با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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Analyzing patient health information based on IoT sensor with AI for improving patient assistance in the future direction
تجزیه و تحلیل اطلاعات سلامت بیمار مبتنی بر حسگر اینترنت اشیا با هوش مصنوعی برای بهبود کمک به بیمار در مسیر آینده-2020 Internet of Things (IoT) and Artificial Intelligence (AI) play a vital role in the upcoming years to improve
the assistance systems. The IoT devices utilize several sensor devices that able to collect a large volume of
data in different domains which is processed by AI techniques to make the decision about the assistance
problems. Among several applications, in this work, IoT with AI is used to examine the healthcare sectors
to improve patient assistance and patient care in the future direction. Traditional health care assistance
system fails to predict the exact patient health information and needs which reduces the accuracy of
patient assistance process. For these issues, an IoT sensor with AI is used to predict the exact patient
details such as fitness tracker, medical reports, health activity, body mass, temperature, and other health
care information which helps to choose the right assistance process. Healthcare mobile application is
used to achieve this goal and collect the patient’s information. This information is shared in the cloud
environment, which is accessed and processed by applying the optimized machine learning techniques.
The gathered patient details are processed according to the iterative golden section optimized deep belief
neural network (IGDBN). The introduced network examines the patient’s details from the previous health
information which helps to predict the exact patient health condition in the future direction. The efficiency
of IoT sensor with an AI-based health assistance prediction process is developed using MATLAB
tool. Excellence is determined in terms of precision (99.87), loss error (0.045), simple matching coefficient
(99.71%), Matthews correlation coefficient (99.10%) and accuracy (99.86%). Keywords: IoT | Sensor | AI | Patient health condition | Mobile application | MATLAB |
مقاله انگلیسی |
2 |
IoV distributed architecture for real-time traffic data analytics
معماری توزیع شده IoV را برای تحلیل داده های ترافیک در زمان واقعی-2018 In this paper, we present necessary premises for the deployment of the Internet of Vehicles (IoV) integrating Big Data analytics
of road network traffic measurements of the city of Mohammedia, Morocco. Thus, we introduce an architecture based on three
main layers such as IoV, Fog Computing and Cloud Computing Layer. We specifically put more focus on Fog Computing layer
in which we develop a framework for a real-time collecting and processing events generated by intelligent vehicles as well as
visualizing traffic state on each road section. Furthermore, we consider deployment and test of the proposed framework using
events retrieved from a Vanets-type micro simulation. Finally, we present and discuss the first obtained results as well as the
advantages and limitations of the proposed architecture.
Keywords: IoV, Big Data analytics, Fog computing, Real-time data analytics, Traffic control |
مقاله انگلیسی |
3 |
Privacy-aware Big Data Analytics as a service for public health policies in smart cities
تجزیه و تحلیل داده های بزرگ تجزیه و تحلیل اطلاعات خصوصی به عنوان یک سرویس برای سیاست های بهداشت عمومی در شهرهای هوشمند-2018 Smart cities make use of a variety of technologies, protocols, and devices to support and improve the quality of
everyday activities of their inhabitants. An important aspect for the development of smart cities are innovative
public policies, represented by requirements, actions, and plans aimed at reaching a specific goal for improving
the societys welfare. With the advent of Big Data, the definition of such policies could be improved and reach an
unprecedented effectiveness on several dimensions, e.g., social or economic. On the other hand, however, the
safeguard of the privacy of its citizens is part of the quality of life of a smart city. In this paper, we focus on
balancing quality of life and privacy protection in smart cities by providing a new Big Data-assisted public policy
making process implementing privacy-by-design. The proposed approach is based on a Big Data Analytics as a
Service approach, which is driven by a Privacy Compliance Assessment derived from the European Unions
GDPR, and discussed in the context of a public health policy making process.
Keywords: Big Data , Privacy , Public policy making |
مقاله انگلیسی |
4 |
Big data and a bewildered lay analyst
داده های بزرگ و یک تحلیلگر بی نظیر-2018 Lay analysts often test hypotheses incorrectly. They also need help to find interesting
hypotheses. They usually do not know what to do next after testing an initial hypothesis.
We discuss their common mistakes, and also suggest practical tactics for their problems.
Keywords: Hypothesis testing ، Hypothesis exploration ، Data analysis tactics ، Exception ، Trend reversal ، Trend enhancement |
مقاله انگلیسی |
5 |
Destinations and crisis. Profiling tourists budget share from 2006 to 2012
مقصد و بحران سهم بودجه گردشگران را از 2006 تا 2012 اختصاص می دهد-2018 Tourist spending behavior is not only relevant in terms of volume but also in terms of trip budget
composition or allocation (share or proportion of total trip budget allocated to transportation, accom
modation or activities). This paper aims to profile expenditure patterns before, during and after the
economic crisis, and how they affect destinations. Clustering methods and compositional data analysis is
used as an appropriate statistical approach to analyze share. Incoming tourists to Spain are segmented by
trip budget share using repeated cross-sections of official statistics data (2006–2012). One of the main
findings is that segments are heterogeneous in their cutting back on expenditure during the economic
crisis, and segments increasing in size during the crisis not only spend less but also have the lowest
activity share. Furthermore, the paper identifies one of the segments being particularly attractive for
destinations in terms both of total expenditure and expenditure profile, with a high activity expenditure
share and usually flying with low-cost airlines. The paper contributes to understanding tourist consumer
behavior in terms of expenditure pattern at micro level in times of economic recession and its im
plications for particular destinations.
Keywords: Cluster analysis ، Trip budget share ، Compositional data analysis (CODA) ، Economic crisis ، Expenditure segmentation |
مقاله انگلیسی |
6 |
ClimateSpark: An in-memory distributed computing framework for big climate data analytics
ClimateSpark: یک چارچوب محاسباتی توزیع شده در حافظه برای تحلیل داده های آب و هوایی بزرگ-2018 The unprecedented growth of climate data creates new opportunities for climate studies, and yet big climate data
pose a grand challenge to climatologists to efficiently manage and analyze big data. The complexity of climate
data content and analytical algorithms increases the difficulty of implementing algorithms on high performance
computing systems. This paper proposes an in-memory, distributed computing framework, ClimateSpark, to
facilitate complex big data analytics and time-consuming computational tasks. Chunking data structure improves
parallel I/O efficiency, while a spatiotemporal index is built for the chunks to avoid unnecessary data reading and
preprocessing. An integrated, multi-dimensional, array-based data model (ClimateRDD) and ETL operations are
developed to address big climate data variety by integrating the processing components of the climate data
lifecycle. ClimateSpark utilizes Spark SQL and Apache Zeppelin to develop a web portal to facilitate the inter
action among climatologists, climate data, analytic operations and computing resources (e.g., using SQL query
and Scala/Python notebook). Experimental results show that ClimateSpark conducts different spatiotemporal data
queries/analytics with high efficiency and data locality. ClimateSpark is easily adaptable to other big multiple
dimensional, array-based datasets in various geoscience domains.
Keywords: Big data ، High performance computing ، Array-based data model ، Climate data analytics ، Apache spark ، Geospatial cyberinfrastructure ، Cloud computing |
مقاله انگلیسی |
7 |
Discovering socially important locations of social media users
کشف مکان های اجتماعی مهم از کاربران رسانه های اجتماعی-2017 Socially important locations are places that are frequently visited by social media users in their social
media life. Discovering socially interesting, popular or important locations from a location based social
network has recently become important for recommender systems, targeted advertisement applications,
and urban planning, etc. However, discovering socially important locations from a social network is chal
lenging due to the data size and variety, spatial and temporal dimensions of the datasets, the need for
developing computationally efficient approaches, and the difficulty of modeling human behavior. In the
literature, several studies are conducted for discovering socially important locations. However, majority of
these studies focused on discovering locations without considering historical data of social media users.
They focused on analysis of data of social groups without considering each user’s preferences in these
groups. In this study, we proposed a method and interest measures to discover socially important loca
tions that consider historical user data and each user’s (individual’s) preferences. The proposed algorithm
was compared with a naïve alternative using real-life Twitter dataset. The results showed that the pro
posed algorithm outperforms the naïve alternative.
Keywords: Socially important locations mining | Spatial social media mining | Historical social media data analysis | Social media networking sites | Twitter |
مقاله انگلیسی |
8 |
Pervasive social networking forensics_ Intelligence and evidence from mobile device extracts
اطلاعات جغرافیایی پزشکی قانونی شبکه های اجتماعی فراگیر : اطلاعات و شواهد از عصاره دستگاه های سیار-2017 In pervasive social networking forensics, mobile devices (e.g. mobile phones) are a typical source of evidence.
For example, figures from an Australian law enforcement agency show the number of mobile phones submitted
for analysis increasing at an average of 60% per annum since 2006, and data from FBI regional computer
forensics laboratory showing an increase of 67% per annum for mobile phone examinations. When coupled with
the growth in capacity of memory card and device storage, which doubles approximately every 15 months, there
is an ongoing and increasing growth in the volume of data available for evidence and intelligence analysis. There
is a potential for information relevant to a range of crimes within the extracted data, such as terrorism and
organised crime investigations, with potential cross-device and cross-case linkages. In this paper, we propose
the Digital Forensic Intelligence Analysis Cycle (DFIAC). Using mobile device extracts from an Australian law
enforcement agency, we demonstrate the utility of DFIAC in locating information across an increasing volume of
forensically extracted data from mobile devices, and a greater understanding of the developing trends in relation
to mobile device forensic analysis.
Keywords: Digital forensic intelligence analysis cycle | Forensic intelligence analysis | Mobile device forensic extracts | Pervasive social networking forensics Social communication forensics | Social networking app forensics | Big forensic data |
مقاله انگلیسی |
9 |
Big Data Challenges, Techniques, Technologies, and Applications and How Deep Learning can be Used
Big Data Challenges, Techniques, Technologies, and Applications and How Deep Learning can be Used-2016 It is already true that Big Data has drawn huge attention from researchers in information sciences, policy and decision makers
in governments and enterprises. A large number of fields and sectors, ranging from economic and business activities to public
administration, from national security to scientific researches in many areas, involve with Big Data problems. This talk is aimed
to demonstrate a close-up view about Big Data, including Big Data applications, Big Data opportunities and challenges, as well as
the state-of-the-art techniques and technologies that we currently adopt to deal with the Big Data problems. The second part is to
discuss the deep learning role in Big Data. In recent years, deep learning caves out a research wave in machine learning. With
outstanding performance, more and more applications of deep learning in pattern recognition, image recognition, speech
recognition, and video processing have been developed. Restricted Boltzmann machine (RBM) plays an important role in current
deep learning techniques, as most of existing deep networks are based on or related to it. This talk will also discuss how the big
data relates with the deep learning.
Keywords: Machine learning | Pattern recognition | Big data | Natural language processing | Data processing | Information analysis |
مقاله انگلیسی |
10 |
Energy Information Analysis Using Data Algorithms Based on Big Data Platform
تجزیه و تحلیل اطلاعات انرژی با استفاده از الگوریتم های داده ها مبتنی بر پلت فرم بزرگ داده ها-2016 With the development of the IoT market, collectable
data is increasing exponentially. Recently, various methods for
big data analysis are being suggested. Existing general
research on data analysis has some problem that if the size of
data is getting bigger, the processing speed is rapidly slow. In
this paper, we find out the optimal algorithm that efficiently
manage the energy data based on Big data by comparing data
which is analyzed using three algorithms.
Keywords: Big data | Data analysis | Algorithms | Energy | Machine learning |
مقاله انگلیسی |