An efficient density-based local outlier detection approach for scattered data
After the local outlier factor was first proposed, there is a large family of local outlier detection
approaches derived from it. Since the existing approaches only focus on the extent of overall …
approaches derived from it. Since the existing approaches only focus on the extent of overall …
Collect and analysis of agro-biodiversity data in a participative context: A business intelligence framework
Abstract In France and Europe, farmland represents a large fraction of land cover. The study
and assessment of biodiversity in farmland is therefore a major challenge. To monitor …
and assessment of biodiversity in farmland is therefore a major challenge. To monitor …
A Meta-learner approach to multistep-ahead time series prediction
The utilization of machine learning has become ubiquitous in addressing contemporary
challenges in data science. Moreover, there has been significant interest in democratizing …
challenges in data science. Moreover, there has been significant interest in democratizing …
Automating data mart construction from semi-structured data sources
The global food and agricultural industry has a total market value of USD 8 trillion in 2016,
and decision makers in the Agri sector require appropriate tools and up-to-date information …
and decision makers in the Agri sector require appropriate tools and up-to-date information …
N2DLOF: A new local density-based outlier detection approach for scattered data
Since the Local Outlier Factor (LOF) was first proposed, there is a large family of approaches
that is derived from it. For the reason that the existing local outliers detection approaches …
that is derived from it. For the reason that the existing local outliers detection approaches …
Detecting feature interactions in agricultural trade data using a deep neural network
Agri-analytics is an emerging sector which uses data mining to inform decision making in the
agricultural sector. Machine learning is used to accomplish data mining tasks such as …
agricultural sector. Machine learning is used to accomplish data mining tasks such as …
Using artificial intelligence to automate meat cut identification from the semimembranosus muscle on beef boning lines
The identification of different meat cuts for labeling and quality control on production lines is
still largely a manual process. As a result, it is a labor-intensive exercise with the potential for …
still largely a manual process. As a result, it is a labor-intensive exercise with the potential for …
Multi-resolution forecast aggregation for time series in agri datasets
A wide variety of phenomena are characterized by time dependent dynamics that can be
analyzed using time series methods. Various time series analysis techniques have been …
analyzed using time series methods. Various time series analysis techniques have been …
A method for automated transformation and validation of online datasets
S McCarthy, A McCarren… - 2019 IEEE 23rd …, 2019 - ieeexplore.ieee.org
While using online datasets for machine learning is commonplace today, the quality of these
datasets impacts on the performance of prediction algorithms. One method for improving the …
datasets impacts on the performance of prediction algorithms. One method for improving the …
Detecting multi-relationship links in sparse datasets
D Nie, M Roantree - 2019 - doras.dcu.ie
Application areas such as healthcare and insurance see many patients or clients with their
lifetime record spread across the databases of different providers. Record linkage is the task …
lifetime record spread across the databases of different providers. Record linkage is the task …