Machine Learning Aids Classification and Discrimination of Noncanonical DNA Folding Motifs by an Arrayed Host:Guest Sensing System

2021 ◽  
Author(s):  
Junyi Chen ◽  
Adam D. Gill ◽  
Briana L. Hickey ◽  
Ziting Gao ◽  
Xinping Cui ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sensing System ◽  
Learning Aids ◽  
Dna Folding

Machine Learning Aids Stormwater Fee Assessment

2020 ◽  
Vol 90 (2) ◽  
pp. 38-40
Author(s):  
Catherine A. Cardno
Keyword(s):  
Machine Learning ◽  
Learning Aids

scholarly journals Corn Nitrogen Status Diagnosis with an Innovative Multi-Parameter Crop Circle Phenom Sensing System

2021 ◽  
Vol 13 (3) ◽  
pp. 401
Author(s):  
Cadan Cummings ◽  
Yuxin Miao ◽  
Gabriel Dias Paiao ◽  
Shujiang Kang ◽  
Fabián G. Fernández
Keyword(s):  
Machine Learning ◽  
Chlorophyll Content ◽  
Vegetation Index ◽  
Soil Drainage ◽  
Management Information ◽  
Area Index ◽  
Sensing System ◽  
Extreme Gradient Boosting ◽  
Split Plot ◽  
N Status

Accurate and non-destructive in-season crop nitrogen (N) status diagnosis is important for the success of precision N management (PNM). Several active canopy sensors (ACS) with two or three spectral wavebands have been used for this purpose. The Crop Circle Phenom sensor is a new integrated multi-parameter proximal ACS system for in-field plant phenomics with the capability to measure reflectance, structural, and climatic attributes. The objective of this study was to evaluate this multi-parameter Crop Circle Phenom sensing system for in-season diagnosis of corn (Zea mays L.) N status across different soil drainage and tillage systems under variable N supply conditions. The four plant metrics used to approximate in-season N status consist of aboveground biomass (AGB), plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). A field experiment was conducted in Wells, Minnesota during the 2018 and the 2019 growing seasons with a split-split plot design replicated four times with soil drainage (drained and undrained) as main block, tillage (conventional, no-till, and strip-till) as split plot, and pre-plant N (PPN) rate (0 to 225 in 45 kg ha−1 increment) as the split-split plot. Crop Circle Phenom measurements alongside destructive whole plant samples were collected at V8 +/−1 growth stage. Proximal sensor metrics were used to construct regression models to estimate N status indicators using simple regression (SR) and eXtreme Gradient Boosting (XGB) models. The sensor derived indices tested included normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), estimated canopy chlorophyll content (eCCC), estimated leaf area index (eLAI), ratio vegetation index (RVI), canopy chlorophyll content index (CCCI), fractional photosynthetically active radiation (fPAR), and canopy and air temperature difference (ΔTemp). Management practices such as drainage, tillage, and PPN rate were also included to determine the potential improvement in corn N status diagnosis. Three of the four replicated drained and undrained blocks were randomly selected as training data, and the remaining drained and undrained blocks were used as testing data. The results indicated that SR modeling using NDVI would be sufficient for estimating AGB compared to more complex machine learning methods. Conversely, PNC, PNU, and NNI all benefitted from XGB modeling based on multiple inputs. Among different approaches of XGB modeling, combining management information and Crop Circle Phenom measurements together increased model performance for predicting each of the four plant N metrics compared with solely using sensing data. The PPN rate was the most important management metric for all models compared to drainage and tillage information. Combining Crop Circle Phenom sensor parameters and management information is a promising strategy for in-season diagnosis of corn N status. More studies are needed to further evaluate this new integrated sensing system under diverse on-farm conditions and to test other machine learning models.

A Machine Learning Enabled Wireless Intracranial Brain Deformation Sensing System

2020 ◽  
Vol 67 (12) ◽  
pp. 3521-3530
Author(s):  
S. Islam ◽  
V. Shah ◽  
S.T.R. Gidde ◽  
P. Hutapea ◽  
S. H. Song ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sensing System ◽  
Brain Deformation

Field-effect transistor array modified by a stationary phase to generate informative signal patterns for machine learning-assisted recognition of gas-phase chemicals

2019 ◽  
Vol 4 (2) ◽  
pp. 386-389
Author(s):  
Toshihiro Yoshizumi ◽  
Tatsuro Goda ◽  
Rui Yatabe ◽  
Akio Oki ◽  
Akira Matsumoto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stationary Phase ◽  
Gas Phase ◽  
Field Effect ◽  
Field Effect Transistor ◽  
Machine Learning Techniques ◽  
Informative Signal ◽  
Sensing System ◽  
Learning Techniques ◽  
Effect Transistor

We propose an artificial intelligence-based chemical-sensing system integrating a porous gate field-effect transistor (PGFET) array modified by gas chromatography stationary phase materials and machine-learning techniques.

DEVELOPMENT OF NEURAL MECHANISMS FOR MACHINE LEARNING

2005 ◽  
Vol 15 (01n02) ◽  
pp. 41-54
Author(s):  
ARTUR M. ARSENIO
Keyword(s):  
Machine Learning ◽  
Experimental Evaluation ◽  
Infant Development ◽  
Humanoid Robot ◽  
Developmental Stages ◽  
Neural Mechanisms ◽  
Object Representations ◽  
Learning Aids ◽  
Educational Videos ◽  
Visual Properties

The goal of this work is to develop a humanoid robot's perceptual mechanisms through the use of learning aids. We describe methods to enable learning on a humanoid robot using learning aids such as books, drawing materials, boards, educational videos or other children toys. Visual properties of objects are learned and inserted into a recognition scheme, which is then applied to acquire new object representations — we propose learning through developmental stages. Inspired in infant development, we will also boost the robot's perceptual capabilities by having a human caregiver performing educational and play activities with the robot (such as drawing, painting or playing with a toy train on a railway). We describe original algorithms to extract meaningful percepts from such learning experiments. Experimental evaluation of the algorithms corroborates the theoretical framework.

Machine Learning for Molecular Perceptron: A Perception-Based Sensing System

2020 ◽  
Vol MA2020-01 (6) ◽  
pp. 632-632
Author(s):  
Yoona Yang ◽  
Zvi A Yaari ◽  
Alex Settle ◽  
Daniel A. Heller ◽  
Ming Zheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sensing System

scholarly journals A Tale of Two “Forests”: Random Forest Machine Learning Aids Tropical Forest Carbon Mapping

PLoS ONE ◽  
2014 ◽  
Vol 9 (1) ◽  
pp. e85993 ◽  
Author(s):  
Joseph Mascaro ◽  
Gregory P. Asner ◽  
David E. Knapp ◽  
Ty Kennedy-Bowdoin ◽  
Roberta E. Martin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Tropical Forest ◽  
Forest Carbon ◽  
Learning Aids ◽  
Carbon Mapping
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