A complex networks approach for data clustering

This work proposes a method for data clustering based on complex networks theory. A data set is represented as a network by considering different metrics to establish the connection between each pair of objects. The clusters are obtained by taking into account five community detection algorithms. The network-based clustering approach is applied in two real-world databases and two sets of artificially generated data. The obtained results suggest that the exponential of the Minkowski distance is the most suitable metric to quantify the similarities between pairs of objects. In addition, the community identification method based on the greedy optimization provides the best cluster solution. We compare the network-based clustering approach with some traditional clustering algorithms and verify that it provides the lowest classification error rate. (C) 2012 Elsevier B.V. All rights reserved.

Optimal Code Set Selection and Security Issues in Spectral Phase-Encoded Time Spreading (SPECTS) OCDMA Systems

In this paper, we perform a thorough analysis of a spectral phase-encoded time spreading optical code division multiple access (SPECTS-OCDMA) system based on Walsh-Hadamard (W-H) codes aiming not only at finding optimal code-set selections but also at assessing its loss of security due to crosstalk. We prove that an inadequate choice of codes can make the crosstalk between active users to become large enough so as to cause the data from the user of interest to be detected by other user. The proposed algorithm for code optimization targets code sets that produce minimum bit error rate (BER) among all codes for a specific number of simultaneous users. This methodology allows us to find optimal code sets for any OCDMA system, regardless the code family used and the number of active users. This procedure is crucial for circumventing the unexpected lack of security due to crosstalk. We also show that a SPECTS-OCDMA system based on W-H 32(64) fundamentally limits the number of simultaneous users to 4(8) with no security violation due to crosstalk. More importantly, we prove that only a small fraction of the available code sets is actually immune to crosstalk with acceptable BER (<10(-9)) i.e., approximately 0.5% for W-H 32 with four simultaneous users, and about 1 x 10(-4)% for W-H 64 with eight simultaneous users.

Chaos-based communication systems in non-ideal channels

Recently, many chaos-based communication systems have been proposed. They can present the many interesting properties of spread spectrum modulations. Besides, they can represent a low-cost increase in security. However, their major drawback is to have a Bit Error Rate (BER) general performance worse than their conventional counterparts. In this paper, we review some innovative techniques that can be used to make chaos-based communication systems attain lower levels of BER in non-ideal environments. In particular, we succinctly describe techniques to counter the effects of finite bandwidth, additive noise and delay in the communication channel. Although much research is necessary for chaos-based communication competing with conventional techniques, the presented results are auspicious. (C) 2011 Elsevier B. V. All rights reserved.

A New Approach for Evaluating the BER of a Multirate, Multiclass OFFH-CDMA System

In this letter, we propose a new approach to evaluate the bit error rate (BER) of a multirate, multiclass optical fast frequency hopping code-division multiple-access (OFFH-CDMA) system. This proposed approach does not require knowledge of the generated users' code sequences, which makes the system analysis straightforward. Furthermore, the presented formalism can also be successfully applied to most multi-weight multi-length family of codes, as long as the corresponding code parameters are employed.

Reliability, Validity and Classification Accuracy of the South Oaks Gambling Screen in a Brazilian Sample

The purpose of this study was to examine the reliability, validity and classification accuracy of the South Oaks Gambling Screen (SOGS) in a sample of the Brazilian population. Participants in this study were drawn from three sources: 71 men and women from the general population interviewed at a metropolitan train station; 116 men and women encountered at a bingo venue; and 54 men and women undergoing treatment for gambling. The SOGS and a DSM-IV-based instrument were applied by trained researchers. The internal consistency of the SOGS was 0.75 according to the Cronbach`s alpha model, and construct validity was good. A significant difference among groups was demonstrated by ANOVA (F ((2.238)) = 221.3, P < 0.001). The SOGS items and DSM-IV symptoms were highly correlated (r = 0.854, P < 0.01). The SOGS also presented satisfactory psychometric properties: sensitivity (100), specificity (74.7), positive predictive rate (60.7), negative predictive rate (100) and misclassification rate (0.18). However, a cut-off score of eight improved classification accuracy and reduced the rate of false positives: sensitivity (95.4), specificity (89.8), positive predictive rate (78.5), negative predictive rate (98) and misclassification rate (0.09). Thus, the SOGS was found to be reliable and valid in the Brazilian population.

Modeling the growth rate and lag time of different strains of Salmonella enterica and Listeria monocytogenes in ready-to-eat lettuce

The growth parameters (growth rate, mu and lag time, lambda) of three different strains each of Salmonella enterica and Listeria monocytogenes in minimally processed lettuce (MPL) and their changes as a function of temperature were modeled. MPL were packed under modified atmosphere (5% O-2, 15% CO2 and 80% N-2), stored at 7-30 degrees C and samples collected at different time intervals were enumerated for S. enterica and L monocytogenes. Growth curves and equations describing the relationship between mu and lambda as a function of temperature were constructed using the DMFit Excel add-in and through linear regression, respectively. The predicted growth parameters for the pathogens observed in this study were compared to ComBase, Pathogen modeling program (PMP) and data from the literature. High R-2 values (0.97 and 0.93) were observed for average growth curves of different strains of pathogens grown on MPL Secondary models of mu and lambda for both pathogens followed a linear trend with high R2 values (>0.90). Root mean square error (RMSE) showed that the models obtained are accurate and suitable for modeling the growth of S. enterica and L monocytogenes in MP lettuce. The current study provides growth models for these foodborne pathogens that can be used in microbial risk assessment. (C) 2011 Elsevier Ltd. All rights reserved.

The evaluation of an instrument for pediatric oncology patient classification

The objectives of this study were to assess the interrater reproducibility of the instrument to classify pediatric patients with cancer; verify the adequacy of the patient classification instrument for pediatric patients with cancer; and make a proposal for changing the instrument, thus allowing for the necessary adjustments for pediatric oncology patients. A total of 34 pediatric inpatients of a Cancer Hospital were evaluated by the teams of physicians, nurses and nursing technicians. The Kappa coefficient was used to rate the agreement between the scores, which revealed a moderate to high value in the objective classifications, and a low value in the subjective. In conclusion, the instrument is reliable and reproducible, however, it is suggested that to classify pediatric oncology patients, some items should be complemented in order to reach an outcome that is more compatible with the reality of this specific population.

Network-based high level data classification

Traditional supervised data classification considers only physical features (e. g., distance or similarity) of the input data. Here, this type of learning is called low level classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is, here, referred to as high level classification. In this paper, we propose a hybrid classification technique that combines both types of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features or class topologies, while the latter measures the compliance of the test instances to the pattern formation of the data. Our study shows that the proposed technique not only can realize classification according to the pattern formation, but also is able to improve the performance of traditional classification techniques. Furthermore, as the class configuration's complexity increases, such as the mixture among different classes, a larger portion of the high level term is required to get correct classification. This feature confirms that the high level classification has a special importance in complex situations of classification. Finally, we show how the proposed technique can be employed in a real-world application, where it is capable of identifying variations and distortions of handwritten digit images. As a result, it supplies an improvement in the overall pattern recognition rate.

A system for classification of time-series data from industrial non-destructive device

This work proposes a system for classification of industrial steel pieces by means of magnetic nondestructive device. The proposed classification system presents two main stages, online system stage and off-line system stage. In online stage, the system classifies inputs and saves misclassification information in order to perform posterior analyses. In the off-line optimization stage, the topology of a Probabilistic Neural Network is optimized by a Feature Selection algorithm combined with the Probabilistic Neural Network to increase the classification rate. The proposed Feature Selection algorithm searches for the signal spectrogram by combining three basic elements: a Sequential Forward Selection algorithm, a Feature Cluster Grow algorithm with classification rate gradient analysis and a Sequential Backward Selection. Also, a trash-data recycling algorithm is proposed to obtain the optimal feedback samples selected from the misclassified ones.