A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification

Machine learning(ML)practices such as classification have played a very important role in classifying diseases in medical science.Since medical science is a sensitive field,the pre-processing of medical data requires careful handling to make quality clinical decisions.Generally,medical data is considered high-dimensional and complex data that contains many irrelevant and redundant features.These factors indirectly upset the disease prediction and classification accuracy of any ML model.To address this issue,various data pre-processing methods called Feature Selection(FS)techniques have been presented in the literature.However,the majority of such techniques frequently suffer from local minima issues due to large solution space.Thus,this study has proposed a novel wrapper-based Sand Cat SwarmOptimization(SCSO)technique as an FS approach to find optimum features from ten benchmark medical datasets.The SCSO algorithm replicates the hunting and searching strategies of the sand cat while having the advantage of avoiding local optima and finding the ideal solution with minimal control variables.Moreover,K-Nearest Neighbor(KNN)classifier was used to evaluate the effectiveness of the features identified by the proposed SCSO algorithm.The performance of the proposed SCSO algorithm was compared with six state-of-the-art and recent wrapper-based optimization algorithms using the validation metrics of classification accuracy,optimum feature size,and computational cost in seconds.The simulation results on the benchmark medical datasets revealed that the proposed SCSO-KNN approach has outperformed comparative algorithms with an average classification accuracy of 93.96%by selecting 14.2 features within 1.91 s.Additionally,the Wilcoxon rank test was used to perform the significance analysis between the proposed SCSOKNN method and six other algorithms for a p-value less than 5.00E-02.The findings revealed that the proposed algorithm produces better outcomes with an average p-value of 1.82E-02.Moreover,potential future directions are also suggested as a result of the study’s promising findings.

Enhanced Arithmetic Optimization Algorithm Guided by a Local Search for the Feature Selection Problem

High-dimensional datasets present significant challenges for classification tasks.Dimensionality reduction,a crucial aspect of data preprocessing,has gained substantial attention due to its ability to improve classification per-formance.However,identifying the optimal features within high-dimensional datasets remains a computationally demanding task,necessitating the use of efficient algorithms.This paper introduces the Arithmetic Optimization Algorithm(AOA),a novel approach for finding the optimal feature subset.AOA is specifically modified to address feature selection problems based on a transfer function.Additionally,two enhancements are incorporated into the AOA algorithm to overcome limitations such as limited precision,slow convergence,and susceptibility to local optima.The first enhancement proposes a new method for selecting solutions to be improved during the search process.This method effectively improves the original algorithm’s accuracy and convergence speed.The second enhancement introduces a local search with neighborhood strategies(AOA_NBH)during the AOA exploitation phase.AOA_NBH explores the vast search space,aiding the algorithm in escaping local optima.Our results demonstrate that incorporating neighborhood methods enhances the output and achieves significant improvement over state-of-the-art methods.

Hybrid distributed feature selection using particle swarm optimization-mutual information

Feature selection(FS)is a data preprocessing step in machine learning(ML)that selects a subset of relevant and informative features from a large feature pool.FS helps ML models improve their predictive accuracy at lower computational costs.Moreover,FS can handle the model overfitting problem on a high-dimensional dataset.A major problem with the filter and wrapper FS methods is that they consume a significant amount of time during FS on high-dimensional datasets.The proposed“HDFS(PSO-MI):hybrid distribute feature selection using particle swarm optimization-mutual information(PSO-MI)”,is a PSO-based hybrid method that can overcome the problem mentioned above.This method hybridizes the filter and wrapper techniques in a distributed manner.A new combiner is also introduced to merge the effective features selected from multiple data distributions.The effectiveness of the proposed HDFS(PSO-MI)method is evaluated using five ML classifiers,i.e.,logistic regression(LR),k-NN,support vector machine(SVM),decision tree(DT),and random forest(RF),on various datasets in terms of accuracy and Matthew’s correlation coefficient(MCC).From the experimental analysis,we observed that HDFS(PSO-MI)method yielded more than 98%,95%,92%,90%,and 85%accuracy for the unbalanced,kidney disease,emotions,wafer manufacturing,and breast cancer datasets,respectively.Our method shows promising results comapred to other methods,such as mutual information,gain ratio,Spearman correlation,analysis of variance(ANOVA),Pearson correlation,and an ensemble feature selection with ranking method(EFSRank).

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

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.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto 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 ofmore efficient treatment strategies.The proposed hybridmethod 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.

Improved Whale Optimization with Local-Search Method for Feature Selection

Various feature selection algorithms are usually employed to improve classification models’overall performance.Optimization algorithms typically accompany such algorithms to select the optimal set of features.Among the most currently attractive trends within optimization algorithms are hybrid metaheuristics.The present paper presents two Stages of Local Search models for feature selection based on WOA(Whale Optimization Algorithm)and Great Deluge(GD).GD Algorithm is integrated with the WOA algorithm to improve exploitation by identifying the most promising regions during the search.Another version is employed using the best solution found by the WOA algorithm and exploited by the GD algorithm.In addition,disruptive selection(DS)is employed to select the solutions from the population for local search.DS is chosen to maintain the diversity of the population via enhancing low and high-quality solutions.Fifteen(15)standard benchmark datasets provided by the University of California Irvine(UCI)repository were used in evaluating the proposed approaches’performance.Next,a comparison was made with four population-based algorithms as wrapper feature selection methods from the literature.The proposed techniques have proved their efficiency in enhancing classification accuracy compared to other wrapper methods.Hence,the WOA can search effectively in the feature space and choose the most relevant attributes for classification tasks.

SCChOA:Hybrid Sine-Cosine Chimp Optimization Algorithm for Feature Selection

Feature Selection(FS)is an important problem that involves selecting the most informative subset of features from a dataset to improve classification accuracy.However,due to the high dimensionality and complexity of the dataset,most optimization algorithms for feature selection suffer from a balance issue during the search process.Therefore,the present paper proposes a hybrid Sine-Cosine Chimp Optimization Algorithm(SCChOA)to address the feature selection problem.In this approach,firstly,a multi-cycle iterative strategy is designed to better combine the Sine-Cosine Algorithm(SCA)and the Chimp Optimization Algorithm(ChOA),enabling a more effective search in the objective space.Secondly,an S-shaped transfer function is introduced to perform binary transformation on SCChOA.Finally,the binary SCChOA is combined with the K-Nearest Neighbor(KNN)classifier to form a novel binary hybrid wrapper feature selection method.To evaluate the performance of the proposed method,16 datasets from different dimensions of the UCI repository along with four evaluation metrics of average fitness value,average classification accuracy,average feature selection number,and average running time are considered.Meanwhile,seven state-of-the-art metaheuristic algorithms for solving the feature selection problem are chosen for comparison.Experimental results demonstrate that the proposed method outperforms other compared algorithms in solving the feature selection problem.It is capable of maximizing the reduction in the number of selected features while maintaining a high classification accuracy.Furthermore,the results of statistical tests also confirm the significant effectiveness of this method.

A New Hybrid Feature Selection Method Using T-test and Fitness Function

Feature selection(FS)(or feature dimensional reduction,or feature optimization)is an essential process in pattern recognition and machine learning because of its enhanced classification speed and accuracy and reduced system complexity.FS reduces the number of features extracted in the feature extraction phase by reducing highly correlated features,retaining features with high information gain,and removing features with no weights in classification.In this work,an FS filter-type statistical method is designed and implemented,utilizing a t-test to decrease the convergence between feature subsets by calculating the quality of performance value(QoPV).The approach utilizes the well-designed fitness function to calculate the strength of recognition value(SoRV).The two values are used to rank all features according to the final weight(FW)calculated for each feature subset using a function that prioritizes feature subsets with high SoRV values.An FW is assigned to each feature subset,and those with FWs less than a predefined threshold are removed from the feature subset domain.Experiments are implemented on three datasets:Ryerson Audio-Visual Database of Emotional Speech and Song,Berlin,and Surrey Audio-Visual Expressed Emotion.The performance of the F-test and F-score FS methods are compared to those of the proposed method.Tests are also conducted on a system before and after deploying the FS methods.Results demonstrate the comparative efficiency of the proposed method.The complexity of the system is calculated based on the time overhead required before and after FS.Results show that the proposed method can reduce system complexity.

Enhanced Multi-Objective Grey Wolf Optimizer with Lévy Flight and Mutation Operators for Feature Selection

The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be minimized.While it is a multi-objective problem,current methods tend to treat feature selection as a single-objective optimization task.This paper presents enhanced multi-objective grey wolf optimizer with Lévy flight and mutation phase(LMuMOGWO)for tackling feature selection problems.The proposed approach integrates two effective operators into the existing Multi-objective Grey Wolf optimizer(MOGWO):a Lévy flight and a mutation operator.The Lévy flight,a type of random walk with jump size determined by the Lévy distribution,enhances the global search capability of MOGWO,with the objective of maximizing classification accuracy while minimizing the number of selected features.The mutation operator is integrated to add more informative features that can assist in enhancing classification accuracy.As feature selection is a binary problem,the continuous search space is converted into a binary space using the sigmoid function.To evaluate the classification performance of the selected feature subset,the proposed approach employs a wrapper-based Artificial Neural Network(ANN).The effectiveness of the LMuMOGWO is validated on 12 conventional UCI benchmark datasets and compared with two existing variants of MOGWO,BMOGWO-S(based sigmoid),BMOGWO-V(based tanh)as well as Non-dominated Sorting Genetic Algorithm II(NSGA-II)and Multi-objective Particle Swarm Optimization(BMOPSO).The results demonstrate that the proposed LMuMOGWO approach is capable of successfully evolving and improving a set of randomly generated solutions for a given optimization problem.Moreover,the proposed approach outperforms existing approaches in most cases in terms of classification error rate,feature reduction,and computational cost.

SA-MSVM:Hybrid Heuristic Algorithm-based Feature Selection for Sentiment Analysis in Twitter

One of the drastically growing and emerging research areas used in most information technology industries is Bigdata analytics.Bigdata is created from social websites like Facebook,WhatsApp,Twitter,etc.Opinions about products,persons,initiatives,political issues,research achievements,and entertainment are discussed on social websites.The unique data analytics method cannot be applied to various social websites since the data formats are different.Several approaches,techniques,and tools have been used for big data analytics,opinion mining,or sentiment analysis,but the accuracy is yet to be improved.The proposed work is motivated to do sentiment analysis on Twitter data for cloth products using Simulated Annealing incorporated with the Multiclass Support Vector Machine(SA-MSVM)approach.SA-MSVM is a hybrid heuristic approach for selecting and classifying text-based sentimental words following the Natural Language Processing(NLP)process applied on tweets extracted from the Twitter dataset.A simulated annealing algorithm searches for relevant features and selects and identifies sentimental terms that customers criticize.SA-MSVM is implemented,experimented with MATLAB,and the results are verified.The results concluded that SA-MSVM has more potential in sentiment analysis and classification than the existing Support Vector Machine(SVM)approach.SA-MSVM has obtained 96.34%accuracy in classifying the product review compared with the existing systems.

URL Phishing Detection Using Particle Swarm Optimization and Data Mining

The continuous destruction and frauds prevailing due to phishing URLs make it an indispensable area for research.Various techniques are adopted in the detection process,including neural networks,machine learning,or hybrid techniques.A novel detection model is proposed that uses data mining with the Particle Swarm Optimization technique(PSO)to increase and empower the method of detecting phishing URLs.Feature selection based on various techniques to identify the phishing candidates from the URL is conducted.In this approach,the features mined from the URL are extracted using data mining rules.The features are selected on the basis of URL structure.The classification of these features identified by the data mining rules is done using PSO techniques.The selection of features with PSO optimization makes it possible to identify phishing URLs.Using a large number of rule identifiers,the true positive rate for the identification of phishing URLs is maximized in this approach.The experiments show that feature selection using data mining and particle swarm optimization helps tremendously identify the phishing URLs based on the structure of the URL itself.Moreover,it can minimize processing time for identifying the phishing website instead.So,the approach can be beneficial to identify suchURLs over the existing contemporary detecting models proposed before.

Optimization of Sentiment Analysis Using Teaching-Learning Based Algorithm

Feature selection and sentiment analysis are two common studies that are currently being conducted;consistent with the advancements in computing and growing the use of social media.High dimensional or large feature sets is a key issue in sentiment analysis as it can decrease the accuracy of sentiment classification and make it difficult to obtain the optimal subset of the features.Furthermore,most reviews from social media carry a lot of noise and irrelevant information.Therefore,this study proposes a new text-feature selection method that uses a combination of rough set theory(RST)and teaching-learning based optimization(TLBO),which is known as RSTLBO.The framework to develop the proposed RSTLBO includes numerous stages:(1)acquiring the standard datasets(user reviews of six major U.S.airlines)which are used to validate search result feature selection methods,(2)preprocessing of the dataset using text processing methods.This involves applying text processing methods from natural language processing techniques,combined with linguistic processing techniques to produce high classification results,(3)employing the RSTLBO method,and(4)using the selected features from the previous process for sentiment classification using the Support Vector Machine(SVM)technique.Results show an improvement in sentiment analysis when combining natural language processing with linguistic processing for text processing.More importantly,the proposed RSTLBO feature selection algorithm is able to produce an improved sentiment analysis.

Jellyfish Search Optimization with Deep Learning Driven Autism Spectrum Disorder Classification

Autism spectrum disorder(ASD)is regarded as a neurological disorder well-defined by a specific set of problems associated with social skills,recurrent conduct,and communication.Identifying ASD as soon as possible is favourable due to prior identification of ASD permits prompt interferences in children with ASD.Recognition of ASD related to objective pathogenicmutation screening is the initial step against prior intervention and efficient treatment of children who were affected.Nowadays,healthcare and machine learning(ML)industries are combined for determining the existence of various diseases.This article devises a Jellyfish Search Optimization with Deep Learning Driven ASD Detection and Classification(JSODL-ASDDC)model.The goal of the JSODL-ASDDC algorithm is to identify the different stages of ASD with the help of biomedical data.The proposed JSODLASDDC model initially performs min-max data normalization approach to scale the data into uniform range.In addition,the JSODL-ASDDC model involves JSO based feature selection(JFSO-FS)process to choose optimal feature subsets.Moreover,Gated Recurrent Unit(GRU)based classification model is utilized for the recognition and classification of ASD.Furthermore,the Bacterial Foraging Optimization(BFO)assisted parameter tuning process gets executed to enhance the efficacy of the GRU system.The experimental assessment of the JSODL-ASDDC model is investigated against distinct datasets.The experimental outcomes highlighted the enhanced performances of the JSODL-ASDDC algorithm over recent approaches.

A Study on Tropical Land Cover Classification Using ALOS PALSAR 50 m Ortho-Rectified Mosaic Data

The main objective of this study is to find better classifier of mapping tropical land covers using Synthetic Aperture Radar (SAR) imagery. The data used are Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) 50 m ortho-rectified mosaic data. Training data for forest, herbaceous, agriculture, urban and water body in the test area located in Kalimantan were collected. To achieve more accurate classification, a modified slope correction formula was created to calibrate the intensity distortions of SAR data. The accuracy of two classifiers called Sequential Minimal Optimization (SMO) and Random Forest (RF) were applied and compared in this study. We focused on object-based approach due to its capability of providing both spatial and spectral information. Optimal combination of features was selected from 32 sets of features based on layer value, texture and geometry. The overall accuracy of land cover classification using RF classifier and SMO classifier was 46.8% and 55.6% respectively, and that of forest and non-forest classification was 74.4% and 79.4% respectively. This indicates that RF classifier has better performance than SMO classifier.

A Two-Tier Fuzzy Meta-Heuristic Hybrid Optimization for Dynamic Android Malware Detection

Application Programming Interface(API)call feature analysis is the prominent method for dynamic android malware detection.Standard benchmark androidmalware API dataset includes featureswith high dimensionality.Not all features of the data are relevant,filtering unwanted features improves efficiency.This paper proposes fuzzy and meta-heuristic optimization hybrid to eliminate insignificant features and improve the performance.In the first phase fuzzy benchmarking is used to select the top best features,and in the second phase meta-heuristic optimization algorithms viz.,Moth Flame Optimization(MFO),Multi-Verse Optimization(MVO)&Whale Optimization(WO)are run with Machine Learning(ML)wrappers to select the best from the rest.Five ML methods viz.,Decision Tree(DT),Random Forest(RF),K-NearestNeighbors(KNN),Naie Bayes(NB)&NearestCentroid(NC)are compared as wrappers.Several experiments are conducted and among them,the best post reduction accuracy of 98.34% is recorded with 95% elimination of features.The proposed novelmethod outperformed among the existing works on the same dataset.

基于改进人工蜂群算法的Android恶意应用检测

随着互联网和移动终端的飞速发展,手机中存储着很多重要的信息,要保证这些信息安全不被泄露的一个重要方法就是对手机中的恶意应用进行检测与处理。在对恶意应用进行检测前需要对样本进行特征提取,而如何在众多特征中进行有效的选取是恶意应用检测中一个至关重要的过程。文中针对Android平台的应用,参考相关的Android恶意检测方法,建立了一个基于改进人工蜂群算法的Android恶意应用检测模型,通过对特征进行有效的选择,最终得到使分类结果最优的特征组合,从而提高对Android恶意应用检测的检测性能。在静态和动态条件下分别对Android应用特征进行提取,通过多种分类算法对恶意应用检测模型进行检验,结果证实提出的基于改进人工蜂群算法的Android恶意应用检测方法具有可行性与优越性。

Improved Dwarf Mongoose Optimization Algorithm for Feature Selection:Application in Software Fault Prediction Datasets

Feature selection(FS)plays a crucial role in pre-processing machine learning datasets,as it eliminates redundant features to improve classification accuracy and reduce computational costs.This paper presents an enhanced approach to FS for software fault prediction,specifically by enhancing the binary dwarf mongoose optimization(BDMO)algorithm with a crossover mechanism and a modified positioning updating formula.The proposed approach,termed iBDMOcr,aims to fortify exploration capability,promote population diversity,and lastly improve the wrapper-based FS process for software fault prediction tasks.iBDMOcr gained superb performance compared to other well-esteemed optimization methods across 17 benchmark datasets.It ranked first in 11 out of 17 datasets in terms of average classification accuracy.Moreover,iBDMOcr outperformed other methods in terms of average fitness values and number of selected features across all datasets.The findings demonstrate the effectiveness of iBDMOcr in addressing FS problems in software fault prediction,leading to more accurate and efficient models.

Optimal Deep Belief Network Based Lung Cancer Detection and Survival Rate Prediction

The combination of machine learning(ML)approaches in healthcare is a massive advantage designed at curing illness of millions of persons.Several efforts are used by researchers for detecting and providing primary phase insights as to cancer analysis.Lung cancer remained the essential source of disease connected mortality for both men as well as women and their frequency was increasing around the world.Lung disease is the unrestrained progress of irregular cells which begin off in one or both Lungs.The previous detection of cancer is not simpler procedure however if it can be detected,it can be curable,also finding the survival rate is a major challenging task.This study develops an Ant lion Optimization(ALO)with Deep Belief Network(DBN)for Lung Cancer Detection and Classification with survival rate prediction.The proposed model aims to identify and classify the presence of lung cancer.Initially,the proposed model undergoes min-max data normalization approach to preprocess the input data.Besides,the ALO algorithm gets executed to choose an optimal subset of features.In addition,the DBN model receives the chosen features and performs lung cancer classification.Finally,the optimizer is utilized for hyperparameter optimization of the DBN model.In order to report the enhanced performance of the proposed model,a wide-ranging experimental analysis is performed and the results reported the supremacy of the proposed model.

Improved Harmony Search with Optimal Deep Learning Enabled Classification Model

Due to drastic increase in the generation of data,it is tedious to examine and derive high level knowledge from the data.The rising trends of high dimension data gathering and problem representation necessitates feature selection process in several machine learning processes.The feature selection procedure establishes a generally encountered issue of global combinatorial optimization.The FS process can lessen the number of features by the removal of unwanted and repetitive data.In this aspect,this article introduces an improved harmony search based global optimization for feature selection with optimal deep learning(IHSFS-ODL)enabled classification model.The proposed IHSFS-ODL technique intends to reduce the curse of dimensionality and enhance classification outcomes.In addition,the IHSFSODL technique derives an IHSFS technique by the use of local search method with traditional harmony search algorithm(HSA)for global optimization.Besides,ODL based classifier including quantum behaved particle swarm optimization(QPSO)with gated recurrent unit(GRU)is applied for data classification process.The utilization of HSA for the choice of features and QPSO algorithm for hyper parameter tuning processes helps to accomplish maximum classification performance.In order to demonstrate the enhanced outcomes of the IHSFS-ODL technique,a series of simulations were carried out and the results reported the betterment over its recent state of art approaches.

An Improved Evolutionary Algorithm for Data Mining and Knowledge Discovery

Recent advancements in computer technologies for data processing,collection,and storage have offered several chances to improve the abilities in production,services,communication,and researches.Data mining(DM)is an interdisciplinary field commonly used to extract useful patterns from the data.At the same time,educational data mining(EDM)is a kind of DM concept,which finds use in educational sector.Recently,artificial intelligence(AI)techniques can be used for mining a large amount of data.At the same time,in DM,the feature selection process becomes necessary to generate subset of features and can be solved by the use of metaheuristic optimization algorithms.With this motivation,this paper presents an improved evolutionary algorithm based feature subsets election with neuro-fuzzy classification(IEAFSS-NFC)for data mining in the education sector.The presented IEAFSS-NFC model involves data pre-processing,feature selection,and classification.Besides,the Chaotic Whale Optimization Algorithm(CWOA)is used for the selection of the highly related feature subsets to accomplish improved classification results.Then,Neuro-Fuzzy Classification(NFC)technique is employed for the classification of education data.The IEAFSS-NFC model is tested against a benchmark Student Performance DataSet from the UCI repository.The simulation outcome has shown that the IEAFSS-NFC model is superior to other methods.

Colliding Bodies Optimization with Machine Learning Based Parkinson’s Disease Diagnosis

Parkinson’s disease(PD)is one of the primary vital degenerative diseases that affect the Central Nervous System among elderly patients.It affect their quality of life drastically and millions of seniors are diagnosed with PD every year worldwide.Several models have been presented earlier to detect the PD using various types of measurement data like speech,gait patterns,etc.Early identification of PD is important owing to the fact that the patient can offer important details which helps in slowing down the progress of PD.The recently-emerging Deep Learning(DL)models can leverage the past data to detect and classify PD.With this motivation,the current study develops a novel Colliding Bodies Optimization Algorithm with Optimal Kernel Extreme Learning Machine(CBO-OKELM)for diagnosis and classification of PD.The goal of the proposed CBO-OKELM technique is to identify whether PD exists or not.CBO-OKELM technique involves the design of Colliding Bodies Optimization-based Feature Selection(CBO-FS)technique for optimal subset of features.In addition,Water Strider Algorithm(WSA)with Kernel Extreme Learning Machine(KELM)model is also developed for the classification of PD.CBO algorithm is used to elect the optimal set of fea-tures whereas WSA is utilized for parameter tuning of KELM model which alto-gether helps in accomplishing the maximum PD diagnostic performance.The experimental analysis was conducted for CBO-OKELM technique against four benchmark datasets and the model portrayed better performance such as 95.68%,96.34%,92.49%,and 92.36%on Speech PD,Voice PD,Hand PD Mean-der,and Hand PD Spiral datasets respectively.