Fuzzy ARTMAP and a hybrid chaos genetic algorithm for medical pattern classification

In this paper, an Evolutionary Artificial Neural Network (EANN), which combines the Fuzzy ARTMAP (FAM) neural network and a hybrid Chaos Genetic Algorithm (CGA), is proposed for undertaking pattern classification tasks. The hybrid CGA is a modified version of the hybrid real-coded genetic algorithms that includes a Chaotic Mapping Operator (CMO) in its search and adaptation process. It is used to evolve the connection weights in FAM, and the resulting EANN is known as FAM-hybrid CGA. The CMO in the hybrid CGA is used to generate a group of chromosomes that incorporates the characteristics of chaos. The chromosomes are then adapted with an arbitrary small amount of variation in every generation. As the evolution procedure proceeds, chromosomes with considerable differences are produced. Such chromosomes, which are located at different regions of interest in the solution space, are able to provide good solutions to undertake search and adaption problems. The effectiveness of the proposed FAM-hybrid CGA model is first evaluated using benchmark medical data sets from the UCI machine learning repository. Its applicability to medical decision support is then demonstrated using a real database of patient records with suspected Acute Coronary Syndrome. The results indicate that FAM-hybrid CGA is able to outperform its neural network counterpart (i.e., FAM), and it can be employed as a useful pattern classification tool for tackling medical decision support tasks.

Keystroke patterns classification using the ARTMAP-FD neural network

This paper presents the development of a keystroke dynamics-based user authentication system using the ARTMAP-FD neural network. The effectiveness of ARTMAPFD in classifying keystroke patterns is analyzed and compared against a number of widely used machine learning systems. The results show that ARTMAP-FD performs well against many of its counterparts in keystroke patterns classification. Apart from that, instead of using the conventional typing timing characteristics, the applicability of typing pressure to ascertaining user's identity is investigated. The experimental results show that combining both latency and pressure patterns can improve the Equal Error Rate (ERR) of the system.

Malwise-an effective and efficient classification system for packed and polymorphic malware

Signature-based malware detection systems have been a much used response to the pervasive problem of malware. Identification of malware variants is essential to a detection system and is made possible by identifying invariant characteristics in related samples. To classify the packed and polymorphic malware, this paper proposes a novel system, named Malwise, for malware classification using a fast application-level emulator to reverse the code packing transformation, and two flowgraph matching algorithms to perform classification. An exact flowgraph matching algorithm is employed that uses string-based signatures, and is able to detect malware with near real-time performance. Additionally, a more effective approximate flowgraph matching algorithm is proposed that uses the decompilation technique of structuring to generate string-based signatures amenable to the string edit distance. We use real and synthetic malware to demonstrate the effectiveness and efficiency of Malwise. Using more than 15,000 real malware, collected from honeypots, the effectiveness is validated by showing that there is an 88 percent probability that new malware is detected as a variant of existing malware. The efficiency is demonstrated from a smaller sample set of malware where 86 percent of the samples can be classified in under 1.3 seconds.

Interobserver reliability of classification and characterization of proximal humeral fractures: a comparison of two and three-dimensional CT

Interobserver reliability for the classification of proximal humeral fractures is limited. The aim of this study was to test the null hypothesis that interobserver reliability of the AO classification of proximal humeral fractures, the preferred treatment, and fracture characteristics is the same for two-dimensional (2-D) and three-dimensional (3-D) computed tomography (CT). Members of the Science of Variation Group--fully trained practicing orthopaedic and trauma surgeons from around the world--were randomized to evaluate radiographs and either 2-D CT or 3-D CT images of fifteen proximal humeral fractures via a web-based survey and respond to the following four questions: (1) Is the greater tuberosity displaced? (2) Is the humeral head split? (3) Is the arterial supply compromised? (4) Is the glenohumeral joint dislocated? They also classified the fracture according to the AO system and indicated their preferred treatment of the fracture (operative or nonoperative). Agreement among observers was assessed with use of the multirater kappa (κ) measure. Interobserver reliability of the AO classification, fracture characteristics, and preferred treatment generally ranged from "slight" to "fair." A few small but statistically significant differences were found. Observers randomized to the 2-D CT group had slightly but significantly better agreement on displacement of the greater tuberosity (κ = 0.35 compared with 0.30, p < 0.001) and on the AO classification (κ = 0.18 compared with 0.17, p = 0.018). A subgroup analysis of the AO classification results revealed that shoulder and elbow surgeons, orthopaedic trauma surgeons, and surgeons in the United States had slightly greater reliability on 2-D CT, whereas surgeons in practice for ten years or less and surgeons from other subspecialties had slightly greater reliability on 3-D CT. Proximal humeral fracture classifications may be helpful conceptually, but they have poor interobserver reliability even when 3-D rather than 2-D CT is utilized. This may contribute to the similarly poor interobserver reliability that was observed for selection of the treatment for proximal humeral fractures. The lack of a reliable classification confounds efforts to compare the outcomes of treatment methods among different clinical trials and reports.

Using fund transfer pricing for comparing financial performance among business units

This study aims to demonstrate by a simplified analysis how fund transfer pricing can be used to measure financial performance taking into account the flow of resources in a bank with only two branches. The present study was developed in three sections. The first section provides information regarding fund transfer pricing and its characteristics. This is followed by a section with an analysis demonstrating how fund transfer pricing can assist top management to evaluate financial performance in a financial institutions with only two business units. Finally, the third section has the concluding remarks about the benefits and limitations of the use of fund transfer pricing. This study uses a simple version of fund transfer pricing system to address a further complex problem which is the exchange of services among business units in a decentralized organization. The analysis has shown how fund transfer pricing can be used to manage a bank, directing the efforts of branch managers.

EEG signal classification for BCI applications by wavelets and interval type-2 fuzzy logic systems

The nonlinear, noisy and outlier characteristics of electroencephalography (EEG) signals inspire the employment of fuzzy logic due to its power to handle uncertainty. This paper introduces an approach to classify motor imagery EEG signals using an interval type-2 fuzzy logic system (IT2FLS) in a combination with wavelet transformation. Wavelet coefficients are ranked based on the statistics of the receiver operating characteristic curve criterion. The most informative coefficients serve as inputs to the IT2FLS for the classification task. Two benchmark datasets, named Ia and Ib, downloaded from the brain-computer interface (BCI) competition II, are employed for the experiments. Classification performance is evaluated using accuracy, sensitivity, specificity and F-measure. Widely-used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, AdaBoost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The wavelet-IT2FLS method considerably dominates the comparable classifiers on both datasets, and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II by 1.40% and 2.27% respectively. The proposed approach yields great accuracy and requires low computational cost, which can be applied to a real-time BCI system for motor imagery data analysis.

A conceptual classification of parents’ attributions of the role of food advertising in children’s diets

Background : High levels of child obesity are triggering growing concerns about the prevalence and effects of food advertising targeted at children. Efforts to address this advertising are confounded by the expanding repertoire of media and promotional techniques used to reach and attract children. The present study explored parents’ views on food marketing and the strategies parents employ when attempting to ameliorate its effects. As part of an online survey of Australian parents’ attitudes towards a range of food advertisements, respondents were invited to provide additional comment in an open-ended question. The question was optional and asked “Are there any other comments you would like to make?”. One in five of the survey respondents (18%; n = 235) elected to answer this question by discussing their views on food advertising and children’s diets. The responses were imported into NVivo10 for coding and analysis. A grounded approach was used to draw meaning from the data and develop a proposed conceptual classification of parents’ attributions relating to food advertising and its consequences.

Results : The majority of responses related to the negative perceived effects of unhealthy food advertising on children’s diets, with few respondents considering such advertisements to be innocuous. The responses were classified into four conceptual categories reflecting differing attitudes to advertising (negative to neutral) and varying levels of locus of control (low to high). The typical characteristics of parents allocated to the four categories exhibited variation according to weight status, television viewing habits, education level, and family size. The largest number of responses was coded to the category characterized by a negative attitude toward food advertising and a low locus of control. Parents in this category were more likely than others to be overweight/obese and heavy television viewers. Parents in the negative attitude to advertising and high locus of control category nominated a variety of parenting practices that could form the basis of parent education interventions.

Conclusions : The results suggest that many Australian parents may feel disempowered in the face of high levels of advertising for unhealthy foods. The current voluntary regulatory code appears to be inadequate in scope and coverage to address this situation.

The design of charitable fund-raising schemes: matching grants or seed money

For a fixed large donation a matching scheme that simply uses a one-for-one match ratio can actually raise less money than a seed money scheme. But when the match ratio is chosen to reflect the characteristics of the small donor base so as to exhaust the large donor's willingness to give, matching schemes always raise more money and are preferred by both charities and large donors. However, when the large donor chooses the size of her donation, a conflict can arise. The large donor can prefer a smaller leadership gift and more reliance on small donor matching while the charity can prefer seed money.