با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.
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
The use of big data and data mining in nurse practitioner clinical education
استفاده از داده های بزرگ و داده کاوی در آموزش بالینی پزشکان -2020
Nurse practitioner (NP) faculty have not fully used data collected in NP clinical education for data mining. With current advances in database technology including data storage and computing power, NP faculty have an opportunity to data mine enormous amounts of clinical data documented by NP students in electronic clinical management systems. The purpose of this project was to examine the use of big data and data mining from NP clinical education and to establish a foundation for competency-based education. Using a data mining knowledge discovery process, faculty are able to gain increased understanding of clinical practicum experiences to inform competency-based NP education and the use of entrusted professional activities for the future.
Keywords: Big data | Data mining | Nurse practitioner clinical education | Competency-based education | Nurse Practitioner Core Competencies | Entrustable professional activities
Data mining of customer choice behavior in internet of things within relationship network
داده کاوی رفتار انتخاب مشتری در اینترنت اشیایی که در شبکه ارتباطی قرار دارند-2020
Internet of Things has changed the relationship between traditional customer networks, and traditional information dissemination has been affected. Smart environment accelerates the changes in customer behaviors. Apparently, the new customer relationship network, benefitted from the Internet of Things technology, will imperceptibly influence customer choice behaviors for the cyber intelligence. In this work, we selected 298 customers click browsing records as training data, and collected 50 customers who used the platform for the first time as research objects. and use the smart customer relationship network correspond to cyber intelligence to build the customer intelligence decision model in Internet of Things. The results showed that the MAE (Mean Absolute Deviation) of the customer trust evaluation model constructed in this study is 0.215, 45% improvement over the traditional equal assignment method. In addition, customers consumer experience can be enhanced with the support of data mining technology in cyber intelligence. Our work indicated the key to build eliminates confusion in customer choice behavior mechanism is to establish a consumer-centric, effective network of customers and service providers, and to be supported by the Internet of Things, big data analysis, and relational fusion technologies.
Keywords: Internet of things | Customer relationship network | Decision making | Recommendation | Fusion algorithm
Data mining and application of ship impact spectrum acceleration based on PNN neural network
داده کاوی و کاربرد شتاب طیف تأثیر کشتی بر اساس شبکه عصبی PNN-2020
The selection of the smoothing coefficient of the probabilistic neural network directly affects the performance of the network. Traditionally, all the mode layer neurons use a uniform smoothing coefficient, and then the optimal smoothing parameters suitable for this problem are searched by the optimization algorithm. In this study, the smoothing coefficients of the mode layer neurons connected by the same summation layer are set to the same value, which not only reflects the relationship between the training samples of the same pattern, but also highlights the difference between the training samples of different modes. Two probabilistic neural network models are applied to the ship impact environment prediction respectively. The results show that the classification effect of multiple smoothing factors is further improved than the single smoothing factor network.
Keywords: Ship impact environment prediction | Probabilistic neural network | Smoothing coefficient | Optimization algorithm
Pivot-based approximate k-NN similarity joins for big high-dimensional data
پیوندهای شباهت تقریبی k-NN مبتنی بر محوری برای داده های بزرگ با ابعاد بزرگ-2020
Given an appropriate similarity model, the k-nearest neighbor similarity join represents a useful yet costly operator for data mining, data analysis and data exploration applications. The time to evaluate the operator depends on the size of datasets, data distribution and the dimensionality of data representations. For vast volumes of high-dimensional data, only distributed and approximate approaches make the joins practically feasible. In this paper, we investigate and evaluate the performance of multiple MapReduce-based approximate k-NN similarity join approaches on two leading Big Data systems Apache Hadoop and Spark. Focusing on the metric space approach relying on reference dataset objects (pivots), this paper investigates distributed similarity join techniques with and without approximation guarantees and also proposes high-dimensional extensions to previously proposed algorithms. The paper describes the design guidelines, algorithmic details, and key theoretical underpinnings of the compared approaches and also presents the empirical performance evaluation, approximation precision, and scalability properties of the implemented algorithms. Moreover, the Spark source code of all these algorithms has been made publicly available. Key findings of the experimental analysis are that randomly initialized pivot-based methods perform well with big highdimensional data and that, in general, the selection of the best algorithm depends on the desired levels of approximation guarantee, precision and execution time.
Keywords: Hadoop | Spark | MapReduce | k-NN | Approximate similarity join | High-dimensional data
A novel intelligent option price forecasting and trading system by multiple kernel adaptive filters
رویکرد پیش بینی قیمت و گزینه سیستم تجاری با فیلترهای انطباقی چند هسته ای-2020
Derivatives such as options are complex financial instruments. The risk in option trading leads to the demand of trading support systems for investors to control and hedge their risk. The nonlinearity and non-stationarity of option dynamics are the main challenge of option price forecasting. To address the problem, this study develops a multi-kernel adaptive filters (MKAF) for online option trading. MKAF is an improved version of the adaptive filter, which employs multiple kernels to enhance the richness of nonlinear feature representation. The MKAF is a fully adaptive online algorithm. The strength of MKAF is that the weights to the kernels are simultaneous optimally determined in filter coefficient updates. We do not need to design the weights separately. Therefore, MKAF is good at tracking nonstationary nonlinear option dynamics. Moreover, to reduce the computation time in updating the filter, and prevent overadaptation, the number of kernels is restricted by using coherence-based sparsification, which constructs a set of dictionary and uses a coherence threshold to restrict the dictionary size. This study compared the new method with traditional ones, we found the performance improvement is significant and robust. Especially, the cumulated trading profits are substantially increased
Keywords: Artificial intelligence | Adaptive filter | Multiple Kernel Machine | Big data analysis | Data mining | Financial forecasting
Mining discriminative spatial cues for aerial image quality assessment towards big data
استخراج نشانه های مکانی تبعیض آمیز برای ارزیابی کیفیت تصویر هوایی نسبت به داده های بزرگ-2020
Evaluating massive-scale aerial/satellite images quality is useful in computer vision and intelligent applications. Traditional local features-based algorithms have achieved impressive performance. However, spatial cues, i.e., geometric property and topological structure, have not been exploited effectively and explicitly. Thus, in this paper, we propose a novel method for image quality assessment towards aerial/satellite images, where discriminative spatial cues are well encoded. More specifically, in order to mine inherent spatial structure of aerial images, each image is segmented into several basic components such as buildings, airport and playground. Afterwards, a weighted region adjacency graph (RAG) is built based on the basic components to represent the spatial feature of each aerial image. We integrate the spatial feature with other transform domain features, and train a support vector regression model to achieve image quality assessment. Experiments demonstrate that our method shows competitive or even better performance compared with several state-of-the-art algorithms.
Keywords: Big data | Artificial intelligent | Data mining | Image quality assessment
Application of smart safety training and education in network teaching management
کاربرد آموزش ایمنی هوشمند و آموزش در مدیریت آموزش شبکه-2020
Aiming at the problems of poor resource scheduling and low degree of information fusion in the traditional network management method of intelligent security training and education optimization, an intelligent security training and education optimization network management model based on big data mining is proposed. Building intelligent safety training and education of big data fusion analysis model, using the method of association rules mining, complete the intelligent safety training and education statistics analysis, under the Internet environment using quantitative sensing fusion tracking method, the network teaching management information fusion processing, build large data information scheduling model based on network teaching management, fuzzy information fusion method to reconstruct 3 d information of the network teaching management, to establish the network teaching management big data spectral analysis model, the introduction of phase space reconstruction method, the network resource scheduling optimization of teaching management. The experimental results show that the proposed method has better resource scheduling performance, higher degree of information fusion, and can improve the ability of intelligent security training and education management.
Keywords: Smart safety training | Education | Network teaching management | Big data | Integration | Resource scheduling
IFC-based process mining for design authoring
استخراج فرآیند مبتنی بر IFC برای تألیف طراحی-2020
Building Information Modelling (BIM) is defined as the process of creation and management of digital replica for building products in a collaborative design set-up. On this basis, BIM as a digital collaboration platform in AECO (Architecture, Engineering, Construction, and Operation) industry, can be upgraded to assist monitoring, control and improvement of the business processes related to planning, design, construction and operation of building facilities. The main problem in this regard, is the wastage of data related to activities completed by different actors during the project; and subsequently, the lack of analytics to discover latent patterns in collaboration and execution of such processes. The present study aims to enable BIM to capture digital footprints of project actors and create event logs for design authoring phase of building projects. This is done using files in IFC (Industry Foundation Classes) format, archived during the design process. We have developed algorithms to create event logs from such archives, and analyzed the event logs using process mining (i.e. process discovery, conformance checking and bottleneck analysis), to identify measures derived from as-happened processes. BIM managers can implement such measures in monitoring, controlling and re-engineering work processes related to design authoring. Two case studies were completed to validate and verify the products and findings of the research. Our results show that process models discovered/fine-tuned at various resolutions and from different perspectives (including ‘actor-centric’ and ‘phase-centric’ views) can provide a realistic view of the BIM project execution. This includes understanding the structure of collaboration and hand-over of work; evaluation of compliance with the BIM execution plan; and detection of bottlenecks and re-works. While the scope of the study has been limited to design authoring processes, this mindset can be extended to other BIM uses, and other phases (such as construction and operation) of building projects. Given the growing efforts on upgrading BIM to capture and formalize the lifecycle data on the products, processes and actors, this study can strongly support BIM managers with documentation and evaluation of the business processes and workflows in their project teams.
Keywords: Building Information Modeling | Business processes management | Data mining | Process mining | BIM management | BIM Execution Planning
Intelligent-ReaxFF: Evaluating the reactive force field parameters with machine learning
Intelli-ReaxFF: ارزیابی پارامترهای میدان نیروی واکنش با یادگیری ماشین-2020
Machine learning has been widely used in quantum chemistries, such as data mining in quantum mechanics calculation and representations of potential energy surface by neural networks. In this study, we report our efforts on the optimization of the ReaxFF parameters with machine learning frameworks. Although deep neural network potentials like High-Dimensional Neural Network Potentials (HDNNP) have achieved much success in applications such as materials modeling, factors like the memory usage, training time, and accuracies are still problems when the training data set is big. On the other hand, classical potentials like ReaxFF and REBO does not have these problems, and a combination of two is an ideal solution. Machine learning has generated techniques such as automatic differentiation and backpropagation, with which we can optimize deep neural networks or complexed interatomic potentials like ReaxFF. With the TensorFlow coding platform, we have constructed an Intelligent ReaxFF (I-ReaxFF) model with terms of matrix (or tensor) operations that can optimize ReaxFF parameters automatically with gradient-based optimizers like adaptive moment solver (Adam) and backpropagations. As inherited from TensorFlow, one significant feature of our code is the GPU acceleration. The training speed can be five times faster with GPU acceleration than pure CPU calculation. Another feather is that it can directly use the ab initio molecular dynamics trajectories with surrounding periodic images as training data, therefore, allowing the data set can be prepared with ease.
Keywords: Neural network | Parameterization | ReaxFF | Materials modeling | Machine learning