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Stacked Gated Recurrent Unit Classifier with CT Images for Liver Cancer Classification

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Computer Systems Science & Engineering 2023年第3期44卷 2309-2322页

作者： mahmoud Ragab Jaber Alyami Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Department of Diagnostic Radiology Faculty of Applied Medical SciencesKing AbdulazizJeddah21589Saudi Arabia Imaging Unit King Fahd Medical Research CentreKing AbdulazizJeddah21589Saudi Arabia

Liver cancer is one of the major diseases with increased mortality in recent years,across the *** detection of liver cancer is a tedious and laborious task due to which Computer Aided Diagnosis(CAD)models have been developed to detect the presence of liver cancer accurately and classify its ***,liver cancer segmentation outcome,using medical images,is employed in the assessment of tumor volume,further treatment plans,and response ***,there is a need exists to develop automated tools for liver cancer detection in a precise *** this motivation,the current study introduces an Intelligent Artificial Intelligence with Equilibrium Optimizer based Liver cancer Classification(IAIEO-LCC)*** proposed IAIEO-LCC technique initially performs Median Filtering(MF)-based pre-processing and data augmentation ***,Kapur’s entropy-based segmentation technique is used to identify the affected regions in ***,VGG-19 based feature extractor and Equilibrium Optimizer(EO)-based hyperparameter tuning processes are also involved to derive the feature *** last,Stacked Gated Recurrent Unit(SGRU)classifier is exploited to detect and classify the liver cancer *** order to demonstrate the superiority of the proposed IAIEO-LCC technique in terms of performance,a wide range of simulations was conducted and the results were inspected under different *** comparison study results infer that the proposed IAIEO-LCC technique achieved an improved accuracy of 98.52%.

关键词： Liver cancer image segmentation artificial intelligence deep learning CT images parameter tuning

Distributed H2/H∞ Filter Design for Discrete-Time Switched Systems

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IEEE/CAA Journal of Automatica Sinica 2020年第1期7卷 158-168页

作者： Nezar M.Alyazidi Magdi S.mahmoud Systems Engineering Department King Fahd University for Petroleum and Minerals

This paper addresses an infinite horizon distributed H2/H∞ filtering for discrete-time systems under conditions of bounded power and white stochastic signals. The filter algorithm is designed by computing a pair of gains namely the estimator and the coupling. Herein, we implement a filter to estimate unknown parameters such that the closed-loop multi-sensor accomplishes the desired performances of the proposed H2 and H∞ schemes over a finite horizon. A switched strategy is implemented to switch between the states once the operation conditions have changed due to disturbances. It is shown that the stability of the overall filtering-error system with H2/H∞ performance can be established if a piecewise-quadratic Lyapunov function is properly constructed. A simulation example is given to show the effectiveness of the proposed approach.

关键词： Bounded power stochastic disturbance discretetime systems H2/H∞ filtering infinite horizon multi-sensor switched systems

Fundamental Issues in Networked Control Systems

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IEEE/CAA Journal of Automatica Sinica 2018年第5期5卷 902-922页

作者： Magdi S.mahmoud a Mutaz M.Hamdan the systems engineering department king fahd university of petroleum and minerals

Abstract--This paper provides a survey on modeling and theories of networked control systems （NCS）. In the first part, modeling of the different types of imperfections that affect NCS is discussed. These imperfections are quantization errors, packet dropouts, variable sampling/transmission intervals, vari- able transmission delays, and communication constraints. Then follows in the second part a presentation of several theories that have been applied for controlling networked systems. These theories include： input delay system approach, Markovian system approach, switched system approach, stochastic system approach, impulsive system approach, and predictive control approach. In the last part, some advanced issues in NCS including decentral- ized and distributed NCS, cloud control system, and co-design of NCS are reviewed. Index Terms--Decentralized networked control systems （NCS）, distributed networked control systems, network constraints, net- worked control system, quantization, time delays.

关键词： Decentralized networked control systems(NCS) distributed networked control systems network constraints networked control system quantization time delays

Estimating the impact of azimuth-angle variations on photovoltaic annual energy production

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Clean Energy 2019年第1期3卷 47-58页

作者： mahmoud Dhimish Santiago Silvestre Laboratory of Photovoltaics University of HuddersfieldQueensgateHD13DHHuddersfieldUK MNT Group Electronic Engineering DepartmentUniversitat Politecnica de Catalunya(UPC)BarcelonaTechC/Jordi Girona 1-3Campus Nord UPC08034 BarcelonaSpain

The performance of a photovoltaic(PV)installation is affected by its tilt and azimuth angles,because these parameters change the amount of solar energy absorbed by the surface of the PV ***,this paper demonstrates the impact of the azimuth angle on the energy production of PV *** different PV sites were studied,where the first comprises PV systems installed at-13°,-4°,+12°and+21°azimuth angles in different geographical locations,whereas the second PV site included adjacent PV systems installed at-87°,-32°,+2°and+17°azimuth *** the investigated PV sites were located in Huddersfield,*** summary,the results indicate that PV systems installed between-4°and+2°presented the maximum energy production over the last 4 years,while the worst energy generation were observed for the PV system installed at an azimuth angle of-87°.Finally,the probability projections for all observed azimuth angles datasets have been *** PV systems are affected by various environmental conditions such as fluctuations in the wind,humidity,solar irradiance and ambient temperature,ultimately,these factors would affect the annual energy generation of the PV *** that reason,we have analysed the disparities and the probability of the annual energy production for multiple PV systems installed at different azimuth angles ranging from-90°to+90°degrees,and affected by different environmental *** analyses are based on the cumulative density function modelling technique as well as the normal distribution function.

关键词： photovoltaics azimuth angle energy production CDF modelling

Complete Monotonicity of Functions Related to Trigamma and Tetragamma Functions

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Computer Modeling in Engineering & Sciences 2022年第4期131卷 263-275页

作者： Mona Anis Hanan Almuashi Mansour mahmoud Faculty of Science Mathematics DepartmentMansoura UniversityMansoura35516Egypt Faculty of Science Mathematics DepartmentJeddah UniversityJeddah21589Saudi Arabia Faculty of Science Mathematics DepartmentKing Abdulaziz UniversityJeddah21589Saudi Arabia

In this paper,we study the completely monotonic property of two functions involving the functionΔ(x)=[ψ′(x)]2+ψ″(x)and deduce the double inequality x^(2)+3x+3/3x^(4)(2x+1)^(2)0which improve some recent results,whereψ(x)is the logarithmic derivative of the Gamma ***,we deduce the completely monotonic degree of a function involvingψ′(x).

关键词： Trigamma function tetragamma function completely monotonic function completely monotonic degree inequality

The Interaction between Control and Computing Theories:New Approaches

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International Journal of Automation and computing 2017年第3期14卷 254-274页

作者： Magdi S.mahmoud Yuanqing Xia Systems Engineering Department KFUPM School of Automation Beijing Institute of Technology

Day by day, networked control system（NCS） methods have been promoted for distributed closed-loop control ***, the integration of control and computing theories enhanced the development of networked control systems through remote control for wide applications employing the internet. Two further directions to networked control technology are LeaderFollower systems and model predictive control systems. Cloud control system is looked at an extension of networked control systems（NCS） using internet of things（IOT） methodologies. In this paper, a comprehensive literature survey of the new technology of control systems application performed on cloud computing is presented.

关键词： Networked control system(NCS) cloud control system network constraints leader-follower system model predictive control

Influence of strong preformed particle gels on low permeable formations in mature reservoirs

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Petroleum Science 2016年第1期13卷 77-90页

作者： mahmoud O.Elsharafi Baojun Bai Mc Coy School of Engineering Midwestern State University Department of Geological Sciences & Engineering Missouri University of Science and Technology

In mature reservoirs,the success of preformed particle gel（PPG） treatment rests primarily on the ability of the PPG to reduce and/or plug the high permeable formations,but not damage the low permeable *** test models（filtration test model and pressure test model）were used to determine the effect of PPG on low permeable *** work used a strong preformed particle gel,Daqing（DQ） gel made by a Chinese *** particle gel sizes were ranged from 30 to 120 mesh for this *** are sized in a millimeter or micrometer,which can absorb over a hundred times their weight in *** gel strength was approximately 6500 Pa for a completely swollen PPG with 1 %（weight percentage） NaCl solution（brine）.0.05 %,1 %,and 10 % NaCl solutions were used in *** core permeability was measured before and after PPG *** relationship between cumulative filtration volumes versus filtration times was *** results indicate that DQ gels of a particle size of 30–80 mesh did not damage the cores of a low permeability of 3–25 m *** DQ gels of a smaller particle size ranging from 100 to 120 mesh damaged the core and a cake was formed on the core *** results also indicate that more damage occurred when a high load pressure（400 psi） was applied on the high permeability cores（290–310 m D）.The penetration of the particle gelsinto the low permeable formations can be decreased by the best selection of gel types,particle sizes,and brine concentrations.

关键词： formations reservoirs mature permeability sandstone permeable filtration rock sizes porosity

Optimal Deep Transfer Learning Model for Histopathological Breast Cancer Classification

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Computers, Materials & Continua 2022年第11期73卷 2849-2864页

作者： mahmoud Ragab Alaa F.Nahhas Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia Center for Artificial Intelligence in Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Biochemistry Department Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia

Earlier recognition of breast cancer is crucial to decrease the severity and optimize the survival *** of the commonly utilized imaging modalities for breast cancer is histopathological *** manual inspection of histopathological images is a challenging task,automated tools using deep learning(DL)and artificial intelligence(AI)approaches need to be *** latest advances of DL models help in accomplishing maximum image classification performance in several application *** this view,this study develops a Deep Transfer Learning with Rider Optimization Algorithm for Histopathological Classification of Breast Cancer(DTLRO-HCBC)*** proposed DTLRO-HCBC technique aims to categorize the existence of breast cancer using histopathological *** accomplish this,the DTLRO-HCBC technique undergoes pre-processing and data augmentation to increase quantitative ***,optimal SqueezeNet model is employed for feature extractor and the hyperparameter tuning process is carried out using the Adadelta ***,rider optimization with deep feed forward neural network(RO-DFFNN)technique was utilized employed for breast cancer *** RO algorithm is applied for optimally adjusting the weight and bias values of the DFFNN *** demonstrating the greater performance of the DTLRO-HCBC approach,a sequence of simulations were carried out and the outcomes reported its promising performance over the current state of art approaches.

关键词： Breast cancer histopathological images machine learning biomedical analysis deep learning computer vision

Optimal Deep Transfer Learning Based Colorectal Cancer Detection and Classification Model

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Computers, Materials & Continua 2023年第2期74卷 3279-3295页

作者： mahmoud Ragab Maged Mosta.a.mahmoud Amer H.Asseri Hani Choudhry Haitham A.Yacoub Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center for Artificial Intelligence in Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Cancer Biology Unit King Fahd Medical Research CenterKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Medical Laboratory Sciences Faculty of Applied Medical SciencesKing Abdulaziz UniversityJeddah22252Saudi Arabia Department of Molecular Genetics and Enzymology Human Genetics and Genome Research InstituteNational Research CentreCairo12622Egypt Biochemistry Department Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia Cell Biology Department Biotechnology Research InstituteNational Research CentreGiza12622Egypt

Colorectal carcinoma(CRC)is one such dispersed cancer globally and also prominent one in causing cancer-based ***,pathologists execute CRC diagnosis through visible scrutinizing under the microscope the resected tissue samples,stained and fixed through Haematoxylin and Eosin(H&E).The advancement of graphical processing systems has resulted in high potentiality for deep learning(DL)techniques in interpretating visual anatomy from high resolution medical *** study develops a slime mould algorithm with deep transfer learning enabled colorectal cancer detection and classification(SMADTL-CCDC)*** presented SMADTL-CCDC technique intends to appropriately recognize the occurrence of colorectal *** accomplish this,the SMADTLCCDC model initially undergoes pre-processing to improve the input image *** addition,a dense-EfficientNet technique was employed to extract feature vectors from the pre-processed ***,SMA with Discrete Hopfield neural network(DHNN)method was applied for the recognition and classification of colorectal *** utilization of SMA assists in appropriately selecting the parameters involved in the DHNN approach.A wide range of experiments was implemented on benchmark datasets to assess the classification performance.A comprehensive comparative study highlighted the better performance of the SMADTL-CDC model over the recent approaches.

关键词： Colorectal cancer deep transfer learning slime mould algorithm hyperparameter optimization biomedical imaging