Hui-Huang Hsu

Bio: Hui-Huang Hsu is an academic researcher from Tamkang University. The author has contributed to research in topics: Video tracking & Motion estimation. The author has an hindex of 13, co-authored 102 publications receiving 861 citations.

Hybrid feature selection by combining filters and wrappers

Abstract: Feature selection aims at finding the most relevant features of a problem domain. It is very helpful in improving computational speed and prediction accuracy. However, identification of useful features from hundreds or even thousands of related features is a nontrivial task. In this paper, we introduce a hybrid feature selection method which combines two feature selection methods - the filters and the wrappers. Candidate features are first selected from the original feature set via computationally-efficient filters. The candidate feature set is further refined by more accurate wrappers. This hybrid mechanism takes advantage of both the filters and the wrappers. The mechanism is examined by two bioinformatics problems, namely, protein disordered region prediction and gene selection in microarray cancer data. Experimental results show that equal or better prediction accuracy can be achieved with a smaller feature set. These feature subsets can be obtained in a reasonable time period.

Feature Selection via Correlation Coefficient Clustering

Abstract: Feature selection is a fundamental problem in machine learning and data mining. How to choose the most problem-related features from a set of collected features is essential. In this paper, a novel method using correlation coefficient clustering in removing similar/redundant features is proposed. The collected features are grouped into clusters by measuring their correlation coefficient values. The most class-dependent feature in each cluster is retained while others in the same cluster are removed. Thus, the most class-related and mutually unrelated features are identified. The proposed method was applied to two datasets: the disordered protein dataset and the Arrhythmia (ARR) dataset. The experimental results show that the method is superior to other feature selection methods in speed and/or accuracy. Detail discussions are given in the paper.

Advanced Data Mining Technologies in Bioinformatics

Abstract: A Sample of Contents: Hierarchical Profiling, Scoring and Applications in Bioinformatics Efficient and Robust Analysis of Large Phylogenetic Datasets Algorithmic Aspects of Protein Threading.

RFID-based human behavior modeling and anomaly detection for elderly care

Abstract: This research aimed at building an intelligent system that can detect abnormal behavior for the elderly at home. Active RFID tags can be deployed at home to help collect daily movement data of the elderly who carries an RFID reader. When the reader detects the signals from the tags, RSSI values that represent signal strength are obtained. The RSSI values are reversely related to the distance between the tags and the reader and they are recorded following the movement of the user. The movement patterns, not the exact locations, of the user are the major concern. With the movement data (RSSI values), the clustering technique is then used to build a personalized model of normal behavior. After the model is built, any incoming datum outside the model can be viewed as abnormal and an alarm can be raised by the system. In this paper, we present the system architecture for RFID data collection and preprocessing, clustering for anomaly detection, and experimental results. The results show that this novel approach is promising.

KNN-DTW Based Missing Value Imputation for Microarray Time Series Data

Abstract: Microarray technology provides an opportunity for scientists to analyze thousands of gene expression profiles simultaneously. However, microarray gene expression data often contain multiple missing expression values due to many reasons. Effective methods for missing value imputation in gene expression data are needed since many algorithms for gene analysis require a complete matrix of gene array values. Several algorithms are proposed to handle this problem, but they have various limitations. In this paper, we develop a novel method to impute missing values in microarray time-series data combining k-nearest neighbor (KNN) and dynamic time warping (DTW). We also analyze and implement several variants of DTW to further improve the efficiency and accuracy of our method. Experimental results show that our method is more accurate compared with existing missing value imputation methods on real microarray time series datasets.

Highly accurate optic flow computation with theoretically justified warping

Abstract: In this paper, we suggest a variational model for optic flow computation based on non-linearised and higher order constancy assumptions. Besides the common grey value constancy assumption, also gradient constancy, as well as the constancy of the Hessian and the Laplacian are proposed. Since the model strictly refrains from a linearisation of these assumptions, it is also capable to deal with large displacements. For the minimisation of the rather complex energy functional, we present an efficient numerical scheme employing two nested fixed point iterations. Following a coarse-to-fine strategy it turns out that there is a theoretical foundation of so-called warping techniques hitherto justified only on an experimental basis. Since our algorithm consists of the integration of various concepts, ranging from different constancy assumptions to numerical implementation issues, a detailed account of the effect of each of these concepts is included in the experimental section. The superior performance of the proposed method shows up by significantly smaller estimation errors when compared to previous techniques. Further experiments also confirm excellent robustness under noise and insensitivity to parameter variations.

시력 손상과 시각 장애(Visual Impairment and Blindness)

Abstract: Acting Under Secretary of Health requested that a working group be established to develop the Veterans Health Initiative (VHI). He envisioned this as a comprehensive program to recognize the connection between certain health effects and military service, to allow military history to be better documented, to prepare health care providers to better serve their veteran patients, and to establish a data base for further study. This was first discussed by the Acting Under Secretary in relation to the health of former prisoners of war. Development was really begun by the former Chief Academic Affairs Officer, Dr. David Stevens, with the Military Service History project. This involves a pocket card for medical residents detailing the important components of a military service history targeting the health risks associated with various periods of service and more generic issues of concern and a website containing references relevant to the issues. Educational modules in the Veterans Health Initiative VHA will assist health care providers in recognizing the connection between certain health effects and military service, prepare health care providers to better serve veteran patients, and will provide a data base for further study. This independent study is designed to provide primary care practitioners with an introduction to the pathologies that lead to sight loss, their functional implications, appropriate method of referrals, training programs, and special considerations for interactions with visually impaired individuals. After completing this independent study, participants would be able to: • Define legal blindness; • Describe the causes of sight loss; • Delineate the functional implications of vision loss • Delineate the psycho/social impact of vision loss on the veteran; • Outline the role of the Visual Impairment Services Team (VIST) in the treatment of legally blind veterans and the referral process; • Describe the special personal and environmental considerations needed for visually impaired patients; • Describe the special medical considerations needed for visually impaired patients; • Describe the primary care practitioner's role in assisting veterans in establishing well-grounded claims for disability related to the loss of vision; and • Describe compensation and pension benefits provided for veterans with eye disabilities. After completing this independent study, you should 1. be able to: state the definition of legal blindness; 2. be able to: associate eye diseases with their visual implications; 3. be able to: demonstrate insight into the functional and Psycho/Social implications of sight-loss; 4. know when referrals to VIST are indicated; 5. understand …

A review of RFID technology and its managerial applications in different industries

Abstract: Radio frequency identification (RFID) is an emerging technology that is increasingly being used in supply chain management RFID technology plays an important role in supporting logistics and supply chain processes because of their ability to identify, trace and track information throughout the supply chain The technology can provide suppliers, manufacturers, distributors and retailers precise real time information about the products This accurate knowledge of the inventory would result in lower labor cost, simplified business processes and improved supply chain efficiency If properly used, it has the potential to cut ordering lead time and cost on inventory control, increase the accuracy of inventory information, help avoid stockouts and boost the number of inventory turns In this paper, we provide an overview of the current state of RFID applications in different industries and its impact on business operations We provide extensive literature survey and develop a framework for future research areas in this field

The Impact of Structural Genomics: Expectations and Outcomes - eScholarship

Abstract: Structural Genomics (SG) projects aim to expand our structural knowledge of biological macromolecules, while lowering the average costs of structure determination. We quantitatively analyzed the novelty, cost, and impact of structures solved by SG centers, and contrast these results with traditional structural biology. The first structure from a protein family is particularly important to reveal the fold and ancient relationships to other proteins. In the last year, approximately half of such structures were solved at a SG center rather than in a traditional laboratory. Furthermore, the cost of solving a structure at the most efficient U.S. center has now dropped to one-quarter the estimated cost of solving a structure by traditional methods. However, top structural biology laboratories are much more efficientthan the average, and comparable to SG centers despite working on very challenging structures. Moreover, traditional structural biology papers are cited significantly more often, suggesting greater current impact.