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Purpose: To develop a clinical prediction model to predict a clinically relevant adrenal disorder for patients with adrenal incidentaloma. Materials and methods: This retrospective study is approved by the institutional review board, with waiver of informed consent. Natural language processing is used for filtering of adrenal incidentaloma cases in all thoracic and abdominal CT reports from 2010 till 2012. A total of 635 patients are identified. Stepwise logistic regression is used to construct the prediction model. The model predicts if a patient is at risk for malignancy or hormonal hyperfunction of the adrenal gland at the moment of initial presentation, thus generates a predicted probability for every individual patient. The prediction model is evaluated on its usefulness in clinical practice using decision curve analysis (DCA) based on different threshold probabilities. For patients whose predicted probability is lower than the predetermined threshold probability, further workup could be omitted. Results: A prediction model is successfully developed, with an area under the curve (AUC) of 0.78. Results of the DCA indicate that up to 11% of patients with an adrenal incidentaloma can be avoided from unnecessary workup, with a sensitivity of 100% and specificity of 11%. Conclusion: A prediction model can accurately predict if an adrenal incidentaloma patient is at risk for malignancy or hormonal hyperfunction of the adrenal gland based on initial imaging features and patient demographics. However, with most adrenal incidentalomas labeled as nonfunctional adrenocortical adenomas requiring no further treatment, it is likely that more patients could be omitting from unnecessary diagnostics.

__Abstract__ Many sentiment analysis methods rely on sentiment lexicons, containing words and their associated sentiment, and are tailored to one specific language. Yet, the ever-growing amount of data in different languages on the Web renders multi-lingual support increasingly important. In this paper, we assess various methods for supporting an additional target language in lexicon-based sentiment analysis. As a baseline, we automatically translate text into a reference language for which a sentiment lexicon is available, and subsequently analyz

Various studies have shown the emergence of cooperative behavior in evolutionary models with spatially distributed agents. We investigate to what extent these findings generalize to evolutionary models of price competition among spatially distributed firms. We consider both one- and two-dimensional models, and we vary the amount of information firms have about competitors in their neighborhood. Our computer simulations show that the emergence of cooperative behavior depends strongly on the amount of information available to firms. Firms tend to behave most cooperatively if they have only a very limited amount of information about their competitors. We provide an intuitive explanation for this phenomenon. Our simulations further indicate that three other factors in our models, namely the accuracy of firms’ information, the probability of experimentation, and the spatial distribution of consumers, have little effect on the emergence of cooperative behavior.

To reveal farmers' motives for on-farm diversification and integration of farming components in the Mekong Delta, Vietnam, we developed a fuzzy logic model (FLM) using a 10-step approach. Farmers' decision-making was mimicked in a three-layer hierarchical architecture of fuzzy inference systems, using data of 72 farms. The model includes three variables for family motives of diversification, six variables related to component integration, next to variables for the production factors and for farmers' appreciation of market prices and know-how on 10 components. To obtain a good classification rate of the less frequent activities, additional individual fine-tuning was necessary after general model calibration. To obtain the desired degree of sensitivity to each variable, it was necessary to use up to five linguistic values for some of the input and output variables in the intermediate hierarchical layers. Model's sensitivity to motivational variables determining diversification and integration was of the same magnitude as its sensitivity to market prices and farmers' know-how of the activities, but less than its sensitivity to labour, capital and land endowment. Modelling to support strategic decision-making seems too elaborate for individual farms, but FLM will be useful to integrate farmers' opinions in strategic decision-making at higher hiera

The area under Receiver Operating Characteristic (ROC) curve, also known as the AUC-index, is commonly used for ranking the performance of data mining models. The AUC has many merits, such as objectivity and ease of interpretation. However, since it is class indifferent, its usefulness while dealing with high

Nowadays railway companies are confronted with high infrastructure maintenance costs. Therefore good strategies are needed to carry out these maintenance activities in a most cost effective way. In this paper we solve the preventive maintenance scheduling problem (PMSP) using genetic algorithms, memetic algorithms and a two-phase heuristic based on opportunities. The aim of the PMSP is to schedule the (short) routine activities and (long) unique projects for one link in the rail network for a certain planning period such that the overall cost is minimized. To reduce costs and inconvenience for the travellers and operators, these maintenance works are clustered as much as possible in the same time period. The performance of the algorithms presented in this paper are compared with the performance of the methods from an earlier work, Budai et al. (2006), using

The Web Ontology Language (OWL) is the most expressive standard language for modeling ontologies on the Semantic Web. In this paper, we present a temporal extension of the very expressive fragment SHIN(D) of the OWL-DL language resulting in the tOWL language. Through a layered approach we introduce 3 extensions: i) Concrete Domains, that allows the representation of restrictions using concrete domain binary predicates, ii) Temporal Representation, that introduces timepoints, relations between timepoints, intervals, and Allen’s 13 interval relations into the language, and iii) TimeSlices/Fluents, that implements a perdurantist view on individuals and allows for the representation of complex temporal aspects, such as process state transitions. We illustrate the expressiveness of the newly introduced language by providing a TBox representation of Leveraged Buy Out (LBO) processes in financial applications and an ABox representation of one specific LBO.

We are concerned with evolutionary algorithms that are employed for economic modeling purposes. We focus in particular on evolutionary algorithms that use a binary encoding of strategies. These algorithms, commonly referred to as genetic algorithms, are popular in agent-based computational economics research. In many studies, however, there is no clear reason for the use of a binary encoding of strategies. We therefore examine to what extent the use of such an encoding may influence the results produced by an evolutionary algorithm. It turns out that the use of a binary encoding can have quite significant effects. Since these effects do not have a meaningful economic interpretation, they should be regarded as artifacts. Our findings indicate that in general the use of a binary encoding is undesirable. They also highlight the importance of employing evolutionary algorithms with a sensible economic interpretation.

Based on a survey of behavioral finance literature, this paper presents a descriptive model of individual investor behavior in which investment decisions are seen as an iterative process of interactions between the investor and the investment environment. This investment process is influenced by a number of interdependent variables and driven by dual mental systems, the interplay of which contributes to boundedly rational behavior where investors use various heuristics and may exhibit behavioral biases. In the modeling tradition of cognitive science and intelligent systems, the investor is seen as a learning, adapting, and evolving entity that perceives the environment, processes information, acts upon it, and updates his or her internal states. This conceptual model can be used to build stylized representations of (classes of) individual investors, and further studied using the paradigm of agent-based artificial financial markets. By allowing us to implement individual investor behavior, to choose various market mechanisms, and to analyze the obtained asset prices, agent-based models can bridge the gap between the micro level of individual investor behavior and the macro level of aggregate market phenomena. It has been recognized, yet not fully explored, that these models could be used as a tool to generate or test various behavioral hypothesis.

In this paper we deal with a common problem found in the operations of security and preventive/corrective maintenance services: that of routing a number of mobile resources to serve foreseen and unforeseen tasks during a shift. We define the (Mobile Re-Allocation Problem) MRAP as the problem of devising a routing strategy to maximize the expected weighted number of tasks served on time. For obtaining a solution to the MRAP, we propose to solve successively a multi-objective optimization problem called the stochastic Team Orienteering Problem with Multiple Time Windows (s-TOP-MTW) so as to consider information about known tasks and the arrival process of new unforeseen tasks. Solving successively the s-TOP-MTW we find that considering information about the arrival process of new unforeseen tasks may aid in maximizing the expected proportion of tasks accomplished on time.

Electrical conductivity is an important indicator for water quality assessment. Since the composition of mineral salts affects the electrical conductivity of groundwater, it is important to understand the relationships between mineral salt composition and electrical conductivity. In this present paper, we develop an adaptive neuro-fuzzy inference system (ANFIS) model for groundwater electrical conductivity based on the concentration of positively charged ions in water. It is shown that the ANFIS model outperforms more traditional methods of modelling electrical conductivity based on the total solids dissolved in the water, even though ANFIS uses less information. Additionally, the fuzzy rules in the ANFIS model provide a categorization of ground water samples in a manner that is consistent with the current understanding of geophysical processes.

A number of experimental studies have investigated whether cooperative behavior may emerge in multi-agent Q-learning. In some studies cooperative behavior did emerge, in others it did not. This report provides a theoretical analysis of this issue. The analysis focuses on multi-agent Q-learning in iterated prisoner’s dilemmas. It is shown that under certain assumptions cooperative behavior may emerge when multi-agent Q-learning is applied in an iterated prisoner’s dilemma. An important consequence of the analysis is that multi-agent Q-learning may result in non-Nash behavior. It is found experimentally that the theoretical results derived in this report are quite robust to violations of the underlying assumptions.

In this paper, we visualize the structure and the evolution of the computational intelligence (CI) field. Based on our visualizations, we analyze the way in which the CI field is divided into several subfields. The visualizations provide insight into the characteristics of each subfield and into the relations between the subfields. By comparing two visualizations, one based on data from 2002 and one based on data from 2006, we examine how the CI field has evolved over the last years. A quantitative analysis of the data further identifies a number of emerging areas within the CI field.

Most agent-based simulation models of financial markets are discrete-time in nature. In this paper, we investigate to what degree such models are extensible to continuous-time, asynchronous modelling of financial markets. We study the behaviour of a learning market maker in a market with information asymmetry, and investigate the difference caused in the market dynamics between the discrete-time simulation and continuous-time, asynchronous simulation. We show that the characteristics of the market prices are different in the two cases, and observe that additional information is being revealed in the continuous-time, asynchronous models, which can be acted upon by the agents in such models. Sin

Distances in the well known fuzzy c-means algorithm of Bezdek (1973) are measured by the squared Euclidean distance. Other distances have been used as well in fuzzy clustering. For example, Jajuga (1991) proposed to use the L_1-distance and Bobrowski and Bezdek (1991) also used the L_infty-distance. For the more general case of Minkowski distance and the case of using a root of the squared Minkowski distance, Groenen and Jajuga (2001) introduced a majorization algorithm to minimize the error. One of the advantages of iterative majorization is that it is a guaranteed descent algorithm, so that every iteration reduces the error until convergence is reached. However, their algorithm was limited to the case of Minkowski parameter between 1 and 2, that is, between the L_1-distance and the Euclidean distance. Here, we extend their majorization algorithm to any Minkowski distance with Minkowski parameter greater than (or equal to) 1. This extension also includes the case of the L_infty-distance. We also investigate how well this algorithm performs and present an empirical application.

Artificial stock markets are built with diffuse priors in mind regarding trading strategies and price formation mechanisms. Diffuse priors are a natural consequence of the unknown relation between the various elements that drive market dynamics and the large variety of market organizations, findings, however, might hold only within the specific market settings. In this paper we propose a framework for building agent-based artificial stock markets. We present the mechanism of the framework based on a previously identified list of organizational and behavioural aspects. Within the framework experiments with arbitrary many trading strategies, acting in various market organizations can be conducted in a flexible way, without changing its architecture. In this way experiments of other artificial stock markets, as well as theoretical models can be replicated and their findings compared. Comparisons of the different experimental results might indicate whether findings are due to traders’ behaviour or to the chosen market structure and could suggest how to improve market quality.

Artificial stock markets are designed with the aim to study and understand market dynamics by representing (part of) real stock markets. Since there is a large variety of real stock markets with several partially observable elements and hidden processes, artificial markets differ regarding their structure and implementation. In this paper we analyze to what degree current artificial stock markets reflect the workings of real stock markets. In order to conduct this analysis we set up a list of factors which influence market dynamics and are as a consequence important to consider for designing market models. We differentiate two categories of factors: general, well-defined aspects that characterize the organization of a market and hidden aspects that characterize the functioning of the markets and the behaviour of the traders.