Effective cardiac health diagnosis using event-driven ECG processing with subband feature extraction and machine learning techniques

2020 ◽  
Author(s):  
Saeed Mian Qaisar ◽  
Syed Fawad Hussain
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Machine Learning Techniques ◽  
Cardiac Health ◽  
Learning Techniques ◽  
Event Driven ◽  
Health Diagnosis

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

scholarly journals Convolutional Neural Network Computation for Autonomous Vehicle

2020 ◽  
pp. 368-372
Author(s):  
Aires Da Conceicao ◽  
Sheshang D. Degadwala
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Autonomous Vehicle ◽  
Human Interaction ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Robust Feature Extraction

Self-driving vehicle is a vehicle that can drive by itself it means without human interaction. This system shows how the computer can learn and the over the art of driving using machine learning techniques. This technique includes line lane tracker, robust feature extraction and convolutional neural network.

A Proficient Scheme for Detecting Breast Cancer by Classification Techniques

2020 ◽  
Vol 17 (8) ◽  
pp. 3453-3457
Author(s):  
Chinka Siva Gopi ◽  
Chidipudi Sivareddy ◽  
K. Mohana Prasad ◽  
R. Sabitha ◽  
K. Ashok Kumar
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
The Body ◽  
Machine Learning Techniques ◽  
Body Parts ◽  
Breast Cancers ◽  
Learning Techniques ◽  
Healthcare Evaluation ◽  
Accuracy Level ◽  
Active Design

Cancer is a risky disease which could affect the particular area in depth and may risk the body parts. Now a days, more females are subject to breast cancers. So that Machine Learning Techniques has proposed to analyze the risky area in which the information is utilized for forecasting additional incidents. Machine Learning is popular scheme within several programs one remaining healthcare evaluation. Image Classification as well as feature extraction will bring the affected area’s image into several analyzing methods. With this proposed system, we’ve suggested an CNN (Convolution Neural Network) active design which fetches a sequence of pictures coming from a healthcare scanner repository so that the pictures are preprocessed as well as additional segmented feature extraction. The effectiveness on the suggested design is examined and it is as opposed along with other Machine Learning procedures and it is found the proposed system has supplies the greater results. The functionality on the unit tends to be more precise as the unit has an iterative method for include removal inside classifying pictures. There are some images are kept for the training and testing. We have achieved the accuracy level of comparing with existing model.

scholarly journals Feature extraction and machine learning techniques for identifying historic urban environmental hazards: New methods to locate lost fossil fuel infrastructure in US cities

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255507
Author(s):  
Jonathan Tollefson ◽  
Scott Frickel ◽  
Maria I. Restrepo
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Gas Production ◽  
Machine Learning Techniques ◽  
Visual Coding ◽  
New Methods ◽  
Fire Insurance ◽  
Learning Techniques ◽  
Site Identification ◽  
Us Cities

U.S. cities contain unknown numbers of undocumented “manufactured gas” sites, legacies of an industry that dominated energy production during the late-19th and early-20th centuries. While many of these unidentified sites likely contain significant levels of highly toxic and biologically persistent contamination, locating them remains a significant challenge. We propose a new method to identify manufactured gas production, storage, and distribution infrastructure in bulk by applying feature extraction and machine learning techniques to digitized historic Sanborn fire insurance maps. Our approach, which relies on a two-part neural network to classify candidate map regions, increases the rate of site identification 20-fold compared to unaided visual coding.

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