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Design network intrusion detection system using support vector machine

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2021年第3期34卷 e4689-e4689页

作者： Ajdani, Mahdi Ghaffary, Hamidreza Islamic Azad Univ Dept Comp Sci Ferdows Branch Ferdows Iran

The growing use of the Internet and the existence of vulnerable points in networks have made the usage of intrusion detection systems as one of the most important security elements. This study aimed to present a method to design an analytical framework of detecting destructive data with respect to three factors including time, users' information, and scale. The design can be applied for big data. In the proposed method, to train data, the time has been divided into subperiods exploiting users' review information during each period of time, and the data have been trained. Also, storing methods have been applied for scalability to enhance the speed and reduce the volume of computations. The method used in this study is a combination of hardware-software method to detect destructive data to cluster them (VIRUS TOTAL Dataset). Also, the proposed method applied a new algorithm of modified vector machine, and the efficiency of the algorithm has promoted support vector machine (SVM), designed to operate better than previous methods. The results showed that the proposed method is more acceptable than other previous methods. The results indicated that the method works with the accuracy of 0.97 which can be fairly accepted.

关键词： classification destructive data intrusion support vector machine algorithm

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Predicting land cover driven ecosystem service value using artificial neural network model

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REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT 2024年 34卷

作者： Hossain, Niamat Ullah Ibne Fattah, Md. Abdul Morshed, Syed Riad Jaradat, Raed Arkansas State Univ Dept Engn Management Jonesboro AR 72467 USA Florida State Univ Dept Geog Tallahassee FL 32306 USA Khulna Univ Engn & Technol Dept Urban & Reg Planning Khulna 9203 Bangladesh Khalifa Univ Sci & Technol Management Sci & Engn Post Box 127788 Abu Dhabi U Arab Emirates

Understanding the synergies and trade-offs of major cities' ecosystem services is vital to mitigating regional ecological and environmental risks and enhancing human well-being in this era of rapid urbanization and global climate change. This study aimed to assess and predict the land use- and land cover (LULC)-driven ecosystem service value (ESV) dynamics in Arkansas's capital city, Little Rock. Historical LULC data were derived by applying support vector machine learning algorithms to Landsat satellite imagery. The benefit transfer method was utilized to identify nine types of ecosystem services and their corresponding economic values. A cellular automata artificial neural network model was used to simulate future potential LULC and ESV patterns. Vegetation accounted for more than 94% of total ESV over the past two decades. However, a 38.40% expansion of built-up areas resulted in a 45.28% decrease in vegetated areas, which reduced total ESV from $3619.73 x 106 to $2563.81 x 106 during 2003-2023. By 2033, the city's urban area will expand to 72.75% of the total area and will witness further declines of 30.35 km2 in vegetation, 19.30 km2 in barren soil, and 1.69 km2 in waterbody areas. Consequently, the ESVs of these natural landscapes will decline by $708.58 x 106, $44.87 x 106, and $15.69 x 106, respectively. Provisioning services will be most affected, followed by supporting, regulating, and cultural services. The study findings provide reference information to policymakers and the local government for use in adopting sustainable land management policies, thereby promoting the ecological value of Little Rock.

关键词： Ecosystem services Ecosystem service valuation Cellular automata artificial neural network model support vector machine algorithm

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Multispectral Portable Fibre-Optic Reflectometer for the Classification of the Origin of Chicken Eggshells in the Case of Mycoplasma synoviae Infections

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SENSORS 2022年第22期22卷 8690-8690页

作者： Pakula, Anna Zolnowski, Wojciech Pasko, Slawomir Kursa, Olimpia Marc, Pawel Jaroszewicz, Leszek R. Warsaw Univ Technol Inst Micromech & Photon Sw A Boboli 8 PL-02525 Warsaw Poland Mil Univ Technol Fac New Technol & Chem Ul Gen Sylwestra Kaliskiego 2 PL-00908 Warsaw Poland Mil Univ Technol Inst Optoelect Ul Gen Sylwestra Kaliskiego 2 PL-00908 Warsaw Poland Natl Vet Res Inst Dept Poultry Dis Al Partyzantow 57 PL-24100 Pulawy Poland

The proper classification of the origins of food products is a crucial issue all over the world nowadays. In this paper, the authors present a device-a multispectral portable fibre-optic reflectometer and signal processing patch-together with a machine-learning algorithm for the classification of the origins of chicken eggshells in the case of Mycoplasma synoviae infection. The sensor device was developed based on previous studies with a continuous spectrum in transmittance and selected spectral lines in reflectance. In the described case, the sensor is based on the integration of reflected spectral data from short spectral bands from the VIS and NIR region, which are produced by single-colour LEDs and introduced to the sample via a fibre bundle. The measurement is carried out in a sequence, and the reflected signal is pre-processed to be put in the machine learning algorithm. The support vector machine algorithm is used together with three different types of data normalization. The obtained results of the F-score factor for classification of the origins of samples show that the percentages of eggs coming from Mycoplasma synoviae infected hens are up to 87% for white and 96% for brown eggshells.

关键词： optical reflectometry machine learning fibre-optic sensors Mycoplasma synoviae origin classification support vector machine algorithm

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Evaluation of urban green space per capita with new remote sensing and geographic information system techniques and the importance of urban green space during the COVID-19 pandemic

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2022年第9期194卷 633-633页

作者： Pouya, Sima Aghlmand, Majid Inonu Univ Fac Fine Arts & Design Dept Landscape Architecture Malatya Turkey Eskisehir Tech Univ Dept Civil Engn Eskisehir Turkey

A recently conducted study by the Centers for Disease Control and Prevention encouraged access to urban green space for the public over the prevalence of COVID-19 in that exposure to urban green space can positively affect the physical and mental health, including the reduction rate of heart disease, obesity, stress, stroke, and depression. COVID-19 has foregrounded the inadequacy of green space in populated cities. It has also highlighted the extant inequities so as to unequal access to urban green space both quantitatively and qualitatively. In this regard, it seems that one of the problems related to Malatya is the uncoordinated distribution of green space in different parts of the city. Therefore, knowing the quantity and quality of these spaces in each region can play an effective role in urban planning. The aim of the present study has been to evaluate urban green space per capita and to investigate its distribution based on the population of the districts of Battalgazi county in Malatya city through developing an integrated methodology (remote sensing and geographic information system). Accordingly, in Google Earth Engine by images of Sentinel-1 and PlanetScope satellites, it was calculated different indexes (NDVI, EVI, PSSR, GNDVI, and NDWI). The data set was prepared and then by combining different data, classification was performed according to support vector machine algorithm. From the landscaping maps obtained, the map was selected with the highest accuracy (overall accuracy: 94.43;and kappa coefficient: 90.5). Finally, by the obtained last map, the distribution of urban green space per capita and their functions in Battalgazi county and its districts were evaluated. The results of the study showed that the existing urban green spaces in the Battalgazi/Malatya were not distributed evenly on the basis of the districts. The per capita of urban green space is twenty-four regions which is more than 9m(2) and in twenty-three ones is less than 9m(2). The recom

关键词： Battalgazi COVID-19 pandemic Google Earth Engine Malatya PSScene4Band Sentinel-1 support vector machine algorithm Urban green spaces per capita Urbanization

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Prediction of neurologic deterioration based on support vector machine algorithms and serum osmolarity equations

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BRAIN AND BEHAVIOR 2018年第7期8卷 e01023-e01023页

作者： Lin, Jixian Jiang, Aihua Ling, Meirong Mo, Yanqing Li, Meiyi Zhao, Jing Fudan Univ Minhang Hosp Dept Neurol 170 Xin Song Rd Shanghai Peoples R China Fudan Univ Minhang Hosp Emergency Dept Shanghai Peoples R China Fudan Univ Minhang Hosp Inst Fudan Minhang Acad Hlth Syst Shanghai Peoples R China

Objective: Dehydration on admission is correlated with neurological deterioration (ND). The primary objective of our study was to use support vector machine (SVM) algorithms to identify an ND prognostic model, based on dehydration equations. Methods: This study included a total of 382 patients hospitalized with acute ischemic stroke. The following parameters were recorded: age, sex, laboratory values (serum sodium, potassium, chlorinum, glucose, and urea), and vascular risk factor data. Receiver operating characteristic (ROC) curve analysis was used to evaluate the discriminative performance of the BUN/Cr ratio as well as each of 38 equations for predicting ND. We used the Boruta algorithm for feature selection. After optimizing the SVM kernel parameters, we built an SVM model to predict ND and used the test set to obtain predictive values for assessing model accuracy. Results: In total, 102 of 382 patients (26.7%) with acute ischemic stroke developed ND. In all patients, the BUN/Cr ratio and each of 38 equations were significant predictors of ND. Equation 20 [1.86 x Na+ + glucose + urea + 9] yielded the maximum area under the ROC curve, and faired best in terms of prognostic performance (a cutoff value of 284.49 mM yielded a sensitivity of 94.12% and specificity of 61.43%). Equation 32 predicted ND poststroke across population groups, and worked well in older as well as young adults;(a cutoff value of 297.08 mM yielded a sensitivity of 93.14% and specificity of 60.00%). Feature selection by the Boruta algorithm was used to decrease the number of variables from 18 to 5 in the condition. The specificity of test samples for the SVM prediction model increased from 44.1% to 89.4%, and the AUC increased from 0.700 to 0.927. Conclusions: SVM algorithms can be used to establish a prediction model for dehydration-associated ND, with good classification results.

关键词： dehydration neurological deterioration support vector machine algorithm

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Epithelial-mesenchymal transition status of circulating tumor cells in breast cancer and its clinical relevance

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Cancer Biology & Medicine 2020年第1期17卷 169-180页

作者： Jiaojiao Zhou Xuan Zhu Shijie Wu Jingxin Guo Kun Zhang Chunjing Xu Huihui Chen Yuxi Jin Yuting Sun Shu Zheng Yiding Chen Department of Surgical Oncology the Second Affiliated HospitalZhejiang University School of MedicineHangzhou 310000China The Key Laboratory of Cancer Prevention and Intervention China National Ministry of EducationZhejiang University School of MedicineHangzhou 310000China Life Science Institute Zhejiang UniversityHangzhou 310000China

Objective:Circulating tumor cells(CTCs)play a critical role in cancer metastasis,but their prevalence and significance remain *** study attempted to track the epithelial-mesenchymal transition(EMT)status of CTCs in breast cancer patients and investigate their clinical ***:In this study,the established negFACS-IF:E/M platform was applied to isolate rare CTCs and characterize their EMT status in breast cancer.A total of 89 breast cancer patients were recruited,including stage 0–III(n=60)and late stage(n=29)***:Using the negFACS-IF:E/M platform,it was found that in human epidermal growth factor receptor 2(HER2)+patients,mesenchymal CTCs usually exhibited a high percentage of HER2+*** IV breast cancer patients had considerably more CTCs than stage 0–III *** stage 0–III breast cancers,the HER2 subtype included a significantly higher percentage of mesenchymal and biphenotypic(epithelial and mesenchymal)CTCs than the luminal A or B *** stage IV patients,CTCs were predominantly epithelial in cases with local recurrence and were more mesenchymal in cases with distant *** applying a support vector machine(SVM)algorithm,the EMT status of CTCs could distinguish between breast cancer cases with metastasis/local recurrence and those without ***:The negFACS-IF:E/M platform provides a flexible and generally acceptable method for the highly sensitive and specific detection of CTCs and their EMT traits in breast *** study demonstrated that the EMT status of CTCs had high clinical relevance in breast cancer,especially in predicting the distant metastasis or local recurrence of breast cancer.

关键词： Circulating tumor cells breast cancer epithelial-to-mesenchymal transition estrogen receptor/human epidermal growth factor receptor 2 expression support vector machine algorithm

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A machine Learning-Based Approach to Discriminating Basaltic Tectonic Settings

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2022年第2期21卷 2250012-2250012页

作者： Liu, Baoshun Shi, Junxia Univ Sci & Technol Beijing Sch Civil & Resource Engn Beijing 100083 Peoples R China

The geochemical characteristics of magmatic rocks can distinguish the tectonic setting of magma formation and their geochemical signatures are discriminated by using the whole-rock geochemical data. As a new attempt of artificial intelligence technology in geochemistry, the machine learning discrimination method is gradually complementary to the classical discriminative graphical method. However, the feature selection of high-dimensional data and the determination of many unknown parameters are the two main factors affecting the classification accuracy of the algorithm. In this paper, a particle swarm optimized support vector machine (PSO-SVM) model is established to classify the tectonic environments of basaltic rocks in the GEOROC database. The model mainly relies on the powerful search capability of the particle swarm algorithm to find the best parameter combination selected by the SVM based on experience to improve the accuracy. In this study, based on the basalt samples in the database and the confusion matrix, the performance of PSO-SVM model is evaluated by simulation experiments. The results show that the model proposed in this paper is more effective in distinguishing the basaltic tectonic environments, with an accuracy of more than 90%. Therefore, compared with the traditional discriminant map method, the machine learning method based on the fusion of two algorithms performs better in the tectonic environment classification problems.

关键词： Basalt tectonic settings discrimination support vector machine algorithm particle swarm optimization

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SmartMouse : Classifying Mouse Actions Using Smartwatch

SmartMouse : Classifying Mouse Actions Using Smartwatch

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作者： Dharmia Rajendran, Karthika California State University Northridge

学位级别：硕士

Smartwatches have become the most popular wearable device available today. In addition to ﬁtness applications, smartwatches provide a rich user interface that has enabled many applications including instant messaging, email and easy notifications. Since the smartwatch is worn on the wrist, it introduces a unique opportunity to understand users' arm, hand and possibly ﬁnger movements, using built-in accelerometer and gyroscope sensors. The mouse is an integral part of the laptop/desktop experience. This thesis explores whether motion sensors from the smartwatch can detect mouse actions, specifically movement in the 2D plan, scrolling, and clicks. The experimental results show that the mouse actions, including right clicking, left clicking, scrolling up or down, and cursor movements across the screen, can be accurately tracked. It is possible to classify in real-time mouse operation using hand and finger gesture recognition from only smart watch motion sensors. Thus, this work opens a new area for human computer interaction, that eliminates the physical mouse in place of the more ubiquitous smart watch.

关键词： Mouse Smartwatch support vector machine algorithm Dynamic Time Warping algorithm Smartmouse accelerometer sensor gyroscope sensor Human computer interaction

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A New Approach in Detectability of Microcalcifications in the Placenta during Pregnancy Using Textural Features and K-Nearest Neighbors algorithm

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JOURNAL OF IMAGING 2022年第3期8卷 81-81页

作者： Miron, Mihaela Moldovanu, Simona Stefanescu, Bogdan Ioan Culea, Mihai Pavel, Sorin Marius Culea-Florescu, Anisia Luiza Dunarea de Jos Univ Galati Fac Automat Comp Elect Engn & Elect Dept Comp Sci & Informat Technol 47 Domneasca Str Galati 800008 Romania Dunarea de Jos Univ Galati Fac Med & Pharm Dept Clin Surg 47 Domneasca Str Galati 800008 Romania Clin Emergency Hosp Sf Ap Andrei Galati Dept Obstet & Gynecol Galati 800578 Romania Dunarea de Jos Univ Galati Fac Automat Comp Elect Engn & Elect Dept Elect & Telecommun 47 Domneasca Str Galati 800008 Romania

(1) Background: Ultrasonography is the main method used during pregnancy to assess the fetal growth, amniotic fluid, umbilical cord and placenta. The placenta's structure suffers dynamic modifications throughout the whole pregnancy and many of these changes, in which placental microcalcifications are by far the most prominent, are related to the process of aging and maturation and have no effect on fetal wellbeing. However, when placental microcalcifications are noticed earlier during pregnancy, they could suggest a major placental dysfunction with serious consequences for the fetus and mother. For better detectability of microcalcifications, we propose a new approach based on improving the clarity of details and the analysis of the placental structure using first and second order statistics, and fractal dimension. (2) Methods: The methodology is based on four stages: (i) cropping the region of interest and preprocessing steps;(ii) feature extraction, first order-standard deviation (SD), skewness (SK) and kurtosis (KR)-and second order-contrast (C), homogeneity (H), correlation (CR), energy (E) and entropy (EN)-are computed from a gray level co-occurrence matrix (GLCM) and fractal dimension (FD);(iii) statistical analysis (t-test);(iv) classification with the K-Nearest Neighbors algorithm (K-NN algorithm) and performance comparison with results from the support vector machine algorithm (SVM algorithm). (3) Results: Experimental results obtained from real clinical data show an improvement in the detectability and visibility of placental microcalcifications.

关键词： placenta calcifications preterm placental calcifications first order feature second order feature t-test K-Nearest Neighbors algorithm support vector machine algorithm

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Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering

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SENSORS 2021年第6期21卷 1997-1997页

作者： Kim, Tae-Yeun Ko, Hoon Kim, Sung-Hwan Kim, Ho-Da Chosun Univ Natl Program Excellence Software Ctr Gwangju 61452 South Korea Chosun Univ IT Res Inst Gwangju 61452 South Korea

Emotion information represents a user's current emotional state and can be used in a variety of applications, such as cultural content services that recommend music according to user emotional states and user emotion monitoring. To increase user satisfaction, recommendation methods must understand and reflect user characteristics and circumstances, such as individual preferences and emotions. However, most recommendation methods do not reflect such characteristics accurately and are unable to increase user satisfaction. In this paper, six human emotions (neutral, happy, sad, angry, surprised, and bored) are broadly defined to consider user speech emotion information and recommend matching content. The "genetic algorithms as a feature selection method" (GAFS) algorithm was used to classify normalized speech according to speech emotion information. We used a support vector machine (SVM) algorithm and selected an optimal kernel function for recognizing the six target emotions. Performance evaluation results for each kernel function revealed that the radial basis function (RBF) kernel function yielded the highest emotion recognition accuracy of 86.98%. Additionally, content data (images and music) were classified based on emotion information using factor analysis, correspondence analysis, and Euclidean distance. Finally, speech information that was classified based on emotions and emotion information that was recognized through a collaborative filtering technique were used to predict user emotional preferences and recommend content that matched user emotions in a mobile application.

关键词： collaborative filtering emotion recognition support vector machine algorithm speech emotion information

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