In system development life cycle (SDLC), a system model can be developed using Data Flow Diagram (DFD). DFD is graphical diagrams for specifying, constructing and visualizing the model of a system. DFD is used in defining the requirements in a graphical view. In this paper, we focus on DFD and its rules for drawing and defining the diagrams. We then formalize these rules and develop the tool based on the formalized rules. The formalized rules for consistency check between the diagrams are used in developing the tool. This is to ensure the syntax for drawing the diagrams is correct and strictly followed. The tool automates the process of manual consistency check between data flow diagrams.

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Abstract. Consistency is the situation where two or more overlapping elements of different diagrams that describe the behavior of system are jointly satisfiable. It is one of the attributes to measure the quality of UML model. Even though the research on consistency between diagrams is rapidly increased, there is still lack of research of consistency driven by use case. Therefore, this paper will define elements of use case and activity diagram, also consistency between them using logic approach. Based on an example of UML model consists of both diagrams, we show how the diagrams fulfilled our proposed consistency rules. Furthermore, the elements involved in the consistency rules are detected and formally reasoned.

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Abstract. Understanding the climate change effects on local crops is vital for adapting new cultivation practices and assuring world food security. Given the volume of palm oil produced in Malaysia, climate change effects on oil palm phenology and fruit production have greater implications at both local and international scenes. In this context, the paper looks at analysing the recent climate change effects on oil palm yield within a five year period (2007)(2008)(2009)(2010)(2011) at the regional scale. The hybrid approach of data mining techniques (association rules) and statistical analyses (regression) used in this research reveal new insights on the effects of climate change on oil palm yield within this small data set insufficient for conventional analyses on their own.

The availability of heterogeneous devices has rapidly changed the way people access the World Wide Web that includes rich content applications such as video streaming, 3D games, video conferencing, and mobile TV. However, most of these devices' (i.e., mobile phone, PDA, smartphone, and tablet) capabilities differ in terms of built-in software and library (what they can display), display size (how the content appears), and battery supply (how long the content can be displayed). In order for the digital contents to fit the target device, content adaptation is required. There have been many projects focused on energy-aware-based content adaptation that have been designed with different goals and approaches. This paper reviews some of the representative content adaptation solutions that have been proposed during the last few years, in relation to energy consumption focusing on wireless multimedia streaming in mobile devices. Also, this paper categorizes the research work according to different classifications of multimedia content adaptation requirements. In addition, we discuss some energy-related challenges content adaptation systems.

This paper investigates the existing practices and prospects of medical data classification based on data mining techniques. It highlights major advanced classification approaches used to enhance classification accuracy. Past research has provided literature on medical data classification using data mining techniques. From extensive literature analysis, it is found that data mining techniques are very effective for the task of classification. This paper analysed comparatively the current advancement in the classification of medical data. The findings of the study showed that the existing classification of medical data can be improved further. Nonetheless, there should be more research to ascertain and lessen the ambiguities for classification to gain better precision.

In the past few years, mobile devices have been increasingly replacing traditional computers as their capabilities such as CPU computation, memory, RAM size, and many more, are being enhanced almost to the level of conventional computers. These capabilities are being exploited by mobile apps developers to produce apps that offer more functionalities and optimized performance. To ensure acceptable quality and to meet their specifications (e.g., design), mobile apps need to be tested thoroughly. As the testing process is often tedious, test automation can be the key to alleviating such laborious activities. In the context of the Android-based mobile apps, researchers and practitioners have proposed many approaches to automate the testing process mainly on the creation of the test suite. Although useful, most existing approaches rely on reverse engineering a model of the application under test for test case creation. Often, such approaches exhibit a lack of comprehensiveness as the application model does not capture the dynamic behavior of the applications extensively due to the incompleteness of reverse engineering approaches. To address this issue, this paper proposes AMOGA, a strategy that uses a hybrid, static-dynamic approach for generating user interface model from mobile apps for modelbased testing. AMOGA implements a novel crawling technique that uses the event list of UI element associated with each event to dynamically exercise the events ordering at the run-time to explore the applications' behavior. An experimental evaluation was performed to assess the effectiveness of our strategy by measuring the code coverage and the fault detection capability through the use of mutation testing concept. Results of the experimental assessment showed that AMOGA represents an alternative approach for model-based testing of mobile apps by generating comprehensive models to improve the coverage of the applications. The strategy proved its effectiveness by achieving high code coverage and mutation score for different applications.