Automated protein function prediction defines the designation of functions of unknown protein functions by using computational methods. This technique is useful to automatically assign gene functional annotations for undefined sequences in next generation genome analysis (NGS). NGS is a popular research method since high-throughput technologies such as DNA sequencing and microarrays have created large sets of genes. These huge sequences have greatly increased the need for analysis. Previous research has been based on the similarities of sequences as this is strongly related to the functional homology. However, this study aimed to designate protein functions by automatically predicting the function of the genome by utilizing InterPro (IPR), which can represent the properties of the protein family and groups of the protein function. Moreover, we used gene ontology (GO), which is the controlled vocabulary used to comprehensively describe the protein function. To define the relationship between IPR and GO terms, three pattern recognition techniques have been employed under different conditions, such as feature selection and weighted value, instead of a binary one.
Frequent packet loss of media data is critical problem that degrades the quality of streaming service in wireless networks. During scalable streaming service, only partial of data can be decoded at the decoder depending on the relationship between frames and layers. Contents provider divides one large media stream into several layers, and then provides each layer to user. The hierarchical structure between frames and layers highly exerts influence the availability of media data. Even if whole data of layer is transmitted successfully, they can not be decoded resulted from absence of reference frames and layers. The complicated relationship between frames and layers in scalable stream increases the volume of the abandoned layers.In this paper, we prove that simple scalable scheme outperforms complicated scheme in error prone network. We suggest the adaptive set top box (AdaptiveSTB) to drop dependency between layers in scalable stream. Also, we measure how much media data can be played through analysis in relationship between layers. AdaptiveSTB enhances the quality of scalable streaming service through removing indirect loss.
Resource management of the main memory and process handler is critical to enhancing the system performance of a web server. Owing to the transaction delay time that affects incoming requests from web clients, web server systems utilize several web processes to anticipate future requests. This procedure is able to decrease the web generation time because there are enough processes to handle the incoming requests from web browsers. However, inefficient process management results in low service quality for the web server system. Proper pregenerated process mechanisms are required for dealing with the clients' requests. Unfortunately, it is difficult to predict how many requests a web server system is going to receive. If a web server system builds too many web processes, it wastes a considerable amount of memory space, and thus performance is reduced. We propose an adaptive web process manager scheme based on the analysis of web log mining. In the proposed scheme, the number of web processes is controlled through prediction of incoming requests, and accordingly, the web process management scheme consumes the least possible web transaction resources. In experiments, real web trace data were used to prove the improved performance of the proposed scheme.
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