Predicting amount of saleable products using neural network metamodels of casthouses

This study aims at developing abstract metamodels for approximating highly nonlinear relationships within a metal casting plant. Metal casting product quality nonlinearly depends on many controllable and uncontrollable factors. For improving the productivity of the system, it is vital for operation planners to predict in advance the amount of high quality products. Neural networks metamodels are developed and applied in this study for predicting the amount of saleable products. Training of metamodels is done using the Levenberg-Marquardt and Bayesian learning methods. Statistical measures are calculated for the developed metamodels over a grid of neural network structures. Demonstrated results indicate that Bayesian-based neural network metamodels outperform the Levenberg-Marquardt-based metamodels in terms of both prediction accuracy and robustness to the metamodel complexity. In contrast, the latter metamodels are computationally less expensive and generate the results more quickly.

Employers' perspectives on work-integrated learning in project-based workplaces

The property and construction industry is uniquely impacted by project-based work environments; this creates special challenges for collaborative education. This research is based on investigating the attitudes of employer’s towards the use of formally assessed internships. The study comprised two stages; firstly a series of pilot interviews were undertaken with employers to test a number known issues. Secondly, the results from the interviews were used to refine a set of questions that were put to a large focus group of employers who were invited from across the property and construction sector. The results showed that many organisations expressed considerable goodwill towards collaborative education with universities. However, the challenges caused by project-based work environments restricted their ability to provide comprehensive learning opportunities. This research focuses on the distinctive issues associated with work-integrated learning in the property and construction industry

Designing for learning : online social networks as a classroom environment

This paper deploys notions of emergence, connections, and designs for learning to conceptualize high school students’ interactions when using online social media as a learning environment. It makes links to chaos and complexity theories and to fractal patterns as it reports on a part of the first author’s action research study, conducted while she was a teacher working in an Australian public high school and completing her PhD. The study investigates the use of a Ning online social network as a learning environment shared by seven classes, and it examines students’ reactions and online activity while using a range of social media and Web 2.0 tools.

The authors use Graham Nuthall’s (2007) “lens on learning” to explore the social processes and culture of this shared online classroom. The paper uses his extensive body of research and analyses of classroom learning processes to conceptualize and analyze data throughout the action research cycle. It discusses the pedagogical implications that arise from the use of social media and, in so doing, challenges traditional models of teaching and learning.

Implementation of the nurse practitioner role within a Victorian healthcare network : an organisational perspective

Objective
This paper presents a discussion of the development of a framework to implement and sustain the nurse practitioner (NP) role within one health service designed to strengthen the capacity of the health system and which could be readily transferable to other health services.
Setting
Eastern Health (EH) is a multi‑campus tertiary health care organisation servicing a population of approximately 800,000 people in the east and outer eastern suburbs of Melbourne, Australia. EH is committed to advancing the nursing profession and exploring innovative, research based models of practice that are responsive to the needs of the community it serves.
Primary argument
The Framework documents the processes of providing a new career pathway for advanced practice nurses that incorporates education and training, and utilises current evidenced‑based practice guidelines to define and promote the scope of practice.
Conclusion
Strong organisational support to facilitate interdisciplinary and multidisciplinary learning opportunities assists integration of the NP role into the healthcare team. Role clarity will assist interprofessional teams to understand and value the role NPs provide.

Case study of e-learning experts in Malaysia : impact of social capital and social networks

This paper examines the experiences of selected academics pioneering e-learning in Malaysian tertiary institutions. It begins with an overview of the broad factors shaping the Malaysian educational environment and then proceeds to examine the experience of individual teachers and e-learning programs. It takes an in-depth qualitative approach to engaging with this case study material drawing heavily on semi-structured interviews with key actors.
Conversations with several respondents suggested that the social networks of mentor relations found in the Malaysian case studies might be aptly described as ‘bamboo networks’. Bamboo, which happens to be plentiful in the Malaysian peninsula where these case studies are based, spreads from clump to clump through a series of underground connections involving a mature clump of bamboo sending out a subterranean runner, often over very long distances that then emerge into the open as a new bamboo clump.
All of those interviewed reported that they have found it difficult to find a support base in their first years of pioneering online developments. Consequently, they tended to fall back on their peer networks linked to the institutions at which they had studied. Prominent individuals championing e-learning in the institutions where they teach tend to form small groups for information sharing and networking. They do look to their management for tacit ‘permission’ rather than direct encouragement. Consequently, the active promotion of e-learning in Malaysia can be described as being ‘middle-down’ rather than ‘top-down’ in nature. That is to say, it is mid-level teachers that inspire those below them to join in the development of e-learning programs. They are internally driven and strongly motivated. In time, their activity should produce new generations of locally developed e-learning experts but this has yet to take place in a substantial fashion. This study shows that both men and women ‘academic guanxi’, or peer networks, play a key role in the adoption of online technologies. Key early adopters become change-agents by inspiring a small network of their peers and via their guanxi networks. It was also discovered that motivation is not simply an individual matter but is also about groups and peer networks or communities of exchange and encouragement. In the development of e-learning in Malaysia, there is very little activity that is not linked to small clusters of developers who are tied into wider networks through personal contacts.
Like clumping bamboo, whilst the local clusters tend to be easily seen, the longer-range ‘subterranean’ personal connections are generally not nearly so immediately obvious. These connections are often the product of previous mentoring relationships, including the relationships between influential teachers and their former postgraduate students. These relationships tend to work like bamboo runners: they run off in multiple directions, subterranean and unseen and then throw up new clumps that then send out fresh runners of their own.

A novel semi-supervised approach for network traffic clustering

Network traffic classification is an essential component for network management and security systems. To address the limitations of traditional port-based and payload-based methods, recent studies have been focusing on alternative approaches. One promising direction is applying machine learning techniques to classify traffic flows based on packet and flow level statistics. In particular, previous papers have illustrated that clustering can achieve high accuracy and discover unknown application classes. In this work, we present a novel semi-supervised learning method using constrained clustering algorithms. The motivation is that in network domain a lot of background information is available in addition to the data instances themselves. For example, we might know that flow ƒ1 and ƒ2 are using the same application protocol because they are visiting the same host address at the same port simultaneously. In this case, ƒ1 and ƒ2 shall be grouped into the same cluster ideally. Therefore, we describe these correlations in the form of pair-wise must-link constraints and incorporate them in the process of clustering. We have applied three constrained variants of the K-Means algorithm, which perform hard or soft constraint satisfaction and metric learning from constraints. A number of real-world traffic traces have been used to show the availability of constraints and to test the proposed approach. The experimental results indicate that by incorporating constraints in the course of clustering, the overall accuracy and cluster purity can be significantly improved.

Student centred approaches : teachers' learning and practice

Student-centred approaches to teaching and learning in mathematics is one of the reforms currently being advocated and implemented to improve mathematics outcomes for students from low SES backgrounds. The models, meanings and practices of student-centred approaches explored in this paper reveal that a constructivist model of student-centred teaching and learning is being promoted and implemented with some success. The ways in which teachers and leaders are being supported through network and school-based professional learning are described.

Identification of concurrent control chart patterns with singular spectrum analysis and learning vector quantization

Identification of unnatural control chart patterns (CCPs) from manufacturing process measurements is a critical task in quality control as these patterns indicate that the manufacturing process is out-of-control. Recently, there have been numerous efforts in developing pattern recognition and classification methods based on artificial neural network to automatically recognize unnatural patterns. Most of them assume that a single type of unnatural pattern exists in process data. Due to this restrictive assumption, severe performance degradations are observed in these methods when unnatural concurrent CCPs present in process data. To address this problem, this paper proposes a novel approach based on singular spectrum analysis (SSA) and learning vector quantization network to identify concurrent CCPs. The main advantage of the proposed method is that it can be applied to the identification of concurrent CCPs in univariate manufacturing processes. Moreover, there are no permutation and scaling ambiguities in the CCPs recovered by the SSA. These desirable features make the proposed algorithm an attractive alternative for the identification of concurrent CCPs. Computer simulations and a real application for aluminium smelting processes confirm the superior performance of proposed algorithm for sets of typical concurrent CCPs.

A hybrid neural network model for noisy data regression

A hybrid neural network model, based on the fusion of fuzzy adaptive resonance theory (FA ART) and the general regression neural network (GRNN), is proposed in this paper. Both FA and the GRNN are incremental learning systems and are very fast in network training. The proposed hybrid model, denoted as GRNNFA, is able to retain these advantages and, at the same time, to reduce the computational requirements in calculating and storing information of the kernels. A clustering version of the GRNN is designed with data compression by FA for noise removal. An adaptive gradient-based kernel width optimization algorithm has also been devised. Convergence of the gradient descent algorithm can be accelerated by the geometric incremental growth of the updating factor. A series of experiments with four benchmark datasets have been conducted to assess and compare effectiveness of GRNNFA with other approaches. The GRNNFA model is also employed in a novel application task for predicting the evacuation time of patrons at typical karaoke centers in Hong Kong in the event of fire. The results positively demonstrate the applicability of GRNNFA in noisy data regression problems.

A neural network-based multi-agent classifier system with a Bayesian formalism for trust measurement

In this paper, a neural network (NN)-based multi-agent classifier system (MACS) utilising the trust-negotiation-communication (TNC) reasoning model is proposed. A novel trust measurement method, based on the combination of Bayesian belief functions, is incorporated into the TNC model. The Fuzzy Min-Max (FMM) NN is used as learning agents in the MACS, and useful modifications of FMM are proposed so that it can be adopted for trust measurement. Besides, an auctioning procedure, based on the sealed bid method, is applied for the negotiation phase of the TNC model. Two benchmark data sets are used to evaluate the effectiveness of the proposed MACS. The results obtained compare favourably with those from a number of machine learning methods. The applicability of the proposed MACS to two industrial sensor data fusion and classification tasks is also demonstrated, with the implications analysed and discussed.

Application of an Adaptive Neural Network to Medical Decision Support

Medical diagnostic and prognostic problems are prime examples of decision making in the face of uncertainty. In this paper, we investigate the applicability of the Fuzzy ARTMAP neural network as an intelligent decision support system in clinical medicine. In particular, Fuzzy ARTMAP is employed as a predictive model for prognosis of complications in patients admitted to the Coronary Care Units. A number of off-line and on-line experiments have been conducted with various network parameter settings, training methods, and learning rules. The results are compared with those from other systems including the logistic regression model. In addition, the outcomes of a set of on-line learning experiments revealed the potential of employing Fuzzy ARTMAP as an autono-mously learning system that is able to learn perpetually and, at the same time, to provide useful decision support.

On Operating Strategies of the Fuzzy Artmap Neural Network: A Comparative Study

In this paper, the effectiveness of three different operating strategies applied to the Fuzzy ARTMAP (FAM) neural network in pattern classification tasks is analyzed and compared. Three types of FAM, namely average FAM, voting FAM, and ordered FAM, are formed for experimentation. In average FAM, a pool of the FAM networks is trained using random sequences of input patterns, and the performance metrics from multiple networks are averaged. In voting FAM, predictions from a number of FAM networks are combined using the majority-voting scheme to reach a final output. In ordered FAM, a pre-processing procedure known as the ordering algorithm is employed to identify a fixed sequence of input patterns for training the FAM network. Three medical data sets are employed to evaluate the performances of these three types of FAM. The results are analyzed and compared with those from other learning systems. Bootstrapping has also been used to analyze and quantify the results statistically. [ABSTRACT FROM AUTHOR].

Short term load forecasting using a hybrid neural network

Short Term Load Forecasting (STLF) is very important from the power systems grid operation point of view. STLF involves forecasting load demand in a short term time frame. The short term time frame may consist of half hourly prediction up to weekly prediction. Accurate forecasting would benefit the utility in terms of reliability and stability of the grid ensuring adequate supply is present to meet with the load demand. Apart from that it would also affect the financial performance of the utility company. An accurate forecast would result in better savings while maintaining the security of the grid. This paper outlines the STLF using a novel hybrid online learning neural network, known as the Gaussian Regression (GR). This new hybrid neural network is a combination of two existing online learning neural networks which are the Gaussian Adaptive Resonance Theory (GA) and the Generalized Regression Neural Network (GRNN). Both GA and GRNN implemented online learning, but each of them suffers from limitation. Originally GA is used for unsupervised clustering by compressing the training samples into several categories. A supervised version of GA is available, namely Gaussian ARTMAP (GAM). However, the GAM is still not capable on solving regression problem. On the other hand, GRNN is designed for solving real value estimation (regression) problem, but the learning process would involve of memorizing all training samples, hence high computational cost. The hybrid GR is considered an enhanced version of GRNN with compression ability while still maintains online learning properties. Simulation results show that GR has comparable prediction accuracy and has less prototype as compared to the original GRNN as well as the Support Vector Regression.

Text-dependent speaker recognition using the Fuzzy ARTMAP neural network

Speaker recognition is the process of automatically recognizing the speaker by analyzing individual information contained in the speech waves. In this paper, we discuss the development of an intelligent system for text-dependent speaker recognition. The system comprises two main modules, a wavelet-based signal-processing module for feature extraction of speech waves, and an artificial-neural-network-based classifier module to identify and categorize the speakers. Wavelet is used in de-noising and in compressing the speech signals. The wavelet family that we used is the Daubechies Wavelets. After extracting the necessary features from the speech waves, the features were then fed to a neural-network-based classifier to identify the speakers. We have implemented the Fuzzy ARTMAP (FAM) network in the classifier module to categorize the de-noised and compressed signals. The proposed intelligent learning system has been applied to a case study of text-dependent speaker recognition problem.