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An Improved Convolutional neural network Model for DnA Classification

Computers, Materials & Continua 2022年第3期70卷 5907-5927页

作者： naglaa.F.Soliman Samia M.Abd-Alhalem Walid El-Shafai Salah Eldin S.E.Abdulrahman n.ismaiel El-Sayed M.El-Rabaie Abeer D.Algarni Fathi E.Abd El-Samie Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt

Recently,deep learning(DL)became one of the essential tools in bioinformatics.A modified convolutional neural network(Cnn)is employed in this paper for building an integratedmodel for deoxyribonucleic acid(DnA)*** any Cnn model,convolutional layers are used to extract features followed by max-pooling layers to reduce the dimensionality of features.A novel method based on downsampling and Cnns is introduced for feature *** downsampling is an improved form of the existing pooling layer to obtain better classification *** two-dimensional discrete transform(2D DT)and two-dimensional random projection(2D RP)methods are applied for *** convert the high-dimensional data to low-dimensional data and transform the data to the most significant feature ***,there are parameters which directly affect how a Cnn model is *** this paper,some issues concerned with the training of Cnns have been *** Cnns are examined by changing some hyperparameters such as the learning rate,size of minibatch,and the number of *** and assessment of the performance of Cnns are carried out on 16S rRnA bacterial *** results indicate that the utilization of a Cnn based on wavelet subsampling yields the best trade-off between processing time and accuracy with a learning rate equal to 0.0001,a size of minibatch equal to 64,and a number of epochs equal to 20.

关键词： DnA classification Cnn downsampling hyperparameters DL 2D DT 2D RP

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Statistical Analyses and Geotechnical Evaluation of nubia Sandstone, Golden Triangle Area, Egypt

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Journal of Geoscience and Environment Protection 2021年第11期9卷 46-60页

作者： Hesham Ahmed Hussein.ismaiel Mohamed Mohamed Askalany Ali Ismail Ali Department of Geology Faculty of Science South Valley University Qena Egypt

Great efforts had been made to use indirect non-destructive tests in the geotechnical evaluation of rocks, especially sandstones, employing different empirical equations. However, most of these equations have been derived from hard and compacted sandstones datan>;therefore, the focus of this research is on weak and weakly compacted sandstones, aiming firstly to obtain empirical equations for estimating their characteristics, secondly to demonstrate and visualize the correlations between the studied variables, and finally to cluster the studied samples based on their characteristics. To attain these aimsn>, twenty oriented block samples were collected from Nubia sandstone, central Eastern Desert, Golden Triangle area, Egypt. These samples were prepared and tested according to standard test methods, including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), Schmidt rebound number (SRN), porosity (n), bulk density (ρ), and ultrasonic P-wave velocity (UPV). The loss on ignition (LOI) was also employed as a physicochemical test for classifying the studied samples and indicating pores filling materials. The results revealed that these sandstones are characterized mainly by high n, low ρ, and low UPV values and these give an indication of weakly compacted and weakly cemented sandstone with shallow burial diagenetic conditions. Based on UCS and elastic modulus values, these sandstones are mainly classified as very low strength and highly yielding rocks. The results of regression analysis show satisfactory correlations between physical and mechanical characteristics, indicating the suitability of obtained empirical equations to deduce these properties. Principal component analysis revealed that the LOI, BTS, SRn, and US

关键词： Geotechnical Evaluation Empirical Equations Statistical Analyses nubia Sandstone non-Destructive Test

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