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作者： Zijian Ma Zhuoyue Wang Zhiqiang Wang Yuheng Lin Yingying Du School of Computer and Information Technology Beijing Jiaotong University Beijing Electronic Science and Technology Institute State Information Center

In recent years, malicious URL attacks against mobile users are increasing, the detection of malicious URLs is of great significance to defend against network attacks. The traditional research on mobile malicious URL detection has the defects of high false positive rate and false positive rate. In response to these questions, this paper mainly constructs a cross-platform applications to classify malicious URLs based on blacklist technology and support vector machines. Moreover, a malicious URL detection system is designed and developed on Android to achieve accurate detection of malicious domain names such as Trojans and viruses. Finally, lots of experiments are done by testing the data of9174 domain names. The accuracy rate is 98.25%, which improves the security of mobile terminals and web application as a whole.

关键词： URL detection svm algorithm Machine learning Feature extraction Model training

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Near-surface snowmelt detection on the Greenland ice sheet from FengYun-3 MWRI data

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第4-Sup期22卷 S8301-S8308页

作者： Wang, Xingdong Wu, Zhankai Li, Xinwu Henan Univ Technol Coll Informat Sci & Engn Zhengzhou Henan Peoples R China Chinese Acad Sci Inst Remote Sensing & Digital Earth Beijing Peoples R China Fujian Normal Univ Key Lab OptoElect Sci & Technol Med Fujian Prov Key Lab Photon Technol Minist Educ Fuzhou Fujian Peoples R China

The melt extent on the Greenland ice sheet plays an important role in energy balance, and the Arctic and global climates. The micro-wave radiation imager (MWRI) is one of the major payloads of Chinese second-generation polar-orbiting meteorological satellite, FengYun-3 (FY-3), and it is similar to the special sensor microwave/image (SSM/I). The cross-polarized gradient ratio (XPGR) is mainly applied in the scanning multichannel microwave radiometer (SMMR) (18 GHz horizontal polarization (18 H) and 37 GHz vertical polarization (37 V)), the advanced microwave scanning radiometer-earth observing system (AMSR-E) (18.7 GHz horizontal polarization (18 H) and 36.5 GHz vertical polarization (36 V)) and SSM/I (19.3 GHz horizontal polarization (19 H) and 37 GHz vertical polarization (37 V)), which increases the differences between dry and wet snow. The hyperplane of support vector machine (svm) is used to detect the melt information based on the XPGR data on the Greenland ice sheet, which has higher detection accuracy comparing with the existing threshold methods in theory. The results were compared with the SSM/I data (threshold = - 0.0154), and the results show that the proposed method (That is XPGR combining with svm) for MWRI data is feasible for the detection of the near-surface snowmelt information on the Greenland ice sheet.

关键词： FengYun-3 satellite Snowmelt detection Greenland ice sheet XPGR algorithm svm algorithm

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An Enhanced Recursive Firefly algorithm for Informative Gene Selection

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2019年第2期10卷 21-33页

作者： Dif, Nassima Elberrichi, Zakaria Univ Djillali Liabes Sidi Bel Abbes EEDIS Lab Sidi Bel Abbes Algeria

Feature selection is the process of identifying good performing combinations of significant features among many possibilities. This preprocess improves the classification accuracy and facilitates the learning task. For this optimization problem, the authors have used a metaheuristics approach. Their main objective is to propose an enhanced version of the firefly algorithm as a wrapper approach by adding a recursive behavior to improve the search of the optimal solution. They applied svm classifier to investigate the proposed method. For the authors experimentations, they have used the benchmark microarray datasets. The results show that the new enhanced recursive FA (RFA) outperforms the standard version with a reduction of dimensionality for all the datasets. As an example, for the leukemia microarray dataset, they have a perfect performance score of 100% with only 18 informative selected genes among the 7,129 of the original dataset. The RFA was competitive compared to other state-of-art approaches and achieved the best results for CNS, Ovarian cancer, MLL, prostate, Leukemia_4c, and lymphoma datasets.

关键词： Classification Data Mining Data Preprocessing Data Reduction Feature Selection Firefly algorithm Metaheuristic Microarray Datasets Recursion svm algorithm

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Research on Personal Credit Risk Assessment based on Machine Learning algorithm

World Scientific Research Journal

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World Scientific Research Journal 2020年第11期6卷 146-156页

作者： Keting Ye

The main purpose of this paper is to use machine learning algorithm to establish different classification models to evaluate and predict personal credit risk. In this paper, we take the data of give me some credit in the kaggle competition as an example. We take the seriousdlqin2yrs variable which is overdue for more than 90 days or worse as the target variable, and take other characteristic variables of the data as independent variables for modeling and analysis. In the stage of data preprocessing, this paper first uses the k-nearest neighbor method to fill in the missing values in the data, then processes the outliers in the data, and tests the multicollinearity among variables. In the process of model construction, logistic regression model is constructed by step-by-step screening method, decision tree model is constructed by cart algorithm, classification prediction model is constructed by svm algorithm, and integrated model is constructed based on three algorithms. With the help of AUC value and ROC curve, by comparing the prediction effect of different models in training data set and test data set, it is found that the integrated learning model performs better, has higher classification effect and has stability.

关键词： Credit evaluation Logistic model Decision tree model svm algorithm Integrated learning

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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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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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Countries Condition of Forestation and Trees Percentage using Machine learning 8

Countries Condition of Forestation and Trees Percentage usin...

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8th International Conference on System Modeling and Advancement in Research Trends (SMART)

作者： Abdullha, Abir Habib, Yeasin Masum, Md Raisul Islam Rabby, A. K. M. Shahariar Azad Daffodil Int Univ Dept CSE Dhaka Bangladesh Apurba Technol Dhaka Bangladesh

ISBN: (纸本)9781728132457

Most countries are now in a dangerous place for forestation and some are in developed forestation. So forestation and trees percentage prediction are to predict the condition of the countries about their condition of forestation and tress percentage. The paper is about a machine learning model to predict the countries condition. We used logistic regression, svm AND Naive Bayes to predict the condition also for matrix. we also find the accuracy of logistic regression, svm, Nave Bayes, Ada boosting classifier, Decision tree, ANN, Linear Discriminant Analysis, Gradient Boosting Classifier, MLP Classifier to find our best accuracy and compare with them with our data. we give details of selected algorithms. We collected some previous data and present data and comparing them to predict the condition of the country. we use some conditions and logic for machine learning. By logistic regression, svm and Nave Bayes will show us the prediction and condition of those chosen countries.

关键词： Logistics algorithm svm algorithm Naive Bayes algorithm Details About algorithms Compare algorithm Accuracy Condition of Countries in Forestation

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Obstacle Avoidance algorithm for Indoor Mobility Robot Based on Improved Support Vector Machine 2019

Obstacle Avoidance Algorithm for Indoor Mobility Robot Based...

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Proceedings of the 2019 The 2nd International Conference on Robotics, Control and Automation Engineering

作者： Jie Hao Northwest Minzu University School of Electrical Engineering Lanzhou China

ISBN: (纸本)9781450376228

With the development of artificial intelligence, robot will be applied to every aspect of our life. In the domain of robotics, especially indoor robotics, the ability of obstacle avoidance is one of the most important questions. In this paper, first of all, some classical machine learning algorithms are applied for indoor robot obstacle avoidance which is in the unknown environment based on multiple ultrasonic sensors. During the move, obstacles will be detected by sensors on the robot, through the machine learning algorithm, the robot will know how to do when the sensor detect an obstacle. Then, proposes an improved Support Vector Machine(svm) algorithm based on the Grid Search algorithm. This algorithm can accurately help robot find the trajectory for moving. Experimental results also presented to show the algorithm improved the generalization ability and real-time performance

关键词： svm algorithm ultrasonic sensor obstacle avoidance Indoor mobile robot machine learning algorithm Grid Search

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KNN and svm Classification for Chainsaw Sound Identification in the Forest Areas

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2019年第12期10卷 531-536页

作者： Gnamele, N'tcho Assoukpou Jean Ouattara, Yelakan Berenger Kobea, Toka Arsene Baudoin, Genevieve Laheurte, Jean-Marc UFHB Lab Signaux & Syst Abidjan Cote Ivoire UFHB Lab Phys Atmosphere & Mecan Fluides Abidjan Cote Ivoire UPEM ESIEE PARIS Marne La Vallee France UPEM ESYCOM FRE 2028 Marne La Vallee France

We present in this paper a comparative study of two classifiers, namely, svm (support vector machine) and KNN (K-Nearest Neighbors), which we combine to MFCC (Mel-Frequency Cepstral Coefficients) in order to make possible the detection of chainsaw's sounds in a forest environment. Optimization's calculation of the relevant characteristics of the sounds recorded in the forest and the judicious choice of the key parameters of the classifiers allows us to obtain a true positive rate of 95.63% for the svm-LOG-KERNEL and 94.02% for the KNN. The svm-LOG-KERNEL classifier offers a better classification result and a processing time 30 times faster than KNN.

关键词： KNN algorithm svm algorithm MFCC sound recognition forest monitoring machine learning

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Research on Data Mining algorithm of Meteorological Observation Based on Data Quality Control algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2018年第2期102卷 2077-2089页

作者： Qing-Dao-Er-Ji, Ren Li, Na Inner Mongolia Univ Technol Sch Informat Engn Hohhot 010051 Peoples R China Inner Mongolia Univ Technol Sch Sci Hohhot 010051 Peoples R China Inner Mongolia Univ Sch Comp Sci Hohhot Peoples R China

The quality of meteorological observation data directly affects the weather forecast and the accuracy of climate prediction. The traditional quality control algorithm is not sensitive to the abnormal changes of the elements and can't meet the needs of the quality control work. Therefore, based on the data mining algorithm, this paper further studied the quality control of meteorological data from two aspects of time correlation and factor correlation. Two different methods of quality control for meteorological observation data were proposed. One is the quality control method of time correlated meteorological observations based on the characteristics of chaos (potential trend and regularity) and the support vector machine algorithm. The other is the quality control method of factor correlated meteorological observations based on BP neural network and the characteristics of different elements. Combining the complementarity and relevance between the two methods, a set of comprehensive quality control scheme is set up. The experimental results show that the proposed scheme can effectively simulate the weather observation data and detect the anomaly value.

关键词： svm algorithm BP neural network Meteorological observation Quality control Data mining algorithm

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