scholarly journals Sales Growth Rate Forecasting Using Improved PSO and SVM

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Xibin Wang ◽  
Junhao Wen ◽  
Shafiq Alam ◽  
Xiang Gao ◽  
Zhuo Jiang ◽  
...  
Keyword(s):  
Growth Rate ◽  
Decisive Role ◽  
Convergence Speed ◽  
Classification Model ◽  
Support Vector ◽  
Local Optimum ◽  
Sales Growth ◽  
Local Optima ◽  
Proposed Model ◽  
Forecasting Performance

Accurate forecast of the sales growth rate plays a decisive role in determining the amount of advertising investment. In this study, we present a preclassification and later regression based method optimized by improved particle swarm optimization (IPSO) for sales growth rate forecasting. We use support vector machine (SVM) as a classification model. The nonlinear relationship in sales growth rate forecasting is efficiently represented by SVM, while IPSO is optimizing the training parameters of SVM. IPSO addresses issues of traditional PSO, such as relapsing into local optimum, slow convergence speed, and low convergence precision in the later evolution. We performed two experiments; firstly, three classic benchmark functions are used to verify the validity of the IPSO algorithm against PSO. Having shown IPSO outperform PSO in convergence speed, precision, and escaping local optima, in our second experiment, we apply IPSO to the proposed model. The sales growth rate forecasting cases are used to testify the forecasting performance of proposed model. According to the requirements and industry knowledge, the sample data was first classified to obtain types of the test samples. Next, the values of the test samples were forecast using the SVM regression algorithm. The experimental results demonstrate that the proposed model has good forecasting performance.

Drug Target Group Prediction with Multiple Drug Networks

2020 ◽  
Vol 23 (4) ◽  
pp. 274-284 ◽  
Author(s):  
Jingang Che ◽  
Lei Chen ◽  
Zi-Han Guo ◽  
Shuaiqun Wang ◽  
Aorigele
Keyword(s):  
Drug Target ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Multiple Drug ◽  
Property A ◽  
Multiple Networks ◽  
Proposed Model ◽  
The One

Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

A Supervised Learning Model for AGV Perception in Unstructured Environment

2015 ◽  
pp. 334-353
Author(s):  
Rizwan Aqeel ◽  
Saif Ur Rehman ◽  
Saira Gillani ◽  
Sohail Asghar
Keyword(s):  
Clustering Algorithm ◽  
Research Problem ◽  
Classification Model ◽  
Support Vector ◽  
Ground Vehicle ◽  
Second Stage ◽  
Initial Stage ◽  
Cluster Area ◽  
Proposed Model ◽  
Unstructured Environment

This chapter focuses on an Autonomous Ground Vehicle (AGV), also known as intelligent vehicle, which is a vehicle that can navigate without human supervision. AGV navigation over an unstructured road is a challenging task and is known research problem. This chapter is to detect road area from an unstructured environment by applying a proposed classification model. The Proposed model is sub divided into three stages: (1) - preprocessing has been performed in the initial stage; (2) - road area clustering has been done in the second stage; (3) - Finally, road pixel classification has been achieved. Furthermore, combination of classification as well as clustering is used in achieving our goals. K-means clustering algorithm is used to discover biggest cluster from road scene, second big cluster area has been classified as road or non road by using the well-known technique support vector machine. The Proposed approach is validated from extensive experiments carried out on RGB dataset, which shows that the successful detection of road area and is robust against diverse road conditions such as unstructured nature, different weather and lightening variations.

scholarly journals Data Driven Leakage Detection and Classification of a Boiler Tube

2019 ◽  
Vol 9 (12) ◽  
pp. 2450 ◽  
Author(s):  
Muhammad Sohaib ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Wavelet Packet ◽  
Thermal Power ◽  
Leak Detection ◽  
Classification Model ◽  
Support Vector ◽  
Boiler Tube ◽  
Proposed Model ◽  
Ae Signals

Boiler heat exchange in thermal power plants involves tubes to transfer heat from the fuel to the water. Boiler tube leakage can cause outages and huge power generation loss. Therefore, early detection of leaks in boiler tubes is necessary to avoid such accidents. In this study, a boiler tube leak detection and classification mechanism was designed using wavelet packet transform (WPT) analysis of the acoustic emission (AE) signals acquired from the boiler tube and a fully connected deep neural network (FC-DNN). WPT analysis of the AE signals enabled the extraction of features associated with the different conditions of the boiler tube, that is, normal and leak conditions. The deep neural network (DNN) effectively explores the salient information from the wavelet packet features through a deep architecture instead of considering shallow networks, such as k-nearest neighbors (k-NN) and support vector machines (SVM). This enhances the classification performance of the leak identification and classification model developed. The proposed model yielded a 99.2 % average classification accuracy when tested with AE signals from the boiler tube. The experimental results prove the efficacy of the proposed model for boiler tube leak detection and classification.

scholarly journals Using Machine Learning to Detect Events on the Basis of Bengali and Banglish Facebook Posts

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2367
Author(s):  
Noyon Dey ◽  
Md. Sazzadur Rahman ◽  
Motahara Sabah Mredula ◽  
A. S. M. Sanwar Hosen ◽  
In-Ho Ra
Keyword(s):  
Social Media ◽  
Detection System ◽  
Classification Model ◽  
Support Vector ◽  
Proposed Model ◽  
Prime Concern ◽  
Use Of Social Media ◽  
Bengali Language ◽  
Language Detection ◽  
Media Data

In modern times, ensuring social security has become the prime concern for security administrators. The widespread and recurrent use of social media sites is creating a huge risk for the lives of the general people, as these sites are frequently becoming potential sources of the organization of various types of immoral events. For protecting society from these dangers, a prior detection system which can effectively detect events by analyzing these social media data is essential. However, automating the process of event detection has been difficult, as existing processes must account for diverse writing styles, languages, dialects, post lengths, and et cetera. To overcome these difficulties, we developed an effective model for detecting events, which, for our purposes, were classified as either protesting, celebrating, religious, or neutral, using Bengali and Banglish Facebook posts. At first, the collected posts’ text were processed for language detection, and then, detected posts were pre-processed using stopwords removal and tokenization. Features were then extracted from these pre-processed texts using three sub-processes: filtering, phrase matching of specific events, and sentiment analysis. The collected features were ultimately used to train our Bernoulli Naive Bayes classification model, which was capable of detecting events with 90.41% accuracy (for Bengali-language posts) and 70% (for the Banglish-form posts). For evaluating the effectiveness of our proposed model more precisely, we compared it with two other classifiers: Support Vector Machine and Decision Tree.

scholarly journals Heterogeneous Ensemble Structure based Universal Spam Profile Detection System for Social Media Networks

2020 ◽  
Vol 9 (1) ◽  
pp. 1028-1039
Keyword(s):  
Social Media ◽  
Detection System ◽  
User Profile ◽  
Classification Model ◽  
Support Vector ◽  
Social Media Networks ◽  
Online Social Media ◽  
Linear Polynomial ◽  
Proposed Model ◽  
Social Media Network

The exponential rise in internet technology and online social media networks have revitalized human-being to connect and socialize globally irrespective of geographical and any demographic boundaries. Additionally, it has revitalized business communities to reach target audiences through social media networks. However, as parallel adverse up-surge the everincreasing presence of malicious users or spam has altered predominant intend of such social media network by propagating biased contents, malicious contents and fraud acts. Avoiding and neutralizing such malefic users on social media network has remained a critical challenge due to gigantically large size and user’s diversity such as Facebook, Twitter, and LinkedIn etc. Though exploiting certain user’s behavior and content types can help identifying malicious users, majority of the existing methods are limited due to confined parametric assessment, and inferior classification approaches. With intend to provide spam profile detection system in this paper a novel heterogeneous ensemblebased method is developed. The proposed model exploits user profile features, user’s activity features, location features and content features to perform spam user profile detection. To ensure optimality of computational significances, we applied multi-phased feature selection method employing Wilcoxon Rank Sum test, Significant Predictor test, and Pearson Correlation test, which assured retaining optimal feature sets for further classification. Subsequently, applying an array of machine learning methods, including Logistic regression, decision tree, Support Vector Machine variants with Linear, Polynomial and RBF kernels, Least Square SVM with linear, polynomial and RBF kernels, ANN with different kernels, etc we constituted a robust ensemble model for spam user profile classification. Simulations revealed that the proposed ensemble classification model achieves accuracy and F-score higher than 98%, which is the highest amongst major works done so far. It affirms suitability and robustness of the proposed model for real time spam profile detection and classification on social media platforms

scholarly journals A Forecasting Approach Combining Self-Organizing Map with Support Vector Regression for Reservoir Inflow during Typhoon Periods

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Gwo-Fong Lin ◽  
Tsung-Chun Wang ◽  
Lu-Hsien Chen
Keyword(s):  
Support Vector Regression ◽  
Support Vector ◽  
Self Organizing Map ◽  
Support Vector Regression Model ◽  
Proposed Model ◽  
Forecasting Performance ◽  
Short Lead Time ◽  
Reservoir Inflow ◽  
Reservoir Watershed ◽  
Self Organizing

This study describes the development of a reservoir inflow forecasting model for typhoon events to improve short lead-time flood forecasting performance. To strengthen the forecasting ability of the original support vector machines (SVMs) model, the self-organizing map (SOM) is adopted to group inputs into different clusters in advance of the proposed SOM-SVM model. Two different input methods are proposed for the SVM-based forecasting method, namely, SOM-SVM1 and SOM-SVM2. The methods are applied to an actual reservoir watershed to determine the 1 to 3 h ahead inflow forecasts. For 1, 2, and 3 h ahead forecasts, improvements in mean coefficient of efficiency (MCE) due to the clusters obtained from SOM-SVM1 are 21.5%, 18.5%, and 23.0%, respectively. Furthermore, improvement in MCE for SOM-SVM2 is 20.9%, 21.2%, and 35.4%, respectively. Another SOM-SVM2 model increases the SOM-SVM1 model for 1, 2, and 3 h ahead forecasts obtained improvement increases of 0.33%, 2.25%, and 10.08%, respectively. These results show that the performance of the proposed model can provide improved forecasts of hourly inflow, especially in the proposed SOM-SVM2 model. In conclusion, the proposed model, which considers limit and higher related inputs instead of all inputs, can generate better forecasts in different clusters than are generated from the SOM process. The SOM-SVM2 model is recommended as an alternative to the original SVR (Support Vector Regression) model because of its accuracy and robustness.

Fabric wrinkle level classification via online sequential extreme learning machine based on improved sine cosine algorithm

2020 ◽  
Vol 90 (17-18) ◽  
pp. 2007-2021 ◽  
Author(s):  
Zhiyu Zhou ◽  
Ruoxi Zhang ◽  
Jianxin Zhang ◽  
Yaming Wang ◽  
Zefei Zhu ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Optimal Parameter ◽  
Classification Model ◽  
Initial Population ◽  
Local Optimum ◽  
Search Spaces ◽  
Proposed Model ◽  
Sine Cosine Algorithm ◽  
Learning Machine ◽  
Hidden Layer

Because it is difficulty to classify level of fabric wrinkle, this paper proposes a fabric winkle level classification model via online sequential extreme learning machine based on improved sine cosine algorithm (SCA). The SCA has excellent global optimization ability, can explore different search spaces, and effectively avoid falling into local optimum. Because the initial population of SCA will have an impact on its optimization speed and quality, the SCA is initialized by differential evolution (DE) to avoid local optimization, and then the output weight and hidden layer bias are optimized; that is, the improved SCA is used to select the optimal parameters of the online sequential extreme learning machine (OSELM) to improve the generalization performance of the algorithm. To verify the performance of the proposed model DE-SCA-OSELM, it will be compared with other algorithms using a fabric wrinkles dataset collected under standard conditions. The experimental results indicate that the proposed model can effectively find the optimal parameter value of OSELM. The average classification accuracy increased by 6.95%, 3.62%, 6.67%, and 3.34%, respectively, compared with the partial algorithms OSELM, SCAELM, RVFL and PSOSVM, which meets expectations.

scholarly journals Mixed Kernel Function Support Vector Regression with Genetic Algorithm for Forecasting Dissolved Gas Content in Power Transformers

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2437 ◽  
Author(s):  
Tusongjiang Kari ◽  
Wensheng Gao ◽  
Ayiguzhali Tuluhong ◽  
Yilihamu Yaermaimaiti ◽  
Ziwei Zhang
Keyword(s):  
Genetic Algorithm ◽  
Gas Content ◽  
Power Transformers ◽  
Gaussian Kernel ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Forecasting Model ◽  
Dissolved Gas ◽  
Proposed Model ◽  
Forecasting Performance

Forecasting dissolved gas content in power transformers plays a significant role in detecting incipient faults and maintaining the safety of the power system. Though various forecasting models have been developed, there is still room to further improve prediction performance. In this paper, a new forecasting model is proposed by combining mixed kernel function-based support vector regression (MKF-SVR) and genetic algorithm (GA). First, forecasting performance of SVR models constructed with a single kernel are compared, and then Gaussian kernel and polynomial kernel are retained due to better learning and prediction ability. Next, a mixed kernel, which integrates a Gaussian kernel with a polynomial kernel, is used to establish a SVR-based forecasting model. Genetic algorithm (GA) and leave-one-out cross validation are employed to determine the free parameters of MKF-SVR, while mean absolute percentage error (MAPE) and squared correlation coefficient (r2) are applied to assess the quality of the parameters. The proposed model is implemented on a practical dissolved gas dataset and promising results are obtained. Finally, the forecasting performance of the proposed model is compared with three other approaches, including RBFNN, GRNN and GM. The experimental and comparison results demonstrate that the proposed model outperforms other popular models in terms of forecasting accuracy and fitting capability.

scholarly journals An SVM-Based Nested Sliding Window Approach for Spectral–Spatial Classification of Hyperspectral Images

2020 ◽  
Vol 13 (1) ◽  
pp. 114
Author(s):  
Jiansi Ren ◽  
Ruoxiang Wang ◽  
Gang Liu ◽  
Yuanni Wang ◽  
Wei Wu
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Sliding Window ◽  
Classification Model ◽  
Support Vector ◽  
Proposed Model ◽  
Two Parameters

This paper proposes a Nested Sliding Window (NSW) method based on the correlation between pixel vectors, which can extract spatial information from the hyperspectral image (HSI) and reconstruct the original data. In the NSW method, the neighbourhood window constructed with the target pixel as the centre contains relevant pixels that are spatially adjacent to the target pixel. In the neighbourhood window, a nested sliding sub-window contains the target pixel and a part of the relevant pixels. The optimal sub-window position is determined according to the average value of the Pearson correlation coefficients of the target pixel and the relevant pixels, and the target pixel can be reconstructed by using the pixels and the corresponding correlation coefficients in the optimal sub-window. By combining NSW with Principal Component Analysis (PCA) and Support Vector Machine (SVM), a classification model, namely NSW-PCA-SVM, is obtained. This paper conducts experiments on three public datasets, and verifies the effectiveness of the proposed model by comparing with two basic models, i.e., SVM and PCA-SVM, and six state-of-the-art models, i.e., CDCT-WF-SVM, CDCT-2DCT-SVM, SDWT-2DWT-SVM, SDWT-WF-SVM, SDWT-2DCT-SVM and Two-Stage. The proposed approach has the following advantages in overall accuracy (OA)—take the experimental results on the Indian Pines dataset as an example: (1) Compared with SVM (OA = 53.29%) and PCA-SVM (OA = 58.44%), NSW-PCA-SVM (OA = 91.40%) effectively utilizes the spatial information of HSI and improves the classification accuracy. (2) The performance of the proposed model is mainly determined by two parameters, i.e., the window size in NSW and the number of principal components in PCA. The two parameters can be adjusted independently, making parameter adjustment more convenient. (3) When the sample size of the training set is small (20 samples per class), the proposed NSW-PCA-SVM approach achieves 2.38–18.40% advantages in OA over the six state-of-the-art models.

Morlet-Kernel Principal Component Analysis Features for Speech Recognition

2020 ◽  
Vol 17 (9) ◽  
pp. 4482-4486
Author(s):  
H. Y. Vani ◽  
M. A. Anusuya ◽  
M. L. Chayadevi
Keyword(s):  
Recognition Rate ◽  
Principal Component ◽  
Morlet Wavelet ◽  
Classification Model ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Data Sets ◽  
Least Mean Square ◽  
Proposed Model ◽  
Comparative Results

The aim of this paper is to present the application of Morlet wavelet to extract the speech features in place of MFCC features. KPCA is applied for selecting and reducing the large features obtained from Morlet wavelet. NLMS (Normalized Least Mean Square) filter is used to reduce additive noise levels ranging from ±5 dB to ±15 dB. Features are modeled using Ensembled Support Vector Machine classification model for FSDD and Kannada multi speaker data sets. The comparative results are discussed over logistic regression model. The proposed model reduces the noise with 99% of recognition rate for isolated words. The efficiency of ensembled classification model is explored.

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