scholarly journals Analisis Data Pada Jaringan Sensor Nirkabel Menggunakan Metode Support Vector Machine

2018 ◽  
Vol 1 (1) ◽  
pp. 8-15
Author(s):  
Caroline Layadi ◽  
Mohammad Fajar ◽  
Hasniati Hasniati ◽  
Izmy Alwiah Musdar
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Gaussian Kernel ◽  
Weather Data ◽  
Support Vector ◽  
Polynomial Evaluation ◽  
Evaluation Result ◽  
Average Accuracy ◽  
Result Show ◽  
Data Validity

The aims of this research are to implement Support Vector Machine for analyze abnormal data on sensor network and evaluate the implementation result. The data collection in the research were done through the searching of related libraries and software evaluate/testing. In this research, temperature, wind speed, and humidity tested using three kernels (linear, Gaussian, and polynomial). Evaluation result show that the implementation of Support Vector Machine can perform the best data validity analysis using Gaussian Kernel with the percentage of average accuracy, temperature 97.83%, humidity 94.5325%, and wind speed 96.93% for weather data 20-28 May and July 28-August 10, 2015. Meanwhile, for weather data June 5-6, 2017 obtained the percentage of average accuracy of temperature 92.855% and humidity 92.43%.

scholarly journals PREDIKSI JUMLAH PENDERITA PENYAKIT TUBERKULOSIS DI KOTA BANDAR LAMPUNG MENGGUNAKAN METODE SVM (SUPPORT VECTOR MACHINE)

2020 ◽  
Vol 7 (3) ◽  
pp. 320
Author(s):  
Favorisen R. Lumbanraja ◽  
Ira Hariati Br Sitepu ◽  
Didik Kurniawan ◽  
Aristoteles Aristoteles
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Mycobacterium Tuberculosis ◽  
Gaussian Kernel ◽  
Weather Data ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Learning Support ◽  
Tuberculosis Patients ◽  
Long Time

<p><em>Tuberkulosis (TB atau TBC) merupakan salah satu penyakit infeksi yang disebabkan oleh Bakteri Mycobacterium tuberculosis. Bakteri tersebut merupakan bakteri yang sangat kuat sehingga dalam pengobatannya memerlukan waktu yang cukup lama. Pengobatan penyakit tuberkulosis dilakukan selama 6-9 bulan secara rutin dengan sedikitnya 3 macam jenis obat. Saat ini kebanyakan masyarakat menganggap batuk dalam jangka waktu berbulan-bulan merupakan batuk biasa, jika dicermati salah satu gejala yang ditimbulkan penyakit tuberkulosis, yaitu batuk dalam jangka waktu yang panjang. Pada penelitian ini digunakan data penderita tuberkulosis di Kota Bandar Lampung, data cuaca dan matrix jarak antara kejadian penderita tuberkulosis yang satu dengan kejadian yang lainnya dalam lingkup kecamatan. Jumlah dari keseluruhan data sebanyak 600 data dengan 44 variabel. Penelitian ini juga menggunakan 3 kernel yaitu, Linear, Gaussian, dan Polynomial dengan menggunakan Metode SVM dengan kernel Linear mendapatkan nilai rata-rata R<sup>2</sup> sebesar 51.43 %, pada percobaan dengan metode SVM dengan kernel Gaussian mendapatkan nilai rata-rata R<sup>2</sup> sebesar 58.53 % dan pada percobaan dengan metode SVM dengan kernel Polynomial mendapatkan nilai rata-rata R<sup>2</sup> sebesar 36.03 %.</em></p><p><strong><em>Kata Kunci</em></strong><em> : Prediksi penderita tuberculosis, tuberculosis, Machine Learning, Support Vector Machine.</em></p><p class="Abstrak"><em>Tuberculosis (TB / TBC) is one of infectious disease caused by Mycobacterium tuberculosis bacteria. These bacteria are very strong bacteria so for the treatment takes a long time. Tuberculosis treatment is carried out for 6-9 months regularly with at least 3 types of drugs. Currently, most of people consider a cough for months is a common cough, if looked by one of the symptoms caused by tuberculosis, which is a cough for a long time. In this research, data on tuberculosis patients in the city of Bandar Lampung were used, weather data and the distance matrix between the case of tuberculosis patients with other case within the district. The total number of data is 600 data with 44 variables. This research also uses 3 kernels</em><em> </em><em>namely, Linear, Gaussian, and Polynomial by using the SVM method with the Linear kernel getting an average R<sup>2</sup> value of 51.43%, in the experiment with the SVM method with a gaussian kernel getting an average R<sup>2</sup> value of 58.53% and at Experiments with the SVM method with the Polynomial kernel obtained an average value of R<sup>2</sup> of 36.03%</em><em> .</em></p><p class="Abstrak"><strong><em>Keywords</em></strong><em> : Prediction of tuberculosis sufferers, tuberculosis, Machine Learning, Support Vector Machine.</em></p>

An application of backtracking search optimization–based least squares support vector machine for prediction of short-term wind speed

2019 ◽  
Vol 44 (3) ◽  
pp. 266-281 ◽  
Author(s):  
Zhongda Tian ◽  
Yi Ren ◽  
Gang Wang
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Prediction Model ◽  
Least Squares ◽  
Support Vector ◽  
Short Term ◽  
Backtracking Search ◽  
Wind Speed Prediction ◽  
Search Optimization ◽  
Speed Prediction

Wind speed prediction is an important technology in the wind power field; however, because of their chaotic nature, predicting wind speed accurately is difficult. Aims at this challenge, a backtracking search optimization–based least squares support vector machine model is proposed for short-term wind speed prediction. In this article, the least squares support vector machine is chosen as the short-term wind speed prediction model and backtracking search optimization algorithm is used to optimize the important parameters which influence the least squares support vector machine regression model. Furthermore, the optimal parameters of the model are obtained, and the short-term wind speed prediction model of least squares support vector machine is established through parameter optimization. For time-varying systems similar to short-term wind speed time series, a model updating method based on prediction error accuracy combined with sliding window strategy is proposed. When the prediction model does not match the actual short-term wind model, least squares support vector machine trains and re-establishes. This model updating method avoids the mismatch problem between prediction model and actual wind speed data. The actual collected short-term wind speed time series is used as the research object. Multi-step prediction simulation of short-term wind speed is carried out. The simulation results show that backtracking search optimization algorithm–based least squares support vector machine model has higher prediction accuracy and reliability for the short-term wind speed. At the same time, the prediction performance indicators are also improved. The prediction result is that root mean square error is 0.1248, mean absolute error is 0.1374, mean absolute percentile error is 0.1589% and R2 is 0.9648. When the short-term wind speed varies from 0 to 4 m/s, the average value of absolute prediction error is 0.1113 m/s, and average value of absolute relative prediction error is 8.7111%. The proposed prediction model in this article has high engineering application value.

scholarly journals Support Vector Machine based Stress Detection System to manage COVID-19 pandemic related stress from ECG signal

2021 ◽  
Vol 20 (1) ◽  
pp. 8-16
Author(s):  
Md Fahim Rizwan ◽  
Rayed Farhad ◽  
Md. Hasan Imam
Keyword(s):  
Support Vector Machine ◽  
Detection System ◽  
Health Condition ◽  
Cardiac Patients ◽  
Gaussian Kernel ◽  
Training Dataset ◽  
Support Vector ◽  
Portable Devices ◽  
Stress Detection ◽  
Specialist Doctor

This study represents a detailed investigation of induced stress detection in humans using Support Vector Machine algorithms. Proper detection of stress can prevent many psychological and physiological problems like the occurrence of major depression disorder (MDD), stress-induced cardiac rhythm abnormalities, or arrhythmia. Stress induced due to COVID -19 pandemic can make the situation worse for the cardiac patients and cause different abnormalities in the normal people due to lockdown condition. Therefore, an ECG based technique is proposed in this paper where the ECG can be recorded for the available handheld/portable devices which are now common to many countries where people can take ECG by their own in their houses and get preliminary information about their cardiac health. From ECG, we can derive RR interval, QT interval, and EDR (ECG derived Respiration) for developing the model for stress detection also. To validate the proposed model, an open-access database named "drivedb” available at Physionet (physionet.org) was used as the training dataset. After verifying several SVM models by changing the ECG length, features, and SVM Kernel type, the results showed an acceptable level of accuracy for Fine Gaussian SVM (i.e. 98.3% for 1 min ECG and 93.6 % for 5 min long ECG) with Gaussian Kernel while using all available features (RR, QT, and EDR). This finding emphasizes the importance of including ventricular polarization and respiratory information in stress detection and the possibility of stress detection from short length data(i.e. form 1 min ECG data), which will be very useful to detect stress through portable ECG devices in locked down condition to analyze mental health condition without visiting the specialist doctor at hospital. This technique also alarms the cardiac patients form being stressed too  much which might cause severe arrhythmogenesis.

Research on Particle Swarm Optimization Least Square Support Vector Machine for Short-Term Wind Speed Prediction

2014 ◽  
Vol 511-512 ◽  
pp. 927-930
Author(s):  
Shuai Zhang ◽  
Hai Rui Wang ◽  
Jin Huang ◽  
He Liu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Wind Speed ◽  
Particle Swarm ◽  
Least Square ◽  
Support Vector ◽  
Short Term ◽  
Swarm Optimization ◽  
Wind Speed Prediction ◽  
Speed Prediction

In the paper, the forecast problems of wind speed are considered. In order to enhance the redaction accuracy of the wind speed, this article is about a research on particle swarm optimization least square support vector machine for short-term wind speed prediction (PSO-LS-SVM). Firstly, the prediction models are built by using least square support vector machine based on particle swarm optimization, this model is used to predict the wind speed next 48 hours. In order to further improve the prediction accuracy, on this basis, introduction of the offset optimization method. Finally large amount of experiments and measurement data comparison compensation verify the effectiveness and feasibility of the research on particle swarm optimization least square support vector machine for short-term wind speed prediction, Thereby reducing the short-term wind speed prediction error, very broad application prospects.

Short-Term Wind Speed and Power Prediction Using Fuzzy Information Granulation-Support Vector Machine

2012 ◽  
Vol 608-609 ◽  
pp. 814-817
Author(s):  
Xiao Fu ◽  
Dong Xiang Jiang
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Wind Farm ◽  
Support Vector ◽  
Fuzzy Information ◽  
Short Term ◽  
Power Fluctuation ◽  
Data Set ◽  
Information Granulation ◽  
Fuzzy Information Granulation

The power fluctuation of wind turbine often causes serious problems in electricity grids. Therefore, short term prediction of wind speed and power as to eliminate the uncertainty determined crucially the development of wind energy. Compared with physical methods, support vector machine (SVM) as an intelligent artificial method is more general and shows better nonlinear modeling capacity. A model which combined fuzzy information granulation with SVM method was developed and implemented in short term future trend prediction of wind speed and power. The data, including the daily wind speed and power, from a wind farm in northern China were used to evaluate the proposed method. The prediction results show that the proposed model performs better and more stable than the standard SVM model when apply them into the same data set.

scholarly journals Infrared–ultrasonic sensor fusion for support vector machine–based fall detection

2018 ◽  
Vol 29 (9) ◽  
pp. 2027-2039 ◽  
Author(s):  
Zhangjie Chen ◽  
Ya Wang
Keyword(s):  
Support Vector Machine ◽  
Sensor Fusion ◽  
Learning Algorithm ◽  
Fall Detection ◽  
Ultrasonic Sensor ◽  
Support Vector ◽  
Detection Accuracy ◽  
Data Recording ◽  
Fusion Algorithm ◽  
Average Accuracy

This article presents an infrared–ultrasonic sensor fusion approach for support vector machine–based fall detection, often required by elderly healthcare. Its detection algorithms and performance evaluation are detailed. The location, size, and temperature profile of the user can be estimated based on a novel sensory fusion algorithm. Different feature sets of the support vector machine–based machine learning algorithm are analyzed and their impact on fall detection accuracy is evaluated and compared empirically. Experiments study three non-fall activities, standing, sitting, and stooping, and two fall actions, forward falling and sideway falling, to simulate daily activities of the elderly. Fall detection accuracy studies are performed based on discretely and continuously (closer to reality) recorded experimental data, respectively. For the discrete data recording, an average accuracy of 92.2% is achieved when the stand-alone Grid-EYE is used and the accuracy is increased to 96.7% when sensor fusion is used. For the continuous data recording (180 training sets, 60 test sets at each distance), an average accuracy less than 70.0% is achieved when the stand-alone Grid-EYE is used and the accuracy is increased to around 90.3% after sensor fusion. New features will be explored in the next step to further increase detection accuracy.

scholarly journals Comparison of New Anomaly Detection Technique for Wind Turbine Condition Monitoring Using Gearbox SCADA Data

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5152
Author(s):  
Conor McKinnon ◽  
James Carroll ◽  
Alasdair McDonald ◽  
Sofia Koukoura ◽  
David Infield ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Anomaly Detection ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Support Vector ◽  
Specific Training ◽  
Average Accuracy ◽  
Operations And Maintenance ◽  
Novel Method ◽  
Isolation Forest

Anomaly detection for wind turbine condition monitoring is an active area of research within the wind energy operations and maintenance (O & M) community. In this paper three models were compared for multi-megawatt operational wind turbine SCADA data. The models used for comparison were One-Class Support Vector Machine (OCSVM), Isolation Forest (IF), and Elliptical Envelope (EE). Each of these were compared for the same fault, and tested under various different data configurations. IF and EE have not previously been used for fault detection for wind turbines, and OCSVM has not been used for SCADA data. This paper presents a novel method of condition monitoring that only requires two months of data per turbine. These months were separated by a year, the first being healthy and the second unhealthy. The number of anomalies is compared, with a greater number in the unhealthy month being considered correct. It was found that for accuracy IF and OCSVM had similar performances in both training regimes presented. OCSVM performed better for generic training, and IF performed better for specific training. Overall, IF and OCSVM had an average accuracy of 82% for all configurations considered, compared to 77% for EE.

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