There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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A Break in the Clouds: Towards a Cloud Definition

The Health Insurance Portability and Accountability Act, also known as HIPAA, was first delivered to congress in 1996 and consisted of just two Titles. It was designed to protect health insurance coverage for workers and their families while between jobs. It establishes standards for electronic health care transactions and addresses the issues of privacy and security when dealing with Protected Health Information (PHI). HIPAA is applicable only in the United States of America.

The Health Insurance Portability and Accountability Act.

We developed a hybrid method for feature selection of classification tasks.Our hybrid method combines Artificial Bee Colony with Differential Evolution.We performed experiments over fifteen datasets from the UCI Repository.Our method selects good features without reducing accuracy of classification.By selecting features with our method, we reduced time required for classification. "Dimensionality" is one of the major problems which affect the quality of learning process in most of the machine learning and data mining tasks. Having high dimensional datasets for training a classification model may lead to have "overfitting" of the learned model to the training data. Overfitting reduces generalization of the model, therefore causes poor classification accuracy for the new test instances. Another disadvantage of dimensionality of dataset is to have high CPU time requirement for learning and testing the model. Applying feature selection to the dataset before the learning process is essential to improve the performance of the classification task. In this study, a new hybrid method which combines artificial bee colony optimization technique with differential evolution algorithm is proposed for feature selection of classification tasks. The developed hybrid method is evaluated by using fifteen datasets from the UCI Repository which are commonly used in classification problems. To make a complete evaluation, the proposed hybrid feature selection method is compared with the artificial bee colony optimization, and differential evolution based feature selection methods, as well as with the three most popular feature selection techniques that are information gain, chi-square, and correlation feature selection. In addition to these, the performance of the proposed method is also compared with the studies in the literature which uses the same datasets. The experimental results of this study show that our developed hybrid method is able to select good features for classification tasks to improve run-time performance and accuracy of the classifier. The proposed hybrid method may also be applied to other search and optimization problems as its performance for feature selection is better than pure artificial bee colony optimization, and differential evolution.

A hybrid approach of differential evolution and artificial bee colony for feature selection

Training artificial neural networks is considered as one of the most challenging machine learning problems. This is mainly due to the presence of a large number of solutions and changes in the search space for different datasets. Conventional training techniques mostly suffer from local optima stagnation and degraded convergence, which make them impractical for datasets with many features. The literature shows that stochastic population-based optimization techniques suit this problem better and are reliably alternative because of high local optima avoidance and flexibility. For the first time, this work proposes a new learning mechanism for radial basis function networks based on biogeography-based optimizer as one of the most well-regarded optimizers in the literature. To prove the efficacy of the proposed methodology, it is employed to solve 12 well-known datasets and compared to 11 current training algorithms including gradient-based and stochastic approaches. The paper considers changing the number of neurons and investigating the performance of algorithms on radial basis function networks with different number of parameters as well. A statistical test is also conducted to judge about the significance of the results. The results show that the biogeography-based optimizer trainer is able to substantially outperform the current training algorithms on all datasets in terms of classification accuracy, speed of convergence, and entrapment in local optima. In addition, the comparison of trainers on radial basis function networks with different neurons size reveal that the biogeography-based optimizer trainer is able to train radial basis function networks with different number of structural parameters effectively.

Training radial basis function networks using biogeography-based optimizer

An effective refined artificial bee colony algorithm for numerical optimisation

The feature selection problem often occurs in pattern recognition, and more specific, classification. Although these patterns could contain a large number of features, some of them could prove to be irrelevant, redundant or even detrimental to classification accuracy. Thus, it is important to remove these kinds of features which in turn leads to problem dimensionality reduction and could eventually improve the classification accuracy. In this paper an approach to dimensionality reduction based on differential evolution which represents a wrapper and explores the solution space is presented. The solutions, subsets of the whole feature set, are evaluated using the k-nearest neighbour algorithm. High quality solutions found during execution of the differential evolution fill the archive. A final solution is obtained by conducting k-fold cross validation on the archive solutions and selecting the best. Experimental analysis was conducted on several standard test sets. Classification accuracy of the k-nearest neighbour algorithm using the full feature set and the accuracy of the same algorithm using only the subset provided by the proposed approach and some other optimization algorithms which were used as wrappers are compared. The analysis has shown that the proposed approach successfully determines good feature subsets which may increase classification accuracy.

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A Differential Evolution Approach to Dimensionality Reduction for Classification Needs

Although cloud computing applies the self-acting principles from autonomic computing, it's possible to achieve even greater synergy In this paper definitions of basic terms concerning cloud computing and autonomic computing are given A new model is presented which incorporates autonomic principles into cloud computing, and that model is later viewed from the aspect of its application to e-health An overview of the current research state covering specific problems and their potential solution through the use of cloud computing and autonomy is also given Based on the proposed model, the application of autonomic paradigm to e-health is considered

E-health Framework Based on Autonomic Cloud Computing

Tracking of attendance is a ubiquitous task in many institutions and during organised events. This, along with subsequent management of collected data can be a cumbersome activity that requires significant time and effort, from all involved parties. Tracking and managing student attendance during lectures and exams is an especially important instance of that task. Automated attendance management systems devised in the literature differ in applied technological solutions and features. However, many of these lack some features that would make them particularly useful for the task of student attendance management. This paper proposes the design of an automatic system tailored for the tracking and management of student attendance data. The design incorporates three main components that interact to deliver a seamless user experience. The key element is the utilisation of simple beacons, which make the system cost-effective and easy to use. Combining them with a mobile platform and a web service enables versatile attendance tracking. Periodic detection of the nearby beacons with authenticated server-side logic makes the proposed design unique in terms of addressing common attendance tracking issues. Also, the design is open for future integration of sophisticated data analysis modules.

Design and development of a smart attendance management system with Bluetooth low energy beacons

Automated defect detection is difficult to achieve in ceramic tile manufacturing today. Computer vision and machine learning based approaches are commonly utilised for this purpose. This paper considers the problem of defect detection in the textured ceramic tiles quality analysis. Instead of detecting defects on the finished tile, the biscuit tile is considered, a pressed, dried, decorated tile before its firing in the kiln. As it is an intermediary product during tile production, classifying them as defected or not before the firing can significantly reduce energy and material costs. To this end, in this paper we propose a new Fourier spectrum annuli feature extraction method. It is based on Fourier spectrum of the surface biscuit tile image and tested on real tile examples from the ceramic tile industry. According to the observed results, it outperforms several well-known methods for feature extraction on real-world tile datasets reaching an F1 score of 0.9236 and 0.8866 on the Black Random Stripes and Stripes Brown Light tile designs respectively.

Bruno Zoric

Papers

An effective refined artificial bee colony algorithm for numerical optimisation

A Differential Evolution Approach to Dimensionality Reduction for Classification Needs

E-health Framework Based on Autonomic Cloud Computing

Design and development of a smart attendance management system with Bluetooth low energy beacons

Classification of biscuit tiles for defect detection using Fourier transform features.