• <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 ?

    Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

    2024-05-25 14:40:24HalaAlShamlanandHalahAlMazrua
    Computers Materials&Continua 2024年4期

    Hala AlShamlan and Halah AlMazrua

    Department of Information Technology,College of Computer and Information Sciences,King Saud University,P.O.Box 145111,Riyadh,4545,Saudi Arabia

    ABSTRACT In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization (GWO) with Harris Hawks Optimization (HHO) for feature selection.The motivation for utilizing GWO and HHO stems from their bio-inspired nature and their demonstrated success in optimization problems.We aim to leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development of more efficient treatment strategies.The proposed hybrid method offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

    KEYWORDS Bio-inspired algorithms;bioinformatics;cancer classification;evolutionary algorithm;feature selection;gene expression;grey wolf optimizer;harris hawks optimization;k-nearest neighbor;support vector machine

    1 Introduction

    Authors are required to adhere to this Microsoft Word template in preparing their manuscripts for submission.It will speed up the review and typesetting process.

    In 2020,cancer claimed the lives of almost 10 million people worldwide,as reported by the World Health Organization(WHO).Disturbingly,there are dire projections indicating a 50%surge,leading to approximately 15 million new cases.Consequently,there is an urgent requirement for effective cancer prevention and treatment strategies[1,2].In cancer,cells within the organs or tissues of the human body exhibit uncontrolled growth,potentially spreading to adjacent regions or,in more advanced stages,to distant organs.Early detection is of paramount importance to enhance survival rates.Therefore,the identification of effective and predictive genes for cancer classification is critical.Achieving this objective entails the careful selection of an adequate number of features(genes)from DNA microarrays.

    It can be difficult to extract meaningful information from gene expression data due to the vast number of genes involved [3].The growing popularity of microarray data in cancer research classification has been attributed to the emergence of gene expression technologies,due mainly to the abundance of gene expression information (features/genes) available for detecting common patterns within a collection of samples.Cancer cells are identified with microarrays,which analyze DNA proteins to gain insight into their genes.Microarray data are organized according to a gene expression matrix,in which rows represent genes,and columns indicate experimental conditions[4].

    Our understanding of disease-gene associations is greatly enhanced by the use of microarray technology.When dimensionality is present,irrelevant genes can complicate cancer classification and data analysis.In order to solve this problem and extract useful information,a feature selection method and classification algorithm are employed.Through the use of these algorithms,cancer-related genes can be removed from microarrays in order to facilitate cancer classification [5].In the context of existing work,previous studies have focused on gene selection methods to improve the accuracy of cancer classification.However,the vast number of genes involved in gene expression data makes it difficult to extract meaningful information[3].This issue has led to the emergence of feature selection techniques,such as filtering,wrapping,and embedding,to identify informative genes and reduce dimensionality[5].Nevertheless,there is a need for more effective approaches that can overcome the limitations of existing methods.Recent publications indicate a growth in hybrid approaches to feature selection as part of the overall framework,as evidenced by the growing number of publications in recent years[6].Gene selection is a process that identifies the most informative and valuable genes for the classification problem.Achieving this is possible by eliminating irrelevant genes and noise in the data,resulting in a more accurate classification of cancer[4].By combining both filter and wrapper techniques,the hybrid method is able to provide the best of both worlds.The development of hybrid approaches has increased in recent years,including the combination of two wrappers and the merger of filter and wrapper methods.For accurate diagnosis,these approaches identify valuable genes.As a result of integrating the strengths of both techniques,hybrid methods achieve optimal results[4].

    In recent years,there has been significant research and development in the field of metaheuristic algorithms to tackle complex optimization problems.Notably,several novel algorithms have emerged,which have shown promising results in various domains.The liver cancer algorithm(LCA)[7] draws inspiration from the behavior of liver cells in response to tumor growth and has been successfully applied in tasks related to liver cancer,such as feature selection and classification.The slime mould algorithm(SMA)[8]replicates the foraging behavior of slime molds and has been used for optimization problems like routing optimization,image segmentation,and clustering.The moth search algorithm(MSA)[9]mimics moths’attraction to light and has found applications in feature selection,image processing,and data clustering.The hunger games search(HGS)[10] algorithm incorporates survival of the fittest principles,and it has been employed in feature selection,scheduling,and parameter tuning.The Runge Kutta method(RUN)[10],originally a numerical integration technique for solving differential equations,has been adapted for optimization problems in engineering design,control systems,and economic modeling.The colony predation algorithm (CPA) [10] is inspired by predator-prey interactions and has been applied to data clustering,image segmentation,and function optimization.The weighted mean of vectors (INFO) [10] algorithm combines candidate solutions using weighted means and has been used in feature selection,image processing,and data mining tasks.These recently proposed meta-heuristic algorithms offer innovative approaches to optimization and have the potential to address complex real-world challenges.

    In this study,we propose a novel hybrid feature selection method that addresses the challenges in cancer classification.Our approach combines two bio-inspired wrapper feature selection techniques and leverages the evolutionary and bio-inspired optimization algorithm GWO-HHO (Grey Wolf Optimizer-Harris Hawks Optimization),which integrates Grey Wolf Optimizer (GWO) and Harris Hawks Optimization(HHO).The main contributions of our research can be summarized as follows:

    Development of a Hybrid Feature Selection Method: We introduce a novel approach that combines bio-inspired wrapper techniques and the GWO-HHO algorithm.This hybrid method aims to overcome the limitations of existing feature selection methods and enhance the accuracy of cancer classification.

    Effective Selection of Relevant and Informative Genes:By utilizing our hybrid feature selection method,we aim to identify the most relevant and informative genes for cancer classification.This process helps to reduce dimensionality,eliminate irrelevant genes,and improve the accuracy of cancer diagnosis.

    Comprehensive Evaluation and Comparison: We conduct a comprehensive evaluation of our proposed method by analyzing six diverse binary and multiclass gene expression microarray datasets.Through this evaluation,we compare the performance of our hybrid approach against recently published algorithms,showcasing its superior accuracy in cancer classification and gene selection.

    Advancements in Cancer Research:By accurately classifying cancer and identifying informative genes,our approach contributes to advancing our understanding of the underlying biology of cancer.This knowledge can potentially lead to the development of more effective diagnostic and therapeutic strategies.

    In summary,our study addresses the challenges in cancer classification through the introduction of a hybrid feature selection method.By effectively selecting relevant and informative genes,our approach overcomes the limitations of existing methods and achieves superior performance in accurately classifying cancer.The results of our research contribute to advancements in cancer research and hold promise for the development of improved diagnostic and therapeutic interventions.

    In the subsequent sections of the paper,we have organized the content as follows: Section 2 provides the necessary background information related to the perspectives discussed in the related works section,which significantly influenced the development of our proposed method.In Section 3,we describe the approaches we propose for gene selection,outlining the methodologies and techniques employed.Section 4 presents the analysis of the data and experimental results,where we evaluate the performance of our method using diverse gene expression microarray datasets.Finally,in Section 5,we provide a comprehensive conclusion that summarizes the key findings of our research and discusses the implications,limitations,and potential future directions.

    2 Related Works

    In recent years,several methods have been proposed for cancer classification and gene selection in bioinformatics research.In this section,we review some of the related works that have addressed similar problems using different optimization algorithms and machine learning techniques.One of the related works is the GWO-SVM method proposed by AlMazrua et al.[11].They combined the Grey Wolf Optimizer (GWO)algorithm with Support Vector Machines(SVM)for feature selection in cancer classification.Their approach aimed to enhance the classification accuracy by selecting the most relevant genes for cancer diagnosis.Another related work is the GWO-KNN method,also proposed by AlMazrua et al.[11].In this study,they employed the GWO algorithm with the k-Nearest Neighbors(KNN)classifier for cancer classification.The GWO algorithm was used to optimize the feature subset selection process,while KNN was utilized for the classification task.

    The HHO-SVM method proposed by AlMazrua et al.[12]is another relevant work in this area.Here,they combined the Harris Hawks Optimization(HHO)algorithm with SVM for gene selection in cancer classification.Their approach aimed to improve the classification performance by identifying the most informative genes associated with cancer.

    Similarly,the HHO-KNN method proposed by AlMazrua et al.[12]utilized the HHO algorithm with KNN for cancer biomarker gene detection.By applying the HHO algorithm,they aimed to identify the most discriminative genes that could serve as potential biomarkers for cancer diagnosis.The HS-GA method[13]is also worth mentioning,as it employed a Hybridization of Harmony Search(HS) and Genetic Algorithm (GA) for gene selection in cancer classification.The hybrid algorithm aimed to improve the search efficiency by combining the exploration capabilities of HS and the exploitation abilities of GA.

    Additionally,the FF-SVM method proposed by Almugren et al.[14] utilized a Fuzzy Expert System(FF)along with SVM for classification of microarray data.Their approach aimed to improve the classification accuracy by incorporating fuzzy logic to handle the uncertainty in gene expression data.Lastly,the GBC method proposed by Alshamlan et al.[15]introduced the Genetic Bee Colony(GBC)algorithm for gene selection in microarray cancer classification.The GBC algorithm aimed to optimize the gene selection process by simulating the foraging behavior of bee colonies.

    In the context of evolutionary algorithms and feature selection,several relevant studies have contributed to the advancement of this field.One study proposed a method for learning correlation information for multi-label feature selection[16].Their approach aimed to enhance the discrimination capability of selected features by integrating correlation learning and feature selection.Another study presented a multi-label feature selection technique that considers label correlations and utilizes a local discriminant model [17].Additionally,a study introduced a multi-label feature selection approach based on label correlations and feature redundancy [18].Their method aimed to select a subset of features that maximized discriminative power while considering relationships among labels and features.Moreover,another study explored manifold learning with a structured subspace for multilabel feature selection[19].They leveraged the inherent data structure to identify informative features for multi-label classification tasks.In addition to these studies,another investigation also focused on manifold learning with a structured subspace for multi-label feature selection[19].Lastly,an approach was proposed that integrates a differential evolution algorithm with dynamic multiple populations based on weighted strategies[20],demonstrating improvements in feature selection performance.

    These related works have contributed to the field of cancer classification and gene selection by employing various optimization algorithms and machine learning techniques.However,the reviewed algorithms have certain limitations regarding the number of selected genes,overfitting,and computational complexity.Firstly,they may not effectively reduce the number of selected genes,resulting in larger feature sets that can be challenging to interpret and may introduce noise into the classification process.This can hinder the identification of the most informative genes and potentially lead to reduced performance.Secondly,the algorithms may not explicitly address the issue of overfitting,which can occur when the model becomes overly complex relative to the available data,leading to poor generalization performance on unseen data.Overfitting can limit the practical utility of the models in real-world applications.Lastly,these methods may lack efficient strategies to handle computational complexity,particularly when dealing with large-scale datasets.The computational demands of these algorithms can become prohibitive,requiring substantial time and resources for analysis.These limitations highlight the need for a hybrid algorithm that combines the strengths of different optimization techniques to overcome these challenges and improve the accuracy,interpretability,and efficiency of gene selection in cancer classification.In the following sections,we will present our novel GWO-HHO hybrid algorithm,which aims to address these limitations and demonstrate its effectiveness in cancer analysis.

    3 Method

    The primary objective of our study is to develop a novel hybrid bio-inspired evolutionary algorithm,termed GWO-HHO,which aims to identify predictive and relevant genes for achieving high classification accuracy in cancer classification tasks.The proposed model consists of three phases:The GWO phase,the HHO phase,and the classification phase.

    In the GWO phase,we employ the Grey Wolf Optimizer algorithm to identify the most informative genes from a pool of candidate genes.This phase focuses on selecting the genes that exhibit significant discriminatory power for cancer classification.Subsequently,in the HHO phase,we utilize the Harris Hawks Optimization algorithm to further refine the selected genes and enhance their predictive power.This phase aims to optimize the selected genes by leveraging the search capabilities of the HHO algorithm.

    Finally,in the classification phase,we employ a classification algorithm to accurately classify cancer samples based on the selected genes.The classification algorithm will be chosen based on its suitability for the dataset and its ability to handle the selected features effectively.

    By integrating these three phases,our proposed hybrid algorithm aims to improve the accuracy of cancer classification by identifying and utilizing the most relevant genes.We believe that this approach has the potential to significantly contribute to the field of cancer research and facilitate the development of more effective diagnostic and treatment strategies.

    For a visual representation,we have included Fig.1,which illustrates the proposed model and the steps involved in the GWO-HHO algorithms.Additionally,Algorithm 1 presents the pseudo code for our proposed algorithm.

    3.1 Proposed GWO-HHO Algorithm

    In this section,we will delve into the various phases of our proposed GWO-HHO algorithm.

    3.1.1 First Phase:Pre-Processing Using Gray Wolf Optimization(GWO)

    The Gray Wolf Optimization (GWO) algorithm represents an advanced swarm intelligence optimization technique,particularly well-suited for wrapper feature selection.It draws inspiration from the coordinated hunting behavior of gray wolves in their natural habitat,where each member of the wolf pack plays a distinct role in identifying and capturing prey.In a similar fashion,the GWO algorithm adopts a strategy reminiscent of gray wolf hunting behavior to discern and select the most informative features for classification tasks.

    The GWO algorithm has exhibited promising outcomes in the realm of feature selection,proving its effectiveness across various applications such as image classification,prediction,and modeling.This algorithm was initially introduced by Mirjalili et al.[21]in 2014.The GWO approach leverages a nature-inspired optimization technique to craft optimal solutions for various problem domains.In the context of enhancing accuracy,GWO proves to be a highly effective feature selection method.Notably,it stands out by not requiring the utilization of a threshold parameter to weed out irrelevant features,a step often necessary in other methods.

    As the number of variables increases,the task of selecting the most relevant subset of features becomes increasingly complex.However,GWO’s wrapper feature selection method is notably less timeconsuming compared to alternative techniques in addressing this complexity.

    GWO possesses a unique advantage in that it circumvents the challenge of getting trapped in local minima,a problem frequently encountered by other bio-inspired optimization methods.For more indepth information regarding GWO and its efficacy in feature selection,interested readers can refer to a previously published paper[11].This paper not only presents a mathematical model for gray wolves but also provides experimental results utilizing GWO as a standalone feature selection algorithm.

    The GWO algorithm was selected as the first phase of the proposed model because of the high dimensionality of the dataset.Among other things,GWO’s demonstrated ability to deal with real-world problems,such as spring tension and compression,welded beams,and pressure vessels,contributed to the decision.This has resulted in GWO being widely recognized as an excellent tool for solving complex optimization problems.The dataset used in this study has a high dimensionality,so it is a great candidate to address it.

    The GWO was designed based on a mathematical model of gray wolves’social hierarchy in which selected the fittest solution as the Alpha(α).Due to this,the second and third best solutions are known as Beta(β)and Delta(δ),respectively.There are no more possible solutions,so Omega(ω)is assumed to be the only solution available.Alpha,beta,and delta are all used in the GWO algorithm in order to lead the search.In this case,theωwolves are pursuing these three wolves.

    There are usually three stages of predation by wolves:Hunting,encircling,and attacking.

    ? Encircling:In GWO,the hunting behavior of gray wolves is emulated,specifically the encircling behavior during a hunt.This behavior is mathematically represented by Eqs.(1)–(3).

    where t is the number of iterations,X(t)is one gray wolf,X(t+1)will be the next position it lands at,and Xp(t)corresponds to one of the following:α,β,δ.coefficient vectors A and C are represented as follows:

    where r1 and r2 are random vectors in the range[0,1]and is a decreasing number in the range[0,2],with a=2-2t/MaxIteration being the most prominent example.

    ? Hunting: In the context of gray wolf hunting behavior,it is noteworthy that these wolves can collectively locate and encircle their prey.Although beta or delta wolves may take part in the hunt at times,it is primarily the alpha wolf that leads the charge.However,when dealing with an abstract search space where the optimal solution(prey)is unknown,we make certain assumptions to mathematically mimic the hunting behavior of gray wolves.In this abstraction,we consider the alpha wolf to represent the best candidate solution,with beta and delta wolves also possessing superior knowledge regarding the probable location of potential prey.As a result,we keep track of the three best solutions that have been developed thus far.We then require other search agents to update their positions based on the information obtained from the positions of these best search agents.This concept is elucidated by Eqs.(5)–(7).

    ? Attacking:The gray wolves encircled the prey and were preparing to catch it(con-165 vergence and get results).Because of A ∈[-2a,2a],this operation was normally 166 performed by lowering a in Eq.(3).When |A| ≥1 occurs,the gray wolves stay 167 away from the prey to achieve global search;when |A|<1 occurs,the gray wolf pack 168 approaches the prey and finally finishes it.

    3.1.2 Second Phase:Gene Selection Using Harris Hawks Optimization(HHO)

    The GWO algorithm operates on a continuous search space,which means that it is not capable of solving the feature selection problem on its own.in addition,as revealed by Niu et al.[22] who analyzed the GWO,and demonstrated that it has a severe structural defect.The algorithm performs best when the solution to an optimization problem is zero.Nevertheless,if the optimal solution is not zero,the same performance cannot be achieved.As a result,the further the function’s optimal solution is from zero,the lower the performance of GWO.To address this limitation,an additional feature selection method must be applied after GWO.To this end,HHO was used in the second phase after GWO.HHO was applied to the dataset produced by GWO to resolve the issue of the continuous search space.By combining these two techniques,the proposed hybrid feature selection algorithm can effectively address the dimensionality problem of the dataset and identify the most informative features for accurate cancer classification.

    HHO is a swarm computation technique that was developed by Heidari et al.in 2019 [23].It draws inspiration from the cooperative hunting and chasing behavior observed in Harris’s hawks,particularly their“surprise pounces”or“the seven kills”.During a cooperative attack,multiple hawks work together and attack a rabbit that has revealed itself.The HHO algorithm employs a similar strategy,where multiple solutions explore the search space and converge on the most effective solution.This cooperative approach enables the algorithm to effectively navigate complex search spaces and identify optimal solutions to problems.In another published paper,AlMazrua et al.[12] discussed the inspiration behind HHO,the mathematical model employed by the algorithm,and presented the results of using HHO as a standalone feature selection method.The authors explained how HHO drew inspiration from the cooperative hunting behavior of Harris’s hawks and elaborated on the mathematical model used to implement the algorithm.Additionally,they presented the results of their experiments,which demonstrated the effectiveness of HHO as a feature selection method.Overall,their study provides valuable insights into the capabilities of HHO and its potential applications in various domains.

    Hawks are known to chase their prey by tracing,encircling,and eventually striking and killing.

    The mathematical model,inspired by the hunting behaviors of Harris’s hawks,encompasses three distinct stages: Exploration,the transition between exploration and exploitation,and exploitation.Throughout each stage of the hunt,the Harris’s hawks represent the candidate solutions,while the targeted prey represents the best candidate solution,nearly approaching the optimal.

    During their search for prey,Harris’s hawks employ two distinct exploration techniques.The candidate solutions aim to position themselves as close to the prey as possible,while the best candidate solution is the one intended to be the prey.

    In the first technique,Harris’s hawks select a location by taking into account the positions of other hawks and their prey.In the second method,the hawks perch atop random tall trees.Eq.(8)enables the simulation of these two methods with equal probabilities denoted as q.

    ? Vector x(t) is the current hawk position,whereas vector x(t+1) is the hawk’s position at the next iteration.

    ? The hawk xrandom(t)is selected at random from the population.

    ? The rabbit position is xrabbit(t).

    ? q,r1,r2,r3 and r4 are randomly generated numbers inside(0,1).

    ? LB and UB are the upper and lower bounds of variables.

    ? xmean(t)is the average position of the current population of hawks,which is calculated as shown in Eq.(9).

    ? t is the total number of iterations.

    ? xi(t)is the position for each hawk in iteration t.

    ? The total number of hawks is represented by N.

    The algorithm switches from exploration to exploitation(transition from exploration to exploitation)depending on the rabbit’s running or escaping energy,as shown in Eq.(10).

    ? E represents the prey’s escaping energy.

    ? The initial state of the energy is indicated by E0,which changes randomly inside(-1,1)at each iteration.

    When|E|≥1,hawks seek out more areas to investigate the rabbit’s whereabouts;alternatively,the exploitation stage begins.The algorithm formulates the rabbit’s escape successp≥0.5 or failurep<0.5 with an equal chance p.The Hawks also will also carry out a soft|E|≥0.5 or hard siege|E|<0.5,based on the rabbit’s energy.The soft siege is defined as in Eqs.(11)–(13).

    ? The difference between the hawk and rabbit positions is represented byΔx(t).

    ? J is a random number used to generate the rabbit’s random jump force.

    A hard siege,on the other hand,can be calculated as follows in Eq.(14):

    A soft siege with repeated fast dives is attempted when|E|≥0.5 andp<0.5,as the rabbit could successfully escape.The hawks have the option of selecting the best dive.Le′vy flight is employed to imitate the prey’s hopping.The hawks’next action is calculated as shown in Eq.(15) to determine whether the dive is successful or not.

    The hawks will dive following Eq.(16),the Le′vy flight L pattern,if the previous dive turns out to be ineffective.

    ? The problem dimension dim is the size of the RandomVector,and dim is the dimension of the problem.

    Eq.(17)has been used to update the final soft-siege rapid dives.

    Eqs.(8) and (9) are used to calculate k and z,respectively.A hard siege with progressive rapid dives occurs when|E|≥0.5 andp<0.5 are not sufficient for the rabbit to flee,as it no longer possesses enough energy.The rabbit’s z is calculated via Eq.(16),while k is updated using Eq.(18).

    3.1.3 Third Phase:Classification

    In this study,two different classifiers,Support Vector Machine(SVM)and K-Nearest Neighbors(KNN),were used.This decision was based on the demonstrated performance of GWO with KNN and HHO with SVM.In the classification phase,the fitness function score was calculated using both classifiers,with the primary objective being to improve classification accuracy while minimizing the number of selected genes.To evaluate the robustness of the proposed approach,a cross-validation method was employed.Specifically,Leave-One-Out Cross-Validation(LOOCV),which is widely used in machine learning,was used to assess the performance of the proposed algorithm.

    In the first phase of the proposed hybrid method,the initial microarray gene dataset is preprocessed and filtered using the GWO gene selection technique.Each gene is evaluated individually and sorted according to its importance.An analysis is then conducted to identify a subset of genes that yield the highest classification accuracy when analyzed using a KNN classifier.The KNN classifier is employed in place of the SVM classifier due to its superior classification accuracy.Through the GWO approach,a dataset is produced that contains fewer redundant and more relevant genes.By using GWO to filter out irrelevant and noisy genes,the computational load during the next phase of the algorithm,which utilizes the HHO technique and SVM classifier,is reduced.This approach allows for the identification of the most informative genes,resulting in improved classification accuracy and reduced computational cost.

    In the second phase of the proposed hybrid method,a gene selection process utilizing HHO is employed to identify the most predictive and informative genes from the GWO dataset,which yield the highest accuracy for the KNN classifier.In the HHO search space,each solution is represented by one or more gene indices drawn from the GWO dataset.The fitness value associated with each solution is determined by the SVM classifier,which measures the classification accuracy of the solution to a gene selection problem.By using HHO,the proposed method can explore the search space more efficiently and identify the optimal subset of genes,resulting in improved classification accuracy.The use of SVM as the fitness function enables the algorithm to identify the most informative genes that yield the best classification results.

    In the final phase of the proposed hybrid method,the informative and predictive genes identified by the GWO algorithm in the first phase are utilized to train a KNN classifier using LOOCV.Next,the HHO algorithm is employed to train an SVM classifier using LOOCV.This phase is designed to evaluate the efficiency of the pro-posed hybrid method.By utilizing both classifiers and LOOCV,the algorithm can identify the most informative genes and achieve the highest possible classification accuracy while minimizing the number of selected genes.The use of LOOCV allows for unbiased estimates of classification accuracy,which is important for assessing the effectiveness of the proposed approach.The aim of this phase is to identify the most significant genes for improving the performance of the SVM and KNN classifiers and determine which classifier performs best when utilized with GWO.GWO is employed as a pre-processing phase to reduce the dimensionality of the microarray data,eliminating duplicate and irrelevant genes.Statistically similar genes are then selected as inputs for the gene selection phase.

    The fitness function,as shown in Eq.(19),is calculated for GWO,allowing the algorithm to exclude as many features as possible while maintaining high levels of accuracy.This approach enables the identification of the most informative genes,which are subsequently utilized to train both classifiers and evaluate their performance.By comparing the performance of the SVM and KNN classifiers,the proposed method aims to determine the most effective approach for cancer classification using the GWO algorithm.

    3.2 Dataset Description

    In this study,two kind of publicly available microarray cancer datasets were utilized,consisting of binary and multiclass datasets.To evaluate the performance and effectiveness of the proposed algorithms,six benchmark microarray datasets were employed.By utilizing these datasets,the study aims to provide a comprehensive evaluation of the proposed approach and demonstrate its potential applicability in real-world cancer classification tasks.The use of benchmark datasets also allows for the comparison of the proposed approach against existing methods,providing valuable insights into its relative effectiveness.The study employed three binary datasets for colon tumors [24],lung cancer[25],and leukemia3[26].Additionally,three multiclass datasets were used,including leukemia2[24],lymphoma[27]and SRBCT[27].A detailed breakdown of the experimental datasets,including information on the number of samples and classes,can be found in Table 1.By utilizing a diverse range of datasets,the study aims to demonstrate the applicability and effectiveness of the proposed approach across various cancer types and classification scenarios.

    Table 1: Description of microarray data sets

    3.3 Parameter Settings

    The best solution was determined by using KNNs and SVMs.We determined the value of k using a trial-and-error approach.Across all datasets in the experiments,k=7 produced the best results.

    The number of iterations(Max_iter)and the size(dim)of a method play an important role in its practicality.Additional parameters to k,dim,up,and lp can be found in Table 2.

    Table 2: Parameter settings for GWO-HHO Algorithm

    3.4 Model Evaluation

    The proposed method will be evaluated based on two factors: Classification accuracy and the number of selected genes.The objective is to minimize the number of selected genes while maximizing the accuracy of classification.To obtain a reliable estimation of the accuracy of a classification algorithm on a small number of samples,LOOCV can be used.In contrast to LOOCV,k-fold cross validation incorporates randomness so that the mean accuracy associated with k-fold cross validation on a data set is not constant.Due to the low number of samples in our datasets,LOOCV is used in the evaluation process.As validation data,LOOCV uses one sample of the original data,whereas residual samples are used for training.The process is repeated in order to ensure that each sample in the data is only used once for the purposes of validation.Therefore,LOOCV is a special case of k-fold cross validation,where k equals the number of samples in the data,and every sample is evaluated exactly once for validity [28].In addition,we applied k-fold cross validation at 5-fold,to compare the results between LOOCV and k-fold in order to determine if there are any overfitting issues.Moreover,LOOCV maximizes data utilization,provides an unbiased estimate of performance,minimizes variance,and ensures robust evaluation.LOOCV evaluates the proposed method on all instances,identifies overfitting,and reduces reliance on specific train-test splits,resulting in a comprehensive and reliable assessment of the method’s effectiveness on the given dataset.

    Furthermore,LOOCV(Leave-One-Out Cross-Validation)was chosen in our study to address the challenges associated with high-dimensional gene expression data,limited sample sizes,and noise.LOOCV is suitable for limited sample sizes as it maximizes the use of available data by leaving out one sample at a time for validation [28].Moreover,it is worth noting that LOOCV has been extensively utilized and demonstrated its effectiveness in handling high-dimensional datasets,as supported by multiple previous studies[11–15,28].It helps mitigate the risk of overfitting in high-dimensional gene expression data by providing a more rigorous assessment of the classification algorithm’s performance.By employing LOOCV,we aimed to ensure a robust and reliable evaluation of our proposed hybrid algorithm’s performance in cancer classification tasks.

    The classification accuracy will be determined using Eq.(20).

    4 Experimental Results and Discussions

    In this section,we will delve into the experimental results of our proposed hybrid method,the GWO-HHO algorithm.Subsequently,we will conduct a comparative analysis with state-of-the-art research to demonstrate the efficiency of our algorithm.

    4.1 The Experiments Results of(GWO-HHO)Method

    The primary objective of this study was to assess and compare the performance of the proposed hybrid feature selection method across a selection of six cancer microarray expression datasets.The aim was to determine its effectiveness in terms of classification accuracy.Additionally,we evaluated the proposed feature selection method using both Leave-One-Out Cross-Validation(LOOCV)and 5-fold Cross-Validation and compared its performance with that of other evolutionary algorithms.Tables 3 and 4 provide insights into how the proposed hybrid feature selection method performs with regard to accuracy and the number of selected features.Notably,LOOCV was employed in Table 3 for assessing classification accuracy,while Table 4 utilized the 5-fold Cross-Validation approach.The experimental results clearly indicate that overfitting does not adversely impact the accuracy of microarray data.

    Table 3: GWO-HHO LOOCV long results

    Table 4: GWO-HHO 5-fold long results

    In the first phase of the proposed method,we applied GWO independently to each dataset and classifier to identify the optimal number of informative genes while achieving high accuracy.Next,HHO was utilized on the GWO-selected dataset to further decrease the number of selected genes and improve accuracy.Based on the experimental results,it was observed that GWO performed best with the KNN classifier while HHO performed best with the SVM classifier.As a result,each phase was associated with a specific classifier.

    As shown in Tables 3 and 4,the results highlight the remarkable performance of the proposed hybrid method.It achieved an impressive 100%accuracy for most datasets,demonstrating its robustness in accurately classifying cancer samples.Notably,the algorithm achieved perfect classification accuracy for leukemia2,leukemia3,lung,and lymphoma datasets.For these datasets,the number of selected genes was found to be less than 5,indicating the algorithm’s ability to identify a small set of highly informative genes for accurate classification.

    For the leukemia3,colon,and SRBCT datasets,the proposed method achieved 100% accuracy while selecting less than 13 genes.This demonstrates the algorithm’s effectiveness in accurately classifying cancer samples with a relatively small number of selected genes.The ability to minimize the number of genes is crucial in reducing the complexity and computational burden associated with cancer diagnosis and treatment.

    Although the colon dataset did not achieve 100% accuracy,the proposed hybrid method still achieved notable results.The algorithm accurately classified the colon dataset with an accuracy above 78%while selecting a small number of genes.This suggests that the selected genes are highly relevant to the classification task,contributing to the algorithm’s performance.

    Overall,the results from Tables 3 and 4 provide strong evidence for the effectiveness of the proposed hybrid method.The algorithm demonstrated high accuracy in most datasets,while simultaneously minimizing the number of selected genes.These findings have significant implications for cancer diagnosis and treatment,as they highlight the potential of the proposed method to accurately classify cancer samples using a small subset of informative genes.

    4.2 Comparison of Results

    In order to assess its effectiveness,we compared the proposed GWO-HHO algorithm with previous metaheuristic bio-inspired methods,focusing on classification accuracy and the number of selected genes as shown in Table 5.

    Table 5: Performance comparison between GWO-HHO algorithm and recent related works in literature based on classification accuracy and number of selected genes

    The results of this comparison revealed that GWO-HHO outperformed other bio-inspired gene selection methods in terms of accuracy.Remarkably,it achieved a remarkable 100% classification accuracy while selecting fewer genes.While it did not yield the smallest number of genes for the lung dataset,it still achieved a perfect accuracy rate of 100%.Additionally,GWO-HHO consistently outperformed its competitors across all datasets except for lung.This demonstrated the algorithm’s ability to accurately classify cancer datasets while keeping the gene selection to a minimum.

    As shown in Table 5,the proposed approach can be applied to real-world cancer classification tasks compared with other state-of-the-art methods.A promising tool for cancer diagnosis and treatment,the GWO-HHO algorithm selects informative genes efficiently and accurately while minimizing computational costs.

    We have integrated several studies to address relevant aspects of our research and identify research gaps in the related works.Al-Khafaji et al.[29]focused on the classification of breast cancer images using new transfer learning techniques,highlighting a research gap in innovative transfer learning techniques specific to breast cancer image classification.Li et al.[30]proposed a dual transfer learning approach for medical image analysis,emphasizing the need for improved methodologies and addressing challenges in transfer learning,which highlights the research gap in the application of this approach for breast cancer detection.Liang et al.[31] introduced an enhanced approach for breast cancer detection by incorporating improved multi-fractal dimension techniques and feature fusion methods,emphasizing the importance of advanced image analysis techniques,and highlighting the research gap in integrating these techniques for breast cancer detection.Additionally,Rahmah et al.[32]conducted a systematic review of computing approaches for breast cancer detection through computer-aided diagnosis,emphasizing the need for a comprehensive understanding of existing approaches and their limitations,which highlights the research gap in developing more effective and accurate computeraided diagnosis systems for breast cancer detection.By incorporating these studies,we provide a clearer understanding of the research landscape and highlight the research gaps that our proposed research aims to address.

    One of the limitations we identified is the reliance on microarray data.While microarray technology has been widely used in cancer research,it is important to acknowledge that the field has seen advancements in other high-throughput technologies such as RNA sequencing.Future studies could explore the effectiveness of the proposed hybrid method on RNA sequencing data to validate its performance across different data modalities.

    5 Conclusion

    This study tackled the challenge of dimensionality within microarray gene expression profiles by introducing a hybrid feature selection approach.This approach integrates the Gray Wolf Optimization(GWO)and Harris Hawks Optimization(HHO)algorithms with the support vector machines(SVM)and K-nearest neighbors(KNN)algorithms.The objective of our method was to enhance the precision of cancer gene selection and classification while simultaneously minimizing the number of genes selected.

    The effectiveness of our hybrid technique was confirmed through the evaluation of multiple cancer microarray datasets.In comparison to other wrapper-based feature selection methods and the standalone GWO and HHO algorithms,our hybrid approach consistently exhibited superior performance in both accuracy and the reduction of selected genes.This underscores the substantial improvements our approach brings not only in terms of performance but also in functionality compared to existing methods.Furthermore,our hybrid approach outperformed recently published hybrid feature selection algorithms,further demonstrating its superiority.

    The theoretical implications of our research lie in the development of a bio-inspired algorithm,the GWO-HHO hybrid,for solving gene selection problems.By combining the strengths of GWO and HHO algorithms,we achieved improved accuracy and efficiency in cancer classification tasks.This contributes to the field of bioinformatics and supports the advancement of gene selection methodologies.

    Practically,our findings have several advantages that can benefit real-world cancer classification problems.The ability of our hybrid method to accurately classify cancer datasets while reducing the number of selected genes holds great potential for clinical applications.The identification of a smaller set of highly informative genes can aid in the development of more targeted and effective diagnostic and therapeutic strategies.

    While our study presents promising results,it is important to acknowledge its limitations.Firstly,our research relied on microarray data,and future investigations should explore the performance of the proposed hybrid method on other data modalities,such as RNA sequencing data,to validate its effectiveness across different technologies.Additionally,our evaluation was based on cross-validation techniques,and further studies should incorporate independent validation on external datasets to assess the generalizability of our method.

    Future research should focus on investigating the integration of the proposed hybrid method with complementary techniques like feature extraction or dimensionality reduction methods,enabling the development of a comprehensive framework for cancer analysis.Additionally,exploring the application of the GWO-HHO hybrid algorithm in other areas of bioinformatics,such as protein structure prediction or biomarker identification,would expand its potential beyond cancer classification.Furthermore,there is a need to consider the development of an interpretable framework that provides insights into the biological relevance of the selected genes,offering a deeper understanding of the underlying mechanisms driving cancer development and progression.Such efforts will contribute to advancements in cancer research and pave the way for improved diagnostic and therapeutic strategies.

    Acknowledgement:The authors extend their appreciation to the Deputyship for Research and Innovation,“Ministry of Education”in Saudi Arabia for supporting this research(IFKSUOR3-014-3).

    Funding Statement:The authors extend their appreciation to the Deputyship for Research and Innovation,“Ministry of Education”in Saudi Arabia for funding this research(IFKSUOR3-014-3).

    Author Contributions:Hala AlShamlan,and Halah AlMazrua equally contributed to the manuscript and revised the codes.All authors approved the manuscript.

    Availability of Data and Materials:The microarray dataset used in this study is available at the following website:Https://csse.szu.edu.cn/staff/zhuzx/datasets.html.

    Conflicts of Interest:All authors declare that they have no competing interests.

    熟妇人妻久久中文字幕3abv| 久久人妻av系列| 51午夜福利影视在线观看| 亚洲第一av免费看| 少妇熟女aⅴ在线视频| 法律面前人人平等表现在哪些方面| 18禁裸乳无遮挡免费网站照片 | 久久精品夜夜夜夜夜久久蜜豆 | 国产精品永久免费网站| 日本一区二区免费在线视频| 久久久久久大精品| 国产三级在线视频| 激情在线观看视频在线高清| 久久久国产成人精品二区| av超薄肉色丝袜交足视频| 亚洲av美国av| 无遮挡黄片免费观看| 一级黄色大片毛片| 国内精品久久久久精免费| 人人妻人人澡人人看| 日韩欧美免费精品| 男女下面进入的视频免费午夜 | 午夜福利在线在线| 成人精品一区二区免费| 欧美黄色淫秽网站| 别揉我奶头~嗯~啊~动态视频| 极品教师在线免费播放| 人人妻人人澡欧美一区二区| 亚洲一区中文字幕在线| 丰满人妻熟妇乱又伦精品不卡| 久99久视频精品免费| 日本a在线网址| 国产精品免费一区二区三区在线| 18禁观看日本| 一本综合久久免费| 夜夜躁狠狠躁天天躁| 午夜老司机福利片| 亚洲精华国产精华精| 国产黄a三级三级三级人| 亚洲专区中文字幕在线| 国产精品99久久99久久久不卡| 国产成人欧美| 国产片内射在线| 国产极品粉嫩免费观看在线| 亚洲精品中文字幕一二三四区| 亚洲,欧美精品.| 变态另类成人亚洲欧美熟女| 女人爽到高潮嗷嗷叫在线视频| 日韩欧美在线二视频| 久久久精品国产亚洲av高清涩受| 俄罗斯特黄特色一大片| 久久99热这里只有精品18| www.自偷自拍.com| 欧美日韩黄片免| 欧美精品啪啪一区二区三区| 91老司机精品| 中文字幕最新亚洲高清| 欧美一级毛片孕妇| 欧美黑人巨大hd| 日韩欧美免费精品| 一区福利在线观看| 国产精品综合久久久久久久免费| 亚洲va日本ⅴa欧美va伊人久久| 真人做人爱边吃奶动态| 国产97色在线日韩免费| 18禁裸乳无遮挡免费网站照片 | 欧美日韩乱码在线| 亚洲精品色激情综合| 亚洲国产看品久久| 自线自在国产av| 曰老女人黄片| 国产精品野战在线观看| 国产人伦9x9x在线观看| 日韩一卡2卡3卡4卡2021年| tocl精华| 国产成人精品久久二区二区91| 丝袜美腿诱惑在线| 免费电影在线观看免费观看| 男女午夜视频在线观看| 在线观看免费视频日本深夜| 国产免费男女视频| 国产亚洲精品综合一区在线观看 | 精品一区二区三区四区五区乱码| 久久久久九九精品影院| 国产亚洲欧美在线一区二区| 精品乱码久久久久久99久播| 亚洲国产毛片av蜜桃av| 天天躁夜夜躁狠狠躁躁| 老熟妇仑乱视频hdxx| 精品福利观看| 成人永久免费在线观看视频| 99精品在免费线老司机午夜| 一级毛片女人18水好多| svipshipincom国产片| 首页视频小说图片口味搜索| 精品欧美国产一区二区三| 久久久久久久精品吃奶| 亚洲av日韩精品久久久久久密| 国产精品香港三级国产av潘金莲| 91成人精品电影| 少妇粗大呻吟视频| 国产精品乱码一区二三区的特点| 在线观看舔阴道视频| 国产亚洲精品综合一区在线观看 | 亚洲色图 男人天堂 中文字幕| 午夜福利在线观看吧| 亚洲狠狠婷婷综合久久图片| 亚洲无线在线观看| 91成年电影在线观看| 波多野结衣高清作品| 午夜a级毛片| 日韩免费av在线播放| 777久久人妻少妇嫩草av网站| 国产亚洲精品一区二区www| 久久青草综合色| 国产99久久九九免费精品| 正在播放国产对白刺激| 欧美一级毛片孕妇| 国产亚洲欧美98| 成年人黄色毛片网站| 老鸭窝网址在线观看| 欧美日韩亚洲综合一区二区三区_| 久久久久久国产a免费观看| 日韩欧美三级三区| 国产精品久久久久久精品电影 | 亚洲七黄色美女视频| 黄频高清免费视频| 成人亚洲精品一区在线观看| 亚洲中文av在线| 欧美又色又爽又黄视频| 国产精品98久久久久久宅男小说| 777久久人妻少妇嫩草av网站| 亚洲精品av麻豆狂野| 日韩欧美在线二视频| 国产人伦9x9x在线观看| 国产高清videossex| 国产又黄又爽又无遮挡在线| www.熟女人妻精品国产| 亚洲av电影不卡..在线观看| 亚洲欧美精品综合久久99| 夜夜夜夜夜久久久久| 在线观看午夜福利视频| 久久性视频一级片| 男人的好看免费观看在线视频 | 国产高清有码在线观看视频 | 亚洲自拍偷在线| 一级a爱片免费观看的视频| 久久人人精品亚洲av| 亚洲精品一区av在线观看| 国产av在哪里看| 欧美av亚洲av综合av国产av| 亚洲第一av免费看| 亚洲片人在线观看| 他把我摸到了高潮在线观看| 久久天堂一区二区三区四区| 免费电影在线观看免费观看| 免费高清视频大片| 国产午夜精品久久久久久| 一个人免费在线观看的高清视频| 日韩欧美免费精品| 亚洲国产精品成人综合色| 国产亚洲精品久久久久5区| 亚洲av第一区精品v没综合| 国产精品九九99| 久久中文字幕一级| 99国产综合亚洲精品| 99国产精品一区二区蜜桃av| 久久天躁狠狠躁夜夜2o2o| 高清毛片免费观看视频网站| 女警被强在线播放| 日本撒尿小便嘘嘘汇集6| 亚洲一区高清亚洲精品| 特大巨黑吊av在线直播 | 欧美一区二区精品小视频在线| 日韩精品青青久久久久久| 国产男靠女视频免费网站| 国产不卡一卡二| 久久99热这里只有精品18| 他把我摸到了高潮在线观看| 国产亚洲欧美在线一区二区| 搡老妇女老女人老熟妇| 一本久久中文字幕| 免费在线观看成人毛片| 成年免费大片在线观看| 午夜福利成人在线免费观看| 男女视频在线观看网站免费 | 欧美日本亚洲视频在线播放| 亚洲片人在线观看| 天堂√8在线中文| 国产精品自产拍在线观看55亚洲| 丝袜美腿诱惑在线| 99久久无色码亚洲精品果冻| 老鸭窝网址在线观看| 麻豆成人午夜福利视频| 国产精品亚洲av一区麻豆| 欧美中文综合在线视频| 人人妻人人看人人澡| 国产精品国产高清国产av| 亚洲成人久久性| 久久亚洲精品不卡| av有码第一页| 日韩大码丰满熟妇| 在线av久久热| 日本一本二区三区精品| 日本熟妇午夜| 亚洲国产高清在线一区二区三 | 99热只有精品国产| 亚洲精品色激情综合| 性色av乱码一区二区三区2| 久久久久国内视频| 欧美色欧美亚洲另类二区| 国产人伦9x9x在线观看| 久久久久免费精品人妻一区二区 | 亚洲成人精品中文字幕电影| 国产成年人精品一区二区| а√天堂www在线а√下载| 麻豆成人午夜福利视频| 久久久久国产一级毛片高清牌| 国产片内射在线| 欧美精品啪啪一区二区三区| 亚洲成人久久性| 婷婷六月久久综合丁香| 亚洲人成77777在线视频| 欧美成人一区二区免费高清观看 | 韩国精品一区二区三区| 精品国产美女av久久久久小说| 欧美日韩福利视频一区二区| 精品欧美国产一区二区三| bbb黄色大片| 国产av一区在线观看免费| 精品卡一卡二卡四卡免费| 一级a爱视频在线免费观看| 波多野结衣高清无吗| 1024香蕉在线观看| 免费女性裸体啪啪无遮挡网站| 1024手机看黄色片| 中文在线观看免费www的网站 | 国产成人精品久久二区二区免费| 曰老女人黄片| 99国产极品粉嫩在线观看| 亚洲av成人av| 欧美zozozo另类| 非洲黑人性xxxx精品又粗又长| 亚洲avbb在线观看| 免费在线观看日本一区| 少妇熟女aⅴ在线视频| av中文乱码字幕在线| 亚洲成人国产一区在线观看| 满18在线观看网站| 国内精品久久久久久久电影| 免费观看精品视频网站| 成年版毛片免费区| 欧美亚洲日本最大视频资源| 久久婷婷人人爽人人干人人爱| 国产精品精品国产色婷婷| www.www免费av| 中文字幕最新亚洲高清| 欧美日韩瑟瑟在线播放| 日本免费一区二区三区高清不卡| 日韩欧美一区视频在线观看| 99国产综合亚洲精品| 可以在线观看的亚洲视频| 最新美女视频免费是黄的| 国产成+人综合+亚洲专区| 亚洲中文字幕一区二区三区有码在线看 | 波多野结衣巨乳人妻| 国产爱豆传媒在线观看 | 丰满的人妻完整版| 亚洲aⅴ乱码一区二区在线播放 | 好男人在线观看高清免费视频 | 97人妻精品一区二区三区麻豆 | 一个人观看的视频www高清免费观看 | 操出白浆在线播放| 看免费av毛片| 又紧又爽又黄一区二区| 成人三级黄色视频| 午夜福利免费观看在线| 国产成人一区二区三区免费视频网站| 啦啦啦观看免费观看视频高清| 熟女电影av网| 国产精品久久久久久精品电影 | 一区二区三区国产精品乱码| 国产乱人伦免费视频| 中文字幕人成人乱码亚洲影| а√天堂www在线а√下载| 亚洲成a人片在线一区二区| 日日爽夜夜爽网站| 在线观看免费视频日本深夜| 亚洲国产看品久久| 久久久久久久精品吃奶| 婷婷丁香在线五月| 国产免费男女视频| 人人妻人人澡欧美一区二区| 哪里可以看免费的av片| 在线观看免费日韩欧美大片| 国产aⅴ精品一区二区三区波| 亚洲色图av天堂| 99re在线观看精品视频| 男女之事视频高清在线观看| 99久久综合精品五月天人人| 欧美乱色亚洲激情| 最好的美女福利视频网| 90打野战视频偷拍视频| 中文字幕人妻熟女乱码| 久久热在线av| 国产精品99久久99久久久不卡| 亚洲片人在线观看| 一夜夜www| 两人在一起打扑克的视频| 日日夜夜操网爽| 色哟哟哟哟哟哟| 国产精品九九99| x7x7x7水蜜桃| 亚洲专区字幕在线| 久久久久亚洲av毛片大全| 十八禁网站免费在线| 嫁个100分男人电影在线观看| 欧美国产日韩亚洲一区| 久热爱精品视频在线9| 18禁国产床啪视频网站| 给我免费播放毛片高清在线观看| 熟女少妇亚洲综合色aaa.| 久久久久免费精品人妻一区二区 | 国产野战对白在线观看| 99在线人妻在线中文字幕| 香蕉国产在线看| 淫妇啪啪啪对白视频| 欧美成人午夜精品| 少妇被粗大的猛进出69影院| 久久精品国产亚洲av高清一级| 国产成人欧美在线观看| 国产成+人综合+亚洲专区| 成人国产一区最新在线观看| 欧美日韩中文字幕国产精品一区二区三区| 日韩有码中文字幕| 一区二区三区国产精品乱码| 最好的美女福利视频网| 俄罗斯特黄特色一大片| 在线观看午夜福利视频| 午夜久久久在线观看| 成年版毛片免费区| 精品国产美女av久久久久小说| 母亲3免费完整高清在线观看| 成人亚洲精品av一区二区| 久久久久国产精品人妻aⅴ院| 99re在线观看精品视频| 老鸭窝网址在线观看| 淫妇啪啪啪对白视频| 久久精品人妻少妇| 亚洲精品粉嫩美女一区| 操出白浆在线播放| 国产1区2区3区精品| 国产精品久久久久久亚洲av鲁大| 香蕉丝袜av| 久久草成人影院| 欧美国产精品va在线观看不卡| 亚洲国产看品久久| 啪啪无遮挡十八禁网站| 午夜福利成人在线免费观看| 免费无遮挡裸体视频| 在线永久观看黄色视频| 给我免费播放毛片高清在线观看| 啦啦啦观看免费观看视频高清| 99久久久亚洲精品蜜臀av| 琪琪午夜伦伦电影理论片6080| 在线av久久热| 欧美国产精品va在线观看不卡| 18禁黄网站禁片免费观看直播| 老鸭窝网址在线观看| 日本一本二区三区精品| 午夜福利高清视频| 免费女性裸体啪啪无遮挡网站| 午夜福利成人在线免费观看| 国产精品99久久99久久久不卡| 国产精品免费一区二区三区在线| 国产精品亚洲av一区麻豆| 一级a爱视频在线免费观看| 在线免费观看的www视频| 十八禁人妻一区二区| 国产精品一区二区免费欧美| 身体一侧抽搐| 免费高清视频大片| 97超级碰碰碰精品色视频在线观看| 老司机深夜福利视频在线观看| 欧美黑人精品巨大| 一区二区三区精品91| 真人做人爱边吃奶动态| 99国产极品粉嫩在线观看| 亚洲欧美一区二区三区黑人| 日本三级黄在线观看| 黄色 视频免费看| 亚洲七黄色美女视频| 国内久久婷婷六月综合欲色啪| 色哟哟哟哟哟哟| 久久人妻福利社区极品人妻图片| 精品午夜福利视频在线观看一区| 老熟妇乱子伦视频在线观看| 国产欧美日韩一区二区三| 午夜福利视频1000在线观看| 91老司机精品| 亚洲真实伦在线观看| 丝袜美腿诱惑在线| 色尼玛亚洲综合影院| 一进一出抽搐gif免费好疼| 久久中文字幕一级| 亚洲天堂国产精品一区在线| 亚洲中文字幕一区二区三区有码在线看 | 成人午夜高清在线视频 | 亚洲国产精品sss在线观看| 午夜福利在线观看吧| 免费在线观看日本一区| 2021天堂中文幕一二区在线观 | 亚洲av片天天在线观看| 一区二区日韩欧美中文字幕| 一级作爱视频免费观看| 国产亚洲精品综合一区在线观看 | 99久久久亚洲精品蜜臀av| 国产精品免费一区二区三区在线| 91国产中文字幕| 亚洲天堂国产精品一区在线| 悠悠久久av| 精品免费久久久久久久清纯| 日本一本二区三区精品| 伊人久久大香线蕉亚洲五| 狠狠狠狠99中文字幕| 欧美日韩亚洲国产一区二区在线观看| 999久久久精品免费观看国产| 久久中文字幕一级| 黑人欧美特级aaaaaa片| av天堂在线播放| 中国美女看黄片| 国产亚洲精品av在线| 一进一出好大好爽视频| 不卡av一区二区三区| 在线观看一区二区三区| 99精品久久久久人妻精品| 亚洲最大成人中文| 国产午夜精品久久久久久| 婷婷丁香在线五月| 国产午夜福利久久久久久| 少妇裸体淫交视频免费看高清 | 欧美成狂野欧美在线观看| 波多野结衣巨乳人妻| 国产高清激情床上av| 中文字幕精品亚洲无线码一区 | av天堂在线播放| www.www免费av| 女人爽到高潮嗷嗷叫在线视频| 国产精品,欧美在线| 中文字幕精品亚洲无线码一区 | 美女午夜性视频免费| 麻豆成人午夜福利视频| 亚洲专区字幕在线| 精品乱码久久久久久99久播| 可以免费在线观看a视频的电影网站| 老司机靠b影院| 亚洲av熟女| 夜夜躁狠狠躁天天躁| 精华霜和精华液先用哪个| 精品欧美国产一区二区三| 婷婷亚洲欧美| 国产一区二区三区在线臀色熟女| 熟女少妇亚洲综合色aaa.| 国产精品久久久久久亚洲av鲁大| 亚洲国产欧美一区二区综合| 久久久国产成人免费| 国产精品乱码一区二三区的特点| 日本精品一区二区三区蜜桃| 精品日产1卡2卡| 亚洲成人久久性| cao死你这个sao货| 99国产精品99久久久久| 一区福利在线观看| 在线看三级毛片| 夜夜看夜夜爽夜夜摸| 亚洲九九香蕉| 怎么达到女性高潮| 婷婷六月久久综合丁香| 国产97色在线日韩免费| 亚洲精品在线美女| 99国产精品一区二区蜜桃av| 色播在线永久视频| 波多野结衣高清作品| 亚洲avbb在线观看| av天堂在线播放| a在线观看视频网站| 中文资源天堂在线| 宅男免费午夜| 久久草成人影院| 99久久久亚洲精品蜜臀av| 欧美黑人巨大hd| 亚洲国产欧美网| 19禁男女啪啪无遮挡网站| 日本三级黄在线观看| 亚洲国产毛片av蜜桃av| 久99久视频精品免费| 熟女电影av网| 国产成人精品久久二区二区91| 日日夜夜操网爽| 日本精品一区二区三区蜜桃| 亚洲av电影不卡..在线观看| 午夜福利在线在线| 久久九九热精品免费| 激情在线观看视频在线高清| 国产精品av久久久久免费| 欧美最黄视频在线播放免费| 免费电影在线观看免费观看| 亚洲国产中文字幕在线视频| 亚洲真实伦在线观看| 免费在线观看黄色视频的| 夜夜看夜夜爽夜夜摸| av超薄肉色丝袜交足视频| 美国免费a级毛片| 女性生殖器流出的白浆| 亚洲av电影不卡..在线观看| 色在线成人网| 18禁黄网站禁片午夜丰满| 亚洲精品中文字幕在线视频| 色精品久久人妻99蜜桃| 欧美 亚洲 国产 日韩一| 欧美黑人欧美精品刺激| 亚洲国产中文字幕在线视频| 亚洲真实伦在线观看| 日本一区二区免费在线视频| 欧美日韩黄片免| 国产成人影院久久av| 曰老女人黄片| 免费观看人在逋| 亚洲av成人一区二区三| 亚洲精品国产精品久久久不卡| 亚洲欧洲精品一区二区精品久久久| 美女午夜性视频免费| 国产日本99.免费观看| 不卡一级毛片| 午夜福利视频1000在线观看| 一二三四在线观看免费中文在| 亚洲七黄色美女视频| 搡老熟女国产l中国老女人| 免费看美女性在线毛片视频| 一本大道久久a久久精品| 成在线人永久免费视频| 成人精品一区二区免费| 国产蜜桃级精品一区二区三区| 精品日产1卡2卡| 无遮挡黄片免费观看| 老司机午夜十八禁免费视频| 久久精品人妻少妇| 麻豆成人午夜福利视频| 精品国产亚洲在线| 亚洲五月婷婷丁香| 女同久久另类99精品国产91| 99riav亚洲国产免费| 黄色成人免费大全| 在线观看午夜福利视频| 国产精品免费视频内射| av超薄肉色丝袜交足视频| 欧美日韩精品网址| 最近最新中文字幕大全免费视频| 亚洲va日本ⅴa欧美va伊人久久| 久久中文字幕一级| 一本综合久久免费| 国产亚洲精品久久久久5区| 91在线观看av| 国产精品爽爽va在线观看网站 | 亚洲欧美精品综合一区二区三区| 亚洲av熟女| 欧美不卡视频在线免费观看 | 日本免费a在线| 亚洲一区二区三区不卡视频| 免费看美女性在线毛片视频| 国产亚洲精品一区二区www| 精品免费久久久久久久清纯| 欧美精品啪啪一区二区三区| 午夜亚洲福利在线播放| 欧美国产精品va在线观看不卡| 亚洲av熟女| 亚洲午夜精品一区,二区,三区| 久久久久久久精品吃奶| 又黄又爽又免费观看的视频| av视频在线观看入口| 亚洲色图av天堂| 一个人观看的视频www高清免费观看 | 正在播放国产对白刺激| 久久天堂一区二区三区四区| 最新美女视频免费是黄的| 国产精品免费一区二区三区在线| 久久国产精品人妻蜜桃| 精品乱码久久久久久99久播| 日本一本二区三区精品| 亚洲av成人av| 真人做人爱边吃奶动态| 淫秽高清视频在线观看| 久久精品国产综合久久久| 日本免费a在线| 激情在线观看视频在线高清| 亚洲精华国产精华精| 在线观看66精品国产| tocl精华| 伊人久久大香线蕉亚洲五| 很黄的视频免费| 色在线成人网| 久久草成人影院| 午夜福利欧美成人| www.熟女人妻精品国产| 日韩欧美三级三区| 免费在线观看日本一区| 久久久国产精品麻豆| 91九色精品人成在线观看| 日日干狠狠操夜夜爽| av免费在线观看网站| 亚洲成av人片免费观看| 大型黄色视频在线免费观看| 97超级碰碰碰精品色视频在线观看| 欧美av亚洲av综合av国产av| 老司机福利观看|