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Fong, S.,Liu, K.,Cho, K.,Wong, R.,Mohammed, S.,Fiaidhi, J. Springer Science + Business Media 2016 The Journal of supercomputing Vol.72 No.10
<P>Big data stream is a new hype but a practical computational challenge founded on data streams that are prevalent in applications nowadays. It is quite well known that data streams that are originated and collected from monitoring sensors accumulate continuously to a very huge amount making traditional batch-based model induction algorithms infeasible for real-time data mining or just-in-time data analytics. In this position paper, following a new data stream mining methodology, namely stream-based holistic analytics and reasoning in parallel (SHARP), a list of data analytic challenges as well as improvised methods are looked into. In particular, two types of decision tree algorithms, batch-mode and incremental-mode, are put under test at sensor data that represents a typical big data stream. We investigate whether and to what extent of two improvised methods-outlier removal and balancing imbalanced class distributions-affect the prediction performance in big data stream mining. SHARP is founded on incremental learning which does not require all the training to be loaded into the memory. This important fundamental concept needs to be supported not only by the decision tree algorithms, but by the other improvised methods usually at the preprocessing stage as well. This paper sheds some light into this area which is often overlooked by data analysts when it comes to big data stream mining.</P>
Fong, Shirley S.M.,Ng, Shamay S.M.,Li, Anthony O.T.,Guo, X. korean Academy of Physical Therapy Rehabilitation 2014 Physical therapy rehabilitation science Vol.3 No.1
Objective: The aim of this study was to compare the radial bone strength, sitting balance ability and global self-esteem of wheelchair martial arts practitioners and healthy control participants. Design: Cross-sectional study. Methods: Nine wheelchair martial art practitioners with physical disabilities and 28 able-bodied healthy individuals participated in the study. The bone strength of the distal radius was assessed using the Sunlight Mini-Omni Ultrasound Bone Sonometer; sitting balance was quantified using the modified functional reach test (with reference to a scale marked on the wall); and the self-administered Rosenberg self-esteem (RSE) scale was used to measure the global self-esteem of the participants. The velocity of the ultrasound wave (speed of sound, m/s) traveling through the outer surface of the radial bone was measured and was then converted into a T-score and a Z-score. These ultrasound T-score and Z-score that represent bone strength; the maximum forward reaching distance in sitting (cm) that represents sitting balance; and the RSE total self-esteem score that indicates global self-esteem were used for analysis. Results: The results revealed that there were no statistically significant between-group differences for radial bone-strength, maximum forward reaching distance, or self-esteem outcomes. Conclusions: The wheelchair martial arts practitioners had similar radial bone strength, sitting balance performance and self-esteem to able-bodied healthy persons. Our results imply that wheelchair martial arts might improve bone strength, postural control and self-esteem in adult wheelchair users. This new sport-wheelchair martial arts-might be an exercise option for people with physical disabilities.
Stream-based Biomedical Classification Algorithms for Analyzing Biosignals
Fong, Simon,Hang, Yang,Mohammed, Sabah,Fiaidhi, Jinan Korea Information Processing Society 2011 Journal of information processing systems Vol.7 No.4
Classification in biomedical applications is an important task that predicts or classifies an outcome based on a given set of input variables such as diagnostic tests or the symptoms of a patient. Traditionally the classification algorithms would have to digest a stationary set of historical data in order to train up a decision-tree model and the learned model could then be used for testing new samples. However, a new breed of classification called stream-based classification can handle continuous data streams, which are ever evolving, unbound, and unstructured, for instance--biosignal live feeds. These emerging algorithms can potentially be used for real-time classification over biosignal data streams like EEG and ECG, etc. This paper presents a pioneer effort that studies the feasibility of classification algorithms for analyzing biosignals in the forms of infinite data streams. First, a performance comparison is made between traditional and stream-based classification. The results show that accuracy declines intermittently for traditional classification due to the requirement of model re-learning as new data arrives. Second, we show by a simulation that biosignal data streams can be processed with a satisfactory level of performance in terms of accuracy, memory requirement, and speed, by using a collection of stream-mining algorithms called Optimized Very Fast Decision Trees. The algorithms can effectively serve as a corner-stone technology for real-time classification in future biomedical applications.
Fong, S.,Cho, K.,Mohammed, O.,Fiaidhi, J.,Mohammed, S. Springer Science + Business Media 2016 The Journal of supercomputing Vol.72 No.10
<P>Biosignal classification is an important non-invasive diagnosis tool in biomedical application, e.g. electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG) that helps medical experts to automatically classify whether a sample of biosignal under test/monitor belongs to the normal type or otherwise. Most biosignals are stochastic and non-stationary in nature, that means their values are time dependent and their statistics vary over different points of time. However, most classification algorithms in data mining are designed to work with data that possess multiple attributes to capture the non-linear relationships between the values of the attributes to the predicted target class. Therefore, it has been a crucial research topic for transforming univariate time series to multivariate dataset to fit into classification algorithms. For this, we propose a pre-processing methodology called statistical feature extraction (SFX). Using the SFX we can faithfully remodel statistical characteristics of the time series via a sequence of piecewise transform functions. The new methodology is tested through simulation experiments over three representative types of biosignals, namely EEG, ECG and EMG. The experiments yield encouraging results supporting the fact that SFX indeed produces better performance in biosignal classification than traditional analysis techniques like Wavelets and LPC-CC.</P>
Fong, J.J.,Li, P.P.,Yang, B.T.,Zhou, Z.Y.,Leache, A.D.,Min, M.S.,Waldman, B. Academic Press 2016 Molecular phylogenetics and evolution Vol.97 No.-
The Oriental fire-bellied toad (Bombina orientalis) is a commonly used study organism, but knowledge of its evolutionary history is incomplete. We analyze sequence data from four genetic markers (mtDNA genes encoding cytochrome c oxidase subunit I, cytochrome b, and 12S-16S rRNA; nuDNA gene encoding recombination activating gene 2) from 188 individuals across its range in Northeast Asia to elucidate phylogeographic patterns and to identify the historic events that shaped its evolutionary history. Although morphologically similar across its range, B. orientalis exhibits phylogeographic structure, which we infer was shaped by geologic, climatic, and anthropogenic events. Phylogenetic and divergence-dating analyses recover four genetically distinct groups of B. orientalis: Lineage 1-Shandong Province and Beijing (China); Lineage 2-Bukhan Mountain (Korea); Lineage 3-Russia, Northeast China, and northern South Korea; and Lineage 4-South Korea. Lineage 2 was previously unknown. Additionally, we discover an area of secondary contact on the Korean Peninsula, and infer a single dispersal event as the origin of the insular Jeju population. Skyline plots estimate different population histories for the four lineages: Lineages 1 and 2 experienced population decreases, Lineage 3 remained stable, while Lineage 4 experienced a sharp increase during the Holocene. The timing of the population expansion of Lineage 4 coincides with the advent of rice cultivation, which may have facilitated the increase in population size by providing additional breeding habitat.
Turbid Density Current Venting through Reservoir Outlets
Fong-Zuo Lee,Jihn-Sung Lai,Yih-Chi Tan,Chia-Chi Sung 대한토목학회 2014 KSCE JOURNAL OF CIVIL ENGINEERING Vol.18 No.2
The planning and design of an outlet operation by releasing a turbid density current from a reservoir requires accurate prediction ofoutflow concentration for sluicing sediment through outlet structures. This study investigates outflow concentration and ventingefficiency through reservoir outlets in a reservoir sluicing operation related to turbid density current. A 3D numerical model isemployed to simulate a venting operation for a turbid density current in typhoon-induced flood events. A simple and efficientformula derived from theoretical analysis with experimental data is proposed for estimating outflow concentration and ventingefficiency. By adopting the proposed formula to avoid time-consuming calculation using the 3D numerical model, the estimatedoutflow concentration and venting efficiency through reservoir outlets have shown good agreement with the measured and simulatedresults in typhoon flood events. This demonstrates that the formula provides an efficient approach for engineering practice in realtimereservoir venting operations.