• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Design of Intelligent Alzheimer Disease Diagnosis Model on CIoT Environment

    2022-08-23 02:21:54AnwerMustafaHilalFahdAlWesabiMohamedTaharBenOthman
    Computers Materials&Continua 2022年6期

    Anwer Mustafa Hilal,Fahd N.Al-Wesabi,Mohamed Tahar Ben Othman,

    Khaled Mohamad Almustafa5,Nadhem Nemri6,Mesfer Al Duhayyim7,Manar Ahmed Hamza1,*and Abu Sarwar Zamani1

    1Department of Computer and Self Development,Preparatory Year Deanship,Prince Sattam bin Abdulaziz University,Al-Kharj,16278,Saudi Arabia

    2Department of Computer Science,King King Khalid University,Muhayel Aseer,62529,Saudi Arabia

    3Faculty of Computer and IT,Sana’a University,Sana’a,61101,Yemen

    4Department of Computer Science,College of Computer,Qassim University,Al-Bukairiyah,52571,Saudi Arabia

    5Department of Information Systems,College of Computer and Information Systems,Prince Sultan University,Saudi Arabia

    6Department of Information Systems,King King Khalid University,Muhayel Aseer,62529,Saudi Arabia

    7Department of Natural and Applied Sciences,College of Community-Aflaj,Prince Sattam bin Abdulaziz University,Al-Kharj,16278,Saudi Arabia

    Abstract: Presently,cognitive Internet of Things(CIoT)with cloud computing(CC)enabled intelligent healthcare models are developed,which enables communication with intelligent devices, sensor modules, and other stakeholders in the healthcare sector to avail effective decision making.On the other hand,Alzheimer disease(AD)is an advanced and degenerative illness which injures the brain cells,and its earlier detection is necessary for suitable interference by healthcare professional.In this aspect, this paper presents a new Oriented Features from Accelerated Segment Test(FAST)with Rotated Binary Robust Independent Elementary Features (BRIEF) Detector (ORB)with optimal artificial neural network(ORB-OANN)model for AD diagnosis and classification on the CIoT based smart healthcare system.For initial pre-processing, bilateral filtering (BLF) based noise removal and region of interest (RoI) detection processes are carried out.In addition, the ORBOANN model includes ORB based feature extractor and principal component analysis (PCA) based feature selector.Moreover, artificial neural network(ANN) model is utilized as a classifier and the parameters of the ANN are optimally chosen by the use of salp swarm algorithm(SSA).A comprehensive experimental analysis of the ORB-OANN model is carried out on the benchmark database and the obtained results pointed out the promising outcome of the ORB-OANN technique in terms of different measures.

    Keywords:Cognitive internet of things;machine learning;parameter tuning;alzheimer’s disease;healthcare;decision making

    1 Introduction

    During this digital age of modern technology, the state-of-art growths from Internet of Things(IoT) in 5G telecommunication networks, an artificial intelligence (AI) which contains Machine Learning(ML)techniques(Extreme Learning Machine(ELM),Reinforcement learning(RL),Random Forest (RF), Convolution Neural Networks (CNN), Decision Tree (DT), Long Short-term Memory Network(LSTM),Naive Bayes(NB),etc.)and deep learning(DL)approaches giving longterm solution for tackling the COVID-19 epidemic[1,2].This technology was used for improving the analysis and cure along with help in the prevention of extent of this disease.This connected technology is support by the group of real-time data in person at remote location utilizing IoT;process,interpret,predict, and decision-making utilizing AI and big data analytics (BDA); back-up the information utilizing cloud computing(CC);and it can be improved using blockchain technology to protect data network[3].

    An IoT that is assumed as an interrelated network of intelligence sensor devices frequently is restricted storing and minimum processing power ability [4].IoT, along with CC that is enamors storing and enough processing power ability is made important services namely smart healthcare[5],feasible in the smart city environments.But the monitor and communicate remotely with patients is essential in such an environment[6].Besides,integrate of IoT and cloud technology is given a seamless and ubiquitous structure to smart health care observing.With big data and their real-time processing approaching as to picture,the investigation community faces several problems for developing a smart and intelligent IoT–cloud structure that is capable of making their individual decisions.Therefore,the cognitive computing structure is established and presented for turning IoT as to brain-powered cognitive IoT (CIoT) [7] that retains a higher level of intelligence.Taking a cognitive IoT–cloud smart health care structure will need that IoT device within patient body (for instance, involved or around him/her) cooperates for sensing his or her disease biomarker,movement,body signal, voice,or monitored signal like electroencephalography (EEG) as well as electrocardiography (ECG); and realize the state of patient.The cognitive healthcare structure has been appropriately intelligent for making the equivalent decisions for making the patient relaxed and chooses the forthcoming course of actions by concerning distinct shareholders of smart cities.

    Alzheimer disease(AD)is an advanced and progressive disease which affect brain cell,and their initial analysis is an important to suitable intervention by health professional [8,9].Non-invasive in vivo neuroimaging approaches namely magnetic resonance imaging (MRI) and positron emission tomography (PET) were mostly used for analyzing and monitoring the development of disease and the results of treatment.The issue of evolving computer-aided diagnosis(CAD)tool for distinguishing images with AD from individuals of healthy brain is widely addressed from the previous years[10].

    Aruchamy et al.[11] present novel techniques for detection of AD utilizing first order statistic features from 3D-brain Magnetic Resonance Imaging(MRI).The presented work utilizes 3D structural brain MRI for separating the white as well as grey matter MRI,remove 2D slices under the axial directions,coronal,and sagittal and elect the key slice for carrying out feature extraction.During the classification stage,distinct classifiers make the selective feature their input for predicting the classes AD or HC(Healthy Control)dependent upon observation under the validation set.Venugopalan et al.[12]utilize DL to essentially analyse imaging(MRI),genetic(single nucleotide polymorphism(SNP)),and medical tested data for classifying persons into AD,mild cognitive impairment(MCI),and control(CN).It can be utilize stacked denoising auto-encoders(AE)for extracting feature in the medical and genetic data and utilize 3D CNN to image data.

    Lella et al.[13] progress an ML structure to the classifier and feature significance study of AD dependent upon communicability at the entire brain level.It is properly related to the efficiency of 3 recent classifier techniques such as support vector machine(SVM),RFs,and ANN,on the connectivity network of balanced cohort of health controlling issues and AD patient from ADNI databases.Asim et al.[14] studies separating the brain depends on distinct atlases and relating the feature removal from this anatomical parcellation to further holistic as well as robust representation.Then,it can be classification AD,MCI,and cognitively normal(CN)issues utilizing those features removed from all atlas templates and the joined features of both atlases.Lahmiri et al.[15] assess the degree to that particular features containing fractals attained from MRI.

    Presently, the analytic conditions for AD are dependent upon clinical and psychometric assessment tests such as clinical dementia rate(CDR)and mini-mental state examination(MMSE)however the decisive analysis of diseases is complete by analysis of brain.There exist an immense essential to the progress of novel techniques to primary identification of AD, the amount of brain imaging approaches facilitate providing non-invasive manners to visualize of brain atrophy[10].The situation previous analysis is not only challenging apart from vital for more treatment.

    This paper presents a new Oriented Features from Accelerated Segment Test (FAST) with Rotated Binary Robust Independent Elementary Features (BRIEF) Detector (ORB) with optimal artificial neural network (ORB-OANN) for AD diagnosis and classification on the CIoT based smart healthcare system.For initial pre-processing,bilateral filtering(BLF)based noise removal and region of interest (RoI) detection processes are carried out.In addition, the ORB-OANN model includes ORB based feature extractor and principal component analysis(PCA)based feature selector.Moreover,ANN model is utilized as a classifier and the parameters of the ANN are optimally chosen by the use of salp swarm algorithm(SSA).A comprehensive experimental analysis of the ORB-OANN model is carried out on the benchmark dataset.

    In short,the paper contribution is given as follows.

    · Proposes a new ORB-OANN model for AD diagnosis and classification on the CIoT based smart healthcare system.

    · Design a BLF based noise removal and RoI detection processes as a pre-processing step

    · Present an ORB based feature extractor and PCA based feature selector for effective classification process

    · Employ an SSA-ANN technique as a classifier and the parameters of the ANN are optimally chosen by the use of SSA

    · Validate the efficacy of the ORB-OANN model on the benchmark dataset and inspect the results in terms of different measures.

    The rest of the paper is arranged as follows.Section 2 briefs the ORB-OANN technique and Section 3 validates the performance of the ORB-OANN technique.Finally,Section 4 concludes the paper.

    2 The Proposed ORB-OANN Model

    Fig.1 demonstrates overall block diagram of proposed ORB-OANN method.In this study,a novel ORB-OANN technique was designed to diagnose AD in the CIoT enabled healthcare environment.Initially, the data acquisition process takes place using smart computed tomography(CT)scanners.

    Figure 1:Block diagram of ORB-OANN model

    They are linked into the network and each scanner acts as an individual node.The node transmits the sensed data to the cloud and then actual processing takes place.Then,the ORB-OANN method contains distinct stages of operations namely BLF based preprocessing, RoI detection, ORB based feature extraction, PCA based feature selection (FS), ANN based classification, and SSA based parameter tuning.

    2.1 Preprocessing

    ROI could not be divisible in their surroundings, due to gray level irregularities and inexistence of solid edge beside their fringe.An image usually has noise that alters the power of pixel pointing at a state,in which the classifier winds up as indeterminate.As,the region based medicinal image compression was implemented the ROI as well as non-ROI were separated independently for expanding the steadiness of the image.

    Next, the BLF technique is employed for eradicating the noise to be image.Since non-linear,edge preserving image filter technique,a BLF handle the intensity value at all the pixels as weighted average of their adjacent pixel intensity values[16].The BLF can able to fix the issue of Gaussian blur in conventional Gaussian convolutional image filter technique since it integrates 2 elements:Euclidean distance and radiometric variance are given as:

    whereD(p)andI(q)denotes image intensity of pixelpindicates output image and pixelqrepresents input image,correspondingly.Rpdenotes collection of pixels adjacent to pixelp.WsandWrindicates range and spatial kernels, where the weights are calculated from Euclidean distancedpqand photometric variancefpqamong pixelspandq, correspondingly.Later, it is generally calculated by image features like texture/intensity.indicates normalized term calculated by(2).In(1),WsandWrboth take a value inverse to the equivalent input and are stated as a Gaussian function.For instance,Wsis estimated as

    In (3),σsrepresents scale variables defining the weight dispersal pattern of kernel.A hugeσsdenotes range Gaussian widens and fattens.

    The BLF exceeds several image filtering methods because of the capacity to attain better filtering performance when maintaining crisp edges.It can be attained by integrating the range and spatial kernels in (1).In smooth areas, BLF executes as Gaussian low pass filter by average away from the smaller, weak connected variances among pixel values affected by noise, because of spatial kernel.But,it could not trivial for BLF to differentiate among thin vessels and noises while employing it in retinal image.This is affected by various features of the certain structure of thin vessels related to a general image edge that is created by dark as well as bright areas.Initially, the pixels of thin vessels conquer small parts of the pixel in local window,producing the vessel pixel to be averaging away by the spatial kernel.Later,the spatial dispersal of vessel pixel is considerably distinct from autonomous,separated image noise,however,the BLF lack functions to entirely capture the relevant features.

    2.2 ORB Based Feature Extractor

    The preprocessed image is passed into the ORB based feature extractor to generate a useful set of features.It works by involving an accurate orientation model for FAST utilizing Intensity Centroid Cloud(ICC)approach and reduces BRIEF rotation invariance by making a variance named steered BRIEF and then improves to r-BRIEF off-spring i.e.,adequately rotation invariants.The orientation modules are added in FAST using ICC approach that employs robust measures of corner orientation.The patch moments are employed to detect centroid as follows:

    wherempqrepresents(p+q)thorder moment of the image its intensity I(x,y)differ as a functions ofxandyimage coordinate.

    Assume the moment in Eq.(4),the centroid is achieved as follows:

    A vector is made from the center to centroidand then the patch orientation become:

    where atan2 denotes quadrant aware version of arc tan.Consider that consequence of illumination parameter of the corner that is not deliberated because of the angle measures remain same irrespective of the corner.The rotational invariant is enhanced by assuring the moment i.e.,evaluated with respect to x and y that remain in the circular area of r radius.An optimum election for patch size represent r which assures run of x,y is from[-r, r].In general,using Hessian measure,the value of|C|become zero,it became unstable but it does not take place using FAST i.e.,favorable for the system’s ability.Then,ORB consist of rotation aware module named r-BRIEF i.e.,a established form of steered BRIEF descriptors integrated with a related learning stage is determined for detecting less correlated binary features.For ensuring an efficient Rotation of BRIEF,a bit string representation of an image patches are made from a collection of binary intensity test[17].For an optimum demonstration of conventional BRIEF,earlier an orientation modules are added to ORB where there is a smooth image patch p.Then,a binary testτcan be expressed as follows:

    Whereas p(x)signifies intensity of patch pat a given points x.

    Consequently,the features are a patch function consider as vector ofnbinary test is given by:

    In this discussion,they use Gaussian distribution near the center of patch and election of vector length n as 256 (displayed to produce reasonable outcome).As one of the critical contributions of ORB is in-plane rotation invariant is discusses on BRIEF,it endures a sharp decline in the existence of in-plane rotation exceeds some degrees.Initially,this method is to steer BRIEF based on orientation of key points(named steered BRIEF).The BRIEF steering is executed by:

    Consider that given feature set ofnbinary tests at certain position(xi,yi), a 2 × n matrix is determined by:

    Then, usingΘ(patch orientation) andRΘ(equal rotation matrix), a steered versionSΘof S is attained by:

    Subsequently,the BRIEF steering function is given by:

    They differentiate the angle to increase(12 degrees)and create lookup table of precomputed BRIEF configurations.Since key point orientation,Θis consistent over the assessments,the precise collection of pointsSΘis utilized for computing descriptors.Additionally,using this method to analyze the difference and association of oriented BRIEF feature and selecting the learning technique for decorrelation the BRIEF features in rotational invariant (to reduce r-BRIEF), they attain powerful efficiency in adjacent applications.

    2.3 PCA Based Feature Selection

    The removed feature F was possibly massive in dimension and so it is needed the reduction.The PCA was well-known approach to reduction dimension as it resolve this challenge of “curse of dimensionality” with no loss of data.The arithmetical model of PCA approach was given consecutively.

    Mean

    It can be measure of central tendencies.The mathematical procedure to mean is written in Eq.(12).Now,Qimplies the random number and size of samples were demonstrated asl.

    Standard deviation(SD)

    It depicts the speed of data.The average distance among the mean as well as point at that data is computed by squaring them[18].It could be arithmetically expressed as Eq.(13).

    Covariance

    It determines the quantity of variances from dimensional in the mean.The mathematical process to covariance has written in Eq.(14).

    If the removed feature was reduced with their dimension, it is normalization and showing to classification.

    2.4 OANN Based Classification

    The chosen features are given as input to the OANN model to classify the existence of AD.The network of Artificial Neuron has layers and neurons attached to the synapses that are weighted.The weights are modified with BP allowing the network to learned.The strength of neurons is controlled by activation function.It is also required to have non-linearity.Assumexis the value of weighted sum of an inputsxiand weightswi,afterwardi=1 ton,now

    Instances of activation functions contain of sigmoid function (Eq.(16)), Rectified Linear Unit(ReLU)(Eq.(18)),Soft-Max Activation Function(Eq.(17))and Hyperbolic Tangent function(tanh)(Eqs.(20)and(21)).

    ?(x)refers the constrained through pair of horizontal asymptotes asx→±1.

    wherenrepresents the count of resultant classes,iandjimplies theithandjthclasses byP(?(x)i)is the forecast probability forithclass.

    Another version of ReLU like Parametric ReLU(PReLU)(Eq.(19))and Leaky ReLU resolves the zero-gradient issue.

    where β refers the parameter learns in training.If β=-1,next ?(x)=xand it attains the version of ReLU known as Absolute Value ReLU[19].If β is little,activation functions are mentioned as leaky ReLU.Fig.2 illustrates the architecture of ANN model.The tanh is provided as follows:

    or

    In order to add read on to choose activation functions, readers are stimulated for reading the work.

    Another essential model of NN is computation of the cost function.The perceptron learned as computing a cost function mentioned as Mean Square Error (MSE).It is several other varieties of cost functions which are utilized for determining the resultant error.In gradient descent (GD) is helpful to this light.But, it needs the cost function is convex resultant in established of Stochastic Gradient Descent(SGD).For tuning the parameter of the ANN technique,the SSA is used and thereby enhances the detection outcomes.

    SSA is determined as an arbitrary population based approach proposed by [20].It is utilized to accelerate the swarming procedure of salp when foraging in the ocean.In the SSA method, the dominants are salps facing the chain,and balances salp is called as follower.The salp locations could be stored in a two dimensional matrix called asz.Furthermore,the food sources are represented asPin search space as the swarm destination.The arithmetical method for SSA is given as follows:The leading salps would change the position in the applications of provided equation:

    The coefficientr1is an essential attribute in SSA since it offers improved management amongst exploitation and exploration stages.In order to change the location of follower, provided equations are employed:

    The summary of step-by-step definitions of this method is provided below:

    1.Upload the variables of SSA such as amount of salps(S),amount of iterations(A),optimal salps location(Z*)and optimum fitness value(f(Z*)).

    2.Upload a population ofSsalp location randomly.

    3.Evaluate the fitness of each salp.

    4.Set amount of iterations to zero.

    5.Update r1.

    6.For each salp,

    a) Whenm==1,upgrade the place of dominant salp using Eq.(22).

    b) Otherwise,upgrade the place of followers’salp using Eq.(25).

    c) Define the fitness of each salp.

    d) UpdateZ*if there is a dominant solution.

    7.Rise a to one.

    8.Follow Steps 5 to 7 untila=Ais met.

    9.Offer an optimum solutionZ*and fitness valuef(Z*).

    3 Performance Validation

    This section examines the performance analysis of the ORB-OANN technique on the diagnosis of AD.The dataset utilized to train our model was gathered from OASIS.The site suggestions a public data repository for study resolves.The dataset covers the cross-sectional brain MRI scan of different issues from the age of 18–96.An image was covering image in the sagittal,coronal,and axial planes of human brain.For testing the presented technique,MRI scan in the smart CT scanner was developed that are utilized at several hospitals linked on the Internet.Fig.3 illustrates some sample images.

    Figure 3:Sample images

    Fig.4 showcases the confusion matrix offered by the ORB-OANN technique with the other two techniques.The ANN technique has classified a set of 86 images into class 1,103 images into class 2,and 91 images into class 3.Moreover,the PCA-ANN approach has classified a set of 87 images into class 1,104 images into class 2,and 91 images into class 3.Eventually,the ORB-OANN method has classified a set of 87 images into class 1,104 images into class 2,and 92 images into class 3.

    Figure 4: Continued

    Figure 4:(a)ANN model(b)PCA-ANN model(c)ORB-OANN model

    Next,a brief AD classification results analysis of the ORB-OANN technique takes place under distinct classes in Tab.1.By looking into the table values, it is noticeable that the ORB-OANN technique has showcased improved AD classification outcomes under all the class labels.The ANN model has obtained an average accuracy of 0.9837,sensitivity of 0.9752,specificity of 0.9876,FPR of 0.0124,and FNR of 0.0248.In line with,the PCA-ANN manner has attained an average accuracy of 0.9884,sensitivity of 0.9821,specificity of 0.9911,FPR of 0.0089,and FNR of 0.0179.Concurrently,the ORB-OANN methodology has gained an average accuracy of 0.9907, sensitivity of 0.9857,specificity of 0.99929,FPR of 0.0071,and FNR of 0.0143.

    Table 1: Result analysis of proposed models in terms of various measures

    Table 1:Continued

    Fig.5 showcases the ROC analysis of the ORB-OANN approach on the applied dataset.The figure demonstrated that the ORB-OANN technique has achieved a maximum ROC of 99.9526.

    Figure 5:Receiver operating characteristic curve(ROC)of ORB-OANN

    A detailed comparative analysis of the AD detection outcome takes place with recent methods such as genetic algorithm with k-nearest neighbors (GA-KNN), grey wolf optimization with KNN(GWO-KNN),GA with convolutional neural networks(GA-CNN),GA with decision tree(GA-DT),GWO with KNN), chaotic GWO-KNN (CGWO-KNN), chaotic GWO-DT (CGWO-DT), chaotic GWO-CNN(CGWO-CNN),ANN,and PCA-ANN.Fig.6 demonstrates the comparison study of the ORB-OANN technique with existing techniques in terms of accuracy.The results exhibited that the GA-KNN and GWO-KNN techniques have obtained the least accuracy of 0.6622 and 0.6923 respectively.In line with,the GA-CNN,GA-DT,and GWO-DT techniques have offered slightly enhanced accuracy of 0.7022, 0.7222, and 0.7623 respectively.Moreover, the CGWO-KNN, CGWO-DT, and GWO-CNN techniques have offered a moderate accuracy of 0.845,0.8852,and 0.8923.Furthermore,the CGWO-CNN,ANN,and PCA-ANN techniques have showcased competitive accuracy values of 0.982,0.9837,and 0.9884 respectively.However,the proposed ORB-OANN technique has resulted to a maximum performance of 0.9907.

    Figure 6:Accuracy analysis of ORB-OANN model with recent approaches

    Fig.7 showcases the comparison analysis of the ORB-OANN method with existing algorithms with respect to sensitivity.The outcomes outperformed that the GA-KNN and GA-CNN techniques have reached a worse sensitivity of 0.6522 and 0.6922 correspondingly.Also, the GWO-KNN, GADT, and GWO-CNN approaches have obtainable somewhat higher sensitivity of 0.7718, 0.7858,and 0.8233 correspondingly.Followed by, the CGWO-KNN, GWO-DT, and CGWO-DT methods have accessible a moderate sensitivity of 0.8452, 0.8520, and 0.8623.In addition, the CGWO-CNN,ANN, and PCA-ANN manners have demonstrated competitive sensitivity values of 0.9745, 0.9752,and 0.9821 correspondingly.But, the presented ORB-OANN methodology has resulted in maximal performance of 0.9857.

    Figure 7:Sensitivity analysis of ORB-OANN model with recent approaches

    Fig.8 rseveals the comparative study of the ORB-OANN manner with recent approaches in terms of specificity.The results portrayed that the GA-DT and GWO-DT methods have gained a minimum specificity of 0.6622 and 0.7822 respectively.Likewise, the GA-KNN, GWO-KNN, and GA-CNN manners have existed slightly improved specificity of 0.7922,0.8210,and 0.8522 respectively.Besides,the CGWO-KNN,GWO-CNN,and CGWO-DT methods have obtainable a moderate specificity of 0.9015,0.9222,and 0.9223.At the same time,the CGWO-CNN,ANN,and PCA-ANN methodologies have depicted competitive specificity values of 0.9545,0.9876,and 0.9911 correspondingly.Eventually,the proposed ORB-OANN algorithm has resulted in a superior performance of 0.9929.From the detailed result analysis,it is ensured that the ORB-OANN technique is found to be a proficient AD diagnosis model in the CIoT enabled healthcare system.

    Figure 8:Specificity analysis of ORB-OANN model with recent approaches

    4 Conclusion

    In this study, a novel ORB-OANN model is designed to diagnose AD in the CIoT enabled healthcare environment.Initially, the data acquisition process takes place using smart CT scanners.Initially, the data acquisition process takes place using smart CT scanners.They are linked into the network and each scanner acts as an individual node.The node transmits the sensed data to the cloud and then actual processing takes place.Then, the ORB-OANN model involves distinct stages of operations namely BLF based preprocessing,RoI detection,ORB based feature extraction,PCA based FS,ANN based classification,and SSA based parameter tuning.A comprehensive experimental analysis of the ORB-OANN model is carried out on the benchmark dataset and the obtained values pointed out the promising performance of the ORB-OANN technique in terms of different measures.In future, the performance of the AD diagnostic outcomes can be boosted by the design of deep learning models.

    Acknowledgement:The authors would like to acknowledge the support of Prince Sultan University,Riyadh, Saudi Arabia for partially supporting this project and for paying the Article Processing Charges(APC)of this publication.

    Funding Statement:The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/23/42),Received by Fahd N.Al-Wesabi.www.kku.edu.sa.

    Conflicts of Interest:The authors declare that they have no conflicts of interest to report regarding the present study.

    欧美日韩精品网址| 一二三四在线观看免费中文在| 青青草视频在线视频观看| √禁漫天堂资源中文www| 男女边吃奶边做爰视频| 午夜久久久在线观看| 久久久久精品国产欧美久久久 | 久久精品国产亚洲av高清一级| 国产黄色免费在线视频| www.精华液| av不卡在线播放| 午夜精品国产一区二区电影| 宅男免费午夜| 后天国语完整版免费观看| 亚洲精品国产色婷婷电影| 9热在线视频观看99| 亚洲欧美色中文字幕在线| 久久国产精品大桥未久av| 在线观看免费日韩欧美大片| 我的亚洲天堂| 婷婷色av中文字幕| 九草在线视频观看| 一区二区av电影网| 丰满少妇做爰视频| 国产亚洲精品第一综合不卡| 黄频高清免费视频| 高清av免费在线| 国产精品熟女久久久久浪| 三上悠亚av全集在线观看| 欧美人与善性xxx| 亚洲av国产av综合av卡| 黄片播放在线免费| 晚上一个人看的免费电影| 黄片小视频在线播放| 亚洲一区二区三区欧美精品| 两个人看的免费小视频| 老汉色∧v一级毛片| 亚洲男人天堂网一区| 国产精品偷伦视频观看了| 欧美精品av麻豆av| 妹子高潮喷水视频| 中文字幕人妻丝袜一区二区| 别揉我奶头~嗯~啊~动态视频 | 国产精品久久久久久精品古装| 国产一卡二卡三卡精品| 国产精品偷伦视频观看了| 中国国产av一级| 久久国产精品影院| 国产又色又爽无遮挡免| 日本av免费视频播放| 91老司机精品| 999久久久国产精品视频| 18禁国产床啪视频网站| 黑丝袜美女国产一区| 国产av精品麻豆| 色网站视频免费| 国产一区二区三区av在线| 9色porny在线观看| 国产欧美日韩一区二区三区在线| 天天添夜夜摸| 色综合欧美亚洲国产小说| 免费日韩欧美在线观看| 午夜免费男女啪啪视频观看| 欧美亚洲 丝袜 人妻 在线| 国产视频首页在线观看| 欧美亚洲日本最大视频资源| 女性被躁到高潮视频| 精品人妻一区二区三区麻豆| 国产熟女欧美一区二区| 最新在线观看一区二区三区 | 亚洲,欧美,日韩| 中文欧美无线码| 青青草视频在线视频观看| 亚洲精品中文字幕在线视频| 国产高清不卡午夜福利| 国产一区亚洲一区在线观看| 欧美日韩精品网址| 一二三四社区在线视频社区8| 成年人黄色毛片网站| 人人澡人人妻人| 啦啦啦啦在线视频资源| 大香蕉久久网| 亚洲伊人久久精品综合| 久久精品亚洲熟妇少妇任你| 免费看不卡的av| 超色免费av| 黑人欧美特级aaaaaa片| 在线亚洲精品国产二区图片欧美| 亚洲成色77777| 两人在一起打扑克的视频| 一本大道久久a久久精品| 国产在线观看jvid| 最新的欧美精品一区二区| 两个人看的免费小视频| 亚洲伊人色综图| 丝袜美腿诱惑在线| 黑人猛操日本美女一级片| 青草久久国产| 久久精品久久精品一区二区三区| 好男人电影高清在线观看| 成人亚洲欧美一区二区av| 最新在线观看一区二区三区 | 老司机影院毛片| 亚洲av男天堂| 亚洲欧美一区二区三区国产| 国产在线免费精品| 91麻豆av在线| 性色av一级| 国产欧美亚洲国产| 日韩中文字幕视频在线看片| 黄色视频在线播放观看不卡| 成年人免费黄色播放视频| 又大又黄又爽视频免费| 美女国产高潮福利片在线看| 久久久久精品人妻al黑| 亚洲国产av新网站| 在线 av 中文字幕| 亚洲久久久国产精品| 操出白浆在线播放| 美国免费a级毛片| 国产无遮挡羞羞视频在线观看| 国产精品一区二区精品视频观看| 菩萨蛮人人尽说江南好唐韦庄| 色视频在线一区二区三区| 免费在线观看影片大全网站 | 国产av精品麻豆| 免费不卡黄色视频| 热re99久久国产66热| 精品国产超薄肉色丝袜足j| 国产真人三级小视频在线观看| 叶爱在线成人免费视频播放| 性少妇av在线| 啦啦啦在线免费观看视频4| 国产亚洲av高清不卡| 日韩 欧美 亚洲 中文字幕| 日韩,欧美,国产一区二区三区| 51午夜福利影视在线观看| 精品熟女少妇八av免费久了| 久久久久久久精品精品| 国产精品国产三级国产专区5o| 巨乳人妻的诱惑在线观看| 香蕉丝袜av| 午夜老司机福利片| 久久国产亚洲av麻豆专区| 啦啦啦啦在线视频资源| 中文字幕人妻丝袜一区二区| 后天国语完整版免费观看| 亚洲色图综合在线观看| 丝袜在线中文字幕| 午夜免费男女啪啪视频观看| 亚洲人成77777在线视频| 天天躁夜夜躁狠狠久久av| 久久这里只有精品19| 各种免费的搞黄视频| 日日夜夜操网爽| 日韩一区二区三区影片| 精品人妻一区二区三区麻豆| 国产午夜精品一二区理论片| 久久精品人人爽人人爽视色| 男人操女人黄网站| 精品少妇久久久久久888优播| 天堂俺去俺来也www色官网| 赤兔流量卡办理| 久久精品国产亚洲av高清一级| 大香蕉久久成人网| 国产成人啪精品午夜网站| 午夜福利影视在线免费观看| 一边摸一边做爽爽视频免费| 波多野结衣av一区二区av| 欧美日本中文国产一区发布| 天天躁夜夜躁狠狠久久av| 久久久国产一区二区| 国产精品久久久久久精品电影小说| 亚洲av在线观看美女高潮| 人妻 亚洲 视频| 欧美老熟妇乱子伦牲交| 国产女主播在线喷水免费视频网站| 菩萨蛮人人尽说江南好唐韦庄| 久久国产亚洲av麻豆专区| 三上悠亚av全集在线观看| 精品少妇黑人巨大在线播放| av网站免费在线观看视频| 香蕉国产在线看| 中国国产av一级| 欧美亚洲日本最大视频资源| 国产在线视频一区二区| 久9热在线精品视频| 久久精品久久精品一区二区三区| 悠悠久久av| 欧美日本中文国产一区发布| 免费观看a级毛片全部| 久久青草综合色| 老司机靠b影院| 日韩,欧美,国产一区二区三区| 汤姆久久久久久久影院中文字幕| 亚洲五月婷婷丁香| 大片电影免费在线观看免费| 一级a爱视频在线免费观看| 午夜福利一区二区在线看| 国产有黄有色有爽视频| 国产一区亚洲一区在线观看| 亚洲精品日本国产第一区| 国产成人系列免费观看| 亚洲av日韩精品久久久久久密 | 国产成人影院久久av| 一级片'在线观看视频| 亚洲精品国产av成人精品| 纯流量卡能插随身wifi吗| 黄片小视频在线播放| 日韩人妻精品一区2区三区| 桃花免费在线播放| 91精品伊人久久大香线蕉| 亚洲国产看品久久| 日本vs欧美在线观看视频| 亚洲精品一卡2卡三卡4卡5卡 | 中文字幕另类日韩欧美亚洲嫩草| 久久久久精品国产欧美久久久 | 成人午夜精彩视频在线观看| av片东京热男人的天堂| 黄色 视频免费看| 日本a在线网址| 一区二区三区四区激情视频| av欧美777| 国产欧美日韩综合在线一区二区| 秋霞在线观看毛片| 亚洲一码二码三码区别大吗| 天天躁日日躁夜夜躁夜夜| 欧美老熟妇乱子伦牲交| 国产精品亚洲av一区麻豆| 久久精品久久久久久久性| 精品免费久久久久久久清纯 | 男女之事视频高清在线观看 | 亚洲视频免费观看视频| 色综合欧美亚洲国产小说| 午夜视频精品福利| 国产精品国产三级专区第一集| 美国免费a级毛片| www.999成人在线观看| 国产一区亚洲一区在线观看| 精品人妻1区二区| 免费人妻精品一区二区三区视频| 手机成人av网站| 亚洲av日韩精品久久久久久密 | 美女中出高潮动态图| 国产极品粉嫩免费观看在线| 久久久久国产精品人妻一区二区| 亚洲七黄色美女视频| 中文字幕av电影在线播放| 在线观看免费视频网站a站| 精品一区二区三区四区五区乱码 | av网站免费在线观看视频| 好男人视频免费观看在线| 亚洲第一青青草原| svipshipincom国产片| 99久久人妻综合| 大香蕉久久成人网| 水蜜桃什么品种好| 久久99一区二区三区| 啦啦啦视频在线资源免费观看| 精品国产一区二区三区四区第35| 一级毛片 在线播放| 婷婷色av中文字幕| 国产黄频视频在线观看| a级毛片黄视频| 亚洲人成77777在线视频| 国产日韩一区二区三区精品不卡| 两个人免费观看高清视频| 18禁裸乳无遮挡动漫免费视频| 欧美成人精品欧美一级黄| 国产精品久久久久久人妻精品电影 | 久久中文字幕一级| 免费在线观看日本一区| 国产成人一区二区在线| 亚洲精品国产色婷婷电影| 天天影视国产精品| 欧美黄色淫秽网站| 国产日韩欧美亚洲二区| 菩萨蛮人人尽说江南好唐韦庄| 超碰97精品在线观看| 亚洲黑人精品在线| 欧美性长视频在线观看| 一级毛片黄色毛片免费观看视频| 欧美日韩国产mv在线观看视频| 精品人妻熟女毛片av久久网站| svipshipincom国产片| 天天躁狠狠躁夜夜躁狠狠躁| 精品一区二区三区四区五区乱码 | 一级,二级,三级黄色视频| 无限看片的www在线观看| 女性生殖器流出的白浆| 日日摸夜夜添夜夜爱| 国产91精品成人一区二区三区 | 亚洲av男天堂| 午夜免费成人在线视频| 久久久精品国产亚洲av高清涩受| 久久久久久久大尺度免费视频| 欧美亚洲 丝袜 人妻 在线| 手机成人av网站| 免费观看人在逋| 丝袜美足系列| 成人免费观看视频高清| 国产成人a∨麻豆精品| 91精品三级在线观看| 多毛熟女@视频| 欧美av亚洲av综合av国产av| 亚洲成国产人片在线观看| 91字幕亚洲| 亚洲熟女精品中文字幕| 国产一级毛片在线| 国产熟女午夜一区二区三区| 亚洲国产欧美日韩在线播放| 国产熟女午夜一区二区三区| 飞空精品影院首页| 一区在线观看完整版| 99热全是精品| 久久99精品国语久久久| 国产视频一区二区在线看| 天天操日日干夜夜撸| 国产在视频线精品| 一级毛片黄色毛片免费观看视频| 又黄又粗又硬又大视频| 成年美女黄网站色视频大全免费| av不卡在线播放| 香蕉丝袜av| 99九九在线精品视频| 女人久久www免费人成看片| 久久久久久久精品精品| 亚洲男人天堂网一区| 国产视频首页在线观看| a级片在线免费高清观看视频| 日本猛色少妇xxxxx猛交久久| 天天影视国产精品| 视频区图区小说| a 毛片基地| 久热这里只有精品99| 19禁男女啪啪无遮挡网站| 日本午夜av视频| 高清不卡的av网站| 免费少妇av软件| 香蕉国产在线看| 欧美乱码精品一区二区三区| 考比视频在线观看| 别揉我奶头~嗯~啊~动态视频 | 国产精品一区二区精品视频观看| 日韩,欧美,国产一区二区三区| av又黄又爽大尺度在线免费看| 亚洲熟女毛片儿| 国产精品av久久久久免费| 最新的欧美精品一区二区| 久久影院123| 欧美黑人精品巨大| 久久久久精品国产欧美久久久 | 九草在线视频观看| 少妇猛男粗大的猛烈进出视频| 日本黄色日本黄色录像| 国产1区2区3区精品| 久久久久精品人妻al黑| 国产1区2区3区精品| 亚洲精品美女久久av网站| 久久热在线av| 国产一区二区三区综合在线观看| 亚洲精品av麻豆狂野| 19禁男女啪啪无遮挡网站| 一区二区三区四区激情视频| 女性生殖器流出的白浆| 亚洲欧美一区二区三区久久| 免费女性裸体啪啪无遮挡网站| 精品高清国产在线一区| 免费高清在线观看日韩| av电影中文网址| 99国产综合亚洲精品| 一级毛片我不卡| 国产熟女欧美一区二区| 亚洲精品日本国产第一区| 国产福利在线免费观看视频| a 毛片基地| 欧美精品人与动牲交sv欧美| 一边摸一边做爽爽视频免费| 视频在线观看一区二区三区| 91九色精品人成在线观看| 午夜免费男女啪啪视频观看| 婷婷色麻豆天堂久久| 制服人妻中文乱码| 建设人人有责人人尽责人人享有的| 日韩av不卡免费在线播放| 水蜜桃什么品种好| 一区二区三区精品91| 免费看av在线观看网站| 视频区图区小说| 亚洲国产精品国产精品| 精品欧美一区二区三区在线| 成人午夜精彩视频在线观看| 久久久久久久久免费视频了| 美女高潮到喷水免费观看| 少妇猛男粗大的猛烈进出视频| 成人亚洲精品一区在线观看| 一本大道久久a久久精品| 大话2 男鬼变身卡| 国产精品免费视频内射| 91国产中文字幕| 黄色 视频免费看| 精品国产乱码久久久久久小说| 成人国产一区最新在线观看 | av欧美777| www.熟女人妻精品国产| 高清视频免费观看一区二区| 在线观看免费高清a一片| 亚洲精品美女久久av网站| 午夜福利视频精品| 国产有黄有色有爽视频| 老司机在亚洲福利影院| 超色免费av| 国产精品久久久久久精品古装| 99香蕉大伊视频| 国产深夜福利视频在线观看| 一本色道久久久久久精品综合| 天天影视国产精品| 国产人伦9x9x在线观看| 国产精品二区激情视频| 丝袜美足系列| 满18在线观看网站| 亚洲熟女毛片儿| 国产熟女午夜一区二区三区| 十八禁高潮呻吟视频| 中文字幕色久视频| 国产精品久久久人人做人人爽| 久久国产精品人妻蜜桃| 黄色一级大片看看| 亚洲精品国产av成人精品| 国产女主播在线喷水免费视频网站| 亚洲欧美精品综合一区二区三区| 青草久久国产| 老熟女久久久| 国产亚洲欧美精品永久| 亚洲国产精品999| 亚洲精品av麻豆狂野| 色网站视频免费| 亚洲精品日本国产第一区| 日韩av在线免费看完整版不卡| 精品国产超薄肉色丝袜足j| 操出白浆在线播放| 欧美精品高潮呻吟av久久| 男女边吃奶边做爰视频| 一二三四社区在线视频社区8| 中文字幕高清在线视频| 最近最新中文字幕大全免费视频 | bbb黄色大片| 免费黄频网站在线观看国产| 亚洲激情五月婷婷啪啪| 韩国高清视频一区二区三区| 日本wwww免费看| 一个人免费看片子| 国产高清videossex| 免费看不卡的av| 亚洲色图综合在线观看| 肉色欧美久久久久久久蜜桃| 秋霞在线观看毛片| bbb黄色大片| 国产成人系列免费观看| 中国国产av一级| 美女高潮到喷水免费观看| 又黄又粗又硬又大视频| 丝袜人妻中文字幕| 欧美+亚洲+日韩+国产| av又黄又爽大尺度在线免费看| 国产又色又爽无遮挡免| 91精品国产国语对白视频| 国产熟女欧美一区二区| 亚洲欧美日韩另类电影网站| 国产国语露脸激情在线看| 免费黄频网站在线观看国产| 成人影院久久| 波野结衣二区三区在线| 国产精品99久久99久久久不卡| 中文欧美无线码| 午夜免费成人在线视频| 黑人猛操日本美女一级片| 成年人免费黄色播放视频| 一本久久精品| 亚洲人成电影观看| 亚洲精品一区蜜桃| 国产精品 欧美亚洲| 十八禁网站网址无遮挡| 欧美乱码精品一区二区三区| 麻豆av在线久日| 久久久久久免费高清国产稀缺| 国产一区二区在线观看av| 国产精品香港三级国产av潘金莲 | 美女脱内裤让男人舔精品视频| 欧美日韩精品网址| 69精品国产乱码久久久| 久久国产精品男人的天堂亚洲| 欧美大码av| 国产精品.久久久| 在现免费观看毛片| 欧美日韩视频精品一区| 国产一区有黄有色的免费视频| 制服人妻中文乱码| 大陆偷拍与自拍| 99国产精品一区二区三区| 日韩大码丰满熟妇| 日本五十路高清| 一级毛片电影观看| 午夜福利免费观看在线| 亚洲精品一卡2卡三卡4卡5卡 | 啦啦啦在线观看免费高清www| 一本一本久久a久久精品综合妖精| 王馨瑶露胸无遮挡在线观看| 男女边摸边吃奶| 狠狠精品人妻久久久久久综合| 国产精品人妻久久久影院| 午夜福利,免费看| 欧美乱码精品一区二区三区| 丝袜人妻中文字幕| 十八禁高潮呻吟视频| 国产片内射在线| 国产成人精品在线电影| 久久国产精品影院| 老司机午夜十八禁免费视频| 午夜免费男女啪啪视频观看| 9191精品国产免费久久| 18在线观看网站| av天堂在线播放| 久久久国产一区二区| 日韩欧美一区视频在线观看| 国产精品免费大片| 久久精品熟女亚洲av麻豆精品| 欧美日韩亚洲国产一区二区在线观看 | 男女床上黄色一级片免费看| 婷婷色综合www| 久久精品亚洲av国产电影网| 男女午夜视频在线观看| 亚洲国产精品一区三区| 少妇裸体淫交视频免费看高清 | 日本91视频免费播放| 成人影院久久| 丝袜美腿诱惑在线| 精品国产超薄肉色丝袜足j| 欧美中文综合在线视频| 国产精品.久久久| 精品福利观看| 国产麻豆69| 热99久久久久精品小说推荐| 亚洲国产最新在线播放| 嫁个100分男人电影在线观看 | 久久ye,这里只有精品| 99精国产麻豆久久婷婷| 韩国高清视频一区二区三区| 中文字幕色久视频| 日韩,欧美,国产一区二区三区| 国产片特级美女逼逼视频| 操出白浆在线播放| 欧美变态另类bdsm刘玥| 咕卡用的链子| 久久精品亚洲av国产电影网| 国产日韩欧美在线精品| 国语对白做爰xxxⅹ性视频网站| 国产精品久久久久久人妻精品电影 | 啦啦啦在线免费观看视频4| 纵有疾风起免费观看全集完整版| 亚洲精品国产av蜜桃| 男女高潮啪啪啪动态图| 国产精品.久久久| 侵犯人妻中文字幕一二三四区| 亚洲欧美成人综合另类久久久| 两个人免费观看高清视频| 99九九在线精品视频| 青草久久国产| 青春草视频在线免费观看| 91精品伊人久久大香线蕉| 丝袜人妻中文字幕| 天天躁狠狠躁夜夜躁狠狠躁| 脱女人内裤的视频| 电影成人av| 国产日韩欧美亚洲二区| 校园人妻丝袜中文字幕| 美女大奶头黄色视频| 97在线人人人人妻| 欧美日韩视频高清一区二区三区二| 高清黄色对白视频在线免费看| 国产高清视频在线播放一区 | 亚洲熟女毛片儿| 久久久久国产一级毛片高清牌| www.av在线官网国产| 又紧又爽又黄一区二区| 国产成人欧美在线观看 | 老司机亚洲免费影院| 99香蕉大伊视频| 久久毛片免费看一区二区三区| 国产在视频线精品| 丝袜人妻中文字幕| 999精品在线视频| 国产麻豆69| 在线观看人妻少妇| av又黄又爽大尺度在线免费看| 久久久精品免费免费高清| 夫妻午夜视频| 欧美黑人欧美精品刺激| 亚洲欧美清纯卡通| 精品国产乱码久久久久久小说| 久久久欧美国产精品| 2021少妇久久久久久久久久久| 别揉我奶头~嗯~啊~动态视频 | 亚洲精品久久久久久婷婷小说| 午夜影院在线不卡| 精品高清国产在线一区| 国产黄频视频在线观看| 久久精品熟女亚洲av麻豆精品| 99九九在线精品视频| 久9热在线精品视频| av视频免费观看在线观看| 老司机深夜福利视频在线观看 | 亚洲国产最新在线播放| 免费观看a级毛片全部| 久久久欧美国产精品|