Comparative Analysis of Detection of Email Spam With the Aid of Machine Learning Approaches

This paper presents the first model for time normalization trained on the SCATE corpus. In the SCATE schema, time expressions are annotated as a semantic composition of time entities. This novel schema favors machine learning approaches, as it can be viewed as a semantic parsing task. In this work, we propose a character level multi-output neural network that outperforms previous state-of-the-art built on the TimeML schema. To compare predictions of systems that follow both SCATE and TimeML, we present a new scoring metric for time intervals. We also apply this new metric to carry out a comparative analysis of the annotations of both schemes in the same corpus.

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Analysis of Naıve Bayes Algorithm for Email Spam Filtering

International Journal for Modern Trends in Science and Technology - RTT2020 ◽

10.46501/ijmtst0701002 ◽

2021 ◽

Vol 7 (01) ◽

pp. 5-9

Author(s):

RajKishore Sahni

Keyword(s):

Machine Learning ◽

Service Providers ◽

Machine Learning Techniques ◽

Research Trend ◽

Learning Approaches ◽

Spam Filtering ◽

Internet Service ◽

Learning Techniques ◽

Bayes Algorithm ◽

Email Spam

The upsurge in the volume of unwanted emails called spam has created an intense need for the development of more dependable and robust antispam filters. Machine learning methods of recent are being used to successfully detect and filter spam emails. We present a systematic review of some of the popular machine learning based email spam filtering approaches. Our review covers survey of the important concepts, attempts, efficiency, and the research trend in spam filtering. The preliminary discussion in the study background examines the applications of machine learning techniques to the email spam filtering process of the leading internet service providers (ISPs) like Gmail, Yahoo and Outlook emails spam filters. Discussion on general email spam filtering process, and the various efforts by different researchers in combating spam through the use machine learning techniques was done. Our review compares the strengths and drawbacks of existing machine learning approaches and the open research problems in spam filtering. We recommended deep learning and deep adversarial learning as the future techniques that can effectively handle the menace of spam emails

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Employee's Attrition Prediction Using Machine Learning Approaches

Machine Learning and Deep Learning in Real-Time Applications - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-7998-3095-5.ch005 ◽

2020 ◽

pp. 121-128

Author(s):

Krishna Kumar Mohbey

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Comparative Analysis ◽

Random Forest ◽

Decision Tree ◽

Experimental Results ◽

Learning Approaches ◽

Employee Attrition ◽

Or Organization ◽

Customer Attrition

In any industry, attrition is a big problem, whether it is about employee attrition of an organization or customer attrition of an e-commerce site. If we can accurately predict which customer or employee will leave their current company or organization, then it will save much time, effort, and cost of the employer and help them to hire or acquire substitutes in advance, and it would not create a problem in the ongoing progress of an organization. In this chapter, a comparative analysis between various machine learning approaches such as Naïve Bayes, SVM, decision tree, random forest, and logistic regression is presented. The presented result will help us in identifying the behavior of employees who can be attired over the next time. Experimental results reveal that the logistic regression approach can reach up to 86% accuracy over other machine learning approaches.

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