Comparative Analysis of Genomic Personalized Cancer Diagnosis by Machine Learning Approaches ROC Curve

2020 ◽  
pp. 511-528
Author(s):  
V. Kakulapti ◽  
P. Lalitha Bhavani ◽  
K. Swathi Reddy ◽  
P. Nissar Ahmed
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Cancer Diagnosis ◽  
Roc Curve ◽  
Learning Approaches ◽  
Personalized Cancer

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

2021 ◽  
Vol 1022 ◽  
pp. 012113
Author(s):  
Mangena Venu Madhavan ◽  
Sagar Pande ◽  
Pooja Umekar ◽  
Tushar Mahore ◽  
Dhiraj Kalyankar
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Learning Approaches ◽  
Email Spam

scholarly journals From Characters to Time Intervals: New Paradigms for Evaluation and Neural Parsing of Time Normalizations

2018 ◽  
Vol 6 ◽  
pp. 343-356 ◽  
Author(s):  
Egoitz Laparra ◽  
Dongfang Xu ◽  
Steven Bethard
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Comparative Analysis ◽  
State Of The Art ◽  
Learning Approaches ◽  
Semantic Parsing ◽  
Time Intervals ◽  
Semantic Composition ◽  
Previous State ◽  
New Scoring

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.

Employee's Attrition Prediction Using Machine Learning Approaches

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.

scholarly journals Face Recognition Comparative Analysis Using Different Machine Learning Approaches

2021 ◽  
Vol 15 (1) ◽  
pp. 265-272
Author(s):  
Nisar Ahmed ◽  
Farhan Khan ◽  
Zain Ullah ◽  
Hasnain Ahmed ◽  
Taimur Shahzad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Comparative Analysis ◽  
Learning Approaches

A Comparative Analysis of Machine Learning Approaches in Personality Prediction Using MBTI

2021 ◽  
pp. 13-23
Author(s):  
Kulsum Akter Nisha ◽  
Umme Kulsum ◽  
Saifur Rahman ◽  
Md. Farhad Hossain ◽  
Partha Chakraborty ◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Learning Approaches ◽  
Personality Prediction
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