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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Role of Big Data and Machine Learning in Diagnostic Decision Support in Radiology
نقش داده های بزرگ و یادگیری ماشین در پشتیبانی از تصمیم گیری تشخیصی در رادیولوژی-2018
The field of diagnostic decision support in radiology is undergoing rapid transformation with the availability of large amounts of patient data and the development of new artificial intelligence methods of machine learning such as deep learning. They hold the promise of providing imaging specialists with tools for improving the accuracy and efficiency of diagnosis and treatment. In this article, we will describe the growth of this field for radiology and outline general trends highlighting progress in the field of diagnostic decision support from the early days of rule-based expert systems to cognitive assistants of the modern era.
Key Words: Diagnostic decision support, artificial intelligence, deep learning, machine learning, cognitive assistants, medical image analysis, knowledge and reasoning
Intelligent and effective informatic deconvolution of “Big Data” and its future impact on the quantitative nature of neurodegenerative disease therapy
هوش انعطاف پذیر و موثر اطلاعاتی "داده های بزرگ" و تأثیر آن بر ماهیت کمی در درمان بیماری های نورودنژراتیک-2018
Biomedical data sets are becoming increasingly larger and a plethora of high-dimensionality data sets (“Big Data”) are now freely accessible for neurodegenerative diseases, such as Alzheimer’s disease. It is thus important that new informatic analysis platforms are developed that allow the organization and interrogation of Big Data resources into a rational and actionable mechanism for advanced therapeutic development. This will entail the generation of systems and tools that allow the cross-platform correlation between data sets of distinct types, for example, transcriptomic, pro teomic, and metabolomic. Here, we provide a comprehensive overview of the latest strategies, including latent semantic analytics, topological data investigation, and deep learning techniques that will drive the future development of diagnostic and therapeutic applications for Alzheimer’s dis ease. We contend that diverse informatic “Big Data” platforms should be synergistically designed with more advanced chemical/drug and cellular/tissue-based phenotypic analytical predictive models to assist in either de novo drug design or effective drug repurposing.
Keywords: Big data; Informatics; High-dimensionality; Alzheimer’s disease; Aging; Molecular signature; Transcriptomics; Metabolomics; Proteomics; Genomics
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
Fault-diagnosis for reciprocating compressors using big data and machine learning
تشخیص گسل برای کمپرسورهای مجاور با استفاده از داده های بزرگ و یادگیری ماشین-2018
Reciprocating compressors are widely used in petroleum industry. A small fault in recipro cating compressor may cause serious issues in operation. Traditional regular maintenance and fault diagnosis solutions cannot efficiently detect potential faults in reciprocating com pressors. This paper proposes a fault-diagnosis system for reciprocating compressors. It applies machine-learning techniques to data analysis and fault diagnosis. The raw data is denoised first. Then the denoised data is sparse coded to train a dictionary. Based on the learned dictionary, potential faults are finally recognized and classified by support vector machine (SVM). The system is evaluated by using 5-year operation data collected from an offshore oil corporation in a cloud environment. The collected data is evenly divided into two halves. One half is used for training, and the other half is used for testing. The results demonstrate that the proposed system can efficiently diagnose potential faults in com pressors with more than 80% accuracy, which represents a better result than the current practice.
Keywords: Reciprocating compressor، Big data ، Cloud computing ، Deep learning ، RPCA ، SVM
An experimental survey on big data frameworks
یک بررسی تجربی در چارچوب داده های بزرگ-2018
Recently, increasingly large amounts of data are generated from a variety of sources. Existing data pro cessing technologies are not suitable to cope with the huge amounts of generated data. Yet, many research works focus on Big Data, a buzzword referring to the processing of massive volumes of (unstructured) data. Recently proposed frameworks for Big Data applications help to store, analyze and process the data. In this paper, we discuss the challenges of Big Data and we survey existing Big Data frameworks. We also present an experimental evaluation and a comparative study of the most popular Big Data frameworks with several representative batch and iterative workloads. This survey is concluded with a presentation of best practices related to the use of studied frameworks in several application domains such as machine learning, graph processing and real-world applications.
Keywords: Big data ، MapReduce ، Hadoop ، HDFS ، Spark ، Flink ، Storm ،Samza ، Batch/stream processing
Fall detection system for elderly people using IoT and Big Data
سیستم تشخیص سقوط برای سالمندان با استفاده از اینترنت اشیا و داده های بزرگ-2018
Falls represent a major public health risk worldwide for the elderly people. A fall not assisted in time can cause functional impairment in an elder and a significant decrease in his mobility, independence and life quality. In that sense, the present work proposes an innovative IoT-based system for detecting falls of elderly people in indoor environments, which takes advantages of low-power wireless sensor networks, smart devices, big data and cloud computing. For this purpose, a 3D-axis accelerometer embedded into a 6LowPAN device wearable is used, which is responsible for collecting data from movements of elderly people in real-time. To provide high efficiency in fall detection, the sensor readings are processed and analyzed using a decision trees based Big Data model running on a Smart IoT Gateway. If a fall is detected, an alert is activated and the system reacts automatically by sending notifications to the groups responsible for the care of the elderly people. Finally, the system provides services built on cloud. From medical perspective, there is a storage service that enables healthcare professional to access to falls data for perform further analysis. On the other hand, the system provides a service leveraging this data to create a new machine learning model each time a fall is detected. The results of experiments have shown high success rates in fall detection in terms of accuracy, precision and gain.
Keywords: Fall detection; Internet-of-Things; Big Data, 6LowPAN; wearable sensor; Smart IoT Gateway; fall detection; decision tree learning algorithm; accelerometer; elderly people.
A survey towards an integration of big data analytics to big insights for value-creation
مروری به سوی تجمیع تحلیل داده های بزرگ به بینشی بزرگ برای ایجاد ارزش-2018
Big Data Analytics (BDA) is increasingly becoming a trending practice that generates an en ormous amount of data and provides a new opportunity that is helpful in relevant decision making. The developments in Big Data Analytics provide a new paradigm and solutions for big data sources, storage, and advanced analytics. The BDA provide a nuanced view of big data development, and insights on how it can truly create value for firm and customer. This article presents a comprehensive, well-informed examination, and realistic analysis of deploying big data analytics successfully in companies. It provides an overview of the architecture of BDA including six components, namely: (i) data generation, (ii) data acquisition, (iii) data storage, (iv) advanced data analytics, (v) data visualization, and (vi) decision-making for value-creation. In this paper, seven Vs characteristics of BDA namely Volume, Velocity, Variety, Valence, Veracity, Variability, and Value are explored. The various big data analytics tools, techniques and tech nologies have been described. Furthermore, it presents a methodical analysis for the usage of Big Data Analytics in various applications such as agriculture, healthcare, cyber security, and smart city. This paper also highlights the previous research, challenges, current status, and future di rections of big data analytics for various application platforms. This overview highlights three issues, namely (i) concepts, characteristics and processing paradigms of Big Data Analytics; (ii) the state-of-the-art framework for decision-making in BDA for companies to insight value-crea tion; and (iii) the current challenges of Big Data Analytics as well as possible future directions.
Keywords: Big data ، Data analytics ، Machine learning ، Big data visualization ، Decision-making ، Smart agriculture ، Smart city application ، Value- reation ، Value-discover ، Value-realization
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
A framework for big data analytics approach to failure prediction of construction firms
چارچوبی برای رویکرد تحلیل داده های بزرگ برای پیش بینی شکست شرکت های ساختمانی-2018
This study explored use of big data analytics (BDA) to analyse data of a large number of construction firms to develop a construction business failure prediction model (CB-FPM). Careful analysis of literature revealed financial ratios as the best form of variable for this problem. Because of MapReduce’s unsuitability for iteration problems involved in developing CB-FPMs, various BDA initiatives for iteration problems were identified. A BDA framework for developing CB-FPM was proposed. It was validated by using 150,000 datacells of 30,000 construction firms, artificial neural network, Amazon Elastic Compute Cloud, Apache Spark and the R software. The BDA CB-FPM was developed in eight seconds while the same process without BDA was aborted after nine hours without success. This shows the issue of not wanting to use large dataset to develop CB-FPM due to tedious duration is resolvable by applying BDA technique. The BDA CB-FPM largely outperformed an ordinary CB-FPM developed with a dataset of 200 construction firms, proving that use of larger sample size with the aid of BDA, leads to better performing CB-FPMs. The high financial and social cost associated with misclassifications (i.e. model error) thus makes adoption of BDA CB-FPMs very important for, among others, financiers, clients and policy makers.
Key Words: Big data analytics; failure prediction models; construction businesses; machine learning; MapReduce/Spark
Local rough set: A solution to rough data analysis in big data
مجموعه ناهموار محلی: راه حل برای تجزیه و تحلیل داده های ناهموار در داده های بزرگ-2018
As a supervised learning method, classical rough set theory often requires a large amount of labeled data, in which concept approximation and attribute reduction are two key issues. With the advent of the age of big data however, labeling data is an expensive and laborious task and sometimes even infeasible, while unlabeled data are cheap and easy to collect. Hence, techniques for rough data analysis in big data using a semi-supervised approach, with limited labeled data, are desirable. Although many concept approximation and attribute reduction algorithms have been proposed in the classical rough set theory, quite often, these methods are unable to work well in the context of limited labeled big data. The challenges to classical rough set theory can be summarized with three issues: limited labeled property of big data, computational inefficiency and over-fitting in attribute reduction. To address these three challenges, we introduce a theoretic framework called local rough set, and develop a series of corresponding concept approximation and attribute reduction algorithms with linear time complexity, which can efficiently and effectively work in limited labeled big data. Theoretical analysis and experimental results show that each of the algorithms in the local rough set significantly outperforms its original counterpart in classical rough set theory. It is worth noting that the performances of the algorithms in the local rough set become more significant when dealing with larger data sets.
Keywords: Rough set theory ، Local rough set ، Concept approximation ، Attribute reduction ، Limited labeled data