scholarly journals Student Learning Prediction Using Machine Learning Techniques

2019 ◽  
Vol 8 (6) ◽  
pp. 4179-4183
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Student Performance ◽  
Machine Learning Techniques ◽  
Data Mining Algorithms ◽  
Learning Techniques ◽  
Final Exams ◽  
E Learning ◽  
Using Data ◽  
Mining Algorithms

Now a day’s e-learning is smartly growing technology. This technology is more helpful for students to communicate with their professors through chats or emails. ELearning also removes the obstacle of physical presence of an Elearner. The main aim of this paper is to predict student performance in their final exams using different machine learning techniques. Information like attendance, marks, assignments, class participation, seminar, CA, projects and semester are collected to predict student performance. This prediction helps the instructors to analyze their students based on their performance. For that we have used WEKA tool for the prediction of the student performance. WEKA (Waikato Environment for Knowledge Analysis) is one of the data mining too which is used for the classification and clustering using data mining algorithms. This prediction helps the students and the staffs to know how much effort their students need to be put in their final exams to get good marks.

scholarly journals Dr. Phish: Phishing Website Detector

2021 ◽  
Vol 297 ◽  
pp. 01032
Author(s):  
Harish Kumar ◽  
Anshal Prasad ◽  
Ninad Rane ◽  
Nilay Tamane ◽  
Anjali Yeole
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Cyber Crime ◽  
Data Mining Algorithms ◽  
Learning Techniques ◽  
Mining Algorithms ◽  
Host Properties ◽  
New Strategies

Phishing is a common attack on credulous people by making them disclose their unique information. It is a type of cyber-crime where false sites allure exploited people to give delicate data. This paper deals with methods for detecting phishing websites by analyzing various features of URLs by Machine learning techniques. This experimentation discusses the methods used for detection of phishing websites based on lexical features, host properties and page importance properties. We consider various data mining algorithms for evaluation of the features in order to get a better understanding of the structure of URLs that spread phishing. To protect end users from visiting these sites, we can try to identify the phishing URLs by analyzing their lexical and host-based features.A particular challenge in this domain is that criminals are constantly making new strategies to counter our defense measures. To succeed in this contest, we need Machine Learning algorithms that continually adapt to new examples and features of phishing URLs.

Homeland Security Data Mining and Link Analysis

2011 ◽  
pp. 566-569
Author(s):  
Bhavani Thuraisingham
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Homeland Security ◽  
Link Analysis ◽  
Machine Learning Techniques ◽  
Data Mining Technique ◽  
Learning Techniques ◽  
Terrorist Groups ◽  
Using Data ◽  
Terrorist Events

Data mining is the process of posing queries to large quantities of data and extracting information often previously unknown using mathematical, statistical, and machine-learning techniques. Data mining has many applications in a number of areas, including marketing and sales, medicine, law, manufacturing, and, more recently, homeland security. Using data mining, one can uncover hidden dependencies between terrorist groups as well as possibly predict terrorist events based on past experience. One particular data-mining technique that is being investigated a great deal for homeland security is link analysis, where links are drawn between various nodes, possibly detecting some hidden links.

Homeland Security Data Mining and Link Analysis

2008 ◽  
pp. 3639-3644
Author(s):  
Bhavani Thuraisingham
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Homeland Security ◽  
Link Analysis ◽  
Machine Learning Techniques ◽  
Data Mining Technique ◽  
Learning Techniques ◽  
Terrorist Groups ◽  
Using Data ◽  
Terrorist Events

Data mining is the process of posing queries to large quantities of data and extracting information often previously unknown using mathematical, statistical, and machine-learning techniques. Data mining has many applications in a number of areas, including marketing and sales, medicine, law, manufacturing, and, more recently, homeland security. Using data mining, one can uncover hidden dependencies between terrorist groups as well as possibly predict terrorist events based on past experience. One particular data-mining technique that is being investigated a great deal for homeland security is link analysis, where links are drawn between various nodes, possibly detecting some hidden links.

scholarly journals A Critical Examination of Different Models for Customer Churn Prediction using Data Mining

2019 ◽  
Vol 8 (6S3) ◽  
pp. 850-854
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Prediction Models ◽  
Machine Learning Techniques ◽  
Critical Examination ◽  
Churn Prediction ◽  
Customer Churn ◽  
Learning Techniques ◽  
Customer Churn Prediction ◽  
Using Data

Due to competition between online retailers, the need for providing improved customer service has grown rapidly. In addition to reduction in sales due to loss of customers, more investments are needed to be done to attract new customers. Companies now are working continuously to improve their perceived quality by way of giving timely and quality service to their customers. Customer churn has become one of the primary challenges that many firms are facing nowadays. Several churn prediction models and techniques are proposed previously in literature to predict customer churn in areas such as finance, telecom, banking etc. Researchers are also working on customer churn prediction in e-commerce using data mining and machine learning techniques. In this paper, a comprehensive review of various models to predict customer churn in e-commerce data mining and machine learning techniques has been presented. A critical review of recent research papers in the field of customer churn prediction in e-commerce using data mining has been done. Thereafter, important inferences and research gaps after studying the literature are presented. Finally, the research significance and concluding remarks are described in the end.

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