Smooth Support Vector Machine Research Articles

Development of Improved Weighed Quantum Lion Optimization with Smooth Support Vector Machine for Alzheimer’s Disease

Accurate diagnosis of Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) was identified on an early stage is essential in the healthcare industry to stop degeneration. The Smooth Support Vector Machine (SSVM) model, Principal Component Analysis (PCA), feature extraction, and Magnetic Resonance Imaging (MRI) image prepossessing are the components for the diagnosis of AD is proposed in this research at early stage. To assist in the classifier's training, we proposed a novel Improved Weighed Quantum Lion Optimization (IWQLO). The SSVM parameters are specifically proposed to be optimized using a new Switching delayed Lion Optimization (SLO) algorithm. The IWQLO-SSVM approach was effectively used to classify AD and MCI utilizing MRI scans of the Alzheimer's disease Neuroimaging Initiative (ADNI) database and Outcome and Assessment Information Set (OASIS) database. For six example scenarios, the classification accuracy of our proposed method is acceptable. Testing show that the proposed approach improves the performance measures such as accuracy, precision, specificity, sensitivity and recall for detecting the early stage AD diagnosis.

Improved SVM Algorithm Financial Management Model for Data Mining

Financial crisis early warning is an important link in financial management. With its relatively simple structure and excellent classification performance, support vector machine is often used in intelligent warning of financial crises. A financial warning model based on improved support vector machine is proposed to address the shortcomings of current financial warning methods that cannot handle large-scale data well, and the accuracy and efficiency are not ideal. In response to the excessive indicators in the financial early warning indicator system, the principal component analysis method is studied for dimensionality reduction, reducing the computational complexity of the model, and improving the training and operational efficiency of the model. In response to the limited practicality of support vector machine, which can only search for the optimal solution under constraint conditions, the use of smooth support vector machine to replace support vector machine for predictive classification is studied. Finally, a financial crisis early warning model is constructed based on smooth support vector machine using principal component analysis for dimensionality reduction of input data. The performance of the model is tested using an internet listed company as an example. The findings demonstrate that Model 1 outperforms the other three models in terms of accuracy rate (95%), MAE, MSE and [Formula: see text] values (0.162, 0.174 and 0.169, respectively). Therefore, the financial crisis early warning model constructed by the research can properly forecast the company’s financial situation, thereby helping the enterprise to develop better.

Faster region based convolution neural network with context iterative refinement for object detection

In this paper, proposed a novel method to improve the localization precision of identified objects. We present a framework for iteratively enhancing image region recommendations to meet ground truth values in this research. The Faster R–CNN (FR-CNN) seems to be an object recognition deep convolutional network. It gives the user the impression that the network is cohesive and single. The network can provide accurate and timely predictions about the whereabouts of a range of objects. We first build a unified model based on rapid predictions to relocate inaccurate area recommendations. Because the emphasis is on object detection, it may be utilised with a wide range of datasets and is compatible with various FR-CNN architectures. Second, we focus on the application of the joint score function to a variety of picture features. This joint score function depicts the location of the concealed object concerning other objects. The picture data and an updated structured production loss function are the only two inputs that influence the parameters of the joint scoring function. The join-score function and iterative context refinement (CIR) are used to generate our final unified model, which is then classified using Smooth Support Vector Machine (SSVM). We measured accuracy using the mean average precision after training FR-CNN + CIR and SSVM on a low-cost GPU using the PASCAL VOC 2012 dataset. Our results are 3.6 % more exact than rival deep learning algorithms on average.

Precision local anomaly positioning technology for large complex electromechanical systems

In recent years, Prognostics Health Management (PHM) technology has become an important reference technology in fields such as avionics and electromechanical systems due to its ability to reduce costs and achieve state based maintenance and autonomous support. However, with the operation of large and complex electromechanical systems (ES), the data generated gradually ages the status of components, and traditional PHM technology is difficult to solve the problem of electromechanical system components becoming more complex. Based on this, this study takes the hydraulic actuator cylinder as an example to construct a local component fault detection model. Firstly, fault data features are extracted using wavelet packet energy spectrum, and then a fault detection model is constructed based on support vector machine (SVM). In response to the shortcomings of SVM, a smooth support vector machine (SSVM) is proposed to replace SVM, and an improved crow search algorithm (ICSA) is used to improve SVM. Finally, an intelligent detection model for hydraulic actuator cylinder faults based on ICSA-SSVM was constructed based on the above algorithms. The experimental results show that the ICSA-SSVM model has the fastest Rate of convergence, among which, the positioning accuracy is 0.96, the fitting degree is 0.984, the fault detection accuracy is 99.16 %, the recall value is 94.52 %, and the AUC value is 0.986, all of which are better than the existing fault detection models. From this, it can be seen that the precise local anomaly localization technology for large-scale complex electromechanical systems based on the ICSA-SSVM algorithm proposed in this study can improve the efficiency and accuracy of fault detection, achieve accurate and intelligent detection of ES local anomalies, and have certain positive significance for the development of China’s industry.

The Construction of a Model for Predicting Users’ Repeat Purchase Behavior and its Impact on the Economic Efficiency of Enterprises

Aiming at the shortcomings in efficiency and accuracy of the current prediction methods of user repeat purchase behavior in e-commerce enterprises, an intelligent prediction model of user repeat purchase behavior based on machine learning was proposed. In order to enhance the quality of the experimental data, Kernel Principal Components Analysis (KPCA) and the synthetic Minority oversampling technique (SMOTE) were first used to preprocess the data. After that, repeat purchase behavior is predicted using a Support Vector Machine (SVM). Then, the Sparrow Search Algorithm (SSA), based on multi-strategy optimization, is suggested to overcome the SSVM’s drawbacks. The Smooth Support Vector Machine (SSVM) is employed as the feature classifier for classification. On this basis, an intelligent prediction model of user repeat purchase behavior based on ISA-SSVM is constructed to achieve efficient prediction of user repeat purchase behavior. The results showed that the fitness value of the ISA-SSVM algorithm was always higher than other algorithms as the number of iterations increases. And its convergence speed is fast, when the number of iterations is 13, the fitness value reaches 94.6%. The error value of this model is 0.14, the loss value is 0.20, the F1 value is 0.957, the recall value is 0.965, the MAE value is 8.52, the fit degree is 0.992, the prediction accuracy is 97.92%, and the AUC value is 0.995, all of which are better than the other two models. As a result, the ISA-SSVM developed in this work outperforms previous models in terms of its ability to forecast customers’ recurrent purchasing behavior. The research approach is helpful for e-commerce businesses to implement precision marketing, which has a good effect on the advantages of e-commerce businesses.

Smooth support vector machine with generalized pinball loss for Pattern Classification

Smooth support vector machine with generalized pinball loss for Pattern Classification

Construction of a new smooth support vector machine model and its application in heart disease diagnosis.

Support vector machine (SVM) is a new machine learning method developed from statistical learning theory. Since the objective function of the unconstrained SVM model is a non-smooth function, a lot of fast optimization algorithms can't be used to find the solution. Firstly, to overcome the non-smooth property of this model, a new padé33 approximation smooth function is constructed by rational approximation method, and a new smooth support vector machine model (SSVM) is established based on the smooth function. Then, by analyzing the performance of the smooth function, we find that the smooth precision is significantly higher than existing smooth functions. Moreover, theoretical and rigorous mathematical analyses are given to prove the convergence of the new model. Finally, it is applied to the heart disease diagnosis. The results show that the Padé33-SSVM model has better classification capability than existing SSVMs.

Learning with smooth Hinge losses

Due to the non-smoothness of the Hinge loss in SVM, it is difficult to obtain a faster convergence rate with modern optimization algorithms. In this paper, we introduce two smooth Hinge losses ψG(α;σ) and ψM(α;σ) which are infinitely differentiable and converge to the Hinge loss uniformly in α as σ tends to 0. By replacing the Hinge loss with these two smooth Hinge losses, we obtain two smooth support vector machines (SSVMs), respectively. Solving the SSVMs with the Trust Region Newton method (TRON) leads to two quadratically convergent algorithms. Experiments in text classification tasks show that the proposed SSVMs are effective in real-world applications. We also introduce a general smooth convex loss function to unify several commonly-used convex loss functions in machine learning. The general framework provides smooth approximation functions to non-smooth convex loss functions, which can be used to obtain smooth models that can be solved with faster convergent optimization algorithms.

A New Approach for Faults Detection and Classification in Overhead Line Systems Using Multiple Methods

Faults in power systems can be described by a sudden increase in current compared with the flow of normal current. The phenomenon of fault affects energy quality, security, and reliability of the system. Several events play a key role in causing faults such as trees falling on overhead lines, insulation breakdown, and lightning strikes. Thus, determining the location of faults is a very important subject, especially in Power Distribution System’s and this can be achieved by applying several methods to classify and detect a fault in order to identify the correct type of faults. this paper, Clarke Method Transformation and Phase Angle Shift techniques are used to classify faults in Power Distribution System and to determine if the fault is grounded or ungrounded. On the other hand, Smooth Support Vector Machine and Discrete Wavelet Transform techniques are used to determine the accurate fault types. The data taken are the faulted voltage phases, which are stored and imported in MATLAB from ATPDraw, which design and build the proposed system of 33 kV power supply, overhead lines with 20 km length and frequency of 50 Hz. The obtained results from MATLAB simulation demonstrate that the proposed algorithm works very properly and it can detect and classify the existence of faults within the transmission line in the Power Distribution System. The inclusion of the multi-phase faults is the future step to enhance the performance of this work.

Urban Traffic Flow Prediction Model with CPSO/SSVM Algorithm under the Edge Computing Framework

Urban traffic flow prediction has always been an important realm for smart city build-up. With the development of edge computing technology in recent years, the network edge nodes of smart cities are able to collect and process various types of urban traffic data in real time, which leads to the possibility of deploying intelligent traffic prediction technology with real-time analysis and timely feedback on the edge. In view of the strong nonlinear characteristics of urban traffic flow, multiple dynamic and static influencing factors involved, and increasing difficulty of short-term traffic flow prediction in a metropolitan area, this paper proposes an urban traffic flow prediction model based on chaotic particle swarm optimization algorithm-smooth support vector machine (CPSO/SSVM). The prediction model has built a new second-order smooth function to achieve better approximation and regression effects and has further improved the computational efficiency of the smooth support vector machine algorithm through chaotic particle swarm optimization. Simulation experiment results show that this model can accurately predict urban traffic flow.

Automatic Modulation Classification Based on Novel Feature Extraction Algorithms

In order to solve the problems that the accuracy of modulation recognition algorithms of MSK and MQAM signals is not ideal under the condition of low signal-to-noise ratio (SNR) in Additional White Gaussian Noise (AWGN) environment, two novel features, the differential nonlinear phase Peak Factor (PF) and the reciprocal of amplitude envelope variance of cyclic spectrum at zero frequency cross section after difference and forth power processing, are constructed, which can complete the recognition of MSK signal and the classification of MPSK and MQAM signals respectively. This paper proposes a mixed recognition algorithm based on the two new features and other classical features, and design a tree shaped multi-layer smooth support vector machine classifier based on feature selection algorithm (FS_DT-SSVM) to recognize eleven kinds of digital modulation signals. The simulation results illustrate that the algorithm can achieve the classification of the modulation signals {2FSK, MSK, 2ASK, 4ASK, 8ASK, 2PSK, 4PSK, 8PSK, 16QAM, 32QAM, 64QAM} with small SNR. When the SNR is not less than -1dB, the recognition rate of the classifier for all signals exceed 97%, which validates the effectiveness of the proposed modulation recognition algorithm.

Forecasting hotel room prices in selected GCC cities using deep learning

This paper extends the research on hotel room prices using traditional and non-traditional statistical models following the analysis by Karathanasopoulos and Shehhi (2018), which discusses how hotel prices can be easily predicted. In this paper, we employed four forecasting models: the seasonal autoregressive integrated moving average (SARIMA) model, the restricted Boltzmann machine as a deep belief network model, the polynomial smooth support vector machine model, and finally, the adaptive network fuzzy interference system (ANFIS) model. Research data was obtained from Smith Travel Research.In this study, we apply advanced forecasting models based on machine learning and artificial intelligence to the hospitality sector. Some of the models used in this study, such as the ANFIS model, contribute to the research conducted in GCC region. The goal of the research was to contribute to the academic literature and assist hotel operators and decision-makers in setting appropriate strategies.

Bio-inspired weighed quantum particle swarm optimization and smooth support vector machine ensembles for identification of abnormalities in medical data

Knowledge mining is an emerging field where various patterns, rules, etc. can be generated which helps us in the analysis of the result. Medical information systems in modern hospitals and medical institutions become larger and larger, it causes great difficulties in extracting useful information for decision support. Especially, when traditional manual data analysis has become inefficient and methods for efficient computer-based analysis are indispensable. Decision support system is a suitable tool for medical application which helps in the extraction of useful data. A new hybrid model, weighted-quantum particle swarm optimization (WQPSO) for data clustering in sequence with smooth support vector machine (SSVM) is proposed for classification. The parameters considered to evaluate the clustering methods are inter-cluster distance, intra-cluster distance, and validation index. Experiments are performed in MATLAB and the performance analysis, comparisons are made with real-world datasets that are retrieved from UC Irvine Machine Learning Repository. The proposed approach overcomes the drawbacks of existing algorithms in terms of accuracy, performance and complexity. To validate the proposed algorithm, experiments are conducted on two data sets, i.e., liver disorders dataset and Wisconsin Breast Cancer Diagnosis (WBCD) dataset. The accuracy of proposed WQPSO-SSVM classification methodology is 83.76% for liver disorder, 98.42% for WBCD, 95.21% of mammography mass data. Among the considered algorithms such as WPSO-SVM, fuzzy, K-means, and fuzzy C-means methods, WQPSO-SSVM is found to yield a better convergence and provide an improved optimal solution.

Analysis of Decision Tree and Smooth Support Vector Machine Methods on Data Mining

This research is about smooth support vector machine (SSVM) and Decision Tree in data mining. Many researchers conduct and develop methods to improve the accuracy and classification of data on good results. This research was conducted by conducting an experiment on STMIK Neumann Medan student data. In this study, it was concluded that Decision Tree performance is better than SSVM, Decision Tree gets very good results that promise to help find the best students to get scholarships. This study is better than SSVM. The training process has a difference of 11.04% and the testing process is 10.08% with each Accuracy.

Smooth Support Vector Machine (SSVM) for classification of Human Development Index

Human Development Index (HDI) is measuring achievements of human development based on basic components of quality of life. Human development index is low score if HDI is less than 60, moderate HDI between 60 to less than 70, high HDI between 70 to less than 80, and equal to 80 and more than 80 belong to high HDI. Smooth Support Vector Machine (SSVM) is a classification technique that is new. The algorithm used is Newton Armijo with linear kernel, polynomial kernel, and Radial Basis Function (RBF) kernel. The result of classification of human development index with SSVM method with linear kernel shows the prediction accuracy 84.77%, polynomial kernel 61.65%, and RBF kernel 100%. Radial Base Function Kernel (RBF) is the most accurate kernel in predicting human development index.

Recognizing Social Function of Urban Regions by Using Data of Public Bicycle Systems

Obtaining the classification of urban functions is an integral part of urban planning. Currently, public bicycle systems are booming in these years. It conveys human mobility and activity information, which can be closely related to the social function of an urban region. This paper discusses the potential use of public bicycle systems for recognizing the social function of urban regions by using one year's rent/return data of public bicycles. We found that rent/return dynamics, extracted from public bicycle systems, exhibited clear patterns corresponding to the urban function classes of these regions. With seven features designed to characterize the rent/return pattern, our method based on Smooth support vector machine (SSVM) is proposed to recognize social function classes of urban regions. We evaluate our method based on the large-scale real-world dataset collected from the public bicycle system of Hangzhou. The results show that our method can efficiently recognize different types of urban function areas. Classification results using the proposed SSVM achieved the best classification accuracy of 96.15%.

Ensembling Downscaling Techniques and Multiple GCMs to Improve Climate Change Predictions in Cryosphere Scarcely-Gauged Catchment

Future projections of climate variables are the key for the development of mitigation and adaptation strategy to changing climate. However, such projections are often subjected to large uncertainties which make implementation of climate change strategies on water resources system a challenging job. Major uncertainty sources are General Circulation models (GCMs), post-processing and climate heterogeneity based on catchment characteristics (e.g. scares data and high-altitude). Here we presents the comparisons between different GCMs, statistical downscaling and bias correction approaches and finally climate projections, with the integration of gridded and converted (monthly to daily) data for a high-altitude, scarcely-gauged Jhelum River basin, Pakistan. Current study relies on climate projections obtained from factorial combination of 5-GCMs, 2 statistical downscaling and 2 bias correction methods. In addition, we applied bias corrected APHRODITE, converted daily data using MODAWEC model and observed data. Further, five GCMs (CGCM3, HadCM3, CCSM3, ECHAM5 and CSIRO-MK3.5) were tested to scrutinize two suitable GCMs integrated with Statistical Downscaling Model (SDSM) and Smooth Support Vector Machine (SSVM). Results illustrate that the CGCM3 and HadCM3 were suitable GCMs for selected study basin. Both downscaling techniques are able to simulate precipitation, however, SSVM performed slightly better than SDSM. We found that the integration of CGCM3 with SSVM (SSVM-CGCM3) generates precipitation and temperature better than the CGCM3 (SDSM-CGCM3) and HadCM3 (SDSM-HadCM3) with SDSM. Furthermore, the low elevation stations were influenced by monsoon, significantly prone to rise in precipitation and temperature, while high-altitude stations were influenced by westerlies circulations, less prone to climate change. The projections indicated rise in basin-wide annual precipitation by 25.51, 36.76 and 45.52 mm and temperature by 0.64, 1.47 and 2.79 °C, during 2030s, 2060s and 2090s, respectively. The methods and results of this study can be adopted to evaluate climate change implications in the catchments of characteristics similar to Jhelum River basin.

Two smooth support vector machines for $$\varepsilon $$ ε -insensitive regression

In this paper, we propose two new smooth support vector machines for \(\varepsilon \)-insensitive regression. According to these two smooth support vector machines, we construct two systems of smooth equations based on two novel families of smoothing functions, from which we seek the solution to \(\varepsilon \)-support vector regression (\(\varepsilon \)-SVR). More specifically, using the proposed smoothing functions, we employ the smoothing Newton method to solve the systems of smooth equations. The algorithm is shown to be globally and quadratically convergent without any additional conditions. Numerical comparisons among different values of parameter are also reported.

THE ANALYSIS PERFORMANCE METHOD NAIVE BAYES ANDSSVM DETERMINE PATTERN GROUPS OF DISEASE

Information is a very important element and into the daily needs of the moment, to get a precise and accurate information is not easy, this research can help decision makers and make a comparison. Researchers perform data mining techniques to analyze the performance of methods and algorithms naïve Bayes methods Smooth Support Vector Machine (ssvm) in the grouping of the disease.The pattern of disease that is often suffered by people in the group can be in the detection area of the collection of information contained in the medical record. Medical records have infromasi disease by patients in coded according to standard WHO. Processing of medical record data to find patterns of this group of diseases that often occur in this community take the attribute address, sex, type of disease, and age. Determining the next analysis is grouping of four ersebut attribute. From the results of research conducted on the dataset fever diabete mellitus, naïve Bayes method produces an average value of 99% and an accuracy and SSVM method produces an average value of 93% accuracy

Analysis and Optimization of Loss Functions for Multiclass, Top-k, and Multilabel Classification.

Top-k error is currently a popular performance measure on large scale image classification benchmarks such as ImageNet and Places. Despite its wide acceptance, our understanding of this metric is limited as most of the previous research is focused on its special case, the top-1 error. In this work, we explore two directions that shed more light on the top-k error. First, we provide an in-depth analysis of established and recently proposed single-label multiclass methods along with a detailed account of efficient optimization algorithms for them. Our results indicate that the softmax loss and the smooth multiclass SVM are surprisingly competitive in top-k error uniformly across all k, which can be explained by our analysis of multiclass top-k calibration. Further improvements for a specific k are possible with a number of proposed top-k loss functions. Second, we use the top-k methods to explore the transition from multiclass to multilabel learning. In particular, we find that it is possible to obtain effective multilabel classifiers on Pascal VOC using a single label per image for training, while the gap between multiclass and multilabel methods on MS COCO is more significant. Finally, our contribution of efficient algorithms for training with the considered top-k and multilabel loss functions is of independent interest.