Building an Intelligent Telemonitoring System for Heart Failure: The Use of the Internet of Things, Big Data, and Machine Learning

2020 ◽  
Author(s):  
Shorouq Eletter ◽  
Tahira Yasmin ◽  
Ghaleb Elrefae ◽  
Hashem Aliter ◽  
Abdullah Elrefae
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Big Data ◽  
Internet Of Things ◽  
The Internet ◽  
Use Of The Internet ◽  
The Internet Of Things

Des Translators neue Kleider. Die Translationwirtschaft in Zeiten von Digitalisierung, Datafizierung und Big Data Management

2017 ◽  
Vol 62 (2) ◽  
Author(s):  
Martin Forstner
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Big Data ◽  
Internet Of Things ◽  
Data Management ◽  
Low Cost ◽  
The Internet ◽  
Computer Linguistics ◽  
Translation Services ◽  
The Internet Of Things

AbstractThe Internet of things will influence all professional environments, including translation services. Advances in machine learning, supported by accelerating improvements in computer linguistics, have enabled new systems that can learn from their own experience and will have repercussions on the workflow processes of translators or even put their services at risk in the expected digitalized society. Outsourcing has become a common practice and working in the cloud and in the crowd tend to enable translating on a very low-cost level. Confronted with promising new labels like

scholarly journals Keyword Recognition Device Cloud Based

2021 ◽  
Vol 9 (VIII) ◽  
pp. 87-89
Author(s):  
Apurv Singh Yadav
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Speech Recognition ◽  
Internet Of Things ◽  
Recognition System ◽  
The Internet ◽  
Speech Recognition System ◽  
The Past ◽  
Use Of The Internet ◽  
The Internet Of Things

Over the past few decades speech recognition has been researched and developed tremendously. However in the past few years use of the Internet of things has been significantly increased and with it the essence of efficient speech recognition is beneficial more than ever. With the significant improvement in Machine Learning and Deep learning, speech recognition has become more efficient and applicable. This paper focuses on developing an efficient Speech recognition system using Deep Learning.

scholarly journals Usages of Spark Framework with Different Machine Learning Algorithms

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Mohamed Ali Mohamed ◽  
Ibrahim Mahmoud El-henawy ◽  
Ahmad Salah
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Internet Of Things ◽  
Large Data ◽  
Machine Learning Algorithms ◽  
Smart Devices ◽  
The Internet ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
The Internet Of Things

Sensors, satellites, mobile devices, social media, e-commerce, and the Internet, among others, saturate us with data. The Internet of Things, in particular, enables massive amounts of data to be generated more quickly. The Internet of Things is a term that describes the process of connecting computers, smart devices, and other data-generating equipment to a network and transmitting data. As a result, data is produced and updated on a regular basis to reflect changes in all areas and activities. As a consequence of this exponential growth of data, a new term and idea known as big data have been coined. Big data is required to illuminate the relationships between things, forecast future trends, and provide more information to decision-makers. The major problem at present, however, is how to effectively collect and evaluate massive amounts of diverse and complicated data. In some sectors or applications, machine learning models are the most frequently utilized methods for interpreting and analyzing data and obtaining important information. On their own, traditional machine learning methods are unable to successfully handle large data problems. This article gives an introduction to Spark architecture as a platform that machine learning methods may utilize to address issues regarding the design and execution of large data systems. This article focuses on three machine learning types, including regression, classification, and clustering, and how they can be applied on top of the Spark platform.

scholarly journals Review of the Complexity of Managing Big Data of the Internet of Things

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
David Gil ◽  
Magnus Johnsson ◽  
Higinio Mora ◽  
Julian Szymański
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Internet Of Things ◽  
State Of The Art ◽  
The State ◽  
The Internet ◽  
Novel Technologies ◽  
Extensible Markup ◽  
Description Framework ◽  
The Internet Of Things

There is a growing awareness that the complexity of managing Big Data is one of the main challenges in the developing field of the Internet of Things (IoT). Complexity arises from several aspects of the Big Data life cycle, such as gathering data, storing them onto cloud servers, cleaning and integrating the data, a process involving the last advances in ontologies, such as Extensible Markup Language (XML) and Resource Description Framework (RDF), and the application of machine learning methods to carry out classifications, predictions, and visualizations. In this review, the state of the art of all the aforementioned aspects of Big Data in the context of the Internet of Things is exposed. The most novel technologies in machine learning, deep learning, and data mining on Big Data are discussed as well. Finally, we also point the reader to the state-of-the-art literature for further in-depth studies, and we present the major trends for the future.

Advanced Deep Learning Applications in Big Data Analytics

2021 ◽  
pp. 1-28
Author(s):  
Venkatesan Manian ◽  
Vadivel P.
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Internet Of Things ◽  
Data Analytics ◽  
Big Data Analytics ◽  
The Internet ◽  
Infrastructure Development ◽  
Learning Approaches ◽  
The Internet Of Things

This chapter analyzes the Internet of Things (IoT), its history, and its tools in brief. This chapter also explores the contribution of IoT towards the recent development in infrastructure development of nations represented as smart world. This chapter also discuss the contribution of IoT towards big data analytics era. This chapter also briefly introduce the smart bio world and how it is made possible with the internet of things. This chapter also introduces the machine learning approaches and also discusses the contribution of Internet of Thing for this machine learning. This chapter also briefly introduces some tools used for IoT developments.

scholarly journals 5G-smart Diabetes:Personalized Diabetes Diagnosis with Human Services Big Data Clouds

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1760-1764
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Internet Of Things ◽  
Cell Phone ◽  
Human Services ◽  
The Internet ◽  
Diabetes Diagnosis ◽  
Treatment Of Diabetes ◽  
5G Systems ◽  
The Internet Of Things

Ongoing advances in remote systems administration and huge information innovations, for example, 5G systems, medicinal huge information investigation, and the Internet of Things, alongside ongoing improvements in wearable figuring and man-made brainpower, are empowering the advancement and usage of imaginative diabetes checking frameworks and applications. Because of the deep rooted and efficient damage endured by diabetes patients, it is basic to plan powerful strategies for the determination and treatment of diabetes. In light of our far reaching examination, this paper characterizes those strategies into Diabetes 1.0 and Diabetes 2.0, which show insufficiencies as far as systems administration and insight. Consequently, our objective is to structure a manageable, financially savvy, and insightful diabetes finding arrangement with customized treatment. In this paper, we initially propose the 5G-Smart Diabetes framework, which joins the best in class advancements, for example, wearable 2.0, machine learning, and huge information to create complete detecting and investigation for patients experiencing diabetes. At that point we present the information sharing system and customized information examination display for 5G-Smart Diabetes. At last, we construct a 5G-Smart Diabetes testbed that incorporates savvy dress, cell phone, and huge information mists. The trial results demonstrate that our framework can successfully give customized analysis and treatment proposals to patients.

THE FUTURE OF SMART INDUSTRY: TECHNOLOGIES, MACRO-TRENDS AND APPLICATION AREAS

10.6036/10342 ◽  
2021 ◽  
Vol 96 (6) ◽  
pp. 561-562
Author(s):  
MIKEL NIÑO
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Big Data ◽  
Data Analysis ◽  
Internet Of Things ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Big Data Technologies ◽  
Smart Industry ◽  
The Internet Of Things

The Smart Industry has been developing has been developing at an accelerated pace since the beginning of the last decade, driven by of the last decade, driven by the by the emergence of technologies such as the Internet of Things, Compute of Things, Cloud Computing and Big Data Cloud Computing and Big Data technologies, as well as their connection and Big Data technologies, as well as their connection with machine learning algorithms for predictive data analysis [1] of data [1].

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