LESH - feature extraction and cognitive machine learning techniques for recognition of lung cancer cells

2021 ◽  
Vol 15 (1) ◽  
pp. 32
Author(s):  
B. Eswara Reddy ◽  
Ummadi Janardhan Reddy ◽  
B. Venkata Ramana Reddy
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Feature Extraction ◽  
Cancer Cells ◽  
Lung Cancer Cells ◽  
Machine Learning Techniques ◽  
Learning Techniques

Prediction of Breast and Lung Cancer, Comparative Review and Analysis Using Machine Learning Techniques

2021 ◽  
pp. 251-271
Author(s):  
Arun Solanki ◽  
Sandeep Kumar ◽  
C. Rohan ◽  
Simar Preet Singh ◽  
Akash Tayal
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Comparative Review

scholarly journals Classification of Macronutrient Deficiencies in Maize Plant Using Machine Learning

2018 ◽  
Vol 8 (6) ◽  
pp. 4197
Author(s):  
Leena N ◽  
K. K. Saju
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Nutrient Management ◽  
Management Strategies ◽  
Nutrient Deficiency ◽  
Crop Productivity ◽  
Machine Learning Techniques ◽  
Nutritional Deficiencies ◽  
Feature Sets ◽  
Learning Techniques

<p>Detection of nutritional deficiencies in plants is vital for improving crop productivity. Timely identification of nutrient deficiency through visual symptoms in the plants can help farmers take quick corrective action by appropriate nutrient management strategies. The application of computer vision and machine learning techniques offers new prospects in non-destructive field-based analysis for nutrient deficiency. Color and shape are important parameters in feature extraction. In this work, two different techniques are used for image segmentation and feature extraction to generate two different feature sets from the same image sets. These are then used for classification using different machine learning techniques. The experimental results are analyzed and compared in terms of classification accuracy to find the best algorithm for the two feature sets.</p>

scholarly journals Statistical Feature Extraction and Classification using Machine Learning Techniques in Brain-Computer Interface

2020 ◽  
Vol 9 (3) ◽  
pp. 1754-1758
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Brain Computer Interface ◽  
Essential Feature ◽  
Average Energy ◽  
Machine Learning Techniques ◽  
Computer Interface ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Learning Techniques

Brain Computer Interface is a paralyzed system. This system is used for direct communication between brain nerves and computer devices. BCI is an imagery movement of the patients who are all unable to communicate with the people. In EEG signals feature extraction plays an important role. Statistical based features are essential feature being used in machine learning applications. Researchers mainly focus on the filters and feature extraction techniques. In this paper data are collected from the BCI Competition III dataset 1a. Statistical features like minimum, maximum, standard deviation, variance, skewnesss, kurtosis, root mean square, average, energy, contrast, correlation and Homogeneity are extracted. Classification is done using machine learning techniques such as Support Vector Machine, Artificial Neural Network and K-Nearest Neighbor. In the proposed system 90.6% accuracy is achieved

Sign in / Sign up

Export Citation Format

Share Document