Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Frederick W. Maier"'
Publikováno v:
AI, Vol 4, Iss 1, Pp 1-15 (2022)
Alfalfa is critical to global food security, and its data is abundant in the U.S. nationally, but often scarce locally, limiting the potential performance of machine learning (ML) models in predicting alfalfa biomass yields. Training ML models on loc
Externí odkaz:
https://doaj.org/article/26944a371f9342859d5c2d55ef3ca2f3
Autor:
Christopher D. Whitmire, Jonathan M. Vance, Hend K. Rasheed, Ali Missaoui, Khaled M. Rasheed, Frederick W. Maier
Publikováno v:
AI, Vol 2, Iss 1, Pp 71-88 (2021)
Predicting alfalfa biomass and crop yield for livestock feed is important to the daily lives of virtually everyone, and many features of data from this domain combined with corresponding weather data can be used to train machine learning models for y
Externí odkaz:
https://doaj.org/article/c783f4f52b2943849e33934d3fa1706a
Publikováno v:
Sensors, Vol 22, Iss 10, p 3688 (2022)
The total boll count from a plant is one of the most important phenotypic traits for cotton breeding and is also an important factor for growers to estimate the final yield. With the recent advances in deep learning, many supervised learning approach
Externí odkaz:
https://doaj.org/article/d86e32e093f040c591d43fe1380c3f2f
Autor:
Matthew Buman, Khaled Rasheed, Lakshmish Ramaswamy, Frederick W. Maier, Jennifer L. Gay, Delaram Yazdansepas, Anzah H. Niazi
Publikováno v:
HEALTHINF
Autor:
Anzah H. Niazi, Delaram Yazdansepas, Jennifer L. Gay, Frederick W. Maier, Lakshmish Ramaswamy, Khaled Rasheed, Matthew P. Buman
Publikováno v:
ICMLA
This paper proposes a multi-level meta-classifier for identifying human activities based on accelerometer data. The training data consists of 77 subjects performing a combination of 23 different activities and monitored using a single hip-worn triaxi
Autor:
Delaram Yazdansepas, Anzah H. Niazi, Jennifer L. Gay, Frederick W. Maier, Lakshmish Ramaswamy, Khaled Rasheed, Matthew P. Buman
Publikováno v:
ICHI
Human activity recognition (HAR) has many important applications in health care. While machine learning-based techniques have been applied for wearable sensor-based HAR, very few researchers have comprehensively studied the effects of various factors