Allometric models for predicting the aboveground biomass of Canada yew (Taxus canadensis Marsh.) from visual and digital cover estimates

2010 ◽  
Vol 40 (10) ◽  
pp. 2003-2014 ◽  
Author(s):  
Thomas C. Quint ◽  
Jeffery P. Dech
Keyword(s):  
Aboveground Biomass ◽  
Training Data ◽  
Total Biomass ◽  
Percent Cover ◽  
Power Functions ◽  
Data Set ◽  
Allometric Models ◽  
Taxus Canadensis ◽  
Testing Data ◽  
Canada Yew

The objectives of this study were to evaluate visual and digital estimates of percent cover as source data and to develop cover-based allometric models for the prediction of aboveground biomass of Canada yew ( Taxus canadensis Marsh.). Cover was determined from visual assessment and digital images captured over 25 plots (1 m2) at a model training site near Timmins, Ontario. Linear and power functions were fit to the cover–biomass data to develop models of foliage, stem, and total aboveground biomass. Both sources of cover data produced models that explained between 70% and 85% of the variance in the training data, with root mean square error estimates ranging from 27 g·m–2 (foliage) to 85 g·m–2 (total). Models based on visual cover data performed consistently better and were tested on independent data. Stem and total biomass were underestimated in the model testing data set; however, prediction statistics indicated that the linear and power forms of foliage biomass models were validated by the testing data. Final models of foliage biomass were developed from the entire data set, with mean absolute errors of 18.3 and 18.7 g·m–2 for the linear and power forms, respectively. Additional variables (e.g., plant height, age) may be required to provide general predictions of the woody biomass of Canada yew.

scholarly journals On Realistically Attacking Tor with Website Fingerprinting

2016 ◽  
Vol 2016 (4) ◽  
pp. 21-36 ◽  
Author(s):  
Tao Wang ◽  
Ian Goldberg
Keyword(s):  
Background Noise ◽  
Laboratory Tests ◽  
Training Data ◽  
Web Traffic ◽  
Training Set ◽  
Data Set ◽  
Laboratory Conditions ◽  
Testing Data ◽  
In The Wild ◽  
New Algorithms

Abstract Website fingerprinting allows a local, passive observer monitoring a web-browsing client’s encrypted channel to determine her web activity. Previous attacks have shown that website fingerprinting could be a threat to anonymity networks such as Tor under laboratory conditions. However, there are significant differences between laboratory conditions and realistic conditions. First, in laboratory tests we collect the training data set together with the testing data set, so the training data set is fresh, but an attacker may not be able to maintain a fresh data set. Second, laboratory packet sequences correspond to a single page each, but for realistic packet sequences the split between pages is not obvious. Third, packet sequences may include background noise from other types of web traffic. These differences adversely affect website fingerprinting under realistic conditions. In this paper, we tackle these three problems to bridge the gap between laboratory and realistic conditions for website fingerprinting. We show that we can maintain a fresh training set with minimal resources. We demonstrate several classification-based techniques that allow us to split full packet sequences effectively into sequences corresponding to a single page each. We describe several new algorithms for tackling background noise. With our techniques, we are able to build the first website fingerprinting system that can operate directly on packet sequences collected in the wild.

scholarly journals Latent Semantic Analysis using a Dennis Coefficient for English Sentiment Classification in a Parallel System

2018 ◽  
Vol 13 (3) ◽  
pp. 408-428 ◽  
Author(s):  
Phu Vo Ngoc
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Sentiment Classification ◽  
Training Data ◽  
The Novel ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Novel Model ◽  
Better Than

We have already survey many significant approaches for many years because there are many crucial contributions of the sentiment classification which can be applied in everyday life, such as in political activities, commodity production, and commercial activities. We have proposed a novel model using a Latent Semantic Analysis (LSA) and a Dennis Coefficient (DNC) for big data sentiment classification in English. Many LSA vectors (LSAV) have successfully been reformed by using the DNC. We use the DNC and the LSAVs to classify 11,000,000 documents of our testing data set to 5,000,000 documents of our training data set in English. This novel model uses many sentiment lexicons of our basis English sentiment dictionary (bESD). We have tested the proposed model in both a sequential environment and a distributed network system. The results of the sequential system are not as good as that of the parallel environment. We have achieved 88.76% accuracy of the testing data set, and this is better than the accuracies of many previous models of the semantic analysis. Besides, we have also compared the novel model with the previous models, and the experiments and the results of our proposed model are better than that of the previous model. Many different fields can widely use the results of the novel model in many commercial applications and surveys of the sentiment classification.

scholarly journals ALLOMETRIC MODELS FOR ESTIMATING ABOVEGROUND BIOMASS AND BIOMASS ALLOCATION OF CAPIXINGUI TREES (Croton floribundus Spreng.) IN AN AGRISILVICULTURAL SYSTEM

2016 ◽  
Vol 40 (2) ◽  
pp. 279-288 ◽  
Author(s):  
Maria Luiza Franceschi Nicodemo ◽  
Marcelo Dias Muller ◽  
Antônio Aparecido Carpanezzi ◽  
Vanderley Porfírio-da-Silva
Keyword(s):  
Aboveground Biomass ◽  
Stem Diameter ◽  
Distribution Model ◽  
Biomass Estimation ◽  
Total Biomass ◽  
Biomass Distribution ◽  
Residual Distribution ◽  
Allometric Models ◽  
Crown Diameter ◽  
Bole Biomass

ABSTRACT The objective of this study was to select allometric models to estimate total and pooled aboveground biomass of 4.5-year-old capixingui trees established in an agrisilvicultural system. Aboveground biomass distribution of capixingui was also evaluated. Single- (diameter at breast height [DBH] or crown diameter or stem diameter as the independent variable) and double-entry (DBH or crown diameter or stem diameter and total height as independent variables) models were studied. The estimated total biomass was 17.3 t.ha-1, corresponding to 86.6 kg per tree. All models showed a good fit to the data (R2ad > 0.85) for bole, branches, and total biomass. DBH-based models presented the best residual distribution. Model lnW = b0 + b1* lnDBH can be recommended for aboveground biomass estimation. Lower coefficients were obtained for leaves (R2ad > 82%). Biomass distribution followed the order: bole>branches>leaves. Bole biomass percentage decreased with increasing DBH of the trees, whereas branch biomass increased.

Selection of optimal external filter for colorimetric camera

2021 ◽  
Vol 2021 (29) ◽  
pp. 141-147
Author(s):  
Michael J. Vrhel ◽  
H. Joel Trussell
Keyword(s):  
Figure Of Merit ◽  
Band System ◽  
Image Data ◽  
Optimal Filter ◽  
Training Data ◽  
Noise Model ◽  
Data Set ◽  
Testing Data ◽  
And Training ◽  
Selection Of

A database of realizable filters is created and searched to obtain the best filter that, when placed in front of an existing camera, results in improved colorimetric capabilities for the system. The image data with the external filter is combined with image data without the filter to provide a six-band system. The colorimetric accuracy of the system is quantified using simulations that include a realistic signal-dependent noise model. Using a training data set, we selected the optimal filter based on four criteria: Vora Value, Figure of Merit, training average ΔE, and training maximum ΔE. Each selected filter was used on testing data. The filters chosen using the training ΔE criteria consistently outperformed the theoretical criteria.

STING Algorithm Used English Sentiment Classification in a Parallel Environment

2017 ◽  
Vol 31 (07) ◽  
pp. 1750021 ◽  
Author(s):  
Nguyen Duy Dat ◽  
Vo Ngoc Phu ◽  
Vo Thi Ngoc Tran ◽  
Vo Thi Ngoc Chau ◽  
Tuan A. Nguyen
Keyword(s):  
Sentiment Classification ◽  
Training Data ◽  
Network Environment ◽  
Data Set ◽  
Political Activities ◽  
New Model ◽  
Commercial Activities ◽  
Testing Data ◽  
Parallel Network ◽  
English Training

Sentiment classification is significant in everyday life of everyone, in political activities, activities of commodity production, commercial activities. In this research, we propose a new model for Big Data sentiment classification in the parallel network environment. Our new model uses STING Algorithm (SA) (in the data mining field) for English document-level sentiment classification with Hadoop Map (M)/Reduce (R) based on the 90,000 English sentences of the training data set in a Cloudera parallel network environment — a distributed system. In the world there is not any scientific study which is similar to this survey. Our new model can classify sentiment of millions of English documents with the shortest execution time in the parallel network environment. We test our new model on the 25,000 English documents of the testing data set and achieved on 61.2% accuracy. Our English training data set includes 45,000 positive English sentences and 45,000 negative English sentences.

scholarly journals Skin Cancer Detection using CNN Algorithm

2020 ◽  
Vol 9 (6) ◽  
pp. 45-49
Keyword(s):  
Neural Network ◽  
Skin Cancer ◽  
Convolutional Neural Network ◽  
Well Being ◽  
Training Data ◽  
Data Sets ◽  
Data Set ◽  
Sequential Model ◽  
Pigmented Lesions ◽  
Testing Data

The project “Disease Prediction Model” focuses on predicting the type of skin cancer. It deals with constructing a Convolutional Neural Network(CNN) sequential model in order to find the type of a skin cancer which takes a huge troll on mankind well-being. Since development of programmed methods increases the accuracy at high scale for identifying the type of skin cancer, we use Convolutional Neural Network, CNN algorithm in order to build our model . For this we make use of a sequential model. The data set that we have considered for this project is collected from NCBI, which is well known as HAM10000 dataset, it consists of massive amounts of information regarding several dermatoscopic images of most trivial pigmented lesions of skin which are collected from different sufferers. Once the dataset is collected, cleaned, it is split into training and testing data sets. We used CNN to build our model and using the training data we trained the model , later using the testing data we tested the model. Once the model is implemented over the testing data, plots are made in order to analyze the relation between the echos and loss function. It is also used to analyse accuracy and echos for both training and testing data.

scholarly journals AI Testing: Ensuring a Good Data Split Between Data Sets (Training and Test) using K-means Clustering and Decision Tree Analysis

2021 ◽  
Vol 12 (1) ◽  
pp. 1-11
Author(s):  
Kishore Sugali ◽  
Chris Sprunger ◽  
Venkata N Inukollu
Keyword(s):  
Decision Tree ◽  
Software Testing ◽  
Training Data ◽  
Data Sets ◽  
Full Data ◽  
Data Set ◽  
Full Dataset ◽  
Development Methodology ◽  
Testing Data ◽  
Long Time

Artificial Intelligence and Machine Learning have been around for a long time. In recent years, there has been a surge in popularity for applications integrating AI and ML technology. As with traditional development, software testing is a critical component of a successful AI/ML application. The development methodology used in AI/ML contrasts significantly from traditional development. In light of these distinctions, various software testing challenges arise. The emphasis of this paper is on the challenge of effectively splitting the data into training and testing data sets. By applying a k-Means clustering strategy to the data set followed by a decision tree, we can significantly increase the likelihood of the training data set to represent the domain of the full dataset and thus avoid training a model that is likely to fail because it has only learned a subset of the full data domain.

scholarly journals ALOCAÇÃO E MODELAGEM DA BIOMASSA EM Dendrocalamus asper

FLORESTA ◽  
2014 ◽  
Vol 45 (1) ◽  
pp. 1 ◽  
Author(s):  
Francelo Mognon ◽  
Aurélio Lourenço Rodrigues ◽  
Carlos Roberto Sanquetta ◽  
Ana Paula Dalla Corte ◽  
Adalberto Brito De Novaes ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Total Biomass ◽  
Major Fraction ◽  
Allometric Models ◽  
Stem Biomass ◽  
Breast Height ◽  
Biomass Models ◽  
Independent Variable ◽  
Collar Diameter ◽  
Dendrocalamus Asper

O objetivo deste trabalho foi quantificar a biomassa seca total individual de plantas de bambu da espécie Dendrocalamus asper (Schult. & Schult. f.) Backer ex K. Heyne, visando conhecer a sua distribuição nos diferentes compartimentos, bem como avaliar modelos de biomassa em função de variáveis biométricas das plantas. Foram avaliados 20 indivíduos, coletados em Bauru, SP. As plantas amostradas foram medidas, abatidas e pesadas. A maior fração da biomassa foi observada na parte aérea, com 86%, sendo 64% para o compartimento colmo, 16% para os galhos e 6% para as folhas. Os rizomas representaram 14% da biomassa total. As variáveis biométricas (diâmetro à altura do peito – DAP, altura total – ht e diâmetro de colo – Dcolo) correlacionaram-se significativamente com as biomassas total e do colmo. O modelo que apresentou o melhor desempenho para a biomassa total teve como variável independente apenas o DAP, enquanto que para a biomassa dos colmos foi a variável combinada dap0,5*lndap. Os ajustes para os demais compartimentos não geraram resultados satisfatórios, em função da baixa correlação entre as variáveis biométricas e suas biomassas. Concluiu-se que é possível expressar a biomassa seca total e do colmo do bambu por meio de modelos alométricos, porém o mesmo não se aplica aos demais compartimentos.Palavras-chave: Bambu; fitomassa; modelos alométricos. AbstractAllocation and modeling of biomass of Dendrocalamus asper. The aim of this research was to quantify the total individual biomass of bamboo plants of the species Dendrocalamus asper (Schult. & Schult. f.) Backer ex K. Heyne, in order to understand its distribution along different compartments, as well as evaluat biomass models according to biometric variables. Twenty individuals collected in Bauru, SP were evaluated. The plants were measured, cut and weighed. The aboveground biomass accounted for the major fraction, representing 86%. The stem compartment represented 64% of total biomass, followed by the branches, with 16% and leaves, with 6%. Rhizomes accounted for 14% of the total biomass. The biometric variables (diameter at breast height - dbh, total height – ht, and collar diameter - dcollar) were significantly correlated with total and stem biomass. The model that revealed best performance for total biomass had only dap as independent variable, for the stems biomass the combined variable was dap0,5*lndap. The adjustments for other compartments were not satisfactory due to low correlation between the biometric variables and their biomass. As conclusion, it is possible to express the total  dry stem biomass and culm mass of bamboo using allometric models, however, the same does not apply to other compartments.Keywords: Bamboo; phytomass; allometric models.

scholarly journals VGG16 Transfer Learning Architecture for Salak Fruit Quality Classification

Telematika ◽  
2021 ◽  
Vol 18 (1) ◽  
pp. 37
Author(s):  
Rismiyati Rismiyati ◽  
Ardytha Luthfiarta
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
State Of The Art ◽  
Learning Rate ◽  
Training Data ◽  
Data Set ◽  
Quality Classification ◽  
Testing Data ◽  
Pixel Value

Purpose: This study aims to differentiate the quality of salak fruit with machine learning. Salak is classified into two classes, good and bad class.Design/methodology/approach: The algorithm used in this research is transfer learning with the VGG16 architecture. Data set used in this research consist of 370 images of salak, 190 from good class and 180 from bad class. The image is preprocessed by resizing and normalizing pixel value in the image. Preprocessed images is split into 80% training data and 20% testing data. Training data is trained by using pretrained VGG16 model. The parameters that are changed during the training are epoch, momentum, and learning rate. The resulting model is then used for testing. The accuracy, precision and recall is monitored to determine the best model to classify the images.Findings/result: The highest accuracy obtained from this study is 95.83%. This accuracy is obtained by using a learning rate = 0.0001 and momentum 0.9. The precision and recall for this model is 97.2 and 94.6.Originality/value/state of the art: The use of transfer learning to classify salak which never been used before.

scholarly journals Allometric Equations for Aboveground Biomass Estimations of Four Dry Afromontane Tree Species

2020 ◽  
Author(s):  
Getaneh Gebeyehu ◽  
Teshome Soromessa ◽  
Tesfaye Bekele ◽  
Demel Teketay
Keyword(s):  
Wood Density ◽  
Aboveground Biomass ◽  
Tree Species ◽  
Model Fit ◽  
Allometric Equations ◽  
Biomass Estimation ◽  
Parameter Estimates ◽  
Total Biomass ◽  
Allometric Models ◽  
Species Specific

Abstract Background: Tree species based developing allometric equations are important because they contain the largest proportion of total biomass and carbon stocks of forests. Studies on developing and validating the species-specific allometric models (SSAM) remain insufficient that may result to biomass estimation errors in the forests. The purpose of this study is to determine the wood density of four tree species and develop and validate the accuracy of allometry for biomass estimations. A total of 103 sample trees representing four species were harvested semi-destructively. The species specific allometric equations (SSAM) were developed using aboveground biomass (AGB in kg) as dependent variable, and three of the predictor’s variables: diameter at beast height (DBH in cm), height (H in m) and wood density (WD in g cm-3). The relation between dependent and independent variables were tested using multiple correlations (R2). The model selection and validation was based on statistical significance of model parameter estimates, Akaike Information Criterion (AIC), adjusted coefficient of determination (R2), residual standard error (RSE) and mean relative error (MRE). Results: The results showed that the AGB correlated significantly with diameter at breast height (R2 > 0.944, P < 0.001), and tree height (R2 > 0.742, P <0.001). The species-specific allometric models, which include DBH, H and WD predicted AGB with high-model fit (R2 > 93.6%, P < 0.001). These models for biomass estimations produced small MRE (1.50–3.40%) and AIC (-7.04 –12.84) compared to a single predictor (MRE:-0.4 – 20.1%; AIC: -7.25 – 35.29). The SSAM also predicted AGB against predictors with high-model fit (R2 > 93.6%, P < 0.001) and small MRE: 1.50 – 3.40% compared to existing general allometric models (MRE: - 31.3 – 11.31%). Conclusions: The research confirmed that the inclusion of DBH, H, and WD in the SSAM predicted AGB with small bias than a single or two predictors. The wood density values of those studied species can be used as the references for biomass estimations using general allometric equations. The study contributes to species-specific allometric models for understanding the total biomass estimation of species. Therefore, the application of species-specific allometric models should be considered in biomass estimations of forests.

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