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Yi-Feng Yang,Ching-Yaw Chen,Yu-Je Lee,Shyh-Hwang Lee 동아시아경상학회 2014 The East Asian Journal of Business Economics Vol.2 No.3
The main purpose of this study intends to study the theoretical interconnection between human response capability and customer relationship management advantage while considering the essential role of service application of information technology as direct, indirect (mediating), and interactive (moderating) influences in the theory. Based on the study sample, the new findings help comprehend the overall interconnected relationship which includes the direct and indirect (mediating) effects of information technology service capability and human response capability as well as their interaction (moderation) on customer relationship management advantage. The new insights interprets the two capabilities (human and information technology) are vital to business because they are the foundation set of service resources significantly to enhance customer relationship management advantage. Keywords: human response capability, information
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Joint feature-sample selection and robust diagnosis of Parkinson's disease from MRI data
Adeli, Ehsan,Shi, Feng,An, Le,Wee, Chong-Yaw,Wu, Guorong,Wang, Tao,Shen, Dinggang Elsevier 2016 NeuroImage Vol.141 No.-
<P><B>Abstract</B></P> <P>Parkinson's disease (PD) is an overwhelming neurodegenerative disorder caused by deterioration of a neurotransmitter, known as dopamine. Lack of this chemical messenger impairs several brain regions and yields various motor and non-motor symptoms. Incidence of PD is predicted to double in the next two decades, which urges more research to focus on its early diagnosis and treatment. In this paper, we propose an approach to diagnose PD using magnetic resonance imaging (MRI) data. Specifically, we first introduce a joint feature-sample selection (JFSS) method for selecting an optimal subset of samples and features, to learn a reliable diagnosis model. The proposed JFSS model effectively discards poor samples and irrelevant features. As a result, the selected features play an important role in PD characterization, which will help identify the most relevant and critical imaging biomarkers for PD. Then, a robust classification framework is proposed to simultaneously de-noise the selected subset of features and samples, and learn a classification model. Our model can also de-noise testing samples based on the cleaned training data. Unlike many previous works that perform de-noising in an unsupervised manner, we perform supervised de-noising for both training and testing data, thus boosting the diagnostic accuracy. Experimental results on both synthetic and publicly available PD datasets show promising results. To evaluate the proposed method, we use the popular Parkinson's progression markers initiative (PPMI) database. Our results indicate that the proposed method can differentiate between PD and normal control (NC), and outperforms the competing methods by a relatively large margin. It is noteworthy to mention that our proposed framework can also be used for diagnosis of other brain disorders. To show this, we have also conducted experiments on the widely-used ADNI database. The obtained results indicate that our proposed method can identify the imaging biomarkers and diagnose the disease with favorable accuracies compared to the baseline methods.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A novel joint feature‐sample selection (JFSS) algorithm is proposed. </LI> <LI> The selected subset best builds a classification model; </LI> <LI> A robust classification framework is proposed that de‐noises the training data, while learning the classification model; </LI> <LI> In addition, the test data are also de-noised based on supervised cleaned training samples; </LI> <LI> The method is applied for Parkinson’s disease (PD) diagnosis, as PD‐data driven methods are scarce and not widely studied. </LI> <LI> New clinically important regions of interest (ROIs) are defined, specifically designed for PD diagnosis. </LI> </UL> </P>
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Wang, Tao,Shi, Feng,Jin, Yan,Yap, Pew-Thian,Wee, Chong-Yaw,Zhang, Jianye,Yang, Cece,Li, Xia,Xiao, Shifu,Shen, Dinggang Hindawi Publishing Corporation 2016 Neural plasticity Vol.2016 No.-
<P>Alzheimer's disease (AD) is the most common form of dementia in elderly people. It is an irreversible and progressive brain disease. In this paper, we utilized diffusion-weighted imaging (DWI) to detect abnormal topological organization of white matter (WM) structural networks. We compared the differences between WM connectivity characteristics at global, regional, and local levels in 26 patients with probable AD and 16 normal control (NC) elderly subjects, using connectivity networks constructed with the diffusion tensor imaging (DTI) model and the high angular resolution diffusion imaging (HARDI) model, respectively. At the global level, we found that the WM structural networks of both AD and NC groups had a small-world topology; however, the AD group showed a significant decrease in both global and local efficiency, but an increase in clustering coefficient and the average shortest path length. We further found that the AD patients had significantly decreased nodal efficiency at the regional level, as well as weaker connections in multiple local cortical and subcortical regions, such as precuneus, temporal lobe, hippocampus, and thalamus. The HARDI model was found to be more advantageous than the DTI model, as it was more sensitive to the deficiencies in AD at all of the three levels.</P>
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