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Vitamin D Status in Saudi Patients with Type 1 Diabetes Mellitus

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Open Journal of Endocrine and Metabolic Diseases 2013年第2期3卷 137-143页

作者： khalid Salim Aljabri S. A. Bok.ari k. A. Alqurashi Department of Endocrinology King Fahad Armed Forces Hospital Jeddah KSA

Aims/Introduction: There are studies in different countries regarding the prevalence of vitamin D deficiency in non diabetic population. Few studies were done in adults with type 1 diabetes mellitus. This study was conducted to determine Vitamin D Status among Saudi patients with type 1 diabetes and to correlate the associated environmental risk.factors. Materials and Methods: A cross-sectional single centre study was conducted in 221 Saudi patients with type 1 diabetes mellitus. These patients were recruited through the Diabetes Centre at k.ng Fahad Armed Forces Hospital between January 2008 and June 2009. 25-hydroxy-vitamin D, Parathyroid hormone, calcium, phosphate and alk.0}ne phosphatase were measured. Results: There were 221 patients with type 1 diabetes mellitus, 92 males (42%) and 129 females (58%). The mean age was 21.3 ± 7.2 and the mean diabetes duration was 7.5 ± 5.7. The frequency of 25-hydroxyvitamin D 50 nmol/l and k.n exposed and duration of sunlight exposure associated significantly with Vitamin D levels (p 0.0001 and p 0.0001 respectively). Age was associated with more vitamin D < 25 nmol/l (the odds ratio (95% confidence interval);4.8 (4.6,14.5), p = 0.005;5.3 (1.8,15.5), p = 0.003;3.9 (1.5,10.5), p = 0.007 for age groups 12 - 15, 16 - 19 and 20 - 24 years old consequently. Male gender and exposing face, arms and legs to sun were associated with vitamin D ≥ 25 nmol/l;the odds ratio (95% confidence interval);0.27 (0.11,0.6), p = 0.001;0.26 (0.09,0.75), p = 0.01 consequently. Conclusions:

关键词： Type 1 Diabetes Mellitus Vitamin D Deficiency Frequency

Electrical Track.ng,Erosion and Fire Retardancy Performance of Silicone Rubber Insulation Containing Aluminum Trihydrate,Graphene and Glass Fiber Additives

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CSEE Journal of Power and Energy Systems 2023年第5期9卷 1918-1925页

作者： M.Tariq Nazir Arslan khalid B.Toan Phung Arup k.Das Peter Cheetham Ashish Paramane Shak.el Ak.am k.oi Loon Wong School of Engineering RMIT UniversityMelbourneVIC 3000Australia. the Center for Advanced Power Systems(CAPS) Florida State UniversityFL 32310USA the Flame Security International Pty Limited Australia the School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW 2052Australia Department of Electrical Engineering National Institute of TechnologySilcharAssam 788010India the College of Electrical Engineering and Control Science Nanjing Tech UniversityNanjingJiangsu 211816China

Silicone rubber composite is a priority electrical insulating material used in high-voltage outdoor insulation *** electrical track.ng/erosion and poor flame resistance performance of silicone rubber once ignited,substantially reduce its work.ng *** paper attempts to investigate track.ng/erosion performance of room temperature vulcanized(RTV)silicone rubber along with flame retardant parameters using aluminum trihydrate(ATH),graphene nanosheets(GN)and milled glass fiber(GF)*** inclined plane test(IPT)was performed in line with criteria defined in IEC 60587 using step-up track.ng voltage method while flame retardancy is evaluated according to ASTM E 1354.0 using a cone *** suggest 30% of ATH assists in improving physical track.ng/erosion resistance of pristine silicone elastomer rubber by impeding development of leak.ge current and a great reduction in maximum average temperatures on the surface of *** improvement in performance of RTV2 is achieved through introduction of 1%of GN and 5% of GF as seen in ***,30% of ATH reduces heat release rate and smok. production rate,and this trend is improved with the introduction of GN/***4 has pop up as the most promising silicone rubber composite with excellent electrical track.ng,erosion,and flame resistance performance relative to its counterparts in this study.

关键词： Dry band arcing electrical track.ng heat release rate leak.ge current silicone rubber smok. production rate

Treatment of spent wash water derived from shredded lettuce processing using a combination of electrocoagulation and germicidal ultraviolet light

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Food Quality and Safety 2017年第2期1卷 147-152页

作者： k.khalid D Alharbi V.Lau C.Liang R.G.Zytner J.Shi k.Warriner Department of Food Science University of GuelphOntarioCanada School of Engineering University of GuelphOntarioCanada Guelph Food Research Center Agriculture Agri Food CanadaGuelphOntarioCanada

Objective:Water recycling is a significant part of an overall water management *** current study evaluated electrocoagulation,used in combination with ultraviolet light(at 254 nm),to reduce the organic content and enhance the microbiological quality,of wash water derived from shredded lettuce ***:The composition of spent wash water derived from a commercial lettuce processing operation was used to prepare a simulated solution to be applied to validate the water recycling *** simulated spent wash water was subjected to an electrocoagulation process followed by filtration and a tertiary ultraviolet(254 nm)*** efficacy of the recycling treatment to decrease turbidity(nephelometric turbidity units,biological oxygen demand(BOD),chemical oxygen demand(COD)and decrease in introduced bacterial ***:Spent wash water sampled from a commercial processing line was found to be colloidal in nature(78±26 NTU)with low total solids content(544±87 mg/L),BOD(230±53 mg/L)and COD(309±53 mg/L).An electrocogaultion process performed for 10 min using 3.48 A/m2 current density at pH 6.5 and conductivity of>100μS/cm supported an 87%removal of turbidity,38%reduction in BOD along with 49%decrease in *** electrocoagulation process was also found to reduce the levels of Escherichia coli,Salmonella and Listeria monocytogenes by 1-2 log *** tertiary UV treatment of water derived from the electro coagulation process,supported further reduction in model pathogens,although it was noted that the D values for inactivation were in the order of 1.01-1.60 mJ/cm^(2),which compares to 0.22-0.31 mJ/cm^(2) in *** apparent increase in bacterial resistance to ultraviolet was lik.ly due to the UV absorbing low molecular weight constituents within wash water that provided protection against ***:In conclusion,the study demonstrated the feasibility of applying electrocoagulation and UV to rapidly treat spent lettuce wash water to fa

关键词： Biological oxygen demand(BOD) Chemical oxygen demand(COD) Electrocoagulation Fresh produce Lettuce UV Washing Water recycling.

Decoherence Effects on Multiplayer Cooperative Quantum Games

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Communications in Theoretical Physics 2011年第8期56卷 228-234页

作者： Salman k.an M.Ramzan M.khalid.k.an Department of Physics Quaid-i-Azam University

We study the behavior of cooperative multiplayer quantum games [Q. Chen, Y. Wang, J.T. Liu, and k.L. Wang, Phys. Lett. A 327 （2004） 98; A.P. Flitney and L.C.L. Hollenberg, Quantum Inf. Comput. 7 （2007） 111] in the presence of decoherence using different quantum channels such as amplitude damping, depolarizing and phase damping. It is seen that the outcomes of the games for the two damping channels with maximum values of decoherence reduce to same value. However, in comparison to phase damping channel, the payoffs of cooperators are strongly damped under the influence amplitude damping channel for the lower values of decoherence parameter. In the case of depolarizing channel, the game is a no-payoff game irrespective of the degree of entanglement in the initial state for the larger values of decoherence parameter. The deeoherenee gets the cooperators worse off.

关键词： multiplayer cooperative quantum games payoffs Nash equilibria

LBP–Bilateral Based Feature Fusion for Breast Cancer Diagnosis

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Computers, Materials & Continua 2022年第11期73卷 4103-4121页

作者： Yassir Edrees Almalk. Maida khalid Sharifa khalid Alduraibi Qudsia Yousaf Maryam Zaffar Shoayea Mohessen Almutiri Muhammad Irfan Mohammad Abd Alk.alik.Basha Alaa khalid Alduraibi Abdulrahman Manaa Alamri k.alaf Alshamrani Hassan A.Alshamrani Division of Radiology Department of MedicineMedical CollegeNajran UniversityNajran 61441Saudi Arabia Department of Computer Science and Information Technology Ibadat International UniversityIslamabad44000Pakistan Department of Radiology College of MedicineQassim UniversityBuraidah 52571Saudi Arabia Department of Radiology King Fahad Specialist HospitalBuraydah 52571Saudi Arabia Electrical Engineering Department College of EngineeringNajran UniversityNajran 61441Saudi Arabia Radiology Department Human Medicine CollegeZagazig UniversityZagazig 44631Egypt Department of Surgery College of MedicineNajran UniversityNajran 61441Saudi Arabia Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran 61441Saudi Arabia

Since reporting cases of breast cancer are on the rise all over the *** in regions such as Pak.stan,Saudi Arabia,and the United *** methods for the early detection and diagnosis of breast cancer are *** usual diagnosis procedures followed by physicians has been updated with modern diagnostic approaches that include computer-aided support for better *** learning based practices has increased the accuracy and efficiency of medical diagnosis,which has helped save lives of many *** is much research in the field of medical imaging diagnostics that can be applied to the variety of data such as magnetic resonance images(MRIs),mammograms,X-rays,ultrasounds,and histopathological images,but magnetic resonance(MR)and mammogram imaging have proved to present the promising *** proposed paper has presented the results of classification algorithms over Breast Cancer(BC)mammograms from a novel dataset tak.n from hospitals in the Qassim health cluster of Saudi *** paper has developed a novel approach called the novel spectral extraction algorithm(NSEA)that uses feature extraction and fusion by using local binary pattern(LBP)and bilateral algorithms,as well as a support vector machine(SVM)as a *** NSEA with the SVM classifier demonstrated a promising accuracy of 94%and an elapsed time of 0.68 milliseconds,which were significantly better results than those of comparative experiments from classifiers named Naïve Bayes,logistic regression,k.Nearest Neighbor(k.N),Gaussian Discriminant Analysis(GDA),AdaBoost and Extreme Learning Machine(ELM).ELM produced the comparative accuracy of 94%however has a lower elapsed time of 1.35 as compared to *** has produced a fairly well accuracy of 82%,k.N has a low accuracy of 66%.However Logistic Regression,GDA and Naïve Bayes have produced the lowest accuracies of 47%,43%and 42%.

关键词： Artificial intelligence machine learning breast cancer mammograms supervised learning classification feature fusion

Performance Evaluation of Deep Dense Layer Neural Network.for Diabetes Prediction

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Computers, Materials & Continua 2023年第7期76卷 347-366页

作者： Niharik. Gupta Baijnath k.ushik.Mohammad khalid Imam Rahmani Saima Anwar Lashari Department of Computer Science and Engineering Shri Mata Vaishno Devi UniversityKatraJ&KIndia College of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia

Diabetes is one of the fastest-growing human diseases worldwide and poses a significant threat to the population’s longer *** prediction of diabetes is crucial to tak.ng precautionary steps to avoid or delay its *** this study,we proposed a Deep Dense Layer Neural Network.DDLNN)for diabetes prediction using a dataset with 768 instances and nine *** also applied a combination of classical machine learning(ML)algorithms and ensemble learning algorithms for the effective prediction of the *** classical ML algorithms used were Support Vector Machine(SVM),Logistic Regression(LR),Decision Tree(DT),k.Nearest Neighbor(k.N),and Naïve Bayes(NB).We also constructed ensemble models such as bagging(Random Forest)and boosting lik. AdaBoost and Extreme Gradient Boosting(XGBoost)to evaluate the performance of prediction *** proposed DDLNN model and ensemble learning models were trained and tested using hyperparameter tuning and k.Fold cross-validation to determine the best parameters for predicting the *** combined ML models used majority voting to select the best outcomes among the *** efficacy of the proposed and other models was evaluated for effective diabetes *** investigation concluded that the proposed model,after hyperparameter tuning,outperformed other learning models with an accuracy of 84.42%,a precision of 85.12%,a recall rate of 65.40%,and a specificity of 94.11%.

关键词： Diabetes prediction hyperparameter tuning k.fold validation machine learning neural network

Optimal Deep Learning Model Enabled Secure UAV Classification for Industry 4.0

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Computers, Materials & Continua 2023年第3期74卷 5349-5367页

作者： khalid A.alissa Mohammed Maray Areej A.Malibari Sana Alazwari Hamed Alqahtani Mohamed k.Nour Marwa Obbaya Mohamed A.Shamseldin Mesfer Al Duhayyim SAUDI ARAMCO Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Mechanical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Emerging technologies such as edge computing,Internet of Things(IoT),5G network.,big data,Artificial Intelligence(AI),and Unmanned Aerial Vehicles(UAVs)empower,Industry 4.0,with a progressive production methodology that shows attention to the interaction between machine and human *** the literature,various authors have focused on resolving security problems in UAV communication to provide safety for vital *** current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification(CSODL-SUAVC)model for Industry 4.0 *** suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image ***,the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation(ML-DWT),CSO-related Optimal Pixel Selection(CSO-OPS),and signcryption-based *** proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover *** secret images,encrypted by signcryption technique,are embedded into cover ***,the image classification process includes three components namely,Super-Resolution using Convolution Neural Network.SRCNN),Adam optimizer,and softmax *** integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV *** proposed CSODLSUAVC model was experimentally validated using benchmark.datasets and the outcomes were evaluated under distinct *** simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.

关键词： Unmanned Aerial Vehicles Artificial Intelligence emerging technologies Deep Learning Industry 4.0 image steganography

Optimal Load Forecasting Model for Peer-to-Peer Energy Trading in Smart Grids

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Computers, Materials & Continua 2022年第1期70卷 1053-1067页

作者： Lijo Jacob Varghese k.Dhayalini Suma Sira Jacob Ihsan ali Abdelzahir Abdelmaboud Taiseer Abdalla Elfadil Eisa Department of Electrical and Electronics Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Electrical and Electronics Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala LumpurMalaysia Department of Information Systems King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia

Peer-to-Peer(P2P)electricity trading is a significant research area that offers maximum fulfilment for both prosumer and *** also decreases the quantity of line loss incurred in Smart Grid(SG).But,uncertainities in demand and supply of the electricity might lead to instability in P2P mark.t for both prosumer and *** recent times,numerous Machine Learning(ML)-enabled load predictive techniques have been developed,while most of the existing studies did not consider its implicit features,optimal parameter selection,and prediction *** order to overcome fulfill this research gap,the current research paper presents a new Multi-Objective Grasshopper Optimisation Algorithm(MOGOA)with Deep Extreme Learning Machine(DELM)-based short-term load predictive technique i.e.,MOGOA-DELM model for P2P Energy Trading(ET)in *** proposed MOGOA-DELM model involves four distinct stages of operations namely,data cleaning,Feature Selection(FS),prediction,and parameter *** addition,MOGOA-based FS technique is utilized in the selection of optimum subset of ***,DELM-based predictive model is also applied in forecasting the load *** proposed MOGOA model is also applied in FS and the selection of optimalDELM parameters to improve the predictive *** inspect the effectual outcome of the proposed MOGOA-DELM model,a series of simulations was performed using Uk.Smart Meter *** the experimentation procedure,the proposed model achieved the highest accuracy of 85.80%and the results established the superiority of the proposed model in predicting the testing data.

关键词： Peer to Peer energy trade smart grid load forecasting machine learning feature selection

Voting Classifier and Metaheuristic Optimization for Network.Intrusion Detection

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Computers, Materials & Continua 2023年第2期74卷 3183-3198页

作者： Doaa Sami k.afaga Faten khalid k.rim Abdelaziz A.Abdelhamid El-Sayed M.El-k.nawy Hend k.Alk.htani Nima k.odadadi Mohammed Hadwan Abdelhameed Ibrahim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt The Department of Civil and Environmental Engineering Florida International UniversityMiamiFLUSA Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Managing physical objects in the network.s periphery is made possible by the Internet of Things(IoT),revolutionizing human *** attack. and unauthorized access are possible with these IoT devices,which exchange data to enable remote *** attack. are often detected using intrusion detection methodologies,although these systems’effectiveness and accuracy are *** paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic *** employed metaheuristic optimizer is a new version of the whale optimization algorithm(WOA),which is guided by the dipper throated optimizer(DTO)to improve the exploration process of the traditionalWOA *** proposed voting classifier categorizes the network.intrusions robustly and *** assess the proposed approach,a dataset created from IoT devices is employed to record the efficiency of the proposed algorithm for binary attack.*** dataset records are balanced using the locality-sensitive hashing(LSH)and Synthetic Minority Oversampling Technique(SMOTE).The evaluation of the achieved results is performed in terms of statistical analysis and visual plots to prove the proposed approach’s effectiveness,stability,and *** achieved results confirmed the superiority of the proposed algorithm for the task.of network.intrusion detection.

关键词： Voting classifier whale optimization algorithm dipper throated optimization intrusion detection internet-of-things