Videogame Analytics, Surveillance, and Memory

There is a growing suite of commercially available analytics tools platforms use within the context of videogaming. Increasingly, these analytics tools are reliant on the capture of data pertaining to player activity—using machine learning techniques to parse these data and create subscription based “guide” services to assist players in their gameplay. This is often spruiked by marketers as a supplement to one’s gameplay and a method for improving one’s own performance. Looking at the example of DotaPlus, a recent kind of machine learning based analytics platform in the popular game Dota 2, this short essay argues that recent developments in gaming analytics might be understood as what Bernard Stiegler calls mnemotechnics—essentially technologies that shape human experience and perception in various, often commercially desirable ways. The theoretical argument I advance here is that the digital traces of player activity, captured and fed back to users in the form of DotaPlus guides, significantly alters the experience of playing Dota 2—done in a way that is economically desirable for the game’s developer.

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Overview of Machine Learning Approaches for Wireless Communication

Research Anthology on Artificial Intelligence Applications in Security ◽

10.4018/978-1-7998-7705-9.ch069 ◽

2021 ◽

pp. 1579-1597

Author(s):

Tolga Ensari ◽

Melike Günay ◽

Yağız Nalçakan ◽

Eyyüp Yildiz

Keyword(s):

Machine Learning ◽

Wireless Communications ◽

Smart Phones ◽

Machine Learning Techniques ◽

Learning Approaches ◽

Research Areas ◽

Learning Techniques ◽

Recent Developments ◽

Security And Reliability ◽

Day By Day

Machine learning is one of the most popular research areas, and it is commonly used in wireless communications and networks. Security and fast communication are among of the key requirements for next generation wireless networks. Machine learning techniques are getting more important day-by-day since the types, amount, and structure of data is continuously changing. Recent developments in smart phones and other devices like drones, wearable devices, machines with sensors need reliable communication within internet of things (IoT) systems. For this purpose, artificial intelligence can increase the security and reliability and manage the data that is generated by the wireless systems. In this chapter, the authors investigate several machine learning techniques for wireless communications including deep learning, which represents a branch of artificial neural networks.

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Overview of Machine Learning Approaches for Wireless Communication

Advances in Wireless Technologies and Telecommunication - Next-Generation Wireless Networks Meet Advanced Machine Learning Applications ◽

10.4018/978-1-5225-7458-3.ch006 ◽

2019 ◽

pp. 123-140 ◽

Cited By ~ 1

Author(s):

Tolga Ensari ◽

Melike Günay ◽

Yağız Nalçakan ◽

Eyyüp Yildiz

Keyword(s):

Machine Learning ◽

Wireless Communications ◽

Smart Phones ◽

Machine Learning Techniques ◽

Learning Approaches ◽

Research Areas ◽

Learning Techniques ◽

Recent Developments ◽

Security And Reliability ◽

Day By Day

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A Survey on Emotion Recognition for Human Robot Interaction

Journal of Computing and Information Technology ◽

10.20532/cit.2020.1004841 ◽

2021 ◽

Vol 28 (2) ◽

pp. 125-146

Keyword(s):

Machine Learning ◽

Emotion Recognition ◽

Human Robot Interaction ◽

Machine Learning Techniques ◽

Emotional States ◽

Robot Interaction ◽

Artificial Intelligent ◽

Learning Techniques ◽

Human Counterpart ◽

Recent Developments

With the recent developments of technology and the advances in artificial intelligent and machine learning techniques, it becomes possible for the robot to acquire and show the emotions as a part of Human-Robot Interaction (HRI). An emotional robot can recognize the emotional states of humans so that it will be able to interact more naturally with its human counterpart in different environments. In this article, a survey on emotion recognition for HRI systems has been presented. The survey aims to achieve two objectives. Firstly, it aims to discuss the main challenges that face researchers when building emotional HRI systems. Secondly, it seeks to identify sensing channels that can be used to detect emotions and provides a literature review about recent researches published within each channel, along with the used methodologies and achieved results. Finally, some of the existing emotion recognition issues and recommendations for future works have been outlined.

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Recent developments in human gait research: parameters, approaches, applications, machine learning techniques, datasets and challenges

Artificial Intelligence Review ◽

10.1007/s10462-016-9514-6 ◽

2016 ◽

Vol 49 (1) ◽

pp. 1-40 ◽

Cited By ~ 47

Author(s):

Chandra Prakash ◽

Rajesh Kumar ◽

Namita Mittal

Keyword(s):

Machine Learning ◽

Machine Learning Techniques ◽

Human Gait ◽

Learning Techniques ◽

Recent Developments

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Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls

Digital Finance ◽

10.1007/s42521-021-00046-2 ◽

2021 ◽

Author(s):

Helmut Wasserbacher ◽

Martin Spindler

Keyword(s):

Machine Learning ◽

Resource Allocation ◽

Simulation Study ◽

Machine Learning Techniques ◽

Automated Extraction ◽

Financial Forecasting ◽

Learning Framework ◽

Learning Techniques ◽

Recent Developments ◽

Data Points

AbstractThis article is an introduction to machine learning for financial forecasting, planning and analysis (FP&A). Machine learning appears well suited to support FP&A with the highly automated extraction of information from large amounts of data. However, because most traditional machine learning techniques focus on forecasting (prediction), we discuss the particular care that must be taken to avoid the pitfalls of using them for planning and resource allocation (causal inference). While the naive application of machine learning usually fails in this context, the recently developed double machine learning framework can address causal questions of interest. We review the current literature on machine learning in FP&A and illustrate in a simulation study how machine learning can be used for both forecasting and planning. We also investigate how forecasting and planning improve as the number of data points increases.

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Machine-learning techniques and their applications in manufacturing

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture ◽

10.1243/095440505x32274 ◽

2005 ◽

Vol 219 (5) ◽

pp. 395-412 ◽

Cited By ~ 50

Author(s):

D T Pham ◽

A A Afify

Keyword(s):

Machine Learning ◽

Inductive Learning ◽

Computer Programs ◽

Machine Learning Techniques ◽

Learning Approaches ◽

Research Issues ◽

Current Trends ◽

Learning Techniques ◽

Recent Developments ◽

Learning Research

Machine learning is concerned with enabling computer programs automatically to improve their performance at some tasks through experience. Manufacturing is an area where the application of machine learning can be very fruitful. However, little has been published about the use of machine-learning techniques in the manufacturing domain. This paper evaluates several machine-learning techniques and examines applications in which they have been successfully deployed. Special attention is given to inductive learning, which is among the most mature of the machine-learning approaches currently available. Current trends and recent developments in machine-learning research are also discussed. The paper concludes with a summary of some of the key research issues in machine learning.

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Using machine learning techniques to reduce data annotation time

PsycEXTRA Dataset ◽

10.1037/e577762012-020 ◽

2006 ◽

Author(s):

Christopher Schreiner ◽

Kari Torkkola ◽

Mike Gardner ◽

Keshu Zhang

Keyword(s):

Machine Learning ◽

Machine Learning Techniques ◽

Data Annotation ◽

Learning Techniques

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Using Machine Learning Algorithms on Prediction of Stock Price

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.2.84 ◽

2020 ◽

Vol 12 (2) ◽

pp. 84-99

Author(s):

Li-Pang Chen

Keyword(s):

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Short Term ◽

Learning Techniques ◽

Historical Database ◽

Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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