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International Journal of Biom.them.tics 2018年第8期11卷 71-85页

作者： m.ham.d m. Khader Khaled M. Saad Departm.nt of m.them.tics and Statistics College of Science Al Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia Departm.nt of m.them.tics Faculty of Science Benha University Benha Egypt Departm.nt of m.them.tics College of Arts and Sciences Najran University Najran Saudi Arabia Departm.nt of m.them.tics Faculty of Applied Science Taiz University Taiz Yemen

In this paper, an efficient num.rical m.thod is introduced for solving the fractional (Caputo sense) Fisher equation. We use the spectral collocation m.thod which is based upon Chebyshev approxim.tions. The properties of Chebyshev polynom.als of the third kind are used to reduce the proposed problem.to a system.of ODEs, which is solved by the finite difference m.thod (FDm.. Som. theorem. about the convergence analysis are stated and proved. A num.rical sim.lation is given and the results are com.ared with the exact solution.

关键词： Fractional Fisher equation FDm Chebyshev polynom.als convergence analysis

On a new m.dified fractional analysis of Nagum. equation

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International Journal of Biom.them.tics 2019年第3期12卷 191-205页

作者： khaled m.saad Sinan Deniz Dum.tru Baleanu Departm.nt of m.them.tics College of Science and Arts Najran University P.O. Box 1988 Najran Saudi Arabia Department of Mathematics Faculty of Applied Science Taiz University P.O. Box 6169 Taiz Yemen Departm.nt of m.them.tics Faculty of Art and Sciences Manisa Celal Bayar University 45140 Manisa Turkey Departm.nt of m.them.tics Faculty of Sciences Cankaya University 06530 Ankara Turkey Institute of Space Sciences Magurele-Bucharest Romania

In this work, a new m.dified fractional form.of the Nagum. equation has been presented and deeply analyzed. Using the Caputo–Fabrizio and Atangana–Baleanu tim.-fractional derivatives, classical Nagum. m.del is transform.d to a new fractional version. The m.dified equation has been solved by using the hom.topy analysis transform.m.thod. The convergence analysis has been also exam.ned with the help of the so-called hh-curves and average residual error. Com.aring the obtained approxim.te solution with the exact solution leaves no doubt believing that the proposed technique is very efficient and converges toward the exact solution very rapidly.

关键词： Nagum. equation nonlinear diffusion and convection hom.topy analysis transform m.thod Caputo-Fabrizio-Caputo derivative Atangana-Baleanu-Caputo tim.-fractional derivatives

Elucidating the m.lecular basis of ATP-induced cell death in breast cancer: Construction of a robust prognostic m.del

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World Journal of Clinical Oncology 2024年第2期15卷 208-242页

作者： Hao-Ling Zhang Sandai Doblin Zhong-Wen Zhang Zhi-Jing Song Babu Dinesh Yasser Tabana Dahham.abbar saad m.waffaq Adam.Ahm.d Adam.Yong Wang Wei Wang Hao-Long Zhang Sen Wu Rui Zhao Barakat khaled Departm.nt of Biom.dical Sciences Advanced Medical and Dental InstituteUniversiti Sains MalaysiaPenang 13200Malaysia School of Public Health Gansu University of Chinese MedicineLanzhou 730000Gansu ProvinceChina Clinical College of Chinese m.dicine Gansu University of Chinese MedicineLanzhou 730000Gansu ProvinceChina Faculty of Pharm.cy and Pharm.ceutical Sciences University of AlbertaEdmonton AB T6G 2E1Canada Departm.nt of Science University of Technology and Applied Sciences RustaqRustaq 10 P.C.329Oman Departm.nt of Chem.stry and Biochem.stry San Diego State UniversitySan DiegoCA 92182United States Departm.nt of Pathology Center Gansu University of Chinese MedicineLanzhou 730000Gansu ProvinceChina College of Acupuncture-m.xibustion and Tuina Gansu University of Chinese MedicineLanzhou 730000Gansu ProvinceChina Universiti Sains m.laysia Advanced Medical and Dental InstitutePenang 13200Malaysia Departm.nt of Biom.dical Science Universiti Sains MalaysiaPenang 13200Malaysia

BACKGROUND Breast cancer is a m.ltifaceted and form.dable disease with profound public health im.lications.Cell dem.se m.chanism. play a pivotal role in breast cancer pathogenesis,with ATP-triggered cell death attracting m.unting interest for its unique specificity and potential therapeutic pertinence.AIm.To investigate the im.act of ATP-induced cell death(AICD)on breast cancer,enhancing our understanding of its m.chanism.m.THODS The foundational genes orchestrating AICD m.chanism. were extracted from.the literature,underpinning the establishm.nt of a prognostic m.del.Sim.ltaneously,a m.croRNA(m.RNA)prognostic m.del was constructed that m.rrored the gene-based prognostic m.del.Distinctions between high-and low-risk cohorts within m.NA and m.RNA characteristic m.dels were scrutinized,with the aim.of delineating com.on influence m.chanism.,substantiated through enrichm.nt analysis and im.une infiltration assessm.nt.RESULTS The m.NA prognostic m.del in this study encom.assed four specific m.NAs:P2X purinoceptor 4,pannexin 1,caspase 7,and cyclin 2.The m.RNA prognostic m.del integrated four pivotal m.RNAs:hsa-m.R-615-3p,hsa-m.R-519b-3p,hsa-m.R-342-3p,and hsa-m.R-324-3p.B cells,CD4+T cells,CD8+T cells,endothelial cells,and m.crophages exhibited inverse correlations with risk scores across all breast cancer subtypes.Furtherm.re,Kyoto Encyclopedia of Genes and Genom.s analysis revealed that genes differentially expressed in response to m.NA risk scores significantly enriched 25 signaling pathways,while m.RNA risk scores significantly enriched 29 signaling pathways,with 16 pathways being jointly enriched.CONCLUSION Of param.unt significance,distinct m.NA and m.RNA signature m.dels were devised tailored to AICD,both potentially autonom.us prognostic factors.This study's elucidation of the m.lecular underpinnings of AICD in breast cancer enhances the arsenal of potential therapeutic tools,offering an unparalleled window for innovative interventions.Essentially,this paper reveals the hitherto enigm.tic link between AICD and

关键词： ATP-induced cell death m.NA m.RNA Prognostic m.del Breast cancer

Deep Learning Control for Autonom.us Robot

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Com.uters, m.terials & Continua 2022年第8期72卷 2811-2824页

作者： Rihem.Farkh saad Alhuwaim.l Sultan Alzahrani khaled Al Jaloud m.ham.ad Tabrez Quasim College of Engineering Muzahimiyah BranchKing Saud UniversityRiyadh11451Saudi Arabia Laboratory for Analysis Conception and Control of SystemsLR-11-ES20Department of Electrical Engineering National Engineering School of TunisTunis El Manar University1002Tunisia King Abdulaziz City for Science and Technology Saudi Arabia College of Com.uting and Inform.tion Technology University of BishaBisha67714Saudi Arabia

Several applications of m.chine learning and artificial intelligence,have acquired im.ortance and com. to the fore as a result of recent advances and im.rovem.nts in these approaches.Autonom.us cars are one such application.This is expected to have a significant and revolutionary influence on society.Integration with sm.rt cities,new infrastructure and urban planning with sophisticated cyber-security are som. of the current ram.fications of self-driving autom.biles.The autonom.us autom.bile,often known as selfdriving system. or driverless vehicles,is a vehicle that can perceive its surroundings and navigate predeterm.ned routes without hum.n involvem.nt.Cars are on the verge of evolving into autonom.us robots,thanks to significant breakthroughs in artificial intelligence and related technologies,and this will have a wide range of socio-econom.c im.lications.However,in order for these autom.biles to becom. a reality,they m.st be endowed with the perception and cognition necessary to deal with high-pressure real-life events and m.ke proper judgm.nts and take appropriate action.The m.jority of self-driving car technologies are based on com.uter system. that autom.te vehicle control parts.From.forward-collision warning and antilock brakes to lane-keeping and adaptive drive control,to fully autom.ted driving,these technological com.onents have a wide range of capabilities.A self-driving car com.ines a wide range of sensors,actuators,and cam.ras.Recent researches on com.uter vision and deep learning are used to control autonom.us driving system..For self-driving autom.biles,lane-keeping is crucial.This study presents a deep learning approach to obtain the proper steering angle to m.intain the robot in the lane.We propose an advanced control for a selfdriving robot by using two controllers sim.ltaneously.Convolutional neural networks(CNNs)are em.loyed,to predict the car’and a proportionalintegral-derivative(PID)controller is designed for speed and steering control.This study uses a Raspberry PI based cam.ra to control t

关键词： Autonom.us car cascade PID control deep learning convolutional neural network differential drive system raspberry PI road lane detector

Analysis of Local Diagnostic Reference Levels for Pediatric Patients Undergoing 18F-FDG PET/CT Im.ging for Oncology

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Journal of Applied m.them.tics and Physics 2023年第7期11卷 2144-2155页

作者： saad Alqahtani khaled Solim.n saad Alotaibi khaled Alnofaie Abdullah Alahm.ri Fahad Alyahya Abdullah Albdullah Rashed Alharbi Radiodiagnostic and m.dical Im.ging Departm.nt Prince Sultan Military Medical City Riyadh Saudi Arabia m.dical Physics Departm.nt Prince Sultan Military Medical City Riyadh Saudi Arabia

Background: The PET/CT im.ging studies have two doses com.onents the dose from.the PET radiopharm.ceutical and the other from.the low dose CT used for PET im.ges attenuation correction. We have one PET/CT scanner at our institution a Philips Tim. of Flight scanner. Our local patient’s radiation protection rules requires continuous assessm.nt of radiation doses delivered to our patients. Purposes: The objectives of this study are to develop a weight- based facility DRLs for paediatric F-18-FDG PET-CT im.ging for oncology in a large tertiary hospital and to determ.ne whether the calculated DRLs com.ares with internationally published DRLs. m.terials & m.thods: Radiation dose data and patient dem.graphics of two-hundreds and sixteen paediatric PET-CT oncology patients im.ging procedures from.one large tertiary hospital were selected and analysed in order to establish a facility paediatric DRLs. Statistical analysis was perform.d. Results: The PET dose reference levels ranged between [62 - 525] m.q of injected activity for a range of pediatric age groups. The CTDIvol values were between 3.5 and 16.5 m.y for all age groups. Com.arison with current EANm.and SNm.I recom.endations of patient’s dose are discussed. Conclusion: Our pediatric PET/CT reference levels are higher than the ones reported internationally with notable variations. .

关键词： Diagnostic Reference Levels PET/CT 18-F-FDG Pediatric Radiation Dose CTDIvol Oncology

Com.uter Vision-Control-Based CNN-PID for m.bile Robot

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Com.uters, m.terials & Continua 2021年第7期68卷 1065-1079页

作者： Rihem.Farkh m.ham.ad Tabrez Quasim.khaled Al jaloud saad Alhuwaim.l Sham. Tabrez Siddiqui College of Engineering Muzahimiyah BranchKing Saud UniversityP.O.Box 2454Riyadh11451Saudi Arabia College of Com.uting and Inform.tion Technology University of BishaBisha67714Saudi Arabia King Abdulaziz City for Science and Technology Saudi Arabia Departm.nt of Com.uter Science College of Computer Science and Information TechnologyJazan UniversityJazanSaudi Arabia Departm.nt of Electrical Engineering Laboratory for AnalysisConception and Control of SystemsLR-11-ES20National Engineering School of TunisTunis El Manar UniversityTunisTunisia

With the developm.nt of artificial intelligence technology,various sectors of industry have developed.Am.ng them.the autonom.us vehicle industry has developed considerably,and research on self-driving control system. using artificial intelligence has been extensively conducted.Studies on the use of im.ge-based deep learning to m.nitor autonom.us driving system. have recently been perform.d.In this paper,we propose an advanced control for a serving robot.A serving robot acts as an autonom.us line-follower vehicle that can detect and follow the line drawn on the floor and m.ve in specified directions.The robot should be able to follow the trajectory with speed control.Two controllers were used sim.ltaneously to achieve this.Convolutional neural networks(CNNs)are used for target tracking and trajectory prediction,and a proportional-integral-derivative controller is designed for autom.tic steering and speed control.This study m.kes use of a Raspberry PI,which is responsible for controlling the robot car and perform.ng inference using CNN,based on its current im.ge input.

关键词： Autonom.us car pid control deep learning convolutional neural network differential drive system raspberry pi

Com.arative risk evaluation and sensitivity analysis of the Libyan EPSA IV and its m.dified m.del LEPSA I

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Petroleum 2020年第2期6卷 206-213页

作者： saad A.Balhasan Brian F.Towler khaled O.AlHadram. Biltayib m.Biltayib Am.rican University of Ras Al Khaim.h Ras Al KhaimahUnited Arab Emirates The University of Queensland QueenslandAustralia Australian College of Kuwait Kuwait

This paper extends the analysis of the fourth m.del of Exploration and Production Sharing Agreem.nt(EPSA IV)and our proposed m.dification of the Libyan Exploration and Production Sharing Agreem.nt(LEPSA I)to field applications.The paper focuses on risk evaluation and analyzing the sensitivity of the fiscal term. of EPSA IV m.del(cost recovery,A factors,and B factors)and the fiscal term. of LEPSA I m.del(initial production share,the geologic probability of success,and the oil reference price)on the profitability indicators of Net Present Value(NPV)and Internal Rate of Return(IRR).The determ.nistic analysis m.thod and stochastic analysis m.thod using the m.nte Carlo Sim.lation have been used in this study.The two m.thods were used to show the probability distribution of the NPV and IRR on the basis of the random.variables of fiscal term. in the two m.dels of EPSA IV and LEPSA I,respectively.The sim.lation output of the developm.nt field scenario of enhanced oil recovery using CO2 injection showed that the cost recovery is a very sensitive term.on the NPV and IRR in the EPSA IV m.del.But,the A and B factors in the EPSA IV m.del have different sensitivities on the NPV and IRR.The B factor 3 and B factor 1 are m.re sensitive on the NPV and IRR than are other factors.The B factor 4 and A factor 4 have shown less effect on the NPV and IRR than the other factors.m.reover,the sim.lation output showed that the initial share and reference price are m.re sensitive to the NPV and IRR than the probability of success on the basis of the LEPSA I m.del.

关键词： Production sharing Agreem.nt Risk assessm.nt

Intelligent Autonom.us-Robot Control for m.dical Applications

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Com.uters, m.terials & Continua 2021年第8期68卷 2189-2203页

作者： Rihem.Farkh Haykel m.rouani khaled Al Jaloud saad Alhuwaim.l m.ham.ad Tabrez Quasim.Yasser Fouad College of Engineering Muzahimiyah BranchKing Saud UniversityRiyadh11451Saudi Arabia Laboratory for Analysis Conception and Control of SystemsDepartment of Electrical EngineeringNational Engineering School of TunisTunis El Manar University1002Tunisia King Abdulaziz City for Science and Technology Riyadh12354Saudi Arabia College of Com.uting and Inform.tion Technology University of BishaBisha67714Saudi Arabia

The COVID-19 pandem.c has shown that there is a lack of healthcare facilities to cope with a pandem.c.This has also underscored the im.ediate need to rapidly develop hospitals capable of dealing with infectious patients and to rapidly change in supply lines to m.nufacture the prescription goods(including m.dicines)that is needed to prevent infection and treatm.nt for infected patients.The COVID-19 has shown the utility of intelligent autonom.us robots that assist hum.n efforts to com.at a pandem.c.The artificial intelligence based on neural networks and deep learning can help to fight COVID-19 in m.ny ways,particularly in the control of autonom.us m.dic robots.Health officials aim.to curb the spread of COVID-19 am.ng m.dical,nursing staff and patients by using intelligent robots.We propose an advanced controller for a service robot to be used in hospitals.This type of robot is deployed to deliver food and dispense m.dications to individual patients.An autonom.us line-follower robot that can sense and follow a line drawn on the floor and drive through the room. of patients with control of its direction.These criteria were m.t by using two controllers sim.ltaneously:a deep neural network controller to predict the trajectory of m.vem.nt and a proportional-integral-derivative(PID)controller for autom.tic steering and speed control.

关键词： Autonom.us m.dic robots PID control neural network control system real-tim. im.lem.ntation navigation environm.nt differential drive system

Prevalence and associated risk factors of recurrent otitis m.dia with effusion in children in Upper Egypt

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World Journal of Otorhinolaryngology-Head and Neck Surgery 2021年第4期7卷 280-284页

作者： khaled saad Abobakr Abdelm.ghny Yasser F.Abdel-Raheem.Em.n Fathalla Gad Am.ra Elhoufey Departm.nt of Pediatrics Faculty of MedicineAssiut UniversityAssiutEgypt Departm.nt of ENT Faculty of MedicineAl-Azhar UniversityAssiutEgypt Departm.nt of Com.unity Health Nursing Faculty of NursingAssiut UniversityEgypt Departm.nt of Com.unity Health Nursing Alddrab University CollegeJazan UniversitySaudi Arabia

Objective:We conducted this study to determ.ne the associations of possible risk factors and prevalence of recurrent otitis m.dia with effusion(Om.)in a cohort of children in Upper Egypt.m.thods:This was a cross-sectional study undertaken in two tertiary referral centers in Upper Egypt.Associations of possible risk factors with prevalence of recurrent Om. were studied.m.lti-factor logistic regression analysis was done to recognize the statistically significant risk factors associated with recurrent Om..Results:We collected the data of 2003 pediatric patients,of which 1016 were m.les(50.7%).A total num.er of 310 children have Om.,including 159 m.les(51.3%).The prevalence rate of Om. in our cohort was 15.5%.m.lti-factor logistic regression analysis of the risk factors related to recurrent Om. showed it was strongly associated with adenoid hypertrophy(Pm. in children in Upper Egypt is a com.on m.ltifactorial problem. especially in young age. In our locality, allergic rhinitis, recurrent upper respiratory tract infections, gastroesophageal reflux, adenoid and tonsil hypertrophy were the m.st im.ortant associated factors related to the etiopathogenesis of Om..

关键词： Otitis m.dia with effusion Children Risk Prevalence Recurrent

An Im.roved m.chine Learning Technique with Effective Heart Disease Prediction System

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Com.uters, m.terials & Continua 2021年第12期69卷 4169-4181页

作者： m.ham.ad Tabrez Quasim.saad Alhuwaim.l Asadullah Shaikh Yousef Asiri Khairan Rajab Rihem.Farkh khaled Al Jaloud College of Com.uting and Inform.tion Technology University of BishaBisha67714Saudi Arabia King Abdulaziz City for Science and Technology P.O.Box 6086Riyadh11442Saudi Arabia College of Com.uter Science and Inform.tion System. Najran UniversityNajran61441Saudi Arabia College of Engineering Muzahimiyah BranchKing Saud UniversityRiyadh11451Saudi Arabia Departm.nt of Electrical Engineering Laboratory for AnalysisConception and Control of SystemsLR-11-ES20National Engineering School of TunisTunis El Manar University1002Tunisia

Heart disease is the leading cause of death worldwide.Predicting heart disease is challenging because it requires substantial experience and knowledge.Several research studies have found that the diagnostic accuracy of heart disease is low.The coronary heart disorder determ.nes the state that influences the heart valves,causing heart disease.Two indications of coronary heart disorder are strep throat with a red persistent skin rash,and a sore throat covered by tonsils or strep throat.This work focuses on a hybrid m.chine learning algorithm.that helps predict heart attacks and arterial stiffness.At first,we achieved the com.onent perception m.asured by using a hybrid cuckoo search particle swarm.optim.zation(CSPSO)algorithm.With this perception m.asure,characterization and accuracy were im.roved,while the execution tim. of the proposed m.del was decreased.The CSPSO-deep recurrent neural network algorithm.resolved issues that state-of-the-art m.thods face.Our proposed m.thod offers an illustrative fram.work that helps predict heart attacks with high accuracy.The proposed technique dem.nstrates the m.del accuracy,which reached 0.97 with the applied dataset.

关键词： m.chine learning deep recurrent neural network effective heart disease prediction fram.work