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In literature, many studies try to analyze healthcare usage and generated decision support systems. In this paper, the aim is to generate a decision support system for insurance companies in the United States according to the federal dataset. The data set contains variables from the United States Healthcare Administration formal data group and they are; group of age, group of healthcare insurance, group of nationality, Corporate Social Responsibility (CSR) usage, and Advance Premium Tax Credit (APTC) usage. The best model for each state in the United States is determined with the dataset from 2014 November to 2015 February. In this study, various statistical models are attempted to generate independent and optimal models for each state. In the model selection process, three different metrics are used. Accuracy rate, f1score and precision score are used in the model selection process and models are compared to each other according to these three metrics for each state uniquely. Consequently, the proposed model is used with a decision support scheme to support customer service workers in healthcare insurance companies. The decision support system under consideration can facilitate a substantial decrease in the duration of interactions between customer service representatives and clients. Additionally, this system can generate more logically sound and efficient customer offers. [ABSTRACT FROM AUTHOR]

• Presents a novel fuzzy DEA based on a general fuzzy measure. • Develops an adjustable and flexible fuzzy DEA model to consider DMUs' preferences. • Applying the adjustable FDEA model for measuring efficiency of hospitals in USA. Possibilistic Data Envelopment Analysis (PDEA) is one of the most applicable and popular approaches in the literature to deal with imprecise and ambiguous data in DEA models. In this approach, with respect to tendency of decision maker (DM) in taking optimistic, pessimistic and compromise attitude, three measures including possibility, necessity and credibility measures are used to form the Fuzzy DEA (FDEA) models, respectively. However, decision makers may have different preference and so it is necessary to customize fuzzy DEA models according to properties of DMUs. This paper proposes a novel fuzzy DEA model based on general fuzzy measure in which the attitude of DMUs could be determined by the optimistic-pessimistic parameters. As a result, the proposed FDEA model is general, applicable, flexible, and adjustable based on each DMUs. A numerical example is used to explain the proposed approach while usefulness and applicability of this approach have been illustrated using a real data set to measure efficiency of 38 hospital in United States. [ABSTRACT FROM AUTHOR]

Abstract: Work related low back disorders (LBDs) due to manual lifting tasks (MLTs) have long been recognized as one of the main occupational disabling injury that affects the quality of life of the industrial working population in the U.S. There have been a number of intensive research efforts devoted to understanding the phenomena of LBDs and building classification models that could effectively distinguish between high risk and low risk MLTs that contribute to LBDs. As of today, however, such models and the occupational exposure limits of different risk factors causing LBDs as well as the guidelines preventing them have not yet been fully proposed. One of the first efforts to comprehend the nature and phenomenon of LBDs was undertaken by . They created a seminal data set and used it to build logistic regression (LR) models to identify significant variables and classify MLTs into high risk and low risk with respect to LBDs. Since then a number of studies have used the same data set to build and test various classifiers to detect the likelihood of LBDs due to manual material handling jobs. This paper summarizes and critiques the previous studies. It also employs this data set to build and test seven classification models, two of which have not been applied in this context yet. The parameters of the models have been calibrated for the best performance, and the models were constructed and validated on the full set and the reduced set of features. Though the performances of our best models are better than those reported in National Institute for Occupational Health and Safety (NIOHS) Guides and two of our previous studies, they are generally less optimistic than those reported in several other studies; this paper proposes a systematic and more reliable approach to creating and validating classifiers to distinguish between low and high risk MLTs that contribute to LBDs. [Copyright &y& Elsevier]

Abstract: This paper has two main purposes. The first one is the development of a rigorous rule-based mechanism for identifying the rounding bottoms (also known as saucers) pattern and resistant levels. The design of this model is based solely on principles of technical analysis, and thus making it a proper system for evaluating the efficacy of the aforementioned technical trading patterns. The second aim of this paper is measuring the predictive power of buy-signals generated by these technical patterns. Empirical results obtained from seven US tech stocks indicate that simple resistant levels outperform saucers patterns. Furthermore, positive statistical significant excess returns are being generated only in first sub-periods of examination. These returns decline or even vanish as the experiment proceeds to recent years. Our findings are aligned with the results reported by various former studies. The proposed identification mechanism can be used as a component of an expert system to assist academic community in evaluating trading strategies where technical patterns are embedded. [Copyright &y& Elsevier]

Abstract: In international trade offset practices (in Taiwan known as industrial cooperation program, ICP) have received increased attention over the past 20 years. In the coming 10 years, the Taiwanese government may expend roughly US$16 billion for purchasing Patriot-III missiles, P-3 long-range anti-submarine planes, and diesel-engine submarines from the United States through foreign military sale, and can achieve US$8 billion ICP credit the largest in Taiwanese history. Offsets or ICP can be regarded as fuzzy multiple criteria decision-making (MCDM) problems, therefore, the fuzziness and uncertainty of subjective perception should be considered. This paper provides an alternative approach, the non-additive fuzzy integral, to deal with the fuzzy MCDM problems especially when there is dependence among considered criteria. The main purpose of this paper is to discuss with Taiwan’s ICP Optimal Offset Transaction Policy and propose a framework of drawing on ICP credit in future. This paper considers the four aspects of policy, ability, economy, and environment, to establish a set of fuzzy AHP multiple criteria decision mode to identify the evaluative criteria variables and project item’s order for ICP project. This decision mode was identified as a workable method. [ABSTRACT FROM AUTHOR]

With the growth of global energy demand, the proportion of the total installed wind capacity continues to increase. Wind speed forecasting is essential to enhance the utilization of wind energy. However, it is not easy to make accurate wind speed forecasting since wind speed time series data has nonlinearity, fluctuation, and intermittence. These features of wind speed time series data make classical models unable to obtain expected forecasting results. Many researchers proposed wind speed forecasting models to overcome the shortage. Nevertheless, some wind speed prediction methods are limited because they cannot mine all implicit features of wind speed data. This paper proposes a novel wind speed forecasting model that can capture all implicit information of wind speed data and obtain accuracy prediction results. To be specific, ensemble empirical mode decomposition (EEMD) is firstly applied to remove the noise in the original wind speed data. Secondly, we build a novel deep learning model based on the attention mechanism and convolutional neural network (CNN). The proposed forecasting model can focus the important part of wind speed data and reduce the high computational complexity of CNN. Finally, a full connect neural network layer is employed to obtain wind speed forecasting results. In order to validate the performance of the proposed model, we take the wind speed data of the M2 tower at 20-meter height of the National Wind Power Technology Center of the United States as an example. The experimental results demonstrate that the forecasting errors of the proposed model are smaller than other comparative models. And the Diebold-Mariano test confirms that the proposed model exhibits a significant difference in performance compared with the comparison models. [ABSTRACT FROM AUTHOR]

This paper presents the theory, methodology and application of a new predictive model for time series within the financial sector, specifically data from 20 companies listed on the U.S. stock exchange market. The main impact of this article is (1) the proposal of a recommender system for financial investment to increase the cumulative gain; (2) an artificial predictor that beats the market in most cases; and (3) the fact that, to the best of our knowledge, this is the first effort to predict time series by learning redundant dictionaries to sparsely reconstruct these signals. The methodology is conducted by finding the optimal set of predicting model atoms through two directions for dictionaries generation: the first one by extracting atoms from past daily return price values in order to build untrained dictionaries; and the second one, by atom extraction followed by training of dictionaries though K-SVD. Prediction of financial time series is a periodic process where each cycle consists of two stages: (1) training of the model to learn the dictionary that maximizes the probability of occurrence of an observation sequence of return values, (2) prediction of the return value for the next coming trading day. The motivation for such research is the fact that a tool, which might generate confidence of the potential benefits obtained from using formal financial services, would encourage more participation in a formal system such as the stock market. Theory, issues, challenges and results related to the application of sparse representation to the prediction of financial time series, as well as the performance of the method, are presented. [ABSTRACT FROM AUTHOR]

Distributed, small-scale solar photovoltaic (PV) systems are being installed at a rapidly increasing rate. This can cause major impacts on distribution networks and energy markets. As a result, there is a significant need for improved forecasting of the power generation of these systems at different time resolutions and horizons. However, the performance of forecasting models depends on the resolution and horizon. Forecast combinations (ensembles), that combine the forecasts of multiple models into a single forecast may be robust in such cases. Therefore, in this paper, we provide comparisons and insights into the performance of five state-of-the-art forecast models and existing forecast combinations at multiple resolutions and horizons. We propose a forecast combination approach based on particle swarm optimization (PSO) that will enable a forecaster to produce accurate forecasts for the task at hand by weighting the forecasts produced by individual models. Furthermore, we compare the performance of the proposed combination approach with existing forecast combination approaches. A comprehensive evaluation is conducted using a real-world residential PV power data set measured at 25 houses located in three locations in the United States. The results across four different resolutions and four different horizons show that the PSO-based forecast combination approach outperforms the use of any individual forecast model and other forecast combination counterparts, with an average Mean Absolute Scaled Error reduction by 3.81% compared to the best performing individual model. Our approach enables a solar forecaster to produce accurate forecasts for their application regardless of the forecast resolution or horizon. • Five solar forecasting models were analysed across multiple resolutions and horizons. • No model is consistently the best, and a forecast combination is necessary. • A forecast combination approach based on particle swarm optimization is proposed. • This approach can be adapted to any resolution or horizon. • In an extensive evaluation, it outperforms individual models and other combinations. [ABSTRACT FROM AUTHOR]

Abstract: The prioritization of advanced-technology projects at the National Aeronautic and Space Administration (NASA) is a difficult task. This difficulty is due to the multiple and often conflicting objectives in addition to the inherent technical complexities and valuation uncertainties involved in the assessment process. As such, a systematic and transparent decision support framework is needed to guide the assessment process, shape the decision outcomes and enable confident choices to be made. Methods for solving Multi-Criteria Decision Making (MCDM) problems have been widely used to select a finite number of alternatives generally characterized by multiple conflicting criteria. However, applying these methods is becoming increasingly difficult for technology assessment in the space industry because there are many emerging risks for which information is not available and decisions are made under significant uncertainty. In this paper, we propose a hybrid fuzzy group decision support framework for technology assessment at NASA. The proposed objective framework is comprised of two modules. In the first module, the complicated structure of the assessment criteria and alternatives are represented and evaluated with the Analytic Network Process (ANP). In the second module, the alternative advanced-technology projects are ranked using a customized fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We demonstrate the applicability of the proposed framework through a case study at the Kennedy Space Center. [Copyright &y& Elsevier]

Abstract: Patent users such as governments, inventors, and manufacturing organizations strive to identify the directions in which new technology is advancing, and their goal is to outline the boundaries of existing knowledge. The paper analyzes patent knowledge to identify research trends. A model based on knowledge extraction from patents and self-organizing maps for knowledge representation is presented. The model was tested on patents from the United States Patent and Trademark Office. The experiments show that the method provides both an overview of the directions of the trends and a drill-down perspective of current trends. [Copyright &y& Elsevier]

Abstract: In this paper, we develop an effective approach to model linear non-Gaussian causal relationships among a composite set of major US macroeconomic factors. The proposed approach first models the linear relationships of the factors using the Vector Autoregression (VAR) model, then the casual relationships are discovered using the linear non-Gaussian Structural Equation Modeling (SEM) method. One advantage of our hybrid approach is that the contemporaneous causal order of macroeconomic variables which is important for VAR practitioners is obtained naturally as a result of the computation. Applying our approach to 11 major US macroeconomic factors reveals that the federal funds rate has the dominating power in the set. This outcome purely based on the underlying data without any prior knowledge is in line with previous studies using other empirical approaches where prior knowledge is often essential. We also provide a global picture depicting the interaction among all the macroeconomic factors of concern, which are often approached individually or in small grouping in the economic research literature in the past and not studied in a unified view as in our approach. [Copyright &y& Elsevier]

Abstract: The delineation of functional economic areas, or market areas, is a problem of high practical relevance, since the delineation of functional sets such as economic areas in the US, Travel-to-Work Areas in the United Kingdom, and their counterparts in other OECD countries are the basis of many statistical operations and policy making decisions at local level. This is a combinatorial optimisation problem defined as the partition of a given set of indivisible spatial units (covering a territory) into regions characterised by being (a) self-contained and (b) cohesive, in terms of spatial interaction data (flows, relationships). Usually, each region must reach a minimum size and self-containment level, and must be continuous. Although these optimisation problems have been typically solved through greedy methods, a recent strand of the literature in this field has been concerned with the use of evolutionary algorithms with ad hoc operators. Although these algorithms have proved to be successful in improving the results of some of the more widely applied official procedures, they are so time consuming that cannot be applied directly to solve real-world problems. In this paper we propose a new set of group-based mutation operators, featuring general operations over disjoint groups, tailored to ensure that all the constraints are respected during the operation to improve efficiency. A comparative analysis of our results with those from previous approaches shows that the proposed algorithm systematically improves them in terms of both quality and processing time, something of crucial relevance since it allows dealing with most large, real-world problems in reasonable time. [Copyright &y& Elsevier]

Abstract: This study is an attempt to compare a comprehensive list of GARCH models in quantifying risks of VaR under stress times. We gather data of stock market indices from both emerging (Brazil and Turkey) and developed (Germany and the USA) markets, over the period of global financial crisis and make use of numerous GARCH specifications to return VaR values. Then we compare the assessments of VaR with the realized returns by Kupiec and Christoffersen Tests. Besides, we calculate Quadratic Losses to evaluate the GARCH specifications in calculating VaR. The results reveal that the ARCH specification is the best performer followed by GARCH(1,1) and the Student’s t distribution is slightly better than the Normal. The other outcome of the paper is that the worst performers are Non-Linear Power GARCH and Non-Linear Power GARCH with a shift. All GARCH estimations are carried out with STATA that uses the Maximum Likelihood method of estimation. [Copyright &y& Elsevier]

Abstract: Based on the principles of technical analysis, this paper proposes an artificial intelligence model, which employs the Adaptive Network Fuzzy Inference System (ANFIS) supplemented by the use of reinforcement learning (RL) as a non-arbitrage algorithmic trading system. The novel intelligent trading system is capable of identifying a change in a primary trend for trading and investment decisions. It dynamically determines the periods for momentum and moving averages using the RL paradigm and also appropriately shifting the cycle using ANFIS-RL to address the delay in the predicted cycle. This is used as a proxy to determine the best point in time to go LONG and visa versa for SHORT. When this is coupled with a group of stocks, we derive a simple form of “riding the cycles – waves”. These are the derived features of the underlying stock movement. It provides a learning framework to trade on cycles. Initial experimental results are encouraging. Firstly, the proposed framework is able to outperform DENFIS and RSPOP in terms of true error and correlation. Secondly, based on the test trading with five US stocks, the proposed trading system is able to beat the market by about 50 percentage points over a period of 13years. [ABSTRACT FROM AUTHOR]

Abstract: This study presents an adaptive intelligent algorithm for forecasting gasoline demand based of artificial neural network (ANN), conventional regression and design of experiment (DOE). To show the superiority and applicability of the proposed algorithm it has been applied for monthly demand estimation of gasoline in Japan, USA, Kuwait, Canada and Iran within 1992–2005. The economic indicators used in this paper are price, GDP (Gross Domestic Production), population, number of vehicles, gasoline demand in the last periods and correlation coefficient between variables. The proposed algorithm may be used to estimate gasoline demand in the future by optimizing parameter values. The proposed algorithm uses ANOVA to select either ANN or regression for future demand estimation. Furthermore, if the null hypothesis in ANOVA and F-test is rejected, the Duncan method is used to identify which model is closer to actual data at α level of significance. Neural network is an intelligent, adaptive and optimizing method for solving the complicated problems. The results show that ANN provides far less error than regression. Moreover, the proposed algorithm provides near optimal solution for gasoline demand estimation due to its flexibility and intelligibility. The intelligent algorithm is therefore ideal for policy making with respect to gasoline consumption estimation. [ABSTRACT FROM AUTHOR]

Accurate estimation of software project effort is crucial for successful management and control of a software project. Recently, multiple additive regression trees (MART) has been proposed as a novel advance in data mining that extends and improves the classification and regression trees (CART) model using stochastic gradient boosting. This paper empirically evaluates the potential of MART as a novel software effort estimation model when compared with recently published models, in terms of accuracy. The comparison is based on a well-known and respected NASA software project dataset. The results indicate that improved estimation accuracy of software project effort has been achieved using MART when compared with linear regression, radial basis function neural networks, and support vector regression models. [Copyright &y& Elsevier]

In this paper, an attempt has been made to develop an expert system using fuzzy approach to investigate the effects of noise pollution on speech interference. The speech interference measured in terms of speech intelligibility is considered to be a function of noise level, distance between speaker and listener, and the age of the listener. The main source of model development is the reports of World Health Organization (WHO) and field surveys conducted by various researchers. It is implemented on Fuzzy Logic Toolbox of MATLAB using both Mamdani and Sugeno techniques. The results are found to be in good agreement with the findings of World Health Organization (WHO) and U.S. Environmental Protection Agency (EPA). The study reveals that for good communication at normal distances (‘short’ and ‘medium’) encountered in ambient environment, the noise level should not exceed 65dB(A) for ‘young’ and ‘middle aged’, and 55dB(A) for ‘old’ persons. [Copyright &y& Elsevier]

Financial evaluation in the public sector can utilize some of the tools developed for evaluation in the private sector. However, the emphasis on the bottom line characterized by productivity measures does not adequately address all of the issues faced by public institutions. Recent collaborations by researchers in management science and public administration have led to the successful development of an analytical approach that combines fuzzy set theory and knowledge based systems to produce a tool for evaluating the general performance of public institutions. Successful implementations have included evaluations of the management of state governments, the financial administration of large cities, and the credit worthiness of public institutions. This paper describes a recent collaborative project, funded by the State of New York, to develop a system to evaluate the financial condition of the state''s nearly 700 school districts. [Copyright &y& Elsevier]

• Introduce Topological Data Analysis Ball Mapper for examining creditworthiness. • Example taken from seminal Altman (1968) Z-Score model and ratios therefrom. • Failing firms shown to only occupy a subset of the "distress zone" of risky Z-Scores. • Visualizing data cloud removes the perceived "black box" of data science. • Practitioners can quickly see how credit seekers place and review credit accordingly. Corporate failure resonates widely, leaving practitioners searching for understanding of default risk. Managers seek to steer away from trouble, credit providers to avoid risky loans and investors to mitigate losses. Applying Topological Data Analysis tools, this paper explores whether failing firms from the United States organise neatly along the five predictors of default proposed by the Z-score models. Each firm is represented as a point in a five-dimensional point cloud, each dimension being one of the five predictors. Visualising that cloud using Ball Mapper reveals failing firms are not always located in similar regions of the point cloud, that is they are not concentrated in an easily split out area of the space. As new modelling approaches vie to better predict firm failure, often using black boxes to deliver potentially over-fitting models, a timely reminder is sounded on the importance of evidencing the identification process. Value is added to the understanding of where in the parameter space failure occurs, and how firms might act to move away from financial distress. Further, lenders may find opportunity amongst subsets of firms that are traditionally considered to be in danger of bankruptcy, but which the Ball Mapper plots developed herein clarify actually sit in characteristic spaces where failure has not occurred. [ABSTRACT FROM AUTHOR]