scholarly journals An Automated ECG Beat Classification System Using Deep Neural Networks with an Unsupervised Feature Extraction Technique

2019 ◽  
Vol 9 (14) ◽  
pp. 2921 ◽  
Author(s):  
Siti Nurmaini ◽  
Radiyati Umi Partan ◽  
Wahyu Caesarendra ◽  
Tresna Dewi ◽  
Muhammad Naufal Rahmatullah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Feature Extraction ◽  
Feature Selection ◽  
Deep Neural Networks ◽  
Machine Learning Technique ◽  
Proposed Model ◽  
Learning Technique ◽  
Processing Steps ◽  
Unsupervised Feature Extraction

An automated classification system based on a Deep Learning (DL) technique for Cardiac Disease (CD) monitoring and detection is proposed in this paper. The proposed DL architecture is divided into Deep Auto-Encoders (DAEs) as an unsupervised form of feature learning and Deep Neural Networks (DNNs) as a classifier. The objective of this study is to improve on the previous machine learning technique that consists of several data processing steps such as feature extraction and feature selection or feature reduction. It is also noticed that the previously used machine learning technique required human interference and expertise in determining robust features, yet was time-consuming in the labeling and data processing steps. In contrast, DL enables an embedded feature extraction and feature selection in DAEs pre-training and DNNs fine-tuning process directly from raw data. Hence, DAEs is able to extract high-level of features not only from the training data but also from unseen data. The proposed model uses 10 classes of imbalanced data from ECG signals. Since it is related to the cardiac region, abnormality is usually considered for an early diagnosis of CD. In order to validate the result, the proposed model is compared with the shallow models and DL approaches. Results found that the proposed method achieved a promising performance with 99.73% accuracy, 91.20% sensitivity, 93.60% precision, 99.80% specificity, and a 91.80% F1-Score. Moreover, both the Receiver Operating Characteristic (ROC) curve and the Precision-Recall (PR) curve from the confusion matrix showed that the developed model is a good classifier. The developed model based on unsupervised feature extraction and deep neural network is ready to be used on a large population before its installation for clinical usage.

scholarly journals Detection of Breast Cancer in the Bio Medical Image using Supervised Machine Learning

2019 ◽  
Vol 9 (2) ◽  
pp. 1140-1142
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Feature Extraction ◽  
Medical Image ◽  
Image Sensors ◽  
Supervised Machine Learning ◽  
Machine Learning Technique ◽  
Biomedical Image ◽  
Learning Technique ◽  
Learning Concept

The detection of disease for diagnosis in the medical image becomes easier, when the machine learning concept is used. In this article, the Breast cancer is detected from the biomedical image using supervised machine learning technique. The bio medical image is acquired from the image sensors and that acquired image is pre processed for further processing. From that pre-processed image, the object detection is performed. The next processes are segmentation and feature extraction. Finally, the supervised learning is implemented in the image for image classification. With the help of machine learning, the different cases of cells are also detected, differentiated and spotted for the further diagnosis

scholarly journals Corn Leaf Disease Detection with Pertinent Feature Selection Model Using Machine Learning Technique with Efficient Spot Tagging Model

2021 ◽  
Vol 35 (6) ◽  
pp. 477-482
Author(s):  
Daneshwari Ashok Noola ◽  
Dayananda Rangapura Basavaraju
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Classification Systems ◽  
Selection Model ◽  
Plant Phenotyping ◽  
Disease Detection ◽  
Machine Learning Technique ◽  
Leaf Disease ◽  
Learning Technique ◽  
Pertinent Feature

Crop diseases constitute a substantial threat to food safety but, due to the lack of a critical basis, their rapid identification in many parts of the world is challenging. The development of accurate techniques in the field of image categorization based on leaves produced excellent results. Plant phenotyping for plant growth monitoring is an important aspect of plant characterization. Early detection of leaf diseases is crucial for efficient crop output in agriculture. Pests and diseases cause crop harm or destruction of a section of the plant, leading to lower food productivity. In addition, in a number of less-developed countries, awareness of pesticide management and control, as well as diseases, is limited. Some of the main reasons for decreasing food production are toxic diseases, poor disease control and extreme climate changes. The quality of farm crops may be influenced by bacterial spot, late blight, septoria and curved yellow leaf diseases. Because of automatic leaf disease classification systems, action is easy after leaf disease signs are detected. Applying image processing and machine learning methodologies, this research offers an efficient Spot Tagging Leaf Disease Detection with Pertinent Feature Selection Model using Machine Learning Technique (SPLDPFS-MLT). Different diseases deplete chlorophyll in leaves generating dark patches on the surface of the leaf. Machine learning algorithms can be used to identify image pre-processing, image segmentation, feature extraction and classification. Compared with traditional models, the proposed model shows that the model performance is better than those existing.

scholarly journals Motor imagery classification in Brain computer interface (BCI) based on EEG signal by using machine learning technique

2019 ◽  
Vol 8 (1) ◽  
pp. 269-275 ◽  
Author(s):  
N. E. Md Isa ◽  
A. Amir ◽  
M. Z. Ilyas ◽  
M. S. Razalli
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Logistic Regression ◽  
Motor Imagery ◽  
Naive Bayes ◽  
Brain Computer Interface ◽  
Naïve Bayes ◽  
Computer Interface ◽  
Machine Learning Technique ◽  
Learning Technique

This paper focuses on classification of motor imagery in Brain Computer Interface (BCI) by using classifiers from machine learning technique. The BCI system consists of two main steps which are feature extraction and classification. The Fast Fourier Transform (FFT) features is extracted from the electroencephalography (EEG) signals to transform the signals into frequency domain. Due to the high dimensionality of data resulting from the feature extraction stage, the Linear Discriminant Analysis (LDA) is used to minimize the number of dimension by finding the feature subspace that optimizes class separability. Five classifiers: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes, Decision Tree and Logistic Regression are used in the study. The performance was tested by using Dataset 1 from BCI Competition IV which consists of imaginary hand and foot movement EEG data. As a result, SVM, Logistic Regression and Naïve Bayes classifier achieved the highest accuracy with 89.09% in AUC measurement.

scholarly journals A Research on Deep Learning Advance for Landslide Classification using Convolutional Neural Networks

2019 ◽  
Vol 8 (6S4) ◽  
pp. 903-906
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Feature Extraction ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Data Set ◽  
Proposed Model

Landslides can easily be tragic to human life and property. Increase in the rate of human settlement in the mountains has resulted in safety concerns. Landslides have caused economic loss between 1-2% of the GDP in many developing countries. In this study, we discuss a deep learning approach to detect landslides. Convolutional Neural Networks are used for feature extraction for our proposed model. As there was no source of an exact and precise data set for feature extraction, therefore, a new data set was built for testing the model. We have tested and compared this work with our proposed model and with other machine-learning algorithms such as Logistic Regression, Random Forest, AdaBoost, K-Nearest Neighbors and Support Vector Machine. Our proposed deep learning model produces a classification accuracy of 96.90% outperforming the classical machine-learning algorithms.

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