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Dubai Primary Health Care Centers Conformation to WHO Age-Friendly Primary Healthcare Recommendations

Advances in Aging Research 2017年第6期6卷 83-92页

作者： Tamer Mohamed Farid Abdellatif Amal Mohamad Saleh Abdulrahim Al Jaziri Manal Mohammad Omran Taryam Nahed AbdulKhaleq Monsef amel ibrahim buharoun Salah Ahmed Mohamed Elbadawi Moulham Saleh Ashtar Elderly Care Unit Health Affairs Department Primary Health Care Services Sector Dubai Health Authority Dubai United Arab Emirates Primary Health Care Services Sector Dubai Health Authority Dubai United Arab Emirates Health Affairs Department Primary Health Care Services Sector Dubai Health Authority Dubai United Arab Emirates Health Centres Department Primary Health Care Services Sector Dubai Health Authority Dubai United Arab Emirates

The WHO project for conforming PHC to requirements of all age groups has resulted in publishing a toolkit for age-friendly PHC in 2008. The toolkit included checklists for physical environment and signage properties. This study matched the current physical environment properties of DHA’s PHC Health Centers against WHO’s recommendations. This is a cross sectional descriptive study that included visits to all 12 Primary Health Care Centers in Dubai city during August-September 2016 with the objective to assess the degree of fulfillment of current properties of Health Centers building to the recommendations of WHO as listed in “Age-friendly Primary Health Care Centres Toolkit” [1]. The study found that 81.86% of physical environment properties are matching the recommendation of WHO, while signage matching was 44.6%. The study concluded that most PHC properties have a physical design that met WHO’s recommendations. The two major deviations were accessibility by public transportations and presence of grab bars. Factors that had a significant impact on design were compliance with multiple international and local standards, the availability of private cars, and the availability of wheel chairs. Signage in DHA’s health centers followed a central plan that differed from WHO’s recommendations.

关键词： World Health Organization (WHO) Primary Health Care (PHC) Elderly Care Unit Dubai Health Authority (DHA)

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Urban Electric Vehicle Charging Station Placement Optimization with Graylag Goose Optimization Voting Classifier

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Computers, Materials & Continua 2024年第7期80卷 1163-1177页

作者： amel Ali Alhussan Doaa Sami Khafaga El-Sayed M.El-kenawy Marwa M.Eid Abdelhameed ibrahim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute for Engineering and TechnologyMansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

To reduce the negative effects that conventional modes of transportation have on the environment,researchers are working to increase the use of electric *** demand for environmentally friendly transportation may be hampered by obstacles such as a restricted range and extended rates of *** establishment of urban charging infrastructure that includes both fast and ultra-fast terminals is essential to address this ***,the powering of these terminals presents challenges because of the high energy requirements,whichmay influence the quality of *** the maximum hourly capacity of each station based on its geographic location is necessary to arrive at an accurate estimation of the resources required for charging *** is vital to do an analysis of specific regional traffic patterns,such as road networks,route details,junction density,and economic zones,rather than making arbitrary conclusions about traffic *** vehicle traffic is simulated using this data and other variables,it is possible to detect limits in the design of the current traffic engineering ***,the binary graylag goose optimization(bGGO)algorithm is utilized for the purpose of feature ***,the graylag goose optimization(GGO)algorithm is utilized as a voting classifier as a decision algorithm to allocate demand to charging stations while taking into consideration the cost variable of traffic *** on the results of the analysis of variance(ANOVA),a comprehensive summary of the components that contribute to the observed variability in the dataset is *** results of the Wilcoxon Signed Rank Test compare the actual median accuracy values of several different algorithms,such as the voting GGO algorithm,the voting grey wolf optimization algorithm(GWO),the voting whale optimization algorithm(WOA),the voting particle swarm optimization(PSO),the voting firefly algorithm(FA),and the voting genetic algori

关键词： Electric vehicle graylag goose optimization metaheuristics optimization machine learning

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模糊数学感官评价结合响应面法优化鲊辣椒混菌发酵工艺

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食品与发酵工业 2024年第7期50卷 83-91,I0004,I0005页

作者：黄璐晗尹小庆阚建全武运戚晨晨彭芸 SAMEH Awad amel ibrahim 杜木英西南大学食品科学学院重庆400715 西南大学中匈食品科学合作研究中心重庆400715 川渝共建特色食品重庆市重点实验室重庆400715 西藏自治区农牧科学院农产品开发与食品科学研究所西藏拉萨850032 新疆农业大学食品科学与药学学院新疆乌鲁木齐830000 新疆新康农业发展有限公司新疆乌鲁木齐830000 亚历山大大学农学院埃及亚历山大999060

以小米椒和玉米粉为原料,植物乳植杆菌XZ3和生香酵母菌Y50为菌种,制作一款混菌发酵鲊辣椒。以鲊辣椒的模糊感官评分为响应值,采用Box-Behnken中心组合设计试验对鲊辣椒混菌发酵工艺进行优化,并测定其产品的功能性成分。结果表明,混菌发... 详细信息

以小米椒和玉米粉为原料,植物乳植杆菌XZ3和生香酵母菌Y50为菌种,制作一款混菌发酵鲊辣椒。以鲊辣椒的模糊感官评分为响应值,采用Box-Behnken中心组合设计试验对鲊辣椒混菌发酵工艺进行优化,并测定其产品的功能性成分。结果表明,混菌发酵鲊辣椒的最优发酵条件为:植物乳植杆菌XZ3与生香酵母菌Y50的比例为1∶1、混菌添加量为2.5%、食盐添加量为4%、发酵时间6 d。在此优化条件下,鲊辣椒的感官评分为8.45分。与不接种自然发酵鲊辣椒和利用植物乳植杆菌XZ3纯种发酵的鲊辣椒进行对比,发现优化后的混菌发酵鲊辣椒的香气更突出。进一步测定其功能性成分发现,混菌发酵鲊辣椒中多酚组分含量最高且种类检出最多,其总酸含量、辣椒碱含量和总黄酮含量都显著高于自然发酵鲊辣椒。除此之外,相比于其他两种,混菌发酵鲊辣椒中甜味氨基酸的含量上升,苦味氨基酸的比例降低。该研究为混菌发酵鲊辣椒的生产提供了有益参考。

关键词：鲊辣椒响应面分析模糊数学感官评定发酵工艺功能性成分

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Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram

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Computer Systems Science & Engineering 2023年第5期45卷 1469-1482页

作者： Doaa Sami Khafaga amel Ali Alhussan Abdelaziz A.Abdelhamid Abdelhameed ibrahim Mohamed Saber El-Sayed M.El-kenawy 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 University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Electronics and Communications Engineering Department Faculty of EngineeringDelta University for Science and TechnologyMansouraEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Arrhythmia has been classified using a variety of *** of the dynamic nature of electrocardiogram(ECG)data,traditional handcrafted approaches are difficult to execute,making the machine learning(ML)solutions more *** with cardiac arrhythmias can benefit from competent monitoring to save their *** arrhythmia classification and prediction have greatly improved in recent *** are a category of conditions in which the heart's electrical activity is abnormally rapid or *** year,it is one of the main reasons of mortality for both men and women,*** the classification of arrhythmias,this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors(KNN)*** proposed method makes advantage of the UCI repository,which has a 279-attribute high-dimensional cardiac arrhythmia *** proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s *** purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia *** method of comprehensive arrhythmia classification outperforms earlier methods presented in the *** achieved classification accuracy using the proposed approach is 99.8%.

关键词： Feature selection electrocardiogram metaheuristics dipper throated algorithm

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Facial Expression Recognition Model Depending on Optimized Support Vector Machine

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Computers, Materials & Continua 2023年第7期76卷 499-515页

作者： amel Ali Alhussan Fatma M.Talaat El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed ibrahim Doaa Sami Khafaga Mona Alnaggar Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Machine Learning&Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Robotics and Intelligent Machines Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt

In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of *** is feasible and useful to convert face photos into collections of visual words and carry out global expression *** main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is *** uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos *** FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization *** discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously *** search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.

关键词： Facial expression recognition machine learning linear dis-criminant analysis(LDA) support vector machine(SVM) grid search

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Validation of the Methods for Detection the Non-Milk Fat in a Mixture of Milk Fat and Palm Oil

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Food and Nutrition Sciences 2023年第1期14卷 1-17页

作者： Marwa El-Nabawy Sameh Awad amel ibrahim Department of Dairy Science and Technology Faculty of Agriculture Alexandria University Alexandria Egypt

Milk fat contains a variety of nutritive and health-promoting compounds that guard against some disease. In the current system of global competition, when the quality of milk and milk products is not an option but rather a requirement, therefore, determining the purity of milk fat is critical. This study aims to validate analytical methods for detecting palm oil in a mixture of milk fat and palm oil. Methods of this study was involved detection of non-milk fat in fat blinders by determining the saponification value, iodine number, refractive index, butyro refractometer reading, Gas chromatography, Reverse Phase High-performance liquid chromatography, and Fourier transforms Infrared. The results of this study revealed that the saponification value, Iodine number, refractive index, and Butyro Reading could be used to detect the addition of palm oil by a level of 10% - 20% or more to the milk. The level of some fatty acids in the milk as determined by GC, such as myristic acid (C14:0), palmitic acid (C16:0), and stearic acid (C18:0), is correlated well with the level of adding palm oil to milk fat. The determination of cholesterol and β-sito-sterol content by RP-HPLC could be used for the detection of the addition of palm oil to milk fat. The spectrum behavior produced by FTIR spectroscopy in this adulterated sample is almost the same, so this technique could not be used to detect the palm oil in milk fat.

关键词： Milk Fat Palm Oil Adulteration Butyro Refractometer Reading GC RP-HPLC FTIR

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Improved Prediction of Metamaterial Antenna Bandwidth Using Adaptive Optimization of LSTM

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Computers, Materials & Continua 2022年第10期73卷 865-881页

作者： Doaa Sami Khafaga amel Ali Alhussan El-Sayed M.El-kenawy Abdelhameed ibrahim Said H.Abd Elkhalik Shady Y.El-Mashad Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Systems Engineering Faculty of Engineering at ShoubraBenha UniversityEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

The design of an antenna requires a careful selection of its parameters to retain the desired ***,this task is time-consuming when the traditional approaches are employed,which represents a significant *** the other hand,machine learning presents an effective solution to this challenge through a set of regression models that can robustly assist antenna designers to find out the best set of design parameters to achieve the intended *** this paper,we propose a novel approach for accurately predicting the bandwidth of metamaterial *** proposed approach is based on employing the recently emerged guided whale optimization algorithm using adaptive particle swarm optimization to optimize the parameters of the long-short-term memory(LSTM)deep *** optimized network is used to retrieve the metamaterial bandwidth given a set of *** addition,the superiority of the proposed approach is examined in terms of a comparison with the traditional multilayer perceptron(ML),Knearest neighbors(K-NN),and the basic LSTM in terms of several evaluation criteria such as root mean square error(RMSE),mean absolute error(MAE),and mean bias error(MBE).Experimental results show that the proposed approach could achieve RMSE of(0.003018),MAE of(0.001871),and MBE of(0.000205).These values are better than those of the other competing models.

关键词： Metamaterial antenna long short term memory(LSTM) guided whale optimization algorithm(Guided WOA) adaptive dynamic particle swarm algorithm(AD-PSO)

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Al-Biruni Earth Radius(BER)Metaheuristic Search Optimization Algorithm

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Computer Systems Science & Engineering 2023年第5期45卷 1917-1934页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed ibrahim Seyedali Mirjalili Nima Khodadad Mona A.Al duailij amel Ali Alhussan Doaa Sami Khafaga Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea Department of Civil and Environmental Engineering Florida International UniversityMiamiUSA Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and *** metaheuristics and evolutionary optimization algorithms have been emerged recently in the literature and gained widespread attention,such as particle swarm optimization(PSO),whale optimization algorithm(WOA),grey wolf optimization algorithm(GWO),genetic algorithm(GA),and gravitational search algorithm(GSA).According to the literature,no one metaheuristic optimization algorithm can handle all present optimization *** novel optimization methodologies are still *** Al-Biruni earth radius(BER)search optimization algorithm is proposed in this *** proposed algorithm was motivated by the behavior of swarm members in achieving their global *** search space around local solutions to be explored is determined by Al-Biruni earth radius calculation method.A comparative analysis with existing state-of-the-art optimization algorithms corroborated the findings of BER’s validation and testing against seven mathematical optimization *** results show that BER can both explore and avoid local *** has also been tested on an engineering design optimization *** results reveal that,in terms of performance and capability,BER outperforms the performance of state-of-the-art metaheuristic optimization algorithms.

关键词： Metaheuristics evolutionary optimization exploration exploitation mutation Al-biruni earth radius

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Meta-heuristics for Feature Selection and Classification in Diagnostic Breast Cancer

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Computers, Materials & Continua 2022年第10期73卷 749-765页

作者： Doaa Sami Khafaga amel Ali Alhussan El-Sayed M.El-kenawy Ali E.Takieldeen Tarek M.Hassan Ehab A.Hegazy Elsayed Abdel Fattah Eid Abdelhameed ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Communications and Computers Engineering Department Faculty of EngineeringDelta University for Science and TechnologyInternational Coastal RoadGamasa CityDakhliyaEgypt Faculty of Applied Health Science Technology Delta University for Science and TechnologyMansoura35712Egypt Faculty of Medicine Head of Internal Medicine DepartmentDelta University for Science and TechnologyMansouraEgypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

One of the most common kinds of cancer is breast *** early detection of it may help lower its overall rates of *** this paper,we robustly propose a novel approach for detecting and classifying breast cancer regions in thermal *** proposed approach starts with data preprocessing the input images and segmenting the significant regions of *** addition,to properly train the machine learning models,data augmentation is applied to increase the number of segmented regions using various scaling *** the other hand,to extract the relevant features from the breast cancer cases,a set of deep neural networks(VGGNet,ResNet-50,AlexNet,and GoogLeNet)are *** resulting set of features is processed using the binary dipper throated algorithm to select the most effective features that can realize high classification *** selected features are used to train a neural network to finally classify the thermal images of breast *** achieve accurate classification,the parameters of the employed neural network are optimized using the continuous dipper throated optimization *** results show the effectiveness of the proposed approach in classifying the breast cancer cases when compared to other recent approaches in the ***,several experiments were conducted to compare the performance of the proposed approach with the other *** results of these experiments emphasized the superiority of the proposed approach.

关键词： Breast cancer image segmentation dipper throated optimization feature selection meta-heuristics

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鲊辣椒生香酵母的分离筛选鉴定及其应用研究

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食品与发酵工业 2023年第17期49卷 145-152页

作者：姚红尹小庆颜宇鸽阚建全武运戚晨晨王治国 Sameh AWAD amel ibrahim 杜木英西南大学食品科学学院重庆400715 中匈食品科学合作研究中心重庆400715 川渝共建特色食品重庆重点实验室重庆400715 西藏自治区农牧科学院农产品开发与食品科学研究所西藏拉萨850030 新疆农业大学食品科学与药学学院新疆乌鲁木齐830052 新疆新康农业发展有限公司新疆乌鲁木齐830022 亚历山大大学农学院埃及亚历山大21532

从自然发酵鲊辣椒中以嗅闻法结合总酯含量作为生香酵母的筛选依据分离筛选到1株生香酵母,经鉴定并分类命名为库德里阿兹威氏毕赤酵母菌(Pichia kudriavzevii)Y50,为探究该菌株混菌发酵生产鲊辣椒的品质提升作用,将Y50与植物乳杆菌混合... 详细信息

从自然发酵鲊辣椒中以嗅闻法结合总酯含量作为生香酵母的筛选依据分离筛选到1株生香酵母,经鉴定并分类命名为库德里阿兹威氏毕赤酵母菌(Pichia kudriavzevii)Y50,为探究该菌株混菌发酵生产鲊辣椒的品质提升作用,将Y50与植物乳杆菌混合发酵鲊辣椒(C),以自然发酵的鲊辣椒(A)以及接种植物乳杆菌发酵的鲊辣椒(B)为对比研究对象,对3种鲊辣椒的基本理化指标、辣椒碱、有机酸和挥发性成分进行比较分析。结果表明,3种鲊辣椒pH值、总酸、亚硝酸盐、胡萝卜素含量较为接近,但鲊辣椒C中有机酸含量达(25.46±0.32)g/kg,比鲊辣椒A和鲊辣椒B分别高出57.8%和32.2%。鲊辣椒A、B、C中分别检出挥发性风味物质90种、67种和72种,其中鲊辣椒C的挥发性物质含量最高,为(10101.11±99.04)μg/kg,酯类、醇类、醛类、酸类挥发性物质含量均有提高,相比A、B分别提升了75%、358%、106%、23%和102%、59%、68%、51%,赋予鲊辣椒更浓郁的酯香、花香和果香,说明添加生香酵母Y50能提升鲊辣椒整体的风味品质,通过气味活度值(odor activity value,OAV)的计算,共检出22种OAV>1的香气化合物,这些关键香味物质赋予了鲊辣椒特有的香气特征,其中β-紫罗兰酮、2-甲基丁酸乙酯、己酸己酯、正庚醇、(+)-柠檬烯、庚醛、愈创木酚是构成鲊辣椒C风味的关键物质。试验表明Y50具有进一步应用于鲊辣椒生产的潜力。

关键词：鲊辣椒生香酵母分离鉴定混菌发酵有机酸挥发性成分香气活度值

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