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A good software architecture design is crucial in successfully realising an object-oriented analysis (OOA) specification with an object-oriented design (OOD) model that meets the specification's functional and non-functional requirements. Most CASE tools and software architecture design notations do not adequately support software architecture modelling and analysis, integration with OOA and OOD methods and tools, and high-level, dynamic architectural visualisations of running systems. We describe SoftArch, an environment that provides flexible software architecture modelling using a concept of successive refinement and an extensible architecture meta-model. SoftArch provides extensible analysis tools enabling developers to analyse their architecture model properties. Run-time visualisation of systems uses dynamic annotation and animation of high-level architectural modelling views. SoftArch is integrated with a component-based CASE tool and run-time monitoring tool, and has facilities for 3rd party tool integration through a common exchange format. This paper discusses the motivation for SoftArch, its modelling, analysis and dynamic visualisation capabilities, and its integration with various analysis, design and implementation tools.

Knowledge-based systems (KBSs) or expert systems (ESs) are able to solve problems generally through the application of knowledge representing a domain and a set of inference rules. In knowledge engineering (KE), the use of KBSs in the real world, three principal disadvantages have been encountered. First, the knowledge acquisition process has a very high cost in terms of money and time. Second, processing information provided by experts is often difficult and tedious. Third, the establishment of mark times associated with each project phase is difficult due to the complexity described in the previous two points. In response to these obstacles, many methodologies have been developed, most of which include a tool to support the application of the given methodology. Nevertheless, there are advantages and disadvantages inherent in KE methodologies, as well. For instance, particular phases or components of certain methodologies seem to be better equipped than others to respond to a given problem. However, since KE tools currently available support just one methodology the joint use of these phases or components from different methodologies for the solution of a particular problem is hindered. This paper presents KEManager, a generic meta-tool that facilitates the definition and combined application of phases or components from different methodologies. Although other methodologies could be defined and combined in the KEManager, this paper focuses on the combination of two well-known KE methodologies, CommonKADS and IDEAL, together with the most commonly-applied knowledge acquisition methods. The result is an example of the ad hoc creation of a new methodology from pre-existing methodologies, allowing for the adaptation of the KE process to an organization or domain-specific characteristics. The tool was evaluated by students at Carlos III University of Madrid (Spain).

Within the framework of a project yielding to the development of an interactive spelling checking/correction system for Modern Greek (M.G.) to run on MS-DOS based computers, our team comprised of several computer engineers and linguists, undertook the following preliminary tasks: The examination and evaluation of pertinent existing research/work from both the computer engineering and linguistic fields, and conducted supplementary research deemed necessary for the purposes of the project. The overall objectives focused on the development of a system that would be convenient to run and use. Unlike similar current systems however, emphasis was given to optimal engineering quality and performance and moreover, optimal linguistic performance attained through substantial linguistic expertise backing.

Knowledge technologies are a subject of permanent interest for software engineers at research organisations, as well as for market analysts in commercial organisations. In this paper, which aims to clarify the role of knowledge management solutions in an enterprise business, we survey the market of commercial knowledge management solutions and analyse their functionalities in domains such as document management, information retrieval, collaboration, decision support, e-learning, business automation and enterprise integration. The survey is based on a thorough study of web resources of knowledge management solution providers. Regarding the role of knowledge solutions in the business process, the present study will show that on an operational level they serve for better utilisation of the enterprise knowledge resources and, on a strategic level, they synthesise new knowledge needed for better management of customers, suppliers and partners. This paper gives an insight into the knowledge management market that can help strategic planners to easily begin a knowledge management initiative.

Background: The study of microbial diversity and community structures heavily relies on the analyses of sequence data, predominantly taxonomic marker genes like the small subunit of the ribosomal RNA (SSU rRNA) amplified from environmental samples. Until recently, the "gold standard" for this strategy was the cloning and Sanger sequencing of amplified target genes, usually restricted to a few hundred sequences per sample due to relatively high costs and labor intensity. The recent introduction of massive parallel tag sequencing strategies like pyrosequencing (454 sequencing) has opened a new window into microbial biodiversity research. Due to its swift nature and relatively low expense, this strategy produces millions of environmental SSU rDNA sequences granting the opportunity to gain deep insights into the true diversity and complexity of microbial communities. The bottleneck, however, is the computational processing of these massive sequence data, without which, biologists are hardly able to exploit the full information included in these sequence data.

Results: The freely available standalone software package JAGUC implements a broad regime of different functions, allowing for efficient and convenient processing of a huge number of sequence tags, including importing custom-made reference data bases for basic local alignment searches, user-defined quality and search filters for analyses of specific sets of sequences, pairwise alignment-based sequence similarity calculations and clustering as well as sampling saturation and rank abundance analyses. In initial applications, JAGUC successfully analyzed hundreds of thousands of sequence data (eukaryote SSU rRNA genes) from aquatic samples and also was applied for quality assessments of different pyrosequencing platforms.

Conclusions: The new program package JAGUC is a tool that bridges the gap between computational and biological sciences. It enables biologists to process large sequence data sets in order to infer biological meaning from hundreds of thousands of raw sequence data. JAGUC offers advantages over available tools which are further discussed in this manuscript.

Background: Diabetes and hypertension are two of the commonest diseases in the world. As they unfavorably affect people of different age groups, they have become a cause of concern and must be predicted and diagnosed well in advance.

Objective: This research aims to determine the effectiveness of artificial neural networks (ANNs) in predicting diabetes and blood pressure diseases and to point out the factors which have a high impact on these diseases.

Sample: This work used two online datasets which consist of data collected from 768 individuals. We applied neural network algorithms to predict if the individuals have those two diseases based on some factors. Diabetes prediction is based on five factors: age, weight, fat-ratio, glucose, and insulin, while blood pressure prediction is based on six factors: age, weight, fat-ratio, blood pressure, alcohol, and smoking.

Method: A model based on the Multi-Layer Perceptron Neural Network (MLP) was implemented. The inputs of the network were the factors for each disease, while the output was the prediction of the disease’s occurrence. The model performance was compared with other classifiers such as Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). We used performance metrics measures to assess the accuracy and performance of MLP. Also, a tool was implemented to help diagnose the diseases and to understand the results.

Result: The model predicted the two diseases with correct classification rate (CCR) of 77.6% for diabetes and 68.7% for hypertension. The results indicate that MLP correctly predicts the probability of being diseased or not, and the performance can be significantly increased compared with both SVM and KNN. This shows MLPs effectiveness in early disease prediction.