• <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 ?

    DNA Sequence Analysis for Brain Disorder Using Deep Learning and Secure Storage

    2022-08-23 02:21:48AlaSalehAlluhaidan
    Computers Materials&Continua 2022年6期

    Ala Saleh Alluhaidan

    Departmemt of Information Systems,College of Computer and Information Science,Princess Nourah Bint Abdulrahman University,Riyadh 11671,Saudi Arabia

    Abstract: Analysis of brain disorder in the neuroimaging of Magnetic Resonance Imaging(MRI),Positron Emission Tomography(PET),and Computed Tomography(CT)needs to understand the functionalities of the brain and it has been performed using traditional methods.Deep learning algorithms have also been applied in genomics data processing.The brain disorder diseases of Alzheimer,Schizophrenia,and Parkinson are analyzed in this work.The main issue in the traditional algorithm is the improper detection of disorders in the neuroimaging data.This paper presents a deep learning algorithm for the classification of brain disorder using Deep Belief Network (DBN) and securely storing the image using a Deoxyribonucleic Acid (DNA) Sequence-based Joint Photographic Experts Group (JPEG) Zig Zag Encryption Algorithm(DBNJZZ).In this work, DBNJZZ implements an efficient and effective prediction model for disorders using the open-access datasets of Alzheimer’s Disease Neuroimaging Initiative(Adni),the Center for Biomedical Research Excellence (Cobre), the Open Access Series of Imaging Studies (Oasis), the Function Biomedical Informatics Research Network (Fbirn), a Parkinson’s dataset of 55 patients and 23 subjects with Parkinson’s syndromes (Ntua),and the Parkinson’s Progression Markers Initiative(Ppmi).This algorithm is implemented and tested using performance metric measures of accuracy,Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and Mean Absolute Percentage Error (MAPE).DBNJZZ gives better performance with an accuracy of 99.21%and also surpasses previous methods on other measures.

    Keywords:DBN;Zig zag;deep learning;MAPE;RMSE;DNA;genomics

    1 Introduction

    Genomics is an associative field of biology that concentrates on the genomes structure,genomes function, genomes evolution, and genomes mapping, and editing.A complete DNA set is called the genome of an organism and includes all its genes.Deep learning algorithms have been applied within the areas of genetics and genomics.When any specific gene is damaged or affected and prone to some disorder it results in what is known as a genetic disorder.The genetic disorder diseases of Alzheimer,Schizophrenia and Parkinson are affecting humans by disrupting normal brain functions[1–3].Medical imaging has become the foremost and effective tool to represent various modalities of an image like X-ray,MRI,CT,mammography,and PET[4].Storing sensitive information of medical images securely and privately also plays a vital role in the medical field.Traditional approaches of DNA-based molecular cryptography design and DNA writing techniques to store images securely became very interesting in the field of research.The main issue with these traditional techniques is that they cannot resist brute force attacks.Therefore,this paper implements a DNA Sequence-based JPEG Zig Zag Encryption Algorithm(DBNJZZ).

    For detecting the above mentioned brain disorders,many traditional algorithms are implemented.The drawbacks of the traditional algorithm are pre-processing and feature extraction which are not clearly defined and are inefficient in handling complicated genomic data.To overcome these drawbacks, the proposed work, DBNJZZ, presents exploring pre-processing methods and feature extractors with the open access datasets of Adni,Cobre,Oasis,Fbirn,Ntua,Ppmi[5–10].

    This proposed work consists of image registration,image enhancement,normalized filtering and smoothening for pre-processing, and implements an unsupervised feature extractor of Deep Belief Network(DBN).This approach of extraction maps the input values with multiple hidden layers.The features extracted in DBN will improve the prediction performance of the image.For performance evaluation, accuracy, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) were calculated.To summarize, the main contributions of this work are:

    1.Implementing the analysis of brain disorder diseases using a deep learning algorithm and showing how to store sensitive information securely in image format using DNA based encryption algorithm.

    2.Evaluating the accuracy of the pre-processed image in terms of image registration, image enhancement,normalized filtering,and smoothening.

    The paper has been organized as follows: Section 2 includes the literature review, Section 3 introduces the proposed algorithm,Section 4 discusses the experiment results,and Section 5 concludes the paper with future directions.

    2 Literature Review

    The rapid development of advanced technology has contributed various tools to diagnose brain disorder diseases,effectively.Deep learning techniques have helped in many ways to tackle the complicated problems of genomic data and analysis the diseases.Neurological diseases such as Alzheimer’s,Schizophrenia,and Parkinson’s are related to the disruption of brain functions.Traditional methods are employed in handling genomic data for brain disorder diseases.The main drawback of these methods is they are still inefficient to handle complicated genomic data for the brain image with disorder.

    This paper presents DBN feature extraction for images of Alzheimer’s, Schizophrenia, and Parkinson’s to improve the quality of performance.For Alzheimer’s disease, data collected in the ADNI dataset, which are derived from MRI, CT, and PET images, was validated and processed.Genetics and biomarkers were used for prediction of disorder disease[11].A deep learning algorithm is designed to diagnose Parkinson’s disease using Single-photon Emission Computed Tomography(SPECT) image dataset.Features of Sparse filtering, a new framework for automated diagnosis of Parkinson’s disease, is designed [12].Using MRI images to diagnose schizophrenia patients, a novel DBN architecture was designed that can explore statistical values from observed data and easily detect the affected region[13].

    DNA sequencing is used to improve the speed of processing genomic data[14].The classification of DNA sequence is performed using a machine learning algorithm for extracting features that will be stored in a vector format.The classification mentioned here is a supervised learning process.Its’drawback is that it cannot read by machine and also it has a high dimensionality of data.The genome sequences extracted from images as features using deep learning algorithms are used in various fields of genomic medicine,bioinformatics application,and medical imaging analysis[15].Tab.1 shows the survey summary of brain disorder analysis using a deep learning algorithm.

    Table 1: Survey Summary of brain disorder analysis

    3 Proposed DNA Sequence-based JPEG Zig Zag Encryption Algorithm(DBNJZZ)

    Medical imaging is an effective tool to diagnose a disease.For analyzing brain disorders,this paper implements a deep learning algorithm of DBN.This workflow(DBNJZZ)consists of two modules:

    Module 1:Pre-processing

    Module 2:Feature extraction of image with disorder and DNA Sequence-based privacy storage of brain image DBN-JPEG Zig Zag encryption algorithm (DBNJZZ).(Proposed) Fig.1 shows the workflow of the DBNJZZ.

    Figure 1:Workflow of DBNJZZ

    3.1 Pre-processing(Module 1)

    Neuroimaging modalities of brain images are CT,MRI,and PET.To improve the quality of an image,it is adjusted in a pre-processing stage.The steps involved in the pre-processing phase are given in Fig.2.

    Figure 2:Pre-processing steps

    3.1.1 Image Registration

    Image registration acquires two or more of same image features with different time frame variations into a single informative image.Linear regression algorithm is used for image registration which includes the functions of rotation,translation,and scaling for an image on the axes of x,y,z.At all angles,the algorithm will align the spatial correlation of the image.In general,image registration is given by:

    where,Ib′is the coordinate value of the image b,βis the set of parametric values of transformation.

    3.1.2 Image Enhancement

    It improves the quality of the image by filtering with contrast Contrast-limited Adaptive Histogram Equalization(CLAHE).This approach will enhance the image brightness with its background to improve visibility.

    3.1.3 Normalization

    It is the process of aligning the image in terms of size and shape to interpret them into common features of the image.This process maps the data point acquired from discrete space value to the reference space value.

    3.1.4 Filtering

    Using Weiner filtering, unwanted features will be removed from the image which consequently will minimize the image noise.

    where k is the low-frequency value of the Wiener filter;the high pass filter value is used to blurred the image.

    3.1.5 Smoothening

    It is the process of reducing the noise of the image.Spatial smoothing is applied which calculates the average value of pixels from the adjacent pixel elements.With smoothing,the Signal-to-noise ratio(SNR)value is enhanced and spatial resolution value is reduced.

    3.2 Feature Extraction of Image with Disorder and DNA Sequence-Based Privacy Storage of Brain Image DBN-JPEG Zig Zag Encryption Algorithm(DBNJZZ).

    In this work, Deep Belief Network (DBN) is used to extract features type from biomarkers of the image.Biomarker acts as a tool for the diagnostic purpose and it is used to identify the abnormal condition of the image.DBN here is based on Restricted Bolztman Machines (RBM) architecture[16–18].DBN is unsupervised feature extractor that extracts the features from the image for performance improvement.In this context,it will extract the normal structure features from the brain image to identify brain-related disorders.Fig.3 shows the workflow of recommended biomarkers.

    DBN architecture is composed of RBM stacks which contain one visible layer and multiple hidden layers.Each layer consists of nodes.The connection between the input layer and hidden layers is established by assigning a weight value.During the process of training the network,the weight vector value will be adjusted.The structure of DBN is given in Fig.4.The architecture of RBM is given in Fig.5.

    Figure 3:Workflow of generating biomarker

    Figure 4:DBN layer

    Figure 5:RBM architecture

    The algorithm for training the DBN is given below:

    Algorithm 1:Training DBN Step 1: Let Ii; 0 ≤i ≤N is input neuron that contains binary values and N is the total number of input neurons.Step 2:Let hii; 0 ≤i ≤K is a hidden neuron that contains binary values and K is the total number hidden neurons.Step 3:Calculate the energy function Er(i,j)between input neurons and hidden neurons by using:Er(I,hi)=∑i biiIi- ∑i ajhij-∑i∑j IiWiijhij (3)Step 4:Train the RBM’s first layer(visible layer)by applying input values to all neurons.Step 5:The output of Step 4 will be used as input of the next layer(hidden layer).Step 6:D(I,hi)= 1 F e-Er(I,hi) (4)The pair values of visible and hidden values are summed up and produce F as a function.Step 7:Evaluate the unbiased value between visible neurons and hidden neuron Dimages/BZ_1737_307_2395_338_2441.pnghij = 1 I =σimages/BZ_1737_566_2395_597_2441.pngbij+∑iIiWiijimages/BZ_1737_860_2395_891_2441.pngimages/BZ_1737_891_2395_922_2441.pngσ is a sigmoid function.Step 8:Repeat steps 6,7 until all layers are evaluated.Step 9:Output Binary Values Bij

    In algorithm 1, the image was trained to detect the disorder in the image.The resulted output will be stored in a secure way using DBNJZZ Encryption Algorithm.DNA is made up of monomers in a polymer structure which are called Deoxyribonucleotides.The basic components of nucleotide are phosphate, deoxyribose sugar, and nitrogenous [19,20].The bases of nitrogenous are Adenine(A),Cytosine(C),Thymine(T),and Guanine(G).After implementation of algorithm 1,the output values are plotted in a matrix format corresponding to the four base variables of DNA:A,C,T,and Gnucleotides.The encoded value of A is[0,0,0,1],C’s encoded value is[0,0,0,1],T’s encoded value is[0,0,0,1]and G’s encoded value is[0,0,0,1].Therefore,the disorder image value can be represented as an equivalent DNA sequence of code.The encryption key of DNA in DBNJZZ is defined by Fig.6.

    Figure 6:JPEG Zig Zag format

    By substituting DNA sequence nucleotides quadruple values by one and translating brain image to one value from randomly selected nucleotides quadruple sequences,a security component is achieved.Specifically,the gene binary sequence value is considered as an encrypted image and is stored securely.The encryption DBNJZZ algorithmis given below:

    ?

    ?

    Both sender and receiver must have the same gene sequence value and store it in a binary format.For each DNA nucleotide sequence in B quadruple value, a binary format file is selected randomly and replaced by an image.Fig.7 shows the result of storing an image using a JPEG Zig Zag pattern.

    Figure 7:Applying JPEG Zig Zag encryption algorithm

    4 Result Analysis

    The four metrics for measuring performance are reported here to evaluate the analysis of DBNJZZ in detecting disorder image.The metrics are:Root Mean Square Error(RMSE),Mean Absolute Error(MAE),accuracy and Mean Absolute Percentage Error(MAPE).

    where,Oiis the observation value of a variable,Piis the prediction value of the variable andNis the number of observations.Eq.(5)is calculated as the square root of the mean of the squared differences between actual outcomes and predictions.Eq.(6)is the absolute difference between the actual or true values and the values that are predicted.The negative sign in the absolute difference result is ignored.Eq.(7) is defined as the error rate of the actual value or observed value minus the forecasted value.Accuracy classification was achieved by 10-fold cross-validation.Tab.2 shows the different disorder diseases and their datasets.

    Table 2: Various disorder diseases and theirs datasets[21]

    In the neurological disorder of Alzheimer’s,the disease is affecting older age people by degrading them mentally and attack the brain function in a specific region.Using Eq.(8) accuracy metric measures for pre-processing activities of Alzheimer’s disease are given in Tab.3 using Tab.2 datasets.

    Table 3: Accuracy of neurological disorder of Alzheimer’s disease

    Tab.3 shows how the proposed work(DBN)has produced a better performance.Using Eq.(8),Tab.4 shows accuracy for a neurological disorder of Schizophrenia disease.Schizophrenia is a psychiatric disorder and it changes a patient’s behavior like emotion and cognition.

    Table 4: Accuracy of neurological disorder analysis of Schizophrenia

    The experiment results in Tab.5 show the accuracy metric measures for Parkinson’s disease in the proposed work DBN.By using Eqs.(5) and (6) RMSE and MAE are plotted in Figs.8–10.Eq.(5)is calculated as the square root of the mean of the squared differences between actual outcomes and predictions.Eq.(6)is the absolute difference between the actual or true values and the values that are predicted.The absolute difference result has a negative sign which is ignored.

    Table 5: Accuracy of neurological disorder analysis of Parkinson disease

    Figure 8:Error Rate using CNN+JPEG ZIG-ZAG

    Figure 9:Error rate using DNN+JPEG ZIG-ZAG

    Figure 10:Error rate using DBN+JPEG ZIG-ZAG

    The experiment results in Figs.8–11 show the superiority of the proposed work, DBN+JPEG ZIG-ZAG.Results prove the better prediction performance of DBN+JPEG ZIG-ZAG compared with existing algorithms in deep learning.In the above analysis,the accuracy metric is used for evaluating deep learning classification algorithms of brain disorder diseases: Alzheimer, Schizophrenia,and Parkinson.Proposed work reveals better performance in the accuracy metric.Visualizing the performance of algorithms in terms of error rate in Fig.11 illustrates the lower error rate of the DBN algorithm and accordingly indicates the correct prediction of the result.

    Figure 11:Result of MAPE in different algorithms DBN+JPEG ZIG-ZAG

    5 Conclusion

    In this research,deep learning algorithms with CNN,DNN,and DBN are evaluated using secure storage of images in the JPEG Zig Zag encryption scheme.The DBN is proposed as an unsupervised feature extractor to extract biomarkers of the image and predict brain disorder diseases of Alzheimer,Schizophrenia,and Parkinson.The proposed work of the DBNJZZ system can process all features of the image and share this image securely.It can also provide a prompt prediction of the disorder using the image.This work has only focused on three diseases.In the future,this work can be extended to cover different case studies with diverse DNA sequences.

    Acknowledgement:This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R234), Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.

    Funding Statement:This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R234), Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.

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

    狂野欧美激情性bbbbbb| av播播在线观看一区| 亚洲国产精品专区欧美| 免费观看性生交大片5| 亚洲国产精品成人久久小说| 国产日韩欧美在线精品| av天堂中文字幕网| 日日啪夜夜撸| 精品久久久久久久久av| 国产精品熟女久久久久浪| 国产亚洲av嫩草精品影院| 成年免费大片在线观看| 亚洲精品一区蜜桃| 国产淫语在线视频| 欧美区成人在线视频| 久久精品国产自在天天线| 一级毛片久久久久久久久女| 欧美丝袜亚洲另类| 美女被艹到高潮喷水动态| 一级毛片黄色毛片免费观看视频| 搡女人真爽免费视频火全软件| 黄色怎么调成土黄色| 麻豆精品久久久久久蜜桃| a级一级毛片免费在线观看| 国产成人精品久久久久久| 我的女老师完整版在线观看| 一区二区三区免费毛片| 蜜桃久久精品国产亚洲av| 十八禁网站网址无遮挡 | 小蜜桃在线观看免费完整版高清| 欧美精品人与动牲交sv欧美| 激情五月婷婷亚洲| 久久精品国产亚洲网站| av在线亚洲专区| 久久久午夜欧美精品| 欧美国产精品一级二级三级 | 久久午夜福利片| 中文字幕亚洲精品专区| 一个人看的www免费观看视频| 精品国产乱码久久久久久小说| 午夜亚洲福利在线播放| 午夜精品国产一区二区电影 | 综合色av麻豆| 天堂俺去俺来也www色官网| 80岁老熟妇乱子伦牲交| av网站免费在线观看视频| 久久人人爽人人爽人人片va| 最近的中文字幕免费完整| 成人欧美大片| 久久99精品国语久久久| 观看美女的网站| 欧美3d第一页| 亚洲色图av天堂| 日韩精品有码人妻一区| 日韩欧美精品免费久久| 丝袜喷水一区| 国内少妇人妻偷人精品xxx网站| 精品久久久久久久久av| 热re99久久精品国产66热6| 亚洲国产精品999| 只有这里有精品99| 精品少妇久久久久久888优播| 热re99久久精品国产66热6| 麻豆成人av视频| 国产午夜精品久久久久久一区二区三区| 我要看日韩黄色一级片| 女人被狂操c到高潮| 欧美激情国产日韩精品一区| 国产一区二区在线观看日韩| 最后的刺客免费高清国语| 老师上课跳d突然被开到最大视频| 免费av不卡在线播放| 99久久精品热视频| 一本久久精品| 久热这里只有精品99| 国内少妇人妻偷人精品xxx网站| 国国产精品蜜臀av免费| 韩国高清视频一区二区三区| 亚洲国产日韩一区二区| 九九久久精品国产亚洲av麻豆| 18禁在线无遮挡免费观看视频| 蜜桃久久精品国产亚洲av| 国产男女超爽视频在线观看| 又粗又硬又长又爽又黄的视频| 麻豆成人午夜福利视频| 亚洲精华国产精华液的使用体验| 最新中文字幕久久久久| 日韩免费高清中文字幕av| 免费少妇av软件| 国产免费一级a男人的天堂| 午夜激情久久久久久久| 建设人人有责人人尽责人人享有的 | 午夜免费鲁丝| 亚洲不卡免费看| 免费av不卡在线播放| 最近最新中文字幕免费大全7| 免费观看的影片在线观看| 午夜精品一区二区三区免费看| 麻豆久久精品国产亚洲av| 国产av不卡久久| 国产精品一区www在线观看| 国产精品国产av在线观看| 蜜臀久久99精品久久宅男| 偷拍熟女少妇极品色| 亚洲精品中文字幕在线视频 | 欧美 日韩 精品 国产| 国产成人精品福利久久| 久久久成人免费电影| 毛片女人毛片| 国产精品不卡视频一区二区| 亚洲欧美日韩无卡精品| 亚洲国产最新在线播放| 国产在线男女| 久久精品久久久久久噜噜老黄| 好男人视频免费观看在线| 在线观看人妻少妇| 美女被艹到高潮喷水动态| 国产极品天堂在线| 天天躁日日操中文字幕| 黄色欧美视频在线观看| 国产精品福利在线免费观看| 好男人视频免费观看在线| 免费黄频网站在线观看国产| 亚洲精品乱码久久久久久按摩| 丰满乱子伦码专区| 中文字幕免费在线视频6| 成人免费观看视频高清| 午夜激情久久久久久久| 秋霞在线观看毛片| 日本熟妇午夜| 一本色道久久久久久精品综合| 人妻少妇偷人精品九色| 黄色配什么色好看| av免费在线看不卡| 51国产日韩欧美| 人人妻人人爽人人添夜夜欢视频 | 天天一区二区日本电影三级| 爱豆传媒免费全集在线观看| 国产精品一二三区在线看| 在线精品无人区一区二区三 | 69av精品久久久久久| 久久精品国产a三级三级三级| 亚洲国产高清在线一区二区三| 国产男女内射视频| 麻豆乱淫一区二区| 欧美最新免费一区二区三区| 色哟哟·www| 久久精品久久久久久久性| 日韩,欧美,国产一区二区三区| 久久久成人免费电影| 老司机影院成人| 人妻 亚洲 视频| 神马国产精品三级电影在线观看| 一区二区三区乱码不卡18| 精品视频人人做人人爽| 亚洲真实伦在线观看| 91久久精品国产一区二区成人| 尤物成人国产欧美一区二区三区| 亚洲精品亚洲一区二区| 国产高清有码在线观看视频| 午夜精品一区二区三区免费看| 午夜日本视频在线| 午夜老司机福利剧场| 欧美潮喷喷水| 少妇人妻 视频| 日本一二三区视频观看| 亚洲无线观看免费| 一本色道久久久久久精品综合| 午夜激情福利司机影院| 成人高潮视频无遮挡免费网站| 国产成人免费观看mmmm| 免费看av在线观看网站| av在线蜜桃| 欧美一区二区亚洲| 久久精品国产鲁丝片午夜精品| 午夜福利网站1000一区二区三区| av在线播放精品| 日本一本二区三区精品| 蜜臀久久99精品久久宅男| 六月丁香七月| 18+在线观看网站| 又大又黄又爽视频免费| 成年人午夜在线观看视频| 亚洲无线观看免费| 777米奇影视久久| 天堂俺去俺来也www色官网| 亚洲成色77777| 国产亚洲午夜精品一区二区久久 | 99久久中文字幕三级久久日本| 少妇人妻精品综合一区二区| 黄色日韩在线| 听说在线观看完整版免费高清| 国产爽快片一区二区三区| 午夜福利网站1000一区二区三区| 久久97久久精品| 国产成人精品一,二区| 国产黄色免费在线视频| 婷婷色综合www| 国产精品国产av在线观看| 亚洲美女视频黄频| 一级黄片播放器| 久久女婷五月综合色啪小说 | 中文在线观看免费www的网站| 精品人妻偷拍中文字幕| videossex国产| 可以在线观看毛片的网站| 免费观看a级毛片全部| 毛片女人毛片| 国产精品一区二区三区四区免费观看| 亚洲精品国产色婷婷电影| 午夜精品国产一区二区电影 | 婷婷色综合www| 免费av观看视频| 亚洲精品一二三| 欧美最新免费一区二区三区| 高清av免费在线| 美女视频免费永久观看网站| 欧美精品人与动牲交sv欧美| 日韩欧美精品免费久久| 日韩在线高清观看一区二区三区| 国产一区亚洲一区在线观看| 国产精品蜜桃在线观看| av福利片在线观看| 色吧在线观看| 久久久久网色| 久久精品熟女亚洲av麻豆精品| 免费高清在线观看视频在线观看| 亚洲精品国产av成人精品| 国产精品不卡视频一区二区| 亚洲国产欧美在线一区| 熟妇人妻不卡中文字幕| 国产精品国产三级专区第一集| 欧美xxxx黑人xx丫x性爽| 毛片女人毛片| 免费av不卡在线播放| 嫩草影院精品99| 国产日韩欧美亚洲二区| 3wmmmm亚洲av在线观看| 亚洲婷婷狠狠爱综合网| 老师上课跳d突然被开到最大视频| 插逼视频在线观看| 国产国拍精品亚洲av在线观看| 国产69精品久久久久777片| 十八禁网站网址无遮挡 | 中文字幕人妻熟人妻熟丝袜美| 日本免费在线观看一区| av国产精品久久久久影院| 男女无遮挡免费网站观看| 日本午夜av视频| 国产老妇伦熟女老妇高清| 欧美激情久久久久久爽电影| 久久久久久国产a免费观看| 久热久热在线精品观看| 久久热精品热| 亚洲av免费高清在线观看| 国产欧美亚洲国产| 亚洲精品国产色婷婷电影| 老师上课跳d突然被开到最大视频| 国产精品偷伦视频观看了| 校园人妻丝袜中文字幕| 内地一区二区视频在线| av福利片在线观看| 免费不卡的大黄色大毛片视频在线观看| 亚洲四区av| 成人欧美大片| 亚洲成人精品中文字幕电影| 最近2019中文字幕mv第一页| 你懂的网址亚洲精品在线观看| 99九九线精品视频在线观看视频| 久热这里只有精品99| 亚洲av.av天堂| 欧美3d第一页| 亚洲av国产av综合av卡| 黄色视频在线播放观看不卡| 国产午夜福利久久久久久| 91精品伊人久久大香线蕉| 人人妻人人看人人澡| 亚洲av免费在线观看| 国产v大片淫在线免费观看| 国产爱豆传媒在线观看| 性色avwww在线观看| 日韩,欧美,国产一区二区三区| a级毛片免费高清观看在线播放| 97在线人人人人妻| 国产伦精品一区二区三区视频9| 亚洲精品成人av观看孕妇| 天堂中文最新版在线下载 | 80岁老熟妇乱子伦牲交| 又粗又硬又长又爽又黄的视频| 精品亚洲乱码少妇综合久久| 精品国产三级普通话版| 国产69精品久久久久777片| 午夜福利高清视频| 国产淫片久久久久久久久| 人人妻人人澡人人爽人人夜夜| 99九九线精品视频在线观看视频| 免费人成在线观看视频色| 高清日韩中文字幕在线| 伊人久久国产一区二区| 日本wwww免费看| 久久精品久久久久久久性| 国产爱豆传媒在线观看| 国产人妻一区二区三区在| 国产av码专区亚洲av| 91在线精品国自产拍蜜月| 中文资源天堂在线| 国产视频首页在线观看| 亚洲av中文字字幕乱码综合| 大码成人一级视频| 真实男女啪啪啪动态图| 成人国产av品久久久| 又大又黄又爽视频免费| 国产精品蜜桃在线观看| 成人黄色视频免费在线看| 最近2019中文字幕mv第一页| 久久久亚洲精品成人影院| 男插女下体视频免费在线播放| 国产成人精品福利久久| 欧美激情在线99| 国产毛片在线视频| 美女xxoo啪啪120秒动态图| 美女被艹到高潮喷水动态| 各种免费的搞黄视频| 99久久人妻综合| 看非洲黑人一级黄片| 日本黄色片子视频| 亚洲一区二区三区欧美精品 | 亚洲精品亚洲一区二区| 亚洲精品日本国产第一区| 国产av码专区亚洲av| av卡一久久| 一个人看视频在线观看www免费| 中国国产av一级| 久久精品熟女亚洲av麻豆精品| 各种免费的搞黄视频| 日日啪夜夜爽| 99热网站在线观看| 亚洲天堂国产精品一区在线| 美女国产视频在线观看| 国产久久久一区二区三区| 亚洲欧美日韩无卡精品| 亚洲精品456在线播放app| 亚洲av欧美aⅴ国产| 国产精品嫩草影院av在线观看| 久久久久久久久久人人人人人人| 久久午夜福利片| 国产白丝娇喘喷水9色精品| 日韩成人av中文字幕在线观看| 精品久久久久久久人妻蜜臀av| 又粗又硬又长又爽又黄的视频| 一级爰片在线观看| 啦啦啦在线观看免费高清www| 尤物成人国产欧美一区二区三区| 亚洲av.av天堂| 尤物成人国产欧美一区二区三区| 成人国产av品久久久| 欧美精品人与动牲交sv欧美| 水蜜桃什么品种好| 亚洲激情五月婷婷啪啪| 最近2019中文字幕mv第一页| 汤姆久久久久久久影院中文字幕| 国产精品国产三级国产专区5o| 日韩人妻高清精品专区| 久久久久精品性色| 在线观看av片永久免费下载| 国产精品一区www在线观看| 国产高潮美女av| 亚洲精品影视一区二区三区av| 亚洲在久久综合| 日本爱情动作片www.在线观看| 搞女人的毛片| 少妇猛男粗大的猛烈进出视频 | 男插女下体视频免费在线播放| 国产有黄有色有爽视频| 午夜福利视频1000在线观看| 制服丝袜香蕉在线| 在线免费十八禁| 成人毛片a级毛片在线播放| 在线 av 中文字幕| 国产乱人视频| 色视频www国产| 国产极品天堂在线| 国产一级毛片在线| 搡女人真爽免费视频火全软件| 蜜桃久久精品国产亚洲av| 亚洲欧美成人综合另类久久久| 欧美极品一区二区三区四区| 亚洲伊人久久精品综合| 免费黄频网站在线观看国产| xxx大片免费视频| 欧美一级a爱片免费观看看| 亚洲av福利一区| 欧美一级a爱片免费观看看| 大片电影免费在线观看免费| 久久久精品免费免费高清| 夫妻午夜视频| 亚洲精华国产精华液的使用体验| 日韩成人av中文字幕在线观看| 成人午夜精彩视频在线观看| 欧美国产精品一级二级三级 | 亚洲在线观看片| 国产成年人精品一区二区| 真实男女啪啪啪动态图| 精品久久久久久久久av| 日韩成人伦理影院| 久久久久久久精品精品| 日韩成人伦理影院| 亚洲av免费在线观看| 蜜桃久久精品国产亚洲av| 国产 一区精品| 嘟嘟电影网在线观看| 日本色播在线视频| 九九爱精品视频在线观看| 久久久久久久久大av| tube8黄色片| 久久久国产一区二区| 日韩欧美一区视频在线观看 | 免费电影在线观看免费观看| 男女那种视频在线观看| 亚洲怡红院男人天堂| 国产69精品久久久久777片| 中文欧美无线码| 久久人人爽人人片av| 插逼视频在线观看| 精品久久久精品久久久| 亚洲精品aⅴ在线观看| 国产精品99久久99久久久不卡 | av黄色大香蕉| 国产精品人妻久久久影院| 青春草视频在线免费观看| 国产一区二区亚洲精品在线观看| 国产精品99久久久久久久久| 六月丁香七月| 日本免费在线观看一区| 狂野欧美白嫩少妇大欣赏| 中文欧美无线码| 中文字幕制服av| 精品一区二区免费观看| 亚洲人成网站高清观看| 亚洲内射少妇av| 国产又色又爽无遮挡免| 日韩成人av中文字幕在线观看| 青春草视频在线免费观看| 自拍欧美九色日韩亚洲蝌蚪91 | 极品少妇高潮喷水抽搐| 国产av不卡久久| 在线观看一区二区三区激情| 精华霜和精华液先用哪个| 少妇熟女欧美另类| 国产永久视频网站| 亚洲最大成人手机在线| 国产精品一区二区在线观看99| 视频中文字幕在线观看| 在线播放无遮挡| 久久97久久精品| 人体艺术视频欧美日本| 午夜爱爱视频在线播放| 亚洲自拍偷在线| 蜜桃久久精品国产亚洲av| 亚州av有码| 99久久精品一区二区三区| av在线天堂中文字幕| 成人免费观看视频高清| 毛片女人毛片| 一区二区三区精品91| 亚洲久久久久久中文字幕| 日韩制服骚丝袜av| 特大巨黑吊av在线直播| 91aial.com中文字幕在线观看| 99热6这里只有精品| 亚洲精品自拍成人| 女的被弄到高潮叫床怎么办| 色吧在线观看| 国产午夜精品一二区理论片| 亚洲欧美日韩卡通动漫| 肉色欧美久久久久久久蜜桃 | 日韩伦理黄色片| 丝袜喷水一区| 亚洲最大成人av| 白带黄色成豆腐渣| 中文乱码字字幕精品一区二区三区| 日产精品乱码卡一卡2卡三| 在线亚洲精品国产二区图片欧美 | 亚洲国产精品999| 国产免费视频播放在线视频| 寂寞人妻少妇视频99o| 久久精品国产亚洲网站| 亚洲国产av新网站| 秋霞伦理黄片| 免费观看性生交大片5| 亚洲国产高清在线一区二区三| 国产黄色视频一区二区在线观看| 日韩伦理黄色片| 成人美女网站在线观看视频| 精品一区在线观看国产| 成人免费观看视频高清| 麻豆成人午夜福利视频| 五月伊人婷婷丁香| 黄色欧美视频在线观看| 视频中文字幕在线观看| 夜夜看夜夜爽夜夜摸| 韩国av在线不卡| 亚洲美女视频黄频| 久热这里只有精品99| 国产av国产精品国产| 欧美三级亚洲精品| 国产69精品久久久久777片| 精品一区二区免费观看| 人人妻人人看人人澡| 男男h啪啪无遮挡| 永久免费av网站大全| 成人鲁丝片一二三区免费| 久久久久久伊人网av| 国产精品麻豆人妻色哟哟久久| 青春草亚洲视频在线观看| 久久久久久久久久久免费av| 下体分泌物呈黄色| 欧美日韩精品成人综合77777| 国产 一区 欧美 日韩| 亚洲精品国产色婷婷电影| 成人亚洲精品一区在线观看 | 欧美成人精品欧美一级黄| 我的女老师完整版在线观看| 欧美日本视频| 欧美xxxx黑人xx丫x性爽| 亚洲精品亚洲一区二区| 国产在线一区二区三区精| 夫妻午夜视频| 国产精品一区二区在线观看99| 亚洲欧美一区二区三区国产| 又大又黄又爽视频免费| 成人午夜精彩视频在线观看| 大片免费播放器 马上看| 交换朋友夫妻互换小说| 亚洲av不卡在线观看| 一边亲一边摸免费视频| 日韩国内少妇激情av| 国产精品嫩草影院av在线观看| 日本色播在线视频| 欧美97在线视频| 国精品久久久久久国模美| 永久免费av网站大全| 欧美日韩亚洲高清精品| 国产亚洲91精品色在线| 国产精品99久久久久久久久| 在现免费观看毛片| 看非洲黑人一级黄片| 青春草视频在线免费观看| 国产av国产精品国产| 男的添女的下面高潮视频| 中文字幕免费在线视频6| 夫妻午夜视频| 成人亚洲欧美一区二区av| 51国产日韩欧美| 免费av毛片视频| 国产精品人妻久久久久久| 成人无遮挡网站| 日日啪夜夜撸| 男女那种视频在线观看| 校园人妻丝袜中文字幕| 边亲边吃奶的免费视频| 内地一区二区视频在线| 综合色av麻豆| 你懂的网址亚洲精品在线观看| 国产高清不卡午夜福利| 少妇熟女欧美另类| 七月丁香在线播放| 在线精品无人区一区二区三 | 80岁老熟妇乱子伦牲交| 好男人视频免费观看在线| 蜜臀久久99精品久久宅男| av在线老鸭窝| 国产高清有码在线观看视频| 国产毛片a区久久久久| 日韩免费高清中文字幕av| 天天躁夜夜躁狠狠久久av| 国产亚洲一区二区精品| 国产精品av视频在线免费观看| a级一级毛片免费在线观看| 亚洲高清免费不卡视频| 2021天堂中文幕一二区在线观| 国产综合懂色| 观看美女的网站| 欧美精品一区二区大全| av线在线观看网站| 超碰av人人做人人爽久久| 99精国产麻豆久久婷婷| 男人狂女人下面高潮的视频| 精品久久国产蜜桃| 在线a可以看的网站| 又爽又黄无遮挡网站| 日韩av免费高清视频| 亚洲经典国产精华液单| 国产亚洲一区二区精品| 国产av不卡久久| 波野结衣二区三区在线| 青春草国产在线视频| 自拍欧美九色日韩亚洲蝌蚪91 | 亚洲人与动物交配视频| 亚洲av.av天堂| 亚洲国产日韩一区二区| 少妇 在线观看| 一级毛片 在线播放| 看免费成人av毛片| 99热全是精品| 自拍欧美九色日韩亚洲蝌蚪91 | 99re6热这里在线精品视频| 好男人在线观看高清免费视频| 国产精品秋霞免费鲁丝片| 日韩成人伦理影院| 男人爽女人下面视频在线观看| 亚洲伊人久久精品综合| 久久久久久久久久成人| 国产淫片久久久久久久久| 国产成人91sexporn|