scholarly journals KOMPARASI MODEL SUPPORT VECTOR MACHINES (SVM) DAN NEURAL NETWORK UNTUK MENGETAHUI TINGKAT AKURASI PREDIKSI TERTINGGI HARGA SAHAM

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
R. Hadapiningradja Kusumodestoni ◽  
Sarwido Sarwido
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Stock Prices ◽  
Prediction Accuracy ◽  
High Accuracy ◽  
Stock Index ◽  
Support Vector ◽  
Accuracy Rate ◽  
Trend Prediction ◽  
Data Set

There are many types of investments to make money, one of which is in the form of shares. Shares is a trading company dealing with securities in the global capital markets. Stock Exchange or also called stock market is actually the activities of private companies in the form of buying and selling investments. To avoid losses in investing, we need a model of predictive analysis with high accuracy and supported by data - lots of data and accurately. The correct techniques in the analysis will be able to reduce the risk for investors in investing. There are many models used in the analysis of stock price movement prediction, in this study the researchers used models of neural networks (NN) and a model of support vector machine (SVM). Based on the background of the problems that have been mentioned in the previous description it can be formulated the problem as follows: need an algorithm that can predict stock prices, and need a high accuracy rate by adding a data set on the prediction, two algorithms will be investigated expected results last researchers can deduce where the algorithm accuracy rate predictions are the highest or accurate, then the purpose of this study was to mengkomparasi or compare between the two algorithms are algorithms Neural Network algorithm and Support Vector Machine which later on the end result has an accuracy rate forecast stock prices highest to see the error value RMSEnya. After doing research using the model of neural network and model of support vector machine (SVM) to predict the stock using the data value of the shares on the stock index hongkong dated July 20, 2016 at 16:26 pm until the date of 15 September 2016 at 17:40 pm as many as 729 data sets within an interval of 5 minute through a process of training, learning, and then continue the process of testing so the result is that by using a neural network model of the prediction accuracy of 0.503 +/- 0.009 (micro 503) while using the model of support vector machine (SVM) accuracy of the predictions for 0477 + / - 0.008 (micro: 0477) so that after a comparison can be concluded that the neural network models have trend prediction accuracy higher than the model of support vector machine (SVM).

Stock Market Trend Prediction Based on Text Mining of Corporate Web and Time Series Data

2014 ◽  
Vol 18 (1) ◽  
pp. 22-31 ◽  
Author(s):  
Hoang T. P. Thanh ◽  
◽  
Phayung Meesad ◽  
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Text Mining ◽  
Stock Market ◽  
Stock Prices ◽  
Time Series Data ◽  
Stock Index ◽  
Series Data ◽  
Support Vector ◽  
Data Set

Predicting the behaviors of the stock markets are always an interesting topic for not only financial investors but also scholars and professionals from different fields, because successful prediction can help investors to yield significant profits. Previous researchers have shown the strong correlation between financial news and their impacts to the movements of stock prices. This paper proposes an approach of using time series analysis and text mining techniques to predict daily stock market trends. The research is conducted with the utilization of a database containing stock index prices and news articles collected from Vietnam websites over 3 years from 2010 to 2012. A robust feature selection and a strong machine learning algorithm are able to lift the forecasting accuracy. By combining Linear Support Vector Machine Weight and Support Vector Machine algorithm, this proposed approach can enhance the prediction accuracy significantly above those of related research approaches. The results show that data set represented by 42 features achieves the highest accuracy by using one-against-one Support Vector Machines (up to 75%) and one-against-one method outperforms one-againstall method in almost all case studies.

scholarly journals Key Feature Recognition Algorithm of Network Intrusion Signal Based on Neural Network and Support Vector Machine

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 380 ◽  
Author(s):  
Kai Ye
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Feature Recognition ◽  
False Positive Rate ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Data Set ◽  
Network Intrusion ◽  
Key Features

When identifying the key features of the network intrusion signal based on the GA-RBF algorithm (using the genetic algorithm to optimize the radial basis) to identify the key features of the network intrusion signal, the pre-processing process of the network intrusion signal data is neglected, resulting in an increase in network signal data noise, reducing the accuracy of key feature recognition. Therefore, a key feature recognition algorithm for network intrusion signals based on neural network and support vector machine is proposed. The principal component neural network (PCNN) is used to extract the characteristics of the network intrusion signal and the support vector machine multi-classifier is constructed. The feature extraction result is input into the support vector machine classifier. Combined with PCNN and SVM (Support Vector Machine) algorithms, the key features of network intrusion signals are identified. The experimental results show that the algorithm has the advantages of high precision, low false positive rate and the recognition time of key features of R2L (it is a common way of network intrusion attack) data set is only 3.18 ms.

scholarly journals Automatic Task Classification via Support Vector Machine and Crowdsourcing

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Hyungsik Shin ◽  
Jeongyeup Paek
Keyword(s):  
Support Vector Machine ◽  
Mobile Devices ◽  
Prediction Accuracy ◽  
Training Data ◽  
Amazon Mechanical Turk ◽  
Support Vector ◽  
Data Set ◽  
English Sentence ◽  
Task Classification ◽  
Personal Assistant

Automatic task classification is a core part of personal assistant systems that are widely used in mobile devices such as smartphones and tablets. Even though many industry leaders are providing their own personal assistant services, their proprietary internals and implementations are not well known to the public. In this work, we show through real implementation and evaluation that automatic task classification can be implemented for mobile devices by using the support vector machine algorithm and crowdsourcing. To train our task classifier, we collected our training data set via crowdsourcing using the Amazon Mechanical Turk platform. Our classifier can classify a short English sentence into one of the thirty-two predefined tasks that are frequently requested while using personal mobile devices. Evaluation results show high prediction accuracy of our classifier ranging from 82% to 99%. By using large amount of crowdsourced data, we also illustrate the relationship between training data size and the prediction accuracy of our task classifier.

scholarly journals Perbandingan Teknik Klasifikasi Neural Network, Support Vector Machine, dan Naive Bayes dalam Mendeteksi Kanker Payudara

2020 ◽  
Vol 7 (1) ◽  
pp. 53
Author(s):  
Derisma Derisma ◽  
Fajri Febrian
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Data Mining ◽  
Support Vector Machine ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Accuracy Rate ◽  
Cancer Disease ◽  
Network Support

Abstrak: Kanker payudara merupakan jenis kanker yang sering ditemukan oleh kebanyakan wanita. Di Indonesia Kanker payudara menempati urutan pertama pada pasien rawat inap di seluruh rumah sakit. Tujuan dari penelitian ini adalah melakukan diagnosis penyakit kanker payudara berbasis komputasi yang dapat menghasilkan bagaimana kondisi kanker seseorang berdasarkan akurasi algoritma. Penelitian ini menggunakan pemrograman orange python dan dataset Wisconsin Breast Cancer untuk pemodelan klasifikasi kanker payudara. Metode data mining yang diterapkan yaitu Neural Network, Support Vector Machine, dan Naive Bayes. Dalam penelitian ini didapat algoritma klasifikasi terbaik yaitu algoritma Kernel SVM dengan tingkat akurasi sebesar  98.9 % dan algoritma terendah yaitu Naive Bayes senilai 96.1 %.   Kata kunci: kanker payudara, neural network, support vector machine, naive bayes   Abstract: Breast cancer is a type of cancer that mostly found in many women. In Indonesia, breast cancer ranks first in hospitalized patients at every hospital. This study aimed to conduct a computation-based diagnose of breast cancer disease that could produce the state of cancer of an individual based on the accuracy of algorithm. This study used python orange programming and Wisconsin Breast Cancer dataset for a modeling and application of breast cancer classification. The data mining methods that were applied in this study were Neural Network, Support Vector Machine, dan Naive Bayes. In this study, Kernel SVM’s algorithm was the best classification algorithm of breast cancer disease with 98.9 % accuracy rate and Naïve Beyes was the lowest with 96.1 % of accuracy rate.   Keywords: breast cancer, neural network, support vector machine, naive bayes

scholarly journals HYBRID FUZZY NEURAL NETWORK TO PREDICT PRICE DIRECTION IN THE GERMAN DAX-30 INDEX

2018 ◽  
Vol 24 (6) ◽  
pp. 2161-2178 ◽  
Author(s):  
Fernando García ◽  
Francisco Guijarro ◽  
Javier Oliver ◽  
Rima Tamošiūnienė
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Stock Index ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Accurate Information ◽  
Investment Strategies ◽  
Trend Prediction ◽  
Fuzzy Neural ◽  
Input Variables

Intraday trading rules require accurate information about the future short term market evolution. For that reason, next-day market trend prediction has attracted the attention of both academics and practitioners. This interest has increased in recent years, as different methodologies have been applied to this end. Usually, machine learning techniques are used such as artificial neural networks, support vector machines and decision trees. The input variables of most of the studies are traditional technical indicators which are used by professional traders to implement investment strategies. We analyse if these indicators have predictive power on the German DAX-30 stock index by applying a hybrid fuzzy neural network to predict the one-day ahead direction of index. We implement different models depending on whether all the indicators and oscillators are used as inputs, or if a linear combination of them obtained through a factor analysis is used instead. In order to guarantee for the robustness of the results, we train and apply the HyFIS models on randomly selected subsamples 10,000 times. The results show that the reduction of the dimension through the factorial analysis generates more profitable and less risky strategies.

scholarly journals Learning Dynamic Factors to Improve the Accuracy of Bus Arrival Time Prediction via a Recurrent Neural Network

2019 ◽  
Vol 11 (12) ◽  
pp. 247
Author(s):  
Xin Zhou ◽  
Peixin Dong ◽  
Jianping Xing ◽  
Peijia Sun
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Public Transportation ◽  
Prediction Accuracy ◽  
Arrival Time ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Support Vector ◽  
Data Set ◽  
Dynamic Factors

Accurate prediction of bus arrival times is a challenging problem in the public transportation field. Previous studies have shown that to improve prediction accuracy, more heterogeneous measurements provide better results. So what other factors should be added into the prediction model? Traditional prediction methods mainly use the arrival time and the distance between stations, but do not make full use of dynamic factors such as passenger number, dwell time, bus driving efficiency, etc. We propose a novel approach that takes full advantage of dynamic factors. Our approach is based on a Recurrent Neural Network (RNN). The experimental results indicate that a variety of prediction algorithms (such as Support Vector Machine, Kalman filter, Multilayer Perceptron, and RNN) have significantly improved performance after using dynamic factors. Further, we introduce RNN with an attention mechanism to adaptively select the most relevant input factors. Experiments demonstrate that the prediction accuracy of RNN with an attention mechanism is better than RNN with no attention mechanism when there are heterogeneous input factors. The experimental results show the superior performances of our approach on the data set provided by Jinan Public Transportation Corporation.

scholarly journals A comparative study of multiple linear regression, artificial neural network and support vector machine for the prediction of dissolved oxygen

2016 ◽  
Vol 48 (5) ◽  
pp. 1214-1225 ◽  
Author(s):  
Xue Li ◽  
Jian Sha ◽  
Zhong-liang Wang
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Linear Regression ◽  
Dissolved Oxygen ◽  
Multiple Linear Regression ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Physical Factors ◽  
Data Set ◽  
Important Indicator

Dissolved oxygen (DO) is an important indicator reflecting the healthy state of aquatic ecosystems. The balance between oxygen supply and consuming in the water body is significantly influenced by physical and chemical parameters. This study aimed to evaluate and compare the performance of multiple linear regression (MLR), back propagation neural network (BPNN), and support vector machine (SVM) for the prediction of DO concentration based on multiple water quality parameters. The data set included 969 samples collected from rivers in China and the 16 predicted variables involved physical factors, nutrients, organic substances, and metal ions, which would affect the DO concentrations directly or indirectly by influencing the water–air exchange, the growth of water plants, and the lives of aquatic animals. The models optimized by particle swarm optimization (PSO) algorithm were calibrated and tested, with nearly 80% and 20% data, respectively. The results showed that the PSO-BPNN and PSO-SVM had better predicted performances than linear regression methods. All of the evaluated criteria, including coefficient of determination, mean squared error, and absolute relative errors suggested that the PSO-SVM model was superior to the MLR and PSO-BPNN for DO prediction in the rivers of China with limited knowledge of other information.

scholarly journals A Fuzzy Based Classification – An Experimental Analysis

2019 ◽  
Vol 8 (10) ◽  
pp. 4634-4638
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Soft Computing ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Hybrid Technique ◽  
Classification Rate ◽  
Data Set ◽  
Computing Technique ◽  
Increase In Accuracy

Soft Computing has become popular in developing systems that encloses human expertise. Imaging technologies and clinical cytology has improved in disease diagnosis. Exact detection is extremely important for proper treatment and cure of disease. Two soft computing technique Neural Network and Support Vector Machine are used for classification of Caridotocography data set. This paper clearly explains the advantages of hybrid technique, when Fuzzy is combined with Neural Network and Support Vector Machine it is clearly noticed that there is an increase in accuracy of classification rate.

scholarly journals Research on the Algorithm System for Generation and Creation by Computer Artificial Intelligence and Support Vector Machine

2021 ◽  
Vol 2083 (4) ◽  
pp. 042039
Author(s):  
Xuan Wu
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Least Square ◽  
Support Vector ◽  
Generation Model ◽  
Algorithmic Composition ◽  
Data Set ◽  
Production Program ◽  
Music Production ◽  
Standard Support Vector Machine

Abstract Algorithmic composition is also called automated composition. It is an attempt to use a specific form of process. Composers make full use of computers to carry out music creation and reduce their access. In this paper, based on the standard support vector machine (SVM) learning neural network, the least square support vector machine (LS-SVM) is combined with the recurrent neural network, and a new least square support vector machine learning neural network is proposed. The article realizes the efficient end-to-end multi-dimensional sound wave time series generation model Music-coder, through which the music style music of the famous singer Jay Chou is generated, and the quantified similarity with the real Jay Chou music data set reaches a maximum of 97.73%. The project in this paper shows that intelligent algorithm as a composition tool for music generation and creation is an effective music production program and will bring new development to music production.

scholarly journals Three-Class Mammogram Classification Based on Descriptive CNN Features

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
M. Mohsin Jadoon ◽  
Qianni Zhang ◽  
Ihsan Ul Haq ◽  
Sharjeel Butt ◽  
Adeel Jadoon
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Curvelet Transform ◽  
Data Matrix ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Accuracy Rate ◽  
Data Set ◽  
Proposed Model ◽  
Mammogram Images

In this paper, a novel classification technique for large data set of mammograms using a deep learning method is proposed. The proposed model targets a three-class classification study (normal, malignant, and benign cases). In our model we have presented two methods, namely, convolutional neural network-discrete wavelet (CNN-DW) and convolutional neural network-curvelet transform (CNN-CT). An augmented data set is generated by using mammogram patches. To enhance the contrast of mammogram images, the data set is filtered by contrast limited adaptive histogram equalization (CLAHE). In the CNN-DW method, enhanced mammogram images are decomposed as its four subbands by means of two-dimensional discrete wavelet transform (2D-DWT), while in the second method discrete curvelet transform (DCT) is used. In both methods, dense scale invariant feature (DSIFT) for all subbands is extracted. Input data matrix containing these subband features of all the mammogram patches is created that is processed as input to convolutional neural network (CNN). Softmax layer and support vector machine (SVM) layer are used to train CNN for classification. Proposed methods have been compared with existing methods in terms of accuracy rate, error rate, and various validation assessment measures. CNN-DW and CNN-CT have achieved accuracy rate of 81.83% and 83.74%, respectively. Simulation results clearly validate the significance and impact of our proposed model as compared to other well-known existing techniques.

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