scholarly journals SVM-Based Detection of Miniature Area of LCLU: A False-Damage Assessment Index for Disaster Management Application

2019 ◽  
Vol 8 (4) ◽  
pp. 10957-10962
Keyword(s):  
Supervised Learning ◽  
Detection Probability ◽  
Damage Assessment ◽  
Learning Algorithm ◽  
Support Vector ◽  
Binary Classifier ◽  
Assessment Index ◽  
Vector Machines ◽  
Management Application ◽  
Two Parameters

In this paper, we reflect on changing the detection environment for addressing the difficulty of detecting miniature area of Land Cover Land Use (LCLU) with a technique using Support Vector Machines (SVMs).We then become accustomed and sophisticatedly changing the Support Vector Machine for designing a supervised learning basis detection that enfolds the False Damage Assessment Index(FDAI). Primarily our proposed detection technique is controls easily the FDAI by simply adjusting two parameters() where it can be facilitate to control sensitivity of detection to the binary classifier and numerical supervised learning algorithm. The experimental results demonstrating about ours proposing detector noticeably improving the detection probability on many existing classifiers in both DAI and FDAI cases .

scholarly journals Automatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines

Sci ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 62
Author(s):  
Jian Zhang ◽  
Rahul Soangra ◽  
Thurmon E. Lockhart
Keyword(s):  
Older Adults ◽  
Support Vector Machines ◽  
Daily Life ◽  
Human Motion ◽  
Function Parameter ◽  
Support Vector ◽  
Activities Of Daily Life ◽  
Svm Algorithm ◽  
Vector Machines ◽  
Two Parameters

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter γ—are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying dynamic and static activities of daily life in the older adults.

New Incremental Learning Algorithm With Support Vector Machines

2019 ◽  
Vol 49 (11) ◽  
pp. 2230-2241 ◽  
Author(s):  
Jie Xu ◽  
Chen Xu ◽  
Bin Zou ◽  
Yuan Yan Tang ◽  
Jiangtao Peng ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Incremental Learning ◽  
Learning Algorithm ◽  
Support Vector ◽  
Vector Machines

scholarly journals Automatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines

Sci ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 60
Author(s):  
Jian Zhang ◽  
Rahul Soangra ◽  
Thurmon E. Lockhart
Keyword(s):  
Older Adults ◽  
Support Vector Machines ◽  
Daily Life ◽  
Human Motion ◽  
Function Parameter ◽  
Support Vector ◽  
Activities Of Daily Life ◽  
Svm Algorithm ◽  
Vector Machines ◽  
Two Parameters

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter γ—are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying dynamic and static activities of daily life in the older adults.

Least Squares Support Vector Machine for Fault Diagnosis Optimization

2013 ◽  
Vol 347-350 ◽  
pp. 505-508
Author(s):  
Si Yang Liang ◽  
Jian Hong Lv
Keyword(s):  
Fault Diagnosis ◽  
Least Squares ◽  
Learning Algorithm ◽  
Test Sample ◽  
Digital Circuit ◽  
Training Sample ◽  
Support Vector ◽  
Vector Machines ◽  
Sample Data ◽  
Diagnosis Method

In order to improve the diagnostic accuracy of digital circuit, the fault diagnosis method based on support vector machines (SVM) is proposed. The input is fault characteristics of digital circuit; the output is the fault style. The connection of fault characteristics and style was established. Network learning algorithm using least squares, the training sample data is formed by the simulation, the test sample data is formed by the untrained simulation. The method achieved the classification of faulted digital circuits, and the results show that the method has the features of fast and high accuracy.

IMPROVING SVM PERFORMANCE IN MULTI-LABEL DOMAINS: THRESHOLD ADJUSTMENT

2013 ◽  
Vol 22 (01) ◽  
pp. 1250038 ◽  
Author(s):  
PEERAPON VATEEKUL ◽  
SAREEWAN DENDAMRONGVIT ◽  
MIROSLAV KUBAT
Keyword(s):  
Classification Performance ◽  
Support Vector ◽  
Binary Classifier ◽  
Research Groups ◽  
Computational Costs ◽  
Vector Machines ◽  
Threshold Adjustment ◽  
Good Classification ◽  
Good Classification Performance ◽  
Suboptimal Behavior

In “multi-label domains,” where the same example can simultaneously belong to two or more classes, it is customary to induce a separate binary classifier for each class, and then use them all in parallel. As a result, some of these classifiers are induced from imbalanced training sets where one class outnumbers the other – a circumstance known to hurt some machine learning paradigms. In the case of Support Vector Machines (SVM), this suboptimal behavior is explained by the fact that SVM seeks to minimize error rate, a criterion that is in domains of this type misleading. This is why several research groups have studied mechanisms to readjust the bias of SVM's hyperplane. The best of these achieves very good classification performance at the price of impractically high computational costs. We propose here an improvement where these cost are reduced to a small fraction without significantly impairing classification.

scholarly journals Using Artificial Intelligence Techniques for Prediction and Estimation of Photovoltaic System Output Power

2020 ◽  
Vol 3 (1) ◽  
pp. 15-21
Author(s):  
Deogratias Nurwaha
Keyword(s):  
Artificial Intelligence ◽  
Output Power ◽  
Gene Expression Programming ◽  
Photovoltaic System ◽  
Support Vector ◽  
Independent Variables ◽  
Vector Machines ◽  
System Output ◽  
Two Parameters ◽  
Statistical Metrics

Two artificial intelligence methods, namely, support vector machines (SVM) and gene expression programming (GEP), were explored for prediction and estimation of the Photovoltaic (PV)output power. Measured values of temperature T (°C) and irradiance E (kWh/㎡) were used as inputs (independent variables) and PV output power P (Kw) was used as output (dependent variable). The statistical metrics were used to assess the predictive performances of the methods. The results of the two models were estimated and compared. The results showed that the two techniques performances are better and similar. Using GEP technique, the relationships between the two parameters and output power were established. Importance of each parameter as contributor to PV output power was also investigated. The results indicated that the SVM and GEP would become the powerful tools that could help estimate the PV output power capacity reserve.

Ensemble Model for the Risk of Anemia in Pediatric Patients With Sickle Cell Disorder

2019 ◽  
Vol 4 (2) ◽  
pp. 33-59
Author(s):  
Jeremiah Ademola Balogun ◽  
Adanze O. Asinobi ◽  
Olawale Olaniyi ◽  
Samuel Ademola Adegoke ◽  
Florence Alaba Oladeji ◽  
...  
Keyword(s):  
Pediatric Patients ◽  
Learning Algorithm ◽  
Tertiary Hospital ◽  
Sub Saharan Africa ◽  
Support Vector ◽  
Ensemble Model ◽  
Multi Layer Perceptron ◽  
Vector Machines ◽  
Sub Saharan ◽  
Cell Disorder

Anemia is a major cause of morbidity and mortality of SCD patients in many parts of the world with the burden much higher in Sub Saharan Africa. This study developed an ensemble of machine learning algorithm for the prediction of the risk of anemia in pediatric SCD patients. Data for this study was collected from 115 pediatric SCD outpatients receiving treatment at a tertiary hospital in South-Western Nigeria. This study adopted a stack-ensemble model composed of deep neural network (DNN), multi-layer perceptron (MLP), and support vector machines (SVM) as base and meta-classifiers using the WEKA software. The ensemble models were compared following the stack-ensemble developed using SVM as a meta-classifier had the best performance with an accuracy of 72.7%. The study concluded that information about socio-demographic and clinical data can be used to assess the risk of anemia among SCD patients.

A Binary Classifier for the Prediction of EC Numbers of Enzymes

2019 ◽  
Vol 16 (5) ◽  
pp. 383-391 ◽  
Author(s):  
Hao Cui ◽  
Lei Chen
Keyword(s):  
Cross Validation ◽  
Enzyme Commission ◽  
Matthews Correlation Coefficient ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Binary Classifier ◽  
Two Samples ◽  
Functional Classes ◽  
Fold Cross Validation

Background: Identification of Enzyme Commission (EC) number of enzymes is quite important for understanding the metabolic processes that produce enough energy to sustain life. Previous studies mainly focused on predicting six main functional classes or sub-functional classes, i.e., the first two digits of the EC number. Objective: In this study, a binary classifier was proposed to identify the full EC number (four digits) of enzymes. Methods: Enzymes and their known EC numbers were paired as positive samples and negative samples were randomly produced that were as many as positive samples. The associations between any two samples were evaluated by integrating the linkages between enzymes and EC numbers. The classic machining learning algorithm, Support Vector Machine (SVM), was adopted as the prediction engine. Results: The five-fold cross-validation test on five datasets indicated that the overall accuracy, Matthews correlation coefficient and F1-measure were about 0.786, 0.576 and 0.771, respectively, suggesting the utility of the proposed classifier. In addition, the effectiveness of the classifier was elaborated by comparing it with other classifiers that were based on other classic machine learning algorithms. Conclusion: The proposed classifier was quite effective for prediction of EC number of enzymes and was specially designed for dealing with the problem addressed in this study by testing it on five datasets containing randomly produced samples.

Sign in / Sign up

Export Citation Format

Share Document