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The Human Eye Pupil Detection System Using BAT Optimized Deep Learning Architecture

Computer Systems Science & Engineering 2023年第7期46卷 125-135页

作者： S.Navaneethan P.Siva Satya Sreedhar S.Padmakala C.Senthilkumar Department of Electronics and Communication Engineering Saveetha Engineering CollegeChennai602105India Department of Information Technology Seshadri Rao Gudlavalleru Engineering CollegeKrishna521356India Department of Computer Science Engineering Saveetha School of EngineeringSIMATSChennai602105India Department of Electronics and Communication Engineering Saveetha School of EngineeringSIMATSChennai602105India

The pupil recognition method is helpful in many real-time systems,including ophthalmology testing devices,wheelchair assistance,and so *** pupil detection system is a very difficult process in a wide range of datasets due to problems caused by varying pupil size,occlusion of eyelids,and *** Convolutional Neural Networks(DCNN)are being used in pupil recognition systems and have shown promising results in terms of *** improve accuracy and cope with larger datasets,this research work proposes BOC(BAT Optimized CNN)-IrisNet,which consists of optimizing input weights and hidden layers of DCNN using the evolutionary BAT algorithm to efficiently find the human eye pupil *** proposed method is based on very deep architecture and many tricks from recently developed popular *** results show that the BOC-IrisNet proposal can efficiently model iris microstructures and provides a stable discriminating iris representation that is lightweight,easy to implement,and of cutting-edge ***,the region-based black box method for determining pupil center coordinates was *** proposed architecture was tested using various IRIS databases,including the CASIA(Chinese academy of the scientific research institute of automation)Iris V4 dataset,which has 99.5%sensitivity and 99.75%accuracy,and the IIT(Indian Institute of Technology)Delhi dataset,which has 99.35%specificity and MMU(Multimedia University)99.45%accuracy,which is higher than the existing architectures.

关键词： BAT algorithm IRIS datasets DCNN pupil detection black box method

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Applied machine learning in greenhouse simulation;new application and analysis

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Information Processing in Agriculture 2018年第2期5卷 253-268页

作者： Morteza Taki Saman Abdanan Mehdizadeh Abbas Rohani Majid Rahnama Mostafa Rahmati-Joneidabad Department of Agricultural Machinery and Mechanization Ramin Agriculture and Natural Resources University of KhuzestanMollasaniIran Department of Biosystems Engineering Faculty of AgricultureFerdowsi University of MashhadMashhadIran Department of Horticultural Science Faculty of AgricultureRamin Agriculture and Natural Resources University of KhuzestanMollasaniIran

Prediction the inside environment variables in greenhouses is very important because they play a vital role in greenhouse cultivation and energy lost especially in cold and hot *** greenhouse environment is an uncertain nonlinear system which classical modeling methods have some problems to solve *** the main goal of this study is to select the best method between Artificial Neural Network(ANN)and Support Vector Machine(SVM)to estimate three different variables include inside air,soil and plant temperatures(Ta,Ts,Tp)and also energy exchange in a polyethylene greenhouse in Shahreza city,Isfahan province,*** environmental factors which influencing all the inside temperatures such as outside air temperature,wind speed and outside solar radiation were collected as data *** this research,13 different training algorithms were used for ANN models(MLPRBF).Based on K-fold cross validation and Randomized Complete Block(RCB)methodology,the best model was *** results showed that the type of training algorithm and kernel function are very important factors in ANN(RBF and MLP)and SVM models performance,*** RBF,MLP and SVM models showed that the performance of RBF to predict Ta,Tp and Ts variables is better according to small values of RMSE and MAPE and large value of R2 *** range of RMSE and MAPE factors for RBF model to predict Ta,Tp and Ts were between 0.07 and 0.12C and 0.28-0.50%,*** and stability of the RBF model with 5-fold cross validation analysis showed that this method can use with small size of data *** performance of best model(RBF)to estimate the energy lost and exchange in the greenhouse with heat transfer models showed that this method can estimate the real data in greenhouse and then predict the energy lost and exchange with high accuracy.

关键词： black box method Energy lost Environmental situation Energy exchange

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