scholarly journals Texture Classification Using Spectral Entropy of Acoustic Signal Generated by a Human Echolocator

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 963
Author(s):  
Raja Abdullah ◽  
Saleh ◽  
Syed Abdul Rahman ◽  
Zamri ◽  
Rashid
Keyword(s):  
Acoustic Signal ◽  
Classification Scheme ◽  
Nearest Neighbor ◽  
Sound Propagation ◽  
Texture Classification ◽  
Synthetic Data ◽  
Spectral Entropy ◽  
K Nearest Neighbor ◽  
Echo Signal ◽  
Classifier Performance

Human echolocation is a biological process wherein the human emits a punctuated acoustic signal, and the ear analyzes the echo in order to perceive the surroundings. The peculiar acoustic signal is normally produced by clicking inside the mouth. This paper utilized this unique acoustic signal from a human echolocator as a source of transmitted signal in a synthetic human echolocation technique. Thus, the aim of the paper was to extract information from the echo signal and develop a classification scheme to identify signals reflected from different textures at various distance. The scheme was based on spectral entropy extracted from Mel-scale filtering output in the Mel-frequency cepstrum coefficient of a reflected echo signal. The classification process involved data mining, features extraction, clustering, and classifier validation. The reflected echo signals were obtained via an experimental setup resembling a human echolocation scenario, configured for synthetic data collection. Unlike in typical speech signals, extracted entropy from the formant characteristics was likely not visible for the human mouth-click signals. Instead, multiple peak spectral features derived from the synthesis signal of the mouth-click were assumed as the entropy obtained from the Mel-scale filtering output. To realize the classification process, K-means clustering and K-nearest neighbor processes were employed. Moreover, the impacts of sound propagation toward the extracted spectral entropy used in the classification outcome were also investigated. The outcomes of the classifier performance herein indicated that spectral entropy is essential for human echolocation.

Dimensionality Reduction with Unsupervised Feature Selection and Applying Non-Euclidean Norms for Classification Accuracy

2011 ◽  
pp. 91-109
Author(s):  
Amit Saxena ◽  
John Wang
Keyword(s):  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Fitness Function ◽  
Synthetic Data ◽  
Feature Subset Selection ◽  
Second Phase ◽  
Data Sets ◽  
Feature Subset ◽  
K Nearest Neighbor ◽  
Two Phase

This paper presents a two-phase scheme to select reduced number of features from a dataset using Genetic Algorithm (GA) and testing the classification accuracy (CA) of the dataset with the reduced feature set. In the first phase of the proposed work, an unsupervised approach to select a subset of features is applied. GA is used to select stochastically reduced number of features with Sammon Error as the fitness function. Different subsets of features are obtained. In the second phase, each of the reduced features set is applied to test the CA of the dataset. The CA of a data set is validated using supervised k-nearest neighbor (k-nn) algorithm. The novelty of the proposed scheme is that each reduced feature set obtained in the first phase is investigated for CA using the k-nn classification with different Minkowski metric i.e. non-Euclidean norms instead of conventional Euclidean norm (L2). Final results are presented in the paper with extensive simulations on seven real and one synthetic, data sets. It is revealed from the proposed investigation that taking different norms produces better CA and hence a scope for better feature subset selection.

Pulmonary Acoustic Signal Classification Using Autoregressive Coefficients and k-Nearest Neighbor

2014 ◽  
Vol 591 ◽  
pp. 211-214 ◽  
Author(s):  
Rajkumar Palaniappan ◽  
Kenneth Sundaraj ◽  
Sebastian Sundaraj ◽  
N. Huliraj ◽  
S.S. Revadi ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Bandpass Filter ◽  
Nearest Neighbor ◽  
Pathological Condition ◽  
Confusion Matrix ◽  
Acoustic Signals ◽  
Signal Classification ◽  
K Nearest Neighbor ◽  
Knn Classifier ◽  
Classifier Performance

— Pulmonary acoustic signals provide important information of the condition of the respiratory system. It can be used to assist medical professionals as an alternative diagnosis tool. In this paper, we intend to discriminate between normal (without any pathological condition), Airway Obstruction (AO) pathology and Interstitial lung disease (ILD) pathology using pulmonary acoustic signals. The proposed method filters the heart sounds and other artifacts using a butterworth bandpass filter and windowed to 256 samples per segment. The autoregressive coefficients (AR coefficients) were extracted as features from the pulmonary acoustic signals. The extracted features are distinguished using k-nearest neighbor (k-nn) classifier. The classifier performance is analysed by using confusion matrix technique. A mean classification accuracy of 96.12% was reported for the proposed method. The performance analysis of the knn classifier using confusion matrix revealed that normal, AO and ILD pathology are classified at 94.36%, 95.18% and 94.68% classification accuracy respectively. The analysis reveals that the proposed method performs better in distinguishing between the normal, AO and ILD.Keywords—Respiratorysound,ARcoefficients,k-nearestneighbor,confusionmatrix

Clustering Based on a Novel Density Estimation Method

2013 ◽  
Vol 748 ◽  
pp. 590-594
Author(s):  
Li Liao ◽  
Yong Gang Lu ◽  
Xu Rong Chen
Keyword(s):  
Density Estimation ◽  
Nearest Neighbor ◽  
Mean Shift ◽  
Estimation Method ◽  
Synthetic Data ◽  
Real Data ◽  
Data Sets ◽  
Clustering Methods ◽  
K Nearest Neighbor ◽  
Data Set

We propose a novel density estimation method using both the k-nearest neighbor (KNN) graph and the potential field of the data points to capture the local and global data distribution information respectively. The clustering is performed based on the computed density values. A forest of trees is built using each data point as the tree node. And the clusters are formed according to the trees in the forest. The new clustering method is evaluated by comparing with three popular clustering methods, K-means++, Mean Shift and DBSCAN. Experiments on two synthetic data sets and one real data set show that our approach can effectively improve the clustering results.

scholarly journals Scattering Transform Framework for Unmixing of Hyperspectral Data

2019 ◽  
Vol 11 (23) ◽  
pp. 2868 ◽  
Author(s):  
Zeng ◽  
Ritz ◽  
Zhao ◽  
Lan
Keyword(s):  
Nearest Neighbor ◽  
Hyperspectral Image ◽  
Three Dimensional ◽  
Synthetic Data ◽  
Low Frequency ◽  
Hyperspectral Data ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Scattering Transform ◽  
Scattering Transforms

The scattering transform, which applies multiple convolutions using known filters targeting different scales of time or frequency, has a strong similarity to the structure of convolution neural networks (CNNs), without requiring training to learn the convolution filters, and has been used for hyperspectral image classification in recent research. This paper investigates the application of the scattering transform framework to hyperspectral unmixing (STFHU). While state-of-the-art research on unmixing hyperspectral data utilizing scattering transforms is limited, the proposed end-to-end method applies pixel-based scattering transforms and preliminary three-dimensional (3D) scattering transforms to hyperspectral images in the remote sensing scenario to extract feature vectors, which are then trained by employing the regression model based on the k-nearest neighbor (k-NN) to estimate the abundance of maps of endmembers. Experiments compare performances of the proposed algorithm with a series of existing methods in quantitative terms based on both synthetic data and real-world hyperspectral datasets. Results indicate that the proposed approach is more robust to additive noise, which is suppressed by utilizing the rich information in both high-frequency and low-frequency components represented by the scattering transform. Furthermore, the proposed method achieves higher accuracy for unmixing using the same amount of training data with all comparative approaches, while achieving equivalent performance to the best performing CNN method but using much less training data.

scholarly journals Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review

Big Data ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 221-248 ◽  
Author(s):  
Haneen Arafat Abu Alfeilat ◽  
Ahmad B.A. Hassanat ◽  
Omar Lasassmeh ◽  
Ahmad S. Tarawneh ◽  
Mahmoud Bashir Alhasanat ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Distance Measure ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Classifier ◽  
Classifier Performance ◽  
Neighbor Classifier

scholarly journals Automated Hertbeat Abnormality Detection Using Realtime R-Assisted Lightweight Feature Extraction Algorithm

2018 ◽  
Author(s):  
Diptangshu Pandit ◽  
Chengyu Liu ◽  
Li Zhang
Keyword(s):  
Feature Extraction ◽  
Classification Scheme ◽  
Nearest Neighbor ◽  
Abnormality Detection ◽  
K Nearest Neighbor ◽  
Heartbeat Classification ◽  
Ecg Signals ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Electrocardiogram Ecg

Automated Electrocardiogram (ECG) processing is an important technique which helps in identifying abnormalities in the heart before any formal diagnosis. This research presents a real-time and lightweight R-assisted feature extraction algorithm and a heartbeat classification scheme which achieves highly accurate abnormality detection. In the proposed algorithm, we extract fifteen features from each heartbeat taken from raw Lead-II ECG signals. The features carry medically valuable information such as locations, amplitude and energy of ECG waves (P, Q, R, S, T waves) which are then used for detection of any abnormality that might be present in the heartbeat using various classification algorithms. We have used four popular databases from Physionet and extracted ten thousand ECG signals from each for training the models and benchmarking results. Four classification models i.e. Naïve Bays, k-Nearest Neighbor, Neural Network, Decision Tree were used for abnormality detection validating the efficiency of the system.

scholarly journals A Novel Approach Based on Decreased Dimension and Reduced Gray Level Range Matrix Features for Stone Texture Classification

2017 ◽  
Vol 7 (5) ◽  
pp. 2502 ◽  
Author(s):  
G. S. N. Murthy ◽  
Srininvasa Rao. V ◽  
T. Veerraju
Keyword(s):  
Nearest Neighbor ◽  
Texture Classification ◽  
Texture Features ◽  
Significant Feature ◽  
Gray Level ◽  
Texture Image ◽  
K Nearest Neighbor ◽  
Classification Rate ◽  
Novel Approach ◽  
Occurrence Matrix

The human eye can easily identify the type of textures in flooring of the houses and in the digital images visually.  In this work, the stone textures are grouped into four categories. They are bricks, marble, granite and mosaic. A novel approach is developed for decreasing the dimension of stone image and for reducing the gray level range of the image without any loss of significant feature information. This model is named as “Decreased Dimension and Reduced Gray level Range Matrix (DDRGRM)” model. The DDRGRM model consists of 3 stages.  In stage 1, each 5×5 sub dimension of the stone image is reduced into 2×2 sub dimension without losing any important qualities, primitives, and any other local stuff.  In stage 2, the gray level of the image is reduced from 0-255 to 0-4 by using fuzzy concepts.  In stage 3, Co-occurrence Matrix (CM) features are derived from the DDRGRM model of the stone image for stone texture classification.  Based on the feature set values, a user defined algorithm is developed to classify the stone texture image into one of the 4 categories i.e. Marble, Brick, Granite and Mosaic. The proposed method is tested by using the K-Nearest Neighbor Classification algorithm with the derived texture features.  To prove the efficiency of the proposed method, it is tested on different stone texture image databases.  The proposed method resulted in high classification rate when compared with the other existing methods.

The Influence of Hubness on NN-Descent

2019 ◽  
Vol 28 (06) ◽  
pp. 1960002 ◽  
Author(s):  
Brankica Bratić ◽  
Michael E. Houle ◽  
Vladimir Kurbalija ◽  
Vincent Oria ◽  
Miloš Radovanović
Keyword(s):  
Nearest Neighbor ◽  
Synthetic Data ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Accurate Approximation ◽  
K Nearest Neighbor ◽  
Major Drawback ◽  
Neighbor Graph ◽  
Nearest Neighbor Graph ◽  
The Cost

The K-nearest neighbor graph (K-NNG) is a data structure used by many machine-learning algorithms. Naive computation of the K-NNG has quadratic time complexity, which in many cases is not efficient enough, producing the need for fast and accurate approximation algorithms. NN-Descent is one such algorithm that is highly efficient, but has a major drawback in that K-NNG approximations are accurate only on data of low intrinsic dimensionality. This paper represents an experimental analysis of this behavior, and investigates possible solutions. Experimental results show that there is a link between the performance of NN-Descent and the phenomenon of hubness, defined as the tendency of intrinsically high-dimensional data to contain hubs – points with large in-degrees in the K-NNG. First, we explain how the presence of the hubness phenomenon causes bad NN-Descent performance. In light of that, we propose four NN-Descent variants to alleviate the observed negative inuence of hubs. By evaluating the proposed approaches on several real and synthetic data sets, we conclude that our approaches are more accurate, but often at the cost of higher scan rates.

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