At present there is an increasing emphasis on both data mining and educational systems, making educational data mining a novel emerging field of research. Educational data mining (EDM) is an attractive interdisciplinary research domain that deals with the development of methods to utilise data originating in an educational context. EDM uses computational methodologies to evaluate educational data in order to study educational questions. The first part of this paper introduces EDM, describes the different types of educational data environments, diverse phases of EDM, the applications and goals of EDM, and some of the most promising future lines of research. Using EDM, the second part of this paper tracks students in Australia from primary school Year 1 through to successful completion of high school, and, thereafter, enrolment in university. The paper makes an assessment of the role of student gender on successive rates of educational completion in Australia. Implications for future lines of enquiry are discussed.

Business Artifacts, as an alternative approach to Business Process Modeling, combines both process and data aspects of a Business into the same model. Many works in the literature have focused on defining Artifact-centric processes and graphical modeling notations. But, to the best of our knowledge, no prior work has directly tackled the problem of generating Database Schemas from Business Artifact Models. In this paper, we propose an algorithm that generates Database Schemas from Business Artifact Models (BAMs). The proposed algorithm not only takes into consideration the different data attribute types of Artifacts’ Information Models, but also supports different Artifacts relationships. We also validate our work with a prototype implementation of a Business Artifact Models Modeler and a Database Schema Generator.

The vagueness in decision-making may be due to ambiguity in the decisional requirements expression. Therefore, in the literature dealing with vagueness in decision systems, studies were concentrated on data vagueness and not on decision requirements. In order to evaluate the expression in decision-making requirements and in order to improve the data warehouses design quality, this paper presents a rigorous fuzzy ontology-based solution. 

Based on the latest Zadeh theory “Ref. [1]”, Authors in “Ref. [2.3]”, propose a solution consisting in using ontologies to provide "an understanding of how the meaning of a proposal can be composed of the meaning of its constituents. One of the limitations of this solution is the fuzziness presence only at the adjective sentence. In some sense, our proposal can be seen as a continuation of that work. We limit our study, in this paper to the “Near negative” operator case. To the best of our knowledge, this case has not been addressed yet in the data warehouse context.

This paper aims to present a computational framework capable of withstanding the effects produced by transient overloads on physical and virtual servers hosted on cloud computing environment. The proposed framework aims at automating management of virtual machines that are hosted in this environment, combining a proactive strategy, which performs load balancing when there is not overload of physical and/or virtual machines with a reactive strategy, which is triggered in the event of overload in these machines. On both strategies, it is observed the service level agreement (SLA) established for each hosted service according to the infrastructure as a service (IaaS) model. The main contribution of this paper is the implementation of a computational framework called Phoenix, capable of handling momentary overloads, considering the CPU, memory and network resources of physical and virtual machines and guaranteeing SLAs. The results demonstrate that Phoenix framework is effective, and it has outstanding performance in handling overloads virtual machine network, which has achieved the isolation of momentary overload on the physical machine preventing the propagation of their effects on the cloud.

With the aim to take forward the digital India mission, it is essential to building a template for intuitive e-commerce shopping site so that users can shop easily, without taking any special training. We have achieved this using several steps. First, we have documented mental model and behavioral patterns of end users while they were interacting with the shopping site. We have mapped existing shopping sites with the mental model, behavioral pattern and as a result, problem themes are identified. Effective procedures are identified to make GUI for the e-commerce shopping sites more intuitive. Based on these procedures, the prototype is designed and validated. Finally, the template for intuitive e-commerce shopping site is formed.

In this paper, we describe various technologies that are being used in virtual garment fitting and simulation. There, we have focused on the usage of anthropometry in the clothing industry and avatar generation of virtual garment fitting. Most commonly used technologies for avatar generation in virtual environment have been discussed in this paper such as generic body model method and laser scanning technologies. Moreover, this paper includes the usage of real-time tracking technologies used in virtual garment fitting like markers and depth cameras. Apart from these, virtual clothing methods such as geometrical, physical and hybrid-based models were also discussed in this paper. As ease allowance has a major impact on virtual cloth fitting, it is also considered in this paper relating to similar research studies. As the final stage, our proposed design has been explained including the steps of the experiment that has been conducted to generate a two-dimensional model of the garment item. Within this paper, all the above-mentioned areas were described thoroughly while stating the existing gap of the virtual garment fitting in online marketplaces and our proposed solution to bridge that gap.

Earthquake is the most dangerous natural disaster in the whole era of human being life. Scientist efforts for predicting earthquake have no prolific result, so far. The earth complexity and geology structures are the main obstacles of these efforts. The importance of time at the occurrence of the earthquake has resulted in using powerful systems for real-time alarming and therefore lessening the casualties of the earthquake. In this paper we have designed a rapid earthquake alarm system and we have implemented it in parallel processing and continuous processing.  We have tried to apply hardware intelligent agents for real-time and parallel processing of data and data fusion of sensors. By applying this technology, the performance of rapid earthquake alarm system will be improved. Through this improvement, the rapid and automated action of rapid earthquake alarm system can lead to reducing the effect of earthquake.

In this paper, we present a study on the design optimization of the 6-RUS Stewart platform using a hybrid algorithm. The geometric and kinematic models are calculated. The optimization problem is formulated after determining the design parameters and defining a set of cost functions related to the size of the workspace and to the indices of the kinematic and static performance, which are the global conditioning index (GCI) and the global stiffness index (GSI).

We started by studying the relation between the design parameters and the proposed cost functions, and then we invested the genetic algorithm to optimize each cost function separately. Moreover, we adopted a weighted cost function method to solve the Multi-Objective optimization problem. 

The convergence performance of the genetic algorithm (GA) and the particle swarm optimization (PSO) were compared, where the PSO algorithm showed better performance. Based on this, a hybrid PSO–PS method was proposed and the results are highly competitive as we obtained better general convergence performance.