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A Self-tuning Multiobjective Genetic algorithm with Application in the svm Classification

Procedia Computer Science 2019年 150卷 503-510页

作者： L.A. Demidova M.M. Egin R.V. Tishkin Ryazan State Radio Engineering University 59/1 Gagarina Str. Ryazan 390005 Russia Limited Liability Company “Areal-98” 18 Lesteva 18 Moscow 115162 Russia

In this paper we suggest the self-tuning multiobjective genetic algorithm (STMGA) based on the NSGA-II. This new algorithm is aimed to improve the svm classification quality. The quality classification indicators such as overall accuracy, specificity, sensitivity and a number of support vectors represent the objective functions in the STMGA. The ways for realizing the self-tuning of the such STMGA parameters as the crossover probability, the crossover distribution index and the mutation distribution index have been proposed and investigated. The considered STMGA is more flexible in the context of selecting its parameters’ values and allows to refuse from the use of the parameters’ values which are set manually. In the case of the radial basis kernel function used for the svm classifier development, the STMGA finds the Pareto-front of such parameters values as the regularization parameter value and the Gaussian kernel parameter value which give the best values in the chosen set of the classification quality indicators. The experimental results obtained on the basis of the model and real datasets of loan scoring, medical and technical diagnostics, etc. confirm the efficiency of the proposed STMGA.

关键词： classification svm algorithm genetic algorithm NSGA-II self-tuning algorithm

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Optimizing drying and storage for edible mushrooms: Study on gamma irradiation levels, drying temperatures, and packaging materials with svm-based predictions

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Case Studies in Thermal Engineering 2025年 72卷

作者： Ehsan Fartash Naeimi Mohammad Hadi Khoshtaghaza Kemal Çağatay Selvi Mariana Ionescu Soleiman Abbasi Department of Agricultural Machinery and Technologies Engineering Faculty of Agriculture Ondokuz Mayıs University 55139 Samsun Türkiye Department of Biosystems Engineering Faculty of Agriculture Tarbiat Modares University 14115-336 Tehran Iran Department of Biotechnical Systems Faculty of Biotechnical Systems Engineering National University of Science and Technology Politehnica Bucharest 060042 Bucharest Romania Department of Food Science and Technology Faculty of Agriculture Tarbiat Modares University 14115-336 Tehran Iran

The control of dried product quality is crucial for preservation and marketability. In this study, the effects of gamma irradiation doses (0, 1.2, 2.4, and 3.6 kGy) and drying temperatures (50, 60, and 70 °C) on the drying rate, lightness (L∗), and texture firmness of mushroom slices were evaluated. In addition, dried samples were packaged in three materials: polyethylene, polypropylene, and a silicone nanoemulsion-based nanocomposite. After four months, the effect of packaging on L∗ and firmness were assessed. Higher irradiation doses and drying temperatures significantly (P < 0.01) enhanced drying rates, ranging from 0.39 g/g∙min for control samples at 50 °C to 0.95 g/g∙min for 3.6 kGy and 70 °C. Higher irradiation doses and lower temperatures yielded better L∗ values (74–78) and texture firmness (41–50 N). Packaging materials and drying conditions significantly affected (P < 0.01) texture firmness, while packaging showed no significant effect (P > 0.01) on L∗. The Support Vector Machine (svm) algorithm accurately predicted change in L∗ and texture firmness after six months of storage, with the Pearson universal kernel producing the highest correlation coefficients (0.996, 1.000, and 1.000). This kernel outperformed the others in estimating parameter changes within the integrated svm-based modeling framework. Overall, gamma irradiation effectively reduced drying time while maintaining product quality, presenting a viable alternative to expensive industrial drying. Nanocomposite packaging preserved the appearance characteristics of the dried mushrooms, and the svm algorithm demonstrated strong potential for predicting quality changes prior to processing.

关键词： Food irradiation Drying rate Combined technology Nanocomposite packaging svm algorithm

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RETRACTED: Analysis of regression prediction model of competitive sports based on svm and artificial intelligence (Retracted Article)

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第4期39卷 5859-5869页

作者： Wang, Jun Qu, Hongjun Qingdao Univ Technol Qingdao 266520 Shandong Peoples R China Shandong Univ Finance & Econ Jinan Shandong Peoples R China

The training effect is not only affected by many environmental disturbance factors, but also related to various factors such as the athlete itself. In this paper, the author analyze the regression prediction model of competitive sports based on svm and artificial intelligence. Traditional statistical modeling simply compares existing data between players and compares them between data. Moreover, it is unable to formulate corresponding tactical strategies according to the situation of the opponent, and targeted training to strengthen the level of individual sports *** com-paring the effects of several kernel functions on the svm modeling side, it is found that the RBF kernel function can make the svm's prediction performance the best when dealing with the speed prediction problem. The experimental results show that this parameter optimization method can significantly improve the performance of the svm regression machine. The prediction model based on support vector machine can effectively improve the prediction direction. Using artificial intelligence and image capture technology in sports can effectively improve the statistical efficiency and prediction effect of competition.

关键词： Artificial intelligence model building svm algorithm machine learning

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Performance prediction and optimization for healthcare enterprises in the context of the COVID-19 pandemic: an intelligent DEA-svm model

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2022年第5期44卷 3778-3791页

作者： Huang, He Zhong, Liwei Shen, Ting Wang, Huixin Univ Shanghai Sci & Technol Business Sch Shanghai Peoples R China Shanghai Univ Tradit Chinese Med Shanghai Municipal Hosp Tradit Chinese Med Shanghai Peoples R China

The coronavirus disease (COVID-19) pandemic has caused significant changes in the external environment of enterprises, resulting in tremendous negative impacts. Accordingly, the irregular fluctuation of business data poses a critical challenge to traditional approaches. Therefore, to combat the effects of the COVID-19 pandemic, an effective model is required to proactively predict an enterprise's performance and simultaneously generate scientific performance optimization solutions. Consequently, at the intersection of artificial intelligence algorithms, operations research, and management science, an intelligent DEA-svm model, which has a theoretical contribution, is developed in this study. The capabilities of this model are verified through sufficient numerical experiments. On the one hand, this model outperforms traditional algorithms in prediction accuracy. On the other hand, effective performance optimization solutions for low-performance enterprises are obtained from the input-output perspective. Moreover, the application value of this model is reflected in its successful implementation in the healthcare industry. Thus, it is a user-friendly tool for realizing the stable operation of enterprises in the context of the COVID-19 pandemic.

关键词： svm algorithm Data envelopment analysis Healthcare management Performance prediction Performance optimization

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Credit risk assessment of small and medium-sized enterprises in supply chain finance based on svm and BP neural network

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NEURAL COMPUTING & APPLICATIONS 2022年第15期34卷 12467-12478页

作者： Zhao, Jingfeng Li, Bo North China Univ Water Resources & Elect Power Sch Management & Econ Zhengzhou 450046 Henan Peoples R China

Our country's market economy is composed of enterprises. However, due to their inherent credit deficiencies and high risks of management, it is very difficult for them to obtain financing support. Based on this, this article studies Error Back Propagation (BP) to establish (SMEs). Based on the relevant concepts of the supply chain management budget model, it explores the main factors influencing the financial impact of SMEs and the benefits of the supply chain budget in solving problems expenditure of SMEs, support vector machine is mainly based on solving the main credit risks of small and medium-sized enterprises, such as poor information transparency, low credit and various risk unknown factors. BP neural network is an algorithm that takes into account the components of supply chain financial financing. This article first gives a simple background and theoretical introduction to the under the current supply chain finance model, and then proposes to use svm and BP neural network algorithms to build and the model has been trained and tested. After collecting relevant references, we will establish authoritative risk assessment rules in accordance with this article according to these standards. These experts are mainly people with many years of experience in the financial industry, and they also have a certain influence in the industry. The risk assessment established for this can be the analysis of factors such as risk indicators and government intervention in this experiment.

关键词： svm algorithm BP neural network algorithm Supply chain finance Small and medium enterprises Credit risk assessment

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Modelling of supercapacitors based on svm and PSO algorithms

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IET ELECTRIC POWER APPLICATIONS 2018年第4期12卷 502-507页

作者： Ding, Shichuan Hang, Jun Wei, Baolei Wang, Qunjing Anhui Univ Sch Elect Engn & Automat Hefei Anhui Peoples R China Anhui Univ Natl Engn Lab Energy Saving Motor & Control Tech Hefei Anhui Peoples R China

Supercapacitors present an attractive energy storage alternative for high-performance applications due to their compact size and high-power density. Therefore, the supercapacitors have broad prospects for the development in the field of electric vehicles and renewable energy. To describe the output characteristics of the supercapacitors with high accuracy for the simulation research and practical application, a dynamic modelling method is proposed for the supercapacitors based on support vector machine (svm) and particle swarm optimisation (PSO) algorithm. In this study, the svm is used to predict the output voltage of the supercapacitors with the key parameters (temperature, current and initial voltage). The PSO algorithm is adopted to optimise the parameters of the svm to improve the performance of the dynamic modelling. An experimental platform is established, where an electric machine drive system powered by the supercapacitors is controlled to operate at frequent acceleration and deceleration modes, thus leading to the frequent charging and discharging of the supercapacitors. The experimental data is collected to validate the effectiveness of the proposed method. The results show that the proposed method can effectively predict the output voltage of the supercapacitors.

关键词： supercapacitors particle swarm optimisation support vector machines power engineering computing electric drives machine control supercapacitors modelling svm algorithm PSO algorithm energy storage dynamic modelling method support vector machine algorithm particle swarm optimisation algorithm output voltage prediction temperature current initial voltage parameter optimization electric machine drive system frequent acceleration mode frequent mode frequent supercapacitor charging frequent supercapacitor discharging

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Plateau underground engineering geological safety risk assessment based on Bootstrap-svm-BPNN

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PROCEEDINGS OF THE INDIAN NATIONAL SCIENCE ACADEMY 2024年 1-13页

作者： Chen, Yongling Hu, Junfeng Yang, Hu Zhan, Chun Wang, Jia China Geol Survey Res Ctr Appl Geol Chengdu 610036 Peoples R China

Underground engineering has become an extremely complex, highly uncertain and dynamic system problem due to the influence of various complex natural geological conditions and many unknown factors. Based on Bootstrap-svm-BPNN optimization model, this paper predicts the underground engineering situation of plateau and analyzes the geological safety risk. The principle of Bootstrap algorithm and svm algorithm is introduced, and the possibility of combining them is realized. The pseudo-sample set was built based on Bootstrap method, and the svm model was used to train and predict it, and the mean and variance of the predicted value were calculated. The random error data set is constructed, and MEA-BPNN is used to train and predict the deviation of random error, so as to realize the hybrid algorithm of Bootstrap-svm-BPNN geological safety prediction. Comparing and analyzing various models, the mixed learning model in this paper has the best prediction effect, R2 is up to 98%, RMSE value is 7.2. Finally, the Bootstrap-svm-BPNN model was applied to practical engineering, and it was found that micro-filled and semi-filled types were still the most common positive samples in the real positive samples. The number of positive samples correctly predicted by interference type increased by 6, and AP increased by 56.15%. The accuracy of the micro-filling model was 41.41%, and the recall rate was 89.13%. The accuracy of the semi-filling type is 57.45%, and the recall rate is 96.43%. The use of the Bootstrap-svm-BPNN optimization model for geological exploration can truly predict various disasters and reduce the negative impact of risk factors, thereby overall lowering the probability level of disaster occurrence.

关键词： Bootstrap-svm-BPNN model MEA-BPNN svm algorithm Pseudo sample set Geological safety

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Classification of Peanut Images Based on Multi-features and svm

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IFAC-PapersOnLine 2018年第17期51卷 726-731页

作者： Li, Zhenbo Niu, Bingshan Peng, Fang Li, Guangyao Yang, Zhaolu Wu, Jing College of Information and Electrical Engineering China Agricultural University Beijing100083 China Key Laboratory of Agricultural Information Acquisition Technology Ministry of Agriculture Beijing100083 China Beijing Engineering and Technology Research Center for Internet of Things in Agriculture Beijing100083 China College of Information and Electrical Engineering Lu Dong University Yantai China

This article provides a method for accurate classification of peanuts. Peanuts can be classified into three categories, including one peanut, two peanuts and three peanuts. Because different peanuts have different prices. The characteristics of peanut images were extracted by three different methods including the convolution neural network of aspect ratio, HOG and Hu invariant moment, and then classifying peanut images respectively by the svm (support vector machine). The accuracy rate of the aspect ratio + svm algorithm, HOG+svm algorithm, Hu invariant moment +svm algorithm respectively is 96.72%, 81.97% and 81.97%, realize the industrialization of peanut classification. © 2018

关键词： Image classification Aspect ratio Oilseeds Support vector machines Accuracy rate Convolution neural network Hu invariant moments Multi features svm algorithm svm(support vector machine) Three categories

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svm for English semantic classification in parallel environment

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INTERNATIONAL JOURNAL OF SPEECH TECHNOLOGY 2017年第3期20卷 487-508页

作者： Vo Ngoc Phu Vo Thi Ngoc Chau Vo Thi Ngoc Tran Duy Tan Univ Inst Res & Dev Da Nang Vietnam Ho Chi Minh City Univ Technol HCMUT Vietnam Natl Univ Comp Sci & Engn Ho Chi Minh City Vietnam Ho Chi Minh City Univ Technol HCMUT Vietnam Natl Univ Sch Ind Management Ho Chi Minh City Vietnam

Semantic analysis is very important and very helpful for many researches and many applications for a long time. svm is a famous algorithm which is used in the researches and applications in many different fields. In this study, we propose a new model using a svm algorithm with Hadoop Map (M)/Reduce (R) for English document-level emotional classification in the Cloudera parallel network environment. Cloudera is also a distributed system. Our English testing data set has 25,000 English documents, including 12,500 English positive reviews and 12,500 English negative reviews. Our English training data set has 90,000 English sentences, including 45,000 English positive sentences and 45,000 English negative sentences. Our new model is tested on the English testing data set and we achieve 63.7% accuracy of sentiment classification on this English testing data set.

关键词： English sentiment classification English document opinion mining svm algorithm Cloudera Parallel environment Distributed system

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An Image Based on svm Classification Technique in Image Retrieval

An Image Based on SVM Classification Technique in Image Retr...

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International Conference on Intelligent and Interactive Systems and Applications (IISA)

作者： Jiang Qianyi Zhong Shaohong Yang Yuwei Cent South Univ Forestry & Technol Coll Comp Sci & Informat Technol Changsha Hunan Peoples R China

ISBN: (纸本)9783319495682;9783319495675

On the frontier of Image Processing, researchers are encountering the challenge of effectively retrieving and using the information contained in the image. As per the prevailing research after the feature extraction of the relevant properties of a high-level image, the resulting image does not add too many features, When operating directly on the image, because of the high Witte sexual performance data are relatively poor, resulting in the traditional classification method does not apply. So this paper uses support vector machine (svm) image classification techniques which can overcome this defect. This paper makes the use of Dense SIFT algorithm to obtain image feature and then build Bag of words model. Subsequently establishing training dictionary database and finally, the test set of images svm classification test. Experimental results show that the use of svm classification accuracy of image retrieval technology enables greatly increased.

关键词： Image retrieval Image classification techniques Feature extraction svm algorithm

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