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Royal Academy of Medicine in Ireland - Section of Bioengineering - Proceedings of meeting held in Dundalk, Co. Louth, in February 1998.

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IRISH JOURNAL OF MEDICAL SCIENCE 1998年第4期167卷 256-276页

作者： [Anonymous] Orthopaedic Research Unit The Queen’s University of Belfast School of Biomedical Sciences The Queen’s University of Belfast Howmedica International Ltd. Raheen Industrial Estate Limerick Department of Mechanical Engineering Trinity College Dublin 2 Mater Hospital Dublin 7 Orthopaedic Research Laboratory Århus University Hospital Denmark University of Limerick Limerick Limerick Regional Hospital Raheen Limerick Boston Scientific Ireland Ltd. Galway Department of Mechanical Engineering University College Dublin Dublin Department of Electrical and Electronic Engineering University College Dublin Dublin Bioengineering Research Centre Department of Mechanical Engineering University College Dublin Dublin Northern Ireland Bio-Engineering Centre (NIBEC) School of Electrical and Mechanical Engineering University of Ulster Newtownabbey Department of Mechanical & Manufacturing Engineering The Ashby Building The Queen’s University of Belfast Johnson and Johnson Professional Orthopaedic Division Queensway England Department of Cardiology and Department of Medical Physics & Bioengineering St. James’s Hospital Dublin 8 Burke House Ellison Street Castlebar Royal College of Surgeons in Ireland Department of Surgery Beaumont Hospital Dublin Department of Electronic and Electrical Engineering Trinity College Dublin 2 Department of Aeronautical Engineering The Queen’s University of Belfast Vascular Unit Belfast City Hospital Department of Medical Physics and Bioengineering St. James’s Hospital Dublin 8 Centre for Biomedical Electronics Department of Electronic and Computer Engineering University of Limerick Limerick Department of Mechanical and Aeronautical Engineering University of Limerick Limerick School of Health Sciences University of Ulster Ireland School of Physiotherapy University of Ulster Newtownabbey Clinical Engineering NI Medical Physics Agency Belfast and Radiology Royal Victoria Hospital Musgrave Park Hospital University of Limerick Biomedical Institute (ULBMI) Uni

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Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer's Disease Classification

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Procedia computer Science 2024年 241卷 146-153页

作者： Hina Tufail Abdul Ahad Ira Puspitasari Ibraheem Shayea Paulo Jorge Coelho Ivan Miguel Pires Department of Computer Science University of Management and Technology Sialkot Pakistan Information System Study Program Faculty of Science and Technology Universitas Airlangga School of Software Northwestern Polytechnical University Xian Shaanxi P.R. China Department of Electronics and Communication Engineering Istanbul Technical University(ITU) Turkey Polytechnic University of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which afects patients’ lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD.

关键词： Alzheimer disease (AD) Deep learning Convolutional Neural Network (CNN) computer-aided diagnosis (CAD)

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Conceptual Design of a Generic Nodal Abstraction (GNA) for a Human-Agent Collaboration systems

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IOP Conference Series: Materials Science and engineering 2019年第1期551卷

作者： Khudhair Abbas Mohammed Mazlina Abdul Majid Mohd Sharifuddin Ahmad Faculty of Computer Systems & Software Engineering University Malaysia Pahang Kuantan Pahang Malaysia College of Computer Science and Information Technology Universiti Tenaga Nasional 43000 Kajang Selangor Malaysia

The issues of modelling software agents in collaborative systems present some problems and difficulties caused by the diversity of tasks and the procedures attached to them. Consequently, such diversity poses a critical challenge to implement scheduled tasks. In this paper, we propose collaborative human-agent system in which a software agent collaborates and cooperates with its human counterpart in several job positions of an organization. The goal of the GNA approach is to improve collaboration between humans when performing tasks within a workflow process. The GNA is built based on the concept of a node which comprises of a human, agents, and their shared functions. This concept espouses the notion that agents could be deployed to assist humans in many common workflow processes and to handle mundane tasks. Furthermore, a multi-agent system is advocated as an application or platform for managing distributed data. Specifically, this paper describes a conceptual design of a generic approach based on the notion of nodes to construct a Generic Nodal Abstraction (GNA) approach that is deployable as a modeling and simulation platform for organizational processes.

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Artificial Intelligence-based Service Chains Scheduling for Medical Emergency in Healthcare

Artificial Intelligence-based Service Chains Scheduling for ...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Mukesh Soni Maher Ali Rusho Haewon Byeon Azzah AlGhamdi Shaikh Abdul Hannan Parth Rameshchandra Dave Dr. D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Tathawade Pune Marashtra Department of Lockheed Martin Engineering Management University of Colorado Boulder Colorado Department of AI and Software & Medical Big Data Research Centre Inje University Gimhae Republic of Korea Computer Information Systems Department College of Computer Science and Information Technology Imam Abdalrhman Bin Faisal University Khobar Saudi Arabia Faculty of Computing and Information AlBaha University AlBaha Kingdom of Saudi Arabia Department of Computer Engineering L. D. College of Engineering Ahmedabad Gujarat India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

This study analyses the weekly scheduling challenge of health and medical information network service function chains to meet medical emergency categorization and grading needs. Emergency services are divided into four sorts by urgency and service area, with priorities and weights. The health and medical information network architecture, service model, and operation method are built accordingly. To reduce weighted completion time, a service function chain scheduling model is created. The function chain scheduling scheme is solved by matching game and Q -learning reinforcement learning algorithms for different issue scales. Finally, simulation studies demonstrate that this approach can accomplish graded medical emergency service, build health and medical information networks, and progress smart healthcare.

关键词： Schedules Job shop scheduling Service function chaining Hospitals Processor scheduling Reinforcement learning Games Emergency services Network function virtualization Standards

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SGNet: Salient Geometric Network for Point Cloud Registration

SGNet: Salient Geometric Network for Point Cloud Registratio...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Qianliang Wu Yaqing Ding Lei Luo Haobo Jiang Shuo Gu Chuanwei Zhou Jin Xie Jian Yang PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Prague Czech Republic State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Intelligence Science and Technology Nanjing University Suzhou China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Point Cloud Registration (PCR) is a critical and challenging task in computer vision and robotics. One of the primary difficulties in PCR is identifying salient and meaningful points that exhibit consistent semantic and geometric properties across different scans. Previous methods have encountered challenges with ambiguous matching due to the similarity among patch blocks throughout the entire point cloud and the lack of consideration for efficient global geometric consistency. To address these issues, we propose a new framework that includes several novel techniques. Firstly, we introduce a semantic-aware geometric encoder that combines object-level and patch-level semantic information. This encoder significantly improves registration recall by reducing ambiguity in patch-level superpoint matching. Additionally, we incorporate a prior knowledge approach that utilizes an intrinsic shape signature to identify salient points. This enables us to extract the most salient super points and meaningful dense points in the scene. Secondly, we introduce an innovative transformer that encodes High-Order (HO) geometric features. These features are crucial for identifying salient points within initial overlap regions while considering global high-order geometric consistency. We introduce an anchor node selection strategy to optimize this high-order transformer further. By encoding inter-frame triangle or polyhedron consistency features based on these anchor nodes, we can effectively learn high-order geometric features of salient super points. These high-order features are then propagated to dense points and utilized by a Sinkhorn matching module to identify critical correspondences for successful registration. The experiments conducted on the 3DMatch/3DLoMatch and KITTI datasets demonstrate the effectiveness of our method.

关键词： Point cloud compression computer vision Accuracy Shape Semantics Transformers Feature extraction Encoding Intelligent robots

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Heuristic Search for Nonlinear Substitutions for Cryptographic Applications

Lecture Notes on Data Engineering and Communications Technol...

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Lecture Notes on Data engineering and Communications Technologies 2023年 180卷 288-298页

作者： Kuznetsov, Oleksandr Frontoni, Emanuele Kandiy, Sergey Smirnov, Oleksii Ulianovska, Yuliia Kobylianska, Olena Department of Political Sciences Communication and International Relations University of Macerata Via Crescimbeni 30/32 Macerata62100 Italy Department of Information and Communication Systems Security Faculty of Comupter Science V. N. Karazin Kharkiv National University 4 Svobody Sq Kharkiv61022 Ukraine Department of Information Engineering Marche Polytechnic University Via Brecce Bianche 12 Ancona60131 Italy Cybersecurity & Software Academic Department Central Ukrainian National Technical University 8 University Avenue Kropyvnytskyi25006 Ukraine Department of Computer Science and Software Engineering University of Customs and Finance Vernadskogo Street 2/4 Dnipro49000 Ukraine

Heuristic algorithms are used to solve complex computational problems quickly in various computer applications. Such algorithms use heuristic functions that rank the search alternatives instead of a full enumeration of possible variants. The algorithm selects, at each iteration, an alternative with the best value for the heuristics. In this paper, we investigate the complex computational problem of finding highly nonlinear substitutions (S-boxes) in the space of 8-bit permutations. The generation of S-boxes is an important field of research, since nonlinear substitutions are widely used in various cryptographic applications. For instance, S-boxes in symmetric ciphers are responsible for cryptographic strength to linear, differential, algebraic, and other types of cryptanalysis. We propose new heuristics in the form of a cost function, calculated with the Walsh-Hadamard transform. We use the Hill Climbing Algorithm to find highly nonlinear substitutions. Our experiments demonstrate that the new heuristics give good results – it takes about 80,000 iterations of the algorithm to generate 8-bit S-boxes with a nonlinearity of 104. For the optimized parameters, the number of iterations of the algorithm is comparable to the best known results, which confirms the significance and value of the research. © The Author(s).

关键词： Cost functions

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Risk factors affecting the construction projects in the developing countries

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IOP Conference Series: Earth and Environmental Science 2019年第1期244卷

作者： R A. Bahamid S. I. Doh M A. Al-Sharaf Faculty of Civil Engineering and Earth Resources Universiti Malaysia Pahang 26300 Gambang Kuantan Pahang Malaysia Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang 26300 Gambang Kuantan Pahang Malaysia

This review focused on the identification of critical risk factors that influence the construction projects conducting in the developing countries. In order to attain this goal, a comprehensive literature review on risk management of the construction projects, particularly emphasizing on the developing countries was carried out. The identified risk factors can be a lead to achieve the goals of the project which includes time, cost and quality. This review used specific criteria to extract the risks factors based on the main purpose of the study. A sum total of 111 risk factors were figured out from the previous studies. Based on the criteria used for the classifications, 56 risk factors were observed as the most crucial risk factors influencing the construction projects of developing countries.

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Risk Factors Influencing the Construction Projects in Yemen from Expert's Perspective

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IOP Conference Series: Materials Science and engineering 2020年第1期712卷

作者： R A Bahamid S I Doh M A Al-Sharafi A R Rahimi Faculty of Civil Engineering Technology Universiti Malaysia Pahang 26300 Gambang Kuantan Pahang Malaysia Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang 26300 Gambang Kuantan Pahang Malaysia Malaysia

This purpose of this study is to validate the important risk factors influencing the construction projects in a developing country of Yemen by using experts' evaluations and literature review. These factors influence the project's quality, time and cost. The semi-structured interviews were employed through online means using thirteen experts to achieve the set objective. Depending on the expert's overviews, out of 56 considered factors, 42 factors were assigned as the most related risk factors influencing the construction projects in Yemen. The obtained outcomes from this study can serve as the risk factors for building blocks in establishing a conceptual model to manage risks of the construction projects in Yemen.

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A method for understanding intentions of indirect speech acts in natural language interfaces

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systems AND computerS IN JAPAN 1996年第1期27卷 77-91页

作者： Mima, H Aoe, J Fuketa, M Hayashi, Y Member Faculty of Engineering The University of Tokushima Tokushima Japan 770 Jun-ichi Aoe received his B.E. and M.E. degrees in Electronic Engineering from the University of Tokushima Tokushima Japan in 1974 and 1976 respectively and his Ph.D. degree in Communication Engineering from the University of Osaka Japan in 1980. Since 1976 he has been with the University of Tokushima. He is currently a Professor in the Department of Information Science and Intelligent Systems at the University of Tokushima. He is the author of about 50 scientific papers. His research interests include computer algorithms in software engineering and natural language processing. Dr. Aoe is editing Computer Algorithms Series of the IEEE Computer Society Press and author of two books: Computer Algorithms-Key Search Strategies in 1991 and String Pattern Matching Strategies in 1994. He received a Best Author Award from the Information Processing Society of Japan in 1993. He is a member of the Association for Computing Machinery the American Association for Artificial Intelligence the Association for Computational Linguistics and the Information Processing Society of Japan the Society for Software Science and Technology the Japanese Society for Artificial Intelligence and the Association for Natural Language Processing of Japan. Nonmember

In an attempt to propose a robust method for understanding natural language (NL) interface commands, a scheme is proposed that infers intentions from an indirect speech-act that does not express users' real intentions explicitly. This method classifies the real intentions of the indirect speech-act into: 1) refusal;2) reversal;3) restriction;4) benefit;and 5) disability. Further, concepts are abstracted for operations, e.g., displaying, moving, and deleting information systems;and constructing the operation knowledge base. This knowledge based comprises operational concepts and the relationships between them. These relationships are assigned the foregoing classifications for intentions. In addition, we construct the knowledge base of objects for the target of operations, e.g., files, figures, strings. This knowledge base contains the relationships: a) antonym;b) exclusive;c) part-of, between the objects;and uses these relations to infer the transitions between the objects. An algorithm is the proposed to infer concepts for operations and concepts for target objects of operations that may represent the user's actual intentions. This proposal scheme was tested with requests on UNIX and a commercially available Japanese Word Processor. The system successfully inferred the intentions for approximately 80 percent of the user's indirect speech-act.

关键词： natural language interface indirect speech act inference of intention

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Modal regression based structured low-rank matrix recovery for multi-view learning

arXiv

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arXiv 2020年

作者： Xu, Jiamiao Wang, Fangzhao Peng, Qinmu You, Xinge Wang, Shuo Jing, Xiao-Yuan Philip Chen, C.L. School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute China State Key Laboratory of Software Engineering School of Computer Wuhan University China Department of Computer and Information Science Faculty of Science and Technology University of Macau 99999 China Dalian Maritime University Dalian116026 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

Low-rank Multi-view Subspace Learning (LMvSL) has shown great potential in cross-view classification in recent years. Despite their empirical success, existing LMvSL based methods are incapable of well handling view discrepancy and discriminancy simultaneously, which thus leads to the performance degradation when there is a large discrepancy among multi-view data. To circumvent this drawback, motivated by the block-diagonal representation learning, we propose Structured Low-rank Matrix Recovery (SLMR), a unique method of effectively removing view discrepancy and improving discriminancy through the recovery of structured low-rank matrix. Furthermore, recent low-rank modeling provides a satisfactory solution to address data contaminated by predefined assumptions of noise distribution, such as Gaussian or Laplacian distribution. However, these models are not practical since complicated noise in practice may violate those assumptions and the distribution is generally unknown in advance. To alleviate such limitation, modal regression is elegantly incorporated into the framework of SLMR (term it MR-SLMR). Different from previous LMvSL based methods, our MR-SLMR can handle any zero-mode noise variable that contains a wide range of noise, such as Gaussian noise, random noise and outliers. The alternating direction method of multipliers (ADMM) framework and half-quadratic theory are used to efficiently optimize MR-SLMR. Experimental results on four public databases demonstrate the superiority of MR-SLMR and its robustness to complicated noise. Copyright © 2020, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

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