Feature-based image fusion quality metrics

Image fusion quality metrics have evolved from image processing quality metrics. They measure the quality of fused images by estimating how much localized information has been transferred from the source images into the fused image. However, this technique assumes that it is actually possible to fuse two images into one without any loss. In practice, some features must be sacrificed and relaxed in both source images. Relaxed features might be very important, like edges, gradients and texture elements. The importance of a certain feature is application dependant. This paper presents a new method for image fusion quality assessment. It depends on estimating how much valuable information has not been transferred.

Praxis modelling : an evaluation of and extension to feature based modelling

This thesis presents Relation Based Modelling as an extension to the Feature Based Modelling approach to student modelling. Relation Based Modelling dynamically creates new terms allowing the instructional designer to specify a set of primitives and operators from which the modelling system will create the necessary elements. Focal modelling is a new technique devised to manipulate and coordinate the addition of new terms. The thesis presents an evaluation of student modelling systems based on predictive accuracy.

Burden of epilepsy: a prevalence-based cost of illness study of direct, indirect and intangible costs for epilepsy

OBJECTIVES: We aimed to gauge the burden of epilepsy in China from a societal perspective by estimating the direct, indirect and intangible costs. METHODS: Patients with epilepsy and controls were enrolled from two tertiary hospitals in China. Patients were asked to complete a Cost-of-Illness (COI), Willingness-to-Pay (WTP) questionnaires, two utility elicitation instruments and Mini Mental State Examination (MMSE). Healthy controls only completed WTP questionnaire, and utility instruments. Univariate analyses were performed to investigate the differences in cost on the basis of different variables, while multivariate analysis was undertaken to explore the predictors of cost/cost component. RESULTS: In total, 141 epilepsy patients and 323 healthy controls were recruited. The median total cost, direct cost and indirect cost due to epilepsy were US$949.29, 501.34 and 276.72, respectively. Particularly, cost of anti-epileptic drugs (AEDs) (US$394.53) followed by cost of investigations (US$59.34), cost of inpatient and outpatient care (US$9.62) accounted for the majority of the direct medical costs. While patients' (US$103.77) and caregivers' productivity costs (US$103.77) constituted the major component of indirect cost. The intangible costs in terms of WTP value (US$266.07 vs. 88.22) and utility (EQ-5D, 0.828 vs. 0.923; QWB-SA, 0.657 vs. 0.802) were both substantially higher compared to the healthy subjects. CONCLUSIONS: Epilepsy is a cost intensive disease in China. According to the prognostic groups, drug-resistant epilepsy generated the highest total cost whereas patients in seizure remission had the lowest cost. AED is the most costly component of direct medical cost probably due to 83% of patients being treated by new generation of AEDs.

Cost-effectiveness of a family-based GP-mediated intervention targeting overweight and moderately obese children

Objective
 To assess from a societal perspective the incremental cost-effectiveness of a family-based GP-mediated intervention targeting overweight and moderately obese children. The intervention was modelled on the LEAP (live, eat and play) trial, a randomised controlled trial conducted by the Centre for Community Child Health, Melbourne, Australia in 2002–2003. This study was undertaken as part of the assessing cost-effectiveness (ACE) in obesity project which evaluated, using consistent methods, 13 interventions targeting unhealthy weight gain in children and adolescents.
Method
A logic pathway was used to model the effects of the intervention compared to no intervention on body mass index (BMI) and health outcomes (disability-adjusted life years—DALYs). Disease costs and health benefits were tracked until the cohort of eligible children reached the age of 100 years or death. Simulation-modelling techniques were used to present a 95% uncertainty interval around the cost-effectiveness ratio. The intervention was also assessed against a series of filters (‘equity’, ‘strength of evidence’, ‘acceptability’, ‘feasibility’, sustainability’ and ‘side-effects’) to incorporate additional factors that impact on resource allocation decisions.
Results
The intervention, as modelled, reached 9685 children aged 5–9 years with a BMI z-score of ≥3.0, and cost $AUD6.3M (or $AUD4.8M excluding time costs). It resulted in an incremental saving of 2300 BMI units which translated to 511 DALYs. The cost-offsets stemming from the intervention totalled $AUD3.6M, resulting in a net cost per DALY saved of $AUD4670 (dominated; $0.1M) (dominated means intervention costs more for less effect).
Conclusion
Compared to a ‘no intervention’ control group, the intervention was cost-effective under current assumptions, although the uncertainty intervals were wide. A key question related to the long-term sustainability of the small incremental weight loss reported, based on the 9-month follow-up results for LEAP.

Cost of stroke in Australia from a societal perspective: results from the north east Melbourne stroke incidence study (NEMESIS)

Background and Purpose—— Accurate information about resource use and costs of stroke is necessary for informed health service planning. The purpose of this study was to determine the patterns of resource use among stroke patients and to estimate the total costs (direct service use and indirect production losses) of stroke (excluding SAH) in Australia for 1997.

Methods—— An incidence-based cost-of-illness model was developed, incorporating data obtained from the North East Melbourne Stroke Incidence Study (NEMESIS). The costs of stroke during the first year after stroke and the present value of total lifetime costs of stroke were estimated.

Results—— The total first-year costs of all first-ever-in-a lifetime strokes (SAH excluded) that occurred in Australia during 1997 were estimated to be A$555 million (US$420 million), and the present value of lifetime costs was estimated to be A$1.3 billion (US$985 million). The average cost per case during the first 12 months and over a lifetime was A$18 956 (US$14 361) and A$44 428 (US$33 658), respectively. The most important categories of cost during the first year were acute hospitalization (A$154 million), inpatient rehabilitation (A$150 million), and nursing home care (A$63 million). The present value of lifetime indirect costs was estimated to be A$34 million.

Conclusions—— Similar to other studies, hospital and nursing home costs contributed most to the total cost of stroke (excluding SAH) in Australia. Inpatient rehabilitation accounts for {approx}27% of total first-year costs. Given the magnitude of these costs, investigation of the cost-effectiveness of rehabilitation services should become a priority in this community.

Optimizing the quality of bootstrap-based prediction intervals

The bootstrap method is one of the most widely used methods in literature for construction of confidence and prediction intervals. This paper proposes a new method for improving the quality of bootstrap-based prediction intervals. The core of the proposed method is a prediction interval-based cost function, which is used for training neural networks. A simulated annealing method is applied for minimization of the cost function and neural network parameter adjustment. The developed neural networks are then used for estimation of the target variance. Through experiments and simulations it is shown that the proposed method can be used to construct better quality bootstrap-based prediction intervals. The optimized prediction intervals have narrower widths with a greater coverage probability compared to traditional bootstrap-based prediction intervals.

A fuzzy decision support method for customer preferences analysis based on Choquet integral

The explosion of the Web 2:0 platforms, with massive volume of user generated data, has presented many new opportunities as well as challenges for organizations in understanding consumer's behavior to support for business planning process. Feature based sentiment mining has been an emerging area in providing tools for automated opinion discovery and summarization to help business managers with achieving such goals. However, the current feature based sentiment mining systems were only able to provide some forms of sentiments summary with respect to product features, but impossible to provide insight into the decision making process of consumers. In this paper, we will present a relatively new decision support method based on Choquet Integral aggregation function, Shapley value and Interaction Index which is able to address such requirements of business managers. Using a study case of Hotel industry, we will demonstrate how this technique can be applied to effectively model the user's preference of (hotel) features. The presented method has potential to extend the practical capability of sentiment mining area, while, research findings and analysis are useful in helping business managers to define new target customers and to plan more effective marketing strategies.

Construction of neural network-based prediction intervals using particle swarm optimization

Prediction intervals (PIs) are excellent tools for quantification of uncertainties associated with point forecasts and predictions. This paper adopts and develops the lower upper bound estimation (LUBE) method for construction of PIs using neural network (NN) models. This method is fast and simple and does not require calculation of heavy matrices, as required by traditional methods. Besides, it makes no assumption about the data distribution. A new width-based index is proposed to quantitatively check how much PIs are informative. Using this measure and the coverage probability of PIs, a multi-objective optimization problem is formulated to train NN models in the LUBE method. The optimization problem is then transformed into a training problem through definition of a PI-based cost function. Particle swarm optimization (PSO) with the mutation operator is used to minimize the cost function. Experiments with synthetic and real-world case studies indicate that the proposed PSO-based LUBE method can construct higher quality PIs in a simpler and faster manner.

A new neural network-based type reduction algorithm for interval type-2 fuzzy logic systems

This paper introduces a new type reduction (TR) algorithm for interval type-2 fuzzy logic systems (IT2 FLSs). Flexibility and adaptiveness are the key features of the proposed non-parametric algorithm. Lower and upper firing strengths of rules as well as their consequent coefficients are fed into a neural network (NN). NN output is a crisp value that corresponds to the defuzzified output of IT2 FLSs. The NN type reducer is trained through minimization of an error-based cost function with the purpose of improving modelling and forecasting performance of IT2 FLS models. Simulation results indicate that application of the proposed TR algorithm greatly enhances modelling and forecasting performance of IT2 FLS models. This benefit is achieved in no cost, as the computational requirement of the proposed algorithm is less than or at most equivalent to traditional TR algorithms.

Prediction interval-based neural network modelling of polystyrene polymerization reactor - a new perspective of data-based modelling

In this paper, prediction interval (PI)-based modelling techniques are introduced and applied to capture the nonlinear dynamics of a polystyrene batch reactor system. Traditional NN models are developed using experimental datasets with and without disturbances. Simulation results indicate that traditional NNs cannot properly handle disturbances in reactor data and demonstrate a poor forecasting performance, with an average MAPE of 22% in the presence of disturbances. The lower upper bound estimation (LUBE) method is applied for the construction of PIs to quantify uncertainties associated with forecasts. The simulated annealing optimization technique is employed to adjust NN parameters for minimization of an innovative PI-based cost function. The simulation results reveal that the LUBE method generates quality PIs without requiring prohibitive computations. As both calibration and sharpness of PIs are practically and theoretically satisfactory, the constructed PIs can be used as part of the decision-making and control process of polymerization reactors. © 2014 The Institution of Chemical Engineers.

Aggregation of Pi-based forecast to enhance prediction accuracy

In contrast to point forecast, prediction interval-based neural network offers itself as an effective tool to quantify the uncertainty and disturbances that associated with process data. However, single best neural network (NN) does not always guarantee to predict better quality of forecast for different data sets or a whole range of data set. Literature reported that ensemble of NNs using forecast combination produces stable and consistence forecast than single best NN. In this work, a NNs ensemble procedure is introduced to construct better quality of Pis. Weighted averaging forecasts combination mechanism is employed to combine the Pi-based forecast. As the key contribution of this paper, a new Pi-based cost function is proposed to optimize the individual weights for NN in combination process. An optimization algorithm, named simulated annealing (SA) is used to minimize the PI-based cost function. Finally, the proposed method is examined in two different case studies and compared the results with the individual best NNs and available simple averaging Pis aggregating method. Simulation results demonstrated that the proposed method improved the quality of Pis than individual best NNs and simple averaging ensemble method.

A new fuzzy-based combined prediction interval for wind power forecasting

This paper makes use of the idea of prediction intervals (PIs) to capture the uncertainty associated with wind power generation in power systems. Since the forecasting errors cannot be appropriately modeled using distribution probability functions, here we employ a powerful nonparametric approach called lower upper bound estimation (LUBE) method to construct the PIs. The proposed LUBE method uses a new framework based on a combination of PIs to overcome the performance instability of neural networks (NNs) used in the LUBE method. Also, a new fuzzy-based cost function is proposed with the purpose of having more freedom and flexibility in adjusting NN parameters used for construction of PIs. In comparison with the other cost functions in the literature, this new formulation allows the decision-makers to apply their preferences for satisfying the PI coverage probability and PI normalized average width individually. As the optimization tool, bat algorithm with a new modification is introduced to solve the problem. The feasibility and satisfying performance of the proposed method are examined using datasets taken from different wind farms in Australia.

Trustworthiness-hypercube-based reliable communication in mobile social networks

In mobile social networks (MSNs), the routing packet is forwarded from any user of in a group to any user of the other group until it reaches the destination group - the group where the destination is located. However, it is inevitable that malicious groups could compromise the quality and reliability of data. To alleviate such effect, analyzing the trustworthiness of a group has a positive influence on the confidence with which a group conducts transactions with that group. In our previous work, the feature-based first-priority relation graph (FPRG) of MSNs is proposed, in which two vertices (groups) are connected iff they have a first-priority relationship. In this paper, the trustworthiness computation of a group is firstly presented in the algorithm TC (Trustworthiness Computing) based on the FPRG. The trustworthiness of a group is evaluated based on the trustworthiness of neighbors and the number of malicious users in the group. We then establish the Trustworthiness-Hypercube-based Reliable Communication (THRC) algorithm in MSNs. The algorithm THRC can provide an effective and reliable data delivery routing. Finally, we also give two scenario simulations to elaborate the processes of the trustworthiness computation and reliable communication.