Comparative Study on Challenges and Detection of Brain Tumor Using Machine Learning Algorithm

2021 ◽  
pp. 21-30
Author(s):  
S. Magesh ◽  
V. R. Niveditha ◽  
Ambeshwar Kumar ◽  
R. Manikandan ◽  
P. S. Rajakumar
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Comparative Study ◽  
Learning Algorithm ◽  
Machine Learning Algorithm

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

scholarly journals A Survey on Fatigue Detection of Workers Using Machine Learning

2020 ◽  
Vol 11 (3) ◽  
pp. 1-8
Author(s):  
Nisha Yadav ◽  
Kakoli Banerjee ◽  
Vikram Bali
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Human Performance ◽  
Learning Algorithm ◽  
Facial Features ◽  
Machine Learning Algorithm ◽  
Computer Screen ◽  
Health Risk Factor ◽  
Fatigue Detection

In the software industry, where the quality of the output is based on human performance, fatigue can be a reason for performance degradation. Fatigue not only degrades quality, but is also a health risk factor. Sleep disorders, depression, and stress are all results of fatigue which can contribute to fatal problems. This article presents a comparative study of different techniques which can be used for detecting fatigue of programmers and data miners who spent lots of time in front of a computer screen. Machine learning can used for worker fatigue detection also, but there are some factors which are specific for software workers. One of such factors is screen illumination. Screen illumination is the light of the computer screen or laptop screen that is casted on the workers face and makes it difficult for the machine learning algorithm to extract the facial features. This article presents a comparative study of the techniques which can be used for general fatigue detection and identifies the best techniques.

scholarly journals Analysis of Brain Tumor Disease Detection Using Convolutional Neural Network

2021 ◽  
Author(s):  
Yogesh Deshmukh ◽  
Samiksha Dahe ◽  
Tanmayeeta Belote ◽  
Aishwarya Gawali ◽  
Sunnykumar Choudhary
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Brain Tumor ◽  
Human Brain ◽  
Convolutional Neural Network ◽  
Learning Algorithm ◽  
Vital Role ◽  
Machine Learning Algorithm ◽  
Different Types ◽  
The Brain

Brain Tumor detection using Convolutional Neural Network (CNN) is used to discover and classify the types of Tumor. Over a amount of years, many researchers are researched and planned ways throughout this area. We’ve proposed a technique that’s capable of detecting and classifying different types of tumor. For detecting and classifying tumor we have used MRI because MRI images gives the complete structure of the human brain, without any operation it scans the human brain and this helps in processing of image for the detection of the Tumor. The prediction of tumor by human from the MRI images leads to misclassification. This motivates us to construct the algorithm for detection of the brain tumor. Machine learning helps and plays a vital role in detecting tumor. In this paper, we tend to use one among the machine learning algorithm i.e. Convolutional neural network (CNN), as CNNs are powerful in image processing and with the help of CNN and MRI images we designed a framework for detection of the brain tumor and classifying its Different types.

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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