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ردیف | عنوان | نوع |
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1 |
Data Mining Strategies for Real-Time Control in New York City
استراتژی داده کاوی برای کنترل زمان واقعی در شهر نیویورک-2105 The Data Mining System (DMS) at New York City Department of Transportation (NYCDOT) mainly consists of four database
systems for traffic and pedestrian/bicycle volumes, crash data, and signal timing plans as well as the Midtown in Motion (MIM)
systems which are used as part of the NYCDOT Intelligent Transportation System (ITS) infrastructure. These database and
control systems are operated by different units at NYCDOT as an independent database or operation system. New York City
experiences heavy traffic volumes, pedestrians and cyclists in each Central Business District (CBD) area and along key arterial
systems. There are consistent and urgent needs in New York City for real-time control to improve mobility and safety for all
users of the street networks, and to provide a timely response and management of random incidents. Therefore, it is necessary to
develop an integrated DMS for effective real-time control and active transportation management (ATM) in New York City.
This paper will present new strategies for New York City suggesting the development of efficient and cost-effective DMS,
involving: 1) use of new technology applications such as tablets and smartphone with Global Positioning System (GPS) and
wireless communication features for data collection and reduction; 2) interface development among existing database and control
systems; and 3) integrated DMS deployment with macroscopic and mesoscopic simulation models in Manhattan. This study
paper also suggests a complete data mining process for real-time control with traditional static data, current real timing data from
loop detectors, microwave sensors, and video cameras, and new real-time data using the GPS data. GPS data, including using taxi
and bus GPS information, and smartphone applications can be obtained in all weather conditions and during anytime of the day.
GPS data and smartphone application in NYCDOT DMS is discussed herein as a new concept.
© 2014 The Authors. Published by Elsevier B.V.
Selection and peer-review under responsibility of Elhadi M. Shakshu
Keywords: Data Mining System (DMS), New York City, real-time control, active transportation management (ATM), GPS data |
مقاله انگلیسی |
2 |
Integrating Human Behavior Modeling and Data Mining Techniques to Predict Human Errors in Numerical Typing
تجمیع مدلسازی رفتار انسان و تکنیک های داده کاوی به منظور پیش بینی خطاهای انسانی در تایپ عددی-2015 Numerical typing errors can lead to serious consequences,
but various causes of human errors and the lack of contextual
clues in numerical typing make their prediction difficult.
Human behavior modeling can predict the general tendency in
making errors, while data mining can recognize neurophysiological
feedback in detecting cognitive abnormality on a trial-by-trial
basis. This study suggests integrating human behavior modeling
and datamining to predict human errors because it utilizes both 1)
top-down inference to transform interactions between task characteristics
and conditions into a general inclination of an average
operator to make errors and 2) bottom-up analysis in parsing psychophysiological
measurements into an individual’s likelihood of
making errors on a trial-by-trial basis. Real-time electroencephalograph
(EEG) features collected in a numerical typing experiment
and modeling features produced by an enhanced human behavior
model (queuing network model human processor) were combined
to improve error classification performance by a linear discriminant
analysis (LDA) classifier. Integrating EEG and modeling
features improved the results of LDA classification by 28.3% in
keenness (d) and by 10.7% in the area under ROC curve (AUC)
from that of using EEG only; it also outperformed the other three
benchmarking scenarios: using behaviors only, using apparent task
features, and using task features plus trial information. The AUC
was significantly increased from using EEG along only if EEG +
Model features were used.
Index Terms—Behavior modeling, data mining, electroencephalograph
(EEG), human errors, linear discriminant analysis,
numerical typing. |
مقاله انگلیسی |
3 |
A case analysis of embryonic data mining success
تجزیه و تحلیل مورد موفقیت داده کاوی جنینی-2015 Within highly competitive business environments, data mining (DM) is viewed as a significant technol-ogy to enhance decision-making processes by transforming data into valuable and actionable informationto gain competitive advantage. There appears, however, to be a dearth of empirical case studies whichconsider in detail the initial stages in DM management to enable apt foundation for its later successfulimplementation. Our research applied a multi-method strategy to determine the critical success factors ofembryonic DM implementation. We propose and validate, through a series of cases, a conceptual frame-work to guide practitioners’ adoption of DM. Our findings reveal additional issues for applied decisionmaking in the context of DM success.
Keywords:
Data mining
Predictive analytics
Critical success factors
Case analysis |
مقاله انگلیسی |
4 |
A methodological review of data mining techniques in predictive medicine: An application in hemodynamic prediction for abdominal aortic aneurysm disease
بررسی روش شناختی تکنیک های داده کاوی در پیشگویی پزشکی: کاربرد در پیش بینی همودینامیک برای بیماری آنوریسم آئورت شکمی-2015 Modern clinics and hospitals need accurate real-time prediction tools. This paper reviews
the importance and present trends of data mining methodologies in predictive medicine by
focusing on hemodynamic predictions in abdominal aortic aneurysm (AAA). It also provides
potential data mining working frameworks for hemodynamic predictions in AAA. These
frameworks either allow the coupling between a typical computational modeling simulation
and various data mining techniques, using the existing medical datasets of real-patient and
mining it directly using various data mining techniques or implementing visual data mining
approach to already available computed results of various hemodynamic features within the
AAA models. These approaches allow the possibility of statistically predicting rupture
potentials of aneurismal patients and ideally provide an alternate solution for substituting
tedious and time-consuming computational modeling. Prediction trends of patient-specific
aneurismal conditions via mining huge volume of medical data can also speed up the
decision making process in real life medicine.
Keywords:
Data mining techniques
Hemodynamic prediction
Abdominal aortic aneurysm |
مقاله انگلیسی |
5 |
Atomic data mining numerical methods, source code SQlite with Python
روش های عددی داده کاوی اتمی، کد منبع sqlite با پایتون-2015 This paper introduces a recently published Python data mining book (chapters, topics, samples of Python source code written
by its authors) to be used in data mining via world wide web and any specific database in several disciplines (economic,
physics, education, marketing. etc). The book started with an introduction to data mining by explaining some of the data
mining tasks involved classification, dependence modelling, clustering and discovery of association rules. The book addressed
that using Python in data mining has been gaining some interest from data miner community due to its open source, general
purpose programming and web scripting language; furthermore, it is a cross platform and it can be run on a wide variety of
operating systens such as Linux, Windows, FreeBSD, Macintosh, Solaris, OS/2, Amiga, AROS, AS/400, BeOS, OS/390,
z/OS, Palm OS, QNX, VMS, Psion, Acorn RISC OS, VxWorks, PlayStation, Sharp Zaurus, Windows CE and even PocketPC.
Finally this book can be considered as a teaching textbook for data mining in which several methods such as machine learning
and statistics are used to extract high-level knowledge from real-world datasets.
© 2012 The Authors. Published by Elsevier Ltd.
Selection and/or peer-review under responsibility of The 2nd International Conference on Integrate
Keywords : Python; atomic data; database; data mining algorithms; data model; collaborative intelligence,
machine learning |
مقاله انگلیسی |
6 |
Data mining as a predictive model for Chelidonium majus extracts production
داده کاوی به عنوان یک مدل پیش بینی برای Chelidonium مجوس عصاره تولید-2015 tChelidonium majus L. (C. majus) is a herbaceous perennial plant of the Papaveraceae family. C. majus isrich of benzylisoquinoline alkaloids: protopine, chelidonine, stylopine, coptisine, berberine, sanguinarineand chelerythrine. Each one of them is endowed with a peculiar pharmacological/toxicological profile.Different C. majus extracts, obtained from different plant parts and under different extraction conditions,contain the above-mentioned alkaloids in different amounts with regard to each other. Consequentlythey are expected to exert an extremely wide range of therapeutic and/or toxic effects. In this paper,data mining techniques, such as multi-linear regressions and Gaussian processes, were efficiently usedto develop a fast and easy approach by which it is possible to predict how different parts of the plant anddifferent extraction conditions lead to C. majus extracts characterized by different chemical composition,in terms of alkaloid contents. In particular, for each alkaloid a model was developed which correlatesspecific parts of the plant and extraction conditions with the quantity of the desired alkaloid. All modelswere statistically validated for their goodness of fit and robustness and are characterized by correlationcoefficient values (R2) up to 0.9618 and cross-validated correlation coefficient values (LOO-CV R2) upto 0.8343. Thus, each model can help in the selection of the proper parts of the plant and extractionconditions to be exploited in order to obtain a particular extract enriched of a particular alkaloid andendowed with desired properties. Another advantage is that the data mining approach presented herecould even be applied to other plants, and for other chemical entities.© 2014 Published by Elsevier B.V.
Keywords:
Chelidonium majus
Plant extracts
Alkaloids
Data mining
Multiple linear regression
Gaussian process regression |
مقاله انگلیسی |
7 |
Data mining – past, present and future – a typical survey on data streams
داده کاوی - گذشته، حال و آینده - یک بررسی معمولی در جریان داده-2015 Data Stream Mining is one of the area gaining lot of practical significance and is progressing at a brisk pace with new methods,
methodologies and findings in various applications related to medicine, computer science, bioinformatics and stock market
prediction, weather forecast, text, audio and video processing to name a few. Data happens to be the key concern in data mining.
With the huge online data generated from several sensors, Internet Relay Chats, Twitter, Face book, Online Bank or ATM
Transactions, the concept of dynamically changing data is becoming a key challenge, what we call as data streams. In this paper,
we give the algorithm for finding frequent patterns from data streams with a case study and identify the research issues in
handling data streams.
Keywords: Clustering; Streams; Mining; Dimensionality reduction; Text stream; Data streams |
مقاله انگلیسی |
8 |
Data mining for feature selection in gene expression autism data
داده کاوی برای انتخاب ویژگی در ژن داده اوتیسم چهره-2015 The paper presents application of data mining methods for recognizing the most significant genes and
gene sequences (treated as features) stored in a dataset of gene expression microarray. The investigations
are performed for autism data. Few chosen methods of feature selection have been applied and their
results integrated in the final outcome. In this way we find the contents of small set of the most important
genes associated with autism. They have been applied in the classification procedure aimed on recognition
of autism from reference group members. The results of numerical experiments concerning
selection of the most important genes and classification of the cases on the basis of the selected genes
will be discussed. The main contribution of the paper is in developing the fusion system of the results
of many selection approaches into the final set, most closely associated with autism. We have also proposed
special procedure of estimating the number of highest rank genes used in classification procedure.
2014 Elsevier Ltd. All rights reserved.
Keywords:
Geneexpressionmicroarrays
Featureselection
Clustering
Classification
Autism |
مقاله انگلیسی |
9 |
Data mining approach for knowledge-based process planning
رویکرد داده کاوی برای برنامه ریزی فرایند مبتنی بر دانش-2015 Concepts like gentelligent products, smart objects or cyber-physical systems have already proven a high potential especially for
decentralized production planning and control. In this context, decentralized communication, new sensor technologies and the
increased application of simulation and monitoring systems lead to an enormous increase of manufacturing data. Additionally, a
new approach for the assessment of manufacturing quality based on process signals from the machine tool is proposed, which
provides current tool state and surface roughness information for every manufacturing process. In order to reuse and evaluate this
data for knowledge-based process planning, an approach to manufacturing data collection and evaluation using data mining
methods was developed. The advantages of the proposed data mining approach for process planning is demonstrated by an
exemplary testing case.
Keywords: process planning; data mining; manufacturing data; process parameters; knowledge discovery |
مقاله انگلیسی |
10 |
Error estimate evaluation in numerical approximations of partial differential equations: A pilot study using data mining methods
ارزیابی برآورد خطا در تقریب عددی معادلات دیفرانسیل با مشتقات جزئی: مطالعه آزمایشی با استفاده از روش داده کاوی-2015 In this Note, we propose a new methodology based on exploratory data mining techniques
to evaluate the errors due to the description of a given real system. First, we decompose
this description error into four types of sources. Then, we construct databases of the
entire information produced by different numerical approximation methods, to assess and
compare the significant differences between these methods, using techniques like decision
trees, Kohonen’s cards, or neural networks. As an example, we characterize specific states
of the real system for which we can locally appreciate the accuracy between two kinds
of finite elements methods. In this case, this allowed us to precise the classical Bramble–
Hilbert theorem that gives a global error estimate, whereas our approach gives a local error
estimate.
Keywords:
Data mining
Error estimate
Vlasov–Maxwell equations
Asymptotic analysis
Paraxial model |
مقاله انگلیسی |