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Driver identification in intelligent vehicle systems using machine learning algorithms

IET INTELLIGENT TRANSPORT SYSTEMS 2019年第1期13卷 40-47页

作者： Li, Zhengping Zhang, Kai Chen, Bokui Dong, Yuhan Zhang, Lin Tsinghua Univ Div Logist & Transportat Grad Sch Shenzhen Shenzhen Peoples R China Natl Univ Singapore Sch Comp Dept Comp Sci Singapore Singapore Tsinghua Univ Div Informat Sci & Technol Grad Sch Shenzhen Shenzhen Peoples R China Tsinghua Berkeley Shenzhen Inst Data Sci & Informat Technol Res Ctr Shenzhen Peoples R China

This study proposes an applicable driver identification method using machine learning algorithms with driving information. The driving data are collected by a 3-axis accelerometer, which records the lateral, longitudinal and vertical accelerations. In this research, a data transformation way is developed to extract interpretable statistics features from raw 3-axis sensor data and utilise machine learning algorithms to identify drivers. To eliminate the bias caused by the sensor installation and ensure the applicability of their approach, they present a data calibration method which proves to be necessary for a comparative test. Four basic supervised classification algorithms are used to perform on the data set for comparison. To improve classification performance, they propose a multiple classifier system, which combines the outputs of several classifiers. Experimental results based on real-world data show that the proposed algorithm is effective on solving driver identification problem. Among the four basic algorithms, random forests (RFs) algorithm has the greatest performance on accuracy, recall and precision. With the proposed multiple classifier system, a greater performance can be achieved in small number of drivers' groups. RFs algorithm takes the lead in running speed. In their experiment, ten drivers are involved and over 5,500,000 driving records per driver are collected.

关键词： learning (artificial intelligence) intelligent transportation systems feature extraction traffic engineering computing image classification calibration statistical analysis RFs algorithm random forests algorithm multiple classifier system supervised classification algorithms data calibration method sensor installation raw 3-axis sensor data interpretable statistics feature extraction data transformation lateral accelerations longitudinal accelerations vertical accelerations 3-axis accelerometer driving data collection machine learning algorithms intelligent vehicle systems driver identification method

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Multiple intra-urban land use simulations and driving factors analysis: a case study in Huicheng, China

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GISCIENCE & REMOTE SENSING 2019年第2期56卷 282-308页

作者： Zhang, Dachuan Liu, Xiaoping Wu, Xiaoyu Yao, Yao Wu, Xinxin Chen, Yimin Sun Yat Sen Univ Sch Geog & Planning Guangdong Key Lab Urbanizat & Geosimulat Guangzhou Guangdong Peoples R China Peking Univ Inst Remote Sensing & Geog Informat Syst Beijing Peoples R China China Univ Geosci Sch Informat Engn Wuhan Hubei Peoples R China Alibaba Grp Dept Data Technol & Prod Hangzhou Zhejiang Peoples R China

Simulations of intra-urban land use changes have gradually attracted more attention as these approaches are extremely helpful in regard to decision making and policy formulation. While prior studies mostly focused on methods of developing intra-urban level simulations, very little research has been conducted explain the factors driving intra-urban land use change. Urban planners are highly concerned with how inner-city structures are formed and how they function. Here, to simulate multiple intra-urban land use changes and to identify the contribution of different driving factors, we developed a random forests (RF) algorithm-based cellular automata (CA) simulation model. In this study, the model applied diverse categories of spatial variables, including traffic location factors, environmental factors, public services, and population density, as the driving factors to enhance our understanding of the dynamics of internal urban land use. The CA model was tested using data from the Huicheng district of Huizhou city in the Guangdong province of China. The Model was validated using actual historical land use data from 2000 to 2010. By applying the validated model, multiple intra-urban land use maps were simulated for 2015. Simultaneously, spatial variable importance measures (VIMs) were calculated by using the out-of-bag (OOB) error estimation approach of the RF algorithm. Based on the calculation results, we assessed and analysed the significance of each intra-urban land use driver for this region. This study provides urban planners and relevant scholars with detailed and targeted information that can aid in the formulation of specific planning strategies for different intra-urban land uses and support the future evolution of this area.

关键词： cellular automata urban planning multiple intra-urban land use simulations driving factors analysis random forests algorithm

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Performance Analysis of E-Mail Spam Classification using different Machine Learning Techniques

Performance Analysis of E-Mail Spam Classification using dif...

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International Conference on Advances in Computing and Communication Engineering (ICACCE)

作者： Vinitha, V. Sri Renuka, D. Karthika Bannari Amman Inst Technol Dept Informat Technol Sathyamangalam Erode India PSG Coll Technol Dept Informat Technol Coimbatore Tamil Nadu India

ISBN: (纸本)9781728132501

Most of the business and general communication is done through email because of its cost effectiveness. This efficiency leads email exposed to various attacks including spamming. Nowadays spam email is the foremost concern for email users. These spams are used for sending fake proposals, advertisements, and harmful contents in the form of executable file to attack user systems or the link to the malicious websites resulting in the unessential consumption of network bandwidth. This paper elucidates the different Machine Learning Techniques such as J48 classifier, Adaboost, K-Nearest Neighbor, Naive Bayes, Artificial Neural Network, Support Vector Machine, and random forests algorithm for filtering spam emails using different email dataset. However, here the comparison of different spam email classification technique is presented and summarizes the overall scenario regarding accuracy rate of different existing approaches.

关键词： Email Spam J48 classifier Adaboost K-Nearest Neighbor Naive Bayes Artificial Neural Network Support Vector Machine random forests algorithm

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Refining Traceability Links Between Vulnerability and Software Component in a Vulnerability Knowledge Graph 18th

Refining Traceability Links Between Vulnerability and Softwa...

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18th International Conference on Web Engineering (ICWE)

作者： Du, Dongdong Ren, Xingzhang Wu, Yupeng Chen, Jien Ye, Wei Sun, Jinan Xi, Xiangyu Gao, Qing Zhang, Shikun Peking Univ Sch Elect Engn & Comp Sci Beijing Peoples R China Peking Univ Sch Software & Microelect Beijing Peoples R China Peking Univ Natl Engn Res Ctr Software Engn Beijing Peoples R China

ISBN: (纸本)9783319916620;9783319916613

Software vulnerabilities and their corresponding software components information are usually stored in different locations with different representations. Building accurate traceability links between them to form a unified knowledge graph can be very helpful for vulnerability spreading analysis, component dependency management, and relationship inference. In this paper, we first propose a software vulnerability knowledge graph model which integrates CVE (Common Vulnerabilities and Exposures) information, Java Component metadata in Maven repository and project collaboration data on Github. To construct the knowledge graph, we then propose two ontology matching approaches. The first one links Maven project and Github project in a URL textmatching way. The second one introduces random forests algorithm to link CVE project version and Maven project version based on 16 well-defined features. Experimental results show that matching between CVE project version and Maven project version are highly promising with an accuracy rate as high as 99.8%. The traceability links between vulnerabilities and software components can be more accurate based on our approach.

关键词： Vulnerability knowledge graph Software security vulnerabilities Software dependencies Vulnerability traceability random forests algorithm

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Identification of immune correlates of protection in Shigella infection by application of machine learning

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JOURNAL OF BIOMEDICAL INFORMATICS 2017年 74卷 1-9页

作者： Arevalillo, Jorge M. Sztein, Marcelo B. Kotloff, Karen L. Levine, Myron M. Simon, Jakub K. Univ Nacl Educ Distancia Dept Stat & Operat Res Paseo Senda del Rey 9 E-28040 Madrid Spain Univ Maryland Sch Med Ctr Vaccine Dev Dept Pediat Baltimore MD 21201 USA Univ Maryland Sch Med Ctr Vaccine Dev Dept Med Baltimore MD 21201 USA Merck & Co Inc Kenilworth NJ USA

Background: Immunologic correlates of protection are important in vaccine development because they give insight into mechanisms of protection, assist in the identification of promising vaccine candidates, and serve as endpoints in bridging clinical vaccine studies. Our goal is the development of a methodology to identify immunologic correlates of protection using the Shigella challenge as a model. Methods: The proposed methodology utilizes the random forests (RF) machine learning algorithm as well as Classification and Regression Trees (CART) to detect immune markers that predict protection, identify interactions between variables, and define optimal cutoffs. Logistic regression modeling is applied to estimate the probability of protection and the confidence interval (CI) for such a probability is computed by bootstrapping the logistic regression models. Results: The results demonstrate that the combination of Classification and Regression Trees and random forests complements the standard logistic regression and uncovers subtle immune interactions. Specific levels of immunoglobulin IgG antibody in blood on the day of challenge predicted protection in 75% (95% CI 67-86). Of those subjects that did not have blood IgG at or above a defined threshold, 100% were protected if they had IgA antibody secreting cells above a defined threshold. Comparison with the results obtained by applying only logistic regression modeling with standard Akaike Information Criterion for model selection shows the usefulness of the proposed method. Conclusion: Given the complexity of the immune system, the use of machine learning methods may enhance traditional statistical approaches. When applied together, they offer a novel way to quantify important immune correlates of protection that may help the development of vaccines. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Classification and Regression Trees random forests algorithm Logistic regression Correlate of protection Shigella

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Regional-scale controls on the spatial activity of rockfalls (Turtmann Valley, Swiss Alps) - A multivariate modeling approach

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GEOMORPHOLOGY 2017年 287卷 29-45页

作者： Messenzehl, Karoline Meyer, Hanna Otto, Jan-Christoph Hoffmann, Thomas Dikau, Richard Univ Bonn Dept Geog Bonn Germany Philipps Univ Marburg Fac Geog Marburg Germany Univ Salzburg Dept Geog & Geol Salzburg Austria Fed Inst Hydrol BfG Koblenz Germany

In mountain geosystems, rockfalls are among the most effective sediment transfer processes, reflected in the regional-scale distribution of talus slopes. However, the understanding of the key controlling factors seems to decrease with increasing spatial scale, due to emergent and complex system behavior and not least to recent methodological shortcomings in rockfall modeling research. In this study, we aim (i) to develop a new approach to identify major regional-scale rockfall controls and (ii) to quantify the relative importance of these controls. Using a talus slope inventory in the Turtmann Valley (Swiss Alps), we applied for the first time the decision-tree based random forest algorithm (RF) in combination with a principal component logistic regression (PCLR) to evaluate the spatial distribution of rockfall activity. This study presents new insights into the discussion on whether periglacial rockfall events are controlled more by topo-climatic, cryospheric, paraglacial or/and rock mechanical properties. (i) Both models explain the spatial rockfall pattern very well, given the high areas under the Receiver Operating Characteristic (ROC) curves of >0.83. Highest accuracy was obtained by the RF, correctly predicting 88% of the rockfall source areas. The RF appears to have a great potential in geomorphic research involving multicollinear data. (ii) The regional permafrost distribution, coupled to the bedrock curvature and valley topography, was detected to be the primary rockfall control. Rockfall source areas cluster within a low-radiation elevation belt (2900-3300 m a.s.l,) consistent with a permafrost probability of >90%. The second most important factor is the time since deglaciation, reflected by the high abundance of rockfalls along recently deglaciated (<100 years), north-faring slopes. However, our findings also indicate a strong rock mechanical control on the paraglacial rockfall activity, declining either exponentially or linearly since deglaciation. The s

关键词： Rockfall Logistic regression model random forests algorithm Rock mechanical properties Paraglacial Permafrost

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A data-driven study of important climate factors on the achievement of LEED-EB credits

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BUILDING AND ENVIRONMENT 2015年 90卷 232-244页

作者： Cheng, Jack C. P. Ma, Lucky J. Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Hong Kong Hong Kong Peoples R China

Developed by the USGBC, LEED is currently the most widely adopted credit-based green building rating program worldwide. Buildings can be graded as Platinum, Gold, Silver, or Certified depending on the number of LEED credit points achieved. Selection of LEED credits is an important step in LEED certification planning and application. The relationships between LEED credit achievement and project factors like budget and schedule have already been studied. Apart from these, climate factors such as temperature and precipitation can also affect the selection of green building technologies and therefore the LEED credits achieved. However, study on the relationships between LEED credit achievement and the climate of the building location is lacking. This paper investigates the relationship between climate factors and LEED credits using data mining techniques. The LEED credits achieved and the local climate conditions of 912 LEED certified existing building projects were collected and analyzed. By setting the climate factors as variables and the credit achievements as the targets, 26 classification models were built using the random forests classification algorithm. The variable importance for each credit was then calculated based on the contribution to the classification performance. For the models with high AUC performance, high importance climate factors were identified and discussed. The results show that some climate factors, such as diurnal temperature range, have a notable correlation with the achievement of certain LEED credits. Implications and contributions of the results are also discussed. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Climate factors Data mining Green building LEED random forests algorithm

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Spatio-temporal interpolation of soil water, temperature, and electrical conductivity in 3D+T: The Cook Agronomy Farm data set

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SPATIAL STATISTICS 2015年第PartA期14卷 70-90页

作者： Gasch, Caley K. Hengl, Tomislav Graeler, Benedikt Meyer, Hanna Magney, Troy S. Brown, David J. Washington State Univ Dept Crop & Soil Sci Pullman WA 99164 USA Wageningen Univ & Res ISRIC World Soil Informat Wageningen Netherlands Univ Munster Inst Geoinformat Munster Germany Univ Marburg Dept Geog Environm Informat D-35032 Marburg Germany Univ Idaho Coll Nat Resources Moscow ID 83843 USA

The paper describes a framework for modeling dynamic soil properties in 3-dimensions and time (3D + T) using soil data collected with automated sensor networks as a case study. Two approaches to geostatistical modeling and spatio-temporal predictions are described: (1) 3D + T predictive modeling using random forests algorithms, and (2) 3D + T kriging model after detrending the observations for depth-dependent seasonal effects. All the analyses used data from the Cook Agronomy Farm (37 ha), which includes hourly measurements of soil volumetric water content, temperature, and bulk electrical conductivity at 42 stations and five depths (0.3, 0.6, 0.9, 1.2, and 1.5 m), collected over five years. This data set also includes 2-and 3-dimensional, temporal, and spatio-temporal covariates covering the same area. The results of (strict) leave-onestation- out cross-validation indicate that both models accurately predicted soil temperature, while predictive power was lower for water content, and lowest for electrical conductivity. The kriging model explained 37%, 96%, and 18% of the variability in water content, temperature, and electrical conductivity respectively versus 34%, 93%, and 5% explained by the random forests model. A less rigorous simple cross-validation of the random forests model indicated improved predictive power when at least some data were available for each station, explaining 86%, 97%, and 88% of the variability in water content, temperature, and electrical conductivity respectively. The high difference between the strict and simple cross-validation indicates high temporal auto-correlation of values at measurement stations. Temporal model components (i.e. day of the year and seasonal trends) explained most of the variability in observations in both models for all three variables. The seamless predictions of 3D + T data produced from this analysis can assist in understanding soil processes and how they change through a season, under different land management

关键词： Digital soil mapping random forests algorithm Regression-kriging Soil sensor network

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Network Traffic Classification - A Comparative Study of Two Common Decision Tree Methods: C4.5 and random Forest 2

Network Traffic Classification - A Comparative Study of Two ...

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2nd International Conference Electronic Design ICED

作者： Munther, Alhamza Othman, Rozmie R. Alalousi, Alabass Anbar, Mohammed Nizam, Shahrul Univ Malaysia Perlis Sch Comp & Commun Engn Perlis Malaysia Univ Thamar Sch Comp & Informat Syst Dept Comp Thamar Yemen Univ Sains Malaysia Natl Adv Ctr Excellence IPv6 George Town Malaysia

ISBN: (纸本)9781479961030

Network traffic classification gains continuous interesting while many applications emerge on the different kinds of networks with obfuscation techniques. Decision tree is a supervised machine learning method used widely to identify and classify network traffic. In this paper, we introduce a comparative study focusing on two common decision tree methods namely: C4.5 and random forest. The study offers comparative results in two different factors are accuracy of classification and processing time. C4.5 achieved high percentage of classification accuracy reach to 99.67 for 24000 instances while random Forest was faster than C4.5 in term of processing time.

关键词： Traffic Classification Machine learning Supervised Learning random forests algorithm

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random forests Based View Generation for Multiview TV

Random Forests Based View Generation for Multiview TV

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IEEE 24th International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Pourazad, Mahsa T. Xu, Di Nasiopoulos, Panos TELUS Commun Inc Vancouver BC Canada

ISBN: (纸本)9780769549156;9781479902279

The appearance of multiview display systems in the consumer market is not far from reality. With technical knowledge in this field constantly improving, production of multiview content is the only other key factor that will determine the successful adoption of this technology. Multiview content can be generated from two or three views and their associated depth maps. Estimating a high quality depth map is challenging. Moreover transmission of depth map information requires extra bandwidth. In this study, we propose and effective algorithm, which utilizes a 3D visual attention model, multiple monocular depth cues and a fraction of depth information for estimating the whole depth map of the scene using the random forests (RF) machine learning algorithm. Having the estimated depth maps and stereo videos, other views may be synthesized. Performance evaluations have shown that the proposed method estimates high quality depth maps for stereo sequence from limited depth information. Implementation of our proposed technique in the future multiview pipeline eliminates the need for estimating and transmitting the whole depth map for all the views, producing high quality multiview content while reducing the required bandwidth.

关键词： multiview TV multiview content depth map estimation 3D visual attention model random forests algorithm

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