On the role of pre and post-processing in environmental data mining

The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed

JMT – Performance Engineering Tools for System Modeling

We present the Java Modelling Tools (JMT) suite, an integrated framework of Java tools for performance evaluation of computer systems using queueing models. The suite offers a rich user interface that simplifies the definition of performance models by means of wizard dialogs and of a graphical design workspace. The performance evaluation features of JMT span a wide range of state-of-the-art methodologies including discrete-event simulation, mean value analysis of product-form networks, analytical identification of bottleneck resources in multiclass environments, and workload characterization with fuzzy clustering. The discrete-event simulator supports several advanced modeling features such as finite capacity regions, load-dependent service times, bursty processes, fork-and-join nodes, and implements spectral estimation for analysis of simulative results. The suite is open-source, released under the GNU general public license (GPL), and it is available for free download at http://jmt.sourceforge.net

Using Xfuzzy environment for the whole design of fuzzy systems

Since 1992, Xfuzzy environment has been improving to ease the design of fuzzy systems. The current version, Xfuzzy 3, which is entirely programmed in Java, includes a wide set of new featured tools that allow automating the whole design process of a fuzzy logic based system: from its description (in the XFL3 language) to its synthesis in C, C++ or Java (to be included in software projects) or in VHDL (for hardware projects). The new features of the current version have been exploited in different application areas such as autonomous robot navigation and image processing.Comisión Interministerial de Ciencia y Tecnología DPI2005-02293 y TEC2005-04359Junta de Andalucía TIC2006-635 y TEP2006-37

New features of the fuzzy logic development environment Xfuzzy

The characteristics of the new version of the fuzzy systems development environment Xfuzzy is presented. The environment covers the aspects related to the specification, verification, adjustment and implementation of fuzzy systems. It is an open environment (in the sense that the user can define many functional and structural aspects) and a free distribution tool that allows proving new formalisms and helps the definition and implementation of complex systems

Web-based strategies in the manufacturing industry

The explosive growth of Internet-based architectures is allowing an efficient access to information resources over geographically dispersed areas. This fact is exerting a major influence on current manufacturing practices. Business activities involving customers, partners, employees and suppliers are being rapidly and efficiently integrated through networked information management environments. Therefore, efforts are required to take advantage of distributed infrastructures that can satisfy information integration and collaborative work strategies in corporate environments. In this research, Internet-based distributed solutions focused on the manufacturing industry are proposed. Three different systems have been developed for the tooling sector, specifically for the company Seco Tools UK Ltd (industrial collaborator). They are summarised as follows. SELTOOL is a Web-based open tool selection system involving the analysis of technical criteria to establish appropriate selection of inserts, toolholders and cutting data for turning, threading and grooving operations. It has been oriented to world-wide Seco customers. SELTOOL provides an interactive and crossed-way of searching for tooling parameters, rather than conventional representation schemes provided by catalogues. Mechanisms were developed to filter, convert and migrate data from different formats to the database (SQL-based) used by SELTOOL.TTS (Tool Trials System) is a Web-based system developed by the author and two other researchers to support Seco sales engineers and technical staff, who would perform tooling trials in geographically dispersed machining centres and benefit from sharing data and results generated by these tests. Through TTS tooling engineers (authorised users) can submit and retrieve highly specific technical tooling data for both milling and turning operations. Moreover, it is possible for tooling engineers to avoid the execution of new tool trials knowing the results of trials carried out in physically distant places, when another engineer had previously executed these trials. The system incorporates encrypted security features suitable for restricted use on the World Wide Web. An urgent need exists for tools to make sense of raw data, extracting useful knowledge from increasingly large collections of data now being constructed and made available from networked information environments. This explosive growth in the availability of information is overwhelming the capabilities of traditional information management systems, to provide efficient ways of detecting anomalies and significant patterns in large sets of data. Inexorably, the tooling industry is generating valuable experimental data. It is a potential and unexplored sector regarding the application of knowledge capturing systems. Hence, to address this issue, a knowledge discovery system called DISKOVER was developed. DISKOVER is an integrated Java-application consisting of five data mining modules, able to be operated through the Internet. Kluster and Q-Fast are two of these modules, entirely developed by the author. Fuzzy-K has been developed by the author in collaboration with another research student in the group at Durham. The final two modules (R-Set and MQG) have been developed by another member of the Durham group. To develop Kluster, a complete clustering methodology was proposed. Kluster is a clustering application able to combine the analysis of quantitative as well as categorical data (conceptual clustering) to establish data classification processes. This module incorporates two original contributions. Specifically, consistent indicators to measure the quality of the final classification and application of optimisation methods to the final groups obtained. Kluster provides the possibility, to users, of introducing case-studies to generate cutting parameters for particular Input requirements. Fuzzy-K is an application having the advantages of hierarchical clustering, while applying fuzzy membership functions to support the generation of similarity measures. The implementation of fuzzy membership functions helped to optimise the grouping of categorical data containing missing or imprecise values. As the tooling database is accessed through the Internet, which is a relatively slow access platform, it was decided to rely on faster Information retrieval mechanisms. Q-fast is an SQL-based exploratory data analysis (EDA) application, Implemented for this purpose

ApriorC4.5 data mining algorithm for enhance the network-based intrusion detection in financial data

The most important cause for the introduction regarding an attack on the law is the Internet's recognition. Economic data safety has become an important issue, an urgent want in imitation of pick out and detects attacks. Intrusion Detection is described as much a pc network in imitation of diagnosing signs about attacks yet malicious endeavor thru a provision over continuous assessment methods. The software program does operate its duties are defined as much intrusion discovery structures (IDS) the need because of economic data. The system advanced separate algorithm provides excellent discovery quantity yet means counterfeit fear rate, certain as an array and shallow learning. Recent research exhibit, as in contrast, including structures using a variety concerning Cascade Algorithm instruction algorithm Shallow development, presents an awful lot better performance. The intrusion detection system, correct detection algorithm using the ratio used to be much less marked. False funk quantity also increased. The algorithm is according to clear up this problem. This dissertation describes the twain hybrid algorithm because of the improvement of intrusion discovery systems. C4.5 selection creeper yet supports the aggregate concerning shallow lessons by maximizing accuracy, a competency regarding C4.5, decreasing the bad alarm rate, and shallow learning talents. The effects showed as the expansion into accuracy, the discovery dimensions then ignoble counterfeit scare rate.&nbsp

Microservices and Machine Learning Algorithms for Adaptive Green Buildings

In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings