Integrating Support Vector Machine Research Articles

MP3: a software tool for the prediction of pathogenic proteins in genomic and metagenomic data.

The identification of virulent proteins in any de-novo sequenced genome is useful in estimating its pathogenic ability and understanding the mechanism of pathogenesis. Similarly, the identification of such proteins could be valuable in comparing the metagenome of healthy and diseased individuals and estimating the proportion of pathogenic species. However, the common challenge in both the above tasks is the identification of virulent proteins since a significant proportion of genomic and metagenomic proteins are novel and yet unannotated. The currently available tools which carry out the identification of virulent proteins provide limited accuracy and cannot be used on large datasets. Therefore, we have developed an MP3 standalone tool and web server for the prediction of pathogenic proteins in both genomic and metagenomic datasets. MP3 is developed using an integrated Support Vector Machine (SVM) and Hidden Markov Model (HMM) approach to carry out highly fast, sensitive and accurate prediction of pathogenic proteins. It displayed Sensitivity, Specificity, MCC and accuracy values of 92%, 100%, 0.92 and 96%, respectively, on blind dataset constructed using complete proteins. On the two metagenomic blind datasets (Blind A: 51–100 amino acids and Blind B: 30–50 amino acids), it displayed Sensitivity, Specificity, MCC and accuracy values of 82.39%, 97.86%, 0.80 and 89.32% for Blind A and 71.60%, 94.48%, 0.67 and 81.86% for Blind B, respectively. In addition, the performance of MP3 was validated on selected bacterial genomic and real metagenomic datasets. To our knowledge, MP3 is the only program that specializes in fast and accurate identification of partial pathogenic proteins predicted from short (100–150 bp) metagenomic reads and also performs exceptionally well on complete protein sequences. MP3 is publicly available at http://metagenomics.iiserb.ac.in/mp3/index.php.

Intelligent Maintenance System Integrating Support Vector Machine, Isometric Mapping, Genetic Algorithm and RFID Technology

With the emerging of RFID technology and increasing pressure on maintenance, higher request is posed on the maintenance action. This paper introduces a combined intelligent system to complete the maintenance task. SVM and SVR model has been trained to classify machine fault types and predict the degradation. The proposed system can carry out maintenance action with the staff position information form RFID tags and the machine condition information. Genetic algorithm will be used to search the best maintenance sequence, then, the combined information will help make most efficient maintenance decision.

Personalization in Mobile Activity Recognition System Using K-Medoids Clustering Algorithm

Nowadays mobile activity recognition (AR) has been creating great potentials in many applications including mobile healthcare and context-aware systems. Human activities could be detected based on sensory data that are available on today's smart phone. In this study, we consider mobile phones as an independent device since sending the data to central server can generate privacy issues. Furthermore, applying AR on mobile phone does not only require an effective accuracy rate but also the lowest power consumption. Normally, an AR model learnt from acceleration data of a specific person is distributed to other people to recognize the same activities instead of generating different models individually. This work often cannot create accurate results on the prediction in broad range of participants. Moreover, such AR model also has to allow each user to update his new activities independently. Therefore, we propose an algorithm that integrates Support Vector Machine classifier and K-medoids clustering method to resolve completely the demand.

Small sample-oriented case-based kernel predictive modeling and its economic forecasting applications under n-splits-k-times hold-out assessment

In most situations, we can only collect small samples for modeling specific economic forecasting problems. Thus, the availability of accurate predictive model for small samples is vital for solving these problems. This research makes an early investigation on the small sample-oriented case-based kernel predictive method (SSOCBKPM) by integrating support vector machine in case reuse of case-based reasoning, and conducts an early application of SSOCBKPM in binary economic forecasting under the n-splits-k-times hold-out method. After business cases consisting of small samples are represented, the most similar cases from small samples to the current problem are retrieved from small case base. The most similar cases retrieved from small samples are then mapped into a higher dimensional space by kernel function to be candidate support vectors, in which dimension a hyper-plane of support vector machine is constructed by reusing the most similar cases. Two datasets for firm failure prediction and one dataset for loan failure prediction were used to test performance of SSOCBKPM. 100 times' random selection of each of the 20%, 35%, 50%, 65%, and 80% of the total samples are respectively used in training to simulate the availability of samples. The results indicate that SSOCBKPM improves accuracy, stability and sensitivity of the classical CBR significantly; and improves performance of SVM significantly when the volume of the training samples becomes smaller. The SSOCBKPM is more useful in economic forecasting than case-based reasoning and support vector machine since the proportion of available samples is commonly small and less than 50% of the population.

Deformation prediction of tunnel surrounding rock mass using CPSO-SVM model

A new method integrating support vector machine (SVM), particle swarm optimization (PSO) and chaotic mapping (CPSO-SVM) was proposed to predict the deformation of tunnel surrounding rock mass. Since chaotic mapping was featured by certainty, ergodicity and stochastic property, it was employed to improve the convergence rate and resulting precision of PSO. The chaotic PSO was adopted in the optimization of the appropriate SVM parameters, such as kernel function and training parameters, improving substantially the generalization ability of SVM. And finally, the integrating method was applied to predict the convergence deformation of the Xiakeng tunnel in China. The results indicate that the proposed method can describe the relationship of deformation time series well and is proved to be more efficient.

A prior feature SVM-MRF based method for mouse brain segmentation

We introduce an automated method, called prior feature Support Vector Machine-Markov Random Field (pSVMRF), to segment three-dimensional mouse brain Magnetic Resonance Microscopy (MRM) images. Our earlier work, extended MRF (eMRF) integrated Support Vector Machine (SVM) and Markov Random Field (MRF) approaches, leading to improved segmentation accuracy; however, the computation of eMRF is very expensive, which may limit its performance on segmentation and robustness. In this study pSVMRF reduces training and testing time for SVM, while boosting segmentation performance. Unlike the eMRF approach, where MR intensity information and location priors are linearly combined, pSVMRF combines this information in a nonlinear fashion, and enhances the discriminative ability of the algorithm. We validate the proposed method using MR imaging of unstained and actively stained mouse brain specimens, and compare segmentation accuracy with two existing methods: eMRF and MRF. C57BL/6 mice are used for training and testing, using cross validation. For formalin fixed C57BL/6 specimens, pSVMRF outperforms both eMRF and MRF. The segmentation accuracy for C57BL/6 brains, stained or not, was similar for larger structures like hippocampus and caudate putamen, (~87%), but increased substantially for smaller regions like susbtantia nigra (from 78.36% to 91.55%), and anterior commissure (from ~50% to ~80%). To test segmentation robustness against increased anatomical variability we add two strains, BXD29 and a transgenic mouse model of Alzheimer's disease. Segmentation accuracy for new strains is 80% for hippocampus, and caudate putamen, indicating that pSVMRF is a promising approach for phenotyping mouse models of human brain disorders.

Integrating support vector machine and genetic algorithm to implement dynamic wafer quality prediction system

The semiconductor and thin-film transistor liquid crystal display (TFT-LCD) industries are currently two of the most important high-tech industries in Taiwan and occupy over 40% of the global market share. Moreover, these two industries need huge investments in manufacture production equipments (PE) and own a large production scale of the global market. Therefore, how to increase the processing quality of PE to raise the production capacity has become an important issue. The statistical process control technique is usually adopted to monitor the important process parameters in the current Fabs. Furthermore, a routine check for machine or a predictive maintenance policy is generally applied to enhance the stability of process and improve yields. However, manufacturing system cannot be obtained online quality measurements during the manufacturing process. When the abnormal conditions occur, it will cause a large number of scrapped substrates and the costs will be seriously raised. In this research, a virtual metrology (VM) system is proposed to overcome those mentioned problems. It not only fulfills real-time quality measurement of each wafer, but also detects the performance degradation of the corresponding machines from the information of manufacturing processes. This paper makes four critical contributions: (1) The more principal component analysis (PCA) we used, the higher the accuracy we obtained by the kernel function approach; (2) the more support vector data description (SVDD) we used, the higher the accuracy we obtain in novelty detection module; (3) our empirical results show that genetic algorithm (GA) and incremental learning methods increase the training/learning of support vector machine (SVM) model; and (4) by developing a wafer quality prediction model, the SVM approach obtains better prediction accuracy than the radial basis function neural network (RBFN) and back-propagation neural network (BPNN) approaches. Therefore, this paper proposes that the artificial intelligent (AI) approach could be a more suitable methodology than traditional statistics for predicting the potential scrapped substrates risk of semiconductor manufacturing companies.

Combining selectivity and affinity predictions using an integrated Support Vector Machine (SVM) approach: An alternative tool to discriminate between the human adenosine A 2A and A 3 receptor pyrazolo-triazolo-pyrimidine antagonists binding sites

G Protein-coupled receptors (GPCRs) selectivity is an important aspect of drug discovery process, and distinguishing between related receptor subtypes is often the key to therapeutic success. Nowadays, very few valuable computational tools are available for the prediction of receptor subtypes selectivity. In the present study, we present an alternative application of the Support Vector Machine (SVM) and Support Vector Regression (SVR) methodologies to simultaneously describe both A 2AR versus A 3R subtypes selectivity profile and the corresponding receptor binding affinities. We have implemented an integrated application of SVM–SVR approach, based on the use of our recently reported autocorrelated molecular descriptors encoding for the Molecular Electrostatic Potential ( autoMEP), to simultaneously discriminate A 2AR versus A 3R antagonists and to predict their binding affinity to the corresponding receptor subtype of a large dataset of known pyrazolo-triazolo-pyrimidine analogs. To validate our approach, we have synthetized 51 new pyrazolo-triazolo-pyrimidine derivatives anticipating both A 2AR/A 3R subtypes selectivity and receptor binding affinity profiles.

Integrating support vector machines and neural networks

Support vector machines (SVMs) are a powerful technique developed in the last decade to effectively tackle classification and regression problems. In this paper we describe how support vector machines and artificial neural networks can be integrated in order to classify objects correctly. This technique has been successfully applied to the problem of determining the quality of tiles. Using an optical reader system, some features are automatically extracted, then a subset of the features is determined and the tiles are classified based on this subset.