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Modelling the daily reference evapotranspiration in semi-arid region of South India: A case study comparing ANFIS and empirical models

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Information Processing in Agriculture 2021年第1期8卷 173-184页

作者： Ramiro T.Gonzalez del Cerro M.S.P Subathra nallapaneni manoj kumar Sebastian Verrastro S.Thomas George Departamento de Ingenieria Electronica Facultad Regional Buenos AiresUniversidad Tecnologica NacionalBuenos AiresArgentina Department of Electrical Sciences Karunya Institute of Technology and SciencesCoimbatore 641114Tamil NaduIndia School of Energy and Environment City University of Hong KongKowloonHong KongChina Sustainable Solutionz T NagarChennai-600017Tamil NaduIndia

The estimation of evapotranspiration(ETo)is one of the main tools for the control of crop growth and to make a rational use of water *** estimate this parameter accurately,it is necessary to have a daily measurement of four meteorological variables,these are:temperature,solar radiation,relative humidity and wind *** is not always possible to count on all the variables,that is why there are empirical methods that use a limited number of variables that make an approximate estimate of the ETo *** of these models are applicable to different regions with completely different *** this paper,a study has been carried out to define the model of ETo estimation that best adapts to the semi-arid region in South *** different datasets for the same period from different meteorological stations were *** addition to the empirical methods for estimating ETo,computer models ANFIS(Adaptive Neuro-Fuzzy Inference System)were *** models consist in the future estimation of a certain parameter by using current variables and a history of variables and past *** results of this work show that ANFIS 11 model makes the best estimate with RMSD=0.002 and r=*** RITCHIE method is the most suitable empirical model for this region,which reaches RMSD=0.507 and r=*** addition,ranking of equations is elaborated for both datasets for daily estimates of ***,comparison is made with the results for each case and thus confirm or reject the convenience of one model over the *** achieve this,a series of statistical indicators were used:Index of agreement(d),MAE(Mean absolute error),SEE(Standard error of estimate)and RMSD(Root mean square difference).Moreover,a sensitivity analysis was performed in order to compare and show the stability of the best models when an error is introduced within the input *** this case,the empirical models demonstrate a better performance than the ANFIS *** work demonstrates that th

关键词： Evapotranspiration ANFIS ANN Semi-arid regions in India Coimbatore RITCHIE method

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COVID-DeepNet: Hybrid Multimodal Deep Learning System for Improving COVID-19 Pneumonia Detection in Chest X-ray Images

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Computers, Materials & Continua 2021年第5期67卷 2409-2429页

作者： A.S.Al-Waisy Mazin Abed Mohammed Shumoos Al-Fahdawi M.S.Maashi Begonya Garcia-Zapirain Karrar Hameed Abdulkareem S.A.Mostafa nallapaneni manoj kumar Dac-Nhuong Le College of Computer Science and Information Technology University of AnbarAnbar31001Iraq College of Computer and Information Sciences King Saud UniversityRiyadh11451Saudi Arabia eVIDA Lab University of Deusto.Avda/UniversidadesBilbao24.48007Spain College of Agriculture Al-Muthanna UniversitySamawah66001Iraq Faculty of Computer Science and Information Technology University Tun Hussein Onn MalaysiaJohor86400Malaysia School of Energy and Environment City University of Hong KongKowloon83Hong Kong Institute of Research and Development Duy Tan UniversityDanang550000Vietnam Faculty of Information Technology Duy Tan UniversityDanang550000Vietnam

Coronavirus(COVID-19)epidemic outbreak has devastating effects on daily lives and healthcare systems *** newly recognized virus is highly transmissible,and no clinically approved vaccine or antiviral medicine is currently *** diagnosis of infected patients through effective screening is needed to control the rapid spread of this *** radiography imaging is an effective diagnosis tool for COVID-19 virus and ***,a novel hybrid multimodal deep learning system for identifying COVID-19 virus in chest X-ray(CX-R)images is developed and termed as the COVID-DeepNet system to aid expert radiologists in rapid and accurate image ***,Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Butterworth bandpass filter were applied to enhance the contrast and eliminate the noise in CX-R images,*** from two different deep learning approaches based on the incorporation of a deep belief network and a convolutional deep belief network trained from scratch using a large-scale dataset were then *** architecture,which provides radiologists a high degree of confidence to distinguish healthy and COVID-19 infected people,was *** proposed COVID-DeepNet system can correctly and accurately diagnose patients with COVID-19 with a detection accuracy rate of 99.93%,sensitivity of 99.90%,specificity of 100%,precision of 100%,F1-score of 99.93%,MSE of 0.021%,and RMSE of 0.016%in a large-scale *** system shows efficiency and accuracy and can be used in a real clinical center for the early diagnosis of COVID-19 virus and treatment follow-up with less than 3 s per image to make the final decision.

关键词： Coronavirus epidemic deep learning deep belief network convolutional deep belief network chest radiography imaging

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