scholarly journals Estimating Occupancy Levels in Enclosed Spaces Using Environmental Variables: A Fitness Gym and Living Room as Evaluation Scenarios

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6579
Author(s):  
Andree Vela ◽  
Joanna Alvarado-Uribe ◽  
Manuel Davila ◽  
Neil Hernandez-Gress ◽  
Hector G. Ceballos
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Environmental Variables ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Living Room ◽  
Air Conditioners ◽  
The Past ◽  
Occupancy Patterns ◽  
Occupancy Estimation

The understanding of occupancy patterns has been identified as a key contributor to achieve improvements in energy efficiency in buildings since occupancy information can benefit different systems, such as HVAC (Heating, Ventilation, and Air Conditioners), lighting, security, and emergency. This has meant that in the past decade, researchers have focused on improving the precision of occupancy estimation in enclosed spaces. Although several works have been done, one of the less addressed issues, regarding occupancy research, has been the availability of data for contrasting experimental results. Therefore, the main contributions of this work are: (1) the generation of two robust datasets gathered in enclosed spaces (a fitness gym and a living room) labeled with occupancy levels, and (2) the evaluation of three Machine Learning algorithms using different temporal resolutions. The results show that the prediction of 3–4 occupancy levels using the temperature, humidity, and pressure values provides an accuracy of at least 97%.

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

Risks ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 4 ◽  
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.

scholarly journals The ethics of algorithms: key problems and solutions

AI & Society ◽  
2021 ◽  
Author(s):  
Andreas Tsamados ◽  
Nikita Aggarwal ◽  
Josh Cowls ◽  
Jessica Morley ◽  
Huw Roberts ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Design Development ◽  
Ethical Problems ◽  
Ethical Implications ◽  
The Past ◽  
Problems And Solutions

AbstractResearch on the ethics of algorithms has grown substantially over the past decade. Alongside the exponential development and application of machine learning algorithms, new ethical problems and solutions relating to their ubiquitous use in society have been proposed. This article builds on a review of the ethics of algorithms published in 2016 (Mittelstadt et al. Big Data Soc 3(2), 2016). The goals are to contribute to the debate on the identification and analysis of the ethical implications of algorithms, to provide an updated analysis of epistemic and normative concerns, and to offer actionable guidance for the governance of the design, development and deployment of algorithms.

scholarly journals Improving Soil Thickness Estimations Based on Multiple Environmental Variables with Stacking Ensemble Methods

2020 ◽  
Vol 12 (21) ◽  
pp. 3609
Author(s):  
Xinchuan Li ◽  
Juhua Luo ◽  
Xiuliang Jin ◽  
Qiaoning He ◽  
Yun Niu
Keyword(s):  
Machine Learning ◽  
Soil Properties ◽  
Environmental Variables ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Accurate Estimation ◽  
Support Vector ◽  
Topographic Wetness Index ◽  
Soil Thickness ◽  
Extreme Gradient Boosting

Spatially continuous soil thickness data at large scales are usually not readily available and are often difficult and expensive to acquire. Various machine learning algorithms have become very popular in digital soil mapping to predict and map the spatial distribution of soil properties. Identifying the controlling environmental variables of soil thickness and selecting suitable machine learning algorithms are vitally important in modeling. In this study, 11 quantitative and four qualitative environmental variables were selected to explore the main variables that affect soil thickness. Four commonly used machine learning algorithms (multiple linear regression (MLR), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGBoost) were evaluated as individual models to separately predict and obtain a soil thickness distribution map in Henan Province, China. In addition, the two stacking ensemble models using least absolute shrinkage and selection operator (LASSO) and generalized boosted regression model (GBM) were tested and applied to build the most reliable and accurate estimation model. The results showed that variable selection was a very important part of soil thickness modeling. Topographic wetness index (TWI), slope, elevation, land use and enhanced vegetation index (EVI) were the most influential environmental variables in soil thickness modeling. Comparative results showed that the XGBoost model outperformed the MLR, RF and SVR models. Importantly, the two stacking models achieved higher performance than the single model, especially when using GBM. In terms of accuracy, the proposed stacking method explained 64.0% of the variation for soil thickness. The results of our study provide useful alternative approaches for mapping soil thickness, with potential for use with other soil properties.

A Survey on Machine Learning Algorithms for the Diagnosis of Breast Masses with Mammograms

2020 ◽  
Vol 16 (6) ◽  
pp. 639-652
Author(s):  
Vaira Suganthi Gnanasekaran ◽  
Sutha Joypaul ◽  
Parvathy Meenakshi Sundaram
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Low Cost ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Computer Aided Detection ◽  
Breast Masses ◽  
Cad System ◽  
The Past ◽  
Mammogram Images

Breast cancer is leading cancer among women for the past 60 years. There are no effective mechanisms for completely preventing breast cancer. Rather it can be detected at its earlier stages so that unnecessary biopsy can be reduced. Although there are several imaging modalities available for capturing the abnormalities in breasts, mammography is the most commonly used technique, because of its low cost. Computer-Aided Detection (CAD) system plays a key role in analyzing the mammogram images to diagnose the abnormalities. CAD assists the radiologists for diagnosis. This paper intends to provide an outline of the state-of-the-art machine learning algorithms used in the detection of breast cancer developed in recent years. We begin the review with a concise introduction about the fundamental concepts related to mammograms and CAD systems. We then focus on the techniques used in the diagnosis of breast cancer with mammograms.

scholarly journals Anticipating Cryptocurrency Prices Using Machine Learning

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Laura Alessandretti ◽  
Abeer ElBahrawy ◽  
Luca Maria Aiello ◽  
Andrea Baronchelli
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Trading Strategies ◽  
Short Term ◽  
The Past ◽  
Term Evolution ◽  
Daily Data ◽  
Abnormal Profits

Machine learning and AI-assisted trading have attracted growing interest for the past few years. Here, we use this approach to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. We analyse daily data for 1,681 cryptocurrencies for the period between Nov. 2015 and Apr. 2018. We show that simple trading strategies assisted by state-of-the-art machine learning algorithms outperform standard benchmarks. Our results show that nontrivial, but ultimately simple, algorithmic mechanisms can help anticipate the short-term evolution of the cryptocurrency market.

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