scholarly journals Identification of Southern Ocean acoustic targets using aggregation backscatter and shape characteristics

2003 ◽  
Vol 60 (3) ◽  
pp. 641-649 ◽  
Author(s):  
Rachel S Woodd-Walker ◽  
Jonathan L Watkins ◽  
Andrew S Brierley
Keyword(s):  
Antarctic Krill ◽  
Target Identification ◽  
Function Analysis ◽  
Substantial Improvement ◽  
Acoustic Energy ◽  
Biomass Estimation ◽  
Accurate Identification ◽  
Linear Discriminant ◽  
Energy Parameters ◽  
Target Fishing

Abstract Acoustic surveys for biomass estimation require accurate identification of echoes from the target species. In one objective technique for identifying Antarctic krill, the difference between mean volume-backscattering strength at two frequencies is used, but can misclassify small krill and other plankton. Here, we investigate ways to improve target identification by including characteristics of backscattering energy and morphology of aggregations. To do this, multi-frequency acoustic data were collected concurrently with target fishing of Antarctic krill and other euphausiid and salp aggregations. Parameter sets for these known aggregations were collated and used to develop empirical classifications. Both linear discriminant-function analysis (DFA) and the artificial neural network technique were employed. In both cases, acoustic-backscattering energy parameters were most important for discriminating between Antarctic krill and other zooplankton. However, swarm morphology and other parameters improved the discrimination, particularly between krill and salps. Our study suggests that for krill-biomass estimates, a simple DFA based on acoustic-energy parameters is a substantial improvement over current dB-difference acoustic methods; but studies requiring the discrimination of zooplankton other than krill must still be supported by target fishing.

Sex Estimation Using Geometric Morphometrics

2021 ◽  
Author(s):  
Valda Black
Keyword(s):  
Geometric Morphometrics ◽  
Classification Accuracy ◽  
Function Analysis ◽  
Dorsal Surface ◽  
Two Dimensions ◽  
Pubic Bone ◽  
Biological Anthropology ◽  
Sex Estimation ◽  
Linear Discriminant ◽  
Shape Changes

Creating and testing efficient techniques for the sex estimation of modern human skeletal remains has been a significant focus in biological anthropology. It is well established that the innominate, particularly the pubic bone, is a sexually dimorphic part of the human skeleton, but prone to fragmentation. Using modern pubic bones of known age and sex, this study aims to capture shape differences using geometric morphometrics (GMM) to test classification accuracy of segments of the pubic bone. The sample consists of 70 left adult pubic bones from the William M. Bass Donated Skeletal Collection, with 35 males and 35 females of mixed age and population affinity. Landmarks were placed on the dorsal surface of the pubic body and ischiopubic ramus to capture their overall shape in two dimensions, so the study is easily replicable and applicable. The scans were separately run through a generalized Procrustes, principal components (PCA), and canonical linear discriminant function analysis (DFA). The DFA results show high classification accuracy for the pubic body (94% males, 100% females) and the ischiopubic ramus (100% females, 97% males), with the PCA DFA allowing a researcher to explore specific shape changes driving the differentiation between groups. GMM was able to quantify and successfully discriminant the shape changes between males and females for small elements of the pubis, which can be applied to fragmentary remains and future morphological methods.

scholarly journals Machine Learning for Gully Feature Extraction Based on a Pan-Sharpened Multispectral Image: Multiclass vs. Binary Approach

2020 ◽  
Vol 9 (4) ◽  
pp. 252 ◽  
Author(s):  
Kwanele Phinzi ◽  
Dávid Abriha ◽  
László Bertalan ◽  
Imre Holb ◽  
Szilárd Szabó
Keyword(s):  
Machine Learning ◽  
Strategic Decision ◽  
Support Vector ◽  
Accurate Identification ◽  
Study Region ◽  
Linear Discriminant ◽  
Binary Approach ◽  
Class Level ◽  
Productive Land ◽  
Number Of Classes

Gullies reduce both the quality and quantity of productive land, posing a serious threat to sustainable agriculture, hence, food security. Machine Learning (ML) algorithms are essential tools in the identification of gullies and can assist in strategic decision-making relevant to soil conservation. Nevertheless, accurate identification of gullies is a function of the selected ML algorithms, the image and number of classes used, i.e., binary (two classes) and multiclass. We applied Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Random Forest (RF) on a Systeme Pour l’Observation de la Terre (SPOT-7) image to extract gullies and investigated whether the multiclass (m) approach can offer better classification accuracy than the binary (b) approach. Using repeated k-fold cross-validation, we generated 36 models. Our findings revealed that, of these models, both RFb (98.70%) and SVMm (98.01%) outperformed the LDA in terms of overall accuracy (OA). However, the LDAb (99.51%) recorded the highest producer’s accuracy (PA) but had low corresponding user’s accuracy (UA) with 18.5%. The binary approach was generally better than the multiclass approach; however, on class level, the multiclass approach outperformed the binary approach in gully identification. Despite low spectral resolution, the pan-sharpened SPOT-7 product successfully identified gullies. The proposed methodology is relatively simple, but practically sound, and can be used to monitor gullies within and beyond the study region.

scholarly journals Morphometric Analysis of Cranial Shape in Fossil and Recent Euprimates

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
C. Verity Bennett ◽  
Anjali Goswami
Keyword(s):  
Function Analysis ◽  
Cranial Morphology ◽  
Evolutionary Patterns ◽  
Linear Discriminant ◽  
Landmark Data ◽  
Components Analysis ◽  
Fossil Primates ◽  
Procrustes Superimposition ◽  
Linear Discriminant Function Analysis ◽  
3D Landmarks

Quantitative analysis of morphology allows for identification of subtle evolutionary patterns or convergences in anatomy that can aid ecological reconstructions of extinct taxa. This study explores diversity and convergence in cranial morphology across living and fossil primates using geometric morphometrics. 33 3D landmarks were gathered from 34 genera of euprimates (382 specimens), including the Eocene adapiforms Adapis and Leptadapis and Quaternary lemurs Archaeolemur, Palaeopropithecus, and Megaladapis. Landmark data was treated with Procrustes superimposition to remove all nonshape differences and then subjected to principal components analysis and linear discriminant function analysis. Haplorhines and strepsirrhines were well separated in morphospace along the major components of variation, largely reflecting differences in relative skull length and width and facial depth. Most adapiforms fell within or close to strepsirrhine space, while Quaternary lemurs deviated from extant strepsirrhines, either exploring new regions of morphospace or converging on haplorhines. Fossil taxa significantly increased the area of morphospace occupied by strepsirrhines. However, recent haplorhines showed significantly greater cranial disparity than strepsirrhines, even with the inclusion of the unusual Quaternary lemurs, demonstrating that differences in primate cranial disparity are likely real and not simply an artefact of recent megafaunal extinctions.

scholarly journals Using a multi-isotope approach to understand waterfowl movement in southern Africa

The Condor ◽  
2019 ◽  
Vol 121 (4) ◽  
Author(s):  
Gregory L Mutumi ◽  
Graeme S Cumming ◽  
S Mažeika P Sullivan ◽  
Alexandre Caron ◽  
Carlos Cáceres
Keyword(s):  
Southern Africa ◽  
Function Analysis ◽  
Linear Discriminant ◽  
Heterogeneous Landscape ◽  
Spatial Coverage ◽  
Key Sites ◽  
Δ 15N ◽  
Global Classification ◽  
Linear Discriminant Function Analysis ◽  
Conservation And Management

Abstract Many far-ranging species depend heavily on relatively small or temporary resources within a heterogeneous landscape. For waterfowl, most species rely on deep, permanent waterbodies as refugia from predators during annual flightless molt periods when synchronous loss and regrowth of the flight feathers occurs. The movements of ducks to and from molt sites are, however, poorly documented for most Afrotropical species and the dependencies of Afrotropical ducks on key sites are unclear, yet this information is integral to conservation and management efforts. We asked whether stable isotopes of wing feathers could be used to determine the molting origins of Afrotropical ducks in southern Africa. We analyzed isotope ratios of carbon, nitrogen, oxygen, and hydrogen in feathers from 4 different species across 5 different sites (wetlands, ponds, lakes) in South Africa, Zimbabwe, Mozambique, and Botswana. We observed differences among sites for all isotopes (P < 0.05), especially δ 13C and δ 15N. Based on these differences, we conducted linear discriminant function analysis (LDA) to assess the utility of these isotopes to assign birds to molt locations. We obtained a global classification accuracy = 0.59, although accuracies differed among sites. Our results demonstrate the potential of a multi-isotope approach to discriminate among specific molt locations and to provide an initial estimate of molt site. Rigorous documentation of molt site from wing feathers is plausible, but will require large sample sizes, extensive spatial coverage, and careful calibration.

Characteristics of Permanent Teeth in Persons with Trisomy G

1976 ◽  
Vol 55 (4) ◽  
pp. 633-638 ◽  
Author(s):  
B. Prahl-Andersen ◽  
J. Oerlemans
Keyword(s):  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Function Analysis ◽  
Permanent Teeth ◽  
Tooth Size ◽  
Linear Discriminant ◽  
Second Molar ◽  
The Mean ◽  
Size And Morphology ◽  
Linear Discriminant Function Analysis

Tooth size and morphology in 35 participants with trisomy G and in 33 controls have been studied. Special attention has been paid to the mean cusp pattern of the upper first and second molar. The classification matrix for the linear discriminant function analysis between participants with trisomy G and controls based on five selected variables showed three misclassifications.

Neural Network Subtyping of Depression

1998 ◽  
Vol 32 (5) ◽  
pp. 687-694 ◽  
Author(s):  
Tony M. Florio ◽  
Gordon Parker ◽  
Marie-Paule Austin ◽  
Ian Hickie ◽  
Philip Mitchell ◽  
...  
Keyword(s):  
Neural Network ◽  
Clinical Decision Making ◽  
Linear Models ◽  
Function Analysis ◽  
Clinical Decision ◽  
Linear Analysis ◽  
Linear Discriminant ◽  
Non Linear ◽  
Depressive Subtypes ◽  
Definition Of

Objective: To examine the applicability of a neural network classification strategy to examine the independent contribution of psychomotor disturbance (PMD) and endogeneity symptoms to the DSM-III-R definition of melancholia. Method: We studied 407 depressed patients with the clinical dataset comprising 17 endogeneity symptoms and the 18-item CORE measure of behaviourally rated PMD. A multilayer perceptron neural network was used to fit non-linear models of varying complexity. A linear discriminant function analysis was also used to generate a model for comparison with the non-linear models. Results: Models (linear and non-linear) using PMD items only and endogeneity symptoms only had similar rates of successful classification, while non-linear models combining both PMD and symptom scores achieved the best classifications. Conclusions: Our current non-linear model was superior to a linear analysis, a finding which may have wider application to psychiatric classification. Our non-linear analysis of depressive subtypes supports the binary view that melancholic and non-melancholic depression are separate clinical disorders rather than different forms of the same entity. This study illustrates how non-linear modelling with neural networks is a potentially fruitful approach to the study of the diagnostic taxonomy of psychiatric disorders and to clinical decision-making.

scholarly journals Possibilities for predicting preeclampsia development in pregnant women with bronchial asthma

2019 ◽  
Vol 68 (4) ◽  
pp. 13-18
Author(s):  
Olga V. Lavrova ◽  
Valery D. Kulikov ◽  
Elena A. Shapovalova ◽  
Anna V. Sablina
Keyword(s):  
Pregnant Women ◽  
Bronchial Asthma ◽  
Function Analysis ◽  
High Reliability ◽  
Significant Risk ◽  
Prediction Algorithm ◽  
Average Dose ◽  
Doppler Measurement ◽  
Linear Discriminant ◽  
Study Results

Hypothesis/aims of study. Currently, preeclampsia is one of the most pressing problems of obstetrics due to the complexity of pathogenesis and to the lack of early and reliable diagnostic criteria. The preeclampsia rate in patients with bronchial asthma is proved higher than in asthma free pregnant women. This study aimed to establish the prediction algorithm of preeclampsia development in in pregnant women suffering from bronchial asthma of varying severity and different level of control. Study design, materials and methods. Asthma duration was studied in 110 pregnant women using the SPSS Discriminant Function Analysis method. Basic therapy and level of asthma control were studied together with respiratory tests, obstetrician medical history, and complications of the first and second trimesters of pregnancy. In addition, serum interleukin panel was assessed and placental Doppler measurement was carried out. Results. Clinical and statistical analysis made it possible out of 87 significant risk factors for the development of hypertensive disorders and preeclampsia to form a highly informative set of signs for a linear discriminant model for predicting preeclampsia: 1) asthma exacerbation in the first trimester of pregnancy; 2) asthma duration severity; 3) average dose of inhaled glucocorticosteroid drugs administered to the exact patient during pregnancy; 4) serum levels of tumor necrosis factor, interferon gamma, and interleukins-4, 6, and 8. Conclusion. The inclusion method of step-by-step discriminant analysis allowed establishing a highly informative four-component complex of clinical predictors for preeclampsia development in pregnant women with asthma. The results of the model testing showed its extremely high reliability (up to 100% within study selection as well as within control selection). Thus, the study results can be recommended for clinical use.

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