Use of GIS and machine learning to predict disease in shrimp farmed on the east coast of the Mekong Delta, Vietnam

2022 ◽  
Author(s):  
Nguyen Minh Khiem ◽  
Yuki Takahashi ◽  
Hiroki Yasuma ◽  
Dang Thi Hoang Oanh ◽  
Tran Ngoc Hai ◽  
...  
Keyword(s):  
Machine Learning ◽  
East Coast ◽  
Mekong Delta

The use of machine learning to predict acute hepatopancreatic necrosis disease (AHPND) in shrimp farmed on the east coast of the Mekong Delta of Vietnam

2020 ◽  
Vol 86 (4) ◽  
pp. 673-683
Author(s):  
Nguyen Minh Khiem ◽  
Yuki Takahashi ◽  
Dang Thi Hoang Oanh ◽  
Tran Ngoc Hai ◽  
Hiroki Yasuma ◽  
...  
Keyword(s):  
Machine Learning ◽  
East Coast ◽  
Mekong Delta ◽  
Acute Hepatopancreatic Necrosis Disease

Salinity intrusion prediction using remote sensing and machine learning in data-limited regions: A case study in Vietnam's Mekong Delta

2021 ◽  
pp. e00424
Author(s):  
Tien Giang Nguyen ◽  
Ngoc Anh Tran ◽  
Phuong Lan Vu ◽  
Quoc-Huy Nguyen ◽  
Huu Duy Nguyen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Mekong Delta ◽  
Salinity Intrusion

An East Coast Perspective on Artificial Intelligence and Machine Learning: Part 1

2020 ◽  
Vol 30 (4) ◽  
pp. 459-466
Author(s):  
Rajiv Gupta ◽  
Sanjith Prahas Krishnam ◽  
Pamela W. Schaefer ◽  
Michael H. Lev ◽  
R. Gilberto Gonzalez
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
East Coast

An East Coast Perspective on Artificial Intelligence and Machine Learning

2020 ◽  
Vol 30 (4) ◽  
pp. 467-478
Author(s):  
Rajiv Gupta ◽  
Sanjith Prahas Krishnam ◽  
Pamela W. Schaefer ◽  
Michael H. Lev ◽  
R. Gilberto Gonzalez
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
East Coast

scholarly journals Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam

2021 ◽  
Vol 127 ◽  
pp. 107790
Author(s):  
Dang An Tran ◽  
Maki Tsujimura ◽  
Nam Thang Ha ◽  
Van Tam Nguyen ◽  
Doan Van Binh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coastal Aquifers ◽  
Predictive Power ◽  
Learning Algorithms ◽  
Mekong Delta ◽  
Machine Learning Algorithms ◽  
Groundwater Salinity

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Language and Literacy Predictors of Dialect Density Among School-Age African American Children From Two Geographic Regions

2020 ◽  
Vol 51 (3) ◽  
pp. 807-820
Author(s):  
Lena G. Caesar ◽  
Marie Kerins
Keyword(s):  
African American ◽  
Multiple Regression ◽  
Oral Language ◽  
East Coast ◽  
Geographic Region ◽  
Literacy Skills ◽  
School Age ◽  
Regression Analyses ◽  
Geographic Regions ◽  
American Children

Purpose The purpose of this study was to investigate the relationship between oral language, literacy skills, age, and dialect density (DD) of African American children residing in two different geographical regions of the United States (East Coast and Midwest). Method Data were obtained from 64 African American school-age children between the ages of 7 and 12 years from two geographic regions. Children were assessed using a combination of standardized tests and narrative samples elicited from wordless picture books. Bivariate correlation and multiple regression analyses were used to determine relationships to and relative contributions of oral language, literacy, age, and geographic region to DD. Results Results of correlation analyses demonstrated a negative relationship between DD measures and children's literacy skills. Age-related findings between geographic regions indicated that the younger sample from the Midwest outscored the East Coast sample in reading comprehension and sentence complexity. Multiple regression analyses identified five variables (i.e., geographic region, age, mean length of utterance in morphemes, reading fluency, and phonological awareness) that accounted for 31% of the variance of children's DD—with geographic region emerging as the strongest predictor. Conclusions As in previous studies, the current study found an inverse relationship between DD and several literacy measures. Importantly, geographic region emerged as a strong predictor of DD. This finding highlights the need for a further study that goes beyond the mere description of relationships to comparing geographic regions and specifically focusing on racial composition, poverty, and school success measures through direct data collection.

Machine Learning for Speaker Recognition

2020 ◽  
Author(s):  
Man-Wai Mak ◽  
Jen-Tzung Chien
Keyword(s):  
Machine Learning ◽  
Speaker Recognition

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining
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