检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

Fault Recognition Method for Isolating Switch Infrared Image...

引用

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

来源：评论

学校读者我要写书评

暂无评论

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...

引用

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

来源：评论

学校读者我要写书评

暂无评论

SmartMouse : Classifying Mouse Actions Using Smartwatch

SmartMouse : Classifying Mouse Actions Using Smartwatch

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Protein-protein interaction based on pairwise similarity

引用

BMC BIOINFORMATICS 2009年第1期10卷 1-12页

作者： Zaki, Nazar Lazarova-Molnar, Sanja El-Hajj, Wassim Campbell, Piers UAE Univ Coll Informat Technol Dept Comp Sci Bioinformat Lab Al Ain 17551 U Arab Emirates UAE Univ Coll Informat Technol Dept Informat Secur Al Ain 17551 U Arab Emirates UAE Univ Coll Informat Technol Dept Informat Syst Al Ain 17551 U Arab Emirates

Background: Protein-protein interaction (PPI) is essential to most biological processes. Abnormal interactions may have implications in a number of neurological syndromes. Given that the association and dissociation of protein molecules is crucial, computational tools capable of effectively identifying PPI are desirable. In this paper, we propose a simple yet effective method to detect PPI based on pairwise similarity and using only the primary structure of the protein. The PPI based on Pairwise Similarity (PPI-PS) method consists of a representation of each protein sequence by a vector of pairwise similarities against large subsequences of amino acids created by a shifting window which passes over concatenated protein training sequences. Each coordinate of this vector is typically the E-value of the Smith-Waterman score. These vectors are then used to compute the kernel matrix which will be exploited in conjunction with support vector machines. Results: To assess the ability of the proposed method to recognize the difference between "interacted" and "non-interacted" proteins pairs, we applied it on different datasets from the available yeast saccharomyces cerevisiae protein interaction. The proposed method achieved reasonable improvement over the existing state-of-the-art methods for PPI prediction. Conclusion: Pairwise similarity score provides a relevant measure of similarity between protein sequences. This similarity incorporates biological knowledge about proteins and it is extremely powerful when combined with support vector machine to predict PPI.

关键词： support vector machine Protein Pair Pairwise Similarity Domain Pair support vector machine algorithm

来源：评论

学校读者我要写书评

暂无评论

Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering

引用

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

来源：评论

学校读者我要写书评

暂无评论

Demonstration of two novel methods for predicting functional siRNA efficiency

引用

BMC BIOINFORMATICS 2006年第1期7卷 271-271页

作者： Jia, Peilin Shi, Tieliu Cai, Yudong Li, Yixue Chinese Acad Sci Shanghai Inst Biol Sci Bioinformat Ctr Shanghai 200031 Peoples R China Chinese Acad Sci Grad Sch Beijing 100039 Peoples R China Shanghai Ctr Bioinformat Technol Shanghai 200235 Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci Partner Inst Computat Biol CAS MPG Shanghai Peoples R China Shanghai Jiao Tong Univ Life Sci Sch Shanghai Peoples R China Univ Manchester Dept Biomol Sci Manchester M60 1QD Lancs England

Background: siRNAs are small RNAs that serve as sequence determinants during the gene silencing process called RNA interference (RNAi). It is well know that siRNA efficiency is crucial in the RNAi pathway, and the siRNA efficiency for targeting different sites of a specific gene varies greatly. Therefore, there is high demand for reliable siRNAs prediction tools and for the design methods able to pick up high silencing potential siRNAs. Results: In this paper, two systems have been established for the prediction of functional siRNAs: (1) a statistical model based on sequence information and (2) a machine learning model based on three features of siRNA sequences, namely binary description, thermodynamic profile and nucleotide composition. Both of the two methods show high performance on the two datasets we have constructed for training the model. Conclusion: Both of the two methods studied in this paper emphasize the importance of sequence information for the prediction of functional siRNAs. The way of denoting a bio-sequence by binary system in mathematical language might be helpful in other analysis work associated with fixed-length bio-sequence.

关键词： support vector machine support vector machine Model Nucleotide Composition support vector machine algorithm support vector machine Method

来源：评论

学校读者我要写书评

暂无评论

Large-scale prediction of protein-protein interactions from structures

引用

BMC BIOINFORMATICS 2010年第1期11卷 144-144页

作者： Hue, Martial Riffle, Michael Vert, Jean-Philippe Noble, William S. Univ Washington Dept Genome Sci Seattle WA 98195 USA Mines ParisTech Ctr Computat Biol F-77305 Fontainebleau France Inst Curie F-75248 Paris France INSERM U900 F-75248 Paris France Univ Washington Dept Biochem Seattle WA 98195 USA Univ Washington Dept Comp Sci & Engn Seattle WA 98195 USA

Background: The prediction of protein-protein interactions is an important step toward the elucidation of protein functions and the understanding of the molecular mechanisms inside the cell. While experimental methods for identifying these interactions remain costly and often noisy, the increasing quantity of solved 3D protein structures suggests that in silico methods to predict interactions between two protein structures will play an increasingly important role in screening candidate interacting pairs. Approaches using the knowledge of the structure are presumably more accurate than those based on sequence only. Approaches based on docking protein structures solve a variant of this problem, but these methods remain very computationally intensive and will not scale in the near future to the detection of interactions at the level of an interactome, involving millions of candidate pairs of proteins. Results: Here, we describe a computational method to predict efficiently in silico whether two protein structures interact. This yes/no question is presumably easier to answer than the standard protein docking question, "How do these two protein structures interact?" Our approach is to discriminate between interacting and non-interacting protein pairs using a statistical pattern recognition method known as a support vector machine (SVM). We demonstrate that our structure-based method performs well on this task and scales well to the size of an interactome. Conclusions: The use of structure information for the prediction of protein interaction yields significantly better performance than other sequence-based methods. Among structure-based classifiers, the SVM algorithm, combined with the metric learning pairwise kernel and the MAMMOTH kernel, performs best in our experiments.

关键词： support vector machine Near Neighbor Protein Pair support vector machine algorithm Candidate Pair

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：