scholarly journals Detecting the Spatial Variability of Seagrass Meadows and Their Consequences on Associated Macrofauna Benthic Activity Using Novel Drone Technology

2021 ◽  
Vol 14 (1) ◽  
pp. 160
Author(s):  
Subhash Chand ◽  
Barbara Bollard
Keyword(s):  
Seasonal Change ◽  
Near Infrared ◽  
Remotely Sensed ◽  
Supervised Machine Learning ◽  
Fine Scale ◽  
Spectral Indices ◽  
Machine Learning Classification ◽  
Detection Techniques ◽  
Seagrass Meadows ◽  
Low Altitude

Seagrass meadows are undergoing significant decline locally and globally from human and climatic impacts. Seagrass decline also impacts seagrass-dependent macrofauna benthic activity, interrupts their vital linkage with adjacent habitats, and creates broader degradation through the ecosystem. Seagrass variability (gain and loss) is a driver of marine species diversity. Still, our understanding of macrofauna benthic activity distribution and their response to seagrass variability from remotely sensed drone imagery is limited. Hence, it is critical to develop fine-scale seasonal change detection techniques appropriate to the scale of variability that will apply to dynamic marine environments. Therefore, this research tested the performance of the VIS and VIS+NIR sensors from proximal low altitude remotely piloted aircraft system (RPAS) to detect fine-scale seasonal seagrass variability using spectral indices and a supervised machine learning classification technique. Furthermore, this research also attempted to identify and quantify macrofauna benthic activity from their feeding burrows and their response to seagrass variability. The results from VIS (visible spectrum) and VIS+NIR (visible and near-infrared spectrum) sensors produced a 90–98% classification accuracy. This accuracy established that the spectral indices were fundamental in this study to identify and classify seagrass density. The other important finding revealed that seagrass-associated macrofauna benthic activity showed increased or decreased abundance and distribution with seasonal seagrass variability from drone high spatial resolution orthomosaics. These results are important for seagrass conservation because managers can quickly detect fine-scale seasonal changes and take mitigation actions before the decline of this keystone species affects the entire ecosystem. Moreover, proximal low-altitude, remotely sensed time-series seasonal data provided valuable contributions for documenting spatial ecological seasonal change in this dynamic marine environment.

scholarly journals Ganoderma boninense Disease Detection by Near-Infrared Spectroscopy Classification: A Review

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3052
Author(s):  
Mas Ira Syafila Mohd Hilmi Tan ◽  
Mohd Faizal Jamlos ◽  
Ahmad Fairuz Omar ◽  
Fatimah Dzaharudin ◽  
Suramate Chalermwisutkul ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Early Detection ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Predictive Analytics ◽  
Detection System ◽  
Imaging Techniques ◽  
Ganoderma Boninense ◽  
Machine Learning Classification ◽  
Detection Techniques

Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a serious threat to the palm oil industry. This catastrophic disease ultimately destroys the basal tissues of oil palm, causing the eventual death of the palm. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease. This review describes past and future prospects of integrated research of near-infrared spectroscopy (NIRS), machine learning classification for predictive analytics and signal processing towards an early G. boninense detection system. This effort could reduce the cost of plantation management and avoid production losses. Remarkably, (i) spectroscopy techniques are more reliable than other detection techniques such as serological, molecular, biomarker-based sensor and imaging techniques in reactions with organic tissues, (ii) the NIR spectrum is more precise and sensitive to particular diseases, including G. boninense, compared to visible light and (iii) hand-held NIRS for in situ measurement is used to explore the efficacy of an early detection system in real time using ML classifier algorithms and a predictive analytics model. The non-destructive, environmentally friendly (no chemicals involved), mobile and sensitive leads the NIRS with ML and predictive analytics as a significant platform towards early detection of G. boninense in the future.

scholarly journals Machine Learning Classification and Regression Approaches for Optical Network Traffic Prediction

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1578
Author(s):  
Daniel Szostak ◽  
Adam Włodarczyk ◽  
Krzysztof Walkowiak
Keyword(s):  
Machine Learning ◽  
Optical Networks ◽  
Network Traffic ◽  
Optical Network ◽  
Optimization Methods ◽  
Supervised Machine Learning ◽  
Traffic Prediction ◽  
Machine Learning Classification ◽  
Classification And Regression ◽  
Network Technologies

Rapid growth of network traffic causes the need for the development of new network technologies. Artificial intelligence provides suitable tools to improve currently used network optimization methods. In this paper, we propose a procedure for network traffic prediction. Based on optical networks’ (and other network technologies) characteristics, we focus on the prediction of fixed bitrate levels called traffic levels. We develop and evaluate two approaches based on different supervised machine learning (ML) methods—classification and regression. We examine four different ML models with various selected features. The tested datasets are based on real traffic patterns provided by the Seattle Internet Exchange Point (SIX). Obtained results are analyzed using a new quality metric, which allows researchers to find the best forecasting algorithm in terms of network resources usage and operational costs. Our research shows that regression provides better results than classification in case of all analyzed datasets. Additionally, the final choice of the most appropriate ML algorithm and model should depend on the network operator expectations.

scholarly journals Effects of Training Set Size on Supervised Machine-Learning Land-Cover Classification of Large-Area High-Resolution Remotely Sensed Data

2021 ◽  
Vol 13 (3) ◽  
pp. 368
Author(s):  
Christopher A. Ramezan ◽  
Timothy A. Warner ◽  
Aaron E. Maxwell ◽  
Bradley S. Price
Keyword(s):  
Machine Learning ◽  
Sample Size ◽  
Remotely Sensed ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Sample Sizes ◽  
Remotely Sensed Data ◽  
Large Area ◽  
Training Set ◽  
Set Size

The size of the training data set is a major determinant of classification accuracy. Nevertheless, the collection of a large training data set for supervised classifiers can be a challenge, especially for studies covering a large area, which may be typical of many real-world applied projects. This work investigates how variations in training set size, ranging from a large sample size (n = 10,000) to a very small sample size (n = 40), affect the performance of six supervised machine-learning algorithms applied to classify large-area high-spatial-resolution (HR) (1–5 m) remotely sensed data within the context of a geographic object-based image analysis (GEOBIA) approach. GEOBIA, in which adjacent similar pixels are grouped into image-objects that form the unit of the classification, offers the potential benefit of allowing multiple additional variables, such as measures of object geometry and texture, thus increasing the dimensionality of the classification input data. The six supervised machine-learning algorithms are support vector machines (SVM), random forests (RF), k-nearest neighbors (k-NN), single-layer perceptron neural networks (NEU), learning vector quantization (LVQ), and gradient-boosted trees (GBM). RF, the algorithm with the highest overall accuracy, was notable for its negligible decrease in overall accuracy, 1.0%, when training sample size decreased from 10,000 to 315 samples. GBM provided similar overall accuracy to RF; however, the algorithm was very expensive in terms of training time and computational resources, especially with large training sets. In contrast to RF and GBM, NEU, and SVM were particularly sensitive to decreasing sample size, with NEU classifications generally producing overall accuracies that were on average slightly higher than SVM classifications for larger sample sizes, but lower than SVM for the smallest sample sizes. NEU however required a longer processing time. The k-NN classifier saw less of a drop in overall accuracy than NEU and SVM as training set size decreased; however, the overall accuracies of k-NN were typically less than RF, NEU, and SVM classifiers. LVQ generally had the lowest overall accuracy of all six methods, but was relatively insensitive to sample size, down to the smallest sample sizes. Overall, due to its relatively high accuracy with small training sample sets, and minimal variations in overall accuracy between very large and small sample sets, as well as relatively short processing time, RF was a good classifier for large-area land-cover classifications of HR remotely sensed data, especially when training data are scarce. However, as performance of different supervised classifiers varies in response to training set size, investigating multiple classification algorithms is recommended to achieve optimal accuracy for a project.

Near-Infrared Spectroscopy Monitoring of Cerebral Oxygenation and Influencing Factors in Neonates from High-Altitude Areas

Neonatology ◽  
2021 ◽  
pp. 1-6
Author(s):  
Bi Ze ◽  
Lili Liu ◽  
Ge Sang Yang Jin ◽  
Minna Shan ◽  
Yuehang Geng ◽  
...  
Keyword(s):  
Heart Disease ◽  
Infrared Spectroscopy ◽  
Brain Injury ◽  
Oxygen Saturation ◽  
High Altitude ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cerebral Oxygenation ◽  
Low Altitude ◽  
Normal Neonates

<b><i>Background:</i></b> Accurate detection of cerebral oxygen saturation (rSO<sub>2</sub>) may be useful for neonatal brain injury prevention, and the normal range of rSO<sub>2</sub> of neonates at high altitude remained unclear. <b><i>Objective:</i></b> To compare cerebral rSO<sub>2</sub> and cerebral fractional tissue oxygen extraction (cFTOE) at high-altitude and low-altitude areas in healthy neonates and neonates with underlying diseases. <b><i>Methods:</i></b> 515 neonates from low-altitude areas and 151 from Tibet were enrolled. These neonates were assigned into the normal group, hypoxic-ischemic encephalopathy (HIE) group, and other diseases group. Near-infrared spectroscopy was used to measure rSO<sub>2</sub> in neonates within 24 h after admission. The differences of rSO<sub>2</sub>, pulse oxygen saturation (SpO<sub>2</sub>), and cFTOE levels were compared between neonates from low- and high-altitude areas. <b><i>Results:</i></b> (1) The mean rSO<sub>2</sub> and cFTOE levels in normal neonates from Tibet were 55.0 ± 6.4% and 32.6 ± 8.5%, significantly lower than those from low-altitude areas (<i>p</i> &#x3c; 0.05). (2) At high altitude, neonates with HIE, pneumonia (<i>p</i> &#x3c; 0.05), anemia, and congenital heart disease (<i>p</i> &#x3c; 0.05) have higher cFTOE than healthy neonates. (3) Compared with HIE neonates from plain areas, neonates with HIE at higher altitude had lower cFTOE (<i>p</i> &#x3c; 0.05), while neonates with heart disease in plateau areas had higher cFTOE than those in plain areas (<i>p</i> &#x3c; 0.05). <b><i>Conclusions:</i></b> The rSO<sub>2</sub> and cFTOE levels in normal neonates from high-altitude areas are lower than neonates from the low-altitude areas. Lower cFTOE is possibly because of an increase in blood flow to the brain, and this may be adversely affected by disease states which may increase the risk of brain injury.

scholarly journals Mapping Sub-Metre 3D Land-Sea Coral Reefscapes Using Superspectral WorldView-3 Satellite Stereoimagery

Oceans ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 315-329
Author(s):  
Antoine Collin ◽  
Mark Andel ◽  
David Lecchini ◽  
Joachim Claudet
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Sustainable Management ◽  
Near Infrared ◽  
Value Added ◽  
Land Use Land Cover ◽  
Fine Scale ◽  
Mid Infrared ◽  
Stereo Imagery ◽  
Sea Use

Shallow coral reefs ensure a wide portfolio of ecosystem services, from fish provisioning to tourism, that support more than 500 million people worldwide. The protection and sustainable management of these pivotal ecosystems require fine-scale but large-extent mapping of their 3D composition. The sub-metre spaceborne imagery can neatly produce such an expected product using multispectral stereo-imagery. We built the first 3D land-sea coral reefscape mapping using the 0.3 m superspectral WorldView-3 stereo-imagery. An array of 13 land use/land cover and sea use/sea cover habitats were classified using sea-, ground- and air-truth data. The satellite-derived topography and bathymetry reached vertical accuracies of 1.11 and 0.89 m, respectively. The value added of the eight mid-infrared (MIR) channels specific to the WorldView-3 was quantified using the classification overall accuracy (OA). With no topobathymetry, the best combination included the eight-band optical (visible + near-infrared) and the MIR8, which boosted the basic blue-green-red OA by 9.58%. The classes that most benefited from this MIR information were the land use “roof” and land cover “soil” classes. The addition of the satellite-derived topobathymetry to the optical+MIR1 produced the best full combination, increasing the basic OA by 9.73%, and reinforcing the “roof” and “soil” distinction.

Evaluating disaster-related tweet credibility using content-based and user-based features

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nasser Assery ◽  
Yuan (Dorothy) Xiaohong ◽  
Qu Xiuli ◽  
Roy Kaushik ◽  
Sultan Almalki
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Emergency Response ◽  
Learning Model ◽  
Performance Comparison ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Content Type ◽  
Machine Learning Classification

Purpose This study aims to propose an unsupervised learning model to evaluate the credibility of disaster-related Twitter data and present a performance comparison with commonly used supervised machine learning models. Design/methodology/approach First historical tweets on two recent hurricane events are collected via Twitter API. Then a credibility scoring system is implemented in which the tweet features are analyzed to give a credibility score and credibility label to the tweet. After that, supervised machine learning classification is implemented using various classification algorithms and their performances are compared. Findings The proposed unsupervised learning model could enhance the emergency response by providing a fast way to determine the credibility of disaster-related tweets. Additionally, the comparison of the supervised classification models reveals that the Random Forest classifier performs significantly better than the SVM and Logistic Regression classifiers in classifying the credibility of disaster-related tweets. Originality/value In this paper, an unsupervised 10-point scoring model is proposed to evaluate the tweets’ credibility based on the user-based and content-based features. This technique could be used to evaluate the credibility of disaster-related tweets on future hurricanes and would have the potential to enhance emergency response during critical events. The comparative study of different supervised learning methods has revealed effective supervised learning methods for evaluating the credibility of Tweeter data.

scholarly journals Upscale or downscale: applications of fine scale remotely sensed data to Chagas disease in Argentina and schistosomiasis in Kenya

2006 ◽  
Vol 1 (1) ◽  
pp. 49 ◽  
Author(s):  
Uriel Kitron ◽  
Julie A. Clennon ◽  
M. Carla Cecere ◽  
Ricardo E. Gürtler ◽  
Charles H. King ◽  
...  
Keyword(s):  
Chagas Disease ◽  
Remotely Sensed ◽  
Fine Scale ◽  
Remotely Sensed Data

scholarly journals Augmenting biologging with supervised machine learning to studyin situbehavior of the medusaChrysaora fuscescens

2019 ◽  
Author(s):  
Clara Fannjiang ◽  
T. Aran Mooney ◽  
Seth Cones ◽  
David Mann ◽  
K. Alex Shorter ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Laboratory Data ◽  
Supervised Machine Learning ◽  
Motion Sensors ◽  
Fine Scale ◽  
Video Footage ◽  
Research Findings ◽  
Summary Statement

AbstractZooplankton occupy critical roles in marine ecosystems, yet their fine-scale behavior remains poorly understood due to the difficulty of studying individualsin situ. Here we combine biologging with supervised machine learning (ML) to demonstrate a pipeline for studyingin situbehavior of larger zooplankton such as jellyfish. We deployed the ITAG, a biologging package with high-resolution motion sensors designed for soft-bodied invertebrates, on 8Chrysaora fuscescensin Monterey Bay, using the tether method for retrieval. Using simultaneous video footage of the tagged jellyfish, we develop ML methods to 1) identify periods of tag data corrupted by the tether method, which may have compromised prior research findings, and 2) classify jellyfish behaviors. Our tools yield characterizations of fine-scale jellyfish activity and orientation over long durations, and provide evidence that developing behavioral classifiers onin siturather than laboratory data is essential.Summary StatementHigh-resolution motion sensors paired with supervised machine learning can be used to infer fine-scalein situbehavior of zooplankton for long durations.

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