Electrocardiogram (ECG) Feature Extraction plays a significant role in diagnosing most of the cardiac diseases. ECG is a non-linear, nonstationary signal. It is used for the primary diagnosis of heart abnormalities. The ECG signals were taken from MIT-BIH arrhythmias database for analysis. Noises in the ECG signal such as powerline interference, baseline wandering and muscle noises were removed using bandpass filter. Different statistical and morphological features were extracted for both normal as well as abnormal cases. These features include R-R interval, heart rate arithmetic mean, median, variance, skewness, kurtosis etc. The values of the feature vector reveal the information regarding status of cardiac health. Platform used for implementation is MATLAB. This paper includes comparative analysis of various transform-based techniques. For differentiating normal and abnormal signals, we have used KNN classifier. We have achieved an accuracy of 86.95%, sensitivity of 87.09% and specificity of 86.66% for 60% training dataset using this classifier.
A novel deep dictionary learning and coding technique to detect plant diseases based on leaf image proposed through this paper. The two concepts of Deep learning & Dictionary Learning is combined here. The proposed deep dictionary learning DDLCN framework amalgamate the advantages of both dictionary learning and deep learning methods. In this dictionary learning and coding layer, which can fill-ins the convolution layer in the standard deep learning architectures. Along with DDLCN we proposed DDLCN for 2 layers and 3 layers. Specifically, DDLCN-2 uses fusion of dictionary learning and codinglayers: the first layer aims to learn a dictionary to represent the input image and the second layer target to take in a dictionary to represent the activated atoms in the first layer.
A web based online application is built to detect the diseases present on plant leaves. This will help farmer/end user to find out which diseases are present on that leaves. In older times, this task requires much time & resources to do the detection. A approach has of DDLCN has been taken in this project which will help to find out the best match features. In this approach. I have taken up to 3 layers of DDLCN which will filter the most match features of the diseases present on plant leaves. The proposed DDLCN combines the two things that is deep learning & dictionary learning. Deep learning which the branch of AI. It has a structures inspired by the human brain. It has the artificial neural networks which helps to train the data. Now taking about the dictionary learning which has a literal meaning having large number of data set. Dictionary learning is also called as sparse representation which will help to arrange the data in proper manner and without wasting the storage. Because it will only count the non zeros numbers present in the sparse matrix.
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