scholarly journals Species-specific audio detection: a comparison of three template-based detection algorithms using random forests

2017 ◽  
Vol 3 ◽  
pp. e113 ◽  
Author(s):  
Carlos J. Corrada Bravo ◽  
Rafael Álvarez Berríos ◽  
T. Mitchell Aide
Keyword(s):  
Random Forest ◽  
Random Forests ◽  
Random Forest Classifier ◽  
Statistical Features ◽  
Web Based ◽  
Detection Algorithms ◽  
Average Accuracy ◽  
Species Specific ◽  
Web Based System

We developed a web-based cloud-hosted system that allow users to archive, listen, visualize, and annotate recordings. The system also provides tools to convert these annotations into datasets that can be used to train a computer to detect the presence or absence of a species. The algorithm used by the system was selected after comparing the accuracy and efficiency of three variants of a template-based detection. The algorithm computes a similarity vector by comparing a template of a species call with time increments across the spectrogram. Statistical features are extracted from this vector and used as input for a Random Forest classifier that predicts presence or absence of the species in the recording. The fastest algorithm variant had the highest average accuracy and specificity; therefore, it was implemented in the ARBIMON web-based system.

scholarly journals Species-specific audio detection: A comparison of three template-based classification algorithms using random forests

2017 ◽  
Author(s):  
Carlos J Corrada Bravo ◽  
Rafael Álvarez Berríos ◽  
T. Mitchell Aide
Keyword(s):  
Random Forest ◽  
Random Forests ◽  
Random Forest Classifier ◽  
Classification Algorithms ◽  
Statistical Features ◽  
Web Based ◽  
Average Accuracy ◽  
Species Specific ◽  
Web Based System

We developed a web-based cloud-hosted system that allow users to archive, listen, visualize, and annotate recordings. The system also provides tools to convert these annotations into datasets that can be used to train a computer to detect the presence or absence of a species. The algorithm used by the system was selected after comparing the accuracy and efficiency of three variants of a template-based classification. The algorithm computes a similarity vector by comparing a template of a species call with time increments across the spectrogram. Statistical features are extracted from this vector and used as input for a Random Forest classifier that predicts presence or absence of the species in the recording. The fastest algorithm variant had the highest average accuracy and specificity; therefore, it was implemented in the ARBIMON web-based system.

scholarly journals Species-specific audio detection: A comparison of three template-based classification algorithms using random forests

2017 ◽  
Author(s):  
Carlos J Corrada Bravo ◽  
Rafael Álvarez Berríos ◽  
T. Mitchell Aide
Keyword(s):  
Random Forest ◽  
Random Forests ◽  
Random Forest Classifier ◽  
Classification Algorithms ◽  
Statistical Features ◽  
Web Based ◽  
Average Accuracy ◽  
Species Specific ◽  
Web Based System

We developed a web-based cloud-hosted system that allow users to archive, listen, visualize, and annotate recordings. The system also provides tools to convert these annotations into datasets that can be used to train a computer to detect the presence or absence of a species. The algorithm used by the system was selected after comparing the accuracy and efficiency of three variants of a template-based classification. The algorithm computes a similarity vector by comparing a template of a species call with time increments across the spectrogram. Statistical features are extracted from this vector and used as input for a Random Forest classifier that predicts presence or absence of the species in the recording. The fastest algorithm variant had the highest average accuracy and specificity; therefore, it was implemented in the ARBIMON web-based system.

scholarly journals ECG-Based Subject Identification Using Statistical Features and Random Forest

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Turky N. Alotaiby ◽  
Saud Rashid Alrshoud ◽  
Saleh A. Alshebeili ◽  
Latifah M. Aljafar
Keyword(s):  
Random Forest ◽  
Random Forest Classifier ◽  
Ecg Signal ◽  
Identification Algorithm ◽  
Statistical Features ◽  
Accuracy Rate ◽  
Subject Identification ◽  
Signal Segment ◽  
Electrocardiogram Ecg ◽  
Ecg Database

In this work, a nonfiducial electrocardiogram (ECG) identification algorithm based on statistical features and random forest classifier is presented. Two feature extraction approaches are investigated: direct and band-based approaches. In the former, eleven simple statistical features are directly extracted from a single-lead ECG signal segment. In the latter, the single-lead ECG signal is first decomposed into bands, and the statistical features are extracted from each segment of a given band and concatenated to form the feature vector. Nonoverlapping segments of different lengths (i.e., 1, 3, 5, 7, 10, or 15 sec) are examined. The extracted feature vectors are applied to a random forest classifier, for the purpose of identification. This study considers 290 reference subjects from the ECG database of the Physikalisch-Technische Bundesanstalt (PTB). The proposed identification algorithm achieved an accuracy rate of 99.61% utilizing the single limb lead (I) with the band-based approach. A single chest lead (V1), augmented limb lead (aVF), and Frank’s lead (Vx) achieved an accuracy rate of 99.37%, 99.76%, and 99.76%, respectively, using the same approach.

scholarly journals Learning a Mid-Level Representation for Multiview Action Recognition

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Cuiwei Liu ◽  
Zhaokui Li ◽  
Xiangbin Shi ◽  
Chong Du
Keyword(s):  
Random Forest ◽  
Action Recognition ◽  
Random Forests ◽  
Random Forest Classifier ◽  
Multiple Views ◽  
Human Actions ◽  
Single View ◽  
Learning Framework ◽  
Camera View ◽  
Action Category

Recognizing human actions in videos is an active topic with broad commercial potentials. Most of the existing action recognition methods are supposed to have the same camera view during both training and testing. And thus performances of these single-view approaches may be severely influenced by the camera movement and variation of viewpoints. In this paper, we address the above problem by utilizing videos simultaneously recorded from multiple views. To this end, we propose a learning framework based on multitask random forest to exploit a discriminative mid-level representation for videos from multiple cameras. In the first step, subvolumes of continuous human-centered figures are extracted from original videos. In the next step, spatiotemporal cuboids sampled from these subvolumes are characterized by multiple low-level descriptors. Then a set of multitask random forests are built upon multiview cuboids sampled at adjacent positions and construct an integrated mid-level representation for multiview subvolumes of one action. Finally, a random forest classifier is employed to predict the action category in terms of the learned representation. Experiments conducted on the multiview IXMAS action dataset illustrate that the proposed method can effectively recognize human actions depicted in multiview videos.

scholarly journals Pemodelan dan Simulasi Monte Carlo dalam Meningkatkan Pendapatan Penjualan Peralatan Motor (Studi Kasus : Bengkel AMI Motor)

2020 ◽  
Author(s):  
Wita Siska Moza ◽  
Yuhandri Yunus
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Business Strategies ◽  
Web Based ◽  
Sales Revenue ◽  
Know How ◽  
The Monte Carlo Method ◽  
Average Accuracy ◽  
Almost All ◽  
Web Based System

AMI Motor shop is a various shop that is engaged in sales by selling various motorcycle equipment. Sales transactions vary in stores, but almost all products have increased and decreased, so it is necessary to know how the product data is related to consumer demand. Sales simulation is an estimate that can provide benefits in making decisions to increase sales revenue. The purpose of this study is to predict what motorcycle equipment stock should be increased and decreased in sales in the following year. The data used is motor equipment sales data in 2018 and 2019 which are processed using the Monte Carlo method. In speeding up data processing, this system is applied to a web-based system using the PHP (Hypertext Processor) programming language. Based on the results of testing prediction levels of motorcycle equipment sales, average accuracy is 95,92%, making it easier for company leaders to make decisions on developing business strategies to increase sales revenue.

scholarly journals Iterative Update of a Random Forest Classifier for Diabetic Retinopathy

2021 ◽  
Author(s):  
Jordi Pascual-Fontanilles ◽  
Aida Valls ◽  
Antonio Moreno ◽  
Pedro Romero-Aroca
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Random Forest ◽  
Random Forests ◽  
Random Forest Classifier ◽  
Diabetic Patients ◽  
Similar Data ◽  
Machine Learning Classification ◽  
Inherent Ambiguity ◽  
New Iterative Method

Random Forests are well-known Machine Learning classification mechanisms based on a collection of decision trees. In the last years, they have been applied to assess the risk of diabetic patients to develop Diabetic Retinopathy. The results have been good, despite the unbalance of data between classes and the inherent ambiguity of the problem (patients with similar data may belong to different classes). In this work we propose a new iterative method to update the set of trees in the Random Forest by considering trees generated from the data of the new patients that are visited in the medical centre. With this method, it has been possible to improve the results obtained with standard Random Forests.

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