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检索条件"任意字段=5th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2004"

共 182 条记录，以下是51-60 订阅

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Classifying Ransomware Using Machine learning Algorithms 20th

Classifying Ransomware Using Machine Learning Algorithms

引用

20th international conference on intelligent data engineering and automated learning (ideal)

作者： Egunjobi, Samuel Parkinson, Simon Crampton, Andrew Univ Huddersfield Sch Comp & Engn Dept Comp Sci Huddersfield HD1 3DH W Yorkshire England

ISBN: (纸本)9783030336172;9783030336165

Ransomware is a continuing threat and has resulted in the battle between the development and detection of new techniques. Detection and mitigation systems have been developed and are in wide-scale use;however, their reactive nature has resulted in a continuing evolution and updating process. this is largely because detection mechanisms can often be circumvented by introducing changes in the malicious code and its behaviour. In this paper, we demonstrate a classification technique of integrating both static and dynamic features to increase the accuracy of detection and classification of ransomware. We train supervised machine learning algorithms using a test set and use a confusion matrix to observe accuracy, enabling a systematic comparison of each algorithm. In this work, supervised algorithms such as the Naive Bayes algorithm resulted in an accuracy of 96% with the test set result, SVM 99.5%, random forest 99.5%, and 96%. We also use Youden's index to determine sensitivity and specificity.

关键词： Ransomware Malware Machine learning

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A framework for mining association rules in data warehouses

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5th international conference on intelligent data engineering and automated learning, ideal 2004

作者： Tjioe, Haorianto Cokrowijoyo Taniar, David School of Business Systems Monash University Clayton VIC3800 Australia

ISBN: (纸本)3540228810

the effort of data mining, especially in relation to association rules in real world business applications, is significantly important. Recently, association rules algorithms have been developed to cope with multidimensional data. In this paper we are concerned with mining association rules in data warehouses by focusing on its measurement of summarized data. We propose two algorithms: HAvg and VAvg, to provide the initialization data for mining association rules in data warehouses by concentrating on the measurement of aggregate data. these algorithms are capable of providing efficient initialized data extraction from data warehouses and are used for mining association rules in data warehouses. © Springer-Verlag Berlin Heidelberg 2004.

关键词： data warehouses

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Clustering and Visualizing SOM Results

Clustering and Visualizing SOM Results

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11th international conference on intelligent data engineering and automated learning

作者： Costa, Jose Alfredo F. Fed Univ Dept Elect Engn Natal RN Brazil

ISBN: (纸本)9783642153808

Self-organizing maps (SOM) had been used for input data quantization and visual display of data, an important property that does not exist in most of clustering algorithms. Effective data clustering using SOM involves two or three steps procedure. After proper network training, units can be clustered generating regions of neurons which are related to data clusters. the basic assumption relies on the data density approximation by the neurons through unsupervised learning. this paper presents a gradient-based SOM visualization method and compares it with U-matrix. It also discusses steps toward clustering using SOM and morphological operators. Results using benchmark datasets show that the new method is more robust to choice of parameters in the filtering phase than the conventional method. the paper also proposes an enhancing method to map visualization taking advantage of the neurons activity, which improve cluster detection especially in small maps.

关键词： data clustering visualization self-organizing maps

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A Two-stage H.264 based Video Compression Method for Automotive Cameras 5

A Two-stage H.264 based Video Compression Method for Automot...

引用

IEEE 5th international conference on Industrial Cyber-Physical Systems (ICPS)

作者： Wang, Yiting Chan, Pak Hung Donzella, Valentina Univ Warwick WMG Coventry W Midlands England

ISBN: (纸本)9781665497701

With the development of automated vehicles and advanced driver assistance systems, the compression of the large amount of data generated by the vehicle camera sensors becomes a necessary processing step to improve the automated driving system efficiency. H.264 is a widely adopted video compression scheme, and it has been designed for human vision. Rate control in H.264 uses fixed quantisation parameter, however, this process can lead to fluctuation in different regions of the image quality of each frame. In this paper, we propose a two-stage H.264 based video compression framework, named "Two Stage Compression (TSC)", to compress the automotive camera videos with different values of compression rate in different regions of each frame. In the first stage, each frame will be divided into the region-of-interest and the region-out-of-interest. In the second stage, different compression ratios will be applied based on the importance of the region. the experimental results show that under the same overall compression ratio, our proposed TSC increments the semantic-aware PSNR by 3.213 dB compared to uniform H.264 compression. Our method is also compared to uniform H.264 compression using a segmentation algorithm, with an improvement of 1.77% in mIOU, the average Intersection over Union.

关键词： Automotive camera data video compression semantic segmentation machine learning intelligent Vehicles automated and Assisted driving

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Interval Filter: A Locality-Aware Alternative to Bloom Filters for Hardware Membership Queries by Interval Classification

Interval Filter: A Locality-Aware Alternative to Bloom Filte...

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11th international conference on intelligent data engineering and automated learning

作者： Quislant, Ricardo Gutierrez, Eladio Plata, Oscar Zapata, Emilio L. Univ Malaga Dept Comp Architecture ETSI Informat E-29071 Malaga Spain

ISBN: (纸本)9783642153808

Bloom filters are data structures that can efficiently represent a set of elements providing operations of insertion and membership testing. Nevertheless, these filters may yield false positive results when testing for elements that have not been previously inserted. In general, higher false positive rates are expected for sets with larger cardinality with constant filter size. this paper shows that for sets where a distance metric can be defined, reducing the false positive rate is possible if elements to be inserted exhibit locality according to this metric. In this way, a hardware alternative to Bloom filters able to extract spatial locality features is proposed and analyzed.

关键词： data structures

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Freight Vehicle Travel Time Prediction Using Sparse Gaussian Processes Regression with Trajectory data 1

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17th international conference on intelligent data engineering and automated learning (ideal)

作者： Li, Xia Bai, Ruibin Univ Nottingham Ningbo China Ningbo 315100 Zhejiang Peoples R China

ISBN: (数字)9783319462578

ISBN: (纸本)9783319462578;9783319462561

Travel time prediction is important for freight transportation companies. Accurate travel time prediction can help these companies make better planning and task scheduling. For several reasons, most companies are not able to obtain traffic flow data from traffic management authorities, but a large amount of trajectory data were collected everyday which has not been fully utilised. In this study, we aim to fill this gap and performed travel time prediction for freight vehicles at individual level using sparse Gaussian processes regression (SGPR) models with trajectory data. the results show that the prediction performance can be gradually improved by adding more mean speed estimates of traveled distance from the first 5 min as the real-time information. the overall performances of SGPR models are very similar to full GP, supported vector regression (SVR) and artificial neural network (ANN) models. the computational complexity of SGPR models is O(mn(2)), and it does not require lengthy model fitting process as SVR and ANN. this makes GP models more practicable for real-world practice in large-scale transportation data analyses.

关键词： Travel time prediction Machine learning Gaussian Processes Sparse approximation Trajectory data Freight vehicle

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the Penalty Avoiding Rational Policy Making Algorithm in Continuous Action Spaces

The Penalty Avoiding Rational Policy Making Algorithm in Con...

引用

11th international conference on intelligent data engineering and automated learning

作者： Miyazaki, Kazuteru Natl Inst Acad Degrees & Univ Evaluat Kodaira Tokyo 1878587 Japan

ISBN: (纸本)9783642153808

Reinforcement learning involves learning to adapt to environments through the presentation of rewards - special input - serving as clues. To obtain quick rational policies, profit sharing, rational policy making algorithm, penalty avoiding rational policy making algorithm (PARP), PS-r* and PS-r# are used. they are called Exploitation-oriented learning (XoL). When applying reinforcement learning to actual problems, treatment of continuous-valued input and output are sometimes required. A method based on PARP is proposed as a XoL method corresponding to the continuous-valued input, but continuous-valued output cannot be treated. We study the treatment of continuous-valued output suitable for a XoL method in which the environment includes both a reward and a penalty. We extend PARP in the continuous-valued input to continuous-valued output. We apply our proposal to the pole-cart balancing problem and confirm its validity.

关键词： Reinforcement learning

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automated, Nomenclature Based data Point Selection for Industrial Event Log Generation 19th

Automated, Nomenclature Based Data Point Selection for Indus...

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Koehler, Wolfgang Jing, Yanguo Coventry Univ Fac Engn Environm & Comp Sch Comp Elect & Math Priory St Coventry CV1 5FB W Midlands England

ISBN: (纸本)9783030034962;9783030034955

Within the automotive industry today, data collection, for legacy manufacturing equipment, largely relies on the data being pushed from the machine's PLCs to an upper system. Not only does this require programmers' efforts to collect and provide the data, but it is also prone to errors or even intentional manipulation. External monitoring, is available through Open Platform Communication (OPC), but it is time consuming to set up and requires expert knowledge of the system as well. A nomenclature based methodology has been devised for the external monitoring of unknown controls systems, adhering to a minimum set of rules regarding the naming and typing of the data points of interest, which can be deployed within minutes without human intervention. the validity of the concept will be demonstrated through implementation within an automotive body shop and the quality of the created log will be evaluated. the impact of such a fine grained monitoring effort on the communication infrastructure will also be measured within the manufacturing facility. It is concluded that, based on the methodology provided in this paper, it is possible to derive OPC groups and items from a PLC program without human intervention in order to obtain a detailed event log.

关键词： Terminology

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Video based human behavior identification using frequency domain analysis

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5th international conference on intelligent data engineering and automated learning, ideal 2004

作者： Wang, Jessica Jun Lin Singh, Sameer ATR Lab Computer Science Department School of Engineering Computer Science and Mathematics University of Exeter Harrison Building Exeter United Kingdom

ISBN: (纸本)3540228810

the identification of human activity in video, for example whether a person is walking, clapping, waving, etc. is extremely important for video interpretation. Since different people would perform the same action across different number of frames, matching training and test actions is not a trivial task. In this paper we discuss a new technique for video shot matching where the shots matched are of different sizes. the proposed technique is based on frequency domain analysis of feature data and it is shown to achieve very high recognition accuracy on a number of different human actions with synthetic data and real life data. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Frequency domain analysis

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Extracting Features from an Electrical Signal of a Non-Intrusive Load Monitoring System

Extracting Features from an Electrical Signal of a Non-Intru...

引用

11th international conference on intelligent data engineering and automated learning

作者： Figueiredo, Marisa B. de Almeida, Ana Ribeiro, Bernardete Martins, Antonio Univ Coimbra Ctr Informat & Syst Polo 2 P-3030290 Coimbra Portugal ISA P-3030320 Coimbra Portugal

ISBN: (纸本)9783642153808

Improving energy efficiency by monitoring household electrical consumption is of significant importance with the present-day climate change concerns. A solution for the electrical consumption management problem is the use of a non-intrusive load monitoring system (NILM). this system captures the signals from the aggregate consumption, extracts the features from these signals and classifies the extracted features in order to identify the switched on appliances. An effective device identification (ID) requires a signature to be assigned for each appliance. Moreover, to specify an ID for each device, signal processing techniques are needed for extracting the relevant features. this paper describes a technique for the steady-states recognition in an electrical digital signal as the first stage for the implementation of an innovative NILM. Furthermore, the final goal is to develop an intelligent system for the identification of the appliances by automated learning. the proposed approach is based on the ratio value between rectangular areas defined by the signal samples. the computational experiments show the method effectiveness for the accurate steady-states identification in the electrical input signals.

关键词： automated learning and identification feature extraction and classification non-intrusive load monitoring

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