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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A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system
معماری جدید اینترنت اشیاء و اکوسیستم داده های بزرگ برای نظارت بر سیستم مراقبت سلامت هوشمند و سیستم هشدار دهنده امن-2018
Wearable medical devices with sensor continuously generate enormous data which is often called as big data mixed with structured and unstructured data. Due to the complexity of the data, it is difficult to process and analyze the big data for finding valuable information that can be useful in decision making. On the other hand, data security is a key requirement in healthcare big data system. In order to overcome this issue, this paper proposes a new architecture for the implementation of IoT to store and process scalable sensor data (big data) for health care applications. The Proposed architecture consists of two main sub architectures, namely, Meta Fog-Redirection (MF-R) and Grouping and Choosing (GC) architecture. MF-R architecture uses big data technologies such as Apache Pig and Apache HBase for collection and storage of the sensor data (big data) generated from different sensor devices. The proposed GC architecture is used for securing integration of fog computing with cloud computing. This architecture also uses key management service and data categorization function (Sensitive, Critical and Normal) for providing security services. The framework also uses MapReduce based prediction model to predict the heart diseases. Performance evaluation parameters such as throughput, sensitivity, accuracy, and f-measure are calculated to prove the efficiency of the proposed architecture as well as the prediction model.
Keywords: Wireless sensor networks ، Internet of Things ، Big data analytics ، Cloud computing and health car
BDWatchdog: Real-time monitoring and profiling of Big Data applications and frameworks
BDWatchdog: نظارت بر زمان واقعی و چارچوب و پروفایل برنامه های کاربردی داده های بزرگ -2018
Current Big Data applications are characterized by a heavy use of system resources (e.g., CPU, disk) generally distributed across a cluster. To effectively improve their performance there is a critical need for an accurate analysis of both Big Data workloads and frameworks. This means to fully understand how the system resources are being used in order to identify potential bottlenecks, from resource to code bottlenecks. This paper presents BDWatchdog, a novel framework that allows real-time and scalable analysis of Big Data applications by combining time series for resource monitorization and flame graphs for code profiling, focusing on the processes that make up the workload rather than the underlying instances on which they are executed. This shift from the traditional system-based monitorization to a process-based analysis is interesting for new paradigms such as software containers or serverless computing, where the focus is put on applications and not on instances. BDWatchdog has been evaluated on a Big Data cloud-based service deployed at the CESGA supercomputing center. The experimental results show that a process-based analysis allows for a more effective visualization and overall improves the understanding of Big Data workloads. BDWatchdog is publicly available at http://bdwatchdog.dec.udc.es.
Keywords: Big data ، Monitoring ، Profiling ، Time series ، Flame graphs ، Process-based analysis
Leveraging hospital big data to monitor flu epidemics
استفاده از داده های بزرگ بیمارستان برای کنترل اپیدمی های آنفولانزا-2018
Background and Objective: Influenza epidemics are a major public health concern and require a costly and time-consuming surveillance system at different geographical scales. The main challenge is being able to predict epidemics. Besides traditional surveillance systems, such as the French Sentinel network, several studies proposed prediction models based on internet-user activity. Here, we assessed the potential of hospital big data to monitor influenza epidemics. Methods: We used the clinical data warehouse of the Academic Hospital of Rennes (France) and then built different queries to retrieve relevant information from electronic health records to gather weekly influenza-like illness activity. Results: We found that the query most highly correlated with Sentinel network estimates was based on emergency reports concerning discharged patients with a final diagnosis of influenza (Pearson’s correla tion coefficient (PCC) of 0.931). The other tested queries were based on structured data (ICD-10 codes of influenza in Diagnosis-related Groups, and influenza PCR tests) and performed best (PCC of 0.981 and 0.953, respectively) during the flu season 2014–15. This suggests that both ICD-10 codes and PCR re sults are associated with severe epidemics. Finally, our approach allowed us to obtain additional patients’ characteristics, such as the sex ratio or age groups, comparable with those from the Sentinel network. Conclusions: Conclusions: Hospital big data seem to have a great potential for monitoring influenza epi demics in near real-time. Such a method could constitute a complementary tool to standard surveillance systems by providing additional characteristics on the concerned population or by providing information earlier. This system could also be easily extended to other diseases with possible activity changes. Ad ditional work is needed to assess the real efficacy of predictive models based on hospital big data to predict flu epidemics.
Keywords: Health big data ، Clinical data warehouse ، Information retrieval system ، Health Information Systems ، Influenza ، Sentinel surveillance
Towards an Intelligent Approach for Ventilation Systems Control using IoT and Big Data Technologies
به سوی رویکرد هوشمند برای کنترل سیستم های تهویه با استفاده از فناوری های اینترنت اشیا و داده های بزرگ-2018
Heating, ventilation and air conditioning systems are generally deployed in buildings for maintaining occupants’ comfort. They are the most considered systems in improving the energy saving while sustaining occupants’ comfort. Several approaches have been proposed, in the past few years, to develop an optimal control for ventilation systems. However, these approaches could not be efficiently performed under diverse contexts. In fact, we introduce an intelligent approach that selects the most appropriate control among three existing strategies. This paves the way to approaches in which an antifragile platform learns and adapts which strategy to enact. The proposed approach is implemented using IoT devices and recent Big-data technologies for real-time monitoring and data processing. A case study was deployed in our EEBLab test site for real testing. Experiments have been conducted and preliminary results show the effectiveness of using adaptive control approaches for ventilation systems control.
Keywords: EEB; ventilation system; algorithm’s selection; real-time processing; IoT and Big Data Technologies
Big Data and Clinical Research in Traumatic Brain Injury
داده های بزرگ و تحقیقات بالینی در ضایعات مغزی آسیب دیده-2018
“T alk and die” in traumatic brain injury (TBI) was initially described in 1975 by Reilly et al clinically deteriorated after initial evaluation suggested 1 in patients who signs of mild brain injury. Description of the talk and die phenomenon evolved into theories of secondary injury in TBI, in which postinjury inflammation, edema, and loss of autoregulation exacerbated the primary injury, and was associated with worse outcomes.2 Since the 1970s, advances in medical care have allowed for a much better understanding of TBI as a multifaceted disease process. Initial clinical evaluation is interpreted in a nexus of imaging, neuromonitoring, and critical care. Over time, we have learned that “talking” after TBI tells only a small part of the story.
Key words : Mortality ، Risk factor ، Skull fracture ، Subdural hematoma ، Talk and die ، Traumatic brain injury
Conception and Exploration of Using Data as a Service in Tunnel Construction with the NATM
مفهوم و اکتشاف استفاده از داده ها به عنوان یک سرویس در ساخت تونل با NATM-2018
The New Austrian Tunneling Method (NATM) has been widely used in the construction of mountain tun nels, urban metro lines, underground storage tanks, underground power houses, mining roadways, and so on. The variation patterns of advance geological prediction data, stress–strain data of supporting struc tures, and deformation data of the surrounding rock are vitally important in assessing the rationality and reliability of construction schemes, and provide essential information to ensure the safety and scheduling of tunnel construction. However, as the quantity of these data increases significantly, the uncertainty and discreteness of the mass data make it extremely difficult to produce a reasonable con struction scheme; they also reduce the forecast accuracy of accidents and dangerous situations, creating huge challenges in tunnel construction safety. In order to solve this problem, a novel data service system is proposed that uses data-association technology and the NATM, with the support of a big data environ ment. This system can integrate data resources from distributed monitoring sensors during the construc tion process, and then identify associations and build relations among data resources under the same construction conditions. These data associations and relations are then stored in a data pool. With the development and supplementation of the data pool, similar relations can then be used under similar con ditions, in order to provide data references for construction schematic designs and resource allocation. The proposed data service system also provides valuable guidance for the construction of similar projects.
Keywords: New Austrian Tunneling Method ، Big data environments ، Data as a service ، Tunnel construction
SLA based healthcare big data analysis and computing in cloud network
تحلیل داده های بزرگ سلامت مبتنی بر sla و محاسبات در شبکه های ابری-2018
Large volume of multi-structured and low-latency patient data are generated in healthcare services, which is challenging task to process and analyze within the Service Level Agreement (SLA). In this paper, a Parallel Semi-Naive Bayes (PSNB) based probabilistic method is used to process the healthcare big data in cloud for future health condition prediction. In order to improve the accuracy of PSNB method, a Modified Conjunctive Attribute (MCA) algorithm is proposed for reducing the dimension. Emergency condition of the patient is considered by setting a global priority among the patients and an Optimal Data Distribution (ODD) algorithm is proposed to position both batch and streaming patient data into the Spark nodes. Further, a Dynamic Job Scheduling (DJS) algorithm is designed to schedule the jobs efficiently to the most suitable nodes for processing the data taking SLA into account. Our proposed PSNB algorithm provides better accuracy of 87.8% for both batch and streaming data, which is 12.8% higher than the original Naive Bayes (NB) algorithm and can conveniently be employed in various patient monitoring applications.
Keywords: Big Data ، cloud computing ،healthcare, spark
Assessing learners satisfaction in collaborative online courses through a big data approach
ارزیابی رضایتمندی دانشجویان در دوره های آنلاین همکاری از طریق رویکرد داده ای بزرگ-2018
Monitoring learners satisfaction (LS) is a vital action for collecting precious information and design valuable online collaborative learning (CL) experiences. Todays CL platforms allow students for per forming many online activities, thus generating a huge mass of data that can be processed to provide insights about the level of satisfaction on contents, services, community interactions, and effort. Big Data is a suitable paradigm for real-time processing of large data sets concerning the LS, in the final aim to provide valuable information that may improve the CL experience. Besides, the adoption of Big Data offers the opportunity to implement a non-intrusive and in-process evaluation strategy of online courses that complements the traditional and time-consuming ways to collect feedback (e.g. questionnaires or surveys). Although the application of Big Data in the CL domain is a recent explored research area with limited applications, it may have an important role in the future of online education. By adopting the design science research methodology, this article describes a novel method and approach to analyse individual students contributions in online learning activities and assess the level of their satisfaction towards the course. A software artefact is also presented, which leverages Learning Analytics in a Big Data context, with the goal to provide in real-time valuable insights that people and systems can use to intervene properly in the program. The contribution of this paper can be of value for both researchers and practitioners: the former can be interested in the approach and method used for LS assessment; the latter can find of interest the system implemented and how it has been tested in a real online course.
Keywords: Big data ، Clustering ، Collaborative learning ، Learning analytics ، Learning satisfaction ، Sentiment analysis
Artificial neural network based prediction of malaria abundances using big data: A knowledge capturing approach
پیشگیری از فراوانی مالاریا بر اساس شبکه عصبی مصنوعی با استفاده از داده های بزرگ: رویکرد جذب دانش-2018
Background and objective: Malaria is one of the most prevalent diseases in urban areas. Malaria flourishes in subtropical countries and affect the public health. The impact is very high, where health monitoring facilities are very limited. To minimize the impact of malaria population in sub-tropical domains, a suitable disease prediction model is required. The objective of this study is to determine the malaria abundances using clinical and environmental variables with Big Data on the geographical location of Khammam district, Telanagana, India. Methods: Prediction model is based on the data collected from primary health centres of department of vector borne diseases (DVBD) of Khammam district and satellite data such as rain fall, relative humidity, temperature and vegetation taken for the time period of 1995–2014. In this study, we test the efficacy of the artificial neural network (ANN) for mosquito abundance prediction. Prediction model was developed for the period of 2015 using a feed forward neural network and compared with the observed values. Results and conclusions: The results vary from area to area based on clinical variables and rainfall in the prediction model corresponding to areas. The average error of the prediction model ranges from 18% to 117%. Clinical data such as number of patients treated with symptoms and without symptoms can improve the prediction level when combined with environmental variables. We perform preliminary findings of malaria abundances by collecting clinical big data across different seasons. Further, more exploration is required in prediction of malaria using big data to improve the accuracy in real practice. In this manuscript, we perform some preliminary findings of malaria abundances by collecting larger data across different seasons. Till today, many models have been developed to examine the malaria prediction with different approaches, but malaria prediction with environmental and clinical data is a new approach with big data analysis.
Keywords: Malaria prediction ، Primary health centers (PHCs) ، Big data ، Artificial neural networks (ANNs)
Cyber Physical System and Big Data enabled energy efficient machining optimisation
سیستم فیزیکی سایبری و بهینه سازی ماشین انرژی توانای داده های بزرگ-2018
Due to increasingly customised manufacturing, unpredictable ambient working conditions in shop floors and stricter requirements on sustainability, it is challenging to achieve energy efficient optimisation for machining processes. This paper presents a novel Cyber Physical System (CPS) and Big Data enabled machining optimisation system to address the above challenge. The innovations and characteristics of the system include the following four aspects: (1) a novel process of “scheduling, monitoring/learning, rescheduling” is designed to enhance system adaptability during manufacturing lifecycles; (2) an innovative energy model to support energy efficient optimisation over manufacturing lifecycles is developed. The energy model, which is enabled by CPS, Big Data analytics and intelligent learning al gorithms, considers dynamic and aging conditions of machine tool systems during manufacturing life cycles; (3) an effective evolutional algorithm based on Fruit Fly Optimisation (FFO), is applied to generate an adaptive energy efficient schedule, and improve schedule when there are significantly varying working conditions and adjustments on the schedule are necessary (that is rescheduling); (4) the system has been successfully deployed into European machining companies to verify capabilities. According to the results, around 40% energy saving and 30% productivity improvement have been achieved in the companies. A practical case study presented in this paper demonstrates the effectiveness and great potential of applicability of the system in practice.
Keywords: Cyber physical system ، Big data ، Energy efficient machining ، Scheduling optimisation