scholarly journals A PSO MODEL FOR DISEASE PATTERN DETECTION ON LEAF SURFACES

2015 ◽  
Vol 34 (3) ◽  
pp. 209 ◽  
Author(s):  
Kanthan Muthukannan ◽  
Pitchai Latha
Keyword(s):  
Feature Extraction ◽  
Image Segmentation ◽  
Disease Classification ◽  
Hybrid Features ◽  
New Approach ◽  
Disease Patterns ◽  
Leaf Surfaces ◽  
Disease Pattern ◽  
Hybrid Feature Extraction

The main objective of this paper is to segment the disease affected portion of a plant leaf and extract the hybrid features for better classification of different disease patterns. A new approach named as Particle Swarm Optimization (PSO) is proposed for image segmentation. PSO is an automatic unsupervised efficient algorithm which is used for better segmentation and better feature extraction. Features extracted after segmentation are important for disease classification so that the hybrid feature extraction components controls the accuracy of classification for different diseases. The approach named as Hybrid Feature Extraction (HFE), which has three components namely color, texture and shape based features. The performance of the preprocessing result was compared and the best result was taken for image segmentation using PSO. Then the hybrid feature parameters were extracted from the gray level co-occurrence matrices of different leaves. The proposed method was tested on different images of disease affected leaves, and the experimental results exhibit its effectiveness.

A Hybrid Feature Extraction Method for Heart Disease Classification using ECG Signals

2021 ◽  
Author(s):  
Emir Akcin ◽  
Kemal Sami Isleyen ◽  
Enes Ozcan ◽  
Alaa Ali Hameed ◽  
Erdal Alimovski ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Heart Disease ◽  
Extraction Method ◽  
Disease Classification ◽  
Feature Extraction Method ◽  
Ecg Signals ◽  
Hybrid Feature Extraction

EPILEPTIC EEG IDENTIFICATION BASED ON HYBRID FEATURE EXTRACTION

2020 ◽  
Vol 20 (06) ◽  
pp. 2050025 ◽  
Author(s):  
XIAOCHEN LIU ◽  
JIZHONG SHEN ◽  
WUFENG ZHAO
Keyword(s):  
Feature Extraction ◽  
Classification Accuracy ◽  
Energy Operator ◽  
Typical Feature ◽  
Frequency Domain Analysis ◽  
Identification System ◽  
Eeg Signals ◽  
Hybrid Features ◽  
Interictal Eeg ◽  
Hybrid Feature Extraction

Electroencephalogram (EEG) signals are widely used as an effective method for epilepsy analysis and diagnosis. For the establishment of an accurate and efficient epilepsy EEG identification system, it is very important to properly extract the features of EEG signals and select appropriate combination features. This paper proposes an automatic epileptic EEG identification method based on hybrid feature extraction. It uses temporal and frequency domain analysis, nonlinear analysis and one-dimensional local pattern recognition method to extract epileptic EEG features. Gradient energy operator and local speed pattern are proposed to better reflect typical feature in the active EEG signals measured during seizure-free intervals. The genetic algorithm is used to select the obtained hybrid features; then the AdaBoost classifier is used to classify epileptic EEG under various classification conditions. Classification results on the dataset developed by University of Bonn show that the proposed method can be used to classify normal EEG, interictal EEG and seizure activity with only a few features. Compared with related researches using the same dataset, the proposed method can obtain an equally satisfactory classification accuracy while the feature amount is reduced by 61–95%. In particular, the classification accuracy of the interictal and normal EEG can reach 99%.

Hybrid Features Extraction for Adaptive Face Images Retrieval

2020 ◽  
Vol 11 (1) ◽  
pp. 17-26 ◽  
Author(s):  
Adel Alti
Keyword(s):  
Feature Extraction ◽  
Recognition Accuracy ◽  
Face Image ◽  
Human Face ◽  
Gradient Vector ◽  
Facial Image ◽  
Hybrid Features ◽  
Face Images ◽  
Hybrid Feature Extraction ◽  
Face Emotion Recognition

Existing methods of face emotion recognition have been limited in performance in terms of recognition accuracy and execution time. It is highly important to use efficient techniques for improving this performance. In this article, the authors present an automatic facial image retrieval combining the advantages of color normalization by texture estimators with the gradient vector. Starting from a query face image, an efficient algorithm for human face by hybrid feature extraction provides very interesting results.

scholarly journals Performance Measures of Respiratory Disease Classification using Deep Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 884-892
Author(s):  
G. Rama Janani
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Performance Measures ◽  
Respiratory Infections ◽  
Respiratory Illness ◽  
Extraction Methods ◽  
Disease Classification ◽  
Deep Cnn

The paper is based on classification of respiratory illness like covid 19 and pneumonia by using deep learning. The symptoms of COVID-19 and pneumonia are similar. Due to this, it is often difficult to identify what is causing your condition without being tested for COVID-19 or other respiratory infections. To find out how COVID-19 and pneumonia differs from one another, this paper presents that a novel Convolutional Neural Network in Tensor Flow and Keras based Covid-19 pneumonia classification. The proposed system supported implements CNN using Pneumonia images to classify the Covid-19, normal, pneumonia. The knowledge from these studies can potentially help in diagnosis of the concerned disease. It is predicted that the success of the anticipated results will increase if the CNN method is supported by adding extra feature extraction methods for classifying covid-19 and pneumonia successfully thereby improving the efficacy and potential of using deep CNN to pictures.

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