Generating high-resolution daily soil moisture by using spatial downscaling techniques: a comparison of six machine learning algorithms

2020 ◽  
Vol 141 ◽  
pp. 103601 ◽  
Author(s):  
Yangxiaoyue Liu ◽  
Wenlong Jing ◽  
Qi Wang ◽  
Xiaolin Xia
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
High Resolution ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Spatial Downscaling

Spatiotemporal modeling of soil moisture in an Atlantic forest through machine learning algorithms

2021 ◽  
Author(s):  
Vinicius Augusto Oliveira ◽  
André Ferreira Rodrigues ◽  
Marco Antônio Vieira Morais ◽  
Marcela de Castro Nunes Santos Terra ◽  
Li Guo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Atlantic Forest ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Spatiotemporal Modeling

scholarly journals Skin Characterizations by Using Contact Capacitive Imaging and High-Resolution Ultrasound Imaging with Machine Learning Algorithms

2021 ◽  
Vol 11 (18) ◽  
pp. 8714
Author(s):  
Elena Chirikhina ◽  
Andrey Chirikhin ◽  
Sabina Dewsbury-Ennis ◽  
Francesco Bianconi ◽  
Perry Xiao
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Stratum Corneum ◽  
Image Classification ◽  
Water Content ◽  
Ultrasound Imaging ◽  
Skin Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Body Parts

We present our latest research on skin characterizations by using Contact Capacitive Imaging and High-Resolution Ultrasound Imaging with Machine Learning algorithms. Contact Capacitive Imaging is a novel imaging technology based on the dielectric constant measurement principle, with which we have studied the skin water content of different skin sites and performed image classification by using pre-trained Deep Learning Neural Networks through Transfer Learning. The results show lips and nose have the lowest water content, whilst cheek, eye corner and under-eye have the highest water content. The classification yields up to 83.8% accuracy. High-Resolution Ultrasound Imaging is a state-of-the-art ultrasound technology, and can produce high-resolution images of the skin and superficial soft tissue to a vertical resolution of about 40 microns, with which we have studied the thickness of different skin layers, such as stratum corneum, epidermis and dermis, around different locations on the face and around different body parts. The results show the chin has the highest stratum corneum thickness, and the arm has the lowest stratum corneum thickness. We have also developed two feature-based image classification methods which yield promising results. The outcomes of this study could provide valuable guidelines for cosmetic/medical research, and methods developed in this study can also be extended for studying damaged skin or skin diseases. The combination of Contact Capacitive Imaging and High-Resolution Ultrasound Imaging could be a powerful tool for skin studies.

scholarly journals Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6926 ◽  
Author(s):  
Xiangyu Ge ◽  
Jingzhe Wang ◽  
Jianli Ding ◽  
Xiaoyi Cao ◽  
Zipeng Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Moisture Content ◽  
Arid Regions ◽  
Soil Moisture Content ◽  
Hyperspectral Imagery ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Spectral Indices ◽  
Processing Scheme

Soil moisture content (SMC) is an important factor that affects agricultural development in arid regions. Compared with the space-borne remote sensing system, the unmanned aerial vehicle (UAV) has been widely used because of its stronger controllability and higher resolution. It also provides a more convenient method for monitoring SMC than normal measurement methods that includes field sampling and oven-drying techniques. However, research based on UAV hyperspectral data has not yet formed a standard procedure in arid regions. Therefore, a universal processing scheme is required. We hypothesized that combining pretreatments of UAV hyperspectral imagery under optimal indices and a set of field observations within a machine learning framework will yield a highly accurate estimate of SMC. Optimal 2D spectral indices act as indispensable variables and allow us to characterize a model’s SMC performance and spatial distribution. For this purpose, we used hyperspectral imagery and a total of 70 topsoil samples (0–10 cm) from the farmland (2.5 × 104 m2) of Fukang City, Xinjiang Uygur AutonomousRegion, China. The random forest (RF) method and extreme learning machine (ELM) were used to estimate the SMC using six methods of pretreatments combined with four optimal spectral indices. The validation accuracy of the estimated method clearly increased compared with that of linear models. The combination of pretreatments and indices by our assessment effectively eliminated the interference and the noises. Comparing two machine learning algorithms showed that the RF models were superior to the ELM models, and the best model was PIR (R2val = 0.907, RMSEP = 1.477, and RPD = 3.396). The SMC map predicted via the best scheme was highly similar to the SMC map measured. We conclude that combining preprocessed spectral indices and machine learning algorithms allows estimation of SMC with high accuracy (R2val = 0.907) via UAV hyperspectral imagery on a regional scale. Ultimately, our program might improve management and conservation strategies for agroecosystem systems in arid regions.

scholarly journals Characterizing Swallows From People With Neurodegenerative Diseases Using High-Resolution Cervical Auscultation Signals and Temporal and Spatial Swallow Kinematic Measurements

2021 ◽  
pp. 1-16
Author(s):  
Cara Donohue ◽  
Yassin Khalifa ◽  
Shitong Mao ◽  
Subashan Perera ◽  
Ervin Sejdić ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Neurodegenerative Diseases ◽  
Learning Algorithms ◽  
Healthy Adults ◽  
Machine Learning Algorithms ◽  
Cervical Auscultation ◽  
Laryngeal Vestibule ◽  
Temporal And Spatial ◽  
Kinematic Measures

Purpose The prevalence of dysphagia in patients with neurodegenerative diseases (ND) is alarmingly high and frequently results in morbidity and accelerated mortality due to subsequent adverse events (e.g., aspiration pneumonia). Swallowing in patients with ND should be continuously monitored due to the progressive disease nature. Access to instrumental swallow evaluations can be challenging, and limited studies have quantified changes in temporal/spatial swallow kinematic measures in patients with ND. High-resolution cervical auscultation (HRCA), a dysphagia screening method, has accurately differentiated between safe and unsafe swallows, identified swallow kinematic events (e.g., laryngeal vestibule closure [LVC]), and classified swallows between healthy adults and patients with ND. This study aimed to (a) compare temporal/spatial swallow kinematic measures between patients with ND and healthy adults and (b) investigate HRCA's ability to annotate swallow kinematic events in patients with ND. We hypothesized there would be significant differences in temporal/spatial swallow measurements between groups and that HRCA would accurately annotate swallow kinematic events in patients with ND. Method Participants underwent videofluoroscopic swallowing studies with concurrent HRCA. We used linear mixed models to compare temporal/spatial swallow measurements ( n = 170 ND patient swallows, n = 171 healthy adult swallows) and deep learning machine-learning algorithms to annotate specific temporal and spatial kinematic events in swallows from patients with ND. Results Differences ( p < .05) were found between groups for several temporal and spatial swallow kinematic measures. HRCA signal features were used as input to machine-learning algorithms and annotated upper esophageal sphincter (UES) opening, UES closure, LVC, laryngeal vestibule reopening, and hyoid bone displacement with 66.25%, 85%, 68.18%, 70.45%, and 44.6% accuracy, respectively, compared to human judges' measurements. Conclusion This study demonstrates HRCA's potential in characterizing swallow function in patients with ND and other patient populations.

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