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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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Bidirectional LSTM-Based Energy Consumption Forecasting:Advancing AI-Driven Cloud Integration for Cognitive City Energy Management

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Computers, Materials & Continua 2025年第5期83卷 2907-2926页

作者： Sheik Mohideen Shah Meganathan Selvamani Mahesh Thyluru Ramakrishna Surbhi Bhatia Khan Shakila Basheer Wajdan Al Malwi Mohammad Tabrez Quasim Department of Computer Science and Engineering Srinivasa Ramanujan CentreSASTRA Deemed UniversityKumbakonam612001India Department of Computer Science and Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bangalore562112India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and TechnologyChitkara UniversityRajpura140401PunjabIndia Department of Information Systems College ofComputer and Information SciencePrincessNourah bintAbdulrahmanUniversityP.O.Box 84428Riyadh11671Saudi Arabia College of Computer Science Informatics and Computer Systems Department King Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and *** study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)*** a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear *** bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal *** design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and *** to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these *** results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive *** integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.

关键词： Deep learning bidirectional LSTM energy consumption forecasting time-series analysis predictive modeling machine learning in energy management

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Attribute Assailability and Sensitive Attribute Frequency based Data Generalization Algorithm for Privacy Preservation

Attribute Assailability and Sensitive Attribute Frequency ba...

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2021 International Conference on Forensics, Analytics, Big Data, Security, FABS 2021

作者： Basapur, Satish B. Shylaja, B.S. Venkatesh Dr. Ambedkar Institute of Technology Department of Information Science and Engineering Bengaluru560056 India Dr. Ambedkar Institute of Technology Department OfComputer Science and Engineering Bengaluru560056 India University Visvesvaraya College of Engineering Bangalore University Department of Computer Science and Engineering Bengaluru560 001 India

ISBN: (纸本)9781665420051

The data publishing and analysis expose users' and users group's sensitive data and their identities. As a result, the adversary initiate inference attack using exposed users' identifies and associated sensitive information. Some of the users' attribute and users' group attributes values are highly vulnerable and their contiguity increase privacy breach. The traditional k-anonymity based privacy models prevent identity disclosure but fails to prevent sensitive attribute values disclosure and ignore repercussion of assailable attributes on users' group privacy. This research propose a novel data generalization algorithm that enhances users' group privacy and data utility. The proposed method calculate the susceptibility of quasi-identifier attributes, frequency of sensitive attributes and their association. These values are used for form equivalence class that satisfies k-anonymity and β-likeness property. The proposed generalization algorithm considers both susceptibility, frequency of sensitive attributes and their association while anonymizing data. The extensive experiments are carried out on open source Apache Spark environment. The simulation results demonstrate that efficiency and effectiveness of proposed algorithm. This research work outperform other state-art data privacy model in terms of thwarting inference attack, information loss and accuracy of model. © 2021 IEEE.

关键词： Sensitive data

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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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A scenario-based model to improve the quality of software inspection process

A scenario-based model to improve the quality of software in...

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4th International Conference on Computational Intelligence, Modelling and Simulation, CIMSim 2012

作者： Taba, Navid Hashemi Ow, Siew Hock Department of Software Engineering Faculty OfComputer Science and Information Technology University of Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9781467331135

Independent researches of expert groups and scientific papers show that , although some testing models and automated and manual tools are mostly used, but still some software and software projects deal with important defects that may lead to project failure. Most of software inspection models are independent of software developing process and concentrating on inspection artifacts. In this article, the proposed model is based on software developing phases adapted predefined scenarios. This inspection approach removes some possible defects in each of software development phase and avoids amplifying these defects in each of the next phases. Defect learning is the interesting point of scenario-based proposed model. Learning is a basic factor for a model to be intelligent. In this model a learning plan is created and executed through which, according to founded results, inspection process is modified and updated. The efficiency of model is evaluated through a case study. © 2012 IEEE.

关键词： Software testing

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A novel approach for personalizing the web using user profiling ontologies

A novel approach for personalizing the web using user profil...

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International Conference on Advanced Computing, ICAC

作者： S. Vigneshwari M. Aramudhan LResearch Scholar Department ofComputer Science and Engineering Sathyabama University Chennai Tamil Nadu India Department of Information Technology Perunthalaivar Kamarajar Institute of Science and Technology Karaikal Tamil Nadu India

Web personalization has become the recent trend in the internet era. There are various means for web personalization. On such approach is by creating ontologies and personalizing the web. Ontologies play an important role in the creation of the semantic web. In this paper a novel approach has been proposed for creation of user profiling ontologies(UPO) which is useful for personalizing the web. In this approach a cross ontology mechanism has been applied. The user queries are continuously monitored. Then the stemming process takes place and important keywords are extracted and the frequently visited web pages are ranked. Based on this UPO is automatically generated. Here ontology mapping mechanism is followed for mapping the UPO with that of the global ontology and thus the web pages are personalized which are compatible to user queries. Data samples are collected and the results show a good improvement using this approach.

关键词： Ontologies Data mining Mutual information Knowledge based systems Web pages Indexes Data models

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Global optimization based image inpainting andits implementation on GPU

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Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (engineering science) 2011年第2期45卷 247-252页

作者： Liu, Jian-Ming Lu, Dong-Ming Ge, Rong College of Computer Science and Technology Zhejiang University Hangzhou 310027 China Department ofComputer and Information Engineering Jiangxi Normal University Nanchang 330022 China

Since greedy image inpainting algorithms might cause visual inconsistency, and global optimization based inpainting algorithms didn't consider structure information, a new fast image inpainting algorithm based on global optimization is proposed. The image inpainting was formulated as a global optimization problem by defining an improved energy function, and a non-local mean based label pruning algorithm was adopted to cut down the number of labels for each node so as to reduce the complexity of the optimization algorithm significantly. Graphic processing unit (GPU) was used to further improve the computing speed. Compared with greedy synthesis and global optimization based image inpainting methods, the proposed approach not only avoids the inter-block discontinuity but also achieves better efficiency.

关键词： Graphics processing unit

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Assessing and improving authentication confidence management

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information Management and Computer Security 2010年第2期18卷 124-139页

作者： Pearce, Michael Zeadally, Sherali Hunt, Ray Department of Computer Science and Software Engineering University of Canterbury Christchurch New Zealand Department of Computer Science and Information Technology University of the District of Columbia Washington DC United States Department OfComputer Science and Software Engineering University of Canterbury Christchurch New Zealand

Purpose-The purpose of this paper is to address some weaknesses in the handling of current multi-factor authentication, suggests some criteria for overcoming these weaknesses and presents a simple proof of concept authentication system. Design/methodology/approach-First, this paper evaluates some of the underlying practices and assumptions in multi-factor authentication systems. Next, the paper assesses the implications of these when compared to a quantitative authentication risk management approach. Based upon these implications this paper next note the requirements for an improved system and detail some related research areas that meet these requirements. Finally, this paper discussed how a system that meets these requirements through the application of that research could provide benefits and outlined a simple points-based authentication system. Findings-The paper proposes that many of the weaknesses in authentication confidence management could be effectively mitigated through the deployment of a factor independent multi-modal fusion quantitative authentication-based system. This paper details a simple point-based approach that does this and discuss how addressing the problems in handling authentication confidence could further optimise risk management in multi-factor authentication systems. Practical implications-This paper's suggestions for optimising multi-factor authentication have many implications within medium to high-security commercial and government applications. Correct authentication risk handling enables decisions regarding risk and authentication to be made more accurately. Originality/value-This implications of the issues discussed in this paper have relevance to anyone who deploys or uses any medium to high-security authentication system. As the bottom end of the medium to high-security range includes online banking, there are implications for a wide range of stakeholders. © Emerald Group Publishing Limited.

关键词： Authentication

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A decision tree based quasi-identifier perturbation technique for preserving privacy in data mining

A decision tree based quasi-identifier perturbation techniqu...

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2009 3rd International Conference on Research Challenges in information science, RCIS 2009

作者： Dai, Bi-Ru Lin, Yang-Tze Department ofComputer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (纸本)9781424428656

Classification is an important issue in data mining, and decision tree is one of the most popular techniques for classification analysis. Some data sources contain private personal information that people are unwilling to reveal. The disclosure of person-specific data is possible to endanger thousands of people, and therefore the dataset should be protected before it is released for mining. However, techniques to hide private information usually modify the original dataset without considering influences on the prediction accuracy of a classification model. In this paper, we propose an algorithm to protect personal privacy for classification model based on decision tree. Our goal is to hide all person-specific information with minimized data perturbation. Furthermore, the prediction capability of the decision tree classifier can be maintained. As demonstrated in the experiments, the proposed algorithm can successfully hide private information with fewer disturbances of the classifier. © 2009 IEEE.

关键词： Decision trees

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An integration platform for developing context-aware applications based on context reasoning and service automation techniques

An integration platform for developing context-aware applica...

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11th International Conference on Advanced Communication Technology, ICACT 2009

作者： Huang, Po-Cheng Chien, Cheng-Vi Lee, Kuan-Rong Kuo, Yau-Hwang Wu, Ming-Zoo Department ofComputer Science and Information Engineering National Cheng Kung University Tainan Taiwan Department of Information Engineering Kun Shan University Yung-Kang Tainan Taiwan Institute for Information Industry Taiwan

ISBN: (纸本)9788955191387

This paper proposes an integration platform composed of Agent platform and Open Services Gateway Initiative (OSGi) framework to assist in developing context-aware applications. For developing context-aware applications, we firstly have to gather data through sensor networks and reason the meaningful context information. Secondly, context-aware applications must make the decision according to the context information, and provide the user-centric service through selecting the suitable device and deciding the service flow. In the integration platform, the agent platform providing personalization and learning capabilities to assist in reasoning context information. Furthermore, OSGi providing the modularization bundles of applications which aid to management the devices and services. Finally, the elder mental care scenario is implemented to verify the performance of the integration platform.

关键词： Sensor networks

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