Ieee Transactions on Information Technology in Biomedicine a Publication of the Ieee Engineering in Medicine and Biology Society, Oct 1, 2009
Plantar lesions induced by biomechanical dysfunction pose a considerable socioeconomic health car... more Plantar lesions induced by biomechanical dysfunction pose a considerable socioeconomic health care challenge, and failure to detect lesions early can have significant effects on patient prognoses. Most of the previous works on plantar lesion identification employed the analysis of biomechanical microenvironment variables like pressure and thermal fields. This paper focuses on foot kinematics and applies kernel principal component analysis (KPCA) for nonlinear dimensionality reduction of features, followed by Fisher's linear discriminant analysis for the classification of patients with different types of foot lesions, in order to establish an association between foot motion and lesion formation. Performance comparisons are made using leave-one-out cross-validation. Results show that the proposed method can lead to approximately 94% correct classification rates, with a reduction of feature dimensionality from 2100 to 46, without any manual preprocessing or elaborate feature extraction methods. The results imply that foot kinematics contain information that is highly relevant to pathology classification and also that the nonlinear KPCA approach has considerable power in unraveling abstract biomechanical features into a relatively low-dimensional pathology-relevant space.
Currently, renewable and sustainable energy is a hot issue to meet the predicted future challenge... more Currently, renewable and sustainable energy is a hot issue to meet the predicted future challenges regarding energy and environment because of the fossil fuel depletion and drastic environmental effects. Biodiesel is designated as renewable, biodegradable, less CO2 and NOx emission fuel as compared to petroleum sourced fuel. Biodiesel from microalgae have emerged as one of the promising sources to displace the petrodiesel. This new approach can contribute to solve few major problems. This study was conducted to determine the characteristic of five microalgae species, two were marine type (Nannochloropsis aculata, Dunaliella sp.) while other three were freshwater type (Chlorella vulgaris, Chlamydomonas reinhardtii, Selenastrum capricornutum). Overall growth rate of marine microalgae was higher than freshwater type among the algal strains under investigation. Oil yields of Dunaliella sp. and Nannochloropsis aculata were higher, 34%, 31.4% respectively based dry weight of microalgae. A...
The performance of the non-linear prognostic model `Partial Logistic Artificial Neural Network wi... more The performance of the non-linear prognostic model `Partial Logistic Artificial Neural Network with Automatic Relevance Determination' (PLANN-ARD) is observed here and compared to a variant of the model that is trained using a compensating mechanism to account for the skewed distribution in the neural network target vector. The application dataset in this survival analysis is cancer patient information namely diagnosed with Intra-Ocular Melanoma. The outcomes of the two models are compared with the empirical cumulative hazard curve derived from the Kaplan-Meier survival function for a particular population. Three forms of out-come from both the compensated and non compensated models are obtained: the network output itself, marginalised network output and the median of the network output distribution.
2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007
A three stage development process for the production of a hierarchical rule based prognosis tool ... more A three stage development process for the production of a hierarchical rule based prognosis tool is described. The application for this tool is specific to breast cancer patients that have a positive expression of the HER 2 gene. The first stage is the development of a Bayesian classification neural network to classify for cancer specific mortality. Secondly, low-order Boolean rules are extracted form this model using an Orthogonal Search based Rule Extraction (OSRE) algorithm. Further to these rules additional information is gathered from the Kaplan-Meier survival estimates of the population, stratified by the categorizations of the input variables. Finally, expert knowledge is used to further simplify the rules and to rank them hierarchically in the form of a decision tree. The resulting decision tree groups all observations into specific categories by clinical profile and by event rate. The practical clinical value of this decision support tool will in future be tested by external validation with additional data from other clinical centres.
The current gold standard method in the clinical assessment of swallowing is the visual inspectio... more The current gold standard method in the clinical assessment of swallowing is the visual inspection of videofluoroscopic frames. Specific clinical measurements are estimated based on various anatomical and bolus positional information with respect to time (or frame number). However, due to the subjective nature of visual inspection clinicians face intra- and inter-observer repeatability issues and bias when making these estimations. The correct demarcations of reference lines highlighting the positions of important anatomical landmarks would serve as a visual aid and could also be used in conjunction with bolus detection methods to objectively determine these desirable measurements. In this paper, we introduce and test the reliability of applying a 16-point Active Shape Model as a deformable template to demarcate the boundaries of salient anatomical boundaries with minimal user input. A robust end and corner point detection algorithm is also used to provide image information for the suggested movement of the template during the fitting stage. Results show the model deformation constraints calculated from a training set of images are clinically coherent. The Euclidean distances between the fitted model points against their corresponding target points were measured. Test images were taken from two different data sets from frames acquired using two different videofluoroscopy units. Overall, fitting was found to be more reliable on the vertebrae and inferior points of the larynx compared to the superior laryngeal points and hyoid bone, with the model always fitting the C7 vertebra with discrepancies no higher than a distance of 23 pixels (3.2% of the image width, approximately 7.6mm).
2008 Seventh International Conference on Machine Learning and Applications, 2008
Abstract This paper describes the evaluation of a regularized Bayesian neural network model in pr... more Abstract This paper describes the evaluation of a regularized Bayesian neural network model in prognostic applications. A total sample size of 5442 subjects treated with ocular melanoma in two centers; Liverpool and Paris was used to carry out external validation ...
IET 3rd International Conference MEDSIP 2006. Advances in Medical, Signal and Information Processing, 2006
ABSTRACT This paper presents a Bayesian Neural Network for the analysis of Competing Risk (CR) da... more ABSTRACT This paper presents a Bayesian Neural Network for the analysis of Competing Risk (CR) data model. Based on a previously developed non-linear model namely Partial Logistic Artificial Neural Network (PLANN) with Automatic Relevance Determination (ARD), this paper proposes an extension for the flexible joint estimation of cause-specific hazards depending on both discrete and continuous covariates (PLANN-CR-ARD) and for censored data. The Bayesia analysis uses Gaussian priors for the neural network parameters and the likelihood function based on the competing risk data is identified as the cross-entropy function. The PLANN-CR-ARD model is illustrated with analyses of an Intra-Ocular Melanoma dataset and comparison with the non-parametric Nelson-Allen estimates of the cause-specific cumulative hazards functions.
International Journal of Knowledge Engineering and Soft Data Paradigms, 2009
This paper describes a multicentre longitudinal cohort study to evaluate the predictive accuracy ... more This paper describes a multicentre longitudinal cohort study to evaluate the predictive accuracy of a regularised Bayesian neural network model in a prognostic application. The study sample (n= 5442) comprises subjects treated with intraocular melanoma in two ...
ABSTRACT A low viscosity polyol has been functionalized from crude Jatropha oil via epoxidation a... more ABSTRACT A low viscosity polyol has been functionalized from crude Jatropha oil via epoxidation and subsequent ring-opening. Starting with the crude Jatropha oil, the double bonds are functionalized by introducing epoxy groups and ring-opened to produce hydroxyl groups. The experiment employs more concentrated 50% hydrogen peroxide and effectively produce solvent-free epodixidized Jatropha oil within shorter reaction time of 5 h with maximum oxirane oxygen content of 4.30% and viscosity of 0.57–0.60 Pa.s. The epoxidized Jatropha oil is then transform into Jatropha-based polyol with hydroxyl number of 171–179 mg KOH/g, low viscosity of 0.92–0.98 Pa.s. and functionality of 5.1–5.3. The epoxidation and ring-opening process are monitored by viscometer and FTIR. The produced polyol permit more time for molding and additives addition during polyurethane due to its low viscosity.
Ieee Transactions on Information Technology in Biomedicine a Publication of the Ieee Engineering in Medicine and Biology Society, Oct 1, 2009
Plantar lesions induced by biomechanical dysfunction pose a considerable socioeconomic health car... more Plantar lesions induced by biomechanical dysfunction pose a considerable socioeconomic health care challenge, and failure to detect lesions early can have significant effects on patient prognoses. Most of the previous works on plantar lesion identification employed the analysis of biomechanical microenvironment variables like pressure and thermal fields. This paper focuses on foot kinematics and applies kernel principal component analysis (KPCA) for nonlinear dimensionality reduction of features, followed by Fisher's linear discriminant analysis for the classification of patients with different types of foot lesions, in order to establish an association between foot motion and lesion formation. Performance comparisons are made using leave-one-out cross-validation. Results show that the proposed method can lead to approximately 94% correct classification rates, with a reduction of feature dimensionality from 2100 to 46, without any manual preprocessing or elaborate feature extraction methods. The results imply that foot kinematics contain information that is highly relevant to pathology classification and also that the nonlinear KPCA approach has considerable power in unraveling abstract biomechanical features into a relatively low-dimensional pathology-relevant space.
Currently, renewable and sustainable energy is a hot issue to meet the predicted future challenge... more Currently, renewable and sustainable energy is a hot issue to meet the predicted future challenges regarding energy and environment because of the fossil fuel depletion and drastic environmental effects. Biodiesel is designated as renewable, biodegradable, less CO2 and NOx emission fuel as compared to petroleum sourced fuel. Biodiesel from microalgae have emerged as one of the promising sources to displace the petrodiesel. This new approach can contribute to solve few major problems. This study was conducted to determine the characteristic of five microalgae species, two were marine type (Nannochloropsis aculata, Dunaliella sp.) while other three were freshwater type (Chlorella vulgaris, Chlamydomonas reinhardtii, Selenastrum capricornutum). Overall growth rate of marine microalgae was higher than freshwater type among the algal strains under investigation. Oil yields of Dunaliella sp. and Nannochloropsis aculata were higher, 34%, 31.4% respectively based dry weight of microalgae. A...
The performance of the non-linear prognostic model `Partial Logistic Artificial Neural Network wi... more The performance of the non-linear prognostic model `Partial Logistic Artificial Neural Network with Automatic Relevance Determination' (PLANN-ARD) is observed here and compared to a variant of the model that is trained using a compensating mechanism to account for the skewed distribution in the neural network target vector. The application dataset in this survival analysis is cancer patient information namely diagnosed with Intra-Ocular Melanoma. The outcomes of the two models are compared with the empirical cumulative hazard curve derived from the Kaplan-Meier survival function for a particular population. Three forms of out-come from both the compensated and non compensated models are obtained: the network output itself, marginalised network output and the median of the network output distribution.
2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007
A three stage development process for the production of a hierarchical rule based prognosis tool ... more A three stage development process for the production of a hierarchical rule based prognosis tool is described. The application for this tool is specific to breast cancer patients that have a positive expression of the HER 2 gene. The first stage is the development of a Bayesian classification neural network to classify for cancer specific mortality. Secondly, low-order Boolean rules are extracted form this model using an Orthogonal Search based Rule Extraction (OSRE) algorithm. Further to these rules additional information is gathered from the Kaplan-Meier survival estimates of the population, stratified by the categorizations of the input variables. Finally, expert knowledge is used to further simplify the rules and to rank them hierarchically in the form of a decision tree. The resulting decision tree groups all observations into specific categories by clinical profile and by event rate. The practical clinical value of this decision support tool will in future be tested by external validation with additional data from other clinical centres.
The current gold standard method in the clinical assessment of swallowing is the visual inspectio... more The current gold standard method in the clinical assessment of swallowing is the visual inspection of videofluoroscopic frames. Specific clinical measurements are estimated based on various anatomical and bolus positional information with respect to time (or frame number). However, due to the subjective nature of visual inspection clinicians face intra- and inter-observer repeatability issues and bias when making these estimations. The correct demarcations of reference lines highlighting the positions of important anatomical landmarks would serve as a visual aid and could also be used in conjunction with bolus detection methods to objectively determine these desirable measurements. In this paper, we introduce and test the reliability of applying a 16-point Active Shape Model as a deformable template to demarcate the boundaries of salient anatomical boundaries with minimal user input. A robust end and corner point detection algorithm is also used to provide image information for the suggested movement of the template during the fitting stage. Results show the model deformation constraints calculated from a training set of images are clinically coherent. The Euclidean distances between the fitted model points against their corresponding target points were measured. Test images were taken from two different data sets from frames acquired using two different videofluoroscopy units. Overall, fitting was found to be more reliable on the vertebrae and inferior points of the larynx compared to the superior laryngeal points and hyoid bone, with the model always fitting the C7 vertebra with discrepancies no higher than a distance of 23 pixels (3.2% of the image width, approximately 7.6mm).
2008 Seventh International Conference on Machine Learning and Applications, 2008
Abstract This paper describes the evaluation of a regularized Bayesian neural network model in pr... more Abstract This paper describes the evaluation of a regularized Bayesian neural network model in prognostic applications. A total sample size of 5442 subjects treated with ocular melanoma in two centers; Liverpool and Paris was used to carry out external validation ...
IET 3rd International Conference MEDSIP 2006. Advances in Medical, Signal and Information Processing, 2006
ABSTRACT This paper presents a Bayesian Neural Network for the analysis of Competing Risk (CR) da... more ABSTRACT This paper presents a Bayesian Neural Network for the analysis of Competing Risk (CR) data model. Based on a previously developed non-linear model namely Partial Logistic Artificial Neural Network (PLANN) with Automatic Relevance Determination (ARD), this paper proposes an extension for the flexible joint estimation of cause-specific hazards depending on both discrete and continuous covariates (PLANN-CR-ARD) and for censored data. The Bayesia analysis uses Gaussian priors for the neural network parameters and the likelihood function based on the competing risk data is identified as the cross-entropy function. The PLANN-CR-ARD model is illustrated with analyses of an Intra-Ocular Melanoma dataset and comparison with the non-parametric Nelson-Allen estimates of the cause-specific cumulative hazards functions.
International Journal of Knowledge Engineering and Soft Data Paradigms, 2009
This paper describes a multicentre longitudinal cohort study to evaluate the predictive accuracy ... more This paper describes a multicentre longitudinal cohort study to evaluate the predictive accuracy of a regularised Bayesian neural network model in a prognostic application. The study sample (n= 5442) comprises subjects treated with intraocular melanoma in two ...
ABSTRACT A low viscosity polyol has been functionalized from crude Jatropha oil via epoxidation a... more ABSTRACT A low viscosity polyol has been functionalized from crude Jatropha oil via epoxidation and subsequent ring-opening. Starting with the crude Jatropha oil, the double bonds are functionalized by introducing epoxy groups and ring-opened to produce hydroxyl groups. The experiment employs more concentrated 50% hydrogen peroxide and effectively produce solvent-free epodixidized Jatropha oil within shorter reaction time of 5 h with maximum oxirane oxygen content of 4.30% and viscosity of 0.57–0.60 Pa.s. The epoxidized Jatropha oil is then transform into Jatropha-based polyol with hydroxyl number of 171–179 mg KOH/g, low viscosity of 0.92–0.98 Pa.s. and functionality of 5.1–5.3. The epoxidation and ring-opening process are monitored by viscometer and FTIR. The produced polyol permit more time for molding and additives addition during polyurethane due to its low viscosity.
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