Support Vector
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Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Muhammad Ali ◽  
Dost Muhammad Khan ◽  
Muhammad Aamir ◽  
Amjad Ali ◽  
Zubair Ahmad

Prediction of financial time series such as stock and stock indexes has remained the main focus of researchers because of its composite nature and instability in almost all of the developing and advanced countries. The main objective of this research work is to predict the direction movement of the daily stock prices index using the artificial neural network (ANN) and support vector machine (SVM). The datasets utilized in this study are the KSE-100 index of the Pakistan stock exchange, Korea composite stock price index (KOSPI), Nikkei 225 index of the Tokyo stock exchange, and Shenzhen stock exchange (SZSE) composite index for the last ten years that is from 2011 to 2020. To build the architect of a single layer ANN and SVM model with linear, radial basis function (RBF), and polynomial kernels, different technical indicators derived from the daily stock trading, such as closing, opening, daily high, and daily low prices and used as input layers. Since both the ANN and SVM models were used as classifiers; therefore, accuracy and F-score were used as performance metrics calculated from the confusion matrix. It can be concluded from the results that ANN performs better than SVM model in terms of accuracy and F-score to predict the direction movement of the KSE-100 index, KOSPI index, Nikkei 225 index, and SZSE composite index daily closing price movement.

2021 ◽  
Vol 11 (23) ◽  
pp. 11400
Andra-Maria Mircea-Vicoveanu ◽  
Elena Rezuș ◽  
Florin Leon ◽  
Silvia Curteanu

This study is based on the consideration that the patients with rheumatoid arthritis and ankylosing spondylitis undergoing biological therapy have a higher risk of developing tuberculosis. The QuantiFERON-TB Gold test result was the output of the models and a series of features related to the patients and their treatments were chosen as inputs. A distribution of patients by gender and biological therapy, followed at the time of inclusion in the study, and at the end of the study, is made for both rheumatoid arthritis and ankylosing spondylitis. A series of classification algorithms (random forest, nearest neighbor, k-nearest neighbors, C4.5 decision trees, non-nested generalized exemplars, and support vector machines) and attribute selection algorithms (ReliefF, InfoGain, and correlation-based feature selection) were successfully applied. Useful information was obtained regarding the influence of biological and classical treatments on tuberculosis risk, and most of them agreed with medical studies.

Risks ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 216
Boualem Djehiche ◽  
Björn Löfdahl

We propose a hybrid classical-quantum approach for modeling transition probabilities in health and disability insurance. The modeling of logistic disability inception probabilities is formulated as a support vector regression problem. Using a quantum feature map, the data are mapped to quantum states belonging to a quantum feature space, where the associated kernel is determined by the inner product between the quantum states. This quantum kernel can be efficiently estimated on a quantum computer. We conduct experiments on the IBM Yorktown quantum computer, fitting the model to disability inception data from a Swedish insurance company.

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Xiaolei Chen ◽  
Sikun Ge

Based on the parallel K-means algorithm, this article conducts in-depth research on the related issues of marketing node detection under the Internet, including designing a new Internet marketing node detector and a location summary network based on FCN (Full Convolutional Network) to input the preprocessing of the node and verify its performance under the data sets. At the same time, to solve the problem of insufficient data sets of Internet marketing nodes, the Internet data sets are artificially generated and used for detector training. First, the multiclass K-means algorithm is changed to two categories suitable for Internet marketing node detection: marketing nodes and background categories. Secondly, the weights in the K-means algorithm are mostly only applicable to target detection tasks. Therefore, when processing Internet marketing node detection tasks, the K-means algorithm is used to regress the training set and calculate 5 weights. During the simulation experiment, the weight calculation formula is used to calculate the weight of the feature term. The basic idea is that if a feature word appears more often in this document but less frequently in other nodes, the word will be assigned higher. At the same time, this article focuses on k. Some shortcomings of the mean clustering algorithm have been specifically improved. By standardizing the data participating in the clustering, the data participating in the clustering is transformed from an irregular distribution to a cluster-like distribution, thereby facilitating the clustering process. The density is introduced to determine the initial center of the cluster, and the purity metric is introduced to determine the appropriate density radius of the cluster center, to achieve the most effective reduction of the support vector machine training samples.

2021 ◽  
Vol 8 (1-2) ◽  
pp. 72-86
Shubham Sharma ◽  
Suraj Kumar Singh ◽  
Shruti Kanga ◽  
Nikola Kranjčić ◽  
Bojan Đurin

Urban Land use changes, measurements, and the analysis of rate trends of growth would help in resources management and planning, etc. In this study, we analyze the urban change dynamics using a support vector machine model. This method derives the urban and rural land-use change and various components, such as population growth, built-up areas, and other utilities. Urban growth increases rapidly due to exponential growth of population, industrial growth, etc. The population growth also affects the availability of various purposes in its spatial distribution. In this present study, we carried out using multi-temporal satellite remote sensing data Landsat MSS (Multispectral scanner), ETM+ (Enhanced thematic mapper), OLI (Operational land imager) for the analysis of urban change dynamics between years 1980-1990, 1990-2003, 2012-2020 in Kanpur Nagar city in the state of Uttar Pradesh in India. In our study, we used SVM (Support Vector Machine) Model to analyze the urban change dynamics. A support vector machine classification technique was applied to generate the LULC maps using Landsat images of the years 1980, 1990, 2003, and 2020. Envi and ArcGIS software had used to identify the land cover changes and the applying urban simulation model (CA- Markov model) in Idrisi selva edition 17.0 software. The LULC maps of 2003 and 2020 were used to simulate the LULC projected map for 2050 using (Cellular automata) CA- Markov based simulation model.

2021 ◽  
Vol 7 ◽  
pp. e799
Zhenlong Sun ◽  
Jing Yang ◽  
Xiaoye Li ◽  
Jianpei Zhang

Support vector machine (SVM) is a robust machine learning method and is widely used in classification. However, the traditional SVM training methods may reveal personal privacy when the training data contains sensitive information. In the training process of SVMs, working set selection is a vital step for the sequential minimal optimization-type decomposition methods. To avoid complex sensitivity analysis and the influence of high-dimensional data on the noise of the existing SVM classifiers with privacy protection, we propose a new differentially private working set selection algorithm (DPWSS) in this paper, which utilizes the exponential mechanism to privately select working sets. We theoretically prove that the proposed algorithm satisfies differential privacy. The extended experiments show that the DPWSS algorithm achieves classification capability almost the same as the original non-privacy SVM under different parameters. The errors of optimized objective value between the two algorithms are nearly less than two, meanwhile, the DPWSS algorithm has a higher execution efficiency than the original non-privacy SVM by comparing iterations on different datasets. To the best of our knowledge, DPWSS is the first private working set selection algorithm based on differential privacy.

2021 ◽  
Vol 5 (2) ◽  
pp. 258
Andi Taufik ◽  
Robi Sopandi

Legitimasi sebagai kelompok yang paling mewakili umat terus menjadi hal yang diperebutkan oleh kelompok Islam saat ini. Di satu sisi ada Nahdlatul Ulama (NU) dengan wacana Islam dan nasionalisme, di sisi lain ada Gerakan Nasional Pembela Fatwa Ulama (GNPF-Ulama) dengan wacana yang hanya menekankan pada aspek Islam. Pertarungan wacana kedua kelompok besar ini termasuk di media sosial Twitter sontak menimbulkan kebingungan pada umat Islam terutama yang awam, ditambah lagi banyaknya Buzzer dan Influencer yang berafiliasi kepada masing-masing kelompok. Penelitian ini akan melakukan klasifikasi tweet dari Influencer yang berafiliasi dengan NU dan GNPF-Ulama di media sosial Twitter. Algoritma yang digunakan adalah Naive Bayes dan Support Vector Machine. Data Twitter diambil menggunakan library tweepy, proses preprocessing menggunakan Python dengan penggunaan Library Sastrawi untuk melakukan stemming kata bahasa Indonesia. Klasifikasi dengan metode Naive Bayes dan Support Vector Machine dilakukan menggunakan aplikasi RapidMiner. Dari rangkaian proses yang dilakukan, metode Support Vector Machine menghasilkan nilai akurasi dan AUC yang lebih baik dari Naive Bayes yakni sebesar 77.28% dan AUC sebesar 0.863.

Xiao-Xiang Cheng ◽  
Jian-Hua Fan ◽  
Zhi-Hong Xiao

This paper investigates the efficiency of three surrogate model-based dynamic finite element model updating methods, the response surface, the support vector regression, and the radial basis function neural network, using the engineering background of the Tsing Ma Bridge tower. The influences of two different sampling methods (central composite design sampling and Box–Behnken design sampling) on the model updating results are also assessed. It was deduced that the impact of the surrogate model type on the updating results is not significant. More precisely, the models updated using the response surface method and the support vector regression method are similar in terms of reproducing the dynamic characteristics of the physical truth. However, the effects of the employed sampling method on the model updating results are significant as the model updating quality using the central composite design sampling method is higher than that using the Box–Behnken design sampling method in some considered cases.

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