scholarly journals 10.21162/PAKJAS/21.1541

2021 ◽  
Vol 58 (04) ◽  
pp. 1395-1403
Author(s):  
Ayesha Hakim
Keyword(s):  
Population Density ◽  
Euclidean Distance ◽  
Bird Species ◽  
Similarity Measures ◽  
Principal Component ◽  
Cosine Similarity ◽  
Mutual Relationship ◽  
Frequency Representation ◽  
Novel Method ◽  
Fold Cross Validation

Acoustic recordings of birds have been used by conservationists and ecologists to determine the population density and bio- diversity of bird species in a region. However, it is hard to analyze and visualize the presence/absence of a specific bird species by aurally hearing these recordings even by an expert bird song specialist. In this paper, we present a computational tool to cluster and recognize bird species based on their sounds and visualize relationships of within-species and between-species sounds based on their similarity measures. The tool has been evaluated on two datasets of varying complexity containing acoustic recordings of eleven birds’ songs and calls using various similarity measures. Principal Component Analysis (PCA) was used for feature selection. Euclidean distance, Mahalanobis distance, and cosine similarity among features was used for pair-wise similarity calculation. The results of similarity measures have been compared using 3-fold cross-validation and validated by spectrograms patterns obtained from frequency representation of acoustic recordings of the selected birds’ songs and calls. Cosine similarity performed better to measure underlying patterns of birds’ sounds and identify mutual relationship among species. It was concluded that the proposed tool can be used as a novel method for conversationalists, ecologists, ornithologists, and evolutionary scientists as well as tourists and bird watchers to recognize different birds’ species, study their mutual relationship, locate the area with highest population density, estimating the predators, and biodiversity in a specific region.

Wheat planted area detection from the MODIS NDVI time series classification using the nearest neighbour method calculated by the Euclidean distance and cosine similarity measures

2019 ◽  
Vol 35 (13) ◽  
pp. 1400-1414 ◽  
Author(s):  
Miriam Rodrigues da Silva ◽  
Osmar Abílio de Carvalho ◽  
Renato Fontes Guimarães ◽  
Roberto Arnaldo Trancoso Gomes ◽  
Cristiano Rosa Silva
Keyword(s):  
Time Series ◽  
Euclidean Distance ◽  
Similarity Measures ◽  
Cosine Similarity ◽  
Nearest Neighbour ◽  
Time Series Classification ◽  
Modis Ndvi ◽  
Ndvi Time Series ◽  
Cosine Similarity Measures

scholarly journals Normalizing clinical terms using learned edit distance patterns

2015 ◽  
Vol 23 (2) ◽  
pp. 380-386 ◽  
Author(s):  
Rohit J Kate
Keyword(s):  
Similarity Measures ◽  
Cosine Similarity ◽  
Training Dataset ◽  
The Novel ◽  
Clinical Text ◽  
Unified Medical Language System ◽  
Normalization Methods ◽  
Novel Method ◽  
Semantic Types ◽  
F Measure

Abstract Background Variations of clinical terms are very commonly encountered in clinical texts. Normalization methods that use similarity measures or hand-coded approximation rules for matching clinical terms to standard terminologies have limited accuracy and coverage. Materials and Methods In this paper, a novel method is presented that automatically learns patterns of variations of clinical terms from known variations from a resource such as the Unified Medical Language System (UMLS). The patterns are first learned by computing edit distances between the known variations, which are then appropriately generalized for normalizing previously unseen terms. The method was applied and evaluated on the disease and disorder mention normalization task using the dataset of SemEval 2014 and compared with the normalization ability of the MetaMap system and a method based on cosine similarity. Results Excluding the mentions that already exactly match in UMLS and the training dataset, the proposed method obtained 64.7% accuracy on the rest of the test dataset. The accuracy was calculated as the number of mentions that correctly matched the gold-standard concept unique identifiers (CUIs) or correctly matched to be without a CUI. In comparison, MetaMap’s accuracy was 41.9% and cosine similarity’s accuracy was 44.6%. When only the output CUIs were evaluated, the proposed method obtained 54.4% best F -measure (at 92.1% precision and 38.6% recall) while MetaMap obtained 19.4% best F -measure (at 38.0% precision and 13.0% recall) and cosine similarity obtained 38.1% best F -measure (at 70.3% precision and 26.1% recall). Conclusions The novel method was found to perform much better than the MetaMap system and the cosine similarity based method in normalizing disease mentions in clinical text that did not exactly match in UMLS. The method is also general and can be used for normalizing clinical terms of other semantic types as well.

scholarly journals Content-Based Estimation of Brain MRI Tilt in Three Orthogonal Directions

2021 ◽  
Author(s):  
Pooja Prabhu ◽  
A. K. Karunakar ◽  
Sanjib Sinha ◽  
N. Mariyappa ◽  
G. K. Bhargava ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Mri ◽  
Similarity Measures ◽  
Principal Component ◽  
Brain Anatomy ◽  
Morphological Operations ◽  
Mr Images ◽  
Tilt Correction ◽  
Brain Mr Images ◽  
The Brain

AbstractIn a general scenario, the brain images acquired from magnetic resonance imaging (MRI) may experience tilt, distorting brain MR images. The tilt experienced by the brain MR images may result in misalignment during image registration for medical applications. Manually correcting (or estimating) the tilt on a large scale is time-consuming, expensive, and needs brain anatomy expertise. Thus, there is a need for an automatic way of performing tilt correction in three orthogonal directions (X, Y, Z). The proposed work aims to correct the tilt automatically by measuring the pitch angle, yaw angle, and roll angle in X-axis, Z-axis, and Y-axis, respectively. For correction of the tilt around the Z-axis (pointing to the superior direction), image processing techniques, principal component analysis, and similarity measures are used. Also, for correction of the tilt around the X-axis (pointing to the right direction), morphological operations, and tilt correction around the Y-axis (pointing to the anterior direction), orthogonal regression is used. The proposed approach was applied to adjust the tilt observed in the T1- and T2-weighted MR images. The simulation study with the proposed algorithm yielded an error of 0.40 ± 0.09°, and it outperformed the other existing studies. The tilt angle (in degrees) obtained is ranged from 6.2 ± 3.94, 2.35 ± 2.61, and 5 ± 4.36 in X-, Z-, and Y-directions, respectively, by using the proposed algorithm. The proposed work corrects the tilt more accurately and robustly when compared with existing studies.

scholarly journals Hyperspectral Image Classification with Localized Graph Convolutional Filtering

2021 ◽  
Vol 13 (3) ◽  
pp. 526
Author(s):  
Shengliang Pu ◽  
Yuanfeng Wu ◽  
Xu Sun ◽  
Xiaotong Sun
Keyword(s):  
Hyperspectral Image ◽  
Principal Component ◽  
Representation Learning ◽  
Classification Performance ◽  
Hyperspectral Data ◽  
Spectral Graph Theory ◽  
Feature Reduction ◽  
Graph Representation ◽  
Novel Method ◽  
Local Graph

The nascent graph representation learning has shown superiority for resolving graph data. Compared to conventional convolutional neural networks, graph-based deep learning has the advantages of illustrating class boundaries and modeling feature relationships. Faced with hyperspectral image (HSI) classification, the priority problem might be how to convert hyperspectral data into irregular domains from regular grids. In this regard, we present a novel method that performs the localized graph convolutional filtering on HSIs based on spectral graph theory. First, we conducted principal component analysis (PCA) preprocessing to create localized hyperspectral data cubes with unsupervised feature reduction. These feature cubes combined with localized adjacent matrices were fed into the popular graph convolution network in a standard supervised learning paradigm. Finally, we succeeded in analyzing diversified land covers by considering local graph structure with graph convolutional filtering. Experiments on real hyperspectral datasets demonstrated that the presented method offers promising classification performance compared with other popular competitors.

scholarly journals Modelling the Impacts of Habitat Changes on the Population Density of Eurasian Skylark (Alauda arvensis) Based on Its Landscape Preferences

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 306
Author(s):  
Nándor Csikós ◽  
Péter Szilassi
Keyword(s):  
Population Density ◽  
Land Cover ◽  
Arable Land ◽  
Bird Species ◽  
Landscape Patterns ◽  
Percentage Error ◽  
Alauda Arvensis ◽  
Rural Landscapes ◽  
Farmland Bird ◽  
Shape Complexity

The dramatic decline of the abundance of farmland bird species can be related to the level of land-use intensity or the land-cover heterogeneity of rural landscapes. Our study area in central Europe (Hungary) included 3049 skylark observation points and their 600 m buffer zones. We used a very detailed map (20 × 20 m minimum mapping unit), the Hungarian Ecosystem Basemap, as a land-cover dataset for the calculation of three landscape indices: mean patch size (MPS), mean fractal dimension (MFRACT), and Shannon diversity index (SDI) to describe the landscape structure of the study areas. Generalized linear models were used to analyze the effect of land-cover types and landscape patterns on the abundance of the Eurasian skylark (Alauda arvensis). According to our findings, the proportions of arable land, open sand steppes, closed grassland patches, and shape complexity and size characteristics of these land cover patches have a positive effect on skylark abundance, while the SDI was negatively associated with the skylark population. On the basis of the used statistical model, the abundance density (individuals/km*) of skylarks could be estimated with 37.77% absolute percentage error and 2.12 mean absolute error. We predicted the skylark population density inside the Natura 2000 Special Protected Area of Hungary which is 0–6 individuals/km* and 23746 ± 8968 skylarks. The results can be implemented for the landscape management of rural landscapes, and the method used are adaptable for the density estimation of other farmland bird species in rural landscapes. According to our findings, inside the protected areas should increase the proportion, the average size and shape complexity of arable land, salt steppes and meadows, and closed grassland land cover patches.

NUMERICAL SIMULATION AND VALIDITY OF A NOVEL METHOD FOR THE PREDICTION OF ARTERY STENOSIS VIA INPUT IMPEDANCE AND SUPPORT VECTOR MACHINE

2014 ◽  
Vol 26 (01) ◽  
pp. 1450002 ◽  
Author(s):  
Hanguang Xiao
Keyword(s):  
Support Vector Machine ◽  
Input Impedance ◽  
Recursive Algorithm ◽  
Artery Stenosis ◽  
Support Vector ◽  
Arterial Tree ◽  
Novel Method ◽  
The Mean ◽  
Fold Cross Validation ◽  
Systemic Arterial Tree

The early detection and intervention of artery stenosis is very important to reduce the mortality of cardiovascular disease. A novel method for predicting artery stenosis was proposed by using the input impedance of the systemic arterial tree and support vector machine (SVM). Based on the built transmission line model of a 55-segment systemic arterial tree, the input impedance of the arterial tree was calculated by using a recursive algorithm. A sample database of the input impedance was established by specifying the different positions and degrees of artery stenosis. A SVM prediction model was trained by using the sample database. 10-fold cross-validation was used to evaluate the performance of the SVM. The effects of stenosis position and degree on the accuracy of the prediction were discussed. The results showed that the mean specificity, sensitivity and overall accuracy of the SVM are 80.2%, 98.2% and 89.2%, respectively, for the 50% threshold of stenosis degree. Increasing the threshold of the stenosis degree from 10% to 90% increases the overall accuracy from 82.2% to 97.4%. Increasing the distance of the stenosis artery from the heart gradually decreases the overall accuracy from 97.1% to 58%. The deterioration of the stenosis degree to 90% increases the prediction accuracy of the SVM to more than 90% for the stenosis of peripheral artery. The simulation demonstrated theoretically the feasibility of the proposed method for predicting artery stenosis via the input impedance of the systemic arterial tree and SVM.

A Novel Method for Gender and Age Detection Based on EEG Brain Signals

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Haitham Issa ◽  
Sali Issa ◽  
Wahab Shah
Keyword(s):  
Cross Validation ◽  
Image Feature ◽  
Emotional States ◽  
Time Frequency ◽  
Brain Signals ◽  
Average Accuracy ◽  
Gender And Age ◽  
Novel Method ◽  
Fold Cross Validation ◽  
Validation Strategy

This paper presents a new gender and age classification system based on Electroencephalography (EEG) brain signals. First, Continuous Wavelet Transform (CWT) technique is used to get the time-frequency information of only one EEG electrode for eight distinct emotional states instead of the ordinary neutral or relax states. Then, sequential steps are implemented to extract the improved grayscale image feature. For system evaluation, a three-fold-cross validation strategy is applied to construct four different classifiers. The experimental test shows that the proposed extracted feature with Convolutional Neural Network (CNN) classifier improves the performance of both gender and age classification, and achieves an average accuracy of 96.3% and 89% for gender and age classification, respectively. Moreover, the ability to predict human gender and age during the mood of different emotional states is practically approved.

Measuring fiber orientations in nonwoven web images using corner detection by Bézier fitting curves

2017 ◽  
Vol 88 (18) ◽  
pp. 2120-2131 ◽  
Author(s):  
Jue Hou ◽  
Bugao Xu ◽  
Hanchao Gao ◽  
RongWu Wang
Keyword(s):  
Curve Fitting ◽  
Fiber Orientation ◽  
Euclidean Distance ◽  
High Accuracy ◽  
Corner Detection ◽  
Bézier Curve ◽  
Corner Points ◽  
Novel Method ◽  
Web Images ◽  
Orientation Statistics

This paper describes a novel method for measuring fiber orientations in nonwoven web images by using Bézier fitting curves to detect corners of fiber edges and to separate crossing fiber edges. First, the Canny detector was adopted to extract fiber edges. Second, Bézier curve fitting was used to fit each fiber edge for calculating the curvature of every point on the edge. Third, corner points were detected by locating points where the curvatures were minimal on various edges and below the threshold to divide edges into segments for orientation calculations. Last, a formula calculating the fiber orientation statistics based on the Euclidean distance was established. The experiment results demonstrated that the proposed method is robust for analyzing different nonwoven web images, and has a high accuracy for corner detection and fiber orientation calculation.

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