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An Optimization Approach for Convolutional Neural Network Using Non-Dominated Sorted Genetic Algorithm-Ⅱ

Computers, Materials & Continua 2023年第3期74卷 5641-5661页

作者： afia zafar Muhammad Aamir Nazri Mohd Nawi Ali Arshad Saman Riaz Abdulrahman Alruban Ashit Kumar Dutta Badr Almutairi Sultan Almotairi Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaMalaysia School of Electronics Computing and MathematicsUniversity of DerbyUK Department of Computer Science National University of TechnologyIslamabadPakistan Department of Information Technology College of Computer and Information SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Kingdom of Saudi Arabia Department of Information Technology College of Computer Sciences and Information Technology CollegeMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia Department of Information Systems Faculty of Computer and Information SciencesIslamic University of MadinahMadinah42351Saudi Arabia

In computer vision,convolutional neural networks have a wide range of *** representmost of today’s data,so it’s important to know how to handle these large amounts of data *** neural networks have been shown to solve image processing problems ***,when designing the network structure for a particular problem,you need to adjust the hyperparameters for higher *** technique is time consuming and requires a lot of work and domain *** a convolutional neural network architecture is a classic NP-hard optimization *** the other hand,different datasets require different combinations of models or hyperparameters,which can be time consuming and *** approaches have been proposed to overcome this problem,such as grid search limited to low-dimensional space and queuing by random *** address this issue,we propose an evolutionary algorithm-based approach that dynamically enhances the structure of Convolution Neural Networks(CNNs)using optimized *** study proposes a method using Non-dominated sorted genetic algorithms(NSGA)to improve the hyperparameters of the CNN *** addition,different types and parameter ranges of existing genetic algorithms are *** study was conducted with various state-of-the-art methodologies and *** have shown that our proposed approach is superior to previous methods in terms of classification accuracy,and the results are published in modern computing literature.

关键词： Non-dominated sorted genetic algorithm convolutional neural network hyper-parameter optimization

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Screening for arboviruses in healthy blood donors: Experience from Karachi, Pakistan

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Virologica Sinica 2022年第5期37卷 774-777页

作者： Moiz Ahmed Khan Kehkashan Imtiaz Humaira Shafaq Joveria Farooqi Mohammad Hassan afia zafar Maureen T.Long Kelli L.Barr Erum Khan Clinical Microbiology Laboratory Department of Pathology&Lab MedicineAga Khan UniversityKarachi 74800Pakistan Blood Bank Haematology&Transfusion MedicineDepartment of Pathology&Lab MedicineAga Khan UniversityKarachi 74800Pakistan Comparative Diagnostic and Population Medicine University of FloridaGainesville FL32611USA Center for Global Health and Infectious Disease Research University of South FloridaTampa FL33620USA

Dear Editor,Arboviruses of medical importance are maintained in nature in enzootic cycles between haematogenous vectors and susceptible vertebrate hosts(Huang et al.,2019).The rapid increase in human populations around the globe and the associated urbanization are creating irreversible damage to the ecosystem,giving rise to many problems including emergence and intensification of the vector borne diseases(Sutherst,2004).Among vector borne diseases,mosquito-borne arboviruses including dengue virus(DENV),West Nile virus(WNV),Japanese Encephalitis virus(JEV)and Zika virus(ZIKV)are rapidly emerging in the affected regions of the world(Palmer et al.,2011).

关键词： maintained irreversible Pakistan

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