposed algorithm includes collection of datasets, pre-processing, EDA (Exploratory Data Analysis), Feature Selection, data visualizing, Interpreting, saving and evaluating the model. After the data visualization process decision tree algorithm is implemented to diagnose the disease along with the patient chances of living. Keywords: Hepatitis B virus, Machine Learning, Decision Tree, Public Health, EDA

scholarly journals CLINICAL APPLICATIONS OF MACHINE LEARNING ON COVID-19: THE USE OF A DECISION TREE ALGORITHM FOR THE ASSESSMENT OF PERCEIVED STRESS IN MEXICAN HEALTHCARE PROFESSIONALS

2020 ◽  
Author(s):  
Juan Luis Delgado-Gallegos ◽  
Gener Avilés-Rodriguez ◽  
Gerardo R. Padilla-Rivas ◽  
María De los Ángeles Cosio-León ◽  
Héctor Franco-Villareal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Perceived Stress ◽  
Healthcare Professionals ◽  
Statistical Correlation ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Managing Stress ◽  
Applications Of Machine Learning ◽  
C5.0 Decision Tree

AbstractStress and anxiety have shown to be indirect effects of the COVID-19 pandemic, therefore managing stress becomes essential. One of the most affected populations by the pandemic are healthcare professionals. Thus, it is paramount to understand and categorize their perceived levels of stress, as it can be a detonating factor leading to mental illness. In our study, we used a machine learning prediction model to help measure perceived stress; a C5.0 decision tree algorithm was used to analyze and classify datasets obtained from healthcare professionals of the northeast region of Mexico. Our analysis showed that 6 out of 102 instances were incorrectly classified. Missing two cases for mild, three for moderate and 1 for severe (accuracy of 94.1%), statistical correlation analysis was performed to ensure integrity of the method, in addition we concluded that severe stress cases can be related mostly to high levels of Xenophobia and Compulsive stress.

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