scholarly journals Using Machine Learning to Establish the Relationship between Die Casting Parameters and the Casting Quality

2021 ◽  
Author(s):  
Pr. S. Henry Juang ◽  
Yi-Ning Huang ◽  
David A. Kafando
Keyword(s):  
Machine Learning ◽  
Die Casting ◽  
Casting Quality ◽  
The Relationship

Delay Prediction of Aircrafts Based on Health Monitoring Data

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
B. A. Dattaram ◽  
N. Madhusudanan
Keyword(s):  
Machine Learning ◽  
Health Monitoring ◽  
Monitoring Data ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Flight Delay ◽  
Delay Prediction ◽  
The Relationship

Flight delay is a major issue faced by airline companies. Delay in the aircraft take off can lead to penalty and extra payment to airport authorities leading to revenue loss. The causes for delays can be weather, traffic queues or component issues. In this paper, we focus on the problem of delays due to component issues in the aircraft. In particular, this paper explores the analysis of aircraft delays based on health monitoring data from the aircraft. This paper analyzes and establishes the relationship between health monitoring data and the delay of the aircrafts using exploratory analytics, stochastic approaches and machine learning techniques.

scholarly journals Advances of ECG Sensors from Hardware, Software and Format Interoperability Perspectives

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 105
Author(s):  
Khaleel Husain ◽  
Mohd Soperi Mohd Zahid ◽  
Shahab Ul Hassan ◽  
Sumayyah Hasbullah ◽  
Satria Mandala
Keyword(s):  
Machine Learning ◽  
Heart Attack ◽  
Privacy Preservation ◽  
Causes Of Death ◽  
Clinical Settings ◽  
Data Format ◽  
Medical Institutions ◽  
Comprehensive Survey ◽  
Electrocardiogram Ecg ◽  
The Relationship

It is well-known that cardiovascular disease is one of the major causes of death worldwide nowadays. Electrocardiogram (ECG) sensor is one of the tools commonly used by cardiologists to diagnose and detect signs of heart disease with their patients. Since fast, prompt and accurate interpretation and decision is important in saving the life of patients from sudden heart attack or cardiac arrest, many innovations have been made to ECG sensors. However, the use of traditional ECG sensors is still prevalent in the clinical settings of many medical institutions. This article provides a comprehensive survey on ECG sensors from hardware, software and data format interoperability perspectives. The hardware perspective outlines a general hardware architecture of an ECG sensor along with the description of its hardware components. The software perspective describes various techniques (denoising, machine learning, deep learning, and privacy preservation) and other computer paradigms used in the software development and deployment for ECG sensors. Finally, the format interoperability perspective offers a detailed taxonomy of current ECG formats and the relationship among these formats. The intention is to help researchers towards the development of modern ECG sensors that are suitable and approved for adoption in real clinical settings.

scholarly journals The relationship between trust in AI and trustworthy machine learning technologies

2020 ◽  
Author(s):  
Ehsan Toreini ◽  
Mhairi Aitken ◽  
Kovila Coopamootoo ◽  
Karen Elliott ◽  
Carlos Gonzalez Zelaya ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Technologies ◽  
The Relationship

scholarly journals Algorithmic and human prediction of success in human collaboration from visual features

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Martin Saveski ◽  
Edmond Awad ◽  
Iyad Rahwan ◽  
Manuel Cebrian
Keyword(s):  
Machine Learning ◽  
Visual Cues ◽  
Success Factors ◽  
Group Performance ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Adventure Game ◽  
Group Success ◽  
The Relationship ◽  
Better Than

AbstractAs groups are increasingly taking over individual experts in many tasks, it is ever more important to understand the determinants of group success. In this paper, we study the patterns of group success in Escape The Room, a physical adventure game in which a group is tasked with escaping a maze by collectively solving a series of puzzles. We investigate (1) the characteristics of successful groups, and (2) how accurately humans and machines can spot them from a group photo. The relationship between these two questions is based on the hypothesis that the characteristics of successful groups are encoded by features that can be spotted in their photo. We analyze >43K group photos (one photo per group) taken after groups have completed the game—from which all explicit performance-signaling information has been removed. First, we find that groups that are larger, older and more gender but less age diverse are significantly more likely to escape. Second, we compare humans and off-the-shelf machine learning algorithms at predicting whether a group escaped or not based on the completion photo. We find that individual guesses by humans achieve 58.3% accuracy, better than random, but worse than machines which display 71.6% accuracy. When humans are trained to guess by observing only four labeled photos, their accuracy increases to 64%. However, training humans on more labeled examples (eight or twelve) leads to a slight, but statistically insignificant improvement in accuracy (67.4%). Humans in the best training condition perform on par with two, but worse than three out of the five machine learning algorithms we evaluated. Our work illustrates the potentials and the limitations of machine learning systems in evaluating group performance and identifying success factors based on sparse visual cues.

A Machine Learning Approach to Analyzing the Relationship Between Temperatures and Multi-Proxy Tree-Ring Records

2018 ◽  
Vol 74 (2) ◽  
pp. 210-224 ◽  
Author(s):  
Jernej Jevšenak ◽  
Sašo Džeroski ◽  
Saša Zavadlav ◽  
Tom Levanič
Keyword(s):  
Machine Learning ◽  
Tree Ring ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
The Relationship

The Relationship Between Microstructure and Creep Behavior in AE42 Magnesium Die Casting Alloy

2016 ◽  
pp. 275-281 ◽  
Author(s):  
Bob R. Powell ◽  
Vadim Rezhets ◽  
Michael P. Balogh ◽  
Richard A. Waldo
Keyword(s):  
Creep Behavior ◽  
Die Casting ◽  
Casting Alloy ◽  
The Relationship ◽  
Magnesium Die Casting

scholarly journals A Benchmark Dataset and Deep Learning-Based Image Reconstruction for Electrical Capacitance Tomography

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3701 ◽  
Author(s):  
Jin Zheng ◽  
Jinku Li ◽  
Yi Li ◽  
Lihui Peng
Keyword(s):  
Machine Learning ◽  
Image Reconstruction ◽  
Benchmark Dataset ◽  
Electrical Capacitance Tomography ◽  
Electrical Capacitance ◽  
Reconstruction Algorithms ◽  
Reconstruction Methods ◽  
Image Reconstruction Algorithms ◽  
Capacitance Tomography ◽  
The Relationship

Electrical Capacitance Tomography (ECT) image reconstruction has developed for decades and made great achievements, but there is still a need to find a new theoretical framework to make it better and faster. In recent years, machine learning theory has been introduced in the ECT area to solve the image reconstruction problem. However, there is still no public benchmark dataset in the ECT field for the training and testing of machine learning-based image reconstruction algorithms. On the other hand, a public benchmark dataset can provide a standard framework to evaluate and compare the results of different image reconstruction methods. In this paper, a benchmark dataset for ECT image reconstruction is presented. Like the great contribution of ImageNet that transformed machine learning research, this benchmark dataset is hoped to be helpful for society to investigate new image reconstruction algorithms since the relationship between permittivity distribution and capacitance can be better mapped. In addition, different machine learning-based image reconstruction algorithms can be trained and tested by the unified dataset, and the results can be evaluated and compared under the same standard, thus, making the ECT image reconstruction study more open and causing a breakthrough.

scholarly journals The effects of age and body weight on powerlifters: An analysis model of powerlifting performance based on machine learning

2019 ◽  
Vol 18 (3) ◽  
pp. 89-99
Author(s):  
Vinh Huy Chau ◽  
Anh Thu Vo ◽  
Ba Tuan Le
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Body Weight ◽  
Root Mean Square Error ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Analysis Model ◽  
Factors Affecting ◽  
Artificial Intelligence Algorithm ◽  
The Relationship

Abstract As a up and coming sport, powerlifting is gathering more and more attetion. Powerlifters vary in their strength levels and performances at different ages as well as differing in height and weight. Hence the questions arises on how to establish the relationship between age and weight. It is difficult to judge the performance of athletes by artificial expertise, as subjective factors affecting the performance of powerlifters often fail to achieve the desired results. In recent years, artificial intelligence has made groundbreaking strides. Therefore, using artificial intelligence to predict the performance of athletes is among one of many interesting topics in sports competitions. Based on the artificial intelligence algorithm, this research proposes an analysis model of powerlifters’ performance. The results show that the method proposed in this paper can predict the best performance of powerlifters. Coefficient of determination-R2=0.86 and root-mean-square error of prediction-RMSEP=20.98 demonstrate the effectiveness of our method.

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