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Prediction of Uncertainty Estimation and Confidence Calibration Using Fully Convolutional Neural Network

Computers, Materials & Continua 2023年第5期75卷 2557-2573页

作者： karim gasmi Lassaad Ben Ammar Hmoud Elshammari Fadwa Yahya Department of Computer Science College of Arts and Sciences at TabarjalJouf UniversityJoufSaudi Arabia College of Sciences and Humanities Prince Sattam Bin Abdulaziz UniversityAl-KharjSaudi Arabia University of Sfax SfaxTunisia Department of Information Systems College of Computer and Information SciencesJouf UniversityJoufSaudi Arabia

Convolution neural networks(CNNs)have proven to be effective clinical *** study highlighted some of the key issues within these *** is difficult to train these systems in a limited clinical image databases,and many publications present strategies including such learning ***,these patterns are known formaking a highly reliable *** addition,normalization of volume and losses of dice have been used effectively to accelerate and stabilize the ***,these systems are improperly regulated,resulting in more confident ratings for correct and incorrect classification,which are inaccurate and difficult to *** study examines the risk assessment of Fully Convolutional Neural Networks(FCNNs)for clinical image *** contributions have been made to this planned work:1)dice loss and cross-entropy loss are compared on the basis of segment quality and uncertain assessment of FCNNs;2)proposal for a group model for assurance measurement of full convolutional neural networks trained with dice loss and group normalization;And 3)the ability of the measured FCNs to evaluate the segment quality of the structures and to identify test examples outside the *** evaluate the study’s contributions,it conducted a series of tests in three clinical image division applications such as heart,brain and *** findings of the study provide significant insights into the predictive ambiguity assessment and a practical strategies for outside-distribution identification and reliable measurement in the clinical image *** approaches presented in this research significantly enhance the reliability and accuracy rating of CNNbased clinical imaging methods.

关键词： Medical image segmentation confidence calibration uncertainty estimation fully convolutional neural network

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Usability-Driven Mobile Application Development

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Computer Systems Science & Engineering 2023年第6期45卷 3165-3180页

作者： Fadwa Yahya Lassaad Ben Ammar gasmi karim Prince Sattam Bin Abdulaziz University AlkharjSaudi Arabia University of Sfax SfaxTunisia Department of Computer Sciences Faculty of Sciences and Arts in TABARJALJouf UniversityKingdom of Saudi Arabia

Recently,a specific interest is being taken in the development of mobile application(app)via Model-Based User Interface Development(MBUID)*** allows the generation of mobile apps in the target platform(s)from conceptual *** such it simplified the development process of mobile ***,the interest is only focused on the functional aspects of the mobile app while neglecting the non-functional aspects,such as *** latter is largely considered as the main factor leading to the success or failure of any software *** paper aims at addressing non-functional aspects of mobile apps generated using MBUID *** such,we propose a usability-driven approach for the development of mobile *** main stages of the proposed approach are defined in a generic way so that they can be integrated with any MBUID method.A case study is presented,in the paper,with the aim of illustrating the feasibility of this approach.

关键词： Mobile application user interface model-based user interface development mobile usability

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