Conceptual development of resources discovery in the proposed hybrid P2P video streaming

We present the design of a hybrid Peer-to-Peer (P2P) system for video streaming. In this paper, we address the availability, accessibility and lookup service of files. We use the advantages of server-client business model to search and retrieve the information. We implement the base ontology of video domain repository so that the final result may be different and provide more results from the keyword search. To provide the dynamic standby peer, we use checksum value as an indicator to search an identical content in the Peer-to-Peer network. We hypothesize that, by using server-client searching in Peer-to-Peer application, we can reduce the latency lookup services, path length, peer load and network traffic

The application of neural network data mining algorithm into mixed pixel classification in geographic information system environment

With the rapid growth of satellite technology and the increasing of spatial resolution, hyperspectral imaging sensor is frequently used for research and development as well as in some semi-operational scenarios. The hyperspectral image also offers unique applications such as terrain delimitations, object detection, material identification, and atmospheric characterization. However, hyperspectral image systems produce large data sets that are not easily interpretable by visual analysis and therefore require automated processing algorithm. The challenging of pattern recognition associated with hyperspectral images is very complex processing due to the presence of considerable number of mixed pixels. This , paper discusses the development of data mining and pattern recognition algorithm to handle the complexity of hyperspectral remote sensing images in Geographical Information Systems environment. Region growing segmentation and radial basis function algorithms are considered a powerful tool to minimize the mixed pixel classification error

Integrated Features by Administering the Support Vector Machine (SVM) of Translational Initiations Sites in Alternative Polymorphic Contex

Many algorithms and methods have been proposed for classification problems in bioinformatics. In this study, the discriminative approach in particular support vector machines (SVM) is employed to recognize the studied TIS patterns. The applied discriminative approach is used to learn about some discriminant functions of samples that have been labelled as positive or negative. After learning, the discriminant functions are employed to decide whether a new sample is true or false. In this study, support vector machines (SVM) is employed to recognize the patterns for studied translational initiation sites in alternative weak context. The method has been optimized with the best parameters selected; c=100, E=10-6 and ex=2 for non linear kernel function. Results show that with top 5 features and non linear kernel, the best prediction accuracy achieved is 95.8%. J48 algorithm is applied to compare with SVM with top 15 features and the results show a good prediction accuracy of 95.8%. This indicates that the top 5 features selected by the IGR method and that are performed by SVM are sufficient to use in the prediction of TIS in weak contexts

Crystal Method For Accurate Software Duration Estimation

All projects share one common characteristic that is the projection of ideas and activities into new endeavours. The ever-present element of risk and uncertainty to the events and tasks leading to completion can never be foretold with absolute accuracy. The software projects are different from other projects. Underestimation is the root course of many software projects not being able to meet the deadline, or failure. Some of the reasons for inaccurate estimation are as follows: the traditional model not being able to capture the project in detail, quick and reliable strategic analysis. The influence of human factor is not able to incorporate explicitly. Failure to consider rework phenomenon. Failure to capture dynamic interaction between technical development and management policies

The development and the use of clearing house server infrastructure for gis interoperabilty

Rapid development of internet technology allows more spatial and tabular data available and accessible publicly. But those data can not be utilized directly due to the differences in data acquisition techniques, data definition and their semantic meaning. This situation reveals the need of interoperable GIS to support seamless information sharing. This is the future GIS architecture that may increase the reusability of available spatial datasets and reduce data acquisition cost. Under the whole framework of the on going research has identified four components (modules) to support the application of GJS interoperability. This paper discusses the development and the use of clearinghouse component (module) to facilitate end users to search, locate and retrieve the required spatial information or meta data from different organization at different time and places. This allows making further enquire about particular spatial data-information. The detail discussions on GIS Interoperability model as well as the development of architecture of these clearinghouse servers are given. The development of clearing house server employs an open sources approach and open file format. In this regards, fully available source code allows modification and customization without licensing restrictions

Cascade Quality Prediction Method Using Multiple PCA+ID3 for Multi-Stage Manufacturing System

AbstractQuality prediction model, as the key to realize the real-time online quality monitoring process, has been developed using various data mining techniques. However, most of quality prediction models are developed in single-stage manufacturing system, where the relationship between manufacturing operation and quality variables is straightforward. Previous studies show that single-stage quality system cannot solve quality problem in multi-stage manufacturing system due to the complex variable relationships. This study is intended to propose a data mining method to develop quality prediction model which is able to deal with the complex variable relationships in multi-stage manufacturing system. This method, named Cascade Quality Prediction Method (CQPM), is developed by considering the complex variables relationships in multi-stage manufacturing system. CQPM employs the combination of multiple Principal Component Analysis and Iterative Dichotomiser 3 algorithm. A case study in semiconductor manufacturing shows that the prediction model that has been developed using CQPM is performed better in predicting both positive and negative classes compared to others

WSN nodes power consumption using multihop routing protocol for illegal cutting forest

The need for an automation system from a remote area cannot be separated from the role of the wireless sensor network. However, the battery consumption is still a problem that influences the lifetime of the system. This research focused on studying how to characterize the power consumption on each sensor node using multihop routing protocol in the illegal logging field, to get the prediction lifetime of the network. The system is designed by using six sensor nodes in a master-slave connection and implemented in a tree topology. Each sensor node is consisting of a sound sensor, vibration sensor, Xbee communication, current and voltage sensor, and Arduino nano. The system is tested using battery 10050 mAH with several scenarios to have calculated how long the battery lifetime can be predicted. The results stated that the master node on the network depleted the power of the battery faster than the slave node since the more slaves connected to the master, the more energy the battery consumes

Multiple Sensor on Clustering Wireless Sensor Network to Tackle Illegal Cutting

This paper is intended to purpose a designed system using Wireless Sensor Network application. It is multiple sensors to tackle illegal cutting in the stage of a timber harvesting. This paper also discusses network performance and the costs of the purposed system. In every node in the networks, the system was built using a combined sound sensor and vibration sensor in which incorporated using Xbee Pro S2C. It is considered as a communication module at each sensor node and Arduino Nano to process the data.  The Wireless Sensor Network has been designed in three networks with the configuration of master and slave nodes in each network. This system was testing using several scenarios to have the data performance of the networks and the performance of the proposed system in the small forest and the opened area. The costs of the purposed system also compared related to the previous system. The result showed the optimum distance that can be applied in the WSN network as a real-time application using Xbee Pro S2C is less than 30 meters; meanwhile the time consumed to communicate between nodes is below 5 s. Therefore, the more slaves in the subnetwork will affect the performance of the system. The proposed system runs smoothly as predicted in the purposed system. All the testing is 100% completed and can be handled by the proposed system