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 Social media analytics of the Internet of Things

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Jim A. 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

AbstractThe 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 research illuminates the public view of the Internet of Things through a content-based and network 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 2-week period. Using supervised and unsupervised machine learning methods, we have identified trends within the realm of IoT and their interconnecting relationships between the most mentioned industries. We have identified characteristics of language sentiment which can help to predict the popularity of IoT conversation topics. 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. Recent breaches and ransomware events denote that organizations should spend more time communicating about risks and mitigations. 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. We propose an IoT Cybersecurity Communication Scorecard to help organizations benchmark the density and sentiment of their corporate communications regarding security against their specific industry.

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 Automated smart car parking system for smart cities demand employs internet of things technology

2021 ◽  
Vol 10 (1) ◽  
pp. 46
Author(s):  
Rahman Atiqur
Keyword(s):  
Internet Of Things ◽  
Smart Cities ◽  
The Internet ◽  
City Life ◽  
Economic Progress ◽  
Parking System ◽  
Car Park ◽  
Internet Of Things Technology ◽  
The City ◽  
The Internet Of Things

<span>The use of smart cities rises quickly with the fast progress of the Internet of Things (IoT) advances. The smart city idea essentially getting city life; as well raises the capability of municipal jobs and facilities plus form viable economic progress of the city. The point of convergence of this paper is to introduce an automated smart automobile parking system for smart cities demand employs internet of things (IoT) technology. The offered automobile parking system covers an IoT entity sent nearby for getting sorted out the existing parking spots which are quicker contrasted with different frameworks. It is a viewpoint gave as an iOS application for reservation, entrance, supervision, and leaving the car park places.</span>

scholarly journals Scientific and practical foundations and business processes for the sustainable development of Smart City

2021 ◽  
Vol 258 ◽  
pp. 02023
Author(s):  
Inna Krasovskaya ◽  
Elena Schislyaeva ◽  
Felix Shamrai
Keyword(s):  
Internet Of Things ◽  
Smart City ◽  
Business Processes ◽  
Smart Cities ◽  
Economic Environment ◽  
High Tech ◽  
The Internet ◽  
Territorial Planning ◽  
Internet Of Things Technology ◽  
The Internet Of Things

The article investigates scientific and practical issues of transport logistics; the conceptual and methodological mechanisms of formation of the “Smart City” strategy are considered and its main business processes are interpreted; the author’s description of the Internet of Things as a high-tech network of deterministic mechanisms, logistic algorithms, business processes and technical and technological devices, interconnected with each other and with the external socio-economic environment is presented; the international experience of the formation and functioning of transport and logistics business processes of “smart cities” was critically rethought; reducing costs, ensuring socio-economic growth and sustainability, while improving the quality of services and the life of citizens, are justified as fundamental tasks of smart city projects; studied the socio-economic objectives of the Smart City of St. Petersburg project, in particular, the formation of a list of measures to optimize business processes, the creation of an external economic environment that would facilitate the attraction of non-budgetary funding sources for the implementation of projects, the development of a methodology for monitoring indicators of socio-economic development and territorial planning of St. Petersburg; identified the main socio-economic advantages of the introduction of smart parking in relation to the infrastructures of smart cities and their residents; empirically confirmed the effectiveness of the implementation of an intelligent parking management system based on the “Internet of Things” technology in the social and economic conditions of St. Petersburg; the characteristic of transport and logistics advantages of the strategy “Smart city of St. Petersburg” is provided.

scholarly journals VariantSpark, A Random Forest Machine Learning Implementation for Ultra High Dimensional Data

2019 ◽  
Author(s):  
Arash Bayat ◽  
Piotr Szul ◽  
Aidan R. O’Brien ◽  
Robert Dunne ◽  
Oscar J. Luo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Internet Of Things ◽  
Random Forests ◽  
Life Sciences ◽  
High Dimensional ◽  
The Internet ◽  
Machine Learning Methods ◽  
High Dimensional Datasets ◽  
The Internet Of Things

AbstractThe demands on machine learning methods to cater for ultra high dimensional datasets, datasets with millions of features, have been increasing in domains like life sciences and the Internet of Things (IoT). While Random Forests are suitable for “wide” datasets, current implementations such as Google’s PLANET lack the ability to scale to such dimensions. Recent improvements by Yggdrasil begin to address these limitations but do not extend to Random Forest. This paper introduces CursedForest, a novel Random Forest implementation on top of Apache Spark and part of the VariantSpark platform, which parallelises processing of all nodes over the entire forest. CursedForest is 9 and up to 89 times faster than Google’s PLANET and Yggdrasil, respectively, and is the first method capable of scaling to millions of features.

Analysis of risk factors in financial supply chain based on machine learning and IoT technology

2020 ◽  
pp. 1-11
Author(s):  
Sun Hongjin
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Supply Chain ◽  
Internet Of Things ◽  
Learning Algorithm ◽  
The Internet ◽  
Supply Chain Risk ◽  
Financing Model ◽  
Internet Of Things Technology ◽  
The Internet Of Things

The financial supply chain is affected by many factors, so an artificial intelligence model is needed to identify supply chain risk factors. This article combines the actual situation of the financial supply chain, improves the traditional machine learning algorithm, and takes the actual company as an example to build a corresponding risk factor recognition model. From the perspective of optimizing the supply chain financial model, this paper combines the functions of the Internet of Things technology and the characteristics of the supply chain financial inventory pledge financing model to design a new type of inventory pledge financing model. The new model makes up for the defects of the original model through the functions of intelligent identification, visual tracking and cloud computing big data processing of the Internet of Things technology. In addition, this study verifies the performance of the system, uses a large amount of data in Internet finance as an object, and obtains the corresponding results through mathematical statistical analysis. The research results show that the model proposed in this paper has a certain effect on the identification and analysis of financial supply chain risk factors.

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