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8th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2023

作者： Yussof, Soffri Syed Zainol Abidin, Syed Nasirin Universiti Malaysia Sabah Decision Support Systems Research Group Faculty of Computing and Informatics Labuan Malaysia

ISBN: (纸本)9798350315516

The role of Small and Medium Enterprises (SMEs) in Malaysia plays a significant component in driving the nation's economic growth the present analysis focuses on a limited number of studies investigating SMEs' systematic methods for pursuing outbound innovation strategies. Hence, the focus is to evaluate the imperative need for outbound innovation strategies that can effectively contribute to a comprehensive strategic approach. This study aims to develop a comprehensive operational research framework that will be incorporated into the formulation of the strategic plan the strategic plan will encompass external technology commercialization strategies specifically designed for SMEs the operational research framework comprises three distinct phases. Phase 1 involves conducting semi-structured interviews. Phase 2 focuses on formulating the Outbound Innovation Strategic Plan (OISP). Lastly, Phase 3 entails evaluating the suitability of the OISP the outcome of this research involves the development of an OISP specifically tailored for Malaysian SMEs in the software industry. © 2023 IEEE.

关键词： Strategic planning

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Refining System's Needs Analysis Using systems Engineering Process Approach 18

Refining System's Needs Analysis Using Systems Engineering P...

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18th Iberian Conference on Information systems and Technologies, CISTI 2023

作者： Ismail, Hadzariah Yussof, Soffri Nasirin, Syed Amboala, Tamrin Nizam, Dinna Nina Mohd Naim, Nur Faraha Mohd Kadir, Abdul Universiti Malaysia Sabah Faculty of Computing and Informatics Labuan Malaysia Universiti Malaysia Sabah Decision Support Systems Research Group Labuan Malaysia Universitas Sari Mulia Kalimantan Selatan Banjarmasin Indonesia

ISBN: (纸本)9789893347928

This paper carefully examines the literature concerning the system's need analysis, as many studies provided a partial picture of how to conduct the analysis. It also discusses the usual mistakes in conducting a need analysis for application design and development exercises. The final part of the paper discusses the common misunderstanding of need analysis which led to the inaccuracy of the exercise. A clear need analysis framework is thus produced to clear this misunderstanding. © 2023 ITMA.

关键词： Need analysis system verfication systems engineering process. systems needs

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Outbound Innovation Strategic Plan for Malaysian SMEs in the Software Industry: An Operational research Framework

Outbound Innovation Strategic Plan for Malaysian SMEs in the...

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Recent Advances and Innovations in Engineering (ICRAIE)

作者： Soffri Yussof Syed Nasirin Syed Zainol Abidin Decision Support Systems Research Group Faculty of Computing and Informatics Universiti Malaysia Sabah Labuan Malaysia

The role of Small and Medium Enterprises (SMEs) in Malaysia plays a significant component in driving the nation’s economic growth. The present analysis focuses on a limited number of studies investigating SMEs' systematic methods for pursuing outbound innovation strategies. Hence, the focus is to evaluate the imperative need for outbound innovation strategies that can effectively contribute to a comprehensive strategic approach. This study aims to develop a comprehensive operational research framework that will be incorporated into the formulation of the strategic plan. The strategic plan will encompass external technology commercialization strategies specifically designed for SMEs. The operational research framework comprises three distinct phases. Phase 1 involves conducting semi-structured interviews. Phase 2 focuses on formulating the Outbound Innovation Strategic Plan (OISP). Lastly, Phase 3 entails evaluating the suitability of the OISP. The outcome of this research involves the development of an OISP specifically tailored for Malaysian SMEs in the software industry.

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Investigating the Practicality of the systems Engineering Process Approach as an Alternative to SDLC in Developing Health Information systems 13

Investigating the Practicality of the Systems Engineering Pr...

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13th International Conference on Information Science and Technology, ICIST 2023

作者： Husin, Noor Hassanah Naim, Nur Faraha Mohd Ismail, Hadzariah Nasirin, Syed Lada, Suddin Yussof, Soffri Miskon, Suraya Tahir, Abdullah M. Kadir, Abdul Faculty of International Finance Universiti Malaysia Sabah Labuan Malaysia Faculty of Computing & Informatics Universiti Malaysia Sabah Labuan Malaysia Decision Support Systems Research Group Universiti Malaysia Sabah Labuan Malaysia Faculty of Management Universiti Teknologi Malaysia Johor Bahru Malaysia Faculty of Engineering Universiti Malayasia Sabah Kota Kinabalu Malaysia Universitas Sari Mulia Banjarmasin Indonesia

ISBN: (纸本)9798350313925

Many hospitals in developing regions like East Malaysia are increasingly adopting health information systems (HIS) to enhance operational efficiency. The systems development life-cycle (SDLC) has been the traditional approach to developing HIS. Still, the systems engineering process (SEP) approach is gaining popularity as an option. This paper investigates the practicality of the SEP approach as an alternative to the SDLC in developing HIS. The study utilized semi-structured interviews with HIS development experts. This case study discusses the effectiveness of utilizing the SEP methodology in developing HIS for government hospitals. The study highlights the inherent complexity of the healthcare system and the involvement of various stakeholders, which justify the use of SEP. The iterative approach of SEP, coupled with extensive collaboration, ensures that the HIS is tailored to meet the specific goals of the hospital. The SEP approach's testing and integration phase enable seamless integration with existing systems, enhancing interoperability. © 2023 IEEE.

关键词： Hospitals

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Refining System’s Needs Analysis Using systems Engineering Process Approach

Refining System’s Needs Analysis Using Systems Engineering ...

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Iberian Conference on Information systems and Technologies (CISTI)

作者： Hadzariah Ismail Soffri Yussof Syed Nasirin Tamrin Amboala Dinna Nina Mohd Nizam Nur Faraha Mohd Naim Abdul Kadir Faculty of Computing & Informatics Universiti Malaysia Sabah Labuan Malaysia Decision Support Systems Research Group Universiti Malaysia Sabah Labuan Malaysia Universitas Sari Mulia Banjarmasin Kalimantan Selatan

This paper carefully examines the literature concerning the system’s need analysis, as many studies provided a partial picture of how to conduct the analysis. It also discusses the usual mistakes in conducting a need analysis for application design and development exercises. The final part of the paper discusses the common misunderstanding of need analysis which led to the inaccuracy of the exercise. A clear need analysis framework is thus produced to clear this misunderstanding.

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Investigating the Practicality of the systems Engineering Process Approach as an Alternative to SDLC in Developing Health Information systems

Investigating the Practicality of the Systems Engineering Pr...

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International Conference on Information Science and Technology (ICIST)

作者： Noor Hassanah Husin Syed Nasirin Suraya Miskon Nur Faraha Mohd Naim Suddin Lada Abdullah M. Tahir Hadzariah Ismail Soffri Yussof Abdul Kadir Faculty of International Finance Universiti Malaysia Sabah Labuan Malaysia Faculty of Computing & Informatics Universiti Malaysia Sabah Labuan Malaysia Faculty of Management Universiti Teknologi Malaysia Johor Bahru Malaysia Decision Support Systems Research Group Universiti Malaysia Sabah Labuan Malaysia Faculty of Engineering Universiti Malayasia Sabah Kota Kinabalu Malaysia Universitas Sari Mulia Banjarmasin Indonesia

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An interview based study of pioneering experiences in teaching and learning complex systems in higher education

arXiv

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arXiv 2018年

作者： Lizier, Joseph T. Harré, Michael S. Mitchell, Melanie DeDeo, Simon Finn, Conor Lindgren, Kristian Lizier, Amanda L. Sayama, Hiroki Centre for Complex Systems Complex Systems Research Group Faculty of Engineering and IT University of Sydney NSW Australia Department of Computer Science Portland State University PortlandOR United States Santa Fe Institute Santa FeNM United States School of Informatics and Computing Indiana University BloomingtonIN United States Department of Social and Decision Sciences Carnegie Mellon University PittsburghPA United States CSIRO Data 61 MarsfieldNSW Australia Department of Space Earth and Environment Chalmers University of Technology Göteborg Sweden School of Education University of Technology Sydney NSW Australia Center for Collective Dynamics of Complex Systems Department of Systems Science and Industrial Engineering Binghamton University State University of New York NY United States

Due to the interdisciplinary nature of complex systems as a field, students studying complex systems at University level have diverse disciplinary backgrounds. This brings challenges (e.g. wide range of computer programming skills) but also opportunities (e.g. facilitating interdisciplinary interactions and projects) for the classroom. However, there is little published regarding how these challenges and opportunities are handled in teaching and learning Complex systems as an explicit subject in higher education, and how this differs in comparison to other subject areas. We seek to explore these particular challenges and opportunities via an interview-based study of pioneering teachers and learners (conducted amongst the authors) regarding their experiences. We compare and contrast those experiences, and analyse them with respect to the educational literature. Our discussions explored: approaches to curriculum design, how theories/models/frameworks of teaching and learning informed decisions and experience, how diversity in student backgrounds was addressed, and assessment task design. We found a striking level of commonality in the issues expressed as well as the strategies to handle them, for example a significant focus on problem-based learning, and the use of major student-led creative projects for both achieving and assessing learning outcomes. Copyright © 2018, The Authors. All rights reserved.

关键词： Students

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Author Correction: Federated learning enables big data for rare cancer boundary detection

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Nature communications 2023年第1期14卷 436页

作者： Sarthak Pati Ujjwal Baid Brandon Edwards Micah Sheller Shih-Han Wang G Anthony Reina Patrick Foley Alexey Gruzdev Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Vollmuth Gianluca Brugnara Chandrakanth J Preetha Felix Sahm Klaus Maier-Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey Rudie Javier Villanueva-Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y Huang Ken Chang Carmen Balaña Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Gaurav Shukla Spencer Liem Gregory S Alexander Joseph Lombardo Joshua D Palmer Adam E Flanders Adam P Dicker Haris I Sair Craig K Jones Archana Venkataraman Meirui Jiang Tiffany Y So Cheng Chen Pheng Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A Vogelbaum J Ross Mitchell Joaquim Farinhas Joseph A Maldjian Chandan Ganesh Bangalore Yogananda Marco C Pinho Divya Reddy James Holcomb Benjamin C Wagner Benjamin M Ellingson Timothy F Cloughesy Catalina Raymond Talia Oughourlian Akifumi Hagiwara Chencai Wang Minh-Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre Xavier Falcão Samuel B Martins Bernardo C A Teixeira Flávia Sprenger David Menotti Diego R Lucio Pamela LaMontagne Daniel Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz Rajan Jain Matthew Lee Yvonne W Lui Richard McKinley Johannes Slotboom Piotr Radojewski Raphael Meier Roland Wiest Derrick Murcia Eric Fu Rourke Haas John Thompson David Ryan Ormond Chaitra Badve Andrew E Sloan Vachan Vadmal Kristin Waite Rivka R Colen Linmin Pei Murat Ak Ashok Srinivasan J Rajiv Bapuraj Arvind Rao Nicholas Wang Ota Yoshiaki Toshio Moritani Sevcan Turk Joonsang Lee Snehal Prabhudesai Fanny Morón Jacob Mandel Konstantinos Kamnitsas Ben Glocker Luke V M Dixon Matthew Williams Peter Zamp Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA. Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Informatics Technical University of Munich Munich Bavaria Germany. Intel Corporation Santa Clara CA USA. Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany. Clinical Cooperation Unit Neuropathology German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Neuropathology Heidelberg University Hospital Heidelberg Germany. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany. Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany. Neurology Clinic Heidelberg University Hospital Heidelberg Germany. Department of Radiology & Biomedical Imaging University of California San Francisco San Francisco CA USA. Symbiosis Center for Medical Image Analysis Symbiosis International University Pune Maharashtra India. Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore Karnataka India. Department of Radiology School of Medicine and Public Health University of Wisconsin Madison WI USA. Department of Medical Physics School of Medicine and Public Health University of Wisconsin Madison WI USA. Leeds Teaching Hospitals Trust Department of Radiology Leeds UK. Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. Catalan Institute of Oncology Badalona Spain. Consorci MAR Parc de Salut de Barcelona Catalonia Spain. Department of Radiology (IDI

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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