scholarly journals Drones can monitor beach litter 40x faster than people

2018 ◽  
Author(s):  
OCTO
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Local Level ◽  
Scaling Up ◽  
Machine Learning Techniques ◽  
Considerable Effort ◽  
Marine Litter ◽  
Learning Techniques ◽  
Monitoring Process ◽  
Trained Individual ◽  
Beach Litter

Marine litter is predominately plastic, which typically floats in water, causing much debris to wash up on the world’s beaches. While some studies have focused on estimating the total debris floating in the ocean, estimates of beached litter are typically only compiled at the local level. On beaches, marine litter is typically estimated visually -- often with a small group of trained volunteers who sample the debris within random transects. This process can take a few hours for each beach, thus scaling up to a considerable effort if one is intending to monitor long stretches of beach multiple times a year. Unmanned aerial vehicles (UAVs), colloquially known as drones, could offer a much faster and efficient monitoring process. The authors present a methodology for using drones to take pictures of beaches, and then using machine learning techniques to automatically count and categorize litter in these photos. Ideally, this methodology would take just one trained individual a few minutes to sample an entire beach.

scholarly journals Local mortality estimates during the COVID-19 pandemic in Italy

2021 ◽  
Author(s):  
Augusto Cerqua ◽  
Roberta Di Stefano ◽  
Marco Letta ◽  
Sara Miccoli
Keyword(s):  
Machine Learning ◽  
Excess Mortality ◽  
Control Method ◽  
Local Level ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Mortality Data ◽  
Official Method ◽  
Learning Techniques ◽  
Mortality Estimates

AbstractEstimates of the real death toll of the COVID-19 pandemic have proven to be problematic in many countries, Italy being no exception. Mortality estimates at the local level are even more uncertain as they require stringent conditions, such as granularity and accuracy of the data at hand, which are rarely met. The “official” approach adopted by public institutions to estimate the “excess mortality” during the pandemic draws on a comparison between observed all-cause mortality data for 2020 and averages of mortality figures in the past years for the same period. In this paper, we apply the recently developed machine learning control method to build a more realistic counterfactual scenario of mortality in the absence of COVID-19. We demonstrate that supervised machine learning techniques outperform the official method by substantially improving the prediction accuracy of the local mortality in “ordinary” years, especially in small- and medium-sized municipalities. We then apply the best-performing algorithms to derive estimates of local excess mortality for the period between February and September 2020. Such estimates allow us to provide insights about the demographic evolution of the first wave of the pandemic throughout the country. To help improve diagnostic and monitoring efforts, our dataset is freely available to the research community.

scholarly journals A Holistic Quality Assurance Approach for Machine Learning Applications in Cyber-Physical Production Systems

2021 ◽  
Vol 11 (20) ◽  
pp. 9590
Author(s):  
Hajo Wiemer ◽  
Alexander Dementyev ◽  
Steffen Ihlenfeldt
Keyword(s):  
Machine Learning ◽  
Quality Assurance ◽  
Process Model ◽  
Production Systems ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Monitoring Process ◽  
Machine Learning Applications ◽  
Cyber Physical Production Systems ◽  
Automation Technology

With the trend of increasing sensors implementation in production systems and comprehensive networking, essential preconditions are becoming required to be established for the successful application of data-driven methods of equipment monitoring, process optimization, and other relevant automation tasks. As a protocol, these tasks should be performed by engineers. Engineers usually do not have enough experience with data mining or machine learning techniques and are often skeptical about the world of artificial intelligence (AI). Quality assurance of AI results and transparency throughout the IT chain are essential for the acceptance and low-risk dissemination of AI applications in production and automation technology. This article presents a conceptual method of the stepwise and level-wise control and improvement of data quality as one of the most important sources of AI failures. The appropriate process model (V-model for quality assurance) forms the basis for this.

Towards spatial machine learning to reveal hidden patterns and relationships in national and international geochemical databases

2020 ◽  
Author(s):  
Chaosheng Zhang
Keyword(s):  
Machine Learning ◽  
Geographically Weighted Regression ◽  
Hot Spot ◽  
Local Level ◽  
Environmental Geochemistry ◽  
Machine Learning Techniques ◽  
Weighted Regression ◽  
Hot Spot Analysis ◽  
Learning Techniques ◽  
Spot Analysis

<p>Environmental geochemistry is playing an increasingly important role in mineral exploration, environmental management, agricultural practices as well as links with health. With rapidly growing databases available at regional, national, and global scales, environmental geochemistry is facing the challenges in the “big data” era. One of the main challenges is to find out useful information hidden in a large volume of data, with the existence of spatial variation found at all the sizes of global, regional (in square kilometers), field (in square meters) and micro scales (in square centimeters). Meanwhile, the rapidly developing techniques in machine learning become useful tools for classification, identification of clusters/patterns, identification of relationships and prediction. This presentation demonstrates the potential uses of a few practical spatial machine learning techniques (spatial analyses) in environmental geochemistry: neighborhood statistics, hot spot analysis and geographically weighted regression.</p><p> </p><p>Neighborhood (local) statistics are calculated using data within a neighborhood such as a moving window. In this way, spatial variation at the local level can be quantified and more details are revealed. Hot spot analysis techniques are capable of revealing hidden spatial patterns. The techniques of hot spot analysis including local index of spatial association (LISA) and Getis Ord Gi* are investigated using examples of geochemical databases in Ireland, China, the UK and the USA. The geographically weighted regression (GWR) explores the relationships between geochemical parameters and their influencing factors at the local level, which is effective in identifying the complex spatially varying relationships. Machine learning techniques are expected to play more important roles in environmental geochemistry. Challenges for more effective “data analytics” are currently emerging in the era of “big data”.</p><p> </p>

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

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.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques
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