scholarly journals Computer Science in Sport – Research and Practice: A book review

2016 ◽  
Vol 15 (1) ◽  
pp. 59-63
Author(s):  
Morgan Stuart
Keyword(s):  
Machine Learning ◽  
Computer Science ◽  
Machine Learning Algorithms ◽  
Sensor Technology ◽  
Sports Analytics ◽  
Research And Practice ◽  
Sports Science ◽  
Technology Changes ◽  
Comprehensive Text ◽  
Systematic Understanding

Abstract Sports informatics and computer science in sport are perhaps the most exciting and fast-moving disciplines across all of sports science. The tremendous parallel growth in digital technology, non-invasive sensor devices, computer vision and machine learning have empowered sports analytics in ways perhaps never seen before. This growth provides great challenges for new entrants and seasoned veterans of sports analytics alike. Keeping pace with new technological innovations requires a thorough and systematic understanding of many diverse topics from computer programming, to database design, machine learning algorithms and sensor technology. Nevertheless, as quickly as the state of the art technology changes, the foundation skills and knowledge about computer science in sport are lasting. Furthermore, resources for students and practitioners across this range of areas are scarce, and the new-release textbook Computer Science in Sport: Research and Practice edited by Professor Arnold Baca, provides much of the foundation knowledge required for working in sports informatics. This is certainly a comprehensive text that will be a valuable resource for many readers.

Introduction and Implementation of Machine Learning Algorithms in R

2019 ◽  
pp. 126-147
Author(s):  
S. R. Mani Sekhar ◽  
G. M. Siddesh
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Computer Science ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Linear Discriminant ◽  
The Given

Machine learning is one of the important areas in the field of computer science. It helps to provide an optimized solution for the real-world problems by using past knowledge or previous experience data. There are different types of machine learning algorithms present in computer science. This chapter provides the overview of some selected machine learning algorithms such as linear regression, linear discriminant analysis, support vector machine, naive Bayes classifier, neural networks, and decision trees. Each of these methods is illustrated in detail with an example and R code, which in turn assists the reader to generate their own solutions for the given problems.

What is Machine Learning? A Primer for the Epidemiologist

2019 ◽  
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.

scholarly journals Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1029 ◽  
Author(s):  
Thomas Kundinger ◽  
Nikoletta Sofra ◽  
Andreas Riener
Keyword(s):  
Machine Learning ◽  
User Study ◽  
Driving Simulator ◽  
Machine Learning Algorithms ◽  
Sensor Technology ◽  
Physiological Data ◽  
Promising Alternative ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Medical Grade

Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to be a promising alternative. However, in a dynamic environment such as driving, only non- or minimal intrusive methods are accepted, and vibrations from the roadbed could lead to degraded sensor technology. This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor. To check accuracy and feasibility, results are compared with reference data from a medical-grade ECG device. A user study with 30 participants in a high-fidelity driving simulator was conducted. Several machine learning algorithms for binary classification were applied in user-dependent and independent tests. Results provide evidence that the non-intrusive setting achieves a similar accuracy as compared to the medical-grade device, and high accuracies (>92%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human–machine interaction in a car and especially for driver state monitoring in the field of automated driving.

scholarly journals Economy-on-demand and the fairness of algorithms

2019 ◽  
Vol 10 (1) ◽  
pp. 3-16
Author(s):  
Claudia Schubert ◽  
Marc-Thorsten Hütt
Keyword(s):  
Machine Learning ◽  
Computer Science ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Rule Based ◽  
Legal Standards ◽  
On Demand ◽  
Legal Enforcement ◽  
Digital Agency

Algorithms are the key instrument for the economy-on-demand using platforms for its clients, workers and self-employed. An effective legal enforcement must not be limited to the control of the outcome of the algorithm but should also focus on the algorithm itself. This article assesses the present capacities of computer science to control and certify rule-based and data-centric (machine learning) algorithms. It discusses the legal instruments for the control of algorithms and their enforcement and institutional pre-conditions. It favours a digital agency that concentrates expertise and bureaucracy for the certification and official calibration of algorithms and promotes an international approach to the regulation of legal standards.

scholarly journals Measurement of Ethical Issues in Software Products

2020 ◽  
Author(s):  
Francesco Di Tria
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Science ◽  
Ethical Issues ◽  
Research Field ◽  
Machine Learning Algorithms ◽  
Software Products ◽  
Ethics Research ◽  
Software Product ◽  
Formal Requirements

Ethics is a research field that is obtaining more and more attention in Computer Science due to the proliferation of artificial intelligence software, machine learning algorithms, robot agents (like chatbot), and so on. Indeed, ethics research has produced till now a set of guidelines, such as ethical codes, to be followed by people involved in Computer Science. However, a little effort has been spent for producing formal requirements to be included in the design process of software able to act ethically with users. In the paper, we investigate those issues that make a software product ethical and propose a set of metrics devoted to quantitatively evaluate if a software product can be considered ethical or not.

scholarly journals Software Defect Estimation using Machine Learning Algorithms

2021 ◽  
Vol 10 (1) ◽  
pp. 204-208
Author(s):  
Mandi Akif Hussain* ◽  
Revoori Veeharika Reddy ◽  
Kedharnath Nagella ◽  
Vidya S.
Keyword(s):  
Machine Learning ◽  
Software Engineering ◽  
Computer Science ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
System Software ◽  
Software Defect ◽  
Learning Techniques ◽  
And Training

Software Engineering is a branch of computer science that enables tight communication between system software and training it as per the requirement of the user. We have selected seven distinct algorithms from machine learning techniques and are going to test them using the data sets acquired for NASA public promise repositories. The results of our project enable the users of this software to bag up the defects are selecting the most efficient of given algorithms in doing their further respective tasks, resulting in effective results.

scholarly journals Prediction of Network Attacks using Bio-Inspired Machine Learning Algorithm

2020 ◽  
Vol 8 (6) ◽  
pp. 5482-5485
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Computer Science ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Algorithm Engineering ◽  
Machine Learning Algorithm ◽  
Network Intrusion

Most of the times, data is created for the Intrusion Detection System (IDS) only when the set of all real working environments are explored under all the possibilities of attacks, which is an expensive task. Network Intrusion Detection software shields a system and computer network from staff and non-authorized users. The detector’s ultimate task is to build a foreboding classifier (i.e. a model) which would help in distinguishing between friendly and non-friendly connections, known as attacks or intrusions.This problem in network sectors is prevented by predicting whether the connection is attacked or not attacked from the dataset. We are using i.e. KDDCup99 using bio inspired machine learning techniques (like Artificial Neural Network). Bio inspired algorithm is a game changer in computer science. The extent of this field is really magnificent as compared to nature around it, complications of computer science are only a subset of it, opening a new era in next generation computing, modelling and algorithm engineering. The aim is to investigate bio inspired machine learning based techniques for better packet connection transfers forecasting by prediction results in best accuracy and to propose this machine learning-based method to accurately predict the DOS, R2L, U2R, Probe and overall attacks by predicting results in the form of best accuracy from comparing supervised classification machine learning algorithms. Furthermore, to compare and discuss the performance of various ML algorithms from the provided dataset with classification and evaluation report, finding and analysing the confusion matrix and for classifying data from the priority and result shows that the effectiveness of the proposed system i.e. bio inspired machine learning algorithm technique can be put on test with best accuracy along with precision, specificity, sensitivity, F1 Score and Recall

Quantum-Computing-Inspired Algorithms in Machine Learning

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.

scholarly journals Meta-analysis of Unmanned Aerial Vehicle (UAV) Imagery for Agro-environmental Monitoring Using Machine Learning and Statistical Models

2020 ◽  
Vol 12 (21) ◽  
pp. 3511
Author(s):  
Roghieh Eskandari ◽  
Masoud Mahdianpari ◽  
Fariba Mohammadimanesh ◽  
Bahram Salehi ◽  
Brian Brisco ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Environmental Monitoring ◽  
Unmanned Aerial Vehicle ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Effective Means ◽  
Machine Learning Algorithms ◽  
Sensor Technology ◽  
Aerial Vehicle

Unmanned Aerial Vehicle (UAV) imaging systems have recently gained significant attention from researchers and practitioners as a cost-effective means for agro-environmental applications. In particular, machine learning algorithms have been applied to UAV-based remote sensing data for enhancing the UAV capabilities of various applications. This systematic review was performed on studies through a statistical meta-analysis of UAV applications along with machine learning algorithms in agro-environmental monitoring. For this purpose, a total number of 163 peer-reviewed articles published in 13 high-impact remote sensing journals over the past 20 years were reviewed focusing on several features, including study area, application, sensor type, platform type, and spatial resolution. The meta-analysis revealed that 62% and 38% of the studies applied regression and classification models, respectively. Visible sensor technology was the most frequently used sensor with the highest overall accuracy among classification articles. Regarding regression models, linear regression and random forest were the most frequently applied models in UAV remote sensing imagery processing. Finally, the results of this study confirm that applying machine learning approaches on UAV imagery produces fast and reliable results. Agriculture, forestry, and grassland mapping were found as the top three UAV applications in this review, in 42%, 22%, and 8% of the studies, respectively.

scholarly journals Enhanced PEMS Performance and Regulatory Compliance through Machine Learning

2018 ◽  
Vol 3 (4) ◽  
pp. 329
Author(s):  
Gregorio Ciarlo ◽  
Daniele Angelosante ◽  
Marco Guerriero ◽  
Giorgio Saldarini ◽  
Nunzio Bonavita
Keyword(s):  
Machine Learning ◽  
Strong Support ◽  
Primary Source ◽  
Regulatory Compliance ◽  
Machine Learning Algorithms ◽  
Sensor Technology ◽  
Software Systems ◽  
Process Data ◽  
Emission Monitoring ◽  
Actual Field

<p><em>Modeling technologies</em><em> can pro</em><em>vide strong support to existing emission management systems, by means of what is known as a Predictive Emission Monitoring System (PEMS). These systems do not measure emissions through any hardware device, but use computer models to predict emission concentrations on the ground of process data (e.g., fuel flow, load) and ambient parameters (e.g., air temperature, relative humidity). They actually represent a relevant application arena for the so-called Inferential Sensor technology which has quickly proved to be invaluable in modern process automation and optimization strategies (Qin et al., 1997; Kadlec et al., 2009). While lots of applications demonstrate that software systems provide accuracy comparable to that of hardware-based Continuous Emission Monitoring Systems (CEMS), virtual analyzers are able to offer additional features and capabilities which are often not properly considered by end-users. Depending on local regulations and constraints, PEMS can be exploited either as primary source of emission monitoring or as a back-up of hardware-based CEMS able to validate analyzers’ readings and extend their service factor. PEMS consistency (and therefore its acceptance from environmental authorities) is directly linked to the accuracy and reliability of each parameter used as input of the models. While environmental authorities are steadily opening to PEMS, it is easy to foresee that major recognition and acceptance will be driven by extending PEMS robustness in front of possible sensor failures. Providing reliable instrument fail-over procedures is the main objective of Sensor Validation (SV) strategies. In this work, the capabilities of a class of machine learning algorithms will be presented, showing the results based on tests performed actual field data gathered at a fluid catalytic cracking unit.</em></p>

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