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ENGINEERING REPORTS 2024年第12期6卷

作者： Rokni, Mohsen Naghizadeh Tavasoli, Omid Esmaeilabadi, Reza Saraf, Amirpouya Islamic Azad Univ Dept Civil Engn Roudehen Branch Roudehen Iran Islamic Azad Univ Dept Civil Engn East Tehran Branch Tehran Iran

This paper investigates the effect of Leca-filled barriers, both single and double-walled trenches, on mitigating ground vibrations due to harmonic loads. A three-dimensional finite element program, validated in comparison by aforementioned studies, was used alongside automated models created via Plaxis and Python integration. This approach facilitated the evaluation of trench effectiveness in both active and passive design scenarios. Our findings suggest that optimal trench dimensions for effective vibration reduction in active designs are a depth and width of approximately 1 lambda r and 0.2 lambda r, respectively. In passive designs, while trench depth becomes less significant, width plays a crucial role in both single and double-wall systems. Additionally, a support vector machine algorithm was developed to forecast the performance of single-wall trenches, showing a high correlation with numerical model outcomes. This underscores the algorithm's utility in predicting trench efficiency, highlighting the practical application of machine learning in geotechnical engineering. Three dimensional investigating on ground vibration attenuation through leca-filled trenches. image

关键词： ground vibration harmonic load Leca-filled trenches numerical analysis support vector machine algorithm

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ANOMALY-BASED NETWORK INTRUSION DETECTION ENHANCEMENT BY PREDICTION THRESHOLD ADAPTATION OF BINARY CLASSIFICATION MODELS

ANOMALY-BASED NETWORK INTRUSION DETECTION ENHANCEMENT BY PRE...

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作者： Amjad Mohamed Al Tobi University of St Andrews

学位级别：博士

Network traffic exhibits a high level of variability over short periods of time. This variability impacts negatively on the performance (accuracy) of anomaly-based network Intrusion Detection Systems (IDS) that are built using predictive models in a batch-learning setup. This thesis investigates how adapting the discriminating threshold of model predictions, specifically to the evaluated traffic, improves the detection rates of these Intrusion Detection models. Specifically, this thesis studied the adaptability features of three well known machine Learning algorithms: C5.0, Random Forest, and support vector machine. The ability of these algorithms to adapt their prediction thresholds was assessed and analysed under different scenarios that simulated real world settings using the prospective sampling approach. A new dataset (STA2018) was generated for this thesis and used for the analysis. This thesis has demonstrated empirically the importance of threshold adaptation in improving the accuracy of detection models when training and evaluation (test) traffic have different statistical properties. Further investigation was undertaken to analyse the effects of feature selection and data balancing processes on a model's accuracy when evaluation traffic with different significant features were used. The effects of threshold adaptation on reducing the accuracy degradation of these models was statistically analysed. The results showed that, of the three compared algorithms, Random Forest was the most adaptable and had the highest detection rates. This thesis then extended the analysis to apply threshold adaptation on sampled traffic subsets, by using different sample sizes, sampling strategies and label error rates. This investigation showed the robustness of the Random Forest algorithm in identifying the best threshold. The Random Forest algorithm only needed a sample that was 0.05% of the original evaluation traffic to identify a discriminating threshold with an overall ac

关键词： Intrusion detection system Anomaly-based IDS Threshold adaptation Prediction accuracy improvement machine learning STA2018 dataset C5.0 algorithm Random forest algorithm support vector machine algorithm

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Fault Recognition Method for Isolating Switch Infrared Images Based on HOG Feature 6

Fault Recognition Method for Isolating Switch Infrared Image...

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6th International Conference on HVDC, HVDC 2024

作者： Guo, Shuangquan Lu, Jun Wang, Jinxin Dong, Zhaoyang State Grid Xinjiang Electric Power Co. Ltd. Electric Power Science Research Institute Urumqi China

ISBN: (纸本)9798350387759

In order to achieve fault recognition for isolating switch infrared images, this paper utilizes an improved SLIC algorithm to segment and label the fault areas of the isolating switch based on color space conversion, effectively improving the accuracy of image segmentation. Leveraging the histogram of oriented gradient (HOG) feature extraction and support vector machine (SVM) algorithm, the paper classifies and categorizes the infrared images of isolating switches to distinguish whether they are operating in normal states. For isolating switches in normal states, the relative temperature difference method is employed to judge their fault states, where a higher relative temperature difference indicates a more severe fault. Experimental results demonstrate that under optimized HOG feature parameters, the highest accuracy in image device recognition can be achieved. By diagnosing faults using infrared images, the severity of defects in isolating switches can be determined, and maintenance recommendations can be provided. The proposed model exhibits high accuracy and reliability. © 2024 IEEE.

关键词： fault diagnosis HOG characteristics isolating switch support vector machine algorithm

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A DDoS attack detection method based on SVM and K-nearest neighbour in SDN environment

A DDoS attack detection method based on SVM and K-nearest ne...

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International Journal of Computational Science and Engineering

作者： Ma, Zhaohui Li, Bohong School of Computer Science South China Normal University Guangzhou510006 China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou510420 China Volgenau School of Engineering George Mason University 4400 University Dr FairfaxVA22030 United States

This paper presents a detection method for DDoS attack in SDN based on K-nearest neighbour (KNN) algorithm and support vector machine (SVM) algorithm. This method makes use of the characteristics of SDN centralised control, collects flow characteristic information efficiently, classifies the flow, screens out the attack flow, and determines whether the system is attacked or not. Experiments show that the resource consumption rate of this model is only 11% when detecting DDOS attack. Meanwhile, the accuracy rate exceeded 99%. © 2020 Inderscience Enterprises Ltd.. All rights reserved.

关键词： Denial-of-service attack Nearest neighbor search support vector machines Accuracy rate Attack flow Detection methods Flow charac teristics K nearest neighbours (k NN) K nearest neighbours Resource consumption support vector machine algorithm

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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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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

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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Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering

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SENSORS 2021年第6期21卷 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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Evaluation of Using Sentinel-1 and-2 Time-Series to Identify Winter Land Use in Agricultural Landscapes

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REMOTE SENSING 2019年第1期11卷

作者： Denize, Julien Hubert-Moy, Laurence Betbeder, Julie Corgne, Samuel Baudry, Jacques Pottier, Eric Univ Rennes CNRS UMR 6164 IETR F-35000 Rennes France Univ Rennes LETG UMR 6554 F-35000 Rennes France Ctr Cooperat Int Rech Agron Dev CIRAD Internal Res Unit Forests & Soc F-34398 Montpellier France INRA UMR Biodivers Agroecol & Amenagement Paysage F-35000 Rennes France

Monitoring vegetation cover during winter is a major environmental and scientific issue in agricultural areas. From an environmental viewpoint, the presence and type of vegetation cover in winter influences the transport of pollutants to water resources. From a methodological viewpoint, characterizing spatio-temporal dynamics of land cover and land use at the field scale is challenging due to the diversity of farming strategies and practices in winter. The objective of this study was to evaluate the respective advantages of Sentinel optical and SAR time-series to identify land use in winter. To this end, Sentinel-1 and -2 time-series were classified using support vector machine and Random Forest algorithms in a 130 km(2) agricultural area. From the classification, the Sentinel-2 time-series identified winter land use more accurately (overall accuracy (OA) = 75%, Kappa index = 0.70) than that of Sentinel-1 (OA = 70%, Kappa = 0.66) but a combination of the Sentinel-1 and -2 time-series was the most accurate (OA = 81%, Kappa = 0.77). Our study outlines the effectiveness of Sentinel-1 and -2 for identify land use in winter, which can help to change agricultural practices.

关键词： agricultural monitoring earth observing sensors multi-temporal classification optical and SAR time-series Random Forest algorithm support vector machine algorithm

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Automatic detection of acute lymphoblastic leukaemia based on extending the multifractal features (vol 14, pg 132, 2020)

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IET IMAGE PROCESSING 2020年第5期14卷 995-995页

作者： Abbasi, M. Kermani, S. Talebi, Ardeshir Isfahan University of Medical Sciences P.O. Box 319 Hezar-Jerib Ave. Isfahan 81746 73461 Iran Faculty of advanced Technologies Isfahan University of Medical Sciences P.O. Box 319 Hezar-Jerib Ave. Isfahan 81746 73461 Iran School of Medicine Isfahan University of Medical Sciences P.O. Box 319 Hezar-Jerib Ave. Isfahan 81746 73461 Iran Department of Medical Physics and Biomedical Engineering School of Medicine Tehran University of Medical Sciences (TUMS) Tehran Iran Islamic Azad University North Tehran Branch Vafadar Blvd. Shahid Sadoughi St. Hakimiyeh Exit Shahid Babaee Highway Tehran *** Iran

The main purpose of this study is to introduce a new species of features to improve the diagnosis efficiency of acute lymphoblastic leukaemia from microscopic images. First, the authors segmented nuclei by the k -means and watershed algorithms. They extracted three sets of geometrical, statistical, and chaotic features from nuclei images. Six chaotic features were extracted by calculating the fractal dimension from five sub-images driven from the nuclei images, with their grey levels being modified. The authors classified the images into binary and multiclass types via the support vector machine algorithm. They conducted principal component analysis for dimensional reduction of feature space and then evaluated the proposed algorithm for the overfitting problem. The obtained overall results represent 99% accuracy, 99% specificity, and 97% sensitivity values in the classification of six-cell groups. The difference between the train and test errors was <3%, which proves that the classification performance had improved by using the multifractal features.

关键词： chaotic features multiclass types Patient diagnostic methods and instrumentation image enhancement image colour analysis support vector machine algorithm Biology and medical computing multifractal features Computer vision and image processing techniques Knowledge engineering techniques feature space Other topics in statistics acute lymphoblastic leukaemia blood Optical, image and video signal processing automatic detection image segmentation cancer image classification principal component analysis feature extraction microscopic images pattern classification nuclei images fractal dimension medical image processing Biomedical measurement and imaging support vector machines image denoising watershed algorithms binary types diagnosis efficiency fractals

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Building Financial Enterprise Risk Assessment System Based on Big Data and Integrated Learning Technology

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Procedia Computer Science 2024年 247卷 900-907页

作者： Yaling Peng Beibei Wen School of Information and Business Management Chengdu Neusoft University Chengdu China

This article aims to explore the use of big data and integrated learning technologies to build a more efficient and accurate financial enterprise risk assessment system. By analyzing the limitations of current financial risk assessment methods, this paper proposes a comprehensive framework based on random forests. The performance of a financial enterprise risk assessment system based on random forest was evaluated through four experiments in the experimental stage. Through systematic comparative experiments, it was found that the random forest method showed better performance compared to the support vector machine (SVM) algorithm, with an accuracy of 92%, SVM at 85%, recall index at 90%, better than SVM at 82%, and F1 score of 91%, higher than SVM at 83%. In the multi scenario risk assessment experiment, the accuracy of random forest in conventional market scenarios is 85%, the recall rate is 80%, and the F1 score is 82%. In real-time data evaluation experiments, the accuracy of the model gradually increased from the initial 85% to 95% at 10 different time points. The above experimental data results collectively confirm that systems based on random forests have strong adaptability and efficient risk assessment capabilities in diverse financial environments.

关键词： Financial risk assessment random forest algorithm big data technology support vector machine algorithm real-time data processing

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