Abstract Computational management and analysis of proteomics data is providing important insights into the biological and clinical sciences. In this research study, a knowledge management strategy for 1D SDS PAGE gel protein image information has been developed and implemented in the New Vision to Protein intensity Analysis System (NeVPAS) application. This application focused on the analysis of skin mucus protein changes... Computational management and analysis of proteomics data is providing important insights into the biological and clinical sciences. In this research study, a knowledge management strategy for 1D SDS PAGE gel protein image information has been developed and implemented in the New Vision to Protein intensity Analysis System (NeVPAS) application. This application focused on the analysis of skin mucus protein changes during a key developmental stage of Atlantic salmon (Salmo salar), the smoltifi cation period. It includes a database to archive proteomics data. In this paper, knowledge management is expressed in terms of data acquisition, hierarchical organisation of data, knowledge retrieval by means of an information retrieval technique and knowledge presentation using text and graphics. This application of proteomics knowledge management in the marine fi eld may also be extended to clinical analysis and environmental monitoring. Read More Read Less

Abstract In this paper, we present a model based on the Neural Network (NN) for classifying Arabic texts. We propose the use of Singular Value Decomposition (SVD) as a preprocessor of NN to reduce the data in terms of both size as well as dimensionality so that the input data become more classifi able and faster for the convergence of the... In this paper, we present a model based on the Neural Network (NN) for classifying Arabic texts. We propose the use of Singular Value Decomposition (SVD) as a preprocessor of NN to reduce the data in terms of both size as well as dimensionality so that the input data become more classifi able and faster for the convergence of the training process used in the NN model. To test the effectiveness of the proposed model, experiments were conducted using an in-house collected Arabic corpus for text categorization. The results showed that the proposed model was able to achieve high categorization effectiveness as measured by precision, recall and F-measure. Experimental result shows that the ANN model using SVD is better than the basic ANN on Arabic text classifi cation. Read More Read Less

Abstract Recently, e-learning systems have begun to be used in companies and education institutions. The spread of e-learning is mainly due to development of the learning management system (LMS), which includes SCORM and WebCT. Therefore, the introduction of learning the web digital web digital texts have become much simpler. The increase of contents of the web digital texts has realized a... Recently, e-learning systems have begun to be used in companies and education institutions. The spread of e-learning is mainly due to development of the learning management system (LMS), which includes SCORM and WebCT. Therefore, the introduction of learning the web digital web digital texts have become much simpler. The increase of contents of the web digital texts has realized a wider range of learning. As a result, we are concerned that learners will be confused with selecting the best learning web digital texts. In our research, we are developing a bidirectional recommendation system that extracts the relationship among learning the web digital texts with historical logs and recommends an effective web digital text for learners. In this paper, we fi rst discuss the design of the bidirectional recommendation system, and second, we show its evaluation results. Finally, we conclude that our recommendation system is useful for learners. Read More Read Less

Abstract In peer-to-peer (P2P) networks that support fi le-sharing services, the level of access demand can vary widely between different fi les. Since fewer nodes store fi les for which there is a lower demand, these fi les are more likely to be lost from the P2P networks if users leave the network or delete the fi les. File loss can... In peer-to-peer (P2P) networks that support fi le-sharing services, the level of access demand can vary widely between different fi les. Since fewer nodes store fi les for which there is a lower demand, these fi les are more likely to be lost from the P2P networks if users leave the network or delete the fi les. File loss can cause users to seek alternatives to P2P services, and lead to degradation in service quality. We propose and evaluate a replication method based on demand forecasting that aims to prevent the loss of low-demand fi les. In this method, the number of fi le replicas to be placed is determined based on the forecast demand for the fi le, so that the loss of low-demand fi les is likely prevented by placing replicas at nodes that frequently use P2P services. Based on simulation results, we compared our proposed method with basic replication methods in terms of the number of fi les and the amount of storage used. Our experimental results show that the proposed method prevents fi le loss by preserving lowdemand fi les over extended periods of time. We also confi rmed that the node storage resources consumed by this method are effi ciently used. Read More Read Less

Abstract Muti-objective optimization deals with the simultaneous optimization of two or more conflicting objective functions in real-life systems. This paper deals with the multi-objective optimization in service systems. The goal of service systems is to provide cost-efficient service to customers, while at the same time, reducing the customer waiting time for service. In general, a low cost in system operation leads... Muti-objective optimization deals with the simultaneous optimization of two or more conflicting objective functions in real-life systems. This paper deals with the multi-objective optimization in service systems. The goal of service systems is to provide cost-efficient service to customers, while at the same time, reducing the customer waiting time for service. In general, a low cost in system operation leads to longer waiting times, while a higher cost in system operation leads to shorter waiting times. The two objectives – service cost (operational cost) and waiting time (customer satisfaction) are, therefore, conflicting in nature. We use the novel Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to optimize the two conflicting objective functions simultaneously. MOPSO is a fairly recent swarm intelligence meta-heuristic algorithm known for its simplicity in programming and its rapid convergence. The multi-objective optimization procedure is illustrated with the example of a practical service system. MOPSO produces a family of well-spread Pareto fronts for the two objective functions in the practical service system. Read More Read Less

Abstract Schema matching is a basic problem in many database application domains, such as data integration. The problem of schema matching can be formulated as follows, “given two schemas, Si and Sj , find the most plausible correspondences between the elements of Si and Sj , exploiting all available information, such as the schemas, instance data, and auxiliary sources” [24]. Given... Schema matching is a basic problem in many database application domains, such as data integration. The problem of schema matching can be formulated as follows, “given two schemas, Si and Sj , find the most plausible correspondences between the elements of Si and Sj , exploiting all available information, such as the schemas, instance data, and auxiliary sources” [24]. Given the rapidly increasing number of data sources to integrate and due to database heterogeneities, manually identifying schema matches is a tedious, time consuming, error-prone, and therefore expensive process. As systems become able to handle more complex databases and applications, their schemas become large, further increasing the number of matches to be performed. Thus, automating this process, which attempts to achieve faster and less labor-intensive, has been one of the main tasks in data integration. However, it is not possible to determine fully automatically the different correspondences between schemas, primarily because of the differing and often not explicated or documented semantics of the schemas. Several solutions in solving the issues of schema matching have been proposed. Nevertheless, these solutions are still limited, as they do not explore most of the available information related to schemas and thus affect the result of integration. This paper presents an approach for matching schemas of heterogeneous relational databases that utilizes most of the information related to schemas, which indirectly explores the implicit semantics of the schemas, that further improves the results of the integration. Read More Read Less

Abstract This paper proposes the method to extract the friendship relations of children by using motion sensors. Children learn to fit into society through living in a group, and it’s greatly influenced by their friend relations. Although preschool teachers need to observe them to assist in the growth of children’s social progress and support the development each child’s personality, only experienced... This paper proposes the method to extract the friendship relations of children by using motion sensors. Children learn to fit into society through living in a group, and it’s greatly influenced by their friend relations. Although preschool teachers need to observe them to assist in the growth of children’s social progress and support the development each child’s personality, only experienced teachers can watch over children while providing high-quality guidance. To resolve the problem, this paper proposes a mathematical and objective method that assists teachers with observation. It uses numerical data of activity level recorded by pedometers, and the authors make tree diagram called dendrogram based on hierarchical clustering with recorded activity level. Also, the authors calculate children’s “breadth” and “depth” of friend relations by using more than one dendrogram. When the authors record children’s activity level in a certain kindergarten for two months and evaluated the proposed method, the results usually coincide with remarks of teachers about the children. Read More Read Less

Abstract This paper presents fuzzy clustering algorithms to establish a grassroots ontology – a machine-generated weak ontology – based on folksonomies. Furthermore, it describes a search engine for vaguely associated terms and aggregates them into several meaningful cluster categories, based on the introduced weak grassroots ontology. A potential application of this ontology, weblog extraction, is illustrated using a simple example. Added... This paper presents fuzzy clustering algorithms to establish a grassroots ontology – a machine-generated weak ontology – based on folksonomies. Furthermore, it describes a search engine for vaguely associated terms and aggregates them into several meaningful cluster categories, based on the introduced weak grassroots ontology. A potential application of this ontology, weblog extraction, is illustrated using a simple example. Added value and possible future studies are discussed in the conclusion. Read More Read Less