scholarly journals Machine Learning Applications: The Past and Current Research Trend in Diverse Industries

Inventions ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 8 ◽  
Author(s):  
Omid Ameri Sianaki ◽  
Ashkan Yousefi ◽  
Azadeh Tabesh ◽  
Mehregan Mahdavi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Collection ◽  
Internet Of Things ◽  
Smart Cities ◽  
Research Trend ◽  
The Internet ◽  
Process Data ◽  
Learning Techniques ◽  
Machine Learning Applications

Dramatic changes in the way we collect and process data has facilitated the emergence of a new era by providing customised services and products precisely based on the needs of clients according to processed big data. It is estimated that the number of connected devices to the internet will pass 35 billion by 2020. Further, there has also been a massive escalation in the amount of data collection tools as Internet of Things devices generate data which has big data characteristics known as five V (volume, velocity, variety, variability and value). This article reviews challenges, opportunities and research trends to address the issues related to the data era in three industries including smart cities, healthcare and transportation. All three of these industries could greatly benefit from machine learning and deep learning techniques on big data collected by the Internet of Things, which is named as the internet of everything to emphasise the role of connected devices for data collection. In the smart grid portion of this paper, the recently developed deep reinforcement learning techniques and their applications in Smart Cities are also presented and reviewed.

scholarly journals Advances in Architectures, Big Data, and Machine Learning Techniques for Complex Internet of Things Systems

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-3 ◽  
Author(s):  
David Gil ◽  
Magnus Johnsson ◽  
Higinio Mora ◽  
Julian Szymanski
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Internet Of Things ◽  
Machine Learning Techniques ◽  
Learning Techniques

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 Social Media Analytics of the Internet of Things

2021 ◽  
Author(s):  
Jim Scheibmeir ◽  
Yashwant K. Malaiya
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Internet Of Things ◽  
Smart Cities ◽  
The Internet ◽  
Social Media Analytics ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Internet Of Things Technology ◽  
The Internet Of Things

Abstract The Internet of Things technology offers convenience and innovation in areas such as smart homes and smart cities. Internet of Things solutions require careful management of devices and the risk mitigation of potential vulnerabilities within cyber-physical systems. The Internet of Things concept, its implementations, and applications are frequently discussed on social media platforms. This article illuminates the public view of the Internet of Things through a content-based analysis of contemporary conversations occurring on the Twitter platform. Tweets can be analyzed with machine learning methods to converge the volume and variety of conversations into predictive and descriptive models. We have reviewed 684,503 tweets collected in a two-week period. Using supervised and unsupervised machine learning methods, we have identified interconnecting relationships between trending themes and the most mentioned industries. We have identified characteristics of language sentiment which can help to predict popularity within the realm of IoT conversation. We found the healthcare industry as the leading use case industry for IoT implementations. This is not surprising as the current Covid-19 pandemic is driving significant social media discussions. There was an alarming dearth of conversations towards cybersecurity. Only 12% of the tweets relating to the Internet of Things contained any mention of topics such as encryption, vulnerabilities, or risk, among other cybersecurity-related terms.

scholarly journals SocialTERM-Extractor: Identifying and Predicting Social-Problem-Specific Key Noun Terms from a Large Number of Online News Articles Using Text Mining and Machine Learning Techniques

2019 ◽  
Vol 11 (1) ◽  
pp. 196 ◽  
Author(s):  
Jong Hwan Suh
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Text Mining ◽  
Social Problem ◽  
Social Problems ◽  
Online News ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Related Data ◽  
Learning Techniques

In the digital age, the abundant unstructured data on the Internet, particularly online news articles, provide opportunities for identifying social problems and understanding social systems for sustainability. However, the previous works have not paid attention to the social-problem-specific perspectives of such big data, and it is currently unclear how information technologies can use the big data to identify and manage the ongoing social problems. In this context, this paper introduces and focuses on social-problem-specific key noun terms, namely SocialTERMs, which can be used not only to search the Internet for social-problem-related data, but also to monitor the ongoing and future events of social problems. Moreover, to alleviate time-consuming human efforts in identifying the SocialTERMs, this paper designs and examines the SocialTERM-Extractor, which is an automatic approach for identifying the key noun terms of social-problem-related topics, namely SPRTs, in a large number of online news articles and predicting the SocialTERMs among the identified key noun terms. This paper has its novelty as the first trial to identify and predict the SocialTERMs from a large number of online news articles, and it contributes to literature by proposing three types of text-mining-based features, namely temporal weight, sentiment, and complex network structural features, and by comparing the performances of such features with various machine learning techniques including deep learning. Particularly, when applied to a large number of online news articles that had been published in South Korea over a 12-month period and mostly written in Korean, the experimental results showed that Boosting Decision Tree gave the best performances with the full feature sets. They showed that the SocialTERMs can be predicted with high performances by the proposed SocialTERM-Extractor. Eventually, this paper can be beneficial for individuals or organizations who want to explore and use social-problem-related data in a systematical manner for understanding and managing social problems even though they are unfamiliar with ongoing social problems.

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.

Machine Learning for Internet of Things

2019 ◽  
pp. 57-83 ◽  
Author(s):  
Ramgopal Kashyap
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Internet Of Things ◽  
Information Quality ◽  
Physical World ◽  
Machine Learning Techniques ◽  
Iot Applications ◽  
Learning Techniques ◽  
The Way

Fast advancements in equipment, programming, and correspondence advances have permitted the rise of internet-associated tangible gadgets that give perception and information estimation from the physical world. It is assessed that the aggregate number of internet-associated gadgets being utilized will be in the vicinity of 25 and 50 billion. As the numbers develop and advances turn out to be more develop, the volume of information distributed will increment. Web-associated gadgets innovation, alluded to as internet of things (IoT), keeps on broadening the present internet by giving network and cooperation between the physical and digital universes. Notwithstanding expanded volume, the IoT produces big data described by speed as far as time and area reliance, with an assortment of numerous modalities and changing information quality. Keen handling and investigation of this big data is the way to creating shrewd IoT applications. This chapter evaluates the distinctive machine learning techniques that deal with the difficulties in IoT information.

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