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Prediction of traffic congestion based on time series dataset number of vehicles using neural network algorithm

AIP Conference Proceedings 2024年第1期2982卷

作者： Prasetyo Wibowo Yunanto Rahmat Gernowo Oky Dwi Nurhayati Doctoral Program of Information System Diponegoro University Jl. Imam Bardjo SH No.5 Semarang Indonesia Information Systems and Technology Universitas Negeri Jakarta Jl. Rawamangun Muka Jakarta Indonesia Department of Physics Diponegoro University Jl. Prof. H. Soedarto Semarang Indonesia Computer Engineering Department Diponegoro University Jl. Prof. H. Soedarto Semarang Indonesia

The increasing number of vehicles that was not accompanied by an adequate road infrastructure readiness causes traffic jams. Traffic jams often occur repeatedly every day, especially at certain times, such as when people are going to and from work. The same traffic jams can also occur at the beginning or the end of the week; this usually repeats every week. As a result, if the congestion dataset is known, the repeating traffic congestion for daily and weekly congestion can be anticipated. In this study, traffic congestion predictions are modeled using the Neural Network Algorithm based on traffic congestion data collected within 24 hours for one to two weeks. The parameters used in optimizing Neural Network performance are learning rate, momentum, and epochs (training cycles). Based on these experiments, the Neural Network Algorithm successfully modeled traffic density patterns which are recurring congestion patterns, with quite good results.

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Radiotherapy Communication Skills Training using Chatbot-Based Prototype (SCIMORT)

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Journal of Medical Imaging and Radiation Sciences 2022年第4期53卷 S1-S1页

作者： Nor, A Nur, S Muhamad, K Rozilawati, A Noorazrul, Y Muhammad, F Nani, S Noraini, W Saiful, H Liyana, S Diagnostic Imaging and Radiotherapy Program School of Diagnostic and Applied Health Sciences Faculty of Health Sciences Universiti Kebangsaan Malaysia Jalan Raja Muda Abdul Aziz 50300 Kuala Lumpur Malaysia Department of Mathematical Sciences Faculty of Science and Technology Universiti Kebangsaan Malaysia UKM Bangi 43600 Selangor Darul Ehsan Malaysia Department of Information Systems Faculty of Computer Science and Information Technology University of Malaya 50603 Kuala Lumpur Malaysia

IntroductionEffective communication skills are integral to providing adequate patient care and are fundamental to its effectiveness. This is particularly important for radiotherapy students while interacting with patients. Students tend to feel disempowered and inadequate to communicate effectively due to repeated miscommunication between students and patients. For students seeking to develop their abilities, confidence, knowledge, and critical thinking, blended learning-based communication skills training may have been an alternative approach. In this study, a chatbot-based prototype model (SCIMORT module) was designed to effectively improve the communication skills of undergraduates in Diagnostic Imaging and Radiotherapy at Universiti Kebangsaan Malaysia. MethodsSCIMORT was developed in compliance with the Clinical Practice for Radiotherapy course's academic syllabus and learning objectives. SCIMORT was constructed using Blender with final exportation to Bot Libre. The research study is divided into two phases. The first phase focuses on developing the SCIMORT module, while the second phase includes properly evaluating the prototype created in the first phase via a survey questionnaire. ResultsThe virtual patient created in the first phase was displayed a visual cue (e.g. blinking, eye movement) and auditory cues (e.g. verbal response, intonation of voice). In the second phase, the expert validation of the questionnaire resulted in a high Content Validity Ratio of more than 0.83 percent and a Cronbach Alpha Coefficient of more than 70%. Both the control group's (users' acceptance = 3.919 ± 1.245; users' engagement = 3.526 ± 1.270) and Trial group's (users' acceptance = 3953 ± 1.114; users' engagement = 3.568 ± 1.142) users' acceptance and engagement with SCIMORT were neutral. ConclusionThe SCIMORT has proven to be an effective and acceptable tool for pre-clinical learning, but with limited levels of engagement. The SCIMORT module should be continually improved by

关键词： SCIMORT module communication skill virtual patients virtual clinical experience breast cancer simulation

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AVID Teacher Leadership: Administrator and Teacher Perceptions of Leadership Attributes

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Journal of School Leadership 2014年第1期24卷 146 - 163页

作者： Shirley J. Mills Jeffery J. Huerta Karen M. Watt Jorge Martinez Member of the Department of Educational Leadership at The University of Texas–Pan American. Her research interests include leadership at all levels in public schools issues of gender in leadership and the physical education policy decisions of the state of Texas and their impact on the health of children in South Texas. Data and research coordinator for the AVID Center's Special Research Projects Office at The University of Texas–Pan American. With interests and concentrations in higher education administration and research he has been working with college preparatory programs in areas of research and evaluation for 10 years. Department of Educational Leadership at The University of Texas–Pan American. She has been engaged in research on the AVID program since 1999 and has 20 published journal articles and 9 book chapters in the field of education. Senior majoring in computer information systems at The University of Texas–Pan American and recently completed his third year as a research assistant in the AVID Center's Special Research Projects Office.

This study examined the perceptions of teachers and administrators with regard to AVID (Advancement via Individual Determination) teacher leaders. The purpose was to compare whether teachers and administrators agree on the types of attributes needed for teacher leaders involved in implementing AVID as a school reform effort. Results revealed that there is agreement among teachers and among administrators regarding the importance of a teacher's personal attributes and attributes related to classroom environment. When compared to teachers, administrators generally feel that certain leadership attributes are more important, such as open communication with the principal, creative problem solving, collegiality on campus, organizational skills, and respect for other teachers.

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Challenges for Free Access to Law in a Multi-Jurisdictional Developing Country: Building the Legal information Institute of India

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Journal of National Law University Delhi 2018年第1期1卷 63–93页

作者： Graham Greenleaf VC Vivekanandan Philip Chung Ranbir Singh Andrew Mowbray A Graham Greenleaf Professor of Law D Information Systems University of New South Wales. B Dr.V.C.Vivekanandan Ministry of HRD IP Chair Professor NALSAR University of Law Hyderabad. C Philip Chung Lecturer in Law University of Technology Sydney and Conjoint Lecturer in Law UNSW. D Prof. (Dr.) Ranbir Singh Vice-Chancellor National Law University Delhi. E Andrew Mowbray Professor of Law D Computer Science at the University of Technology Sydney. This article first appeared in 8(3) SCRIPTed 292 (2011). The assistance of Rajan Sharma system developer for LII of India at AustLII Jill Matthews Indexer prior AustLII staff and SCRIPTed editors and referees is gratefully acknowledged. An earlier version of this article was presented at the Law via the Internet Conference University of Hong Kong 2011. The article was completed while Graham Greenleaf was a Visiting Fellow at the SCRIPT/AHRC Law and Technology Centre University of Edinburgh. The LII of India has been assisted by an AusAID PSLP (Public Sector Linkages Program) grant concerning access to South Asian legal information and from an ARC (Australian Research Council) LIEF grant concerning CommonLII

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SRBench++: Principled Benchmarking of Symbolic Regression With Domain-Expert Interpretation

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： de Franca, F.O. Virgolin, M. Kommenda, M. Majumder, M.S. Cranmer, M. Espada, G. Ingelse, L. Fonseca, A. Landajuela, M. Petersen, B. Glatt, R. Mundhenk, N. Lee, C.S. Hochhalter, J.D. Randall, D.L. Kamienny, P. Zhang, H. Dick, G. Simon, A. Burlacu, B. Kasak, Jaan Machado, Meera Wilstrup, Casper Cavaz, W.G.L. Federal University of ABC Santo Andre Brazil Evolutionary Intelligence group Centrum Wiskunde and Informatica Science Park Amsterdam Netherlands University of Applied Sciences Upper Austria Hagenberg Austria Computational Health Informatics Program Boston Children’s Hospital Harvard Medical School Boston USA Center for Computational Astrophysics Flatiron Institute and Department of Astrophysical Sciences of Princeton University USA LASIGE Faculdade de Ciências Universidade de Lisboa Lisboa Portugal Computational Engineering Division Lawrence Livermore National Laboratory Livermore USA Department of Mechanical Engineering University of Utah Utah USA Meta FAIR France Victoria University of Wellington School of Engineering and Computer Science New Zealand University of otago Department of Information Science New Zealand Institut für Angewandte Physik Universität TübingenMax Planck Institute for Intelligent Systems Tübingen Germany Abzu AI Orient Plads 1 Nordhavn Denmark

Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main promise of this approach is that it may return an interpretable model that can be insightful to users, while maintaining high accuracy. The current standard for benchmarking these algorithms is SRBench, which evaluates methods on hundreds of datasets that are a mix of real-world and simulated processes spanning multiple domains. At present, the ability of SRBench to evaluate interpretability is limited to measuring the size of expressions on real-world data, and the exactness of model forms on synthetic data. In practice, model size is only one of many factors used by subject experts to determine how interpretable a model truly is. Furthermore, SRBench does not characterize algorithm performance on specific, challenging sub-tasks of regression such as feature selection and evasion of local minima. In this work, we propose and evaluate an approach to benchmarking SR algorithms that addresses these limitations of SRBench by 1) incorporating expert evaluations of interpretability on a domain-specific task, and 2) evaluating algorithms over distinct properties of data science tasks. We evaluate 12 modern symbolic regression algorithms on these benchmarks and present an in-depth analysis of the results, discuss current challenges of symbolic regression algorithms and highlight possible improvements for the benchmark itself. Authors

关键词： Benchmarking

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Development and evaluation of a hospital-wide healthcare-associated surgical site infection detection algorithm

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Journal of Microbiology, Immunology and Infection 2015年第2期48卷 S43-S43页

作者： Yi-Ju Tseng Bo-Chiang Huang Hui-Chi Lin Ming-Yuan Chen Rung-Ji Shang Wang-Huei Sheng Yee-Chun Chen Feipei Lai Shan-Chwen Chang Children's Hospital Informatics Program Boston Children's Hospital Boston MA USA Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taiwan Center for Infection Control National Taiwan University Hospital (NTUH) Taiwan Information Systems Office NTUH Taipei Taiwan Department of Internal Medicine NTUH and College of Medicine Taiwan National Institute of Infectious Diseases and Vaccinology National Health Research Institutes Taiwan Department of Computer Science and Information Engineering National Taiwan University Taiwan

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GAME ON! A PROTOTYPE OF A VIRTUAL CLINICAL RADIOTHERAPY LEARNING APPLICATION

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Journal of Medical Imaging and Radiation Sciences 2023年第3期54卷 S34-S34页

作者： Aniza, Azmi Nor Afiqah, Mohd Sukri Nur Zuan, Lee Xi Chit, Som Khid AL Som Azmie, Yahya Noorazrul Noraini, Ahmad Wahid Izzuan, Hussin Saiful Liyana, Shuib Diagnostic Imaging and Radiotherapy Program Faculty of Health Sciences Universiti Kebangsaan Malaysia 50300 Kuala Lumpur Malaysia National University Hospital Singapore 5 Lower Kent Ridge Road Singapore Diagnostic Radiology Singapore General Hospital Outram Rd Singapore Department of Mathematical Sciences Faculty of Science and Technology Universiti Kebangsaan Malaysia 43600 Selangor Darul Ehsan Malaysia Department of Information Systems Faculty of Computer Science and Information Technology University of Malaya 50603 Kuala Lumpur Malaysia

OBJECTIVE"Game On" is a prototype of a Virtual Clinical Radiotherapy Learning Application (VRLA) designed to provide an immersive pre-clinical learning experience for undergraduates in the Diagnostic Imaging and Radiotherapy (PDR) program at UKM. The VRLA aims to prepare students for their clinical attachments in radiotherapy departments by focusing on patient positioning and immobilization device placement, aligned with the learning outcomes of the Radiotherapy Treatment Planning course. MATERIALS & METHODSThis pilot study involved the development using Unity and Blender software; the VRLA offers a virtual environment where students can engage in interactive gameplay related to radiotherapy procedures. The initial phase of the game model emphasizes key areas such as the lung, breast, and male reproductive regions. The VRLA aims to enhance students' understanding and skills in radiotherapy techniques by employing realistic simulations and game mechanics. A pilot study was conducted with 22 participants, including academicians and PDR students, to assess the effectiveness of the VRLA. Additionally, a BETA test was conducted through an online survey to evaluate user acceptance and engagement. RESULTSThe findings indicate generally positive levels of user acceptance, particularly among the student group. However, the academic group displayed a relatively neutral response regarding user engagement. While the VRLA demonstrates promising effectiveness for pre-clinical learning, further enhancements are necessary to improve user engagement. Recommendations include refining the game's story flow, incorporating additional game elements, and addressing the specific needs of different user groups. CONCLUSIONThis research contributes to advancing virtual clinical learning applications in radiotherapy education. The findings provide insights for developing innovative and engaging tools that can augment traditional educational approaches and enhance the learning experience for ra

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Airr Community Curation and Standardised Representation for Immunoglobulin and T Cell Receptor Germline Sets

SSRN

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SSRN 2022年

作者： Lees, William D. Christley, Scott Peres, Ayelet Kos, Justin T. Corrie, Brian Ralph, Duncan Breden, Felix Cowell, Lindsay G. Yaari, Gur Corcoran, Martin Karlsson Hedestam, Gunilla B. Ohlin, Mats Collins, Andrew M. Watson, Corey T. Busse, Christian E. Institute of Structural and Molecular Biology Birkbeck College London United Kingdom Human-Centered Computing and Information Science Institute for Systems and Computer Engineering Technology and Science Porto Portugal Peter O’Donnell Jr School of Public Health Department of Immunology UT Southwestern Medical Center Texas United States Bioengineering Program Faculty of Engineering Bar-Ilan University Ramat Gan Israel Department of Biochemistry and Molecular Genetics School of Medicine University of Louisville Kentucky United States Department of Biological Sciences Simon Fraser University BurnabyBC Canada Fred Hutchinson Cancer Research Center SeattleWA United States Department of Microbiology Tumor and Cell Biology Karolinska Institute Stockholm Sweden Department of Immunotechnology Lund University Lund Sweden School of Biotechnology and Biomolecular Sciences University of New South Wales SydneyNSW Australia Division of B Cell Immunology German Cancer Research Center Heidelberg Germany

Analysis of an individual’s immunoglobulin or T cell receptor gene repertoire can provide important insights into immune function. High-quality analysis of adaptive immune receptor repertoire sequencing data depends upon accurate and relatively complete germline sets, but current sets are known to be incomplete. Established processes for the review and systematic naming of receptor germline genes and alleles require specific evidence and data types, but the discovery landscape is rapidly changing. To exploit the potential of emerging data, and to provide the field with improved state-of-the-art germline sets, an intermediate approach is needed that will allow the rapid publication of consolidated sets derived from these emerging sources. These sets must use a consistent naming scheme and allow refinement and consolidation into genes as new information emerges. Name changes should be minimised, but, where changes occur, the naming history of a sequence must be traceable. Here we outline the current issues and opportunities for the curation of germline IG/TR genes and present a forward-looking data model for building out more robust germline sets that can dovetail with current established processes and provides transparency based on principles of findability, accessibility, interoperability, and reusability. We describe results obtained by applying the data model and associated principles within the AIRR Community, including supporting software and databases. © 2022, The Authors. All rights reserved.

关键词： Genes

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Author Correction: Effect of de novo transcriptome assembly on transcript quantification

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Scientific reports 2019年第1期9卷 18041页

作者： Ping-Han Hsieh Yen-Jen Oyang Chien-Yu Chen Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei 10617 Taiwan. Department of Computer Science and Information Engineering National Taiwan University Taipei 10617 Taiwan. Department of Bio-Industrial Mechatronics Engineering National Taiwan University Taipei 10617 Taiwan. chienyuchen@ntu.edu.tw. Genome and Systems Biology Program National Taiwan University and Academia sinica Taipei 10617 Taiwan. chienyuchen@ntu.edu.tw.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition

arXiv

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arXiv 2023年

作者： de Franca, F.O. Virgolin, M. Kommenda, M. Majumder, M.S. Cranmer, M. Espada, G. Ingelse, L. Fonseca, A. Landajuela, M. Petersen, B. Glatt, R. Mundhenk, N. Lee, C.S. Hochhalter, J.D. Randall, D.L. Kamienny, P. Zhang, H. Dick, G. Simon, A. Burlacu, B. Kasak, Jaan Machado, Meera Wilstrup, Casper La Cava, W.G. Federal University of ABC Santo Andre Brazil Evolutionary Intelligence Group Centrum Wiskunde & Informatica Science Park 123 Amsterdam Netherlands Computational Health Informatics Program Boston Children's Hospital Harvard Medical School Boston United States University of Applied Sciences Upper Austria Hagenberg Austria Center for Computational Astrophysics Flatiron Institute Department of Astrophysical Sciences Princeton University United States LASIGE Faculdade de Ciências Universidade de Lisboa Lisboa Portugal Computational Engineering Division Lawrence Livermore National Laboratory Livermore United States University of Utah Department of Mechanical Engineering Utah United States Meta FAIR France Victoria University of Wellington School of Engineering and Computer Science New Zealand University of otago Department of Information Science New Zealand Institut für Angewandte Physik Universität Tübingen Max Planck Institute for Intelligent Systems Tübingen Germany Abzu AI Orient Plads 1 Nordhavn 2150 Denmark

Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main attraction of this approach is that it returns an interpretable model that can be insightful to users. Historically, the majority of algorithms for symbolic regression have been based on evolutionary algorithms. However, there has been a recent surge of new proposals that instead utilize approaches such as enumeration algorithms, mixed linear integer programming, neural networks, and Bayesian optimization. In order to assess how well these new approaches behave on a set of common challenges often faced in real-world data, we hosted a competition at the 2022 Genetic and Evolutionary Computation Conference consisting of different synthetic and real-world datasets which were blind to entrants. For the real-world track, we assessed interpretability in a realistic way by using a domain expert to judge the trustworthiness of candidate models. We present an in-depth analysis of the results obtained in this competition, discuss current challenges of symbolic regression algorithms and highlight possible improvements for future competitions. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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