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Gao, Hong Bing, Liao Yang, Xian Zhang, and Chen Cheng. "Application and Experimental Study of Support Vector Machine in Rolling Bearing Fault." Applied Mechanics and Materials 48-49 (February 2011): 241–45. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.241.
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A brief introduction of the basic concepts of the classification interval, the optimal classification surface and support vector; explained derivation of SVM based on Lagrange optimization method; Sigmoid kernel function and so on. It describes three methods of C-SVM、V-SVM and least squares SVM based on Sigmoid kernel function. To a bearing failure as a example to compare three results of SVM training of the kernel linear function, polynomial kernel function, Sigmoid kernel function, The results show that satisfactory fault analysis demand the appropriate kernel function selection. Fault in the gear box, the bearing failure is 19%, In addition, the rate is as high as 30% in other rotating machinery system failure [1,2].Thus, rolling bearing condition monitoring and fault diagnosis are very important to production safety, and many scholars have done numerous studies [3,4]. Support vector machine method is a learning methods based on statistical learning theory Vapnik-Chervonenkis dimension theory and structural risk minimization [5,6].
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Wu, Chong, Lu Wang, and Zhe Shi. "Financial Distress Prediction Based on Support Vector Machine with a Modified Kernel Function." Journal of Intelligent Systems 25, no. 3 (July 1, 2016): 417–29. http://dx.doi.org/10.1515/jisys-2014-0132.
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AbstractFor the financial distress prediction model based on support vector machine, there are no theories concerning how to choose a proper kernel function in a data-dependent way. This paper proposes a method of modified kernel function that can availably enhance classification accuracy. We apply an information-geometric method to modifying a kernel that is based on the structure of the Riemannian geometry induced in the input space by the kernel. A conformal transformation of a kernel from input space to higher-dimensional feature space enlarges volume elements locally near support vectors that are situated around the classification boundary and reduce the number of support vectors. This paper takes the Gaussian radial basis function as the internal kernel. Additionally, this paper combines the above method with the theories of standard regularization and non-dimensionalization to construct the new model. In the empirical analysis section, the paper adopts the financial data of Chinese listed companies. It uses five groups of experiments with different parameters to compare the classification accuracy. We can make the conclusion that the model of modified kernel function can effectively reduce the number of support vectors, and improve the classification accuracy.
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Jain, Paras, CH N. V. S. Praneeth, Iragavarapu Kannan, Potluri Harsha Sai, and Jaba Deva Krupa Abel. "Electrocardiogram Beat Classification Using Data Filtration Technique and Support Vector Machine." Journal of Computational and Theoretical Nanoscience 17, no. 8 (August 1, 2020): 3613–20. http://dx.doi.org/10.1166/jctn.2020.9240.
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This work addresses the automatic classification of arrhythmia beats into four generalized classes as described by the Association for the Advancement of Medical Instrumentation (AAMI) standard. We propose a method that includes time-series, statistical and frequency features of RR-interval, DWT, and EMD analysis of QRS morphology. Also, a data filtration technique using support vector selection and under-sampling is applied to find those features as well as data points having significant prediction capabilities. While testing the above combination on MIT-BIH arrhythmia database, adopting the inter-patient paradigm, we achieved 70%, 99.79%, 64.5%, and 80.55% Se and 61.76%, 94.64%, 83.22%, and 77.48% PPV for F, N, SVEB, and VEB classes respectively. Further, the proposed method reduced the classifier’s complexity through feature selection and computation time by data reduction while maintaining the generalization capability of the model. Another finding includes the significant contribution that RR-interval, 180–360 Hz and 0–45 Hz band power, and non-linear statistical characteristics have in distinguishing the arrhythmia classes. The feature and data selection criterion used is F -score and one-class classification by RBF-SVM respectively. The classifier used for building the final model is SVM with the cubic kernel.
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Audina, Nur, Vincentius P. Siregar, and I. Wayan Nurjaya. "ANALISIS PERUBAHAN SEBARAN MANGROVE MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM) DENGAN CITRA LANDSAT DI KABUPATEN BINTAN KEPULAUAN RIAU." Jurnal Ilmu dan Teknologi Kelautan Tropis 11, no. 1 (April 1, 2019): 49–63. http://dx.doi.org/10.29244/jitkt.v11i1.22468.
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ABSTRAKMangrove berfungsi sebagai pelindung abrasi pantai, kawasan pemijahan serta sebagai habitat alami bagi biota darat dan laut. Mangrove banyak dimanfaatkan sebagai penghasil kayu, kawasan wisata serta wilayah konservasi. Adanya pemanfaatan mangrove tersebut menyebabkan terjadi perubahan luasan mangrove yang akan berdampak pada keseimbangan ekosistem perairan. Penelitian ini bertujuan untuk menganalisis perubahan luasan mangrove menggunakan citra satelit Landsat dengan interval waktu 4 tahun (2005 - 2017). Data yang digunakan adalah citra satelit Landsat 5 (2005, 2009) dan Landsat 8 (2013, 2017) pada 3 lokasi yaitu (Desa Berakit, Bintan Buyu dan Teluk Sesah). Algoritma yang digunakan dalam tahap klasifikasi adalah Maximum Likelihood (MLH) dan Support Vector Machine (SVM) dengan 4 kernel. Perubahan penutup lahan selanjutnya dianalisis berkaitan dengan sebaran muatan padatan tersuspensi (MPT). Hasil penelitian menunjukkan mangrove, pemukiman dan perkebunan mengalami pertambahan luasan pada 3 desa tersebut. Hasil klasifikasi tutupan lahan menunjukkan algoritma SVM kernel Radial Basis Function (RBF) memberikan akurasi yang tinggi, yaitu 70,42% dengan koefisien kappa 0,61, sedangkan hasil uji signifikansi menunjukkan bahwa SVM dengan kernel RBF tidak memiliki perbedaan yang signifikan dengan kernel Sigmoid. Berdasarkan tahun 2005-2017, adanya perubahan alih fungsi lahan memberikan dampak pada konsentrasi MPT karena memiliki korelasi yang tinggi serta berpengaruh terhadap perubahan garis pantai yaitu abrasi (Berakit) dan akresi (Bintan Buyu dan Teluk Sesah). ABSTRACTMangrove serves as a protector for coastal abrasion, spawning ground, and natural habitats of species of terrestrial and marine biota. It is widely used for producing woods, tourist areas and conservation areas. The change of its functions above will therefore affect to altering its area cover that is impacted to an imbalance of aquatic ecosystems. This study aimed to analyze the changes of mangrove extent using the Landsat images with data acquisition (2005- 2017) with interval 4 years. The data used in this study were Landsat 5 (2005, 2009) and Landsat 8 (2013 and 2017) at 3 villages (Berakit, Bintan Buyu and Teluk Sesah). The data were analyzed by using algorithms of Maximum Likelihood (MLH) and Support Vector Machine (SVM) with 4 kernels. The change of mangrove cover was then analyzed according to Total Suspended Solid (TSS). The results showed that mangroves, settlements and plantations had increase in the 3 villages. The land cover classification showed that SVM algorithm with kernel Radial Basis Function (RBF) gave high accuracy of 70.42% with coefficient kappa 0.61 while significance test showed no significant difference with SVM Sigmoid kernel type. Based on 2005-2017, changes in land use change have an impact on MPT concentration because it has a high correlation and has an effect on shoreline changes namely abrasion (Berakit) and accretion (Bintan Buyu and Teluk Sesah).
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Wirasati, Ilsya, Zuherman Rustam, Jane Eva Aurelia, Sri Hartini, and Glori Stephani Saragih. "Comparison some of kernel functions with support vector machines classifier for thalassemia dataset." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (June 1, 2021): 430. http://dx.doi.org/10.11591/ijai.v10.i2.pp430-437.
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<span id="docs-internal-guid-9a30056f-7fff-8ff1-59e1-69f89f4280bd"><span>In the medical field, accurate classification of medical data is really important because of its impact on disease detection and patient’s treatment. Technology, machine learning, is needed to help medical staff to improve accuracy to classify disease. This research discussed some kernel functions, such as gaussian radial basis function (RBF) kernel, Polynomial kernel, and linear kernel with support vector machine (SVM) to classify thalassemia data. Thalassemia is a genetic blood disorder which is also one of the major public health problems. In this paper, there is an explanation about thalassemia, SVM, and some of the kernel functions that serve as a comprehensive source for the next research about this topic. Furthermore, there is a comparison result from three kernel functions to find out which one has the best performance. The result is gaussian RBF kernel with SVM is the best method with an average of accuracy 99,63%. </span></span>
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Liu, Zhi, Shuqiong Xu, Yun Zhang, Xin Chen, and C. L. Philip Chen. "Interval type-2 fuzzy kernel based support vector machine algorithm for scene classification of humanoid robot." Soft Computing 18, no. 3 (July 6, 2013): 589–606. http://dx.doi.org/10.1007/s00500-013-1080-0.
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Lu, Zhengdong, Todd K. Leen, and Jeffrey Kaye. "Kernels for Longitudinal Data with Variable Sequence Length and Sampling Intervals." Neural Computation 23, no. 9 (September 2011): 2390–420. http://dx.doi.org/10.1162/neco_a_00164.
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We develop several kernel methods for classification of longitudinal data and apply them to detect cognitive decline in the elderly. We first develop mixed-effects models, a type of hierarchical empirical Bayes generative models, for the time series. After demonstrating their utility in likelihood ratio classifiers (and the improvement over standard regression models for such classifiers), we develop novel Fisher kernels based on mixture of mixed-effects models and use them in support vector machine classifiers. The hierarchical generative model allows us to handle variations in sequence length and sampling interval gracefully. We also give nonparametric kernels not based on generative models, but rather on the reproducing kernel Hilbert space. We apply the methods to detecting cognitive decline from longitudinal clinical data on motor and neuropsychological tests. The likelihood ratio classifiers based on the neuropsychological tests perform better than than classifiers based on the motor behavior. Discriminant classifiers performed better than likelihood ratio classifiers for the motor behavior tests.
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Trabelsi, Imen, and Med Salim Bouhlel. "Feature Selection for GUMI Kernel-Based SVM in Speech Emotion Recognition." International Journal of Synthetic Emotions 6, no. 2 (July 2015): 57–68. http://dx.doi.org/10.4018/ijse.2015070104.
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Speech emotion recognition is the indispensable requirement for efficient human machine interaction. Most modern automatic speech emotion recognition systems use Gaussian mixture models (GMM) and Support Vector Machines (SVM). GMM are known for their performance and scalability in the spectral modeling while SVM are known for their discriminatory power. A GMM-supervector characterizes an emotional style by the GMM parameters (mean vectors, covariance matrices, and mixture weights). GMM-supervector SVM benefits from both GMM and SVM frameworks. In this paper, the GMM-UBM mean interval (GUMI) kernel based on the Bhattacharyya distance is successfully used. CFSSubsetEval combined with Best first algorithm and Greedy stepwise were also utilized on the supervectors space in order to select the most important features. This framework is illustrated using Mel-frequency cepstral (MFCC) coefficients and Perceptual Linear Prediction (PLP) features on two different emotional databases namely the Surrey Audio-Expressed Emotion and the Berlin Emotional speech Database.
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PACHORI, RAM BILAS, MOHIT KUMAR, PAKALA AVINASH, KORA SHASHANK, and U. RAJENDRA ACHARYA. "AN IMPROVED ONLINE PARADIGM FOR SCREENING OF DIABETIC PATIENTS USING RR-INTERVAL SIGNALS." Journal of Mechanics in Medicine and Biology 16, no. 01 (February 2016): 1640003. http://dx.doi.org/10.1142/s0219519416400030.
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Diabetes Mellitus (DM) which is a chronic disease and difficult to cure. If diabetes is not treated in a timely manner, it may cause serious complications. For timely treatment, an early detection of the disease is of great interest. Diabetes can be detected by analyzing the RR-interval signals. This work presents a methodology for classification of diabetic and normal RR-interval signals. Firstly, empirical mode decomposition (EMD) method is applied to decompose the RR-interval signals in to intrinsic mode functions (IMFs). Then five parameters namely, area of analytic signal representation (AASR), mean frequency computed using Fourier-Bessel series expansion (MFFB), area of ellipse evaluated from second-order difference plot (ASODP), bandwidth due to frequency modulation (BFM) and bandwidth due to amplitude modulation (BAM) are extracted from IMFs obtained from RR-interval signals. Statistically significant features are fed to least square-support vector machine (LS-SVM) classifier. The three kernels namely, Radial Basis Function (RBF), Morlet wavelet, and Mexican hat wavelet kernels have been studied to obtain the suitable kernel function for the classification of diabetic and normal RR-interval signals. In this work, we have obtained the highest classification accuracy of 95.63%, using Morlet wavelet kernel function with 10-fold cross-validation. The classification system proposed in this work can help the clinicians to diagnose diabetes using electrocardiogram (ECG) signals.
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Na, Hyun Seok, and Khae Hawn Kim. "Development of urination recognition technology based on Support Vector Machine using a smart band." Journal of Exercise Rehabilitation 17, no. 4 (August 23, 2021): 287–92. http://dx.doi.org/10.12965/jer.2142474.237.
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The purpose of this study was to explore the feasibility of a urination management system by developing a smart band-based algorithm that recognizes the urination interval of women. We designed a device that recognizes the time and interval of urination based on the patient’s specific posture and posture changes. The technology used for recognition applied the Radial Basis Function kernel-based Support Vector Machine, a teaching and learning method that facilitates multidimensional analysis by simultaneously judging the characteristics of complex learning data. In order to evaluate the performance of the proposed recognition technique, we compared actual urination and device-sensed urination. An experiment was performed to evaluate the performance of the recognition technology proposed in this study. The efficacy of smart band monitoring urination was evaluated in 10 female patients without urination problems. The entire experiment was performed over a total of 3 days. The average age of the participants was 28.73 years (26–34 years), and there were no signs of dysuria. The final accuracy of the algorithm was calculated based on clinical guidelines for urologists. The experiment showed a high average accuracy of 91.0%, proving the robustness of the proposed algorithm. This urination behavior recognition technique shows high accuracy and can be applied in clinical settings to characterize urination patterns in female patients. As wearable devices develop and become more common, algorithms that detect specific sequential body movement patterns that reflect specific physiological behaviors could become a new methodology to study human physiological behavior.
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Rizwan, Md Fahim, Rayed Farhad, and Md Hasan Imam. "Support Vector Machine based Stress Detection System to manage COVID-19 pandemic related stress from ECG signal." AIUB Journal of Science and Engineering (AJSE) 20, no. 1 (April 15, 2021): 8–16. http://dx.doi.org/10.53799/ajse.v20i1.112.
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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.
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S., Nanda, and Sukumar M. "Detection and classification of thyroid nodule using Shearlet coefficients and support vector machine." International Journal of Engineering & Technology 6, no. 3 (June 4, 2017): 50. http://dx.doi.org/10.14419/ijet.v6i3.7705.
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Thyroid nodules have diversified internal components and dissimilar echo patterns in ultrasound images. Textural features are used to characterize these echo patterns. This paper presents a classification scheme that uses shearlet transform based textural features for the classification of thyroid nodules in ultrasound images. The study comprised of 60 thyroid ultrasound images (30 with benign nodules and 30 with malignant nodules). Total of 22 features are extracted. Support vector machine (SVM) and K nearest neighbor (KNN) are used to differentiate benign and malignant nodules. The diagnostic sensitivity, specificity, F1_score and accuracy of both the classifiers are calculated. A comparative study has been carried out with respect to their performances. The sensitivity of SVM with radial basis function (RBF) kernel is 100% as compared to that of KNN with 96.33%. The proposed features can increase the accuracy of the classifier and decrease the rate of misdiagnosis in thyroid nodule classification.
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Br. Pasaribu, Novie Theresia, Timotius Halim, Ratnadewi Ratnadewi, and Agus Prijono. "EEG signal classification for drowsiness detection using wavelet transform and support vector machine." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (June 1, 2021): 501. http://dx.doi.org/10.11591/ijai.v10.i2.pp501-509.
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<span id="docs-internal-guid-ed628156-7fff-8934-2369-94f011b043ca"><span>There are several categories to detect and measure driver drowsiness such as physiological methods, subjective methods and behavioral methods. The most objective method for drowsiness detection is the physiological method. One of the physiological methods used is an electroencephalogram (EEG). In this research wavelet transform is used as a feature extraction and using support vector machine (SVM) as a classifier. We proposed an experiment of retrieval data which is designed by using modified-EAR and EEG signal. From the SVM training process, with the 5-fold cross validation, Quadratic kernel has the highest accuracy 84.5% then others. In testing Driving-2 process 7 respondents were detected as drowsiness class, and 3 respondents were detected as awake class. In the testing of Driving-3 process, 6 respondents were detected as drowsiness class, and 4 respondents were detected as awake class. </span></span>
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Wang, Jiang, Peng Bai, Xiao Hu Duan, and Yan Li. "Research on Parameter Optimization of Mixed Gas IR SVM Calibration Model." Advanced Materials Research 1083 (January 2015): 97–103. http://dx.doi.org/10.4028/www.scientific.net/amr.1083.97.
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A SVM calibration model combined with new information processing method of support vector machine and infrared spectroscopy is established. For the problem of model parameters affecting the analysis results, the optimization of the model parameters is studied through the experiment. The mixed gas containing hydrocarbon is used as an example, spectra data preprocessing, spectra analysis band, spectrometer scanning interval, types of kernel function for SVM calibration model, penalty factorC, and other parameters that affect the measurement results are optimized. The experimental results show that the accuracy of the analysis results can be improved in the case of the SVM calibration model optimized and the model has a practical application value.
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Ramli, Sofea, and Sharifalillah Nordin. "Personality Prediction Based on Iris Position Classification Using Support Vector Machines." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 3 (March 1, 2018): 667. http://dx.doi.org/10.11591/ijeecs.v9.i3.pp667-672.
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<p>Predicting personality generally involves personal interpretations of a person which makes the current methods for personality prediction process less adequate, timely and tedious. Thus, a simple yet efficient alternative method is proposed in this project for detecting iris positions which are used in Neuro-Linguistic Programming as clues for the human internal representational system and mental activity. This study set out to determine several positions of the iris of a person based on the Eye Accessing Cues. The design and the development of a complete system will be undertaken as for the users to use as a medium to predict their personality based on their iris position. Several pre-processing techniques were executed to each of the data before run into the testing and training activities for accuracy gaining. The algorithm used for classification of the positions is Support Vector Machine which by taking rectangle crop of an eye with 9000 pixels as inputs. Radial Basis Function is used for the kernel parameter of the proposed method. The classification will then map into the type of a person with the lists of his personality based on Visual, Auditory and Kinaesthetic theory. The result of the classification of the iris positions is currently 84.9% accurate which in the future might be increased by tuning several other parameters that consisted in Support Vector Machine.</p>
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Oh, Hyun-Taik, Jong-Ick Won, Sung-Choong Woo, and Tae-Won Kim. "Determination of Impact Damage in CFRP via PVDF Signal Analysis with Support Vector Machine." Materials 13, no. 22 (November 18, 2020): 5207. http://dx.doi.org/10.3390/ma13225207.
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Carbon fiber reinforced plastics (CFRPs) have high specific stiffness and strength, but they are vulnerable to transverse loading, especially low-velocity impact loadings. The impact damage may cause serious strength reduction in CFRP structure, but the damage in a CFRP is mainly internal and microscopic, that it is barely visible. Therefore, this study proposes a method of determining impact damage in CFRP via poly(vinylidene fluoride) (PVDF) sensor, which is convenient and has high mechanical and electrical performance. In total, 114 drop impact tests were performed to investigate on impact responses and PVDF signals due to impacts. The test results were analyzed to determine the damage of specimens and signal features, which are relevant to failure mechanisms were extracted from PVDF signals by means of discrete wavelet transform (DWT). Support vector machine (SVM) was used for optimal classification of damage state, and the model using radial basis function (RBF) kernel showed the best performance. The model was validated through a 4-fold cross-validation, and the accuracy was reported to be 92.30%. In conclusion, impact damage in CFRP structures can be effectively determined using the spectral analysis and the machine learning-based classification on PVDF signals.
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Li, Fuqiang, Shiying Zhang, Wenxuan Li, Wei Zhao, Bingkang Li, and Huiru Zhao. "Forecasting Hourly Power Load Considering Time Division: A Hybrid Model Based on K-means Clustering and Probability Density Forecasting Techniques." Sustainability 11, no. 24 (December 6, 2019): 6954. http://dx.doi.org/10.3390/su11246954.
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In comparison with traditional point forecasting method, probability density forecasting can reflect the load fluctuation more effectively and provides more information. This paper proposes a hybrid hourly power load forecasting model, which integrates K-means clustering algorithm, Salp Swarm Algorithm (SSA), Least Square Support Vector Machine (LSSVM), and kernel density estimation (KDE) method. Firstly, the loads at 24 times a day are grouped into three categories according to the K-means clustering algorithm, which correspond to the valley period, flat period, and peak period of the load, respectively. Secondly, the load point forecasting value is obtained by LSSVM method optimized by SSA algorithm. Furthermore, the kernel density estimation method is employed to fit the forecasting error of SSA-LSSVM in different time periods, and the probability density function of the error distribution is obtained. The final load probability density forecasting result is obtained by combining the point forecasting value and the error fitting result, and then the upper and lower limits of the confidence interval under the given confidence level are solved. In this paper, the performance of the model is evaluated by two indicators named interval coverage and interval average width. Meanwhile, in comparison with several other models, it can be concluded that the proposed model can effectively improve the forecasting effect.
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Tang, Li-Juan, Wen Du, Hai-Yan Fu, Jian-Hui Jiang, Hai-Long Wu, Guo-Li Shen, and Ru-Qin Yu. "New Variable Selection Method Using Interval Segmentation Purity with Application to Blockwise Kernel Transform Support Vector Machine Classification of High-Dimensional Microarray Data." Journal of Chemical Information and Modeling 49, no. 8 (July 31, 2009): 2002–9. http://dx.doi.org/10.1021/ci900032q.
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Xue-hua, Zhao, Miao Xu-juan, Zhang Zhen-gang, and Hao Zheng. "Research on Prediction Method of Reasonable Cost Level of Transmission Line Project Based on PCA-LSSVM-KDE." Mathematical Problems in Engineering 2019 (August 1, 2019): 1–11. http://dx.doi.org/10.1155/2019/1649086.
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In order to reduce the investment risk, the evaluation standard of transmission line project investment planning becomes higher, which puts forward higher requirements for the reasonable level prediction of transmission line project cost. This paper combines principal component analysis (PCA) with the least squares support vector machine (LSSVM) model and establishes a point prediction model for transmission line project cost. Based on the analysis of the error of the point prediction model, the kernel density estimation (KDE) method is innovatively introduced to estimate the prediction error, and the probability density function of the error is obtained. Then, according to different confidence levels, the corresponding cost intervals are obtained, which means that the reasonable level of transmission line project cost is obtained. The results show that the coverage rate of the cost prediction interval under 85% confidence level is 88.57%. This conclusion shows that the model has high reliability and can provide a reliable basis for the evaluation of transmission line project investment planning.
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Moradi, Sina, Anthony Agostino, Ziba Gandomkar, Seokhyeon Kim, Lisa Hamilton, Ashish Sharma, Rita Henderson, and Greg Leslie. "Quantifying natural organic matter concentration in water from climatological parameters using different machine learning algorithms." H2Open Journal 3, no. 1 (January 1, 2020): 328–42. http://dx.doi.org/10.2166/h2oj.2020.035.
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Abstract The present understanding of how changes in climate conditions will impact the flux of natural organic matter (NOM) from the terrestrial to aquatic environments and thus aquatic dissolved organic carbon (DOC) concentrations is limited. In this study, three machine learning algorithms were used to predict variations in DOC concentrations in an Australian drinking water catchment as a function of climate, catchment and physical water quality data. Four independent variables including precipitation, temperature, leaf area index and turbidity (n = 5,540) were selected from a large dataset to develop and train each machine learning model. The accuracy of the multivariable linear regression, support vector regression (SVR) and Gaussian process regression algorithms with different kernel functions was determined using adjusted R-squared (adj. R2), root-mean-squared error (RMSE) and mean absolute error (MAE). Model accuracy was very sensitive to the time interval used to average climate observations prior to pairing with DOC observations. The SVR model with a quadratic kernel function and a 12-day time interval between climate and water quality observations outperformed the other machine learning algorithms (adj. R2 = 0.71, RMSE = 1.9, MAE = 1.35). The area under the receiver operating characteristic curve method (AUC) confirmed that the SVR model could predict 92% of the elevated DOC observations; however, it was not possible to estimate DOC values at specific sampling sites in the catchment, probably due to the complex local geological and hydrological changes in the sites that directly surround and feed each sampling point. Further research is required to establish potential relationships between climatological data and NOM concentration in other water catchments – especially in the face of a changing climate.
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Shao, Zhenhua, Tianxiang Chen, Li-an Chen, and Hong Tian. "An Internal Model Controller for Three-Phase APF Based on LS-Extreme Learning Machine." Open Electrical & Electronic Engineering Journal 8, no. 1 (December 31, 2014): 717–22. http://dx.doi.org/10.2174/1874129001408010717.
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Aiming at the problem that the three-phase APF’s dynamic model is a multi-variable, nonlinear and strong coupling system, an internal model controller for three-phase APF based on LS-Extreme Learning Machine is studied in this paper. As a novel single hidden layer feed-forward neural networks, extreme learning machine (ELM) has several advantages: simple net structural, fast learning speed, good generalization performance and so on. In order to improve the controller’s dynamic responses, a least squares extreme learning machine for internal model control is proposed. A least squares ELM regression (LS-ELMR) model for the three-phase APFS on-line monitoring was built from external factors with in-out datum. Moreover, the relative stable error is presented to evaluate the system performance and the features for the internal model control system based on extreme learning machine, neural network, kernel ridge regress and support vector machine. The experimental results show that the LS-internal model control system based on extreme learning machine has good dynamic performance and strong filtering result.
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Xu, Yuanyuan, and Genke Yang. "A Short-Term Wind Speed Forecasting Hybrid Model Based on Empirical Mode Decomposition and Multiple Kernel Learning." Complexity 2020 (November 2, 2020): 1–13. http://dx.doi.org/10.1155/2020/8811407.
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Short-term wind speed forecasting plays an increasingly important role in the security, scheduling, and optimization of power systems. As wind speed signals are usually nonlinear and nonstationary, how to accurately forecast future states is a challenge for existing methods. In this paper, for highly complex wind speed signals, we propose a multiple kernel learning- (MKL-) based method to adaptively assign the weights of multiple prediction functions, which extends conventional wind speed forecasting methods using a support vector machine. First, empirical mode decomposition (EMD) is used to decompose complex signals into several intrinsic mode function component signals with different time scales. Then, for each channel, one multiple kernel model is constructed for forecasting the current sequence signal. Finally, several experiments are carried out on different New Zealand wind farm data, and the relevant prediction accuracy indexes and confidence intervals are evaluated. Extensive experimental results show that, compared with existing machine learning methods, the EMD-MKL model proposed in this paper has better performance in terms of the prediction accuracy evaluation indexes and confidence intervals and shows a better ability to generalize.
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Chen, Yijun, Chongshi Gu, Chenfei Shao, Hao Gu, Dongjian Zheng, Zhongru Wu, and Xiao Fu. "An Approach Using Adaptive Weighted Least Squares Support Vector Machines Coupled with Modified Ant Lion Optimizer for Dam Deformation Prediction." Mathematical Problems in Engineering 2020 (April 13, 2020): 1–23. http://dx.doi.org/10.1155/2020/9434065.
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A dam deformation prediction model based on adaptive weighted least squares support vector machines (AWLSSVM) coupled with modified Ant Lion Optimization (ALO) is proposed, which can be utilized to evaluate the operational states of concrete dams. First, the Ant Lion Optimizer, a novel metaheuristic algorithm, is used to determine the punishment factor and kernel width in the least squares support vector machine (LSSVM) model, which simulates the hunting process of antlions in nature. Second, aiming to solve the premature convergence phenomenon, Levy flight is introduced into the ALO to improve the global optimization ability. Third, according to the statistical characteristics of the datum error, an improved normal distribution weighting rule is applied to update the weighted value of data samples based on the learning result of the LSSVM model. Moreover, taking a concrete arch dam in China as an example, the horizontal displacement recorded by a pendulum is used as a study object. The accuracy and validity of the proposed model are verified and evaluated based on the four evaluating criteria, and the results of the proposed model are compared with those of well-established models. The simulation results demonstrate that the proposed model outperforms other models and effectively overcomes the influence of outliers on the performance of the model. It also has high prediction accuracy, produces excellent generalization performance, and can be a promising alternative technique for the analysis and prediction of dam deformation and other fields, including flood interval prediction, the stock price market, and wind speed forecasting.
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Hussain, Lal, Imtiaz Ahmed Awan, Wajid Aziz, Sharjil Saeed, Amjad Ali, Farukh Zeeshan, and Kyung Sup Kwak. "Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques." BioMed Research International 2020 (February 18, 2020): 1–19. http://dx.doi.org/10.1155/2020/4281243.
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The adaptability of heart to external and internal stimuli is reflected by the heart rate variability (HRV). Reduced HRV can be a predictor of negative cardiovascular outcomes. Based on the nonlinear, nonstationary, and highly complex dynamics of the controlling mechanism of the cardiovascular system, linear HRV measures have limited capability to accurately analyze the underlying dynamics. In this study, we propose an automated system to analyze HRV signals by extracting multimodal features to capture temporal, spectral, and complex dynamics. Robust machine learning techniques, such as support vector machine (SVM) with its kernel (linear, Gaussian, radial base function, and polynomial), decision tree (DT), k-nearest neighbor (KNN), and ensemble classifiers, were employed to evaluate the detection performance. Performance was evaluated in terms of specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). The highest performance was obtained using SVM linear kernel (TA = 93.1%, AUC = 0.97, 95% CI [lower bound = 0.04, upper bound = 0.89]), followed by ensemble subspace discriminant (TA = 91.4%, AUC = 0.96, 95% CI [lower bound 0.07, upper bound = 0.81]) and SVM medium Gaussian kernel (TA = 90.5%, AUC = 0.95, 95% CI [lower bound = 0.07, upper bound = 0.86]). The results reveal that the proposed approach can provide an effective and computationally efficient tool for automatic detection of congestive heart failure patients.
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Lei, Mi, Long Chen, Bisheng Huang, and Keli Chen. "Determination of Magnesium Oxide Content in Mineral Medicine Talcum Using Near-Infrared Spectroscopy Integrated with Support Vector Machine." Applied Spectroscopy 71, no. 11 (September 21, 2017): 2427–36. http://dx.doi.org/10.1177/0003702817727016.
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In this research paper, a fast, quantitative, analytical model for magnesium oxide (MgO) content in medicinal mineral talcum was explored based on near-infrared (NIR) spectroscopy. MgO content in each sample was determined by ethylenediaminetetraacetic acid (EDTA) titration and taken as reference value of NIR spectroscopy, and then a variety of processing methods of spectra data were compared to establish a good NIR spectroscopy model. To start, 50 batches of talcum samples were categorized into training set and test set using the Kennard–Stone (K-S) algorithm. In a partial least squares regression (PLSR) model, both leave-one-out cross-validation (LOOCV) and training set validation (TSV) were used to screen spectrum preprocessing methods from multiplicative scatter correction (MSC), and finally the standard normal variate transformation (SNV) was chosen as the optimal pretreatment method. The modeling spectrum bands and ranks were optimized using PLSR method, and the characteristic spectrum ranges were determined as 11995–10664, 7991–6661, and 4326–3999 cm−1, with four optimal ranks. In the support vector machine (SVM) model, the radical basis function (RBF) kernel function was used. Moreover, the full spectrum data of samples pretreated with SNV, the characteristic spectrum data screened using synergy interval partial least squares (SiPLS), and the scoring data of the first four ranks obtained by a partial least squares (PLS) dimension reduction of characteristic spectrum were taken as input variables of SVM, and the MgO content reference values of various sample were taken as output values. In addition, the SVM model internal parameters were optimized using the grid optimization method (GRID), particle swarm optimization (PSO), and genetic algorithm (GA) so that the optimal C and g-values were determined and the validation model was established. By comprehensively comparing the validation effects of different models, it can be concluded that the scoring data of the first four ranks obtained by PLS dimension reduction of characteristic spectrum were taken as input variables of SVM, and the PLS-SVM regression model established using GRID was the optimal NIR spectroscopy quantitative model of talc. This PLS-SVM regression model (rank = 4) measured that the MgO content of talcum was in the range of 17.42–33.22%, with root mean square error of cross validation (RMSECV) of 2.2127%, root mean square error of calibration (RMSEC) of 0.6057%, and root mean square error of prediction (RMSEP) of 1.2901%. This model showed high accuracy and strong prediction capacity, which can be used for rapid prediction of MgO content in talcum.
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Pandit, Ravi, and Athanasios Kolios. "SCADA Data-Based Support Vector Machine Wind Turbine Power Curve Uncertainty Estimation and Its Comparative Studies." Applied Sciences 10, no. 23 (December 4, 2020): 8685. http://dx.doi.org/10.3390/app10238685.
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Power curves, supplied by turbine manufacturers, are extensively used in condition monitoring, energy estimation, and improving operational efficiency. However, there is substantial uncertainty linked to power curve measurements as they usually take place only at hub height. Data-driven model accuracy is significantly affected by uncertainty. Therefore, an accurate estimation of uncertainty gives the confidence to wind farm operators for improving performance/condition monitoring and energy forecasting activities that are based on data-driven methods. The support vector machine (SVM) is a data-driven, machine learning approach, widely used in solving problems related to classification and regression. The uncertainty associated with models is quantified using confidence intervals (CIs), which are themselves estimated. This study proposes two approaches, namely, pointwise CIs and simultaneous CIs, to measure the uncertainty associated with an SVM-based power curve model. A radial basis function is taken as the kernel function to improve the accuracy of the SVM models. The proposed techniques are then verified by extensive 10 min average supervisory control and data acquisition (SCADA) data, obtained from pitch-controlled wind turbines. The results suggest that both proposed techniques are effective in measuring SVM power curve uncertainty, out of which, pointwise CIs are found to be the most accurate because they produce relatively smaller CIs. Thus, pointwise CIs have better ability to reject faulty data if fault detection algorithms were constructed based on SVM power curve and pointwise CIs. The full paper will explain the merits and demerits of the proposed research in detail and lay out a foundation regarding how this can be used for offshore wind turbine conditions and/or performance monitoring activities.
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Zhang, Lei, Lun Xie, Qinkai Han, Zhiliang Wang, and Chen Huang. "Probability Density Forecasting of Wind Speed Based on Quantile Regression and Kernel Density Estimation." Energies 13, no. 22 (November 22, 2020): 6125. http://dx.doi.org/10.3390/en13226125.
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Based on quantile regression (QR) and kernel density estimation (KDE), a framework for probability density forecasting of short-term wind speed is proposed in this study. The empirical mode decomposition (EMD) technique is implemented to reduce the noise of raw wind speed series. Both linear QR (LQR) and nonlinear QR (NQR, including quantile regression neural network (QRNN), quantile regression random forest (QRRF), and quantile regression support vector machine (QRSVM)) models are, respectively, utilized to study the de-noised wind speed series. An ensemble of conditional quantiles is obtained and then used for point and interval predictions of wind speed accordingly. After various experiments and comparisons on the real wind speed data at four wind observation stations of China, it is found that the EMD-LQR-KDE and EMD-QRNN-KDE generally have the best performance and robustness in both point and interval predictions. By taking conditional quantiles obtained by the EMD-QRNN-KDE model as the input, probability density functions (PDFs) of wind speed at different times are obtained by the KDE method, whose bandwidth is optimally determined according to the normal reference criterion. It is found that most actual wind speeds lie near the peak of predicted PDF curves, indicating that the probabilistic density prediction by EMD-QRNN-KDE is believable. Compared with the PDF curves of the 90% confidence level, the PDF curves of the 80% confidence level usually have narrower wind speed ranges and higher peak values. The PDF curves also vary with time. At some times, they might be biased, bimodal, or even multi-modal distributions. Based on the EMD-QRNN-KDE model, one can not only obtain the specific PDF curves of future wind speeds, but also understand the dynamic variation of density distributions with time. Compared with the traditional point and interval prediction models, the proposed QR-KDE models could acquire more information about the randomness and uncertainty of the actual wind speed, and thus provide more powerful support for the decision-making work.
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Jayagopi, G., and S. Pushpa. "On the classification of arrhythmia using supplementary features from Tetrolet transforms." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (December 1, 2019): 5006. http://dx.doi.org/10.11591/ijece.v9i6.pp5006-5015.
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<span>Heart diseases had been molded as potential threats to human lives, especially to elderly people in recent days due to the dynamically varying food habits among the people. However, these diseases could be easily caught by proper analysis of Electrocardiogram (ECG) signals acquired from individuals. This paper proposes a better method to detect and classify the arrhythmia using 15 features which include 4 R-R interval features, 3 statistical and 6 chaotic features estimated from ECG signals. Additionally, Entropy and Energy features had been gained after converting one dimensional ECG signals to two dimensional data and applied Tetrolet transforms on that. Total numbers of 15 features had been utilized to classify the heart beats from the benchmark MIT-Arrhythmia database using Support Vector Machines (SVM). The classification performance was analyzed under various kernel functions and different Tetrolet decomposition levels. It is found that Radial Basis Function (RBF) kernel could perform better than linear and polynomial kernels. This research attempt yielded an accuracy of 99.35 % against the existing works. Moreover, addition of two more features had introduced a negligible overhead of time. Hence, this method is better suitable to detect and classify the Arrhythmia in both online and offline.</span>
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Daniel, Melvin, Jangkung Raharjo, and Koredianto Usman. "Iris-based Image Processing for Cholesterol Level Detection using Gray Level Co-Occurrence Matrix and Support Vector Machine." Engineering Journal 24, no. 5 (September 30, 2020): 135–44. http://dx.doi.org/10.4186/ej.2020.24.5.135.
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Serious illnesses such as strokes and heart attacks can be triggered by high levels of cholesterol in human blood that exceeds ideal conditions, where the ideal cholesterol level is below 200 mg/dL. To find out cholesterol levels need a long process because the patient must go through a blood sugar test that requires the patient to undergo fasting for 10–12 hours first before the test. Iridology is a branch of science that studies human iris and its relation to the wellness of human internal organs. The method can be used as an alternative for medical analysis. Iridology thus can be used to assess the conditions of organs, body construction, and other psychological conditions. This paper proposes a cholesterol detection system based on the iris image processing using Gray Level Co-Occurrence Matrix (GLCM) and Support Vector Machine (SVM). GLCM is used as the feature extraction method of the image, while SVM acts as the classifier of the features. In addition to GLCM and SVM, this paper also construct a preprocessing method which consist of image resizing, segmentation, and color image to gray level conversion of the iris image. These steps are necessary before the GLCM feature extraction step can be applied. In principle, the GLCM method is a construction of a matrix containing the information about the proximity position of gray level images pixels. The output of GLCM is fed to the SVM that relies on the best hyperplane. Thus, SVM performs as a separator of two data classes of the input space. From the simulation results, the system built was able to detect excess cholesterol levels through iris image and classify into three classes, namely: non–cholesterol (< 200 ), risk of cholesterol (200–239 ) and high cholesterol (> 240 ). The accuracy rate obtained was 94.67% with an average computation time of 0.0696 . It was using each of the 75 training and test data, with the second-order parameters used are contrast–correlation–energy–homogeneity, pixel distance = 1, quantization level = 8, Polynomial kernel types and One Against One Multiclass. Iris has specific advantages which can record all organ conditions, body construction and psychological conditions. Therefore, Iridology as a science based on the arrangement of iris fibers can be an alternative for medical analysis. In this paper proposes a cholesterol detection system through the iris using Gray Level Co-occurence Matrix and Support Vector Machine. Input system is an iris image that will be processed by pre-processing and then extracted features with the Gray Level Co-Occurrence Matrix method which is a matrix containing information about position the proximity of pixels that have a certain gray level. And then classified with the Support Vector Machine method that relies on the best hyper lane which functions as a separator of two data classes in the input space. From the simulation results, the system built was able to detect excess cholesterol levels through iris image and classify into three classes are: risk of cholesterol, high cholesterol and non–cholesterol with an accuracy rate of 96.47% and average computation time was 0.0696 using each of the 75 training and test data, with the second-order parameters used are contrast–correlation–energy–homogeneity, pixel distance = 1, quantization level = 8, Polynomial kernel types and One Against One Multiclass.
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Longato, Enrico, Giada Acciaroli, Andrea Facchinetti, Alberto Maran, and Giovanni Sparacino. "Simple Linear Support Vector Machine Classifier Can Distinguish Impaired Glucose Tolerance Versus Type 2 Diabetes Using a Reduced Set of CGM-Based Glycemic Variability Indices." Journal of Diabetes Science and Technology 14, no. 2 (March 31, 2019): 297–302. http://dx.doi.org/10.1177/1932296819838856.
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Background: Many glycemic variability (GV) indices exist in the literature. In previous works, we demonstrated that a set of GV indices, extracted from continuous glucose monitoring (CGM) data, can distinguish between stages of diabetes progression. We showed that 25 indices driving a logistic regression classifier can differentiate between healthy and nonhealthy individuals; whereas 37 GV indices and four individual parameters, feeding a polynomial-kernel support vector machine (SVM), can further distinguish between impaired glucose tolerance (IGT) and type 2 diabetes (T2D). The latter approach has some limitations to interpretability (complex model, extensive index pool). In this article, we try to obtain the same performance with a simpler classifier and a parsimonious subset of indices. Methods: We analyzed the data of 62 subjects with IGT or T2D. We selected 17 interpretable GV indices and four parameters (age, sex, BMI, waist circumference). We trained a SVM on the data of a baseline visit and tested it on the follow-up visit, comparing the results with the state-of-art methods. Results: The linear SVM fed by a reduced subset of 17 GV indices and four basic parameters achieved 82.3% accuracy, only marginally worse than the reference 87.1% (41-features polynomial-kernel SVM). Cross-validation accuracies were comparable (69.6% vs 72.5%). Conclusion: The proposed SVM fed by 17 GV indices and four parameters can differentiate between IGT and T2D. Using a simpler model and a parsimonious set of indices caused only a slight accuracy deterioration, with significant advantages in terms of interpretability.
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Rustam, Zuherman, Aldi Purwanto, Sri Hartini, and Glori Stephani Saragih. "Lung cancer classification using fuzzy c-means and fuzzy kernel C-Means based on CT scan image." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (June 1, 2021): 291. http://dx.doi.org/10.11591/ijai.v10.i2.pp291-297.
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<span id="docs-internal-guid-94842888-7fff-2ae1-cd5c-026943b95b7f"><span>Cancer is one of the diseases with the highest mortality rate in the world. Cancer is a disease when abnormal cells grow out of control that can attack the body's organs side by side or spread to other organs. Lung cancer is a condition when malignant cells form in the lungs. To diagnose lung cancer can be done by taking x-ray images, CT scans, and lung tissue biopsy. In this modern era, technology is expected to help research in the field of health. Therefore, in this study feature extraction from CT images was used as data to classify lung cancer. We used CT scan image data from SPIE-AAPM Lung CT challenge 2015. Fuzzy C-Means and fuzzy kernel C-Means were used to classify the lung nodule from the patient into benign or malignant. Fuzzy C-Means is a soft clustering method that uses Euclidean distance to calculate the cluster center and membership matrix. Whereas fuzzy kernel C-Means uses kernel distance to calculate it. In addition, the support vector machine was used in another study to obtain 72% average AUC. Simulations were performed using different k-folds. The score showed fuzzy kernel C-Means had the highest accuracy of 74%, while fuzzy C-Means obtained 73% accuracy. </span></span>
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Ma, Junwei, Xiaoxu Niu, Huiming Tang, Yankun Wang, Tao Wen, and Junrong Zhang. "Displacement Prediction of a Complex Landslide in the Three Gorges Reservoir Area (China) Using a Hybrid Computational Intelligence Approach." Complexity 2020 (January 28, 2020): 1–15. http://dx.doi.org/10.1155/2020/2624547.
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Displacement prediction of reservoir landslide remains inherently uncertain since a complete understanding of the complex nonlinear, dynamic landslide system is still lacking. An appropriate quantification of predictive uncertainties is a key underpinning of displacement prediction and mitigation of reservoir landslide. A density prediction, offering a full estimation of the probability density for future outputs, is promising for quantification of the uncertainty of landslide displacement. In the present study, a hybrid computational intelligence approach is proposed to build a density prediction model of landslide displacement and quantify the associated predictive uncertainties. The hybrid computational intelligence approach consists of two steps: first, the input variables are selected through copula analysis; second, kernel-based support vector machine quantile regression (KSVMQR) is employed to perform density prediction. The copula-KSVMQR approach is demonstrated through a complex landslide in the Three Gorges Reservoir Area (TGRA), China. The experimental study suggests that the copula-KSVMQR approach is capable of construction density prediction by providing full probability density distributions of the prediction with perfect performance. In addition, different types of predictions, including interval prediction and point prediction, can be derived from the obtained density predictions with excellent performance. The results show that the mean prediction interval widths of the proposed approach at ZG287 and ZG289 are 27.30 and 33.04, respectively, which are approximately 60 percent lower than that obtained using the traditional bootstrap-extreme learning machine-artificial neural network (Bootstrap-ELM-ANN). Moreover, the obtained point predictions show great consistency with the observations, with correlation coefficients of 0.9998. Given the satisfactory performance, the presented copula-KSVMQR approach shows a great ability to predict landslide displacement.
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Zeng, Bing, Jiang Guo, Wenqiang Zhu, Zhihuai Xiao, Fang Yuan, and Sixu Huang. "A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM." Energies 12, no. 21 (November 1, 2019): 4170. http://dx.doi.org/10.3390/en12214170.
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Dissolved gas analysis (DGA) is a widely used method for transformer internal fault diagnosis. However, the traditional DGA technology, including Key Gas method, Dornenburg ratio method, Rogers ratio method, International Electrotechnical Commission (IEC) three-ratio method, and Duval triangle method, etc., suffers from shortcomings such as coding deficiencies, excessive coding boundaries and critical value criterion defects, which affect the reliability of fault analysis. Grey wolf optimizer (GWO) is a novel swarm intelligence optimization algorithm proposed in 2014 and it is easy for the original GWO to fall into the local optimum. This paper presents a new meta-heuristic method by hybridizing GWO with differential evolution (DE) to avoid the local optimum, improve the diversity of the population and meanwhile make an appropriate compromise between exploration and exploitation. A fault diagnosis model of hybrid grey wolf optimized least square support vector machine (HGWO-LSSVM) is proposed and applied to transformer fault diagnosis with the optimal hybrid DGA feature set selected as the input of the model. The kernel principal component analysis (KPCA) is used for feature extraction, which can decrease the training time of the model. The proposed method shows high accuracy of fault diagnosis by comparing with traditional DGA methods, least square support vector machine (LSSVM), GWO-LSSVM, particle swarm optimization (PSO)-LSSVM and genetic algorithm (GA)-LSSVM. It also shows good fitness and fast convergence rate. Accuracies calculated in this paper, however, are significantly affected by the misidentifications of faults that have been made in the DGA data collected from the literature.
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Tang, Hong, Ziyin Dai, Yuanlin Jiang, Ting Li, and Chengyu Liu. "PCG Classification Using Multidomain Features and SVM Classifier." BioMed Research International 2018 (July 9, 2018): 1–14. http://dx.doi.org/10.1155/2018/4205027.
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This paper proposes a method using multidomain features and support vector machine (SVM) for classifying normal and abnormal heart sound recordings. The database was provided by the PhysioNet/CinC Challenge 2016. A total of 515 features are extracted from nine feature domains, i.e., time interval, frequency spectrum of states, state amplitude, energy, frequency spectrum of records, cepstrum, cyclostationarity, high-order statistics, and entropy. Correlation analysis is conducted to quantify the feature discrimination abilities, and the results show that “frequency spectrum of state”, “energy”, and “entropy” are top domains to contribute effective features. A SVM with radial basis kernel function was trained for signal quality estimation and classification. The SVM classifier is independently trained and tested by many groups of top features. It shows the average of sensitivity, specificity, and overall score are high up to 0.88, 0.87, and 0.88, respectively, when top 400 features are used. This score is competitive to the best previous scores. The classifier has very good performance with even small number of top features for training and it has stable output regardless of randomly selected features for training. These simulations demonstrate that the proposed features and SVM classifier are jointly powerful for classifying heart sound recordings.
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Park, Dajeong, Miran Lee, Sunghee Park, Joon-Kyung Seong, and Inchan Youn. "Determination of Optimal Heart Rate Variability Features Based on SVM-Recursive Feature Elimination for Cumulative Stress Monitoring Using ECG Sensor." Sensors 18, no. 7 (July 23, 2018): 2387. http://dx.doi.org/10.3390/s18072387.
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Routine stress monitoring in daily life can predict potentially serious health impacts. Effective stress monitoring in medical and healthcare fields is dependent upon accurate determination of stress-related features. In this study, we determined the optimal stress-related features for effective monitoring of cumulative stress. We first investigated the effects of short- and long-term stress on various heart rate variability (HRV) features using a rodent model. Subsequently, we determined an optimal HRV feature set using support vector machine-recursive feature elimination (SVM-RFE). Experimental results indicate that the HRV time domain features generally decrease under long-term stress, and the HRV frequency domain features have substantially significant differences under short-term stress. Further, an SVM classifier with a radial basis function kernel proved most accurate (93.11%) when using an optimal HRV feature set comprising the mean of R-R intervals (mRR), the standard deviation of R-R intervals (SDRR), and the coefficient of variance of R-R intervals (CVRR) as time domain features, and the normalized low frequency (nLF) and the normalized high frequency (nHF) as frequency domain features. Our findings indicate that the optimal HRV features identified in this study can effectively and efficiently detect stress. This knowledge facilitates development of in-facility and mobile healthcare system designs to support stress monitoring in daily life.
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Nagasato, Daisuke, Hitoshi Tabuchi, Hideharu Ohsugi, Hiroki Masumoto, Hiroki Enno, Naofumi Ishitobi, Tomoaki Sonobe, et al. "Deep Neural Network-Based Method for Detecting Central Retinal Vein Occlusion Using Ultrawide-Field Fundus Ophthalmoscopy." Journal of Ophthalmology 2018 (November 1, 2018): 1–6. http://dx.doi.org/10.1155/2018/1875431.
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The aim of this study is to assess the performance of two machine-learning technologies, namely, deep learning (DL) and support vector machine (SVM) algorithms, for detecting central retinal vein occlusion (CRVO) in ultrawide-field fundus images. Images from 125 CRVO patients (n=125 images) and 202 non-CRVO normal subjects (n=238 images) were included in this study. Training to construct the DL model using deep convolutional neural network algorithms was provided using ultrawide-field fundus images. The SVM uses scikit-learn library with a radial basis function kernel. The diagnostic abilities of DL and the SVM were compared by assessing their sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic curve for CRVO. For diagnosing CRVO, the DL model had a sensitivity of 98.4% (95% confidence interval (CI), 94.3–99.8%) and a specificity of 97.9% (95% CI, 94.6–99.1%) with an AUC of 0.989 (95% CI, 0.980–0.999). In contrast, the SVM model had a sensitivity of 84.0% (95% CI, 76.3–89.3%) and a specificity of 87.5% (95% CI, 82.7–91.1%) with an AUC of 0.895 (95% CI, 0.859–0.931). Thus, the DL model outperformed the SVM model in all indices assessed (P<0.001 for all). Our data suggest that a DL model derived using ultrawide-field fundus images could distinguish between normal and CRVO images with a high level of accuracy and that automatic CRVO detection in ultrawide-field fundus ophthalmoscopy is possible. This proposed DL-based model can also be used in ultrawide-field fundus ophthalmoscopy to accurately diagnose CRVO and improve medical care in remote locations where it is difficult for patients to attend an ophthalmic medical center.
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Chang, Han, Qifang Wu, Hao Tian, Jinshan Yan, Xuan Luo, and Huirong Xu. "Non-Destructive Identification of Internal Watercore in Apples Based on Online Vis/NIR Spectroscopy." Transactions of the ASABE 63, no. 6 (2020): 1711–21. http://dx.doi.org/10.13031/trans.13844.
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HighlightsA custom-designed online Vis/NIR spectroscopy system was used for real-time detection of watercore in apples.Watercore severity index (WSI) was applied for watercore severity assessment.Higher than 95.0% accuracy was obtained for total samples in classifying sound apples from watercore groups using kNN, BPNN, SVM, and 1D CNN at a detection speed of 3 apples s-1.Linear kernel SVM achieved the best classification accuracy of 96% for samples in the prediction set.Abstract. Watercore, an internal physiological disorder affecting apples, can be characterized by water-soaked, glassy regions near the fruit core. It is used as an indicator of full ripeness, storage suitability, and price of apples in many countries. Therefore, fast and non-destructive detection of watercore plays an important role in improving the commercial value of apples and reducing postharvest costs. In this study, an online visible/near-infrared (Vis/NIR) spectroscopy system was proposed for real-time detection of watercore in ‘Fuji’ apples (Malus pumila Mill.). A total of 318 samples harvested during harvest season in the same orchard were analyzed for both watercore severity index (WSI) and soluble solids content (SSC). According to the USDA watercore classification standard, all samples were classified into one of four classes (sound, slight, moderate, or severe) based on the affected area of watercore. Results showed that, although there was a positive correlation between spectral intensity and affected area of watercore, no significant relationship between affected area size and SSC could be obtained by Pearson test (correlation coefficient ~0.094). Generally, >95.0% accuracy was obtained for total samples at a detection speed of 3 apples s-1 in classifying sound from watercore groups using k-nearest neighbors (kNN) algorithm, back-propagation neural network (BPNN), support vector machine (SVM) classification, and one-dimensional convolutional neural network (1D-CNN). The best classification result was achieved by linear kernel SVM, with an accuracy of 96% for total samples. These classification algorithms showed preliminary feasibility for online screening of apples with watercore using Vis/NIR spectroscopy in industrial applications. Keywords: Apple watercore, Machine learning, Online detection, Vis/NIR spectroscopy, Watercore severity index.
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Nguyen, Ha Thi Thu, Loc Van Nguyen, C. A. J. M. (Kees) de Bie, Ignacio A. Ciampitti, Duc Anh Nguyen, Minh Van Nguyen, Luciana Nieto, Rai Schwalbert, and Long Viet Nguyen. "Mapping Maize Cropping Patterns in Dak Lak, Vietnam Through MODIS EVI Time Series." Agronomy 10, no. 4 (April 1, 2020): 478. http://dx.doi.org/10.3390/agronomy10040478.
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Land use maps specifying up-to-date acreage information on maize (Zea mays L.) cropping patterns are required by many stakeholders in Vietnam. Government statistics, however, lag behind by one year, and the official land use maps are only updated at 5-year intervals. The aim of this study was to apply the Savitzky–Golay algorithm to reconstruct noisy Enhanced Vegetation Index (EVI) time series (2003–2018) from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MOD13Q1) to allow timely detection of changes in maize crop phenology, and then to employ a linear kernel Support Vector Machine (SVM) classifier on the reconstructed EVI time series to prepare the present-day maize cropping pattern map of Dak Lak province of Vietnam. The method was able to specify the spatial extent of areas cropped to maize with an overall map accuracy of 79% and could also differentiate the areas cropped to maize just once versus twice annually. The by-district mapped maize acreage shows a good agreement with the official governmental data, with a 0.93 correlation coefficient (r) and a root mean square deviation (RMSD) of 1624 ha.
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Khodaei, Amin, Mohammad-Reza Feizi-Derakhshi, and Behzad Mozaffari-Tazehkand. "A Markov chain-based feature extraction method for classification and identification of cancerous DNA sequences." BioImpacts 11, no. 2 (March 24, 2020): 87–99. http://dx.doi.org/10.34172/bi.2021.16.
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Introduction: In recent decades, the growing rate of cancer incidence is a big concern for most societies. Due to the genetic origins of cancer disease, its internal structure is necessary for the study of this disease. Methods: In this research, cancer data are analyzed based on DNA sequences. The transition probability of occurring two pairs of nucleotides in DNA sequences has Markovian property. This property inspires the idea of feature dimension reduction of DNA sequence for overcoming the high computational overhead of genes analysis. This idea is utilized in this research based on the Markovian property of DNA sequences. This mapping decreases feature dimensions and conserves basic properties for discrimination of cancerous and non-cancerous genes. Results: The results showed that a non-linear support vector machine (SVM) classifier with RBF and polynomial kernel functions can discriminate selected cancerous samples from non-cancerous ones. Experimental results based on the 10-fold cross-validation and accuracy metrics verified that the proposed method has low computational overhead and high accuracy. Conclusion: The proposed algorithm was successfully tested on related research case studies. In general, a combination of proposed Markovian-based feature reduction and non-linear SVM classifier can be considered as one of the best methods for discrimination of cancerous and non-cancerous genes.
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Teixeira, Alfredo, Severiano R. Silva, Marianne Hasse, José M. H. Almeida, and Luis Dias. "Intramuscular Fat Prediction Using Color and Image Analysis of Bísaro Pork Breed." Foods 10, no. 1 (January 12, 2021): 143. http://dx.doi.org/10.3390/foods10010143.
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This work presents an analytical methodology to predict meat juiciness (discriminant semi-quantitative analysis using groups of intervals of intramuscular fat) and intramuscular fat (regression analysis) in Longissimus thoracis et lumborum (LTL) muscle of Bísaro pigs using as independent variables the animal carcass weight and parameters from color and image analysis. These are non-invasive and non-destructive techniques which allow development of rapid, easy and inexpensive methodologies to evaluate pork meat quality in a slaughterhouse. The proposed predictive supervised multivariate models were non-linear. Discriminant mixture analysis to evaluate meat juiciness by classified samples into three groups—0.6 to 1.1%; 1.25 to 1.5%; and, greater than 1.5%. The obtained model allowed 100% of correct classifications (92% in cross-validation with seven-folds with five repetitions). Polynomial support vector machine regression to determine the intramuscular fat presented R2 and RMSE values of 0.88 and 0.12, respectively in cross-validation with seven-folds with five repetitions. This quantitative model (model’s polynomial kernel optimized to degree of three with a scale factor of 0.1 and a cost value of one) presented R2 and RSE values of 0.999 and 0.04, respectively. The overall predictive results demonstrated the relevance of photographic image and color measurements of the muscle to evaluate the intramuscular fat, rarther than the usual time-consuming and expensive chemical analysis.
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Lu, Juan, Xiaoping Liao, Steven Li, Haibin Ouyang, Kai Chen, and Bing Huang. "An Effective ABC-SVM Approach for Surface Roughness Prediction in Manufacturing Processes." Complexity 2019 (June 13, 2019): 1–13. http://dx.doi.org/10.1155/2019/3094670.
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It is difficult to accurately predict the response of some stochastic and complicated manufacturing processes. Data-driven learning methods which can mine unseen relationship between influence parameters and outputs are regarded as an effective solution. In this study, support vector machine (SVM) is applied to develop prediction models for machining processes. Kernel function and loss function are Gaussian radial basis function and ε-insensitive loss function, respectively. To improve the prediction accuracy and reduce parameter adjustment time of SVM model, artificial bee colony algorithm (ABC) is employed to optimize internal parameters of SVM model. Further, to evaluate the optimization performance of ABC in parameters determination of SVM, this study compares the prediction performance of SVM models optimized by well-known evolutionary and swarm-based algorithms (differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), and ABC) and analyzes ability of these optimization algorithms from their optimization mechanism and convergence speed based on experimental datasets of turning and milling. Experimental results indicate that the selected four evaluation indicators values that reflect prediction accuracy and adjustment time for ABC-SVM are better than DE-SVM, GA-SVM, and PSO-SVM except three indicator values of DE-SVM for AISI 1045 steel under the case that training set is enough to develop the prediction model. ABC algorithm has less control parameters, faster convergence speed, and stronger searching ability than DE, GA, and PSO algorithms for optimizing the internal parameters of SVM model. These results shed light on choosing a satisfactory optimization algorithm of SVM for manufacturing processes.
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Chen, Kai, Rongchun Li, Yong Dou, Zhengfa Liang, and Qi Lv. "Ranking Support Vector Machine with Kernel Approximation." Computational Intelligence and Neuroscience 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/4629534.
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Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (RankSVM with a linear kernel) for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss) objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms.
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Jia, Shi Jie, Jian Ying Zhao, Yan Ping Yang, and Nan Xiao. "Product-Image Classification with Support Vector Machine." Advanced Materials Research 433-440 (January 2012): 6019–22. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.6019.
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SVMs with kernel have been established with good generalization capabilities. This paper proposed a supervised product-image classification method based on SVM and Pyramid Histogram of words(PHOW). We tested several kernel functions on PI100 (Microsoft product-image dataset), such as linear, Radial Basis, Chi-square, histogram intersection and spatial pyramid kernel. Experimental results showed the effectiveness of our algorithm.
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Lihua Fu, Meng Zhang, Zhihui Liu, and Hongwei Li. "Support Vector Machine with Generalized Gaussian Kernel." Journal of Convergence Information Technology 6, no. 12 (December 31, 2011): 51–58. http://dx.doi.org/10.4156/jcit.vol6.issue12.7.
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TAO, Jian-Wen, and Shi-Tong WANG. "Kernel Support Vector Machine for Domain Adaptation." Acta Automatica Sinica 38, no. 5 (December 27, 2012): 797–811. http://dx.doi.org/10.3724/sp.j.1004.2012.00797.
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Li, Haoran, Li Xiong, Lucila Ohno-Machado, and Xiaoqian Jiang. "Privacy Preserving RBF Kernel Support Vector Machine." BioMed Research International 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/827371.
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Data sharing is challenging but important for healthcare research. Methods for privacy-preserving data dissemination based on the rigorous differential privacy standard have been developed but they did not consider the characteristics of biomedical data and make full use of the available information. This often results in too much noise in the final outputs. We hypothesized that this situation can be alleviated by leveraging a small portion of open-consented data to improve utility without sacrificing privacy. We developed a hybrid privacy-preserving differentially private support vector machine (SVM) model that uses public data and private data together. Our model leverages the RBF kernel and can handle nonlinearly separable cases. Experiments showed that this approach outperforms two baselines: (1) SVMs that only use public data, and (2) differentially private SVMs that are built from private data. Our method demonstrated very close performance metrics compared to nonprivate SVMs trained on the private data.
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Kurita, Takio. "Support Vector Machine and Generalization." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 2 (March 20, 2004): 84–92. http://dx.doi.org/10.20965/jaciii.2004.p0084.
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The support vector machine (SVM) has been extended to build up nonlinear classifiers using the kernel trick. As a learning model, it has the best recognition performance among the many methods currently known because it is devised to obtain high performance for unlearned data. This paper reviews how to enhance generalization in learning classifiers centering on the SVM.
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Qu, Xi Long, Mi An Dai, and Zhen Hui Li. "Key Problem in Support Vector Machine Model." Applied Mechanics and Materials 34-35 (October 2010): 1351–54. http://dx.doi.org/10.4028/www.scientific.net/amm.34-35.1351.
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This study found the development direction of SVM, the research content is the most crucial and fundamental nature in SVM, if achieve this paper targets, it will promote the further application of SVM, and have important theoretical value; In addition, this study are The basic work of nuclear analytical methods, the results can be directly applied to the field of recognition pattern based on nuclear analytical methods (such as Kernel Principal Component Analysis and Kernel Fisher method), so the research results of this paper has good generalized values.
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Han, Henry, and Xiaoqian Jiang. "Overcome Support Vector Machine Diagnosis Overfitting." Cancer Informatics 13s1 (January 2014): CIN.S13875. http://dx.doi.org/10.4137/cin.s13875.
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Support vector machines (SVMs) are widely employed in molecular diagnosis of disease for their efficiency and robustness. However, there is no previous research to analyze their overfitting in high-dimensional omics data based disease diagnosis, which is essential to avoid deceptive diagnostic results and enhance clinical decision making. In this work, we comprehensively investigate this problem from both theoretical and practical standpoints to unveil the special characteristics of SVM overfitting. We found that disease diagnosis under an SVM classifier would inevitably encounter overfitting under a Gaussian kernel because of the large data variations generated from high-throughput profiling technologies. Furthermore, we propose a novel sparse-coding kernel approach to overcome SVM overfitting in disease diagnosis. Unlike traditional ad-hoc parametric tuning approaches, it not only robustly conquers the overfitting problem, but also achieves good diagnostic accuracy. To our knowledge, it is the first rigorous method proposed to overcome SVM overfitting. Finally, we propose a novel biomarker discovery algorithm: Gene-Switch-Marker (GSM) to capture meaningful biomarkers by taking advantage of SVM overfitting on single genes.
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Prathyusha, Annavarapu Naga. "Diabetic Prediction Using Kernel Based Support Vector Machine." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 2 (April 25, 2020): 1178–83. http://dx.doi.org/10.30534/ijatcse/2020/43922020.
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