Fault Diagnosis for Wireless Sensor by Twin Support Vector Machine

Various data mining techniques have been applied to fault diagnosis for wireless sensor because of the advantage of discovering useful knowledge from large data sets. In order to improve the diagnosis accuracy of wireless sensor, a novel fault diagnosis for wireless sensor technology by twin support vector machine (TSVM) is proposed in the paper. Twin SVM is a binary classifier that performs classification by using two nonparallel hyperplanes instead of the single hyperplane used in the classical SVM. However, the parameter setting in the TSVM training procedure significantly influences the classification accuracy. Thus, this study introduces PSO as an optimization technique to simultaneously optimize the TSVM training parameter. The experimental results indicate that the diagnosis results for wireless sensor of twin support vector machine are better than those of SVM, ANN

Mean field variational Bayesian inference for support vector machine classification

A mean field variational Bayes approach to support vector machines (SVMs) using the latent variable representation on Polson & Scott (2012) is presented. This representation allows circumvention of many of the shortcomings associated with classical SVMs including automatic penalty parameter selection, the ability to handle dependent samples, missing data and variable selection. We demonstrate on simulated and real datasets that our approach is easily extendable to non-standard situations and outperforms the classical SVM approach whilst remaining computationally efficient.Comment: 18 pages, 4 figure

A Support Vector Regression Approach for Three–Level Longitudinal Data

Background: Longitudinal data structure is frequently observed in health science. This introduces correlation to the data that needs to be handled in modelling process. Recently, machine learning approaches have been introduced in the context of longitudinal data for prediction of the response variable purpose. In this paper a mixed-effects least squares support vector regression model is presented for three-level longitudinal data. In the proposed model, multiple random-effect terms are used for considering the existing correlation structures in longitudinal data. The proposed model is flexible in modelling (non-)linear and complex relationships between predictors and response, while it takes into account the hierarchical structure of data and is computationally efficient.  Methods Both random intercept and random trend models with a special correlation structure of errors are illustrated. A real data example on human Brucellosis rate is analysed and two simulation studies are performed to illustrate the proposed model. The fitting and generalisation performance of the proposed model are investigated and compared with the ordinary least squares support vector regression and linear mixed-effects models.  Results: Based on the human Brucellosis rate example and two simulation studies, the proposed models had the best performance in generalisation. Also, the fitting performances of the proposed models were better than that of the classic models.  Conclusion: Our study revealed that in the presence of nonlinear relationship between covariates and outcome, the proposed MLS-SVR model has the best fitting and generalisation performance and can capture correlation of the data

Multiple kernel learning with random effects for predicting longitudinal outcomes and data integration

Predicting disease risk and progression is one of the main goals in many clinical research studies. Cohort studies on the natural history and etiology of chronic diseases span years and data are collected at multiple visits. Although kernel-based statistical learning methods are proven to be powerful for a wide range of disease prediction problems, these methods are only well studied for independent data but not for longitudinal data. It is thus important to develop time-sensitive prediction rules that make use of the longitudinal nature of the data. In this paper, we develop a novel statistical learning method for longitudinal data by introducing subject-specific short-term and long-term latent effects through a designed kernel to account for within-subject correlation of longitudinal measurements. Since the presence of multiple sources of data is increasingly common, we embed our method in a multiple kernel learning framework and propose a regularized multiple kernel statistical learning with random effects to construct effective nonparametric prediction rules. Our method allows easy integration of various heterogeneous data sources and takes advantage of correlation among longitudinal measures to increase prediction power. We use different kernels for each data source taking advantage of the distinctive feature of each data modality, and then optimally combine data across modalities. We apply the developed methods to two large epidemiological studies, one on Huntington's disease and the other on Alzheimer's Disease (Alzheimer's Disease Neuroimaging Initiative, ADNI) where we explore a unique opportunity to combine imaging and genetic data to study prediction of mild cognitive impairment, and show a substantial gain in performance while accounting for the longitudinal aspect of the data

A mixed effects least squares support vector machine model for classification of longitudinal data

A mixed effects least squares support vector machine (LS-SVM) classifier is introduced to extend the standard LS-SVM classifier for handling longitudinal data. The mixed effects LS-SVM model contains a random intercept and allows to classify highly unbalanced data, in the sense that there is an unequal number of observations for each case at non-fixed time points. The methodology consists of a regression modeling and a classification step based on the obtained regression estimates. Regression and classification of new cases are performed in a straightforward manner by solving a linear system. It is demonstrated that the methodology can be generalized to deal with multi-class problems and can be extended to incorporate multiple random effects. The technique is illustrated on simulated data sets and real-life problems concerning human growth. © 2011 Elsevier B.V. All rights reserved.status: publishe

Evaluation of Adnexal Mass in Reproductive and Perimenopausal Age Group

INTRODUCTION: The ovaries are the organs which can give rise to both benign and malignant tumors throught the life of women. The ovarian cancer remains to held the fixth leading cause of cancer related deaths. The most important is the family history as 10% of patients have inherited genetic predisposition. Ovarian mass are a frequent finding in general gynecology and most are cystic.histo ; ogically ovarian cysts are often divided into neoplastic growth (ovarian cystic neoplasms) and those created by disruption of normal ovulation (functional ovarian cysts). Angiogenesis is an essential component of both the follicular and luteal phases of ovarian cycles. It is also a component of various pathologic ovarian cycles. It is also a component of various pathologic ovarian process including follicular cyst formation, PCOS, ovarian hyperstimulation syndrome, benign and malignant ovarian neoplasms. Functional ovarian cysts make up large proportion. Neoplasms fill the remaining category which are predominantly benign. AIMS & OBJECTIVE: Primary Objective: The primary objective of my study is to evaluat the ADNEXAL MASS in reproductive and perimenopausal age group in view of analyzingthe percentage of malignant adnexal tumors in this age group. METHODOLOY: The study included patients in the reproductive and perimenopasual age group group admitted in ISOKGH for evaluation in 1 year duration. From all patients basic data (age, occupation, education and address) and gynaecological data (menarche age, parity, last menstrual cycle, symptoms and family history) were obtained. Further more the blood analysis, tumor marker, clinical and ultrasonography, CT findings of pelvic organs and hpe reports were performed. The risk of malignancy index (RMI) for all patients was calculated. IOTA – Simple rules. Reliable triage test to differentiate between benign and malignant masses. The outcome for all patients assessed. Inclusion Criteria: All reproductive and perimenopausal age group admitted in ISOKGH. Exclusion Criteria: The patients below 15 and above 50. The patients treated as outpatients. RESULTS AND CONCLUSION: ◈ Adnexal mass presentation was found to be more common in the middle age females particularly in the perimenopasual women and the usual presentation was with symptoms of abdominal pain and distension along with dysfunctional uterine bleeding. ◈ Parity and sterilization procedures did not have any association with the occurrence of adnexal mass. ◈ Adnexal mass did not have any associated pathology in cervic, vagina or uterus. ◈ Per vagina findings shows forniceal fullness in most of the patients with adnexal mass. ◈ Right sided ovarian mass found to be more common than left side or bilateral. ◈ Right adnexal mass was the most common clinical diagnosis ◈ Right sided ovarian cyst was the most common USG finding. ◈ Mean uterine length and breadth was almost in normal size. ◈ Multi-loculated septa was seen in 30% of the patients in the mass lesion. ◈ Solid components was present in 14% of the lesions. ◈ Papillary projections was seen in 12% of the lesions. ◈ Of all the adnexal mass 15% were malignant lesion, 6% were borderline lesions and the remaining were benign lesions. ◈ Simple Ovarian cyst and mucinous cystadenoma were the most common benign lesions and the most common malignant lesion was cystadenocarcinoma