Output Of Support Vector Machine Research Articles

Machine learning that predicts well may not learn the correct physical descriptions of glassy systems

The complexity of glasses makes it challenging to explain their dynamics. Machine learning (ML) has emerged as a promising pathway for understanding glassy dynamics by linking their structural features to rearrangement dynamics. Support vector machine (SVM) was one of the first methods used to detect such correlations. Specifically, a certain output of SVMs trained to predict dynamics from structure, the distance from the separating hyperplane, was interpreted as being linearly related to the activation energy for the rearrangement. By numerical analysis of toy models, we explore under which conditions it is possible to infer the energy barrier to rearrangements from the distance to the separating hyperplane. We observe that such successful inference is possible only under very restricted conditions. Typical tests, such as the apparent Arrhenius dependence of the probability of rearrangement on the inferred energy and the temperature, or high cross-validation accuracy do not guarantee success. Since even in such relatively simple toy models, prediction success of ML models does not necessarily translate into success of learning the underlying physics, we suggest that more careful investigations are needed when such claims are made. For this, we propose practical approaches for measuring the quality of the energy inference and for modifying the inferred model to improve the inference, which should be usable in the context of realistic datasets. Published by the American Physical Society 2024

Bimodal moment-by-moment coupling in perceptual multistability.

Multistable perception occurs in all sensory modalities, and there is ongoing theoretical debate about whether there are overarching mechanisms driving multistability across modalities. Here we study whether multistable percepts are coupled across vision and audition on a moment-by-moment basis. To assess perception simultaneously for both modalities without provoking a dual-task situation, we query auditory perception by direct report, while measuring visual perception indirectly via eye movements. A support-vector-machine (SVM)-based classifier allows us to decode visual perception from the eye-tracking data on a moment-by-moment basis. For each timepoint, we compare visual percept (SVM output) and auditory percept (report) and quantify the co-occurrence of integrated (one-object) or segregated (two-objects) interpretations in the two modalities. Our results show an above-chance coupling of auditory and visual perceptual interpretations. By titrating stimulus parameters toward an approximately symmetric distribution of integrated and segregated percepts for each modality and individual, we minimize the amount of coupling expected by chance. Because of the nature of our task, we can rule out that the coupling stems from postperceptual levels (i.e., decision or response interference). Our results thus indicate moment-by-moment perceptual coupling in the resolution of visual and auditory multistability, lending support to theories that postulate joint mechanisms for multistable perception across the senses.

Interference fading suppression for distributed acoustic sensor using frequency-shifted delay loop

In order to suppress the interference fading of phase-sensitive optical time domain reflectometer (Φ-OTDR), a multi-frequency detection method based on frequency-shifted delay loop (FSDL) is proposed. A probe pulse train with up to five consistent frequency components are generated by periodic frequency-shifted, amplification, delay and filtering processing in FSDL. The piecewise aggregate approximation (PAA) is introduced to compress the amount of operational data. Wavelet energy spectrum analysis and support vector machine (SVM) are used to extract features and realize the fading classification. With the help of fading label making and restoration, the multi-frequency signals are intelligently aggregated using rotated-vector-sum (RVS). Experimental results show that PAA has applicability in data compression of beat signals. After 5-layer wavelet energy spectrum analysis, the fading binary classification accuracy can reach 96.77 %, and the fading signals identified after segmentation can be restored to the original data. The fading probability based on SVM output labels can be reduced to 1.89 %. The SVM-based classification output labels aggregation shows that the vibration positioning signal-to-noise ratio (SNR) can be increased to 13.52 dB, the phase demodulation curve is smoother, and the demodulation SNR can be up to 17.63 dB. Finally, the R-Square of 0.997 for the amplitude of demodulated vibration signal in repeatability experiment verifies the validity of this effective scheme for fading recognition and suppression.

Dual stage ensemble technique for intrusion detection in cloud computing

A capability of cloud-based IDS in identifying complicated and anonymous attacks is rising in the current era. However, unwanted delays hinder the detection rate. A malicious user might utilize vast quantities of computational power. The cloud provides to perform attacks both within and without the cloud. Furthermore, there are major challenges for intrusion detection due to the ease of the cloud and also the continual restructuring and movement of cloud resources. Intruder detection, feature extraction, and data processing are all included in the novel optimization-based Intrusion Detection System (IDS) paradigm that will be presented in this study. Data normalization is used to first pre-process the input data. Then, appropriate feature extraction is carried out, including the extraction of (a) raw features, (b) statistical features, then (c) higher-order statistical features using suggested kurtosis. The detection phase is then applied to the retrieved features. A two-stage ensemble method is suggested for finding intruders in clouds. Random forest (RF), Support Vector Machine (SVM), optimal Neural Network (NN), and RNN make up the suggested ensemble technique. The RF, SVM, and Optimized NN algorithms are directly fed the collected features. The output of these classifiers is then provided to the RNN classifier (i.e.), RF output to RNN1, SVM output to RNN2, and optimized NN output to RNN3. Then, the weighted average of RNN 1, 2, and 3 is considered as the final output. A Self Adaptive Salp Swarm Optimization optimizes the weights of NN for exact detection (SA-SSO). Finally, a test is conducted to confirm the developed model’s superiority.

Comparison of Machine Learning Models Performance on Simulating Reservoir Outflow: A Case Study of Two Reservoirs in Illinois, U.S.A.

ABSTRACTReservoir outflow is an important variable for understanding hydrological processes and water resource management. Natural streamflow variation, in addition to the streamflow regulation provided by dams and reservoirs, can make streamflow difficult to understand and predict. This makes them a challenge to accurately simulate hydrologic processes at a daily scale. In this study, three Machine Learning (ML) algorithms, Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were examined and compared to model reservoir outflow. Past, current, and future hydrologic and meteorological data were used as model inputs, and the outflow of next day was used as prediction. Simulation results demonstrated that all three models can reasonably simulate reservoir outflow. For Carlyle Lake, the coefficient of determination and Nash–Sutcliffe efficiency were each close to one for the three models. The coefficient of determination, relative mean bias, and root mean square error indicated that the SVM performed better than the RF and ANN, but the SVM output displayed a larger relative mean bias than that from RF and ANN. For Lake Shelbyville, the ANN model performed better than RF and SVM when considering the coefficient of determination, Nash–Sutcliffe efficiency, relative mean bias, and root mean square error. The study results demonstrate that the three ML algorithms (RF, SVM, and ANN) are all promising tools for simulating reservoir outflow. Both the accuracy and efficacy of the three ML algorithms are considered to support practitioners in planning reservoir management.

The effect of CT texture-based analysis using machine learning approaches on radiologists' performance in differentiating focal-type autoimmune pancreatitis and pancreatic duct carcinoma

PurposeTo develop a support vector machine (SVM) classifier using CT texture-based analysis in differentiating focal-type autoimmune pancreatitis (AIP) and pancreatic duct carcinoma (PD), and to assess the radiologists’ diagnostic performance with or without SVM.Materials and methodsThis retrospective study included 50 patients (20 patients with focal-type AIP and 30 patients with PD) who underwent dynamic contrast-enhanced CT. Sixty-two CT texture-based features were extracted from 2D images of the arterial and portal phase CTs. We conducted data compression and feature selections using principal component analysis (PCA) and produced the SVM classifier. Four readers participated in this observer performance study and the statistical significance of differences with and without the SVM was assessed by receiver operating characteristic (ROC) analysis.ResultsThe SVM performance indicated a high performance in differentiating focal-type AIP and PD (AUC = 0.920). The AUC for all 4 readers increased significantly from 0.827 to 0.911 when using the SVM outputs (p = 0.010). The AUC for inexperienced readers increased significantly from 0.781 to 0.905 when using the SVM outputs (p = 0.310). The AUC for experienced readers increased from 0.875 to 0.912 when using the SVM outputs, however, there was no significant difference (p = 0.018).ConclusionThe use of SVM classifier using CT texture-based features improved the diagnostic performance for differentiating focal-type AIP and PD on CT.

Short-Term PV Power Forecasting Using a Regression-Based Ensemble Method

One of the most critical aspects of integrating renewable energy sources into the smart grid is photovoltaic (PV) power generation forecasting. This ensemble forecasting technique combines several forecasting models to increase the forecasting accuracy of the individual models. This study proposes a regression-based ensemble method for day-ahead PV power forecasting. The general framework consists of three steps: model training, creating the optimal set of weights, and testing the model. In step 1, a Random forest (RF) with different parameters is used for a single forecasting method. Five RF models (RF1, RF2, RF3, RF4, and RF5) and a support vector machine (SVM) for classification are established. The hyperparameters for the regression-based method involve learners (linear regression (LR) or support vector regression (SVR)), regularization (least absolute shrinkage and selection operator (LASSO) or Ridge), and a penalty coefficient for regularization (λ). Bayesian optimization is performed to find the optimal value of these three hyperparameters based on the minimum function. The optimal set of weights is obtained in step 2 and each set of weights contains five weight coefficients and a bias. In the final step, the weather forecasting data for the target day is used as input for the five RF models and the average daily weather forecasting data is also used as input for the SVM classification model. The SVM output selects the weather conditions, and the corresponding set of weight coefficients from step 2 is combined with the output from each RF model to obtain the final forecasting results. The stacking recurrent neural network (RNN) is used as a benchmark ensemble method for comparison. Historical PV power data for a PV site in Zhangbin Industrial Area, Taiwan, with a 2000 kWp capacity is used to test the methodology. The results for the single best RF model, the stacking RNN, and the proposed method are compared in terms of the mean relative error (MRE), the mean absolute error (MAE), and the coefficient of determination (R2) to verify the proposed method. The results for the MRE show that the proposed method outperforms the best RF method by 20% and the benchmark method by 2%.

A new approach for writer verification based on segments of handwritten graphemes

Abstract Traditional literature considers complex biometric sources such as words, letters and signatures for writer verification/identification. In this work the use of small segments of the handwritten stroke for writer verification is proposed. A grapheme is defined as the concatenation of smaller segments or fragments. Two models of grapheme are developed based on the idea that the segments are parts of a circle with or without direction. The average of Gray Level of the Perpendicular Line to the Skeleton and Local Binary Pattern are adopted as descriptors. A database of 3,000 images of 50 writers, with 6 types of segments and with 10 samples per segment has been developed. A binary output support vector machine was applied as classifier. Thus, 50 classifiers were trained using 100 balanced data sets generated using subsampling of the majority class. Experiments are carried out with the proposed models, with an identity verification hit rate of 97 % on average. The positive results of experiments show that it is possible to achieve high precision in writer verification using segment of graphemes.

Early Risk Prediction of Diabetes Based on GA-Stacking

Early risk prediction of diabetes could help doctors and patients to pay attention to the disease and intervene as soon as possible, which can effectively reduce the risk of complications. In this paper, a GA-stacking ensemble learning model is proposed to improve the accuracy of diabetes risk prediction. Firstly, genetic algorithms (GA) based on Decision Tree (DT) is used to select individuals with high adaptability, that is, a subset of attributes suitable for diabetes risk prediction. Secondly, the optimized convolutional neural network (CNN) and support vector machine (SVM) are used as the primary learners of stacking to learn attribute subsets, respectively. Then, the output of CNN and SVM is used as the input of the mate learner, the fully connected layer, for classification. Qingdao desensitization physical examination data from 1 January 2017 to 31 December 2019 is used, which includes body temperature, BMI, waist circumference, and other indicators that may be related to early diabetes. We compared the performance of GA-stacking with K-nearest neighbor (KNN), SVM, logistic regression (LR), Naive Bayes (NB), and CNN before and after adding GA through the average prediction time, accuracy, precision, sensitivity, specificity, and F1-score. Results show that prediction efficiency can be improved by adding GA. GA-stacking has higher prediction accuracy. Moreover, the strong generalization ability and high prediction efficiency of GA-stacking have also been verified on the early-stage diabetes risk prediction dataset published by UCI.

Fuzzy Output Support Vector Machine Based Incident Ticket Classification

Fuzzy Output Support Vector Machine Based Incident Ticket Classification

Multiple graph kernel learning based on GMDH-type neural network

Multiple kernel learning (MKL), as a principled classification method, selects and combines base kernels to increase the categorization accuracy of Support Vector Machines (SVMs). The group method of data handling neural network (GMDH-NN) has been applied in many fields of optimization, data mining, and pattern recognition. It can automatically seek interrelatedness in data, select an optimal structure for the model or network, and enhance the accuracy of existing algorithms. We can utilize the advantages of the GMDH-NN to build a multiple graph kernel learning (MGKL) method and enhance the categorization performance of graph kernel SVMs. In this paper, we first define a unitized symmetric regularity criterion (USRC) to improve the symmetric regularity criterion of GMDH-NN. Second, a novel structure for the initial model of the GMDH-NN is defined, which uses the posterior probability output of graph kernel SVMs. We then use a hybrid graph kernel in the H1-space for MGKL in combination with the GMDH-NN. This way, we can obtain a pool of optimal graph kernels with different kernel parameters. Our experiments on standard graph datasets show that this new MGKL method is highly effective.

Bank CRM Optimization Using Predictive Classification Based on the Support Vector Machine Method

ABSTRACT This paper proposes a predictive approach to segmenting credit card users, based on their value to the bank. The approach combines the Recency, Frequency and Monetary (RFM) method, clustering using the k-means method, and predictive classification by the Support Vector Machine (SVM) method. Clustering by non-encoded RFM attributes overcomes the subjectivity in selecting the number of segments and losing information (small differences in the values of these attributes) which are problems of classic RFM segmentation. In order to overcome the problem of class imbalance in predictive classification (which occurs due to the small number of valuable customers), the Support Vector Machine (SVM) method was applied as a pre-processor of data due to its extraordinary generalization capabilities. The end result of predictive classification should be a set of rules that describes the identified customer segments in order to tailor the offer to each segment individually. The extraction of rules from the SVM output was achieved using the Decision Tree (DT) classification method. Using a proposed approach that addresses the issue of the small class, marketing managers can more effectively target the most valuable customers, thereby increasing revenue, but also reducing unnecessary costs due to wrongly targeted valuable clients.

Identifying tracé alternant activity in neonatal EEG using an inter-burst detection approach.

Electroencephalography (EEG) is an important clinical tool for reviewing sleep-wake cycling in neonates in intensive care. Tracé alternant (TA)-a characteristic pattern of EEG activity during quiet sleep in term neonates-is defined by alternating periods of short-duration, high-voltage activity (bursts) separated by lower-voltage activity (inter-bursts). This study presents a novel approach for detecting TA activity by first detecting the inter-bursts and then processing the temporal map of the bursts and inter-bursts. EEG recordings from 72 healthy term neonates were used to develop and evaluate performance of 1) an inter-burst detection method which is then used for 2) detection of TA activity. First, multiple amplitude and spectral features were combined using a support vector machine (SVM) to classify bursts from inter-bursts within TA activity, resulting in a median area under the operating characteristic curve (AUC) of 0.95 (95% confidence interval, CI: 0.93 to 0.98). Second, post-processing of the continuous SVM output, the confidence score, was used to produce a TA envelope. This envelope was used to detect TA activity within the continuous EEG with a median AUC of 0.84 (95% CI: 0.80 to 0.88). These results validate how an inter-burst detection approach combined with post processing can be used to classify TA activity. Detecting the presence or absence of TA will help quantify disruption of the clinically important sleep-wake cycle.

Probabilistic SVM classifier ensemble selection based on GMDH-type neural network

Support vector machine (SVM) provides a good classification and regression ability, especially, for small sample learning. However, in practice, the learning ability of implemented SVM is occasionally far from the expected level. Group method of data handling neural network (GMDH-NN) has been applied in various fields for pattern recognition and data mining. It makes it possible to automatically find interrelations in data, to select an optimal structure of network or model and to improve the accuracy of existing algorithms. In this work we propose to take the advantages of GMDH-NN for further increasing the classification performance of SVM. One weakness of the symmetric regularity criterion of GMDH-NN is that if one of the input attributes has a relatively big range, then it may overcome the other attributes. Thus, we first define a standardized symmetric regularity criterion (SSRC) to evaluate and select the candidate models, and optimize a classifier ensemble selection approach. Secondly, we define a novel structure of initial model of GMDH-NN which is from the posterior probability outputs of SVMs. These probabilistic outputs are generated from the improved Platt’s probabilistic outputs. Thirdly, in real classification tasks, different classifiers usually have different classification advantages. So we use probabilistic SVM as base learner and integrate the probabilistic SVMs with GMDH-NN, and then propose a special classifier ensemble selection approach for probabilistic SVM classifiers based on GMDH-NN called GMDH-PSVM. Moreover, we use the Borda sorting and Random weighted Borda sorting to discuss the results of our experiments. Experiments on standard UCI datasets demonstrate the effectiveness of our method.

A framework for automatic classification of mobile LiDAR data using multiple regions and 3D CNN architecture

ABSTRACT This paper proposes a framework for automatic classification of mobile laser scanner (MLS) point cloud using multi-faceted multi-object convolutional neural network (MMCN). The proposed method takes a full three-dimensional (3D) point cloud as input and outputs a class label for each point. Unlike other existing classification methods for MLS data, the proposed method is not dependent on any parameter or its tuning. The proposed MMCN uses multiple objects of a sample, defined by different sizes of the sample, in addition to the different facets obtained by rotating about the various axes, thus adding more information during the training and testing stages. The proposed framework uses manually extracted samples for training the MMCN. Automatically extracted multiple regions based on the various radii of spherical neighbourhoods around MLS points are passed through the trained MMCN for determining their probabilities of belonging to different classes. The class probabilities of different sized regions are then used as a feature vector to train a support vector machine (SVM), and the final decision for the class of a point is based on the SVM output. The proposed framework has been trained for five classes, viz., Ground, House, Pole, Tree, and Car and has been tested on Oakland and Paris-Lille 3D MLS datasets. The total accuracy and kappa coefficient (κ) reach up to 96.5% and 93.8%, respectively, for the framework. The MMCN together with the SVM is able to achieve parameter-free classification of MLS data, thereby eliminating the need for manual parameter tuning as in the existing methods. Therefore, besides the use for classification of MLS data for mapping purpose, the approach is also suitable for classification of light detection and ranging (LiDAR) data resulting from autonomous vehicle sensors. The accuracy of this work can be further improved by incorporating more and varied training samples and deeper convolutional neural network (CNN) with better hardware resources.

Automated Fundoscopy for Glaucoma Detection and Classifiction

glaucoma is leading chronic eye dieses in the world that leads to vision lost. The main cause of Glaucoma is intrinsic deterioration of the optic nerve which leads high intraocular pressure of the eye. Manually detection of glaucoma is tedious and costly. In our work we are providing automated system for glaucoma detection which is based on fully connected conditional random filed (FC-CRF) model, it works on long and thin structure. Conditional random filed provide a platform for structure prediction. Taking benefit of current results, validating assumption and parameters of our system learned automatically with the help of structured output support vector machine. Our system trained both quantitatively and qualitatively on publically existing data sets: DRIVE, STARE, CHASEDB1 and HRF. Once we obtain segmentation results further classification is done by SVM and K-NN classifier results of our proposed system is analyzed with gold standard labeling provided each data sets in terms of TP,TN,FP and FN. importance of our proposed system is it works for enlarge structure which can provide a platform to other biomedical and biological applications.

Automatic Process Parameters Tuning and Surface Roughness Estimation for Laser Cleaning

An image analysis-based two-stage process parameters tuning and Surface Roughness (SR) estimation algorithm is proposed for the laser cleaning application. A Cartesian coordinate robot is utilized to collect image and implement cleaning. Before cleaning, in order to tune the proper laser parameters, first, the environment lighting is controlled for the metal image collection. Second, lots of classification features are computed for the images above. The Gray-Level Co-occurrence Matrix (GLCM) texture features, the concavo-convex region features, the histogram symmetry difference feature, and the imaging thermophysical property features are computed. Third, the initial laser parameters are created randomly and an iteration computation is performed: a Support Vector Machine (SVM) is used to forecast the cleaning effect; its inputs include the classification features and the initial laser parameters; its output is the cleaning effect degree. If the SVM output cannot fulfill user’s demand, the laser parameters will be updated randomly. This iteration will be implemented constantly until the SVM output becomes valid. Then the laser cleaning will be performed. When estimating SR for the cleaned metal, multiple image features are calculated for the images after cleaning. The features include the Tamura coarseness, some GLCM features, and the convex region feature. To improve the prediction precision, different feature combinations are used for different cleaning effects. The linear function and the 3-order polynomial function are considered for the SR estimation. After tests, the accuracies of SVM, the SR prediction function, and the integrated SR control and estimation algorithm can be 90.0%, 80.0% and 80.0% approximately.

Wind weather prediction based on multi-output least squares support vector regression optimised by bat algorithm

As a kind of clean energy, wind energy is widely disseminated and has been widely researched. Compared with other methods, the support vector machine algorithm is more logical. Least squares support vector machine can improve training efficiency. Therefore, the method of multi-output least squares support vector regression is used to forecast the wind speed and wind direction in this paper. The bat algorithm is simple in structure and easy to understand. It has been applied to solve optimisation problems with MSVR. Compared with single output support vector machines, multi-output support vector machine readily solves problems of complex structure. The simulation model is established to predict the value of wind speed and wind direction by using different algorithms. The simulation results show that the multi-output least squares support vector machines prediction model based on bat optimisation algorithm has better feasibility and effectiveness.

Wind weather prediction based on multi-output least squares support vector regression optimised by bat algorithm

As a kind of clean energy, wind energy is widely disseminated and has been widely researched. Compared with other methods, the support vector machine algorithm is more logical. Least squares support vector machine can improve training efficiency. Therefore, the method of multi-output least squares support vector regression is used to forecast the wind speed and wind direction in this paper. The bat algorithm is simple in structure and easy to understand. It has been applied to solve optimisation problems with MSVR. Compared with single output support vector machines, multi-output support vector machine readily solves problems of complex structure. The simulation model is established to predict the value of wind speed and wind direction by using different algorithms. The simulation results show that the multi-output least squares support vector machines prediction model based on bat optimisation algorithm has better feasibility and effectiveness.

Dynamic Collaborative Tracking

Correlation filter has been demonstrated remarkable success for visual tracking recently. However, most existing methods often face model drift caused by several factors, such as unlimited boundary effect, heavy occlusion, fast motion, and distracter perturbation. To address the issue, this paper proposes a unified dynamic collaborative tracking framework that can perform more flexible and robust position prediction. Specifically, the framework learns the object appearance model by jointly training the objective function with three components: target regression submodule, distracter suppression submodule, and maximum margin relation submodule. The first submodule mainly takes advantage of the circulant structure of training samples to obtain the distinguishing ability between the target and its surrounding background. The second submodule optimizes the label response of the possible distracting region close to zero for reducing the peak value of the confidence map in the distracting region. Inspired by the structure output support vector machines, the third submodule is introduced to utilize the differences between target appearance representation and distracter appearance representation in the discriminative mapping space for alleviating the disturbance of the most possible hard negative samples. In addition, a CUR filter as an assistant detector is embedded to provide effective object candidates for alleviating the model drift problem. Comprehensive experimental results show that the proposed approach achieves the state-of-the-art performance in several public benchmark data sets.