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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Hossain, Mohammad Alamgir Metwally, Abdelnasser Abdelwakil Khan, Asif Ali Amin, Sherif Tawfik Hamdan, Alfadil Ahmed Limkar, Suresh Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department ofComputer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department ofComputer and Network Engineering College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Artificial Intelligence & Data Science AISSMS Institute of Information Technology Maharashtra Pune India

ISBN: (纸本)9789819765805

CVD continues to pose a substantial global health burden, demanding the need for the creation of precise predictive models that can aid in the timely identification and evaluation of risks. This research paper aims to provide a thorough examination of the prediction of CVD by utilizing CNN in aggregation with hyperparameter tuning techniques. This paper examines the effectiveness of three widely recognized CNN architectures, namely ResNet, VGGNet, and Xception CVD. The Tree-structured Parzen Estimator (TPE) is utilized as a method for tuning hyperparameters in order to optimize the CNN models. The study commences with a comprehensive examination of the dataset, encompassing a wide array of cardiovascular CT image data. The chosen CNN architectures serve as the fundamental framework for our predictive models. The selection of these architectures is based on their established track record in effectively addressing image analysis tasks and their capacity to accurately capture complex patterns and features present in medical images. The models undergo training using a dataset in order to acquire knowledge about distinctive features that are linked to various cardiovascular conditions. The experimental findings presented in the study indicate that the utilization of the Xception architecture, in aggregation with TPE for hyperparameter optimization, yields the most favorable predictive accuracy. Specifically, the achieved accuracy rate of 98.26% is notably remarkable. This result highlights the significance of not only choosing advanced CNN architectures but also optimizing their hyperparameters to fully exploit their potential in predicting cardiovascular disease. This establishes it as a promising tool for the early diagnosis and risk assessment of CVD. This study establishes a fundamental basis for future investigations in the advancement of precise and dependable predictive models applicable to various medical imaging contexts. © The Author(s), under exclusive license

关键词： Diseases

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Windows Security Patch Auto-Management System Based on XML

Windows Security Patch Auto-Management System Based on XML

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International Conference on Advanced Communication Technology (ICACT)

作者： Jung-jin Park Jin-sub Park Bong-hoi Kim Jeong-gi Lee Geum-boon Lee Beom-joon Cho Department ofComputer Engineering Daejeon University South Korea Korea Electronics and Technology Institute South Korea Ubiquitous Network Communications Corporation South Korea Department ofComputer Engineering Chosun University South Korea

In recent days, damages to information systems and network due to worm and virus using vulnerabilities of windows security have been rapidly increasing. How to deal with the attack using vulnerabilities of windows program is to install patch appropriately and rapidly. This study suggests security patch auto-management system which installs security patch file automatically to clients through automatic downloading of the patch from MS download center based on XML as existing patch management system needs intervention of managers.

关键词： XML Information security File servers Computer security Technology management Computer networks Pervasive computing Computer worms Data security Communication system security

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The European Reference Genome Atlas: piloting a decentralised approach to equitable biodiversity genomics

NPJ Biodiversity

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NPJ Biodiversity 2024年第1期3卷 28页

作者： Mc Cartney, Ann M. Formenti, Giulio Mouton, Alice De Panis, Diego Marins, Luísa S. Leitão, Henrique G. Diedericks, Genevieve Kirangwa, Joseph Morselli, Marco Salces-Ortiz, Judit Escudero, Nuria Iannucci, Alessio Natali, Chiara Svardal, Hannes Fernández, Rosa De Pooter, Tim Joris, Geert Strazisar, Mojca Wood, Jonathan M. D. Herron, Katie E. Seehausen, Ole Watts, Phillip C. Shaw, Felix Davey, Robert P. Minotto, Alice Fernández, José M. Böhne, Astrid Alegria, Carla Alioto, Tyler Alves, Paulo C. Amorim, Isabel R. Aury, Jean-Marc Backstrom, Niclas Baldrian, Petr Baltrunaite, Laima Barta, Endre Bedhom, Bertrand Belser, Caroline Bergsten, Johannes Bertrand, Laurie Bilandija, Helena Binzer-Panchal, Mahesh Bista, Iliana Blaxter, Mark Borges, Paulo A. V. Dias, Guilherme Borges Bosse, Mirte Brown, Tom Bruggmann, Rémy Buena-Atienza, Elena Burgin, Josephine Buzan, Elena Cariani, Alessia Casadei, Nicolas Chiara, Matteo Chozas, Sergio Jr, Fedor Čiampor Crottini, Angelica Cruaud, Corinne Cruz, Fernando Dalen, Love Biase, Alessiode Delcampo, Javier Delic, Teo Dennis, Aliceb. Derks, Martijn F. L. Diroma, Maria Angela Djan, Mihajla Duprat, Simone Eleftheriadi, Klara Feulner, Philine G. D. Flot, Jean-François Forni, Giobbe Fosso, Bruno Fournier, Pascal Fournier-Chambrillon, Christine Gabaldon, Toni Garg, Shilpa Gissi, Carmela Giupponi, Luca Gomez-Garrido, Jessica González, Josefa Grilo, Miguel L. Grüning, Björn Guerin, Thomas Guiglielmoni, Nadege Gut, Marta Haesler, Marcel P. Hahn, Christoph Halpern, Balint Harrison, Peter W. Heintz, Julia Hindrikson, Maris Höglund, Jacob Howe, Kerstin Hughes, Graham M. Istace, Benjamin Cock, Mark J. Janžekovič, Franc Jonsson, Zophonias O. Joye-Dind, Sagane Koskimäki, Janne J. Krystufek, Boris Kubacka, Justyna Kuhl, Heiner Kusza, Szilvia Labadie, Karine Lähteenaro, Meri Lantz, Henrik Lavrinienko, Anton Leclère, Lucas Lopes, Ricardo Jorge Madsen, Ole Magdelenat, Ghislaine Magoga, Giulia Manousaki, Tereza Mappes, Tapio Marques, Joao Pedro Redondo, Gemma I. Martinez Maumus, Florian McCarthy, Shane A. M Genomics Institute University of California SantaCruz CA United States TheVertebrate Genome Laboratory The Rockefeller University NewYork NY United States Department of Biology University of Florence Sesto Fiorentino Italy InBios-Conservation Genetics Laboratory University of Liege Liege Belgium Leibniz Institut für Zoo und Wildtierforschung Berlin Germany Berlin Center for Genomics in Biodiversity Research Berlin Germany Department of Biology University of Antwerp Antwerp Belgium Institute of Zoology University of Cologne Cologne Germany Department of Chemistry Life Sciences and Environmental Sustainability University of Parma Parma Italy Institute of Evolutionary Biology (CSIC-Universitat Pompeu Fabra Barcelona Spain Naturalis Biodiversity Center Leiden Netherlands Neuromics Support Facility VIB Center for Molecular Neurology VIB Antwerp Belgium Neuromics Support Facility Department of Biomedical SciencesUniversity ofAntwerp Antwerp Belgium Tree of Life WellcomeSanger Institute Cambridge Hinxton United Kingdom School of Biology and Environmental Science University College Dublin Belfield Ireland Aquatic Ecology & Evolution Institute of Ecology & Evolution University of Bern Bern Switzerland Department of Fish Ecology & Evolution Eawag Kastanienbaum Switzerland Department of Biological and Environmental Science University of Jyvaskyla Jyvaskyla Finland The EarlhamInstitute NorwichResearchPark Norwich United Kingdom Digital Science London United Kingdom Barcelona Supercomputing Center Spanish National Bioinformatics Institute ELIXIR Spain Getafe Spain Leibniz Institute for the Analysis of Biodiversity Change Museum Koenig Bonn Bonn Germany CE3C—Centre for Ecology Evolution and Environmental Changes & CHANGE—Global Change and Sustainability Institute Faculdade de Ciências Universidade de Lisboa Campo Grande Lisboa Portugal Centro Nacional de Análisis Genómico (CNAG) Barcelona Spain Universitat de Barcelona (UB) Barcelona Spain CIBIO Cen

A genomic database of all Earth’s eukaryotic species could contribute to many scientific discoveries;however, only a tiny fraction of species have genomic information available. In 2018, scientists across the world united under the Earth BioGenome Project (EBP), aiming to produce a database of highquality reference genomes containing all ~1.5 million recognized eukaryotic species. As the European node of the EBP, the European Reference Genome Atlas (ERGA) sought to implement a new decentralised, equitable and inclusive model for producing reference genomes. For this, ERGA launched a Pilot Project establishing the first distributed reference genome production infrastructure and testing it on 98 eukaryotic species from 33 European countries. Here we outline the infrastructure and explore its effectiveness for scaling high-quality reference genome production, whilst considering equity and inclusion. The outcomes and lessons learned provide a solid foundation for ERGA while offering key learnings to other transnational, national genomic resource projects and the EBP. © The Author(s) 2024, corrected publication 2024.

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