scholarly journals Characterization of Road Condition with Data Mining Based on Measured Kinematic Vehicle Parameters

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Johannes Masino ◽  
Jakob Thumm ◽  
Guillaume Levasseur ◽  
Michael Frey ◽  
Frank Gauterin ◽  
...  
Keyword(s):  
Data Mining ◽  
Support Vector ◽  
Matlab Toolbox ◽  
Data Set ◽  
The Road ◽  
Acceleration Sensors ◽  
Road Surfaces ◽  
Road Condition ◽  
Sensor Signals

This work aims at classifying the road condition with data mining methods using simple acceleration sensors and gyroscopes installed in vehicles. Two classifiers are developed with a support vector machine (SVM) to distinguish between different types of road surfaces, such as asphalt and concrete, and obstacles, such as potholes or railway crossings. From the sensor signals, frequency-based features are extracted, evaluated automatically with MANOVA. The selected features and their meaning to predict the classes are discussed. The best features are used for designing the classifiers. Finally, the methods, which are developed and applied in this work, are implemented in a Matlab toolbox with a graphical user interface. The toolbox visualizes the classification results on maps, thus enabling manual verification of the results. The accuracy of the cross-validation of classifying obstacles yields 81.0% on average and of classifying road material 96.1% on average. The results are discussed on a comprehensive exemplary data set.

scholarly journals Characterizing Malignant Melanoma Clinically Resembling Seborrheic Keratosis Using Deep Knowledge Transfer

Cancers ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 6300
Author(s):  
Panagiota Spyridonos ◽  
George Gaitanis ◽  
Aristidis Likas ◽  
Ioannis Bassukas
Keyword(s):  
Public Access ◽  
Classification Model ◽  
Bayesian Optimization ◽  
Support Vector ◽  
Seborrheic Keratosis ◽  
Convolutional Network ◽  
Data Set ◽  
Computer Based ◽  
Hyperparameter Selection

Malignant melanomas resembling seborrheic keratosis (SK-like MMs) are atypical, challenging to diagnose melanoma cases that carry the risk of delayed diagnosis and inadequate treatment. On the other hand, SK may mimic melanoma, producing a ‘false positive’ with unnecessary lesion excisions. The present study proposes a computer-based approach using dermoscopy images for the characterization of SΚ-like MMs. Dermoscopic images were retrieved from the International Skin Imaging Collaboration archive. Exploiting image embeddings from pretrained convolutional network VGG16, we trained a support vector machine (SVM) classification model on a data set of 667 images. SVM optimal hyperparameter selection was carried out using the Bayesian optimization method. The classifier was tested on an independent data set of 311 images with atypical appearance: MMs had an absence of pigmented network and had an existence of milia-like cysts. SK lacked milia-like cysts and had a pigmented network. Atypical MMs were characterized with a sensitivity and specificity of 78.6% and 84.5%, respectively. The advent of deep learning in image recognition has attracted the interest of computer science towards improved skin lesion diagnosis. Open-source, public access archives of skin images empower further the implementation and validation of computer-based systems that might contribute significantly to complex clinical diagnostic problems such as the characterization of SK-like MMs.

CHARACTERIZATION OF ROAD PROFILES BASED ON FRACTAL PROPERTIES AND CONTACT MECHANICS

2017 ◽  
Vol 90 (2) ◽  
pp. 405-427 ◽  
Author(s):  
Mehran Motamedi ◽  
Saied Taheri ◽  
Corina Sandu ◽  
Pierrick Legrand
Keyword(s):  
Contact Theory ◽  
The Road ◽  
Frictional Properties ◽  
Wet Friction ◽  
Fractal Properties ◽  
Fractal Parameters ◽  
Road Surfaces ◽  
Friction Measurements ◽  
Friction Prediction

ABSTRACT A major challenge in tire and road engineering is to understand the intricate mechanisms of friction. Pavement texture is a feature of the road surface that determines most tire–road interactions, and it can be grouped into two classes of macro-texture and micro-texture. Since the effects of micro-texture and macro-texture dominate the friction measurements at low and high slip speeds, they can help provide sufficient resistance to skidding, if maintained at high levels. A non-contact profilometer is used to measure the macro- and micro-texture of several different road surfaces. The friction number for each surface is measured using the Michigan Department of Transportation's (MDOT) single axle friction trailer. Some fractal parameters of the measured profiles are estimated, and it is proved that all measured profiles display strong fractal behavior. The correlation between texture and fractal parameters and friction is investigated. It is shown that while global fractal quantities fail to classify pavement profiles, the pointwise Hölder exponent as a local fractal parameter, and also the mean square roughness, can discriminate profiles that have different frictional properties. For five road surfaces, two-dimensional (2D) characterization is done using one-dimensional (1D) profile measurements. The hysteretic coefficient of friction is estimated using the contact theory developed by B.N.J. Persson. Good correlation is observed between the wet friction measurements and friction prediction results.

Empirical study on the effect of using synthetic attributes on classification algorithms

2017 ◽  
Vol 10 (2) ◽  
pp. 111-129 ◽  
Author(s):  
Ali Hasan Alsaffar
Keyword(s):  
Data Mining ◽  
Empirical Study ◽  
Student Performance ◽  
Software Tool ◽  
Real Data ◽  
Support Vector ◽  
Classification Algorithms ◽  
Past Performance ◽  
Data Set ◽  
Content Type

Purpose The purpose of this paper is to present an empirical study on the effect of two synthetic attributes to popular classification algorithms on data originating from student transcripts. The attributes represent past performance achievements in a course, which are defined as global performance (GP) and local performance (LP). GP of a course is an aggregated performance achieved by all students who have taken this course, and LP of a course is an aggregated performance achieved in the prerequisite courses by the student taking the course. Design/methodology/approach The paper uses Educational Data Mining techniques to predict student performance in courses, where it identifies the relevant attributes that are the most key influencers for predicting the final grade (performance) and reports the effect of the two suggested attributes on the classification algorithms. As a research paradigm, the paper follows Cross-Industry Standard Process for Data Mining using RapidMiner Studio software tool. Six classification algorithms are experimented: C4.5 and CART Decision Trees, Naive Bayes, k-neighboring, rule-based induction and support vector machines. Findings The outcomes of the paper show that the synthetic attributes have positively improved the performance of the classification algorithms, and also they have been highly ranked according to their influence to the target variable. Originality/value This paper proposes two synthetic attributes that are integrated into real data set. The key motivation is to improve the quality of the data and make classification algorithms perform better. The paper also presents empirical results showing the effect of these attributes on selected classification algorithms.

scholarly journals Building attack detection system base on machine learning

2021 ◽  
Vol 6 (2) ◽  
pp. 018-032
Author(s):  
Rasha Thamer Shawe ◽  
Kawther Thabt Saleh ◽  
Farah Neamah Abbas
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Network Security ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Attack Detection ◽  
Support Vector ◽  
Data Set ◽  
Kdd Cup 99

These days, security threats detection, generally discussed to as intrusion, has befitted actual significant and serious problem in network, information and data security. Thus, an intrusion detection system (IDS) has befitted actual important element in computer or network security. Avoidance of such intrusions wholly bases on detection ability of Intrusion Detection System (IDS) which productions necessary job in network security such it identifies different kinds of attacks in network. Moreover, the data mining has been playing an important job in the different disciplines of technologies and sciences. For computer security, data mining are presented for serving intrusion detection System (IDS) to detect intruders accurately. One of the vital techniques of data mining is characteristic, so we suggest Intrusion Detection System utilizing data mining approach: SVM (Support Vector Machine). In suggest system, the classification will be through by employing SVM and realization concerning the suggested system efficiency will be accomplish by executing a number of experiments employing KDD Cup’99 dataset. SVM (Support Vector Machine) is one of the best distinguished classification techniques in the data mining region. KDD Cup’99 data set is utilized to execute several investigates in our suggested system. The experimental results illustration that we can decrease wide time is taken to construct SVM model by accomplishment suitable data set pre-processing. False Positive Rate (FPR) is decrease and Attack detection rate of SVM is increased .applied with classification algorithm gives the accuracy highest result. Implementation Environment Intrusion detection system is implemented using Mat lab 2015 programming language, and the examinations have been implemented in the environment of Windows-7 operating system mat lab R2015a, the processor: Core i7- Duo CPU 2670, 2.5 GHz, and (8GB) RAM.

scholarly journals Prediction and Classification into Benign and Malignant using the Clinical Testing Features

2020 ◽  
Vol 9 (10) ◽  
pp. 55-61
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Image Classification ◽  
Naive Bayes ◽  
Malignant Tumors ◽  
Naïve Bayes ◽  
Support Vector ◽  
Natural Image ◽  
Data Set ◽  
Classification Techniques

Breast Cancer is the most often identified cancer among women and a major reason for the increased mortality rate among women. As the diagnosis of this disease manually takes long hours and the lesser availability of systems, there is a need to develop the automatic diagnosis system for early detection of cancer. The advanced engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. Data mining techniques contribute a lot to the development of such a system, Classification, and data mining methods are an effective way to classify data. For the classification of benign and malignant tumors, we have used classification techniques of machine learning in which the machine learns from the past data and can predict the category of new input. This study is a relative study on the implementation of models using Support Vector Machine (SVM), and Naïve Bayes on Breast cancer Wisconsin (Original) Data Set. With respect to the results of accuracy, precision, sensitivity, specificity, error rate, and f1 score, the efficiency of each algorithm is measured and compared. Our experiments have shown that SVM is the best for predictive analysis with an accuracy of 99.28% and naïve Bayes with an accuracy of 98.56%. It is inferred from this study that SVM is the well-suited algorithm for prediction.

On the application of compression-based metrics to identifying anomalous behaviour in web traffic

2020 ◽  
Vol 28 (4) ◽  
pp. 546-557
Author(s):  
Gonzalo de la Torre-Abaitua ◽  
Luis F Lago-Fernández ◽  
David Arroyo
Keyword(s):  
Feature Vector ◽  
Support Vector ◽  
Web Traffic ◽  
Data Set ◽  
Security Breaches ◽  
Web Attacks ◽  
Web Logs ◽  
Security Event ◽  
Classification Procedures

Abstract In cybersecurity, there is a call for adaptive, accurate and efficient procedures to identifying performance shortcomings and security breaches. The increasing complexity of both Internet services and traffic determines a scenario that in many cases impedes the proper deployment of intrusion detection and prevention systems. Although it is a common practice to monitor network and applications activity, there is not a general methodology to codify and interpret the recorded events. Moreover, this lack of methodology somehow erodes the possibility of diagnosing whether event detection and recording is adequately performed. As a result, there is an urge to construct general codification and classification procedures to be applied on any type of security event in any activity log. This work is focused on defining such a method using the so-called normalized compression distance (NCD). NCD is parameter-free and can be applied to determine the distance between events expressed using strings. As a first step in the concretion of a methodology for the integral interpretation of security events, this work is devoted to the characterization of web logs. On the grounds of the NCD, we propose an anomaly-based procedure for identifying web attacks from web logs. Given a web query as stored in a security log, a NCD-based feature vector is created and classified using a support vector machine. The method is tested using the CSIC-2010 data set, and the results are analyzed with respect to similar proposals.

Snow and Ice Control System Based on Slipperiness Data Transmitted by Drivers: Usefulness of Subjective Slipperiness Data

1996 ◽  
Vol 1533 (1) ◽  
pp. 44-49
Author(s):  
Takashi Nakatsuji ◽  
Takashi Fujiwara ◽  
Toru Hagawara ◽  
Yuki Onodera
Keyword(s):  
Control Systems ◽  
Weather Conditions ◽  
Friction Coefficients ◽  
The Road ◽  
The Third ◽  
Road Surfaces ◽  
Road Condition ◽  
The Relationship ◽  
Ice Control ◽  
Snow And Ice

In Japan, the regulation of studded tires requires the establishment of new countermeasures for effective ice control on slippery roads in winter. The most important information for snow and ice control systems is determining the slipperiness of road surfaces. To detect the slipperiness simply and precisely, a monitoring system was examined in which drivers judged the slipperiness. To evaluate the suitability of such slipperiness data, three investigations were carried out: (a) the relationship between the road condition classification and the slipperiness index, (b) the effectiveness of the subdivision of road classification, and (c) the comparison of slipperiness indexes with the actual friction coefficients. To address the first problem, the road conditions were investigated for 1 month with the cooperation of 10 taxi companies. It was found that the subjective slipperiness index was more sensitive to changes in weather conditions than the road classifications, and that icy roads do not always correspond to slippery roads. That is, there was a limitation on expressing road conditions by road classification. For the second problem, a similar investigation was performed by subdividing the road conditions into more classes. It was concluded that the subdivision of road classification is not so effective in precisely representing the slipperiness of roads. For the third problem, it was clarified that the subjective slipperiness indexes more or less agree with the actual friction coefficients. As for the results, the slipperiness index showed potential for use in snow and ice control systems.

Uncertainty in the standardized method "Characterization of the acoustic properties of road surfaces" by CEN TC227 WG5

2021 ◽  
Vol 263 (6) ◽  
pp. 314-322
Author(s):  
Gijsjan van Blokland ◽  
Luc Goubert
Keyword(s):  
Calculation Model ◽  
Acoustic Properties ◽  
Noise Emission ◽  
Road Vehicles ◽  
The Road ◽  
Road Pavement ◽  
Road Surfaces ◽  
Standardized Method ◽  
Limited Accuracy

TC227 of CEN has developed a method to determine the effect of the road pavement on the sound emission of road vehicles. The proposed methods can be applied to define the acoustic label value of a generic or proprietary pavement type, to check compliance of a pavement with the specifications for that pavement type and to monitor the development of the acoustic properties over the lifetime of the product. With the procedure one can additionally derive the coefficients for the pavement correc tion in the noise emission formulae for road vehicles in the CNOSSOS-EU calculation model. The application of the method exhibits a limited accuracy. The paper investigates the sources of uncertainty of the standardized method and combine the contributions into a single overall uncertainty according to the procedures laid down in Guide 98-3 of ISO. The uncertainty is determined for each of the listed application areas. From the uncertainty analysis the major contributions are identified. Improvement of the method shall focus on only these contributions.

Characterization of the road surfaces in real time

2016 ◽  
Vol 105 ◽  
pp. 93-98 ◽  
Author(s):  
David Ibarra ◽  
Ricardo Ramírez-Mendoza ◽  
Saúl Ibarra
Keyword(s):  
Real Time ◽  
The Road ◽  
Road Surfaces

Lubricated Friction of Rubber. I. Introduction

1968 ◽  
Vol 41 (4) ◽  
pp. 832-842 ◽  
Author(s):  
E. M. Bevilacqua ◽  
E. P. Percarpio
Keyword(s):  
Skid Resistance ◽  
Relative Importance ◽  
The Road ◽  
Quantitative Estimates ◽  
Road Surfaces ◽  
Quantitative Basis ◽  
Improved Technique ◽  
Tread Rubber ◽  
Wet Skid Resistance

Abstract This review introduces a series of reports on a quantitative study of friction of rubber on wet surfaces. It was derived from concern over safety aspects of skidding on wet roads; this first paper deals with the relation between safety and traction. Subsequent papers deal with: A quantitative approach to characterization of road surfaces, identification of the surface features of importance, and estimation of their relative contributions to lubricated friction. Quantitative estimates of effects of properties of rubber materials on lubricated friction and an analysis of their relative importance in interaction with the significant features of the road surface. A quantitative basis for evaluation of wet skid resistance of roads, the choice of the rubber to be used in this evaluation, and methods of testing. An improved technique to measure the property of tread rubber important for wet skid resistance of tires. Identification and interpretation of the nature of friction on ice at low temperatures.

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