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.
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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
Power-aware gateway connectivity in battery-powered dynamic IoT networks
اتصال دروازه ای توان - آگاه در شبکه های پویای اینترنت اشیای کار کننده با باتری-2018
The paradigm of Internet of Things (IoT) is on rapid rise in today’s world of communication. Every networking device is being connected to the Internet to develop specific and dedicated applications. Data from these devices, called as IoT devices, is transmitted to the Internet through IoT Gateways (IGWs). IGWs support all the technologies in an IoT network. In order to reduce the cost involved with the deployment of IGWs, specialized low-cost devices called Solution Specific Gateways (SSGWs) are also employed alongside IGWs. These SSGWs are similar to IGWs except they support a subset of technologies supported by IGWs. A large number of applications are being designed which require IGWs and SSGWs to be deployed in remote areas. More often than not, gateways in such areas have to be run on battery power. Hence, power needs to be conserved in such networks for extending network life along with maintaining total connectivity. In this paper, we propose a dynamic spanning tree based algorithm for power-aware connectivity called SpanIoTPower-Connect which determines (near) optimal power consumption in battery-powered IoT networks. SpanIoTPower-Connect computes the spanning tree in the network in a greedy manner in order to minimize the power consumption and achieve total connectivity. Additionally, we propose an algorithm to conserve power in dynamic IoT networks where the connectivity demand changes with time. Our simulation results show that our algorithm performs better than Static Spanning Tree based algorithm for power-aware connectivity (Static ST) and a naive connectivity algorithm where two neighboring SSGWs are connected through every available technology. To the best of our knowledge, our work is the first attempt at achieving power-aware connectivity in battery-powered dynamic IoT networks.
keywords: Internet of Things| IoT gateway| IoT network| Power-aware| Performance evaluation
Text Mining Based on Tax Comments as Big Data Analysis Using SVM and Feature Selection
متن کاوی براساس نکات مالیاتی به عنوان تجزیه و تحلیل داده های بزرگ با استفاده از SVM و انتخاب ویژگی-2018
The tax gives an important role for the contributions of the economy and development of a country. The improvements to the taxation service system continuously done in order to increase the State Budget. One of consideration to know the performance of taxation particularly in Indonesia is to know the public opinion as for the object service. Text mining can be used to know public opinion about the tax system. The rapid growth of data in social media initiates this research to use the data source as big data analysis. The dataset used is derived from Facebook and Twitter as a source of data in processing tax comments. The results of opinions in the form of public sentiment in part of service, website system, and news can be used as consideration to improve the quality of tax services. In this research, text mining is done through the phases of text processing, feature selection and classification with Support Vector Machine (SVM). To reduce the problem of the number of attributes on the dataset in classifying text, Feature Selection used the Information Gain to select the relevant terms to the tax topic. Testing is used to measure the performance level of SVM with Feature Selection from two data sources. Performance measured using the parameters of precision, recall, and Fmeasure.
Keywords: Text Mining; Tax Comments; Support Vector Machine; Feature Selection
Food Trend Based on Social Media for Big Data Analysis Using K-Mean Clustering and SAW
روند تغذیه بر اساس رسانه های اجتماعی برای تجزیه و تحلیل داده های بزرگ با استفاده از خوشه بندی K-Mean و SAW-2018
tracking customer preferences is an important aspect of business success. Having information on hand about most favorite food is a key success for everyone who takes apart in the culinary business. Exact sales data on certain food is hardly available to the public. Restaurant owner tends to keep their data for their own business strategy. Therefore, generating a food trend in a certain community is hardly possible using food sales data. This paper discussed extracting food general trend from social media, with the case study on Twitter data with a certain regional area of interest. Social media provides a tremendous amount of data including people choice of food when they visit the certain place. However, the available data is unstructured in human language. The challenge is twofold: to grasp the meaning and extract the relevant information to the food trends. We proposed a bag of words technique to gather relevant information in the Indonesian language for feature extracting purpose. While K-mean Clustering and Simple Additive Weighting (SAW) algorithm are proposed to draw up the food rank. In order to measure the accuracy, we compare our result with the sales data of some restaurants in Yogyakarta. We test the algorithm using 4 weeks of data, the result is compared against the available data and an accuracy of 72.75 % is achieved
Keywords: social media; food trend; big data; bag of words; K mean clustering; simple additive weighting
Big data requirements in current and next fusion research experiments
الزامات داده های بزرگ در آزمایش های فعلی و بعدی همجوشی-2018
The present and future data management requirements for fusion experiments are presented along with the currently adopted solutions. Even if the presented solution fulfil the requirements of the current experiments, the next generation fusion devices are likely to produce/require an unpreceded amount of data. For this reason, the solutions adopted nowadays, and also foreseen for the experiments under construction, might prove not enough scalable. Information Technology already provides efficient solutions for big data management, successfully employed for large cloud applications and social media. In particular, MongoDB, Cassandra and Hadoop represent promising candidates for the next generation experiments because their combined usage covers the specific data requirements for fusion research.
Keywords: Big Data ; Nuclear Fusion Experiment ; Data Acquisition ; Databases
Big data for internet of things: A survey
داده های بزرگ برای اینترنت اشیا: یک مرور-2018
With the rapid development of the Internet of Things (IoT), Big Data technolo gies have emerged as a critical data analytics tool to bring the knowledge within IoT infrastructures to better meet the purpose of the IoT systems and support critical decision making. Although the topic of Big Data analytics itself is ex tensively researched, the disparity between IoT domains (such as healthcare, energy, transportation and others) has isolated the evolution of Big Data ap proaches in each domain. Thus, the mutual understanding across IoT domains can possibly advance the evolution of Big Data research in IoT. In this work, we therefore conduct a survey on Big Data technologies in different IoT domains to facilitate and stimulate knowledge sharing across the IoT domains. Based on our review, this paper discusses the similarities and differences among Big Data technologies used in different IoT domains, suggests how certain Big Data technology used in one IoT domain can be re-used in another IoT domain, and develops a conceptual framework to outline the critical Big Data technologies across all the reviewed IoT domains.
Keywords: Big Data, data analytics, Internet of Things, healthcare, energy, transportation, building automation, Smart Cities
A novel adaptive e-learning model based on Big Data by using competence-based knowledge and social learner activities
یک مدل تطبیقی جدید یادگیری الکترونیکی مبتنی بر داده های بزرگ با استفاده ازدانش مبتنی بر شایستگی و فعالیت های یادگیرنده اجتماعی-2018
The e-learning paradigm is becoming one of the most important educational methods, which is a deci sive factor for learning and for making learning relevant. However, most existing e-learning platforms offer traditional e-learning system in order that learners access the same evaluation and learning con tent. In response, Big Data technology in the proposed adaptive e-learning model allowed to consider new approaches and new learning strategies. In this paper, we propose an adaptive e-learning model for providing the most suitable learning content for each learner. This model based on two levels of adaptive e-learning. The first level involves two steps: (1) determining the relevant future educational objectives through the adequate learner e-assessment method using MapReduce-based Genetic Algo rithm, (2) generating adaptive learning path for each learner using the MapReduce-based Ant Colony Optimization algorithm. In the second level, we propose MapReduce-based Social Networks Analysis for determining the learner motivation and social productivity in order to assign a specific learning rhythm to each learner. Finally, the experimental results show that the presented algorithms implemented on Big Data environment converge much better than those implementations with traditional concurrent works. Also, this work provides main benefit because it describes how Big Data technology transforms e-learning paradigm.
Keywords: Adaptive e-learning ، Big data ، MapReduce ، Genetic algorithm ، Personalized learning path ، Ant colony optimization algorithms ، Social networks analysis ، Motivation and productivity ، Learning content
Big Data Challenges and Data Aggregation Strategies in Wireless Sensor Networks
چالش های داده بزرگ و استراتژی های جمع آوری داده ها در شبکه های حسگر بی سیم-2018
The emergence of new data handling technologies and analytics enabled the organization of big data in processes as an innovative aspect in wireless sensor networks (WSNs). Big data paradigm, combined with WSN technology, involves new challenges that are necessary to resolve in parallel. Data aggregation is a rapidly emerging research area. It represents one of the processing challenges of big sensor networks. This paper introduces the big data paradigm, its main dimensions that represent one of the most challenging concepts, and its principle analytic tools which are more and more introduced in the WSNs technology. The paper also presents the big data challenges that must be overcome to efficiently manipulate the voluminous data, and proposes a new classification of these challenges based on the necessities and the challenges of WSNs. As the big data aggregation challenge represents the center of our interest, this paper surveys its proposed strategies in WSNs.
INDEX TERMS: Big data, data aggregation, wireless sensor networks
رفع ابهام ازبیتکوین: پرده برداری از افسانه ارز دیجیتال
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 16 - تعداد صفحات فایل doc فارسی: 20
بیتکوین و سیستم معاملاتی عجیب و غیر متمرکز آن، در حال حاضر مورد علاقه معامله گران حرفه ای و خرده پای در جستجوی سود و مورد توجه اقتصاددانان و کارشناسان قانونی به دنبال قوانین احتمالی برای محدود کردن استفاده های غیر قانونی، است. ما به بررسی موفقیت غیر منتظره و مداوم بیتکوین از یک دیدگاه جامعه شناختی علاقه مند هستیم، که سوال اول در این راستا، سیستم مشروعیت غیر معمولی آن، که به جای اختیارات دولتی، بافن آوری بلاکچین پشتیبانی می شود، است. سپس به جمع آوری داده و المان هایی پرداختیم که تاریخچه بیتکوین را به عنوان یک رمزنگاری ارزی (ارز دیجیتال) بازسازی می کند که از داستان پر رمز و راز آن آغاز می شود که تولد آن را در برگرفته است. ما سپس، گسترش و توسعه آن را از طریق شبکه های اجتماعی و زبان کلمات، همراه با شیوع و توسعه عظیم ناگهانی آن پیگیری می کنیم و در آخر پیشنهاد می دهیم که مصرف کنندگان و قانون گزاران نهادی باید نسبت به ریسک های بیتکوین و قدرت ادعایی آن که مفهوم پول ما را به چالش کشیده است، هوشیار باشند.
کلمات کلیدی: بیتکوین | ارز دیجیتال | تکنولوژی بلاکچین (زنجیره بلوکی) | طرح های پونزی | اعتماد به پول
|مقاله ترجمه شده|
Uncovering Student Perceptions of a First-Year Online Writing Course
کشف مشاهدات دانش آموز در یک دوره آنلاین نوشتن سال اولی-2018
This article examined student perceptions of the Writing Program Administrators (WPA) learning outcomes for first-year writing through a fully online first-year writing course. A second research question explored how the course content focused on technology, visual rhetoric, and social media impacted students’ overall perceptions about their learning. The method used in this study is qualitative in nature, based on a Likert-scale survey and end of semester open-ended surveys with students. The findings indicate that students perceived their abilities to improve not only in the four areas delineated by the WPA outcomes, but also through the ability to see writing as the primary method of communication and have more time to reflect in an online environment. Findings also suggest that instructor feedback and relevant course content both positively impact student perceptions of an online course. This article concludes with encouragement for additional research and studies relating to first-year writing courses in an online environment.
keywords: First-year writing| Online course| WPA outcomes