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2022 International Conference on Emerging Technologies in Electronics, computing and Communication, ICETECC 2022

作者： Susa, Julie Ann B. Dellosa, Rhowel M. Doculan, Jo Ann D. Jallorina, Roldan D. Evangelista, Ryan Soriente Zapanta, Godofredo S. Mindoro, Jennalyn N. Southern Luzon State University Dept. of Computer Engineering Quezon Lucban Philippines University of Northern Philippines University Research and Development Office Vigan Philippines Ifugao State University College of Computing Sciences Ifugao Philippines Southern Luzon State University Agriculture Department Quezon Infanta Philippines Sulu State College School of Computer Studies and Engineering Sulu Philippines Pamantasan Ng Lungsod Ng Pasig College of Engineering Pasig Philippines Technological Institute of the Philippines Department of Computer Engineering Manila Philippines

ISBN: (纸本)9781665490870

Despite the advancements in many chemical medications used to treat diseases, medicinal plants have also been very effective in doing so. The global use of herbal products is increasing due to the growing contribution of science and technology to its ethical and scientific growth. In the Philippines, medicinal plants or herbs are popular in treating illness. Although the traditional identification method is effective and requires the expertise of skilled practitioners, it is time-consuming and prone to mistakes. Studies identifying the therapeutic use of naturally occurring plant chemicals have now been publicly disclosed that automatically detect the species of medicinal plants and leaves using machine vision and deep learning. This led to the first phase of this work, which used the readily available Philippine medicinal plants to identify the leaves automatically. With transfer learning using YOLOv3, this study seeks to identify Philippine medicinal plants in real-time. Only segmented leaves of Basella alba (alugbati), Mentha (mint), Moringa oleifera (malungay), Nerium oleander (adelfa), and Psidium guajava (bayabas) are used in the identification of medicinal leaves. The identification of the images utilized an inference approach in the real-time application based on the extracted features using YOLOv3. The result of the test performed illustrates an optimal outcome in the detection of different medicinal leaves. The optimal model performance results to mAP 98.63%. The test summary demonstrates a high detection accuracy and produces results at a fast speed. Thus, various model inferences using input images, live feed, and video inputs exemplify the model's effectiveness in detecting the inputs medical leaves classes. © 2022 IEEE.

关键词： Deep learning

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Identification of Philippine Therapeutic Leave using Deep Learning

Identification of Philippine Therapeutic Leave using Deep Le...

引用

Emerging Technologies in Electronics, computing and Communication (ICETECC), International Conference on

作者： Julie Ann B. Susa Rhowel M. Dellosa Jo Ann D. Doculan Roldan D. Jallorina Ryan Soriente Evangelista Godofredo S. Zapanta Jennalyn N. Mindoro Dept. of Computer Engineering Southern Luzon State University Lucban Quezon Philippines University Research and Development Office University of Northern Philippines Vigan Philippines College of Computing Sciences Ifugao State University Ifugao Philippines Agriculture Department Southern Luzon State University Infanta Quezon Philippines School of Computer Studies and Engineering Sulu State College Sulu Philippines College of Engineering Pamantasan ng Lungsod ng Pasig Pasig Philippines Department of Computer Engineering Technological Institute of the Philippines Manila Philippines

Despite the advancements in many chemical medications used to treat diseases, medicinal plants have also been very effective in doing so. The global use of herbal products is increasing due to the growing contribution of science and technology to its ethical and scientific growth. In the Philippines, medicinal plants or herbs are popular in treating illness. Although the traditional identification method is effective and requires the expertise of skilled practitioners, it is time-consuming and prone to mistakes. Studies identifying the therapeutic use of naturally occurring plant chemicals have now been publicly disclosed that automatically detect the species of medicinal plants and leaves using machine vision and deep learning. This led to the first phase of this work, which used the readily available Philippine medicinal plants to identify the leaves automatically. With transfer learning using YOLOv3, this study seeks to identify Philippine medicinal plants in real-time. Only segmented leaves of Basella alba (alugbati), Mentha (mint), Moringa oleifera (malungay), Nerium oleander (adelfa), and Psidium guajava (bayabas) are used in the identification of medicinal leaves. The identification of the images utilized an inference approach in the real-time application based on the extracted features using YOLOv3. The result of the test performed illustrates an optimal outcome in the detection of different medicinal leaves. The optimal model performance results to mAP 98.63%. The test summary demonstrates a high detection accuracy and produces results at a fast speed. Thus, various model inferences using input images, live feed, and video inputs exemplify the model’s effectiveness in detecting the inputs medical leaves classes.

关键词： Deep learning Image segmentation Computational modeling Transfer learning Object segmentation Feature extraction Real-time systems

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Enhance the Mammogram Images for Both Segmentation and Feature Extraction Using Wavelet Transform

Enhance the Mammogram Images for Both Segmentation and Featu...

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2019 International Conference on Advanced Science and Engineering, ICOASE 2019

作者： Zebari, Dilovan Asaad Haron, Habibollah Zeebaree, Subhi R.M. Zeebaree, Diyar Qader Faculty of Engineering School of Computing University Teknologi Malaysia Johor Malaysia University Teknologi Malaysia Faculty of Computing Department of Computer Science Johor Malaysia IT Dept. Technical College of Informatics Akre Duhok Scientific Research and Development Center Nawroz University Duhok Iraq

ISBN: (纸本)9781538693438

Breast cancer (BC) is a main killer disease for women and men. It can be cured and controlled only if it is detected at its early detection. BC initial identification can be realized by the help of computer support identification approaches. From the detailed study on previous researches, it is found that, there is no system producing high accuracy because of one or more reasons. Absence of effective preprocessing is the discussed reason that obstructs the detection accuracy of Computer-Aided diagnosis (CAD) method. Noise removal and contrast enhancement are the two types of preprocessing. There is no system performs the preprocessing on mammogram image. This work is an attempt to develop an enhanced preprocessing method for CAD of breast cancer by incorporating suitable noise reduction and contrast enhancement methods in the conventional CAD system. Contrast enhancement after noise reduction double enhances the mammogram image and the proposed methods MSE value for the mammogram image mdb072 has been 1.44% reduced. Reduction in MSE increases the PSNR to 0.16%. Many mammogram images have been tested and the result shows that, increase in contrast, decrease in mean square error and increase in peak signal to noise ratio when comparing to existing methods. © 2019 IEEE.

关键词： Wavelet transforms

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Design of Tree-Like Database Structure for Solving Test Modeling Tasks of Energy Equipment Training

Design of Tree-Like Database Structure for Solving Test Mode...

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International Conference on Information Technologies in Engineering Education (Inforino)

作者： Ivan Komarov Andrey Vegera Ali Zein Svetlana Borisova Svetlana Blazhenova Alexander Gavrilov Center for Innovative Development National Research University "MPEI" Moscow Russia Dep.of Innovative Technologies for High-Tech Industries National Research University "MPEI" Moscow Russia Dept. of Computing Machines Systems and Networks National Research University "MPEI" Moscow Russia

ISBN: (数字)9781728148106

ISBN: (纸本)9781728148113

Large enterprises of power engineering (PJSC Power Machines, JSC ROTEK, etc.) often have a large amount of experimental data that is used as a verification basis for the results of numerical simulation. In this case, the task arises of storing the received data and its use for training employees. Small companies do not have full experience and sufficient data to select the optimal settings, which can lead to high accuracy modeling. The solution is to create a database containing recommendations for choosing the settings of the grid and the solver module of modeling software. Recommendations can be generated by verification experiences and simulation results using experimental data.

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research of Attention-Based Bi-GRU-CRF for Slot Filling

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Journal of Physics: Conference Series 2021年第1期1757卷

作者： Luwang Zhou Zhimin Huang Ying Nie Dept. of Geography Graphics and Image Research and Development North China Institute of Computing Technology No.211 North Fourth Ring Road Beijing 100084 PR China

Slot Filling (SF) is a critical part of spoken language understanding (SLU) which targets to capture semantic constituents from a specific utterance. It is considered as a sequence labeling issue. Currently, recurrent neural networks have shown promising effectiveness in this issue. Considering the effects of interrelated information within adjacent words and labels, we present that a novel approach consists of bi-directional gate recurrent unit (Bi-GRU), attention mechanism, and conditional random field (CRF). Our model can utilize interrelated information from words in the neighborhood, highlight key information, and exploit the dependencies within labels corresponding to surrounding words. The empirical experiments illustrate that our model significantly boosts the F1 score with around 1% and 5.1% relative enhancement on two public benchmark dataset ATIS and SNIPS separately.

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1O Kinetics and determinants of blood ESR1 mutation under AI and palbociclib in HR+/HER2- metastatic breast cancer patients in the PADA-1 trial

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ESMO Open 2025年 10卷 104556-104556页

作者： L. Cabel T. Bachelot A-C. Hardy-Bessard B. Pistilli F. Dalenc T. De La Motte Rouge C. Callens R. Sabatier J-S. Frenel S. Ladoire J. Grenier L. Stefani H. Vegas I. Bieche S. Delaloge A. Pradines J. Lemonnier F. Berger F.C. Bidard Medical Oncology Dept. Institut Curie Paris France Medical Oncology Department Centre Léon Bérard Lyon France Cotes d'Armor CARIO - Centre Armoricain de Radiothérapie d’Imagerie Médicale et d’Oncologie Plérin France Breast Cancer Group Institut Gustave Roussy Villejuif France Medical Oncology Centre Hospitalier Universitaire de Toulouse - Hopital Rangueil Toulouse France Medical Oncology Dept. Centre Eugene - Marquis Rennes France Genetics Institut Curie Paris France Medical Oncology Dept. IPC - Institut Paoli-Calmettes Marseille France Medical Oncology Department ICO Institut de Cancerologie de l'Ouest René Gauducheau Saint-Herblain France Oncology Dept. Centre Georges-François Leclerc (Dijon) Dijon France Oncology Institut Ste Catherine Avignon France Oncology Centre Hospitalier Annecy Genevois Annecy France Oncology CHRU Hopitaux de Tours - Hopital Bretonneau Tours France Genetics Department Institut Curie Paris France Breast Oncology Department Institut Gustave Roussy Villejuif France Biology IUCT - Institut Universitaire du Cancer de Toulouse - Oncopole Toulouse France Research And Development Dept. Unicancer Paris France Statistics Institut Curie Saint-Cloud France Medical Oncology Department Institut Curie Paris France

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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-dept. 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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Overcoming the disconnect between energy system and climate modelling

arXiv

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arXiv 2022年

作者： Craig, Michael T. Wohland, Jan Stoop, Laurens P. Kies, Alexander Pickering, Bryn Bloomfield, Hannah C. Browell, Jethro De Felice, Matteo Dent, Chris J. Deroubaix, Adrien Frischmuth, Felix Gonzalez, Paula L.M. Grochowicz, Aleksander Gruber, Katharina Härtel, Philipp Kittel, Martin Kotzur, Leander Labuhn, Inga Lundquist, Julie K. Pflugradt, Noah van der Wiel, Karin Zeyringer, Marianne Brayshaw, David J. School for Environment and Sustainability University of Michigan United States Institute for Environmental Decisions ETH Zurich Switzerland Helmholtz-Zentrum Hereon Germany Information and Computing Science Copernicus Institute of Sustainable Development Utrecht University Netherlands TenneT TSO B.V. Arnhem Netherlands Royal Netherlands Meteorological Institute Netherlands Frankfurt Institute for Advanced Studies Germany Department of Electrical and Computer Engineering Aarhus University Denmark ETH Zürich Switzerland School of Geographical Sciences University of Bristol United Kingdom Department of Meteorology University of Reading United Kingdom University of Glasgow United Kingdom European Commission Joint Research Centre Netherlands School of Mathematics University of Edinburg United Kingdom The Alan Turing Institute United Kingdom Max Planck Institute Germany Fraunhofer Institute for Energy Economics and Energy System Technology Germany Department of Mathematics University of Oslo Norway University of Natural Resources and Life Sciences Austria German Institute for Economic Research Germany Institute of Energy and Climate Research Techno-economic Systems Analysis Germany University of Bremen Institute of Geography Germany Dept. of Atmospheric and Oceanic Science University of Colorado Boulder United States National Renewable Energy Laboratory United States Renewable and Sustainable Energy Institute United States Forschungszentrum Jülich Germany Department of Technology Systems University of Oslo Norway

Energy system models underpin decisions by energy system planners and operators. Energy system modelling faces a transformation: accounting for changing meteorological conditions imposed by climate change. To enable that transformation, a community of practice in energy-climate modelling has started to form that aims to better integrate energy system models with weather and climate models. Here, we evaluate the disconnects between the energy system and climate modelling communities, then lay out a research agenda to bridge those disconnects. In the near-term, we propose interdisciplinary activities for expediting uptake of future climate data in energy system modelling. In the long-term, we propose a transdisciplinary approach to enable development of (1) energy-system-tailored climate datasets for historical and future meteorological conditions and (2) energy system models that can effectively leverage those datasets. This agenda increases the odds of meeting ambitious climate mitigation goals by systematically capturing and mitigating climate risk in energy sector decision making. © 2022, CC BY-NC-ND.

关键词： Decision making

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Promoting Secure Email Communication and Authentication

Promoting Secure Email Communication and Authentication

引用

Mensch und Computer 2017, MuC 2017 - Workshopband - Human and Computer 2017, MuC 2017 - Workshop Proceedings

作者： Zimmermann, Verena Henhapl, Birgit Gerber, Nina Enzmann, Matthias Research Group Work and Engineering Psychology Technische Universität Darmstadt Germany Research Group Security Usability & Society Technische Universität Darmstadt Germany Dept. for Cloud Computing and Identity & Privacy Fraunhofer SIT Darmstadt Germany

Nowadays, the possibility to communicate securely is crucial for users in the private as well as in the business context. However, to do so they have to face problems regarding mismatching mental models of encryption and bad usability not only concerning the encryption, but also the authentication process. To solve this problem, we evaluate users’ perception on encryption and authentication schemes in order to (1) derive a process, which is more in line with their expectations and (2) use authentication schemes which provide security but also achieve a high acceptance rate from users. We plan to integrate our findings into a prototypical software in order to evaluate users’ acceptance for our technical approach. Copyright (C) 2017 bei den Autoren.

关键词： Authentication

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A review of cloud computing simulation platforms & related environments 7

A review of cloud computing simulation platforms & related e...

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7th International Conference on cloud computing and Services Science, CLOSER 2017

作者： Byrne, James Svorobej, Sergej Giannoutakis, Konstantinos M. Tzovaras, Dimitrios Byrne, P.J. Östberg, Per-Olov Gourinovitch, Anna Lynn, Theo Irish Centre for Cloud Computing and Commerce Dublin City University Glasnevin Dublin 9 Ireland Centre for Research and Technology Hellas Information Technologies Institute 6th km Xarilaou-Thermi Thessaloniki57001 Greece Dept. Computing Science and HPC2N Umeå University UmeaSE-901 87 Sweden

ISBN: (纸本)9789897582431

Recent years have seen an increasing trend towards the development of Discrete Event Simulation (DES) platforms to support cloud computing related decision making and research. The complexity of cloud environments is increasing with scale and heterogeneity posing a challenge for the efficient management of cloud applications and data centre resources. The increasing ubiquity of social media, mobile and cloud computing combined with the Internet of Things and emerging paradigms such as Edge and Fog computing is exacerbating this complexity. Given the scale, complexity and commercial sensitivity of hyperscale computing environments, the opportunity for experimentation is limited and requires substantial investment of resources both in terms of time and effort. DES provides a low risk technique for providing decision support for complex hyperscale computing scenarios. In recent years, there has been a significant increase in the development and extension of tools to support DES for cloud computing resulting in a wide range of tools which vary in terms of their utility and features. Through a review and analysis of available literature, this paper provides an overview and multi-level feature analysis of 33 DES tools for cloud computing environments. This review updates and extends existing reviews to include not only autonomous simulation platforms, but also on plugins and extensions for specific cloud computing use cases. This review identifies the emergence of cloudSim as a de facto base platform for simulation research and shows a lack of tool support for distributed execution (parallel execution on distributed memory systems). ©2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

关键词： Fog computing

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