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Article
Publication date: 26 August 2014

Bilal M’hamed Abidine, Belkacem Fergani, Mourad Oussalah and Lamya Fergani

The task of identifying activity classes from sensor information in smart home is very challenging because of the imbalanced nature of such data set where some activities…

Abstract

Purpose

The task of identifying activity classes from sensor information in smart home is very challenging because of the imbalanced nature of such data set where some activities occur more frequently than others. Typically probabilistic models such as Hidden Markov Model (HMM) and Conditional Random Fields (CRF) are known as commonly employed for such purpose. The paper aims to discuss these issues.

Design/methodology/approach

In this work, the authors propose a robust strategy combining the Synthetic Minority Over-sampling Technique (SMOTE) with Cost Sensitive Support Vector Machines (CS-SVM) with an adaptive tuning of cost parameter in order to handle imbalanced data problem.

Findings

The results have demonstrated the usefulness of the approach through comparison with state of art of approaches including HMM, CRF, the traditional C-Support vector machines (C-SVM) and the Cost-Sensitive-SVM (CS-SVM) for classifying the activities using binary and ubiquitous sensors.

Originality/value

Performance metrics in the experiment/simulation include Accuracy, Precision/Recall and F measure.

Details

Kybernetes, vol. 43 no. 8
Type: Research Article
ISSN: 0368-492X

Keywords

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Article
Publication date: 18 April 2017

Yanjie Wang, Zhengchao Xie, InChio Lou, Wai Kin Ung and Kai Meng Mok

The purpose of this paper is to examine the applicability and capability of models based on a genetic algorithm and support vector machine (GA-SVM) and a genetic algorithm…

Abstract

Purpose

The purpose of this paper is to examine the applicability and capability of models based on a genetic algorithm and support vector machine (GA-SVM) and a genetic algorithm and relevance vector machine (GA-RVM) for the prediction of phytoplankton abundances associated with algal blooms in a Macau freshwater reservoir, and compare their performances with an artificial neural network (ANN) model.

Design/methodology/approach

The hybrid models GA-SVM and GA-RVM were developed for the optimal control of parameters for predicting (based on the current month’s variables) and forecasting (based on the previous three months’ variables) phytoplankton dynamics in a Macau freshwater reservoir, MSR, which has experienced cyanobacterial blooms in recent years. There were 15 environmental parameters, including pH, SiO2, alkalinity, bicarbonate (HCO3−), dissolved oxygen (DO), total nitrogen (TN), UV254, turbidity, conductivity, nitrate (NO3−), orthophosphate (PO43−), total phosphorus (TP), suspended solids (SS) and total organic carbon (TOC) selected from the correlation analysis, with eight years (2001-2008) of data for training, and the most recent three years (2009-2011) for testing.

Findings

For both accuracy performance and generalized performance, the ANN, GA-SVM and GA-RVM had similar predictive powers of R2 of 0.73-0.75. However, whereas ANN and GA-RVM models showed very similar forecast performances, GA-SVM models had better forecast performances of R2 (0.862), RMSE (0.266) and MAE (0.0710) with the respective parameters of 0.987, 0.161 and 0.032 optimized using GA.

Originality/value

This is the first application of GA-SVM and GA-RVM models for predicting and forecasting algal bloom in freshwater reservoirs. GA-SVM was shown to be an effective new way for monitoring algal bloom problem in water resources.

Details

Engineering Computations, vol. 34 no. 2
Type: Research Article
ISSN: 0264-4401

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Article
Publication date: 21 May 2021

Saddam Bensaoucha, Youcef Brik, Sandrine Moreau, Sid Ahmed Bessedik and Aissa Ameur

This paper provides an effective study to detect and locate the inter-turn short-circuit faults (ITSC) in a three-phase induction motor (IM) using the support vector

Abstract

Purpose

This paper provides an effective study to detect and locate the inter-turn short-circuit faults (ITSC) in a three-phase induction motor (IM) using the support vector machine (SVM). The characteristics extracted from the analysis of the phase shifts between the stator currents and their corresponding voltages are used as inputs to train the SVM. The latter automatically decides on the IM state, either a healthy motor or a short-circuit fault on one of its three phases.

Design/methodology/approach

To evaluate the performance of the SVM, three supervised algorithms of machine learning, namely, multi-layer perceptron neural networks (MLPNNs), radial basis function neural networks (RBFNNs) and extreme learning machine (ELM) are used along with the SVM in this study. Thus, all classifiers (SVM, MLPNN, RBFNN and ELM) are tested and the results are compared with the same data set.

Findings

The obtained results showed that the SVM outperforms MLPNN, RBFNNs and ELM to diagnose the health status of the IM. Especially, this technique (SVM) provides an excellent performance because it is able to detect a fault of two short-circuited turns (early detection) when the IM is operating under a low load.

Originality/value

The original of this work is to use the SVM algorithm based on the phase shift between the stator currents and their voltages as inputs to detect and locate the ITSC fault.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. 40 no. 3
Type: Research Article
ISSN: 0332-1649

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Article
Publication date: 27 February 2020

Seyed Amin Bagherzadeh

This paper aims to propose a nonlinear model for aeroelastic aircraft that can predict the flight parameters throughout the investigated flight envelopes.

Abstract

Purpose

This paper aims to propose a nonlinear model for aeroelastic aircraft that can predict the flight parameters throughout the investigated flight envelopes.

Design/methodology/approach

A system identification method based on the support vector machine (SVM) is developed and applied to the nonlinear dynamics of an aeroelastic aircraft. In the proposed non-parametric gray-box method, force and moment coefficients are estimated based on the state variables, flight conditions and control commands. Then, flight parameters are estimated using aircraft equations of motion. Nonlinear system identification is performed using the SVM network by minimizing errors between the calculated and estimated force and moment coefficients. To that end, a least squares algorithm is used as the training rule to optimize the generalization bound given for the regression.

Findings

The results confirm that the SVM is successful at the aircraft system identification. The precision of the SVM model is preserved when the models are excited by input commands different from the training ones. Also, the generalization of the SVM model is acceptable at non-trained flight conditions within the trained flight conditions. Considering the precision and generalization of the model, the results indicate that the SVM is more successful than the well-known methods such as artificial neural networks.

Practical implications

In this paper, both the simulated and real flight data of the F/A-18 aircraft are used to provide aeroelastic models for its lateral-directional dynamics.

Originality/value

This paper proposes a non-parametric system identification method for aeroelastic aircraft based on the SVM method for the first time. Up to the author’s best knowledge, the SVM is not used for the aircraft system identification or the aircraft parameter estimation until now.

Details

Aircraft Engineering and Aerospace Technology, vol. 92 no. 3
Type: Research Article
ISSN: 1748-8842

Keywords

Content available
Article
Publication date: 28 July 2020

Harleen Kaur and Vinita Kumari

Diabetes is a major metabolic disorder which can affect entire body system adversely. Undiagnosed diabetes can increase the risk of cardiac stroke, diabetic nephropathy…

Abstract

Diabetes is a major metabolic disorder which can affect entire body system adversely. Undiagnosed diabetes can increase the risk of cardiac stroke, diabetic nephropathy and other disorders. All over the world millions of people are affected by this disease. Early detection of diabetes is very important to maintain a healthy life. This disease is a reason of global concern as the cases of diabetes are rising rapidly. Machine learning (ML) is a computational method for automatic learning from experience and improves the performance to make more accurate predictions. In the current research we have utilized machine learning technique in Pima Indian diabetes dataset to develop trends and detect patterns with risk factors using R data manipulation tool. To classify the patients into diabetic and non-diabetic we have developed and analyzed five different predictive models using R data manipulation tool. For this purpose we used supervised machine learning algorithms namely linear kernel support vector machine (SVM-linear), radial basis function (RBF) kernel support vector machine, k-nearest neighbour (k-NN), artificial neural network (ANN) and multifactor dimensionality reduction (MDR).

Details

Applied Computing and Informatics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2634-1964

Keywords

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Book part
Publication date: 30 September 2020

B. G. Deepa and S. Senthil

Breast cancer (BC) is one of the leading cancer in the world, BC risk has been there for women of the middle age also, it is the malignant tumor. However, identifying BC…

Abstract

Breast cancer (BC) is one of the leading cancer in the world, BC risk has been there for women of the middle age also, it is the malignant tumor. However, identifying BC in the early stage will save most of the women’s life. As there is an advancement in the technology research used Machine Learning (ML) algorithm Random Forest for ranking the feature, Support Vector Machine (SVM), and Naïve Bayes (NB) supervised classifiers for selection of best optimized features and prediction of BC accuracy. The estimation of prediction accuracy has been done by using the dataset Wisconsin Breast Cancer Data from University of California Irvine (UCI) ML repository. To perform all these operation, Anaconda one of the open source distribution of Python has been used. The proposed work resulted in extemporize improvement in the NB and SVM classifier accuracy. The performance evaluation of the proposed model is estimated by using classification accuracy, confusion matrix, mean, standard deviation, variance, and root mean-squared error.

The experimental results shows that 70-30 data split will result in best accuracy. SVM acts as a feature optimizer of 12 best features with the result of 97.66% accuracy and improvement of 1.17% after feature reduction. NB results with feature optimizer 17 of best features with the result of 96.49% accuracy and improvement of 1.17% after feature reduction.

The study shows that proposal model works very effectively as compare to the existing models with respect to accuracy measures.

Details

Big Data Analytics and Intelligence: A Perspective for Health Care
Type: Book
ISBN: 978-1-83909-099-8

Keywords

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Article
Publication date: 6 January 2021

Miao Fan and Ashutosh Sharma

In order to improve the accuracy of project cost prediction, considering the limitations of existing models, the construction cost prediction model based on SVM (Standard…

Abstract

Purpose

In order to improve the accuracy of project cost prediction, considering the limitations of existing models, the construction cost prediction model based on SVM (Standard Support Vector Machine) and LSSVM (Least Squares Support Vector Machine) is put forward.

Design/methodology/approach

In the competitive growth and industries 4.0, the prediction in the cost plays a key role.

Findings

At the same time, the original data is dimensionality reduced. The processed data are imported into the SVM and LSSVM models for training and prediction respectively, and the prediction results are compared and analyzed and a more reasonable prediction model is selected.

Originality/value

The prediction result is further optimized by parameter optimization. The relative error of the prediction model is within 7%, and the prediction accuracy is high and the result is stable.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 14 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

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Article
Publication date: 19 December 2019

Ganesh Narayanan, Milan Joshi, Prasun Dutta and Kanak Kalita

Computational fluid dynamics (CFD) technique is the most commonly used numerical approach to simulate fluid flow behaviour. Owing to its computationally, cost-intensive…

Abstract

Purpose

Computational fluid dynamics (CFD) technique is the most commonly used numerical approach to simulate fluid flow behaviour. Owing to its computationally, cost-intensive nature CFD models may not be easily and quickly deployable. In this regard, this study aims to present a support vector machine (SVM)-based metamodelling approach that can be easily trained and quickly deployed for carrying out large-scale studies.

Design/methodology/approach

Radial basis function and ε^*-insensitive loss function are used as kernel function and loss function, respectively. To prevent overfitting of the model, five-fold cross-validation root mean squared error is used while training the SVM metamodel. Rather than blindly using any SVM tuning parameters, a particle swarm optimisation (PSO) is used to fine-tune them. The developed SVM metamodel is tested using various error metrics on disjoint test data.

Findings

Using the SVM metamodel, a parametric study is conducted to understand the effect of various factors influencing the behaviour of the turbulent fluid flow in the pipe bend with CFD simulation data set. Based on the parametric study carried out, it is seen that the diametric position has the most effect on dimensionless axial velocity, whereas Reynolds number has the least effect.

Originality/value

This paper provides an effective PSO-tuned SVM metamodelling approach, which may be used as a significant cost-saving approach to quickly and accurately estimate fluid flow characteristics that, in general, require the use of expensive CFD models.

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Article
Publication date: 23 August 2019

Janani Balakumar and S. Vijayarani Mohan

Owing to the huge volume of documents available on the internet, text classification becomes a necessary task to handle these documents. To achieve optimal text…

Abstract

Purpose

Owing to the huge volume of documents available on the internet, text classification becomes a necessary task to handle these documents. To achieve optimal text classification results, feature selection, an important stage, is used to curtail the dimensionality of text documents by choosing suitable features. The main purpose of this research work is to classify the personal computer documents based on their content.

Design/methodology/approach

This paper proposes a new algorithm for feature selection based on artificial bee colony (ABCFS) to enhance the text classification accuracy. The proposed algorithm (ABCFS) is scrutinized with the real and benchmark data sets, which is contrary to the other existing feature selection approaches such as information gain and χ2 statistic. To justify the efficiency of the proposed algorithm, the support vector machine (SVM) and improved SVM classifier are used in this paper.

Findings

The experiment was conducted on real and benchmark data sets. The real data set was collected in the form of documents that were stored in the personal computer, and the benchmark data set was collected from Reuters and 20 Newsgroups corpus. The results prove the performance of the proposed feature selection algorithm by enhancing the text document classification accuracy.

Originality/value

This paper proposes a new ABCFS algorithm for feature selection, evaluates the efficiency of the ABCFS algorithm and improves the support vector machine. In this paper, the ABCFS algorithm is used to select the features from text (unstructured) documents. Although, there is no text feature selection algorithm in the existing work, the ABCFS algorithm is used to select the data (structured) features. The proposed algorithm will classify the documents automatically based on their content.

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Article
Publication date: 14 June 2011

A. Ghosh, T. Guha, R.B. Bhar and S. Das

The purpose of this paper is to address a solution to the problem of defect recognition from images using the support vector machines (SVM).

Abstract

Purpose

The purpose of this paper is to address a solution to the problem of defect recognition from images using the support vector machines (SVM).

Design/methodology/approach

A SVM‐based multi‐class pattern recognition system has been developed for inspecting commonly occurring fabric defects such as neps, broken ends, broken picks and oil stain. A one‐leave‐out cross validation technique is applied to assess the accuracy of the SVM classifier in classifying fabric defects.

Findings

The investigation indicates that the fabric defects can be classified with a reasonably high degree of accuracy by the proposed method.

Originality/value

The paper outlines the theory and application of SVM classifier with reference to pattern classification problem in textiles. The SVM classifier outperforms the other techniques of machine learning systems such as artificial neural network in terms of efficiency of calculation. Therefore, SVM classifier has great potential for automatic inspection of fabric defects in industry.

Details

International Journal of Clothing Science and Technology, vol. 23 no. 2/3
Type: Research Article
ISSN: 0955-6222

Keywords

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