Extracting hidden patterns from dates' product data using a machine learning technique

<div style="’text-align: justify;">Mining in data is an important step for knowledge discovery, which leads to extract new patterns from datasets. It is a widespread methodology that has the capability to help ministries, companies, and experts for diving into the data to find important insights and patterns to help them take suitable decisions. The farmers and marketers of the date product in the production regions lack to discover the most important characteristics of dates types from the economically, healthy, and the type of consumers point of view to achieve the highest profits by choosing the best types and the most consumed. The research objective is to extract interesting patterns from the dates’ product dataset, using Machine Learning, based on association rules generation. This, in turn, will support the farmers, and marketers to discover new features related to the production, consumption, and marketing processes. This research used a real dataset collected from KSA, Qassim region, which is the first region of cultivation of palm, that produces the best types of dates in the Arab region. The data preprocessed and analyzed by the Apriori algorithm. The results show important features and insights related to the health benefits of dates, production, its consumption, consumers types, and marketing. Consequently, these results can be employed, for instance, to encourage individuals to consume dates for their nutritional value and their important health benefits., furthermore, the results encourage producers to focus on the production of preferable types and to improve the marketing policies of the other types.</div>

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Individual Yarn Fibre Extraction from Micro CT: Multilevel Machine Learning Approach

10.31224/osf.io/m4fjr ◽

2020 ◽

Author(s):

Petr Henys ◽

Lukáš Čapek

Keyword(s):

Machine Learning ◽

Point Of View ◽

Machine Learning Technique ◽

Computational Point ◽

Textile Materials ◽

Machine Learning Approach ◽

Learning Technique ◽

The Individual ◽

Woven Textile ◽

Tomography Data

The internal structure and mechanics of the fibre materials, such as yarn or woven textile, are highly complex. Exploring the fibre structure is an essential step in material engineering either from the experimental or computational point of view. In this study, a new method to extract geometrical and morphological parameters of fibre structures is proposed. The method benefits from standard image analysis and machine learning technique to efficiently extract fibre segments from microcomputer tomography data. The proposed algorithm is tested on the yarn and woven textile materials with different resolution and quality. The developed method can extract the individual fibres with varying accuracy from 73-100% with processing time 2-5s on the tested samples.

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What Should Investors Care About? Mutual Fund Ratings by Analysts vs. Machine Learning Technique

SSRN Electronic Journal ◽

10.2139/ssrn.3702749 ◽

2020 ◽

Author(s):

Si Cheng ◽

Ruichang Lu ◽

Xiaojun Zhang

Keyword(s):

Machine Learning ◽

Mutual Fund ◽

Machine Learning Technique ◽

Learning Technique

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The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽

10.3390/atmos11010111 ◽

2020 ◽

Vol 11 (1) ◽

pp. 111 ◽

Cited By ~ 2

Author(s):

Chul-Min Ko ◽

Yeong Yun Jeong ◽

Young-Mi Lee ◽

Byung-Sik Kim

Keyword(s):

Machine Learning ◽

Heavy Rainfall ◽

Extreme Rainfall ◽

Machine Learning Techniques ◽

Precipitation Forecast ◽

Machine Learning Technique ◽

Rainfall Forecast ◽

Quantitative Precipitation Forecast ◽

Correction Technique ◽

Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

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