کارابرن عزیز، مقالات isi بالاترین کیفیت ترجمه را دارند، ترجمه آنها کامل و دقیق می باشد (محتوای جداول و شکل های نیز ترجمه شده اند) و از بهترین مجلات isi انتخاب گردیده اند. همچنین تمامی ترجمه ها دارای ضمانت کیفیت بوده و در صورت عدم رضایت کاربر مبلغ عینا عودت داده خواهد شد.
از نرم افزار winrar برای باز کردن فایل های فشرده استفاده می شود. برای دانلود آن بر روی لینک زیر کلیک کنید
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
Toward modeling and optimization of features selection in Big Data based social Internet of Things
به سوی مدل سازی و بهینه سازی انتخاب ویژگی ها در داده های بزرگ مبتنی بر اینترنت اشیا اجتماعی-2018
The growing gap between users and the Big Data analytics requires innovative tools that address the challenges faced by big data volume, variety, and velocity. Therefore, it becomes computationally inefficient to analyze and select features from such massive volume of data. Moreover, advancements in the field of Big Data application and data science poses additional challenges, where a selection of appropriate features and High-Performance Computing (HPC) solution has become a key issue and has attracted attention in recent years. Therefore, keeping in view the needs above, there is a requirement for a system that can efficiently select features and analyze a stream of Big Data within their requirements. Hence, this paper presents a system architecture that selects features by using Artificial Bee Colony (ABC). Moreover, a Kalman filter is used in Hadoop ecosystem that is used for removal of noise. Furthermore, traditional MapReduce with ABC is used that enhance the processing efficiency. Moreover, a complete four-tier architecture is also proposed that efficiently aggregate the data, eliminate unnecessary data, and analyze the data by the proposed Hadoop-based ABC algorithm. To check the efficiency of the proposed algorithms exploited in the proposed system architecture, we have implemented our proposed system using Hadoop and MapReduce with the ABC algorithm. ABC algorithm is used to select features, whereas, MapReduce is supported by a parallel algorithm that efficiently processes a huge volume of data sets. The system is implemented using MapReduce tool at the top of the Hadoop parallel nodes with near real time. Moreover, the proposed system is compared with Swarm approaches and is evaluated regarding efficiency, accuracy and throughput by using ten different data sets. The results show that the proposed system is more scalable and efficient in selecting features.
Keywords: SIoT ، Big Data ، ABC algorithm، Feature selection
Interactive visualization and analysis of antihypertensive prescriptions using National Health Insurance claims data
بصری سازی تعاملی و تحلیل نسخه های ضد فشار خون با استفاده از ادعاهای بیمه ملی بهداشت و درمان-2018
Interactive visualization is an important approach to help to understand and to explain large amounts of data, particularly in light of decision support. Although data visualization have been introduced in healthcare and clinical fields, analytics has often been performed by data experts, focused on specific subjects, or insufficient statistical evidence. Therefore, this study suggests the procedures of effective and efficient visualization of big data for general healthcare researchers. Specifically, the procedure includes conventional regression analyses followed by interactive data visualization for prescription patterns of antihypertensive drugs. Methods: As a large-scale nationally representative prescription data, the Korean National Health Insurance claims data were collected. Conventional descriptive and regression analyses were conducted for therapy decision and prescription patterns using the software R. Then, based on the statistically significant findings, dashboards were developed to visualize interactively the patterns of prescriptions using the software Tableau. Results: Major characteristics (genders, age groups, healthcare institutions, and comorbidities) explained the differences in therapy and the average number of drugs prescribed as well as differences among most commonly prescribed drug classes. Two interactive dashboards were created for visualizing prescription patterns with incorporation of horizontal bar charts, packed bubble charts, treemaps, filled maps, radar charts, box and whisker plots, and filters. Conclusion: In the current big data era, interactive data visualization offers substantial opportunities to have comprehensive view, extract insights and evidence from the flood of vast amounts of data. This study’s interactive visualizations can provide healthcare professionals insight into prescription patterns and demonstrate the value of creating interactive dashboards to support informed and timely decision-making. Exploring big data using interactive visualization is expected to deliver many future benefits in healthcare fields.
Keywords: Prescriptions; National Health Insurance Claims database; Hypertension; Interactive Visualization
Differential Privacy Preserving of Training Model in Wireless Big Data with Edge Computing
حفظ حریم خصوصی دیفرانسیل حفظ مدل آموزش در داده های بزرگ بی سیم با محاسبات لبه-2018
With the popularity of smart devices and the widespread use of machine learning methods, smart edges have become the mainstream of dealing with wireless big data. When smart edges use machine learning models to analyze wireless big data, nevertheless, some models may unintentionally store a small portion of the training data with sensitive records. Thus, intruders can expose sensitive information by careful analysis of this model. To solve this privacy issue, in this paper, we propose and implement a machine learning strategy for smart edges using differential privacy. We focus our attention on privacy protection in training datasets in wireless big data scenario. Moreover, we guarantee privacy protection by adding Laplace mechanisms, and design two different algorithms Output Perturbation (OPP) and Objective Perturbation (OJP), which satisfy differential privacy. In addition, we consider the privacy preserving issues presented in the existing literatures for differential privacy in the correlated datasets, and further provided differential privacy preserving methods for correlated datasets, guaranteeing privacy by theoretical deduction. Finally, we implement the experiments on the TensorFlow, and evaluate our strategy on four datasets, i.e., MNIST, SVHN, CIFAR-10 and STL-10. The experiment results show that our methods can efficiently protect the privacy of training datasets and guarantee the accuracy on benchmark datasets.
Index Terms: Wireless Big Data, Smart Edges, Differential Privacy, Training Data Privacy, Machine Learning, Correlated Datasets, Laplacian Mechanism, TensorFlow
A Joint Power Efficient Server and Network Consolidation approach for virtualized data centers
یک سرور توانی کارآمد مشترک و دیدگاه یکپارچه سازی شبکه برای مراکز داده ای مجازی-2018
Cloud computing and virtualization are enabling technologies for designing energy-aware resource management mechanisms in virtualized data centers. Indeed, one of the main challenges of big data centers is to decrease the power consumption, both to cut costs and to reduce the environmental impact. To this extent, Virtual Machine (VM) consolidation is often used to smartly reallocate the VMs with the objective of reducing the power consumption, by exploiting the VM live migration. The consolidation problem consists in finding the set of migrations that allow to keep turned on the minimum number of servers needed to host all the VMs. However, most of the proposed consolidation approaches do not consider the network related consumption, which represents about 10–20% of the total energy consumed by IT equipment in real data centers. This paper proposes a novel joint server and network consolidation model that takes into account the power efficiency of both the switches forwarding the traffic and the servers hosting the VMs. It powers down switch ports and routes traffic along the most energy efficient path towards the least energy consuming server under QoS constraints. Since the model is complex, a fast Simulated Annealing based Resource Consolidation algorithm (SARC) is proposed. Our numerical results demonstrate that our approach is able to save on average 50% of the network related power consumption compared to a network unaware consolidation.
keywords: Cloud| Virtualization| Power| Green computing| Simulated annealing
Post-colonial dilemmas in the construction of Ghanaian citizenship education: National unity, human rights and social inequalities
مسائل پسا - استعماری در ایجاد آموزش شهروندی غنایی ها: اتحاد ملی، حقوق انسانی و نابرابری های اجتماعی-2018
This article contributes to the growing interest in the compromises which African models of citizenship education make between Western and indigenous curricular agendas. It traces how Nkrumah’s educational ideals were reshaped by the teaching of human rights, individual independence, enterprise and economic development. We employ historical policy research, a critical literature review and interviews with key officials to construct a chronology of Ghanaian civic education, providing insights into postcolonial dilemmas around promoting national unity over social difference, critical learning and child-centred pedagogy, the valuing of indigenous cultures, challenging social inequalities and the need for the ‘decolonisation of the mind’ (Sefa Dei 2005b).
keywords: Youth |Ghana |Human rights |Social inequality |Citizenship education
“What is the problem represented to be?” Two decades of research on Roma and education in Europe
مسئله به چه شکلی باید بیان شود؟ دو دهه تحقیق روی رم و آموزش در اروپا-2018
This review article offers an analysis of research on Roma and education. A total of 151 peer-reviewed research articles were sampled through systematic searches in four databases, covering the period 1997–2016. Inspired by critical approaches in policy analysis, we draw on the concept of problem representations to identify dominant discourses in the research material. The analysis identifies nine problem representations; absence from school, academic achievement, socioeconomic issues, cultural differences, invisibility, teachers’ competencies, hostility, segregation and misguided policy and action. The content of these problem representations suggests that Roma is often framed as either victims or problems in educational research, and that cultural differences are much more dominant as a problem representation in the field than structural aspects such as socioeconomic issues. This critical review can contribute to raise awareness regarding how we frame research questions in the field of Roma and education.
keywords: Roma |Gypsy |Traveller |Intercultural education |Problem representations
Exploring religious tourist experiences in Jerusalem: The intersection of Abrahamic religions
بررسی تجربیات گردشگری مذهبی در اورشلیم: تقاطع مذهب های ابراهیمی-2018
By considering the importance of religious tourism for travel and the tourism industry, this study aims to identify religious tourists experiences in Jerusalem, as one of the most important holy cities. By a survey, 848 data were collected from the Jewish, Christian, and Muslim religious tourists. Results showed that religious tourism experience was a multi-faceted construct, which consists of engaging mentally, discovering new things, interacting & belonging, connecting spiritually & emotionally, and relaxing & finding peace dimensions. By using these dimensions, perceived experience differences of tourists were examined depending on religion. Moreover, religious tourism experience was identified to significantly affect overall tourist satisfaction with Jerusalem. The study concluded with discussion of the findings and their implications.
keywords: Religious tourism| Religious tourist experience| Overall satisfaction| Jerusalem
The structure of information release and the factor structure of returns
ساختار افشای اطلاعات و ساختار عاملی بازگشت ها-2018
We model how firms releasing information on different dates causes the CAPM to fail, requiring an additional factor based on the information structure to price assets. We exemplify this mechanism’s empirical relevance using quarterly earnings announcements, which cluster across months along size and book-to-market. Seventy percent of the alpha reduction from including SMB and HML occurs in the four main earnings announcement months. The information structure factor accounts for all of SMB and HML’s seasonal alpha reduction and one third of their overall alpha reduction. Controlling for size and book-to-market, exposures to SMB and HML vary with firms’ earnings announcement month.
keywords: Earnings announcements |CAPM|Factor models |SMB|HML
All’s well that ends well? On the importance of how returns are achieved
چیزی خوب است که خوب تمام شود؟ درمورد اهمیت چگونگی به دست آمدن بازگشت ها-2018
We demonstrate that investor satisfaction and investment behavior are influenced substantially by the price path by which the final investor return is achieved. In a series of experiments, we analyze various different price paths. Investors are most satisfied if their assets first fall in value and then recover, and they are least satisfied with the opposite pattern, independent of whether the final return is positive or negative. Price paths systematically influence risk preferences, return beliefs, and ultimately trading decisions. Our results enable a much more holistic perspective on a wide range of topics in finance, such as the disposition effect, risk-taking behavior after previous gains and losses, and behavioral asset pricing.
keywords: Investor satisfaction |Reference points |Risk tolerance |Investor behavior |Experimental finance