Artificial Intelligence Based Body Sensor Network Framework—Narrative Review: Proposing an End-to-End Framework using Wearable Sensors, Real-Time Location Systems and Artificial Intelligence/Machine Learning Algorithms for Data Collection, Data Mining and Knowledge Discovery in Sports and Healthcare

AbstractWith the rising amount of data in the sports and health sectors, a plethora of applications using big data mining have become possible. Multiple frameworks have been proposed to mine, store, preprocess, and analyze physiological vitals data using artificial intelligence and machine learning algorithms. Comparatively, less research has been done to collect potentially high volume, high-quality ‘big data’ in an organized, time-synchronized, and holistic manner to solve similar problems in multiple fields. Although a large number of data collection devices exist in the form of sensors. They are either highly specialized, univariate and fragmented in nature or exist in a lab setting. The current study aims to propose artificial intelligence-based body sensor network framework (AIBSNF), a framework for strategic use of body sensor networks (BSN), which combines with real-time location system (RTLS) and wearable biosensors to collect multivariate, low noise, and high-fidelity data. This facilitates gathering of time-synchronized location and physiological vitals data, which allows artificial intelligence and machine learning (AI/ML)-based time series analysis. The study gives a brief overview of wearable sensor technology, RTLS, and provides use cases of AI/ML algorithms in the field of sensor fusion. The study also elaborates sample scenarios using a specific sensor network consisting of pressure sensors (insoles), accelerometers, gyroscopes, ECG, EMG, and RTLS position detectors for particular applications in the field of health care and sports. The AIBSNF may provide a solid blueprint for conducting research and development, forming a smooth end-to-end pipeline from data collection using BSN, RTLS and final stage analytics based on AI/ML algorithms.

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Npc Behavior Plugin Development for Game Engine Unity

Russian Digital Libraries Journal ◽

10.26907/1562-5419-2020-23-5-1044-1057 ◽

2020 ◽

Vol 23 (5) ◽

pp. 1044-1057

Author(s):

Leonid Nikolaevich Parenyuk ◽

Vlada Vladimirovna Kugurakova

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Real Time ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Game Engine ◽

Game Development ◽

Advantages And Disadvantages

There are various approaches for creating artificial intelligence in games, and each has both advantages and disadvantages. This study describes an authoring implementation of the NPC behavior task using machine learning algorithms that will be associated with the Unity environment in real time. This approach can be used in game development.

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Machine Learning and Data Mining in Bioinformatics

Machine Learning ◽

10.4018/978-1-60960-818-7.ch401 ◽

2012 ◽

pp. 695-703

Author(s):

George Tzanis ◽

Christos Berberidis ◽

Ioannis Vlahavas

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Data Mining ◽

Reinforcement Learning ◽

Supervised Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

The World ◽

Supervised Learning Algorithms ◽

Computational Systems

Machine learning is one of the oldest subfields of artificial intelligence and is concerned with the design and development of computational systems that can adapt themselves and learn. The most common machine learning algorithms can be either supervised or unsupervised. Supervised learning algorithms generate a function that maps inputs to desired outputs, based on a set of examples with known output (labeled examples). Unsupervised learning algorithms find patterns and relationships over a given set of inputs (unlabeled examples). Other categories of machine learning are semi-supervised learning, where an algorithm uses both labeled and unlabeled examples, and reinforcement learning, where an algorithm learns a policy of how to act given an observation of the world.

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Machine Learning and Data Mining in Bioinformatics

Handbook of Research on Innovations in Database Technologies and Applications ◽

10.4018/978-1-60566-242-8.ch066 ◽

2009 ◽

pp. 612-621 ◽

Cited By ~ 2

Author(s):

George Tzanis ◽

Christos Berberidis ◽

Ioannis Vlahavas

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Data Mining ◽

Reinforcement Learning ◽

Supervised Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

The World ◽

Supervised Learning Algorithms ◽

Computational Systems

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Introduction to Machine Learning and Its Implementation Techniques

Handbook of Research on Applications and Implementations of Machine Learning Techniques - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-9902-9.ch018 ◽

2020 ◽

pp. 334-358

Author(s):

Arul Murugan R. ◽

Sathiyamoorthi V.

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Big Data ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

High Speeds ◽

Learning Techniques ◽

Implementation Techniques ◽

Gain Access

Machine learning (ML) is one of the exciting sub-fields of artificial intelligence (AI). The term machine learning is generally stated as the ability to learn without being explicitly programmed. In recent years, machine learning has become one of the thrust areas of research across various business verticals. The technical advancements in the field of big data have provided the ability to gain access over large volumes of diversified data at ease. This massive amount of data can be processed at high speeds in a reasonable amount of time with the help of emerging hardware capabilities. Hence the machine learning algorithms have been the most effective at leveraging all of big data to provide near real-time solutions even for the complex business problems. This chapter aims in giving a solid introduction to various widely adopted machine learning techniques and its applications categorized into supervised, unsupervised, and reinforcement and will serve a simplified guide for the aspiring data and machine learning enthusiasts.

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Artificial Intelligence and Machine Learning Algorithms

Advances in Computer and Electrical Engineering - Challenges and Applications for Implementing Machine Learning in Computer Vision ◽

10.4018/978-1-7998-0182-5.ch008 ◽

2020 ◽

pp. 188-219

Author(s):

Amit Kumar Tyagi ◽

Poonam Chahal

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Computer Vision ◽

Big Data ◽

Deep Learning ◽

Internet Of Things ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Learning Techniques

With the recent development in technologies and integration of millions of internet of things devices, a lot of data is being generated every day (known as Big Data). This is required to improve the growth of several organizations or in applications like e-healthcare, etc. Also, we are entering into an era of smart world, where robotics is going to take place in most of the applications (to solve the world's problems). Implementing robotics in applications like medical, automobile, etc. is an aim/goal of computer vision. Computer vision (CV) is fulfilled by several components like artificial intelligence (AI), machine learning (ML), and deep learning (DL). Here, machine learning and deep learning techniques/algorithms are used to analyze Big Data. Today's various organizations like Google, Facebook, etc. are using ML techniques to search particular data or recommend any post. Hence, the requirement of a computer vision is fulfilled through these three terms: AI, ML, and DL.

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Quantum-Computing-Inspired Algorithms in Machine Learning

Quantum-Inspired Intelligent Systems for Multimedia Data Analysis - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-5225-5219-2.ch001 ◽

2018 ◽

pp. 1-26

Author(s):

Deeksha Kaul ◽

Harika Raju ◽

B. K. Tripathy

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Decision Making ◽

Quantum Computing ◽

Computer Science ◽

Real Time ◽

Learning Algorithms ◽

Machine Learning Algorithms

In this chapter, the authors discuss the use of quantum computing concepts to optimize the decision-making capability of classical machine learning algorithms. Machine learning, a subfield of artificial intelligence, implements various techniques to train a computer to learn and adapt to various real-time tasks. With the volume of data exponentially increasing, solving the same problems using classical algorithms becomes more tedious and time consuming. Quantum computing has varied applications in many areas of computer science. One such area which has been transformed a lot through the introduction of quantum computing is machine learning. Quantum computing, with its ability to perform tasks in logarithmic time, aids in overcoming the limitations of classical machine learning algorithms.

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Thesis on Machine Learning Methods and Its Applications

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.38506 ◽

2021 ◽

Vol 9 (10) ◽

pp. 746-757

Author(s):

Dharmapriya M S

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Big Data ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Machine Learning Methods ◽

Processing Data ◽

History Of ◽

Learning Research ◽

Learning Machine

Abstract: In the 1950s, the concept of machine learning was discovered and developed as a subfield of artificial intelligence. However, there were no significant developments or research on it until this decade. Typically, this field of study has developed and expanded since the 1990s. It is a field that will continue to develop in the future due to the difficulty of analysing and processing data as the number of records and documents increases. Due to the increasing data, machine learning focuses on finding the best model for the new data that takes into account all the previous data. Therefore, machine learning research will continue in correlation with this increasing data. This research focuses on the history of machine learning, the methods of machine learning, its applications, and the research that has been conducted on this topic. Our study aims to give researchers a deeper understanding of machine learning, an area of research that is becoming much more popular today, and its applications. Keywords: Machine Learning, Machine Learning Algorithms, Artificial Intelligence, Big Data.

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Big Data Mining Algorithms

Encyclopedia of Information Science and Technology, Fifth Edition - Advances in Information Quality and Management ◽

10.4018/978-1-7998-3479-3.ch052 ◽

2021 ◽

pp. 768-777

Author(s):

M. Govindarajan

Keyword(s):

Machine Learning ◽

Data Mining ◽

Big Data ◽

Unsupervised Learning ◽

Supervised Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Data Sets ◽

Big Data Mining ◽

Supervised Learning Algorithms

Big data mining involves knowledge discovery from these large data sets. The purpose of this chapter is to provide an analysis of different machine learning algorithms available for performing big data analytics. The machine learning algorithms are categorized in three key categories, namely, supervised, unsupervised, and semi-supervised machine learning algorithm. The supervised learning algorithms are trained with a complete set of data, and thus, the supervised learning algorithms are used to predict/forecast. Example algorithms include logistic regression and the back propagation neural network. The unsupervised learning algorithms starts learning from scratch, and therefore, the unsupervised learning algorithms are used for clustering. Example algorithms include: the Apriori algorithm and K-Means. The semi-supervised learning combines both supervised and unsupervised learning algorithms. The semi-supervised algorithms are trained, and the algorithms also include non-trained learning.

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