Application of Machine Learning Algorithms to the IoE

The internet of everything is a network that connects people, data, process, and things, making it easier to understand that many subfields of knowledge are discussable while addressing this subject. This chapter makes a survey on the application of machine learning algorithms to the internet of everything. This survey is particularly focused in computational frameworks for the development of intelligent systems and applications of machine learning algorithms as possible engines of wealth creation. A final example shows how to develop a simple end-to-end system.

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Detecting Abnormal Behavior of an IoT Device in the Network Based on a Traffic Model

Telecom IT ◽

10.31854/2307-1303-2019-7-3-50-55 ◽

2019 ◽

Vol 7 (3) ◽

pp. 50-55

Author(s):

D. Saharov ◽

D. Kozlov

Keyword(s):

Machine Learning ◽

Internet Of Things ◽

Mobile Networks ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Abnormal Behavior ◽

Traffic Model ◽

The Internet ◽

Wide Spread ◽

The Internet Of Things

The article deals with the СoAP Protocol that regulates the transmission and reception of information traf-fic by terminal devices in IoT networks. The article describes a model for detecting abnormal traffic in 5G/IoT networks using machine learning algorithms, as well as the main methods for solving this prob-lem. The relevance of the article is due to the wide spread of the Internet of things and the upcoming update of mobile networks to the 5g generation.

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Machine Learning in P&C Insurance: A Review for Pricing and Reserving

Risks ◽

10.3390/risks9010004 ◽

2020 ◽

Vol 9 (1) ◽

pp. 4 ◽

Cited By ~ 1

Author(s):

Christopher Blier-Wong ◽

Hélène Cossette ◽

Luc Lamontagne ◽

Etienne Marceau

Keyword(s):

Machine Learning ◽

Insurance Industry ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Actuarial Science ◽

Science Field ◽

The Past ◽

Highly Nonlinear ◽

Computer Scientists ◽

Applications Of Machine Learning

In the past 25 years, computer scientists and statisticians developed machine learning algorithms capable of modeling highly nonlinear transformations and interactions of input features. While actuaries use GLMs frequently in practice, only in the past few years have they begun studying these newer algorithms to tackle insurance-related tasks. In this work, we aim to review the applications of machine learning to the actuarial science field and present the current state of the art in ratemaking and reserving. We first give an overview of neural networks, then briefly outline applications of machine learning algorithms in actuarial science tasks. Finally, we summarize the future trends of machine learning for the insurance industry.

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The Applications of Machine Learning Algorithms in the Modeling of Estrogen-Like Chemicals

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/138620709788489037 ◽

2009 ◽

Vol 12 (5) ◽

pp. 490-496 ◽

Cited By ~ 14

Author(s):

Huanxiang Liu ◽

Xiaojun Yao ◽

Paola Gramatica

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Applications Of Machine Learning

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Movie Recommenadation System

International Journal on Recent and Innovation Trends in Computing and Communication ◽

10.17762/ijritcc.v9i4.5460 ◽

2021 ◽

Vol 9 (4) ◽

pp. 13-16

Author(s):

Gandhali Malve ◽

Lajree Lohar ◽

Tanay Malviya ◽

Shirish Sabnis

Keyword(s):

Machine Learning ◽

Recommender System ◽

Recommendation System ◽

Learning Algorithms ◽

Recommendation Systems ◽

Machine Learning Algorithms ◽

The Internet ◽

Amount Of Information ◽

System Recommendation ◽

User Ratings

Today the amount of information in the internet growth very rapidly and people need some instruments to find and access appropriate information. One of such tools is called recommendation system. Recommendation systems help to navigate quickly and receive necessary information. Many of us find it difficult to decide which movie to watch and so we decided to make a recommender system for us to better judge which movie we are more likely to love. In this project we are going to use Machine Learning Algorithms to recommend movies to users based on genres and user ratings. Recommendation system attempt to predict the preference or rating that a user would give to an item.

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Distributing Epistemic Functions and Tasks - A Framework for Augmenting Human Analytic Power With Machine Learning in Science Education Research

10.31219/osf.io/sg9jk ◽

2022 ◽

Author(s):

Marcus Kubsch ◽

Christina Krist ◽

Joshua Rosenberg

Keyword(s):

Machine Learning ◽

Science Education ◽

Education Research ◽

Educational Research ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Science Education Research ◽

Computational Power ◽

Machine Learning Methods ◽

Applications Of Machine Learning

Machine learning has become commonplace in educational research and science education research, especially to support assessment efforts. Such applications of machine learning have shown their promise in replicating and scaling human-driven codes of students’ work. Despite this promise, we and other scholars argue that machine learning has not achieved its transformational potential. We argue that this is because our field is currently lacking frameworks for supporting creative, principled, and critical endeavors to use machine learning in science education research. To offer considerations for science education researchers’ use of ML, we present a framework, Distributing Epistemic Functions and Tasks (DEFT), that highlights the functions and tasks that pertain to generating knowledge that can be carried out by either trained researchers or machine learning algorithms. Such considerations are critical decisions that should occur alongside those about, for instance, the type of data or algorithm used. We apply this framework to two cases, one that exemplifies the cutting-edge use of machine learning in science education research and another that offers a wholly different means of using machine learning and human-driven inquiry together. We conclude with strategies for researchers to adopt machine learning and call for the field to rethink how we prepare science education researchers in an era of great advances in computational power and access to machine learning methods.

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Machine Learning Algorithms

Computational Intelligence in the Internet of Things - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-7955-7.ch009 ◽

2019 ◽

pp. 210-233

Author(s):

Namrata Dhanda ◽

Stuti Shukla Datta ◽

Mudrika Dhanda

Keyword(s):

Machine Learning ◽

Intelligent Systems ◽

Learning Algorithm ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Clear Distinction ◽

Human Intelligence ◽

Training Algorithms ◽

Smart Systems ◽

Supervised And Unsupervised Learning

Human intelligence is deeply involved in creating efficient and faster systems that can work independently. Creation of such smart systems requires efficient training algorithms. Thus, the aim of this chapter is to introduce the readers with the concept of machine learning and the commonly employed learning algorithm for developing efficient and intelligent systems. The chapter gives a clear distinction between supervised and unsupervised learning methods. Each algorithm is explained with the help of suitable example to give an insight to the learning process.

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Machine learning algorithms for smart data analysis in the Internet of things: an overview

Intelligent Wireless Communications ◽

10.1049/pbte094e_ch12 ◽

2021 ◽

pp. 307-327

Author(s):

Mohammed H. Alsharif ◽

Anabi Hilary Kelechi ◽

Imran Khan ◽

Mahmoud A. Albreem ◽

Abu Jahid ◽

...

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Internet Of Things ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

The Internet ◽

Smart Data ◽

The Internet Of Things

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Machine Learning Techniques for IoT-Based Indoor Tracking and Localization

10.4018/978-1-7998-4186-9.ch007 ◽

2022 ◽

pp. 123-145

Author(s):

Pelin Yildirim Taser ◽

Vahid Khalilpour Akram

Keyword(s):

Machine Learning ◽

Indoor Localization ◽

Learning Algorithms ◽

Distance Estimation ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

The Internet ◽

Traditional Methods ◽

Learning Techniques ◽

Localization Systems

The GPS signals are not available inside the buildings; hence, indoor localization systems rely on indoor technologies such as Bluetooth, WiFi, and RFID. These signals are used for estimating the distance between a target and available reference points. By combining the estimated distances, the location of the target nodes is determined. The wide spreading of the internet and the exponential increase in small hardware diversity allow the creation of the internet of things (IoT)-based indoor localization systems. This chapter reviews the traditional and machine learning-based methods for IoT-based positioning systems. The traditional methods include various distance estimation and localization approaches; however, these approaches have some limitations. Because of the high prediction performance, machine learning algorithms are used for indoor localization problems in recent years. The chapter focuses on presenting an overview of the application of machine learning algorithms in indoor localization problems where the traditional methods remain incapable.

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What is Machine Learning? A Primer for the Epidemiologist

American Journal of Epidemiology ◽

10.1093/aje/kwz189 ◽

2019 ◽

Cited By ~ 6

Author(s):

Qifang Bi ◽

Katherine E Goodman ◽

Joshua Kaminsky ◽

Justin Lessler

Keyword(s):

Machine Learning ◽

Big Data ◽

Computer Science ◽

Research Methods ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Epidemiologic Research ◽

Learning Techniques ◽

Applications Of Machine Learning

Abstract Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on “Big Data,” it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. Here, we provide an overview of the concepts and terminology used in machine learning literature, which encompasses a diverse set of tools with goals ranging from prediction to classification to clustering. We provide a brief introduction to 5 common machine learning algorithms and 4 ensemble-based approaches. We then summarize epidemiologic applications of machine learning techniques in the published literature. We recommend approaches to incorporate machine learning in epidemiologic research and discuss opportunities and challenges for integrating machine learning and existing epidemiologic research methods.

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