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Improving the prediction accuracy of soil nutrient classification by optimizing extreme learning machine parameters

Information Processing in Agriculture 2020年第1期7卷 72-82页

作者： M.S.Suchithra Maya L.Pai Department of Computer Science and IT Amrita School of Arts and SciencesKochiAmrita Vishwa Vidyapeetham682024India

The soil,Soul of Infinite Life,is the entity responsible for sustaining life on earth.In spite of significant advances in the service sector,agriculture remains the major provider of employment and source of revenue in India.Soil testing is a valuable tool for evaluating the available nutrient status of soil and helps to determine the proper amount of nutrients to be added to a given soil based on its fertility and crop needs.In the current study,the soil test report values are used to classify several significant soil features like village wise soil fertility indices of Available Phosphorus(P),Available Potassium(K),Organic Carbon(OC)and Boron(B),as well as the parameter Soil Reaction(pH).The classification and prediction of the village wise soil parameters aids in reducing wasteful expenditure on fertilizer inputs,increase profitability,save the time of chemical soil analysis experts,improves soil health and environmental quality.These five classification problems are solved using the fast learning classification technique known as Extreme Learning Machine(ELM)with different activation functions like gaussian radial basis,sine-squared,hyperbolic tangent,triangular basis,and hard limit.After the performance analysis of ELMs with diverse activation functions for these soil parameter classifications,the gaussian radial basis function attains the maximum performance for four out of five problems,which goes above 80%in most of the accuracy rate calculations in every problem,followed by hyperbolic tangent,hard limit,triangular basis,and sine-squared.However,the performance of the final classification problem,i.***.the pH classification,gives moderate values with the gaussian radial basis and best performance(near 90%),with the hyperbolic tangent.

关键词： Extreme learning machine activation functions Soil fertility indices Soil pH Classification Prediction

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Observer Design Based on Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network

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Tsinghua Science and Technology 2016年第5期21卷 544-551页

作者： Xin Wen Xin Li Department of Aeronautics and Astronautics Shenyang Aerospace University Shenyang 110136 China School Astronautics Nanjing University of Aeronautics and Astronautics Nanjing 210016 China

In this paper, we propose and construct an observer design based on a Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network（SRCPFWNN） for a class of nonlinear system. We use a Self-Recurrent Wavelet Neural Network（SRWNN） to construct a self-recurrent consequent part for each rule of the Takagi-Sugeno-Kang（TSK） model in the SRCPFWNN and analyze the structure of the fuzzy wavelet neural network model. Based on the Direct Adaptive Control Theory（DACT） and a back propagation-based learning algorithm, all parameters of the consequent parts are updated online in the SRCPFWNN. On this basis, we propose a design method using an adaptive state observer based on an SRCPFWNN for nonlinear systems. Using the Lyapunov function, we then prove the stability of this observer design method. Our simulation results confirm that the observer can accurately and quickly estimate the state values of the system.

关键词： Takagi-Sugeno-Kang （TSK） fuzzy model activation functions state observer nonlinear systems simulation

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Efficient Deep CNN Model for COVID-19 Classification

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Computers, Materials & Continua 2022年第3期70卷 4373-4391页

作者： Walid El-Shafai Amira A.Mahmoud El-Sayed M.El-Rabaie Taha E.Taha Osama F.Zahran Adel S.El-Fishawy Mohammed Abd-Elnaby Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Coronavirus(COVID-19)infection was initially acknowledged as a global pandemic in Wuhan in China.World Health Organization(WHO)stated that the COVID-19 is an epidemic that causes a 3.4%death rate.Chest X-Ray(CXR)and Computerized Tomography(CT)screening of infected persons are essential in diagnosis applications.There are numerous ways to identify positive COVID-19 cases.One of the fundamental ways is radiology imaging through CXR,or CT images.The comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high quality.Hence,automated classification techniques are required to facilitate the diagnosis process.Deep Learning(DL)is an effective tool that can be utilized for detection and classification this type of medical images.The deep Convolutional Neural Networks(CNNs)can learn and extract essential features from different medical image datasets.In this paper,a CNN architecture for automated COVID-19 detection from CXR and CT images is offered.Three activation functions as well as three optimizers are tested and compared for this task.The proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train it.The performance is tested and investigated on the CT and CXR datasets.Three activation functions:Tanh,Sigmoid,and ReLU are compared using a constant learning rate and different batch sizes.Different optimizers are studied with different batch sizes and a constant learning rate.Finally,a comparison between different combinations of activation functions and optimizers is presented,and the optimal configuration is determined.Hence,the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early stage.The proposed model achieves a classification accuracy of 91.67%on CXR image dataset,and a classification accuracy of 100%on CT dataset with training times of 58 min and 46 min on CXR and CT datasets,respectively.The best results are

关键词： COVID-19 image classification CNN DL activation functions optimizers

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