M. Nordin A. Rahman

The development of temporal-based multimedia data management application using web services

Multimedia data management is an important application in multimedia system domain. The application scopes are to retrieve, collect, store and preserve multimedia data in a distributed environment. A reliable or appropriate format of record structure in multimedia data management is required in order to trace information validation. Hence, the concept of temporal database can be applied to overcome this situation. The paper presents the development of temporal based multimedia data management model. Two elements of time are incorporated into the model; transaction time and valid time. This time elements will monitor the changes (event) and transaction that happened to the data in database. The use of time elements provides a new schema to query multimedia data based on past, current and future time. The designed model is implemented under web services based environment by utilizing J2EE platform.

Rough Set Theory Approach for Classifying Multimedia Data

The huge size of multimedia data requires for efficient data classification and organization in providing effective multimedia data manipulation. Those valuable data must be captured and stored for potential purposes. One of the main problems in Multimedia Information System (MIS) is the management of multimedia data. As a consequence, multimedia data management has emerged as an important research area for querying, retrieving, inserting and updating of these vast multimedia data. This research considers the rough set theory technique to organize and categorize the multimedia data. Rough set theory method is useful for exploring multimedia data and simplicity to construct multimedia data classification. Classification will help to improve the performance of multimedia data retrieving and organizing process.

Meta-Model Interaction for Collaborative School Subject Management

Subject management is a process of developing and coordinating of teaching materials. A collaborative subject module management is used to allow participants to share and manage course modules via Internet technology. Currently, Malaysian school does not have collaborative platform to manage subject materials. Teachers usually used personalized tools to manage their subject modules. This article will discuss the modelling of cloud-based subject module management. Raquel’s interaction meta-model is adopted as the central framework of the model. Based on developed model, a prototype has been developed which implemented under cloud computing platform. In the experiment, the prototype is evaluated using four cloud quality of service metrics. The results show that the model has accomplished the metrics of efficiency of 62%, effectiveness of 64%, learnability of 62%, and attractiveness of 68%. The proposed model could help to facilitate the development and management collaboration of subject module in Malaysian schools. It provides a reliable collaborative platform among teachers, students, and school management.

Parallel Guided Image Processing Model for Ficus Deltoidea (Jack) Moraceae Varietal Recognition

Nowadays, with the huge number of leaves data, plant species recognition process becomes computationally expensive. Many computer scientists have suggested that the usage of parallel and distributed computing should be strongly considered as mandatory for handling computationally intensive programs. The availability of high performance multi-cores architecture results the complex recognition system to become popular in parallel computing area. This paper emphasizes on the computational flow design to enable the execution of the complex image processing tasks for Ficus deltoidea varietal recognition to be processed on parallel computing environment. Multi-cores computer is used whereas one of them acts as a master processor of the process and the other remaining processors act as worker processors. The master processor responsibles for controlling the main system operations such as data partitioning, data allocation, and data merging which results from worker processors. Experiments showed that a multi-cores parallel environment is a very appropriate platform for pipeline image processing. From the results, the sequential complex image processing model and computational flow design are significantly improved when executed through parallel model under multi-cores computer system. As the number of cores increases, the computational time taken by the parallel algorithm becomes less.Nowadays, with the huge number of leaves data, plant species recognition process becomes computationally expensive. Many computer scientists have suggested that the usage of parallel and distributed computing should be strongly considered as mandatory for handling computationally intensive programs. The availability of high performance multi-cores architecture results the complex recognition system to become popular in parallel computing area. This paper emphasizes on the computational flow design to enable the execution of the complex image processing tasks for Ficus deltoidea varietal recognition to be processed on parallel computing environment. Multi-cores computer is used whereas one of them acts as a master processor of the process and the other remaining processors act as worker processors. The master processor responsibles for controlling the main system operations such as data partitioning, data allocation, and data merging which results from worker processors. Experiments showed that a multi-cores parallel environment is a very appropriate platform for pipeline image processing. From the results, the sequential complex image processing model and computational flow design are significantly improved when executed through parallel model under multi-cores computer system. As the number of cores increases, the computational time taken by the parallel algorithm becomes less.

The direction of cloud computing for Malaysian education sector in 21st century

In 21st century, technology has turned learning environment into a new way of education to make learning systems more effective and systematic. Nowadays, education institutions are faced many challenges to ensure the teaching and learning process is running smoothly and manageable. Some of challenges in the current education management are lack of integrated systems, high cost of maintenance, difficulty of configuration and deployment as well as complexity of storage provision. Digital learning is an instructional practice that use technology to make learning experience more effective, provides education process more systematic and attractive. Digital learning can be considered as one of the prominent application that implemented under cloud computing environment. Cloud computing is a type of network resources that provides on-demands services where the users can access applications inside it at any location and no time border. It also promises for minimizing the cost of maintenance and provides a flexibl...

A Gamification Model for Resource Sharing in Malaysian Schools Using Cloud Computing Platform

Cloud computing is a technology that delivers a pool of configurable computing resources to the users. The insufficient systems for sharing of resources, poor extended pattern of resource sharing system, inadequate network and computing facilities have become an issue in school management. This article discusses a model for resource sharing in Malaysian schools using cloud computing platform. The sharing of resources is modelled based on Multi-Person Prisoner’s Dilemma and Peer-Approved incentive mechanism that gives justification to users in the resource utilization. The concept of software as a service is adopted to make the model can be managed in the cloud environment. Based on the proposed model, a prototype is developed and deployed into cloud using OpenStack architecture. The prototype is evaluated using questionnaire instrument that is based on four attributes of usability metrics. The result shows that the model usability achieved 79.28% of effectiveness, 81.35% of efficiency, 91.8% of learnability and 93.18% of satisfaction. The proposed model could save the cost of buying computing and storage infrastructure since all the resources are cloud based.