scholarly journals Illegal Logging Detection Based on Acoustic Surveillance of Forest

2020 ◽  
Vol 10 (20) ◽  
pp. 7379
Author(s):  
Iosif Mporas ◽  
Isidoros Perikos ◽  
Vasilios Kelefouras ◽  
Michael Paraskevas
Keyword(s):  
Classification Accuracy ◽  
Recognition Accuracy ◽  
Classification Performance ◽  
Sound Recognition ◽  
Support Vector ◽  
Classification Methods ◽  
Illegal Logging ◽  
Decision Level ◽  
Audio Recordings ◽  
Acoustic Surveillance

In this article, we present a framework for automatic detection of logging activity in forests using audio recordings. The framework was evaluated in terms of logging detection classification performance and various widely used classification methods and algorithms were tested. Experimental setups, using different ratios of sound-to-noise values, were followed and the best classification accuracy was reported by the support vector machine algorithm. In addition, a postprocessing scheme on decision level was applied that provided an improvement in the performance of more than 1%, mainly in cases of low ratios of sound-to-noise. Finally, we evaluated a late-stage fusion method, combining the postprocessed recognition results of the three top-performing classifiers, and the experimental results showed a further improvement of approximately 2%, in terms of absolute improvement, with logging sound recognition accuracy reaching 94.42% when the ratio of sound-to-noise was equal to 20 dB.

scholarly journals Fugl-Meyer hand motor imagination recognition for brain–computer interfaces using only fNIRS

2021 ◽  
Author(s):  
Chenguang Li ◽  
Hongjun Yang ◽  
Long Cheng
Keyword(s):  
Classification Accuracy ◽  
Near Infrared ◽  
Support Vector ◽  
Classification Methods ◽  
Experimental Paradigm ◽  
Functional Near Infrared Spectroscopy ◽  
Machine Method ◽  
Physiological Signal ◽  
Computer Interfaces ◽  
Mode Decomposition

AbstractAs a relatively new physiological signal of brain, functional near-infrared spectroscopy (fNIRS) is being used more and more in brain–computer interface field, especially in the task of motor imagery. However, the classification accuracy based on this signal is relatively low. To improve the accuracy of classification, this paper proposes a new experimental paradigm and only uses fNIRS signals to complete the classification task of six subjects. Notably, the experiment is carried out in a non-laboratory environment, and movements of motion imagination are properly designed. And when the subjects are imagining the motions, they are also subvocalizing the movements to prevent distraction. Therefore, according to the motor area theory of the cerebral cortex, the positions of the fNIRS probes have been slightly adjusted compared with other methods. Next, the signals are classified by nine classification methods, and the different features and classification methods are compared. The results show that under this new experimental paradigm, the classification accuracy of 89.12% and 88.47% can be achieved using the support vector machine method and the random forest method, respectively, which shows that the paradigm is effective. Finally, by selecting five channels with the largest variance after empirical mode decomposition of the original signal, similar classification results can be achieved.

scholarly journals Sentiment polarity classification of tweets using a extended dictionary

2018 ◽  
Vol 21 (62) ◽  
pp. 1
Author(s):  
Jorge E. Camargo ◽  
Vladimir Vargas-Calderon ◽  
Nelson Vargas ◽  
Liliana Calderón-Benavides
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Classification Performance ◽  
Semantic Relations ◽  
Support Vector ◽  
The Real ◽  
Polarity Classification ◽  
Real Academia ◽  
Word Definitions

With the purpose of classifying text based on its sentiment polarity (positive or negative), we proposed an extension of a 68,000 tweets corpus through the inclusion of word definitions from a dictionary of the Real Academia Espa\~{n}ola de la Lengua (RAE). A set of 28,000 combinations of 6 Word2Vec and support vector machine parameters were considered in order to evaluate how positively would affect the inclusion of a RAE's dictionary definitions classification performance. We found that such a corpus extension significantly improve the classification accuracy. Therefore, we conclude that the inclusion of a RAE's dictionary increases the semantic relations learned by Word2Vec allowing a better classification accuracy.

scholarly journals AN APPLICATION OF ROLL-INVARIANT POLARIMETRIC FEATURES FOR CROP CLASSIFICATION FROM MULTI-TEMPORAL RADARSAT-2 SAR DATA

2018 ◽  
Vol XLII-1 ◽  
pp. 451-456 ◽  
Author(s):  
M. Ustuner ◽  
F. B. Sanli ◽  
S. Abdikan ◽  
M. T. Esetlili ◽  
G. Bilgin
Keyword(s):  
Classification Accuracy ◽  
Alpha Angle ◽  
Test Site ◽  
Classification Performance ◽  
Support Vector ◽  
Observation Data ◽  
Data Set ◽  
Crop Classification ◽  
Sar Data ◽  
Spatio Temporal

<p><strong>Abstract.</strong> Crops are dynamically changing and time-critical in the growing season and therefore multitemporal earth observation data are needed for spatio-temporal monitoring of the crops. This study evaluates the impacts of classical roll-invariant polarimetric features such as entropy (H), anisotropy (A), mean alpha angle (<span style="text-decoration: overline">&amp;alpha;</span>) and total scattering power (SPAN) for the crop classification from multitemporal polarimetric SAR data. For this purpose, five different data set were generated as following: (1) H<span style="text-decoration: overline">&amp;alpha;</span>, (2) H<span style="text-decoration: overline">&amp;alpha;</span>Span, (3) H<span style="text-decoration: overline">&amp;alpha;</span>A, (4) H<span style="text-decoration: overline">&amp;alpha;</span>ASpan and (5) coherency [<i>T</i>] matrix. A time-series of four PolSAR data (Radarsat-2) were acquired as 13 June, 01 July, 31 July and 24 August in 2016 for the test site located in Konya, Turkey. The test site is covered with crops (maize, potato, summer wheat, sunflower, and alfalfa). For the classification of the data set, three different models were used as following: Support Vector Machines (SVMs), Random Forests (RFs) and Naive Bayes (NB). The experimental results highlight that H&amp;alpha;ASpan (91.43<span class="thinspace"></span>% for SVM, 92.25<span class="thinspace"></span>% for RF and 90.55<span class="thinspace"></span>% for NB) outperformed all other data sets in terms of classification performance, which explicitly proves the significant contribution of SPAN for the discrimination of crops. Highest classification accuracy was obtained as 92.25<span class="thinspace"></span>% by RF and H&amp;alpha;ASpan while lowest classification accuracy was obtained as 66.99<span class="thinspace"></span>% by NB and H&amp;alpha;. This experimental study suggests that roll-invariant polarimetric features can be considered as the powerful polarimetric components for the crop classification. In addition, the findings prove the added benefits of PolSAR data investigation by means of crop classification.</p>

scholarly journals EFFECT OF PANSHARPENED IMAGE ON SOME OF PIXEL BASED AND OBJECT BASED CLASSIFICATION ACCURACY

2016 ◽  
Vol XLI-B7 ◽  
pp. 235-239 ◽  
Author(s):  
P. Karakus ◽  
H. Karabork
Keyword(s):  
Spatial Resolution ◽  
Spectral Resolution ◽  
Classification Accuracy ◽  
Multispectral Image ◽  
Classification Method ◽  
Support Vector ◽  
Classification Methods ◽  
Panchromatic Image ◽  
Object Based ◽  
Spot 5

Classification is the most important method to determine type of crop contained in a region for agricultural planning. There are two types of the classification. First is pixel based and the other is object based classification method. While pixel based classification methods are based on the information in each pixel, object based classification method is based on objects or image objects that formed by the combination of information from a set of similar pixels. Multispectral image contains a higher degree of spectral resolution than a panchromatic image. Panchromatic image have a higher spatial resolution than a multispectral image. Pan sharpening is a process of merging high spatial resolution panchromatic and high spectral resolution multispectral imagery to create a single high resolution color image. The aim of the study was to compare the potential classification accuracy provided by pan sharpened image. In this study, SPOT 5 image was used dated April 2013. 5m panchromatic image and 10m multispectral image are pan sharpened. Four different classification methods were investigated: maximum likelihood, decision tree, support vector machine at the pixel level and object based classification methods. SPOT 5 pan sharpened image was used to classification sun flowers and corn in a study site located at Kadirli region on Osmaniye in Turkey. The effects of pan sharpened image on classification results were also examined. Accuracy assessment showed that the object based classification resulted in the better overall accuracy values than the others. The results that indicate that these classification methods can be used for identifying sun flower and corn and estimating crop areas.

Improving the classification performance with group lasso-based ranking method in high dimensional correlated data

2020 ◽  
Vol 19 (03) ◽  
pp. 2040009
Author(s):  
Abhijeet R Patil ◽  
Bong-Jin Choi ◽  
Sangjin Kim
Keyword(s):  
Classification Accuracy ◽  
Geometric Mean ◽  
Classification Performance ◽  
Group Lasso ◽  
Correlated Data ◽  
Support Vector ◽  
Cpg Sites ◽  
Highly Correlated ◽  
Selection Operator ◽  
Sensitivity Specificity

The high-throughput correlated DNA methylation (DNAmeth) dataset generated from Illumina Infinium Human Methylation 27 (IIHM 27K) BeadChip assay. In the DNAmeth data, there are several CpG sites for every gene, and these grouped CpG sites are highly correlated. Most of the current filtering-based ranking (FBR) methods do not consider the group correlation structures. Obtaining the significant features with the FBR methods and applying these features to the classifiers to attain the best classification accuracy in highly correlated DNAmeth data is a challenging task. In this research, we introduce a resampling of group least absolute shrinkage and selection operator (glasso) FBR method capable of ignoring the unrelated features in the data considering the group correlation among the features. The various classifiers, such as random forests (RF), Naive Bayes (NB), and support vector machines (SVM) with the significant CpGs obtained from the proposed resampling of group lasso-based ranking (RGLR) method helped to boost the classification accuracy. Through simulated and experimental prostate DNAmeth data, we showed that higher performance of accuracy, sensitivity, specificity, and geometric mean is achieved by ignoring the unimportant CpG sites through the RGLR method.

scholarly journals AGNES-SMOTE: An Oversampling Algorithm Based on Hierarchical Clustering and Improved SMOTE

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xin Wang ◽  
Yue Yang ◽  
Mingsong Chen ◽  
Qin Wang ◽  
Qin Qin ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Hierarchical Clustering ◽  
Classification Accuracy ◽  
Classification Performance ◽  
Support Vector ◽  
Seed Sample ◽  
Imbalanced Datasets ◽  
Centroid Method ◽  
Svm Classification ◽  
Sampling Process

Aiming at low classification accuracy of imbalanced datasets, an oversampling algorithm—AGNES-SMOTE (Agglomerative Nesting-Synthetic Minority Oversampling Technique) based on hierarchical clustering and improved SMOTE—is proposed. Its key procedures include hierarchically cluster majority samples and minority samples, respectively; divide minority subclusters on the basis of the obtained majority subclusters; select “seed sample” based on the sampling weight and probability distribution of minority subcluster; and restrict the generation of new samples in a certain area by centroid method in the sampling process. The combination of AGNES-SMOTE and SVM (Support Vector Machine) is presented to deal with imbalanced datasets classification. Experiments on UCI datasets are conducted to compare the performance of different algorithms mentioned in the literature. Experimental results indicate AGNES-SMOTE excels in synthesizing new samples and improves SVM classification performance on imbalanced datasets.

scholarly journals Explaining Machine Learning Models for Clinical Gait Analysis

2022 ◽  
Vol 3 (2) ◽  
pp. 1-27
Author(s):  
Djordje Slijepcevic ◽  
Fabian Horst ◽  
Sebastian Lapuschkin ◽  
Brian Horsak ◽  
Anna-Maria Raberger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Reaction Force ◽  
Statistical Parametric Mapping ◽  
Qualitative Evaluation ◽  
Classification Performance ◽  
Healthcare Sector ◽  
Support Vector ◽  
Classification Methods ◽  
Network Support ◽  
Gait Classification

Machine Learning (ML) is increasingly used to support decision-making in the healthcare sector. While ML approaches provide promising results with regard to their classification performance, most share a central limitation, their black-box character. This article investigates the usefulness of Explainable Artificial Intelligence (XAI) methods to increase transparency in automated clinical gait classification based on time series. For this purpose, predictions of state-of-the-art classification methods are explained with a XAI method called Layer-wise Relevance Propagation (LRP). Our main contribution is an approach that explains class-specific characteristics learned by ML models that are trained for gait classification. We investigate several gait classification tasks and employ different classification methods, i.e., Convolutional Neural Network, Support Vector Machine, and Multi-layer Perceptron. We propose to evaluate the obtained explanations with two complementary approaches: a statistical analysis of the underlying data using Statistical Parametric Mapping and a qualitative evaluation by two clinical experts. A gait dataset comprising ground reaction force measurements from 132 patients with different lower-body gait disorders and 62 healthy controls is utilized. Our experiments show that explanations obtained by LRP exhibit promising statistical properties concerning inter-class discriminativity and are also in line with clinically relevant biomechanical gait characteristics.

scholarly journals Improvement of EMG Pattern Recognition Model Performance in Repeated Uses by Combining Feature Selection and Incremental Transfer Learning

2021 ◽  
Vol 15 ◽  
Author(s):  
Qi Li ◽  
Anyuan Zhang ◽  
Zhenlan Li ◽  
Yan Wu
Keyword(s):  
Pattern Recognition ◽  
Feature Selection ◽  
Classification Accuracy ◽  
Fine Tuning ◽  
Support Vector ◽  
Classification Methods ◽  
Data Set ◽  
Feature Vectors ◽  
Emg Pattern Recognition ◽  
Sequential Forward Search

Electromyography (EMG) pattern recognition is one of the widely used methods to control the rehabilitation robots and prostheses. However, the changes in the distribution of EMG data due to electrodes shifting results in classification decline, which hinders its clinical application in repeated uses. Adaptive learning can solve this problem but takes additional time. To address this, an efficient scheme is developed by comparing the performance of 12 combinations of three feature selection methods [no feature selection (NFS), sequential forward search (SFS), and particle swarm optimization (PSO)] and four classification methods [non-adaptive support vector machine (N-SVM), incremental SVM (I-SVM), SVM based on TrAdaBoost (T-SVM), and I-SVM based on TrAdaBoost (TI-SVM)] in the classification of EMG data of 12 subjects for 5 consecutive days. Our results showed that TI-SVM achieved the highest classification accuracy among the classification methods (p &lt; 0.05). The SFS method achieved the same classification accuracy as that of the scheme trained with the feature vectors selected by the NFS method (p = 0.999) while achieving a lower training time than that of TI-SVM combined with the NFS method (p = 0.043). Although the PSO method outperformed the NFS and SFS methods by achieving reduced training and response times (p &lt; 0.05), the PSO method achieved a considerably lower classification accuracy than that of the scheme trained with the feature vectors selected by the NFS (p = 0.001) or SFS (p = 0.001) method. Furthermore, TI-SVM combined with the SFS method outperformed the CNN method with fine-tuning in classification accuracy on a small data set (p = 0.001). The results indicate that TI-SVM combined with the SFS method is suitable for improving the performance of EMG pattern recognition in repeated uses.

scholarly journals Research of the efficiency of application of technologies of wavelet analysis in the tasks of recognition of images

2021 ◽  
Vol 1 (1) ◽  
pp. 23-28
Author(s):  
V. S. Maraev
Keyword(s):  
Wavelet Analysis ◽  
Classification Accuracy ◽  
Experimental Comparison ◽  
Support Vector ◽  
Classification Models ◽  
Classification Methods ◽  
Test Results ◽  
Bayes Classifier ◽  
Fractal Method ◽  
Vector Machines

The work is devoted to the experimental comparison of the accuracy of classification methods on the problem of pattern recognition in images using wavelet analysis technologies and without. In particular, the interaction of the ring-projection wavelet-fractal method for identifying features with classical classification methods such as "Naive Bayes classifier" and "Support vector machines" is investigated. The experimental test results are given in the form of a table. As a result, it is established that the introduction of wavelet analysis into the construction of image classification models is justified, and leads to a relatively small but significant increase in the classification accuracy.

scholarly journals USING PIXEL-BASED AND OBJECT-BASED METHODS TO CLASSIFY URBAN HYPERSPECTRAL FEATURES

2016 ◽  
Vol 42 (3) ◽  
pp. 92-105 ◽  
Author(s):  
Ahmad Hadavand ◽  
Mehdi Mokhtarzadeh ◽  
Mohammad Javad Valadan Zoej ◽  
Saeid Homayouni ◽  
Mohammad Saadatseresht
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
Classification Accuracy ◽  
Support Vector ◽  
Classification Methods ◽  
Spectral Angle Mapper ◽  
Object Based Image Analysis ◽  
Analysis Methods ◽  
Object Based ◽  
Percent Improvement

Object-based image analysis methods have been developed recently. They have since become a very active research topic in the remote sensing community. This is mainly because the researchers have begun to study the spatial structures within the data. In contrast, pixel-based methods only use the spectral content of data. To evaluate the applicability of object-based image analysis methods for land-cover information extraction from hyperspectral data, a comprehensive comparative analysis was performed. In this study, six supervised classification methods were selected from pixel-based category, including the maximum likelihood (ML), fisher linear likelihood (FLL), support vector machine (SVM), binary encoding (BE), spectral angle mapper (SAM) and spectral information divergence (SID). The classifiers were conducted on several features extracted from original spectral bands in order to avoid the problem of the Hughes phenomenon, and obtain a sufficient number of training samples. Three supervised and four unsupervised feature extraction methods were used. Pixel based classification was conducted in the first step of the proposed algorithm. The effective feature number (EFN) was then obtained. Image objects were thereafter created using the fractal net evolution approach (FNEA), the segmentation method implemented in eCognition software. Several experiments have been carried out to find the best segmentation parameters. The classification accuracy of these objects was compared with the accuracy of the pixel-based methods. In these experiments, the Pavia University Campus hyperspectral dataset was used. This dataset was collected by the ROSIS sensor over an urban area in Italy. The results reveal that when using any combination of feature extraction and classification methods, the performance of object-based methods was better than pixel-based ones. Furthermore the statistical analysis of results shows that on average, there is almost an 8 percent improvement in classification accuracy when we use the object-based methods.

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