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Dimensional reduction using conditional entropy for incomplete information systems 1

15th International Conference on Parallel Computing Technologies, PaCT 2019

作者： Deris, Mustafa Mat Senan, Norhalina Abdullah, Zailani Mamat, Rabiei Handaga, Bana Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia Faculty of Entrepreneurship and Business Universiti Malaysia Kelantan Pengkalan Chepa Kota BharuKelantan16100 Malaysia Faculty of Informatics and Applied Mathematics University of Malaysia Terengganu Kuala TerengganuTerengganu21030 Malaysia Program Studi Informatika Universitas Muhammadiah Surakarta SurakartaCentral Java57162 Indonesia

ISBN: (数字)9783030256364

ISBN: (纸本)9783030256357

Dimension reduction approach is one of the main data reduction approaches in order to reduce the storage and processing time while maintaining the integrity of the original data. A wide range of dimension reduction approaches are based on classical approaches such as PCA and Bayer’s, and machine learning approaches such as clustering, and feature selection techniques. However, many of the approaches do not consider the incomplete information systems where some attribute values are missing or incomplete. Only few studies were proposed for the problem in incomplete information systems due to its complexities, specifically on attribute selection. The most popular approaches is based on probability theory to replace missing values with the most common values, or remove the missing objects from the information systems. However, it needs to know the probability distribution of data in advance. To overcome these issues, we propose a new approach based on conditional entropy to reduce dimensionality. The results show that the proposed approach achieves better data reduction with higher accuracy for objects and dimensionality reduction in incomplete information systems. © 2019, Springer Nature Switzerland AG.

关键词： Data reduction

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Comparison Of Multinomial Naive Bayes Algorithm And Logistic Regression For Intent Classification In Chatbot

Comparison Of Multinomial Naive Bayes Algorithm And Logistic...

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International Conference on applied Engineering (ICAE)

作者： Muhammad Yusril Helmi Setyawan Rolly Maulana Awangga Safif Rafi Efendi Applied Bachelor Program of Informatics Engineering Politeknik Pos Indonesia Bandung Indonesia

ISBN: (纸本)9781538680674

Chatbot is software that communicates using natural language. chatbots such as machine conversation systems, Chatterbot, virtual agents, and dialogue systems. This software enables to simulate human conversations. In this research, the chatbot system that will be created must be able to understand the natural language of what is entered by the user, and the chatbot will answer according to what the user is expecting. The researcher proposes a classification method to identify intent rather than user input or called intent classification on the chatbot system; the researcher also wants to know the level of accuracy, precision, and recall on the evaluation results of both methods. The classification method applied in this research is the Naive Bayes method and compared with the Logistic Regression method to determine the class intention. The evaluation results show the level of accuracy precision and recall in the Logistic Regression model is higher than the Naive Bayes model.

关键词： Logistics Training data Bayes methods Mathematical model Data models Classification algorithms Natural languages

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An algorithm based on Voronoi diagrams for the multi-stream multi-source multicast routing problem

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International Journal of Innovative Computing and Applications 2018年第4期9卷 216-229页

作者： De Andrade, Romerito Campos Goldbarg, Marco César Goldbarg, Elizabeth Ferreira Gouveâ Postgraduate Program in Systems and Computation Federal University of Rio Grande Do Norte Campus Universitário Lagoa Nova Natal RN Brazil Department of Informatics and Applied Mathematics Federal University of Rio Grande Do Norte Campus Universitário Lagoa Nova Natal RN Brazil

In this study, we present a new heuristic for the multi-stream multi-source multicast routing problem. The core of the heuristic proposed in this study is based on a generalisation of Voronoi diagrams in graphs. It allows building the trees needed to serve the demands of multiple sessions efficiently. Also, the proposed algorithm supports multiple sources. We performed an extensive experimental analysis of different network and problem configurations such as the number of sessions, nodes, sources and participants per session. We compare the proposed algorithm to heuristics proposed previously. The results of the experiments showed that the heuristic proposed in this study finds high-quality solutions efficiently. © 2018 Inderscience Enterprises Ltd.

关键词： Multicasting

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Stroke Lesion Detection Using Convolutional Neural Networks

Stroke Lesion Detection Using Convolutional Neural Networks

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2018 International Joint Conference on Neural Networks, IJCNN 2018

作者： Pereira, Danillo Roberto Filho, Pedro P. Rebouças De Rosa, Gustavo Henrique Papa, João Paulo De Albuquerque, Victor Hugo C. Department of Computing São Paulo State University Bauru SP Brazil Federal Institute of Education Science and Technology of Ceará CE Brazil Graduate Program in Applied Informatics University of Fortaleza Fortaleza CE Brazil

ISBN: (纸本)9781509060146

Stroke is an injury that affects the brain tissue, mainly caused by changes in the blood supply to a particular region of the brain. As consequence, some specific functions related to that affected region can be reduced, decreasing the quality of life of the patient. In this work, we deal with the problem of stroke detection in Computed Tomography (CT) images using Convolutional Neural Networks (CNN) optimized by Particle Swarm optimization (PSO). We considered two different kinds of strokes, ischemic and hemorrhagic, as well as making available a public dataset to foster the research related to stroke detection in the human brain. The dataset comprises three different types of images for each case, i.e., the original CT image, one with the segmented cranium and an additional one with the radiological density's map. The results evidenced that CNN's are suitable to deal with stroke detection, obtaining promising results. © 2018 IEEE.

关键词： Convolution

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Performance of multimodal prediction models for intracerebral hemorrhage outcomes using real-world data

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International Journal of Medical informatics 2025年 202卷 105989-105989页

作者： Matsumoto, Koutarou Suzuki, Masahiro Ishihara, Kazuaki Tokunaga, Koki Matsuda, Katsuhiko Chen, Jenhui Yamashiro, Shigeo Soejima, Hidehisa Nakashima, Naoki Kamouchi, Masahiro Department of Health Care Administration and Management of Medical Sciences Kyushu University Fukuoka Japan Graduate Degree Program of Applied Data Sciences Sophia University Tokyo Japan Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan Department of Pharmacy Saiseikai Kumamoto Hospital Kumamoto Japan Department of Radiology Saiseikai Kumamoto Hospital Kumamoto Japan Division of Neurosurgery Saiseikai Kumamoto Hospital Kumamoto Japan Institute for Medical Information Research and Analysis Saiseikai Kumamoto Hospital Kumamoto Japan Medical Information Center Kyushu University Hospital Fukuoka Japan Department of Medical Informatics of Medical Sciences Kyushu University Fukuoka Japan Center for Cohort Studies of Medical Sciences Kyushu University Fukuoka Japan

Background: We aimed to develop and validate multimodal models integrating computed tomography (CT) images, text and tabular clinical data to predict poor functional outcomes and in-hospital mortality in patients with intracerebral hemorrhage (ICH). These models were designed to assist non-specialists in emergency settings with limited access to stroke specialists. Methods: A retrospective analysis of 527 patients with ICH admitted to a Japanese tertiary hospital between April 2019 and February 2022 was conducted. Deep learning techniques were used to extract features from three-dimensional CT images and unstructured data, which were then combined with tabular data to develop an L1-regularized logistic regression model to predict poor functional outcomes (modified Rankin scale score 3–6) and in-hospital mortality. The model's performance was evaluated by assessing discrimination metrics, calibration plots, and decision curve analysis (DCA) using temporal validation data. Results: The multimodal model utilizing both imaging and text data, such as medical interviews, exhibited the highest performance in predicting poor functional outcomes. In contrast, the model that combined imaging with tabular data, including physiological and laboratory results, demonstrated the best predictive performance for in-hospital mortality. These models exhibited high discriminative performance, with areas under the receiver operating curve (AUROCs) of 0.86 (95% CI: 0.79–0.92) and 0.91 (95% CI: 0.84–0.96) for poor functional outcomes and in-hospital mortality, respectively. Calibration was satisfactory for predicting poor functional outcomes, but requires refinement for mortality prediction. The models performed similar to or better than conventional risk scores, and DCA curves supported their clinical utility. Conclusion: Multimodal prediction models have the potential to aid non-specialists in making informed decisions regarding ICH cases in emergency departments as part of clinical dec

关键词： Logistic regression

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μ-brkga: A parallel biased random-key genetic algorithm applied to nesting problems 47

μ-brkga: A parallel biased random-key genetic algorithm app...

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47th International Conference on Computers and Industrial Engineering: How Digital Platforms and Industrial Engineering are Transforming Industry and Services, CIE 2017

作者： Amaro, Bonfim Pinheiro, Plácido Rogério Graduate Program in Applied Informatics University of Fortaleza Fortaleza Brazil

The Nesting Problems are a special class of cutting and packing (C&P) problems in which a set of items with arbitrary geometry must be positioned in a surface with a non-fixed length, aiming a minimization of the require area for allocation of all given demand. It is commonly applied in industries that operate in various types of manufacturers and exporters activities (e.g., shipbuilding, clothes, and glass). In this work, we propose a parallel biased random-key genetic algorithm with multiple populations (μ-BRKGA) to the Nesting Problems using a collision-free region (CFR) concept as position method, intending at a more efficient and fastest layout compaction to get "good" solutions. The problem layout of the proposed algorithm, a solution, is represented for placement order in the surface and the corresponding orientation. As the way of to evaluate our (μ-BRKGA) algorithm, computational tests using benchmark instances were applied, discussed and compared with different existing literature methods.

关键词： Genetic algorithms

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Generation of Terahertz Transients from Co2Fe0.4Mn0.6Si Heusler alloy/Heavy-Metal Bilayers

Generation of Terahertz Transients from Co2Fe0.4Mn0.6Si Heus...

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International Conference on Infrared and Millimeter Waves

作者： S. Heidtfeld R. Adam T. Kubota K. Takanashi S. Suga D. Cao C. Greb F. Wang C. Schmitz-Antoniak D.E. Bürgler G. Chen I. Komissarov R. Sobolewski C. M. Schneider Research Centre Jülich Peter Grünberg Institute Jülich Germany University Duisburg-Essen Duisburg Germany Institute for Materials Research Tohoku University Sendai Japan Center for Spintronics Research Network Tohoku University Sendai Japan Center for Science and Innovation in Spintronics (Core Research Cluster) Tohoku University Sendai Japan Institute of Scientific and Industrial Research Osaka University Ibaraki Japan College of Physics National Demonstration Center for Applied Physics Education Qindao University Qingdao China Materials Science Graduate Program University of Rochester Rochester New York USA University of Rochester Rochester New York USA Belarusian State University of Informatics and Radioelectronics Minsk Belarus University of California Davis Davis California USA

We generated electro magnetic transients with a frequency content up to the terahertz regime from Co 2 Fe 0.4 Mn 0.6 Si Heusler alloy/heavy-metal bilayers by excitation with fs-laser pulses. We ascribe the generation process to the inverse spin Hall effect. We compared our results with ferromagnetic resonance measurements to investigate the efficiency of interfacial spin currents. We observed that the efficiency of the THz radiation can be described by the spin-orbit coupling, nonetheless the interface properties play an important role.

关键词： Ferromagnetic resonance Couplings Current measurement Hall effect Transient analysis

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Task assignment to distributed teams based on a qualitative multi-criteria approach 12

Task assignment to distributed teams based on a qualitative ...

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12th Iberian Conference on Information Systems and Technologies, CISTI 2017

作者： Filho, Marum Simao Pinheiro, Placido Rogerio Albuquerque, Adriano Bessa Graduate Program in Applied Informatics University of Fortaleza / 7 de Setembro University Center Fortaleza Brazil Graduate Program in Applied Informatics University of Fortaleza Fortaleza Brazil

ISBN: (纸本)9789899843479

The high growth of companies in recent years has caused the transformation of local markets into global ones. The software industry is not stranger to this transformation and has been looking for effective ways to work in a distributed scenario. A critical activity in distributed projects concerns the assignment of tasks to remote teams. This is a multicriteria decision-making problem and can be supported by Verbal Decision Analysis approaches. This paper describes an approach based on Verbal Decision Analysis methods to aid decision on factors that influence the assignment of tasks to distrubuted teams. The approach is based on the ORCLASS and ZAPROS III-i methods. The first results showed that factors such as 'low turnover rate' and 'team maturity' are among those considered more important, and the factors 'personal trust', 'low labor cost', 'cultural similarities' and 'time zone' were considered less relevant in DSD context. © 2017 AISTI.

关键词： Decision making

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Experiments with universal CEFR classification

arXiv

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arXiv 2018年

作者： Vajjala, Sowmya Rama, Taraka Applied Linguistics and Technology Program Iowa State University United States Department of Informatics University of Oslo Norway

The Common European Framework of Reference (CEFR) guidelines describe language proficiency of learners on a scale of 6 levels. While the description of CEFR guidelines is generic across languages, the development of automated proficiency classification systems for different languages follow different approaches. In this paper, we explore universal CEFR classification using domainspecific and domain-agnostic, theory-guided as well as data-driven features. We report the results of our preliminary experiments in monolingual, cross-lingual, and multilingual classification with three languages: German, Czech, and Italian. Our results show that both monolingual and multilingual models achieve similar performance, and cross-lingual classification yields lower, but comparable results to monolingual classification. Copyright © 2018, The Authors. All rights reserved.

关键词： Classification (of information)

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