Infant and youth mortality has seen a steady decline over the years. However, many issues related to sociodemographic factors still persist. In Portugal, while mortality forecasts are regularly disclosed to the general public by specialised public entities, very few studies have focused on its determinants, and none have taken advantage of the modelling capabilities of Machine Learning (ML) techniques. This work makes use of real-world data in order to identify the main determinants of infant and youth mortality in Portugal using some of these techniques. The data used for this study comprised 178 databases from various authorities, encompassing economic, demographic, environmental, health, education, and mortality variables at the municipal level. No data at the individual level was available. Two different approaches were proposed. For the first one, the problem was framed as a regression problem, with the number of deaths as the target variable and the potential determinants as the predictors. Simple regression models were used, mainly due to their interpretability. A neural network was also employed to enable a comparison between linear and nonlinear models. Feature elimination and feature selection methods were devised in order to ascertain which variables were the most relevant. These include a feature selection method specifically custom for the problem at hand which proved particularly effective, as it led to performance improvements for every model used in this work. The second approach made use of the K-means clustering technique to determine which of the previously selected variables led to better clusters with both the number of deaths and the mortality rate. To this end, the silhouette method was chosen as the evaluation metric. The best regression model achieved an R2of 0.846. The foreign population with legal status of residence in the parents’ place of residence and the average monthly earnings of employees were shown to be the features with the greatest impact on mortality.

Dynamic scheduling has received widespread attention from academia and industry due to the increasing complexity in manufacturing systems. Highly dynamic and adaptable behaviours are necessary for an improved production efficiency in unstable and constantly changing environments. This paper proposes an agent-based hybrid tabu-search heuristic (AB-TSH) to solve dynamic flexible job-shop scheduling problems. The solution is fully implemented and tested in an industrial environment for seven distinct dynamic scenarios derived from a static scenario using the benchmark of AIP-PRIMECA Flexible Manufacturing System. The scheduling plan is obtained by exploitation using a greedy heuristic on tabu search solution points. The hybrid tabu-search heuristic is supported by a multi-agent system that react and re-optimize the scheduling plan in case of disturbances and unpredicted events. The proposed solution demonstrated superior performance in terms of makespan in the majority of dynamic scenarios tested when compared to previous studies in the literature. This improved performance is attributed to the solution’s ability to combine the scheduling plan both statically and dynamically.

The First-Order Autonomous Learning Multi-Model (ALMMo) system was initially introduced as a regressor which could be easily adapted to a binary classifier. In this paper, an extension of the ALMMo algorithm is proposed, enabling it to tackle multi-class classification tasks, without escalating the computational demand substantially. Thus, this paper highlights the flexibility of the method by increasing its range of capabilities. The proposed extension is tested in 3 benchmark datasets, and the obtained results are presented as a proof of the concept. Furthermore, these results are compared to 2 benchmark methods, those being shallow neural-networks and support vector machines; as well as to the ALMMo-0 classifier.

The need to ensure that all funding projects made available by the European Union are fully exploited by each country leads to a robust investigation of the main difficulties of applying to these projects. In this context, Portugal has verified that these funds are not being fully used and detected that one of the main setbacks in the European Union funding application system is the identification of the best call for application. Therefore, this work aims at developing a funds assistant framework to suggest the most suitable set of available calls for application based on the written description of the beneficiaries' projects, via the use of Natural Language Processing and text classification algorithms. Moreover, this problem is addressed as a hierarchical multi-class text classification, taking advantage of the hierarchical structure inside the European funds and aiming to improve the overall performance of the proposed framework. In this context, four models are chosen to compare the classification performance: Naive Bayes, Support Vector Machines, Random Forest and k-Nearest Neighbors. Simulation results highlight that the Support Vector Machine outperforms the rest of the algorithms for almost all classification scenarios. In order to enhance the results, the models are also calibrated to return the second prediction when the likelihood of the first prediction is small, achieving even higher performances for all levels of the hierarchy.

Since most branches of the distribution grid may already be close to their maximum capacity, smart management when charging electric vehicles (EVs) is becoming more and more crucial. In fact, office buildings might not be able to handle several transactions at the same time, especially considering the next generation of fast chargers which are very power expensive. Thus, an efficient charging policy needs to be found. This paper proposes the scheduling of real-time EVs charging through deep reinforcement learning (DRL) techniques. DRL has been chosen because it can adaptively learn from interacting with the surrounding environment. The focus of the optimization is to ensure the completion of the charging transactions in a timely manner, while shifting the load from the times of peak demand. The novelty of the proposed approach lies in its innovative framework: pools of electric vehicles with different characteristics are categorized using a clustering algorithm, a tree-based classifier has been developed to sort new instances of EVs, and a multilayer perceptron artificial deep neural network has been trained to predict the expected duration of each charging session. These features are used as inputs to the DRL agent, and are mapped into actions that adjust the maximum power associated to each charging station. The model has been compared to a traditional charging algorithm and increasingly challenging scenarios have been considered. Results have shown that the developed algorithm fails less than the baseline, with a reduction of the load due to EVs charging of 80% during peak times.

In this paper, a new approach to the usage of 0-order Autonomous Learning Multi-Model (ALMMo-0) classifiers is proposed. ALMMo-0 classifiers are fully automatic and do not rely on any hyper-parameters. The creation of clouds relies on normalizing data points by their norm, which may remove an important degree of freedom from the data itself. The proposed approach consists of adding the initial radius of the clouds as an hyper-parameter, which makes it possible to skip the normalization step. This approach requires the search for the ideal value of the hyper-parameter. This way, upon training a set of models with different values for the initial radius, the user is expected to be able to choose from several models which range from more accurate to less complex. This approach was tested on three benchmark problems and compared to the results obtained using the original approach. Furthermore, this approach was also tested on a real dataset (Acute Kidney Injury). The obtained results enhance the versatility provided by the proposed method, successfully allowing the user to choose the model that fits better the design demands regarding accuracy, training time, and complexity.

This paper proposes a flexible manufacturing system based on intelligent computational agents. A Multi-Agent System composed of 4 types of reactive agents was designed to control the operation of a real implementation in the Intelligent Automation Lab at Instituto Superior Técnico. This implementation was based and constructed analogously to a known benchmark, AIP-PRIMECA. The agents were modelled using Petri nets and agent communications were defined through the combination of FIPA Interaction Protocols. The system was tested under the conditions of static and dynamic scenarios, having its performance validated whenever possible by comparison with results from a Potential Fields Approach in the same benchmark. Overall, the performance exhibited by the proposed MAS was slightly better and it is worth highlighting the simple behaviour of each agent and ability to respond in real-time to all the dynamic scenarios tested.

The papers in this special section focus on recent developments and emerging topics in the area of deep fuzzy models that address some of the problems and limitations above. These models have been known under different names, such as hierarchical fuzzy systems and fuzzy networks. They are usually well suited for performing multiple functional compositions at either crisp or linguistic level. Deep learning has gained significant attention within the computational intelligence community in recent years. Its success has been mainly due to the increased power of modern computational platforms in terms of their ability to collect, store, and process large volumes of data. This has led to a substantial increase in the effectiveness and efficiency of data management. As a result, it has become possible to achieve high accuracy for some benchmark learning tasks, such as object classification and image recognition within a short time frame. The most common implementation of deep learning has been through neural networks due to the ability of their layers of neurons to perform multiple functional compositions as part of a multistage learning process.

Insulin therapy is crucial to control blood sugar levels for critical-care patients. However, there is no consensus on which is the most beneficial glucose control, either intensive or conventional, for these patients to reduce mortality. Through the use of electronic health records and computational intelligent systems, this work intends to increase the understanding of mechanisms that influence survival of adult patients (≥ 16 years old) within the first 24 hours of being admitted ≥to an intensive care unit under insulin therapy to control blood glucose. The cohort for this study is composed by 9098 patients and a total of 187 features. Fuzzy modeling, gradient boosting and logistic regression were selected due to their interpretability and simplicity. Results show that gradient boosting achieved the highest performance (AUC = 0.94), followed by fuzzy modeling (AUC = 0.87) and logistic regression (AUC = 0.86). Logistic regression resulted to be more difficult to interpret than the other models. The most meaningful variables among all models were Glasgow coma scale score, total urine output, both respiratory and infectious diseases, ventilation time and anion gap.

This paper proposes a decision support system based on fuzzy clustering, fuzzy modeling and fuzzy fingerprints, to provide personalized therapy for critically ill patients. It is hypothesized that the ‘collective experience’ from large clinical databases, where clinical decisions are linked with patient outcomes, can be used to identify specific patient sub-groups and build personalized therapy models towards a new era of personalized medicine, allowing the improvement of patient outcomes in the Intensive Care Unit (ICU). The validity of the proposed systems will be tested using the case study of patients admitted to the ICU who then develop acute kidney injury (AKI); Two-thirds of patients with AKI require renal support therapy. Generalized severity scoring systems have consistently performed poorly for patients with AKI.

The expected post-operatory weight loss is not always achieved after bariatric surgery. Efforts have been done to describe the causes. Recently, total weight loss (%TWL) has been pointed out to better assess weight loss in bariatric patients. However, there is no cut off point that delimits the patients who successfully achieve their weight goals after a bariatric surgery. In this work, a method based on fuzzy modeling is implemented to help clinicians setting up the best cut-off point in %TWL for a specific population. The best boundary to delimit success and failure will be selected based on the predictive performance of the assessed cut-off points: 25, 30, 35 and 40%TWL after one and two years of surgery. Area under the receiver operating characteristic curve (AUC) values of 0.70 and 0.75 were achieved for the first and second post-surgery periods, respectively. Further, features not previously described as predictors of weight loss were identified as good predictors of the outcome.

Up to two thirds of critically ill patients in Intensive Care Units (ICU) around the world develop Acute Kidney Injury (AKI). This complication is characterized by a reduction of kidney function and is intrinsically linked to an increased risk of mortality. AKI is also connected to higher costs in healthcare due to longer hospital stay times and ICU readmissions for affected patients. The present study developed a model capable of predicting which patients are at a larger risk of death and therefore allow for a more efficient use of hospital resources. Information regarding several physiological parameters of patients with AKI as well as data regarding their survival was used for the creation of a fuzzy model. The model achieved AUC values of up to 0.92 when using mean values for time series and analyzing a subset of patients with AKI at its most advanced stage. When analysing a general population of ICU patients, the algorithm can achieve AUC values of 0.86. This value can be compared with values obtained in similar studies of 0.85 and surpasses values obtained in traditional risk scores such as APACHE II and SOFA. A model such as this, shows the potential of using this type of predictive applications for scheduling human resources and managing monetary resources on critical patients according to their relative need for attention and treatment.

Logistic regression and Takagi-Sugeno fuzzy models are sequentially trained with categorical and numerical data in an ensemble-based multistage scheme. In the first stage, a logistic regression model is used to transform the binary feature space into a numerical feature that is used to train a second stage of models consisting of an ensemble of two Takagi-Sugeno fuzzy models. In the ensemble, one model is trained in the space of numerical features and first stage prediction values. The other model is trained only with samples that were classified with a low degree of confidence by the first stage model, in the space of numerical variables. The final output is given by the average of the ensemble predictions at second stage. This scheme was devised under the hypothesis that separating binary from numerical features in the modeling process would increase the performance of a single model using both types of features together. The proposed multistage approach is used to solve a clinical classification problem in a Portuguese hospital. The problem consists of predicting comanagement signalling based on patient clinical data, including diagnosis, procedures, comorbidities and numerical scores, collected before surgery. The multistage performed better in the comanagement dataset, and in 2 out of 5 benchmark datasets.

This paper proposes the use of mixed fuzzy clustering (MFC) algorithm to derive Takagi-Sugeno (T-S) fuzzy models (FMs). Mixed fuzzy clustering handles both time invariant and multivariate time variant features, allowing the user to control the weight of each component in the clustering process. Two model designs based on MFC are investigated. In the first, the antecedent fuzzy sets of the T-S model are obtained from the clusters obtained by the MFC algorithm. In the second, FMs based on fuzzy c-means (FCM) are constructed over the input space of the partition matrix generated by MFC. The proposed fuzzy modeling approaches are used in health care classification problems, where time series of unequal lengths are very common. MFC-based T-S FMs outperform FCM-based T-S FMs in four out of five datasets and k-nearest neighbors classifiers in five out of five datasets. Dynamic time warping performs better than the Euclidean distance in one dataset and similarly in the remaining. Given the different nature of time variant and invariant data, the choice of a clustering algorithm that treats data differently should be considered for model construction.

Probabilistic fuzzy systems (PFS) are shown to be valuable methods for conditional density estimation that combine fuzziness or linguistic uncertainty and probabilistic uncertainty. Several PFS applications have shown the added value of the different reasoning mechanisms of PFS and gains from incorporating two types of uncertainty. The effects of parametrization and parameter estimation on the function or conditional density approximations of PFS have not been documented in the literature. This paper aims to fill this gap in the literature by analyzing the parameters of PFS in conditional density estimation and point forecast using synthetic and real data applications. We show that both in-sample and out-of-sample results depend on PFS parametrization and the results deteriorate when the probability parameters of PFS are not optimized correctly, since these parameters allow the system to be fine tuned.

Acute kidney injury (AKI) affects 5 to 7% of all hospitalized patients, with a much higher incidence in the critically ill. Although AKI patients have increased risks of death in the intensive care units (ICU) and in the short term, few studies examined the association between the diagnosis of AKI and long-term outcomes. The aim of this study is to address the prediction of short and long-term mortality in patients that presented AKI diagnosis at their hospital admission. Demographic, clinical, physiologic, and date of death data of ICU patients were extracted from the MIMIC-III database to develop predictive models. Fuzzy models are developed using fuzzy c-means and Gustafson-Kessel algorithms to predict mortality in the ICU within 24 hours, when the patients had an ICD-9 admission diagnosis of AKI. The proposed models achieved an AUC of 0.77, which is slightly better than the results obtained by Celi et al. [1] that obtained an AUC of 0.74. Thus, AKI revealed to be a significant risk factor for in-hospital and long-term mortality for ICU patients.

Patients are often discharged prematurely from Intensive Care Units (ICU) due to clinical resource limitations, economic pressure or poor discharge planning. The readmission of such patients is associated with an increased risk of death and is currently viewed as a marker for poor quality care. Several studies have focused on predicting which patients are likely to be readmitted, using techniques such as logistic regression or machine learning algorithms, and based on physiological data measured during the patients' stay at the ICU. So far, no published algorithms have been able to predict readmissions to a satisfactory degree. In this work we hypothesize that physicians' and nurses' notes could give a better explanation of both ICU discharges and readmissions, and propose using the text notes in an ICU database in order to build classification models for the prediction of readmissions. We tested the use of Fuzzy Fingerprints and other traditional text classifiers and compared them to a previously proposed model based on numerical data, obtaining very relevant improvements in the classification results, namely an AUC=0.8.

This paper proposes a methodology to define the load profiles of residential natural gas consumers using smart metering data. A detailed clustering analysis is performed using the fuzzy c-means clustering algorithm and multiple clustering validity indices. The analysis is based on a sample of more than one thousand households over one year. Compact and well defined annual clusters of natural gas consumers are obtained. The results provide evidence that the consumers' representative profiles are mainly characterized by a morning and an evening peak consumption, the time at which the consumption starts to rise and to decline and the off-peak consumption. The knowledge obtained with this methodology can assist decision makers in the energy utility industry in order to develop demand side management programs, consumer engagement strategies, marketing, demand forecasting tools as well as in designing more innovative and sophisticated tariff systems.

Two new methods for the optimization of probabilistic fuzzy classifiers are proposed. Probabilistic fuzzy systems are specially attractive due to their explicit and simultaneous modelling of two kinds of uncertainty, namely vagueness in linguistic terms (fuzziness) and probabilistic uncertainty. The current method uses the maximization of the likelihood with the stochastic gradient descent, which not only converges to local minima but also does not guarantee the minimization of the misclassification error. The proposed methods address this specific problem by incorporating global search techniques. The first algorithm proposed is a genetic algorithm with simple crossover and mutation operations. The other is a first generation memetic algorithm which combines the genetic algorithm with the stochastic gradient descent. A total of five benchmarks were used to compare the three algorithms. The results show that the proposed methods have an average relative improvement of 2% and 6% for the accuracy with the genetic and memetic algorithms, respectively.

This papers proposes two novel approaches for the identification of Takagi-Sugeno fuzzy models with time variant and invariant features. The proposed Mixed Fuzzy Clustering algorithm is proposed for determining the parameters of Takagi-Sugeno fuzzy models in two different ways: (1) the antecedent fuzzy sets are determined based on the partition matrix generated by the Mixed Fuzzy Clustering algorithm; (2) the input features are transformed using the same algorithm and the antecedent fuzzy sets are derived using Fuzzy C-Means clustering. The proposed approaches are tested on four different health care applications: readmissions in intensive care units, administration of vasopressors and mortality. The results show that the proposed clustering algorithm resulted in an increase of the performance of the fuzzy models in three out of four applications in comparison to the use of Fuzzy C-Means.

The current demands on the energy market, such as efficiency, sustainability and affordability increase the need for customer understanding and data analysis. This paper presents an analysis of the segmentation of electricity consumers based on the fuzzy clustering of time variant electricity consumption data and invariant features like the demographic customer information. The algorithm used is mixed fuzzy clustering (MFC), which allows integrating both variant and invariant features into one clustering. The clustering is evaluated both in its stability over the two years of data, using a entropy measurement and in its general quality given by the three clustering validity indices, Calinski-Harabasz, Davies-Bouldin and Silhouette index.

This paper applies genetic algorithms to optimize the operation of a transmission network with energy storage capabilities, to optimize its costs, which include both generation and storage costs, for cases when the data inherent to the system is assumed to be perfectly known. The problem is formulated through the DC optimal power flow equations, including losses across the transmission lines, therefore allowing solutions regarding the network generation costs to be obtained, with and without storage. In this way, the financial impact inherent to the usage of energy storage can be derived. Since we are dealing with a large combinatorial problem, the search throughout the solution space was done by means of the Genetic Algorithms. The solutions consist of the storage device's charging or discharging rate at which it must be operating during each sub-interval considered for the simulations. The results delivered by the GA have proven the profitability of including energy storage capabilities in the transmission network of São Miguel (Portugal) and the usefulness of such algorithm in a real world application.

This work presents heuristic and metaheuristic approaches for addressing the real-world steel cutting problem of a retail steel distributor as a cutting & packing problem. It consists of the cutting of large steel blocks in order to obtain smaller pieces ordered by clients. The problem was formulated as a 3-dimensional residual bin packing problem for minimization of scrap generation, with guillotine cutting constraint and chips scrap generation. A tabu search and bestfit decreasing (BFD) approaches are proposed and their performance compared to an heuristic and ant colony optimization (ACO) algorithms. It's shown that the tabu search and best-fit decreasing algorithm are able to reduce the generated scrap by up to 52% in comparison with the heuristic in [1]. The orders to suppliers were also reduced by up to 35%. The analysis of the results of the different approaches provide insight onto the most important factors in the problem's scrap minimization.

The aim of this work is to identify groups of patients with similar patterns that are related to a higher risk of readmission to an Intensive Care Unit (ICU). Patients readmissions to ICUs are introduced as a problem associated with increased mortality, morbidity and costs, which complicates the performance of a good clinical management and medical diagnosis. To approach the readmissions classification problem, Fuzzy C-Means (FCM) clustering algorithm was implemented to find the different groups of patients. A multimodel approach was developed using these groups and the best clustering division was assessed through different objective functions. Two decision criteria were used for the multimodel approach, an a priori decision and an a posteriori decision. The data used, from MIMIC II database, consisted on the arithmetic means of time series of variables — acquired during the last 24 hours before discharge. The multimodel using the a priori and the aposteriori decisions were able to predict readmissions with an average AUC of 0.74 and 0.75, respectively. Consequently, the multimodel results overcame the results of previous predictive models developed for the classification of readmissions outcome.

This paper proposes a novel feature selection approach formulated based on the Fish School Search (FSS) optimization algorithm, intended to cope with premature convergence. In order to use this population based optimization algorithm in feature selection problems, we propose the use of a binary encoding scheme for the internal mechanisms of the fish school search, emerging the binary fish school search (BFSS). The suggested algorithm was combined with fuzzy modeling in a wrapper approach for Feature Selection (FS) and tested over three benchmark databases. This hybrid proposal was applied to an ICU (Intensive Care Unit) readmission problem. The purpose of this application was to predict the readmission of ICU patients within 24 to 72 hours after being discharged. We assessed the experimental results in terms of performance measures and the number of features selected by each used FS algorithms. We observed that our proposal can correctly select the discriminating input features.

We propose the application of probabilistic fuzzy systems (PFS) to model the prediction of early readmission in intensive care unit patients and compare it with the gold-standard method - logistic regression based on the APACHE II score. PFS are characterized by the combination of the linguistic description of the system with the statistical properties of data. On one hand, results point that PFS models perform comparably to the gold-standard method, with AUC values of 0.66±0.03. On the other hand, results also show that PFS models use a significant lower number of variables which, from the clinical practice point of view, suggests improved gains in terms of simplicity.

Shock is a life-threatening medical condition requiring the administration of powerful drugs - vasopressors. Early identification of these patients is a worthy goal in order to timely prepare them for therapy. A subset composed of the most frequently sampled and readily available variables in an intensive care unit (ICU) was used for clustering patients. Then, a data exploration process was started through the use of fuzzy clustering with the fuzzy cmeans algorithm, where four clusters were obtained and the groups characteristics were analyzed. A relationship between the clusters obtained and the use of vasopressors was found out and these results were visualized with the help of histograms. First, a single model was derived. Then, four models were trained and used for a multi model approach, one for each identified group of patients. In both cases fuzzy models were used as they are universal approximators. For the multi-model approach, two decision criteria were used. First a decision a priori based on the distance from the clusters centers to the patient characteristics was used. Lastly a decision a posteriori approach where each model was used and the final outcome used is based on the uncertainty of the output response to the threshold of each model. The multi model approach with a posteriori decision had a better performance of the two schemes tested, and also performed better than the single general model approach.

This paper proposes a new optimization methodology for supply chain design, using ant colony optimization. The objective of this methodology is to choose the facilities that will take part in a multi-product closed-loop supply chain, such as factories, warehouses and disassembly centers, in order to minimize the costs related to these facilities and those related to transportation costs, both in the forward and reverse chains. Considering that total production quantities for factories, expected cross-docking stocks for warehouses, and disassembly centers are determined by this methodology, it can be considered that it undertakes both strategic and tactical Supply Chain Management (SCM) problems at once. The developed algorithm, SCant-Design, is sufficiently general to solve any SCM configuration, with linear and nonlinear cost functions and constraints. The algorithm results were compared to a MILP approach for a particular case study and the obtained value for the cost function is very similar, although using less facilities.

This paper proposes the application of a new binary particle swarm optimization (BPSO) method to feature selection problems. Two enhanced versions of binary particle swarm optimization, designed to cope with premature convergence of the BPSO algorithm, are proposed. These methods control the swarm variability using the velocity and the similarity between best swarm solutions. The proposed PSO methods use neural networks, fuzzy models and support vector machines in a wrapper approach, and are tested in a benchmark database. It was shown that the proposed BPSO approaches require an inferior simulation time, less selected features and increase accuracy. The best BPSO is then compared with genetic algorithms (GA) and applied to a real medical application, a sepsis patient database. The objective is to predict the outcome (survived or deceased) of the sepsis patients. It was shown that the proposed BPSO approaches are similar in terms of model accuracy when compared to GA, while requiring an inferior simulation time and less selected features.

In the present work, we propose the application of probabilistic fuzzy systems (PFS) to model the prediction of mortality in septic shock patients. This technique is characterized by the combination of the linguistic description of the system with the statistical properties of data. Preliminary results for this particular clinical problem point that PFS models, besides performing as accurately as first order Takagi-Sugeno fuzzy models, also provide probability measures that provide additional clinical information upon which physicians can act on.

The amount of data generated in the intensive care environment nowadays prohibits the storage of all the information available. The validation process is time consuming, since nurses have to check every certain periods the data acquired from bedside monitors in order to assess their validity and integrity. This work presents an automatic method for data validation in the intensive care environment, based on an artificial intelligence approach, namely artificial neural networks (ANNs). A real world dataset acquired at Beth Israel Deaconess Medical Center (BIDMC) neonatal intensive care unit (NICU) is used to obtain the validation model and assess its performance. The dataset consists of high frequency sampled data of the level of oxygen saturation (SpO2) of neonates. A subset of 100 neonates was considered for modeling purposes. A total of 7,018,662 samples were available, containing 129,075 validated ones. The performance of the validation model, assessed in terms of its AUC, was of up to 0.75. Both the sensitivity and specificity reached acceptable values according to medical review. Future work would involve a prospective study and validation of the methods proposed in this work.

One consequence of the increasing amount of data stored during acquisition processes is that sampled time series are more prone to be collected in a misaligned uneven fashion and/or be partly lost or unavailable (missing data). Due to their severe impact on data mining techniques, this work proposes methods to (a) align misaligned unevenly sampled data, (b) differentiate absent values related to low sampling frequencies, compared to those resulting from missingness mechanisms, and (c) to classify recoverable and non-recoverable segments of missing data by using statistical and fuzzy modeling approaches. These methods were evaluated against randomly simulated test datasets containing different amounts of missing data. Results show that: (1) using the variable most frequently sampled as a template, combined with cubic interpolation, allowed to unshift misaligned uneven data without significant errors; (2) the differentiation of absent values due to low sampling frequencies from those truly missing, can be successfully performed using 95% confidence intervals relative to the mean sampling time; (3) fuzzy modeling returned better classification results for recoverable segments, while the statistical approach performed better in classifying non-recoverable segments. All three methods proposed in this work decreased their performance when the amount of missing data was increased in the test datasets.

Laboratory testing is a frequent activity for patients in intensive care units (ICU). Recent studies demonstrate that frequent laboratory testing does not necessarily relate to better outcomes. We hypothesize that unnecessary laboratory testing can be reduced by predicting which tests are unlikely to influence clinical management. Reducing unnecessary tests could reduce morbidity and hospitalization costs. We analyzed an ICU database containing 26,665 patient records at Beth Israel Deaconess Medical Center, Boston, and selected a subset of patients with gastrointestinal bleeding. Database knowledge discovery was applied involving data preprocessing, feature selection, and classification. Conventional soft computing tools such as fuzzy models and neural networks were utilized in this work, combined with statistical and mathematical tools. The input variables included bedside monitor trends, lab tests, arterial/central catheter information, urine collections, transfusions, indexes and scores calculated for the patients. The outcome variable was a binary classification based on falling levels of hematocrit. Feature selection was performed by a bottom-up strategy, maximizing the area under the ROC curve (AUC), the integrated discrimination improvement (IDI) and a multiobjective function primarily pondering the sensitivity of the models. A leave-one-out cross validation process was used to evaluate the overall models' performance, as well as the additional predictive value of the variables selected. Urine output was selected by all models as the best predictor of useful hematocrit testing. Our results show that it is possible to correctly classify the usefulness of a hematocrit lab test up to 81% of the time by using fuzzy models and neural networks.

Vasopressors belong to a powerful class of drugs used in the management of systemic shock in ill patients. The administration of a vasopressor involves the non-trivial process of inserting a central venous catheter. This procedure carries with it inherent risks which are increased when done under urgency such as in the case of unexpected systemic shock. The ability to predict the transition to vasopressor dependence could be expected to improve overall outcomes associated with the procedure. We use three different approaches combining fuzzy modeling with bottom-up (BU), top-town (TD) and ant feature selection (AFS), to classify requirements for vasopressors in shock. We observe that fuzzy models combined with BU feature selection return higher values of sensitivity; fuzzy models with no feature selection and fuzzy models with TD feature selection return higher values of AUC and specificity; features most commonly selected to classify impending use of vasopressores in pancreatitis patients include levels of Sodium and White Blood Cell counts, while for pneumonia patients include levels of Lactid Acid and White Blood Cell Count; and finally, fuzzy models combined with BU and fuzzy models combined with AFS demonstrate the lowest number of selected variables with no significant loss in accuracy.

Real-world databases often contain missing data and existing correction algorithms deliver varying performance. Also, most modeling techniques are not suitable to deal with them automatically. In this study we examine different approaches to predicting septic shock in the presence of missing data. Some preprocessing techniques for managing missing data include disregarding data, or replacing it with information that by design introduces bias. In this study, we show that predictive performance improves by employing a minimum pre-processing technique, the Zero-Order-Hold (ZOH) method, by applying a Fuzzy C-Means clustering technique based on the partial distance calculation strategy (FCM-PDS) and by computing the final classification regarding the samples from each patient. Performance improvements continue to occur where up to approximately 60% of the data is missing, though for higher percentage the classification performance still is statistically improved. We further validate this approach by making comparisons with previous studies.

Real word data sets often contain many missing elements. Most algorithms that automatically develop a rule-based model are not well suited to deal with incomplete data. The usual technique is to disregard the missing values or substitute them by a best guess estimate, which can bias the results. In this paper we propose a new method for estimating the parameters of a Takagi-Sugeno fuzzy model in the presence of incomplete data. We also propose an inference mechanism that can deal with the incomplete data. The presented method has the added advantage that it does not require imputation or iterative guess-estimate of the missing values. This methodology is applied to fuzzy modeling of a classification and regression problem. The performance of the obtained models are comparable with the results obtained when using a complete data set.

This paper proposes a fault-tolerant control (FTC) approach using evolving fuzzy modeling. FTC is performed in two steps: fault detection and fault accommodation. Fault accommodation uses evolving Takagi-Sugeno fuzzy models, and fault detection uses a model-based approach also based on fuzzy models. Information from fault detection is used for fault accommodation in a model predictive control (MPC) scheme. The evolving fuzzy modeling approach increases the control performance when the process is with faults. The proposed approach continuously evaluate the control performance and perform on-line clustering, if it is necessary. Evolving FTC is used to accommodate two simulated faults in a distillation column process. The considered faults are the load process fault (variation in feed composition) and the change in heating (variation of re-boiler temperature). The fault tolerant control using evolving fuzzy modeling was able to accommodate the simulated faults.

Measuring the performance of a given classifier is not a straightforward or easy task. Depending on the application, the overall classification rate may not be sufficient if one, or more, of the classes fail in prediction. This problem is also reflected in the feature selection process, especially when a wrapper method is used. Cohen's kappa coefficient is a statistical measure of inter-rater agreement for qualitative items. It is generally thought to be a more robust measure than simple percent agreement calculation, since it takes into account the agreement occurring by chance. Considering that kappa is a more conservative measure, then its use in wrapper feature selection is suitable to test the performance of the models. This paper proposes the use of the kappa measure as an evaluation measure in a feature selection wrapper approach. In the proposed approach, fuzzy models are used to test the feature subsets and fuzzy criteria are used to formulate the feature selection problem. Results show that using the kappa measure leads to more accurate classifiers, and therefore it leads to feature subset solutions with more relevant features.

Bayesian networks (BNs) are knowledge representation tools capable of representing dependence or independence relationships among random variables. Learning the structure of BNs from datasets has received increasing attention in the last two decades, due to the BNs' capacity of providing good inference models and discovering the structure of complex domains. One approach for BNs' structure learning from data is to define a scoring metric that evaluates the quality of the candidate networks, given a dataset, and then apply an optimization procedure to explore the set of candidate networks. Among the most frequently used optimization methods for BN score-based learning is greedy hill climbing (GHC) search. This paper proposes a new local discovery ant colony optimization (ACO) algorithm and a hybrid algorithm max-min ant colony optimization (MMACO), based on the local discovery algorithm max-min parents and children (MMPC) and ACO to learn the structure of a BN. In MMACO, MMPC is used to construct the skeleton of the BN and ACO is used to orientate the skeleton edges, thus returning the final structure. The algorithms are applied to several sets of benchmark networks and are shown to outperform the GHC and simulated annealing algorithms.

Bayesian networks (BNs) are knowledge representation tools capable of representing dependence or independence relationships among random variables that compose a problem domain. Bayesian networks learned from data sets are receiving increasing attention within the community of researchers of uncertainty in artificial intelligence, due to their capacity to provide good inference models and to discover the structure of complex domains. One approach to learning BNs from data is to use a scoring metric to evaluate the fitness of any given candidate network for the database, and apply an optimization procedure to explore the set of candidate networks. Among the most frequently used optimization methods for this purpose is greedy search, either deterministic or stochastic. This article proposes a hybrid Bayesian network learning algorithm MMACO, based on the local discovery algorithm max-min parents and children (MMPC) and ant colony optimization (ACO). MMPC is used to construct the skeleton of the Bayesian network and then ACO is used to orientate its edges, thus returning the final structure. We apply MMACO (max-min ACO) to several sets of benchmark networks and show that it outperforms greedy search (GS) and simulated annealing (SA) algorithms.

This paper proposes extracting fuzzy rules from data using fuzzy possibilistic c-means and possibilistic fuzzy c-means algorithms, which provide more than one partition information: the typicality matrix and the membership matrix. Usually to extract fuzzy rules from data only one of the partition matrix is used, resulting in one rule per cluster. In our work we extract rules from both the membership partition matrix and the typicality matrix, resulting in deriving multiple rules for each cluster. These methods are applied to fuzzy modeling of four different classification problems: Iris, Wine, Wisconsin breast cancer and Altman data sets. The performance of the obtained models is compared and we consider the added value of the proposed approach in fuzzy modeling.

This paper proposes the application of fault-tolerant control (FTC) using weighted fuzzy predictive control. The FTC approach is based on two steps, fault detection and isolation (FDI) and fault accommodation. Fault detection is performed by a model-based approach using fuzzy modeling. Fault isolation uses a fuzzy decision making approach. The model of the isolated fault is used in fault accommodation with a model predictive control (MPC) scheme. This paper uses a weighted fuzzy predictive control scheme, where fuzzy goals and fuzzy constraints are described in a fuzzy objective function. Each criterion (goal or constraint) has an associated weight factor, which is chosen by the decision-maker. Two faults were considered in an experimental three tanks process and the respective fuzzy models were identified. The fuzzy FTC scheme was able to accommodate the faults of the experimental process.

One of the most important techniques in data preprocessing for data mining is feature selection. Real-world data analysis, data mining, classification and modeling problems usually involve a large number of candidate inputs or features. Less relevant or highly correlated features decrease, in general, the classification accuracy, and enlarge the complexity of the classifier. The goal is to find a reduced set of features that reveals the best classification accuracy for a fuzzy classifier. This paper proposes an ant colony optimization (ACO) algorithm for feature selection, which minimizes two objectives: the number of features and the error classification. Two pheromone matrices and two different heuristics are used for each objective. The performance of the method is compared to other features selection methods, revealing higher performance.

A new uncalibrated eye-to-hand visual servoing based on inverse fuzzy modeling is proposed in this paper. In classical visual servoing, the Jacobian plays a decisive role in the convergence of the controller, as its analytical model depends on the selected image features. This Jacobian must also be inverted online. Fuzzy modeling is applied to obtain an inverse model of the mapping between image feature variations and joint velocities. This approach is independent from the robot's kinematic model or camera calibration and also avoids the necessity of inverting the Jacobian online. An inverse model is identified for the robot workspace, using measurement data of a robotic manipulator. This inverse model is directly used as a controller. The inverse fuzzy control scheme is applied to a robotic manipulator performing visual servoing for random positioning in the robot workspace. The obtained experimental results show the effectiveness of the proposed control scheme. The fuzzy controller can position the robotic manipulator at any point in the workspace with better accuracy than the classic visual servoing approach.

The management of supply-chain can be performed using distributed optimization based on Ant Colony Optimization (ACO), which uses the pheromone matrix as the mean to exchange information between the several subsystems. However, ACO can be replaced by the hybrid algorithm Beam-ACO, which fuses Beam-Search and ACO algorithms. This optimization method has proven to be more powerful than ACO algorithms for scheduling problems. Since both use the pheromone matrix to achieve the best solution, this work proposes the implementation of Beam-ACO in supply-chain management. Beam-ACO is used in this paper to optimize the supplying and logistic agents of a supply chain. The distribution system is optimized using the standard ACO algorithm, because Beam-ACO is not suitable for this type of optimization problems. Three different instances of supply-chains have been tested. Results show that the use of Beam-ACO improves the local and global results of the supply chain, and that the distributed optimization paradigm can be applied on supply chains where different agents are optimized by different algorithms.

The process under study has four manipulating variables: feed flow rate, washing water, air and rejected flow rates. Column flotation process is an example of a complex, nonlinear and multivariable system. Fuzzy modeling is a well-known modeling technique, which has been applied to complex and nonlinear processes. Fuzzy multivariable modeling with fuzzy model predictive control is used in this paper to control a laboratory setup of a flotation column. Moreover, the control strategy is tested in real-time. Results show that the applied methods led to a good control performance of all the controlled variables.

This paper proposes a fuzzy classification system to perform word indexing in ancient printed documents. The indexing system receives a given word selected by an user. The word is preprocessed using an aspect ratio filter, assuring that only interesting word candidates are considered. The image is classified by oriented feature extraction using Gabor filter banks. The oriented features are used to generate membership functions that characterize the selected word. This target word image is then compared to the potential matches, using a similarity matrix. The indexing system is flexible and lightweight when compared to other optimal recognizers, which allows its use in "real-time" applications. A significant test revealed that the indexer achieved very good results in terms of precision and recall in texts from XVIIth century.

The timely production and distribution of rapidly perishable goods such as ready-mixed concrete is a complex combinatorial optimization problem in the context of supply chain management. The problem involves several tightly interrelated scheduling and routing problems that have to be solved considering a trade-off of production and delivery costs. This paper applies a novel supply chain management paradigm, the distributed optimization, to a real-world case of a concrete delivery supply chain. The production of concrete in several production centers and the distribution of concrete are modeled as job shop problems, where each problem is solved using Ant Colony Optimization. The management methodology consists of allowing each system to exchange information concerning intermediate optimization results through pheromone matrices. In this way, each system finds its own optimization solution based on the information provided by the other systems. A simulation example shows that the proposed coordination mechanism improves the supply chain performance, when compared to another management approach, where both problems are optimized using hybrid methods combining meta-heuristics with constructive heuristics.

The identification of fuzzy models for classification is a very complex task. Often, real world databases have a large number of features and the most relevant ones must be chosen. Therefore, it is necessary to select carefully the variables that are relevant for the feature class. A new automatic feature selection for classification problems is proposed in this paper, to construct compact fuzzy classification models. Several clustering algorithms are used and compared in terms of computational efficiency and accuracy in classification problems. The proposed algorithm was tested in well-known data sets: iris plant, wine, hepatitis, breast cancer and in a difficult real-world problem: the prediction of bankruptcy. The experiments show the advantages of the proposed method for selecting the proper features for classification.

This paper proposes the application of fault-tolerant control (FTC) using fuzzy predictive control. The FTC approach is based on two steps, fault detection and isolation (FDI) and fault accommodation. Fault detection is performed by a model-based approach using fuzzy modeling. Fault isolation uses a fuzzy decision making approach. The model of the isolated fault is used in fault accommodation with a model predictive control (MPC) scheme. This paper uses a fuzzy predictive control scheme, where fuzzy goals and fuzzy constraints are described in a fuzzy objective function. The criteria (goals or constraints) have an associated weight factor, which are chosen by the decision-maker. Two faults were simulated, and the respective fuzzy models were identified. The fuzzy FTC scheme proposed in this paper was able to accommodate the simulated faults.

Model-based fault detection and diagnosis is an interesting method, due to economical and safety related matters. However, in practice it is very difficult to achieve accurate models for complex nonlinear plants. If the plant structure is not precisely known, the diagnosis has to be based primarily on data or heuristic information. The inherent characteristics of fuzzy logic theory make it suitable for fault detection and isolation (FDI). In this paper is proposed a modification to the regularity criterion algorithm, aiming the reduction of computational time without loss of accuracy. The fuzzy models obtained using the modified regularity criterion algorithms are optimized by a real-coded genetic algorithm. An industrial valve simulator is used to obtain several abrupt and incipient faults in the system. These faults are some of the possible faults in the real system. The fuzzy models used in the FDI system were able to detect and isolate the simulated abrupt and incipient faults

This paper proposes an optical character recognition system based on fuzzy logic for ancient printed documents. The recognition process consists of two stages: training with collected character image examples and classification of new character images. The proposed OCR builds fuzzy membership functions from oriented features extracted using Gabor filter banks. Results on a significant test led to a character recognition success rate of 88%

In practice, it is very difficult to achieve an accurate modeling for real-world systems. If the system structure is not precisely known and the number of potential inputs is large, the model has to be based primarily on data or heuristic information. The inherent characteristics of fuzzy logic theory makes it suitable for modeling this type of systems. In this paper a modification to the regularity criterion algorithm is proposed, aiming the reduction of computational time without loss of accuracy. The fuzzy models obtained using the modified regularity criterion algorithm are optimized by a real-coded genetic algorithm. Real data is used for the design and validation of two industrial processes used as examples. The proposed algorithm improves both the accuracy of the models and it reduces the computational time to obtain them

A new approach to improve visual servoing based on fuzzy filters is proposed in this paper. In visual servoing the speed of convergence depends on a constant gain, that directly influences the velocity of the robot manipulator joints. This velocity can achieve undesired behavior due to the intrinsically discrete visual servoing and when regulator control is used. In this paper, the image features error is filtered by means of fuzzy filters, to improve the behavior of the robot joint velocities. Simulation results are presented for a 6 degrees of freedom (DOF) eye-in-hand system, that demonstrates the efficiency of the proposed approach. Experimental results are also presented when a 2 DOF robot manipulator performs kinematic visual servoing, moving to a pre-defined image features position.

The column flotation process is a very complex, nonlinear and multivariable system. Fuzzy modeling is a well-known modeling technique, which has been applied to complex and nonlinear processes. This paper proposes a fuzzy modeling identification technique, where the structure of the model is determined using a regularity criterion, and the rules are identified using fuzzy clustering optimized by a real-coded genetic algorithm. Real data is used for the design and validation of the column flotation fuzzy model. The results are compared to other well-known fuzzy modeling techniques. The validation results show that is possible to find a better model using the identification procedure proposed in this paper.

The model-based fault detection and diagnosis (FDI) is an interesting method, because of economical and safety related matters. However, in practice it is very difficult to achieve an accurate modeling for complex nonlinear systems. If the system structure is not precisely known, the diagnosis has to be based primarily on data or heuristic information. Fuzzy system theory is an interesting tool to handle these situations. The use of characteristics of fuzzy logic theory makes it suitable for fault diagnosis. In this paper, a simulator of an industrial servo-actuated valve is used to simulate several faults in the system. These faults are some of the possible faults in the real system. The fuzzy model-based FDI system developed in this paper was able to detect and isolate all the simulated faults.

This paper addresses the problem of optimizing the schedule of logistic processes using genetic algorithms and fuzzy decision making. We consider here the problem of dynamically assign components to orders and choose the solution that is able to deliver more orders at the correct date, respecting at the same time the priority degree of the orders. A compromise between these conflicting goals is achieved by using a genetic algorithm to optimize a fuzzy weighted function. The simulation results show that the proposed genetic algorithm evolved with fuzzy optimization presents good results for this type of problems.

Model predictive control (MPC) is a well-known control technique, which has been applied to complex and nonlinear processes. This paper integrates different fuzzy issues in multivariable predictive control. Fuzzy predictive control incorporates fuzzy goals and constraints in model predictive control, in a fuzzy decision making framework. Several issues are proposed in this paper for multivariable fuzzy predictive control, namely, the use of weighted fuzzy decision functions and fuzzy predictive filters. Simultaneous weighted satisfaction of various criteria is modeled by using the qualitative extensions of (Archimedean) fuzzy t-norms. The use of fuzzy predictive filters are represented as an adaptive set of control actions multiplied by gain factors. The integration of the several fuzzy issues proposed in this paper is applied to the control of a container gantry crane. Simulation results show the advantages of the proposed methods.

This paper presents the results of one application of target selection in direct marketing: the mailing campaigns of a charity organization, where the clients are selected based on the expected amount of donation they are going to make. Target selection is an important data mining problem for which several modeling techniques have been used. Statistical regression, neural networks, decision trees, and clustering are the most utilized techniques. Fuzzy clustering can also be applied to target selection. In this paper, traditional and fuzzy techniques are compared by using cross-validation measures. The four techniques are applied based on recency, frequency and monetary value measures. The application to mailing campaigns of a charity organization, showed that fuzzy modeling obtains results similar to those of other classical target selection techniques.

This paper addresses the problem of optimizing logistic processes that can be modeled as a birth-and-death process. A fuzzy decision making algorithm is proposed to assign components to orders, which is a common task in a large number of logistic processes. The dynamic assignment of components to orders is the key issue in optimizing logistic processes. This paper proposes several criteria for this optimization. These criteria are combined using weighted fuzzy aggregation in a fuzzy decision making environment. First, a simple but illustrative example shows that the proposed techniques can be applied with good results to this type of processes. Then, the proposed method is applied to a real-world logistic process at Fujitsu-Siemens Computers.

Target selection in direct marketing is an important data mining problem for which fuzzy modeling can be used. The paper compares several fuzzy modeling techniques applied to target selection based on recency, frequency and monetary value measures. The comparison uses cross validation applied to mailing campaigns of a charity organization.

Many practical optimization problems are characterized by some flexibility in the problem constraints, where this flexibility can be exploited for additional trade-off between improving the objective function and satisfying the constraints. Fuzzy sets have proven to be a suitable representation for modeling this type of soft constraints. The paper proposes an extension of this model for satisfying the problem constraints and the goals, where preference for different constraints and goals can be specified by the decision-maker. The difference in the preference for the constraints is represented by a set of associated weight factors, which influence the amount of trade-off between improving the optimization objectives and satisfying various constraints. Simultaneous weighted satisfaction of the problem constraints and goals are demonstrated by using a fuzzy linear programming problem.

Fuzzy predictive control integrates conventional model predictive control with techniques from fuzzy multicriteria decision making, translating the goals and the constraints to predictive control in a transparent way. The information regarding the (fuzzy) goals and the (fuzzy) constraints of the control problem is combined by using a decision function from the theory of fuzzy sets. This paper investigates the use of fuzzy decision making (FDM) in model predictive control (MPG), and compares the results to those obtained from conventional MPG. Attention is also paid to the choice of aggregation operators for fuzzy decision making in control. Experiments on a nonminimum phase, unstable linear system, and on an air-conditioning system with nonlinear dynamics are studied. It is shown that the performance of the model predictive controller can be improved by the use of fuzzy criteria in a fuzzy decision making framework.

Fuzzy predictive control integrates conventional model-based predictive control with techniques from fuzzy multicriteria decision making. The information regarding the fuzzy criteria of the control problem is combined by using a decision function from the fuzzy set-theory. The use of fuzzy criteria in the cost function leads usually to a non-convex optimization problem, which is numerically complex. The numeric optimization problem becomes more tractable by discretizing the control actions, limiting the search of the optimal solution to this space. This paper extends the application of the branch-and-bound optimization technique to predictive control problems with fuzzy cost functions. This approach can reduce significantly the search time, allowing the application of fuzzy predictive control to a broader class of systems, and to real-time control problems. Simulation and real-time results of temperature control in a fan-coil unit show the applicability of the approach. © 2000 John Wiley & Sons, Inc.