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Articles 1 - 6 of 6
Full-Text Articles in Social and Behavioral Sciences
Network Neighbor Effects On Customer Churn In Cell Phone Networks, Pavel N. Krivitsky, Pedro Ferreira, Rahul Telang
Network Neighbor Effects On Customer Churn In Cell Phone Networks, Pavel N. Krivitsky, Pedro Ferreira, Rahul Telang
Faculty of Engineering and Information Sciences - Papers: Part A
No abstract provided.
A Reduced Reference Image Quality Metric Based On Feature Fusion And Neural Networks, Aladine Chetouani, Azeddine Beghdadi, Mohamed Deriche, Abdesselam Bouzerdoum
A Reduced Reference Image Quality Metric Based On Feature Fusion And Neural Networks, Aladine Chetouani, Azeddine Beghdadi, Mohamed Deriche, Abdesselam Bouzerdoum
Faculty of Engineering and Information Sciences - Papers: Part A
A Global Reduced Reference Image Quality Metric (IQM) based on feature fusion using neural networks is proposed. The main idea is the introduction of a Reduced Reference degradation-dependent IQM (RRIQM/D) across a set of common distortions. The first stage consists of extracting a set of features from the wavelet-based edge map. Such features are then used to identify the type of degradation using Linear Discriminant Analysis (LDA). The second stage consists of fusing the extracted features into a single measure using Artificial Neural Networks (ANN). The result is a degradation- dependent IQM measure called the RRIQM/D. The performance of the …
Hierarchical Anatomical Brain Networks For Mci Prediction: Revisiting Volumetric Measures, Luping Zhou, Yaping Wang, Yang Li, Pew-Thian Yap, Dinggang Shen, Alzheimers Disease Neuroimaging Initiative (Adni)
Hierarchical Anatomical Brain Networks For Mci Prediction: Revisiting Volumetric Measures, Luping Zhou, Yaping Wang, Yang Li, Pew-Thian Yap, Dinggang Shen, Alzheimers Disease Neuroimaging Initiative (Adni)
Faculty of Engineering and Information Sciences - Papers: Part A
Owning to its clinical accessibility, T1-weighted MRI (Magnetic Resonance Imaging) has been extensively studied in the past decades for prediction of Alzheimer's disease (AD) and mild cognitive impairment (MCI). The volumes of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) are the most commonly used measurements, resulting in many successful applications. It has been widely observed that disease-induced structural changes may not occur at isolated spots, but in several inter-related regions. Therefore, for better characterization of brain pathology, we propose in this paper a means to extract inter-regional correlation based features from local volumetric measurements. Specifically, our approach …
Hierarchical Anatomical Brain Networks For Mci Prediction By Partial Least Square Analysis, Luping Zhou, Yaping Wang, Yang Li, Pew-Thian Yap, Dinggang Shen
Hierarchical Anatomical Brain Networks For Mci Prediction By Partial Least Square Analysis, Luping Zhou, Yaping Wang, Yang Li, Pew-Thian Yap, Dinggang Shen
Faculty of Engineering and Information Sciences - Papers: Part A
Owning to its clinical accessibility, T1-weighted MRI has been extensively studied for the prediction of mild cognitive impairment (MCI) and Alzheimer's disease (AD). The tissue volumes of GM, WM and CSF are the most commonly used measures for MCI and AD prediction. We note that disease-induced structural changes may not happen at isolated spots, but in several inter-related regions. Therefore, in this paper we propose to directly extract the inter-region connectivity based features for MCI prediction. This involves constructing a brain network for each subject, with each node representing an ROI and each edge representing regional interactions. This network is …
Methods For Generating Complex Networks With Selected Structural Properties For Simulations: A Review And Tutorial For Neuroscientists, Brenton Prettejohn, Matthew Berryman, Mark Mcdonnell
Methods For Generating Complex Networks With Selected Structural Properties For Simulations: A Review And Tutorial For Neuroscientists, Brenton Prettejohn, Matthew Berryman, Mark Mcdonnell
Faculty of Engineering and Information Sciences - Papers: Part A
Many simulations of networks in computational neuroscience assume completely homogenous random networks of the Erdös–Rényi type, or regular networks, despite it being recognized for some time that anatomical brain networks are more complex in their connectivity and can, for example, exhibit the “scale-free” and “small-world” properties. We review the most well known algorithms for constructing networks with given non-homogeneous statistical properties and provide simple pseudo-code for reproducing such networks in software simulations. We also review some useful mathematical results and approximations associated with the statistics that describe these network models, including degree distribution, average path length, and clustering coefficient. We …
Knowledge Sharing Through Virtual Layers In Regional Sustainable Development Networks, Rosemary A. Van Der Meer, Luba Torlina, Jamie Mustard
Knowledge Sharing Through Virtual Layers In Regional Sustainable Development Networks, Rosemary A. Van Der Meer, Luba Torlina, Jamie Mustard
Faculty of Engineering and Information Sciences - Papers: Part A
Our research examines how the organisational structure facilitates knowledge sharing within the group. This case study examines a Victorian regional sustainable group using interviews and social network analysis to identify the group's organisational structure and its effect on knowledge sharing between the members. Our findings indicate that while the mixed membership, lack of hierarchy and layered structure are complex, these elements work together to provide members with a rich body of knowledge. The diversity and differences in membership are complimentary and combined can provide a more in-depth understanding of the regional sustainable development issues.