Richter's Predictor: Modelling Earthquake Damage Using Multi-class Classification Models

2020 ◽  
Author(s):  
Aishwarya Kumaraswamy ◽  
Bhargava N Reddy ◽  
Rithvik Kolla
Keyword(s):  
Earthquake Damage ◽  
Classification Models ◽  
Multi Class Classification

scholarly journals Identification of Autism Subtypes Based on Wavelet Coherence of BOLD FMRI Signals Using Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5256
Author(s):  
Mohammed Isam Al-Hiyali ◽  
Norashikin Yahya ◽  
Ibrahima Faye ◽  
Ahmed Faeq Hussein
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Phase Synchronization ◽  
Binary Classification ◽  
Blood Oxygen Level Dependent ◽  
Autism Spectrum ◽  
Wavelet Coherence ◽  
Good Resolution ◽  
Classification Models ◽  
Multi Class Classification

The functional connectivity (FC) patterns of resting-state functional magnetic resonance imaging (rs-fMRI) play an essential role in the development of autism spectrum disorders (ASD) classification models. There are available methods in literature that have used FC patterns as inputs for binary classification models, but the results barely reach an accuracy of 80%. Additionally, the generalizability across multiple sites of the models has not been investigated. Due to the lack of ASD subtypes identification model, the multi-class classification is proposed in the present study. This study aims to develop automated identification of autism spectrum disorder (ASD) subtypes using convolutional neural networks (CNN) using dynamic FC as its inputs. The rs-fMRI dataset used in this study consists of 144 individuals from 8 independent sites, labeled based on three ASD subtypes, namely autistic disorder (ASD), Asperger’s disorder (APD), and pervasive developmental disorder not otherwise specified (PDD-NOS). The blood-oxygen-level-dependent (BOLD) signals from 116 brain nodes of automated anatomical labeling (AAL) atlas are used, where the top-ranked node is determined based on one-way analysis of variance (ANOVA) of the power spectral density (PSD) values. Based on the statistical analysis of the PSD values of 3-level ASD and normal control (NC), putamen_R is obtained as the top-ranked node and used for the wavelet coherence computation. With good resolution in time and frequency domain, scalograms of wavelet coherence between the top-ranked node and the rest of the nodes are used as dynamic FC feature input to the convolutional neural networks (CNN). The dynamic FC patterns of wavelet coherence scalogram represent phase synchronization between the pairs of BOLD signals. Classification algorithms are developed using CNN and the wavelet coherence scalograms for binary and multi-class identification were trained and tested using cross-validation and leave-one-out techniques. Results of binary classification (ASD vs. NC) and multi-class classification (ASD vs. APD vs. PDD-NOS vs. NC) yielded, respectively, 89.8% accuracy and 82.1% macro-average accuracy, respectively. Findings from this study have illustrated the good potential of wavelet coherence technique in representing dynamic FC between brain nodes and open possibilities for its application in computer aided diagnosis of other neuropsychiatric disorders, such as depression or schizophrenia.

scholarly journals Active Learning of Multi-Class Classification Models from Ordered Class Sets

2019 ◽  
Vol 33 ◽  
pp. 5589-5596
Author(s):  
Yanbing Xue ◽  
Milos Hauskrecht
Keyword(s):  
Active Learning ◽  
Learning Strategy ◽  
Classification Models ◽  
Ordered Sets ◽  
Learning Framework ◽  
Multiple Datasets ◽  
Multi Class Classification ◽  
Active Learning Strategy ◽  
Human Annotator ◽  
New Machine

In this paper, we study the problem of learning multi-class classification models from a limited set of labeled examples obtained from human annotator. We propose a new machine learning framework that learns multi-class classification models from ordered class sets the annotator may use to express not only her top class choice but also other competing classes still under consideration. Such ordered sets of competing classes are common, for example, in various diagnostic tasks. In this paper, we first develop strategies for learning multi-class classification models from examples associated with ordered class set information. After that we develop an active learning strategy that considers such a feedback. We evaluate the benefit of the framework on multiple datasets. We show that class-order feedback and active learning can reduce the annotation cost both individually and jointly.

Mathematical Programming Models for Classification

2014 ◽  
pp. 1503-1516 ◽  
Author(s):  
Minghe Sun
Keyword(s):  
Linear Programming ◽  
Mathematical Programming ◽  
Programming Model ◽  
The Other ◽  
Programming Models ◽  
Mixed Integer ◽  
Classification Models ◽  
Classification Analysis ◽  
Mathematical Programming Models ◽  
Multi Class Classification

Mathematical programming models for discriminant and classification analysis are presented. Specifically, linear programming and mixed integer programming approaches are discussed. For each approach, two-class classification models and multi-class classification models are discussed. The emphasis is on the formulations of these mathematical programming models rather than on their performances. Two illustrative examples, one for two-class and the other for multi-class classification, are used to demonstrate the formulations of these mathematical programming models. An example is used to demonstrate the formulation after a mathematical programming model is presented.

scholarly journals Cost-sensitive probability for weighted voting in an ensemble model for multi-class classification problems

2021 ◽  
Author(s):  
Artittayapron Rojarath ◽  
Wararat Songpan
Keyword(s):  
Ensemble Learning ◽  
Support Vector ◽  
Ensemble Model ◽  
Classification Models ◽  
Classification Problems ◽  
Sensitivity Matrix ◽  
Weighted Voting ◽  
Class Prediction ◽  
Multi Class Classification ◽  
The Individual

AbstractEnsemble learning is an algorithm that utilizes various types of classification models. This algorithm can enhance the prediction efficiency of component models. However, the efficiency of combining models typically depends on the diversity and accuracy of the predicted results of ensemble models. However, the problem of multi-class data is still encountered. In the proposed approach, cost-sensitive learning was implemented to evaluate the prediction accuracy for each class, which was used to construct a cost-sensitivity matrix of the true positive (TP) rate. This TP rate can be used as a weight value and combined with a probability value to drive ensemble learning for a specified class. We proposed an ensemble model, which was a type of heterogenous model, namely, a combination of various individual classification models (support vector machine, Bayes, K-nearest neighbour, naïve Bayes, decision tree, and multi-layer perceptron) in experiments on 3-, 4-, 5- and 6-classifier models. The efficiencies of the propose models were compared to those of the individual classifier model and homogenous models (Adaboost, bagging, stacking, voting, random forest, and random subspaces) with various multi-class data sets. The experimental results demonstrate that the cost-sensitive probability for the weighted voting ensemble model that was derived from 3 models provided the most accurate results for the dataset in multi-class prediction. The objective of this study was to increase the efficiency of predicting classification results in multi-class classification tasks and to improve the classification results.

scholarly journals Eating and Drinking Recognition in Free-Living Conditions for Triggering Smart Reminders

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2803 ◽  
Author(s):  
Diana Gomes ◽  
João Mendes-Moreira ◽  
Inês Sousa ◽  
Joana Silva
Keyword(s):  
Binary Classification ◽  
Developed Countries ◽  
Recognition Algorithm ◽  
Living Conditions ◽  
Classification Model ◽  
Classification Models ◽  
Free Living ◽  
Multi Class Classification ◽  
Medication Taking Behavior ◽  
Free Living Conditions

The increasingly aging society in developed countries has raised attention to the role of technology in seniors’ lives, namely concerning isolation-related issues. Independent seniors that live alone frequently neglect meals, hydration and proper medication-taking behavior. This work aims at eating and drinking recognition in free-living conditions for triggering smart reminders to autonomously living seniors, keeping system design considerations, namely usability and senior-acceptance criteria, in the loop. To that end, we conceived a new dataset featuring accelerometer and gyroscope wrist data to conduct the experiments. We assessed the performance of a single multi-class classification model when compared against several binary classification models, one for each activity of interest (eating vs. non-eating; drinking vs. non-drinking). Binary classification models performed consistently better for all tested classifiers (k-NN, Naive Bayes, Decision Tree, Multilayer Perceptron, Random Forests, HMM). This evidence supported the proposal of a semi-hierarchical activity recognition algorithm that enabled the implementation of two distinct data stream segmentation techniques, the customization of the classification models of each activity of interest and the establishment of a set of restrictions to apply on top of the classification output, based on daily evidence. An F1-score of 97% was finally attained for the simultaneous recognition of eating and drinking in an all-day acquisition from one young user, and 93% in a test set with 31 h of data from 5 different unseen users, 2 of which were seniors. These results were deemed very promising towards solving the problem of food and fluids intake monitoring with practical systems which shall maximize user-acceptance.

scholarly journals Earthquake Damage Assessment in Three Spatial Scale Using Naive Bayes, SVM, and Deep Learning Algorithms

2021 ◽  
Vol 11 (20) ◽  
pp. 9737
Author(s):  
Sajjad Ahadzadeh ◽  
Mohammad Reza Malek
Keyword(s):  
Deep Learning ◽  
Situational Awareness ◽  
Binary Classification ◽  
Pearson Correlation ◽  
Temporal Analysis ◽  
Earthquake Damage ◽  
Damage Estimation ◽  
Svm Algorithm ◽  
Multi Class Classification ◽  
Damage Map

Earthquakes lead to enormous harm to life and assets. The ability to quickly assess damage across a vast area is crucial for effective disaster response. In recent years, social networks have demonstrated a lot of capability for improving situational awareness and identifying impacted areas. In this regard, this study proposed an approach that applied social media data for the earthquake damage assessment at the county, city, and 10 × 10 km grids scale using Naive Bayes, support vector machine (SVM), and deep learning classification algorithms. In this study, classification was evaluated using accuracy, precision, recall, and F-score metrics. Then, for understanding the message propagation behavior in the study area, temporal analysis based on classified messages was performed. In addition, variability of spatial topic concentration in three classification algorithms after the earthquake was examined using location quotation (LQ). A damage map based on the results of the classification of the three algorithms into three scales was created. For validation, confusion matrix metrics, Spearman’s rho, Pearson correlation, and Kendall’s tau were used. In this study, binary classification and multi-class classification have been done. Binary classification was used to classify messages into two classes of damage and non-damage so that their results could finally be used to estimate the earthquake damage. Multi-class classification was used to categorize messages to increase post-crisis situational awareness. In the binary classification, the SVM algorithm performed better in all the indices, gaining 71.22% accuracy, 81.22 F-measure, 79.08% accuracy, 85.62% precision, and 0.634 Kappa. In the multi-class classification, the SVM algorithm performed better in all the indices, gaining 90.25% accuracy, 88.58% F-measure, 84.34% accuracy, 93.26% precision, and 0.825 Kappa. Based on the results of the temporal analysis, most of the damage-related messages were reported on the day of the earthquake and decreased in the following days. Most of the messages related to infrastructure damages and injured, dead, and missing people were reported on the day of the earthquake. In addition, results of LQ indicated Napa as a center of the earthquake as the concentration of damage-related messages in all algorithms were based there. This indicates that our approach has been able to identify the damage well and has considered the earthquake center one of the most affected counties. The findings of the damage estimation showed that going away from the epicenter lowered the amount of damage. Based on the result of the validation of the estimated damage map with official data, the SVM performed better for damage estimation, followed by deep learning. In addition, at the county scale, algorithms showed better performance with Spearman’s rho of 0.8205, Pearson correlation of 0.5217, and Kendall’s tau of 0.6666.

Comparing the Speed and Accuracy of Multi-Label Classification Models

2015 ◽  
Vol 10 (8) ◽  
pp. 829
Author(s):  
Aswin Wibisurya ◽  
Ford Lumban Gaol ◽  
Kuncoro Wastuwibowo
Keyword(s):  
Classification Models ◽  
Speed And Accuracy

Squeak and rattle noise classification using radial basis function neural networks

2020 ◽  
Vol 68 (4) ◽  
pp. 283-293
Author(s):  
Oleksandr Pogorilyi ◽  
Mohammad Fard ◽  
John Davy ◽  
Mechanical and Automotive Engineering, School ◽  
Mechanical and Automotive Engineering, School ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Accuracy ◽  
Training Method ◽  
Vehicle Interior ◽  
Trained Classifier ◽  
Different Types ◽  
Noise Classification ◽  
Automatic Tool ◽  
Multi Class Classification

In this article, an artificial neural network is proposed to classify short audio sequences of squeak and rattle (S&R) noises. The aim of the classification is to see how accurately the trained classifier can recognize different types of S&R sounds. Having a high accuracy model that can recognize audible S&R noises could help to build an automatic tool able to identify unpleasant vehicle interior sounds in a matter of seconds from a short audio recording of the sounds. In this article, the training method of the classifier is proposed, and the results show that the trained model can identify various classes of S&R noises: simple (binary clas- sification) and complex ones (multi class classification).

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