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HHO optimized support vector machine classifier for traditional Chinese medicine syndrome differentiation of diabetic retinopathy

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International Journal of Ophthalmology(English edition) 2024年第6期17卷 991-1000页

作者： Li Xiao Cheng-Wu Wang Ying Deng Yi-Jing Yang Jing Lu Jun-Feng Yan Qing-Hua Peng School of Chinese Medicine Hunan University of Chinese MedicineChangsha 410208Hunan ProvinceChina School of Informatics Hunan University of Chinese MedicineChangsha 410208Hunan ProvinceChina Hunan Provincial Key Laboratory for Prevention and Treatment of Ophthalmology and Otolaryngology Diseases with Chinese Medicine Hunan University of Chinese MedicineChangsha 410208Hunan ProvinceChina Hunan Provincial Engineering and Technological Research Center for Prevention and Treatment of Ophthalmology and Otolaryngology Diseases with Chinese Medicine and Protecting Visual Function Hunan University of Chinese MedicineChangsha 410208Hunan ProvinceChina

AIM:To develop a classifier for traditional Chinese medicine(TCM)syndrome differentiation of diabetic retinopathy(DR),using optimized machine learning algorithms,which can provide the basis for TCM objective and intelligent syndrome differentiation.METHODS:Collated data on real-world DR cases were collected.A variety of machine learning methods were used to construct TCM syndrome classification model,and the best performance was selected as the basic model.Genetic Algorithm(GA)was used for feature selection to obtain the optimal feature combination.Harris Hawk Optimization(HHO)was used for parameter optimization,and a classification model based on feature selection and parameter optimization was constructed.The performance of the model was compared with other optimization algorithms.The models were evaluated with accuracy,precision,recall,and F1 score as indicators.RESULTS:Data on 970 cases that met screening requirements were collected.support vector machine(SVM)was the best basic classification model.The accuracy rate of the model was 82.05%,the precision rate was 82.34%,the recall rate was 81.81%,and the F1 value was 81.76%.After GA screening,the optimal feature combination contained 37 feature values,which was consistent with TCM clinical practice.The model based on optimal combination and SVM(GA_SVM)had an accuracy improvement of 1.92%compared to the basic classifier.SVM model based on HHO and GA optimization(HHO_GA_SVM)had the best performance and convergence speed compared with other optimization algorithms.Compared with the basic classification model,the accuracy was improved by 3.51%.CONCLUSION:HHO and GA optimization can improve the model performance of SVM in TCM syndrome differentiation of DR.It provides a new method and research idea for TCM intelligent assisted syndrome differentiation.

关键词： traditional Chinese medicine diabetic retinopathy Harris Hawk Optimization support vector machine syndrome differentiation

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Comparison of debris flow susceptibility assessment methods:support vector machine,particle swarm optimization,and feature selection techniques

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Journal of Mountain Science 2024年第2期21卷 397-412页

作者： ZHAO Haijun WEI Aihua MA Fengshan DAI Fenggang JIANG Yongbing LI Hui Key Laboratory of Shale Gas and Geoengineering Institute of Geology and GeophysicsChinese Academy of SciencesBeijing 100029China Innovation Academy for Earth Science Chinese Academy of SciencesBeijing 100029China Hebei Province Key Laboratory of Sustained Utilization&Development of Water Recourse Hebei GEO UniversityShijiazhuang 050031China School of Water Resources and Environment Hebei GEO UniversityShijiazhuang 050031China The Sixth Geological Brigade of Geological Mineral Exploration and Development Bureau of Hebei Province Shijiazhuang 050085China

The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning algorithms.In total,133 historical debris flow records and 16 related factors were selected.The support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step process.First,the particle swarm optimization(PSO)algorithm was employed to select the SVM model hyperparameters.Second,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,respectively.Three statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the models.The results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM models.Moreover,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing processes.It was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment results.Moreover,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning algorithms.The high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intensive human activities and heavy rainfall events.

关键词： Chengde Feature selection support vector machine Particle swarm optimization Principal component analysis Debris flow susceptibility

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Enhanced Steganalysis for Color Images Using Curvelet Features and support vector machine

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Computers, Materials & Continua 2024年第1期78卷 1311-1328页

作者： Arslan Akram Imran Khan Javed Rashid Mubbashar Saddique Muhammad Idrees Yazeed Yasin Ghadi Abdulmohsen Algarni Faculty of Computer Science and Information Technology Department of Computer ScienceSuperior UniversityLahore54000Pakistan machine Learning Code(MLC)Lab Meharban HouseHouse Number 209Zafar ColonyOkara56300Pakistan Information Technology Services University of OkaraOkara56310Pakistan Department of Computer Science&Engineering University of Engineering&Technology LahoreNarowal CampusNarowal51601Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain11671Abu Dhabi Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

Algorithms for steganography are methods of hiding data transfers in media files.Several machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image analysis.Images with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for classification.To address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of images.support vector machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or cover.The Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the proposedmethod.Using WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.

关键词： Curvelets fast fourier transformation support vector machine high pass filters steganography

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Performance Analysis of support vector machine (SVM) on Challenging Datasets for Forest Fire Detection

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International Journal of Communications, Network and System Sciences 2024年第2期17卷 11-29页

作者： Ankan Kar Nirjhar Nath Utpalraj Kemprai Aman Department of Computer Science Chennai Mathematical Institute Chennai India Department of Mathematics & Computer Science Chennai Mathematical Institute Chennai India Department of Data Science Chennai Mathematical Institute Chennai India

This article delves into the analysis of performance and utilization of support vector machines (SVMs) for the critical task of forest fire detection using image datasets. With the increasing threat of forest fires to ecosystems and human settlements, the need for rapid and accurate detection systems is of utmost importance. SVMs, renowned for their strong classification capabilities, exhibit proficiency in recognizing patterns associated with fire within images. By training on labeled data, SVMs acquire the ability to identify distinctive attributes associated with fire, such as flames, smoke, or alterations in the visual characteristics of the forest area. The document thoroughly examines the use of SVMs, covering crucial elements like data preprocessing, feature extraction, and model training. It rigorously evaluates parameters such as accuracy, efficiency, and practical applicability. The knowledge gained from this study aids in the development of efficient forest fire detection systems, enabling prompt responses and improving disaster management. Moreover, the correlation between SVM accuracy and the difficulties presented by high-dimensional datasets is carefully investigated, demonstrated through a revealing case study. The relationship between accuracy scores and the different resolutions used for resizing the training datasets has also been discussed in this article. These comprehensive studies result in a definitive overview of the difficulties faced and the potential sectors requiring further improvement and focus.

关键词： support vector machine Challenging Datasets Forest Fire Detection Classification

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support-vector-machine-based Adaptive Scheduling in Mode 4 Communication

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Computers, Materials & Continua 2022年第11期73卷 3319-3331页

作者： Muhammad Adnan Khan Ahmed Abu-Khadrah Shahan Yamin Siddiqui Taher M.Ghazal Tauqeer Faiz Munir Ahmad Sang-Woong Lee Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea College of Computing&Informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia School of Computer Science Minhaj University LahoreLahore54000Pakistan Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)43600BangiSelangorMalaysia School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan

Vehicular ad-hoc networks(VANETs)are mobile networks that use and transfer data with vehicles as the network nodes.Thus,VANETs are essentially mobile ad-hoc networks(MANETs).They allow all the nodes to communicate and connect with one another.One of the main requirements in a VANET is to provide self-decision capability to the vehicles.Cognitive memory,which stores all the previous routes,is used by the vehicles to choose the optimal route.In networks,communication is crucial.In cellular-based vehicle-to-everything(CV2X)communication,vital information is shared using the cooperative awareness message(CAM)that is broadcast by each vehicle.Resources are allocated in a distributed manner,which is known as Mode 4 communication.The support vector machine(SVM)algorithm is used in the SVM-CV2X-M4 system proposed in this study.The k-fold model with different values of k is used to evaluate the accuracy of the SVM-CV2XM4 system.The results show that the proposed system achieves an accuracy of 99.6%.Thus,the proposed system allows vehicles to choose the optimal route and is highly convenient for users.

关键词： Mode-4 communication ad-hoc vehicular network CV2X support vector machine

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Stability prediction of hard rock pillar using support vector machine optimized by three metaheuristic algorithms

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矿业科学技术学报:英文版 2023年第8期33卷 1019-1036页

作者： Chuanqi Li Jian Zhou Kun Du Daniel Dias School of Resources and Safety Engineering Central South UniversityChangsha 410083China Laboratory 3SR CNRS UMR 5521Grenoble Alpes UniversityGrenoble 38000France

Hard rock pillar is one of the important structures in engineering design and excavation in underground mines.Accurate and convenient prediction of pillar stability is of great significance for underground space safety.This paper aims to develop hybrid support vector machine(SVM)models improved by three metaheuristic algorithms known as grey wolf optimizer(GWO),whale optimization algorithm(WOA)and sparrow search algorithm(SSA)for predicting the hard rock pillar stability.An integrated dataset containing 306 hard rock pillars was established to generate hybrid SVM models.Five parameters including pillar height,pillar width,ratio of pillar width to height,uniaxial compressive strength and pillar stress were set as input parameters.Two global indices,three local indices and the receiver operating characteristic(ROC)curve with the area under the ROC curve(AUC)were utilized to evaluate all hybrid models’performance.The results confirmed that the SSA-SVM model is the best prediction model with the highest values of all global indices and local indices.Nevertheless,the performance of the SSASVM model for predicting the unstable pillar(AUC:0.899)is not as good as those for stable(AUC:0.975)and failed pillars(AUC:0.990).To verify the effectiveness of the proposed models,5 field cases were investigated in a metal mine and other 5 cases were collected from several published works.The validation results indicated that the SSA-SVM model obtained a considerable accuracy,which means that the combination of SVM and metaheuristic algorithms is a feasible approach to predict the pillar stability.

关键词： Underground pillar stability Hard rock support vector machine Metaheuristic algorithms

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Differentially Private support vector machines with Knowledge Aggregation

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Computers, Materials & Continua 2024年第3期78卷 3891-3907页

作者： Teng Wang Yao Zhang Jiangguo Liang Shuai Wang Shuanggen Liu School of Cyberspace Security Xi’an University of Posts and TelecommunicationsXi’an710121China

With the widespread data collection and processing,privacy-preserving machine learning has become increasingly important in addressing privacy risks related to individuals.support vector machine(SVM)is one of the most elementary learning models of machine learning.Privacy issues surrounding SVM classifier training have attracted increasing attention.In this paper,we investigate Differential Privacy-compliant Federated machine Learning with Dimensionality Reduction,called FedDPDR-DPML,which greatly improves data utility while providing strong privacy guarantees.Considering in distributed learning scenarios,multiple participants usually hold unbalanced or small amounts of data.Therefore,FedDPDR-DPML enables multiple participants to collaboratively learn a global model based on weighted model averaging and knowledge aggregation and then the server distributes the global model to each participant to improve local data utility.Aiming at high-dimensional data,we adopt differential privacy in both the principal component analysis(PCA)-based dimensionality reduction phase and SVM classifiers training phase,which improves model accuracy while achieving strict differential privacy protection.Besides,we train Differential privacy(DP)-compliant SVM classifiers by adding noise to the objective function itself,thus leading to better data utility.Extensive experiments on three high-dimensional datasets demonstrate that FedDPDR-DPML can achieve high accuracy while ensuring strong privacy protection.

关键词： Differential privacy support vector machine knowledge aggregation data utility

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On the prediction of methane adsorption in shale using grey wolf optimizer support vector machine approach

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Petroleum 2022年第2期8卷 264-269页

作者： Rahmad Syah Mohammad Hossein Towfighi Naeem Reza Daneshfar Hossein Dehdar Bahram Soltani Soulgani DS&CI Research Group Universitas Medan AreaMedanIndonesia Department of Petroleum Engineering Faculty of Chemical EngineeringTarbiat Modares UniversityP.O.Box14155-4838TehranIran Department of Petroleum Engineering Ahwaz Faculty of Petroleum EngineeringPetroleum University of Technology(PUT)AhwazIran

With the advancement of technology,gas shales have become one of the most prominent energy sources all over the world.Therefore,estimating the amount of adsorbed gas in shale resources is necessary for the technical and economic foresight of the production operations.This paper presents a novel machine learning method called grey wolf optimizer support vector machine(GWO-SVM)to predict adsorbed gas.For this purpose,a data set containing temperature,pressure,total organic carbon(TOC),and humidity has been collected from several sources,and the GWO-SVM model was created based on it.The results show that this model has R-squared and root mean square error equal to 0.982 and 0.08,respectively.Also,the results ensure that the proposed model gives an excellent prediction of the amount of adsorbed gas compared to previously proposed models.Besides,according to the sensitivity analysis,among the input parameters,humidity has the highest effect on gas adsorption.

关键词： Gas adsorption Shale machine learning Model support vector machine Grey wolf optimizer

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Lung Cancer Detection Using Modified AlexNet Architecture and support vector machine

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Computers, Materials & Continua 2023年第1期74卷 2039-2054页

作者： Iftikhar Naseer Tehreem Masood Sheeraz Akram Arfan Jaffar Muhammad Rashid Muhammad Amjad Iqbal Faculty of Computer Science and Information Technology The Superior UniversityLahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Faculty of IT University of Central PunjabLahore54100Pakistan

Lung cancer is the most dangerous and death-causing disease indicated by the presence of pulmonary nodules in the lung.It is mostly caused by the instinctive growth of cells in the lung.Lung nodule detection has a significant role in detecting and screening lung cancer in Computed tomography(CT)scan images.Early detection plays an important role in the survival rate and treatment of lung cancer patients.Moreover,pulmonary nodule classification techniques based on the convolutional neural network can be used for the accurate and efficient detection of lung cancer.This work proposed an automatic nodule detection method in CT images based on modified AlexNet architecture and support vector machine(SVM)algorithm namely LungNet-SVM.The proposed model consists of seven convolutional layers,three pooling layers,and two fully connected layers used to extract features.support vector machine classifier is applied for the binary classification of nodules into benign andmalignant.The experimental analysis is performed by using the publicly available benchmark dataset Lung nodule analysis 2016(LUNA16).The proposed model has achieved 97.64%of accuracy,96.37%of sensitivity,and 99.08%of specificity.A comparative analysis has been carried out between the proposed LungNet-SVM model and existing stateof-the-art approaches for the classification of lung cancer.The experimental results indicate that the proposed LungNet-SVM model achieved remarkable performance on a LUNA16 dataset in terms of accuracy.

关键词： Lung cancer alexnet luna16 computed tomography support vector machine

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An enhanced segmentation technique and improved support vector machine classifier for facial image recognition

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International Journal of Intelligent Computing and Cybernetics 2022年第2期15卷 302-317页

作者： Rangayya Virupakshappa Nagabhushan Patil Sharnbasva University KalaburagiIndia Poojya Doddappa Appa College of Engineering GulbargaIndia

Purpose-One of the challenging issues in computer vision and pattern recognition is face image recognition.Several studies based on face recognition were introduced in the past decades,but it has few classification issues in terms of poor performances.Hence,the authors proposed a novel model for face recognition.Design/methodology/approach-The proposed method consists of four major sections such as data acquisition,segmentation,feature extraction and recognition.Initially,the images are transferred into grayscale images,and they pose issues that are eliminated by resizing the input images.The contrast limited adaptive histogram equalization(CLAHE)utilizes the image preprocessing step,thereby eliminating unwanted noise and improving the image contrast level.Second,the active contour and level set-based segmentation(ALS)with neural network(NN)or ALS with NN algorithm is used for facial image segmentation.Next,the four major kinds of feature descriptors are dominant color structure descriptors,scale-invariant feature transform descriptors,improved center-symmetric local binary patterns(ICSLBP)and histograms of gradients(HOG)are based on clour and texture features.Finally,the support vector machine(SVM)with modified random forest(MRF)model for facial image recognition.Findings-Experimentally,the proposed method performance is evaluated using different kinds of evaluation criterions such as accuracy,similarity index,dice similarity coefficient,precision,recall and F-score results.However,the proposed method offers superior recognition performances than other state-of-art methods.Further face recognition was analyzed with the metrics such as accuracy,precision,recall and F-score and attained 99.2,96,98 and 96%,respectively.Originality/value-The good facial recognition method is proposed in this research work to overcome threat to privacy,violation of rights and provide better security of data.

关键词： Face recognition Active contour and Level set-based segmentation Neural network algorithm support vector machine Modified random forest classifier

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