作者：
Duffy, SamQueen Mary University of London
Media and Arts Technology Programme School of Electronic Engineering and Computer Science Mile End Road London E1 4NS United Kingdom
People's willingness to share where they are, what they are doing and with whom using location-based technology has led to the emergence of applications which are being used to create new ways to represent and nav...
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We present a preliminary investigation into a novel approach to natural gas prediction. Experimental data were extracted from the Energy Information Administration of the US Department of Energy. The datasets were pre...
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Software Engineering is a primary subject in many computerscience departments of universities worldwide. Its purpose is to help students understand and apply both disciplined and systematic methods to software develo...
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Software Engineering is a primary subject in many computerscience departments of universities worldwide. Its purpose is to help students understand and apply both disciplined and systematic methods to software development. Due to the ubiquity and visibility of software in the modern world, the study, education and research into software engineering and its practice have retained a high level of interest. Distinct in nature from other computerscience courses, this discipline borrows from and is influenced by other fields such as, systems analysis, design, management, quality control, and communication. In this paper, the practices and methodologies used, developed, and evaluated in the Software Engineering Education (SEE) will be outlined. Further improvements have been planned through the Software Engineering Education Methodology Exploration (SEE_ME) project.
In today's cloud computing environments, where scalability, agility, and resiliency are paramount, microservices architecture stands out as a fundamental keystone of modern software development. While microservice...
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The performance of the Hopfield neural network with mean field annealing for finding optimal or near-optimal solutions to the routing problem in communication network is investigated. The proposed neural network uses ...
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The performance of the Hopfield neural network with mean field annealing for finding optimal or near-optimal solutions to the routing problem in communication network is investigated. The proposed neural network uses mean field annealing to eliminate the constraint terms in the energy function. Unlike other systems which use penalty constraint terms there is no need to tune constraint parameters and the neural network should avoid the problems of scaling. It also avoids the need to pre-determine the minimum number of hops corresponding to the optimal route. We have obtained very encouraging simulation results for the nine node grid network and fourteen node NFSNET-backbone network.
Playing an instrument is a physical skill learned through observation, repetition and rehearsal. Students of orchestral instruments seek one-to-one tuition from expert musicians. However as they become more accomplish...
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Comments on Youtube media provide feedback to the content owner of how their video influence audience. However, some comments may have intention to convey readers not to the original goals of the video content. Moreov...
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This paper presents the development of an assistive educational system with intelligent approach which can be a basic electronic training and treatment tool to assist children with high-functioning autism. The plan is...
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Most supervised neural networks are trained by minimizing the mean squared error of the training set. But there are problems of using mean squared error especially whenever the target output is equal to the actual out...
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Most supervised neural networks are trained by minimizing the mean squared error of the training set. But there are problems of using mean squared error especially whenever the target output is equal to the actual output in which the error signal tends to zero. This will lead to the instability of the internal structure of the network as well. In this paper, we discuss an improved convergence rates of standard backpropagation model with some modifications in its learning strategies. A modified backpropagation model is experimented on XOR problem, data of profitability analysis and Kuala Lumpur Composite Index (KLCI) at Kuala Lumpur Stock Exchange (KLSE) and handwritten/handprinted digits. The results are compared with standard backpropagation model which is based on mean square errors.
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