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1,581 results on '"Peng, Sheng Lung"'

106. Measuring Protein Structural Similarity by Maximum Common Edge Subgraphs

Author

Peng, Sheng-Lung, Tsay, Yu-Wei, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Huang, De-Shuang, editor, Zhang, Xiang, editor, Reyes García, Carlos Alberto, editor, and Zhang, Lei, editor

Published
2010
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107. CAPS Genomic Subtyping on Orthomyxoviridae

Author

Peng, Sheng-Lung, Tsay, Yu-Wei, Lin, Chich-Sheng, Tang, Chuan Yi, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Huang, De-Shuang, editor, Jo, Kang-Hyun, editor, Lee, Hong-Hee, editor, Kang, Hee-Jun, editor, and Bevilacqua, Vitoantonio, editor

Published
2009
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108. Verification of Pathotyping by Quasispecies Model

Author

Peng, Sheng-Lung, Tsay, Yu-Wei, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Huang, De-Shuang, editor, Jo, Kang-Hyun, editor, Lee, Hong-Hee, editor, Kang, Hee-Jun, editor, and Bevilacqua, Vitoantonio, editor

Published
2009
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109. Probe Ptolemaic Graphs

Author

Chandler, David B., Chang, Maw-Shang, Kloks, Ton, Le, Van Bang, Peng, Sheng-Lung, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Hu, Xiaodong, editor, and Wang, Jie, editor

Published
2008
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111. On the Treewidth and Pathwidth of Biconvex Bipartite Graphs

Author

Peng, Sheng-Lung, Yang, Yi-Chuan, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Cai, Jin-Yi, editor, Cooper, S. Barry, editor, and Zhu, Hong, editor

Published
2007
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112. Partitioned Probe Comparability Graphs

Author

Chandler, David B., Chang, Maw-Shang, Kloks, Ton, Liu, Jiping, Peng, Sheng-Lung, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, and Fomin, Fedor V., editor

Published
2006
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113. Recognition of Probe Cographs and Partitioned Probe Distance Hereditary Graphs

Author

Chandler, David B., Chang, Maw-Shang, Kloks, Ton, Liu, Jiping, Peng, Sheng-Lung, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Cheng, Siu-Wing, editor, and Poon, Chung Keung, editor

Published
2006
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114. The PIGs Full Monty – A Floor Show of Minimal Separators

Author

Chang, Gerard Jennhwa, Kloks, Antonius J. J., Liu, Jiping, Peng, Sheng-Lung, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Diekert, Volker, editor, and Durand, Bruno, editor

Published
2005
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115. Roman Domination over Some Graph Classes

Author

Liedloff, Mathieu, Kloks, Ton, Liu, Jiping, Peng, Sheng-Lung, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, and Kratsch, Dieter, editor

Published
2005
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124. Edge and node searching problems on trees

Author

Peng, Sheng-Lung, Ho, Chin-Wen, Hsu, Tsan-sheng, Ko, Ming-Tat, Tang, Chuan-Yi, Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Jiang, Tao, editor, and Lee, D. T., editor

Published
1997
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129. Graph searching on chordal graphs

Author

Peng, Sheng-Lung, Ko, Ming-Tat, Ho, Chin-Wen, Hsu, Tsan-sheng, Tang, Chuan-Yi, Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Asano, Tetsuo, editor, Igarashi, Yoshihide, editor, Nagamochi, Hiroshi, editor, Miyano, Satoru, editor, and Suri, Subhash, editor

Published
1996
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131. Probe Ptolemaic Graphs

Author

Chandler, David B., primary, Chang, Maw-Shang, additional, Kloks, Ton, additional, Le, Van Bang, additional, and Peng, Sheng-Lung, additional

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133. A Pairwise Alignment Algorithm for Long Sequences of High Similarity

Author

Lee, Chien-Tai and Peng, Sheng-Lung

Subjects
Whole-genome alignment, Pairwise alignment, SARS virus, Article
Abstract

Alignment algorithms are important in bioinformatics for comparing the similarity among sequences. The algorithm of Needleman–Wunsch is well known for globally aligning two sequences. However, this algorithm is unsuitable for sequences of long length. Many heuristic algorithms are proposed, such as BLAST and FASTA. However, they are still unsuitable for long sequences. In this paper, we study the alignment problem on highly similar sequences. By taking SARS viruses as an example, our result shows that our algorithm runs faster than Clustalx for aligning two SARS viruses. It implies that our algorithm is suitable for viruses of high similarity.

Published
2017

139. Block-graph width

Author

Chang, Maw-Shang, primary, Hung, Ling-Ju, additional, Kloks, Ton, additional, and Peng, Sheng-Lung, additional

Published
2011
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141. On probe permutation graphs

Author

Chandler, David B., primary, Chang, Maw-Shang, additional, Kloks, Ton, additional, Liu, Jiping, additional, and Peng, Sheng-Lung, additional

Published
2009
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148. Internet of things-assisted intelligent monitoring model to analyse the physical health condition.

Author

Zhuang, Liang, Jumani, Awais Khan, Sbeih, Asma, Balamurugan, S., Muthu, BalaAnand, Peng, Sheng-Lung, and Abd Wahab, Mohd Helmy

Abstract

Background: Nowadays, smart healthcare minimizes medical facilities costs, ease staff burden, achieve unified control of materials and records, and enhance patients' medical experience. Smart healthcare treatments have critical barriers to improving patient outcomes, reducing the regulatory burden, and promoting the transition from volume to benefit.Objective: In this paper, the Internet of Things-assisted Intelligent Monitoring Model (IoT-IMM) has been proposed to improve patient health and maintain health records.Method: The advanced IoT sensors can monitor patient health and insert into the patients' bodies. Information collected can be analyzed, aggregated, and mined to predict diseases at an early stage. For that, an enhanced deep learning network using Bayes theorem (EDLN-BT) benefits to obtain and verify various patient health data in a specific aspect, making it easy to supervise the patient's activities.Results: The IoT-IMM-based EDLN-BT results show the smart health care monitoring has undergone substantial growth, improving patient satisfaction for the quality of the healthcare services offered in hospitals and many other healthcare facilities. It helps predict health diseases with increased accuracy, prediction rate with minimal residual error delay, and energy consumption. [ABSTRACT FROM AUTHOR]

Published
2021
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149. Internet of things-based technological acceptance learning management framework for the physical education system.

Author

Yao, Hongyan, Wang, Yongsheng, Montenegro-Marin, Carlos Enrique, Hsu, Ching-Hsien, Balamurugan, S., Muthu, BalaAnand, Peng, Sheng-Lung, and Abd Wahab, Mohd Helmy

Abstract

Background: Internet of Things (IoT) is a hopeful advancement that is an accurate international link for smart devices for total initiatives. Physical Education (PE) builds students' abilities and trust to engage in various physical activities, both within and outside their classrooms. The challenging characteristics in the learning management system include lack of setting a clear goal, lack of system integration, and failure to find an implementation team is considered as an essential factor.Objective: In this paper, an IoT-based technological acceptance learning management framework (IoT-TALMF) has been proposed to identify the objectives, resource allocation, and effective team for group work in the physical education system.Method: Physical Educators primarily use the learning management framework as databases of increased management components, choosing to interact with students, teammates, organizations. Statistical course content analysis is introduced to identify and set clear goals that motivate students for the physical education system. The course instructor learning technique is incorporated with IoT-TALMF to improve system integration based on accuracy and implement an effective team to handle unexpected cost delays in the physical education system.Results: The numerical results show that the IoT-TALMF framework enhances the identity accuracy ratio of 97.33%, the performance ratio of students 96.2%, and the reliability ratio of 97.12%, proving the proposed framework's reliability. [ABSTRACT FROM AUTHOR]

Published
2021
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150. Internet of things-based cloud computing platform for analyzing the physical health condition.

Author

Cui, Mengyao, Baek, Seung-Soo, Crespo, Rubén González, Premalatha, R., Balamurugan, S., Muthu, BalaAnand, Peng, Sheng-Lung, and Abd Wahab, Mohd Helmy

Abstract

Background: Health monitoring is important for early disease diagnosis and will reduce the discomfort and treatment expenses, which is very relevant in terms of prevention. The early diagnosis and treatment of multiple conditions will improve solutions to the patient's healthcare radically. A concept model for the real-time patient tracking system is the primary goal of the method. The Internet of things (IoT) has made health systems accessible for programs based on the value of patient health.Objective: In this paper, the IoT-based cloud computing for patient health monitoring framework (IoT-CCPHM), has been proposed for effective monitoring of the patients.Method: The emerging connected sensors and IoT devices monitor and test the cardiac speed, oxygen saturation percentage, body temperature, and patient's eye movement. The collected data are used in the cloud database to evaluate the patient's health, and the effects of all measures are stored. The IoT-CCPHM maintains that the medical record is processed in the cloud servers.Results: The experimental results show that patient health monitoring is a reliable way to improve health effectively. [ABSTRACT FROM AUTHOR]

Published
2021
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