Estimation of Size-Specific Molting Probabilities in Adult Decapod Crustaceans Based on Postmolt Indicator Data

1989 ◽  
Vol 46 (10) ◽  
pp. 1819-1830 ◽  
Author(s):  
Michael S. Mohr ◽  
David G. Hankin
Keyword(s):  
Adult Female ◽  
Binary Classification ◽  
Carapace Width ◽  
Estimation Procedure ◽  
Survival Ratio ◽  
Cancer Magister ◽  
Constant Variance ◽  
Indicator Data ◽  
Dungeness Crabs

For crustaceans that exhibit a well-defined molting season, postmolt indicator methods may be used to classify a sample of animals collected after the molting season into those that have molted and those that have failed to molt. This binary classification of a sample may be used to estimate size-specific molting probabilities. We derive maximum likelihood estimators for these molting probabilities, and for the variances of estimated molting probabilities, based on such postmolt indicator data. Estimators assume that the premolt–postmolt relation is linear with an additive and normally distributed error term of constant variance and, in their simplest form, assume that the ratio (Ri) of size-specific survival probabilities through the molting season for molting as compared with nonmolting individuals is known. For the more likely situation in which only a plausible range for Ri is specifiable, an estimation procedure is proposed which minimizes the maximum possible error (mean square error) of the molting probability estimator over this range. We illustrate application of estimators using shell condition data collected from the northern California population of adult female Dungeness crabs (Cancer magister). Estimated annual molting probabilities for adult female Dungeness crabs were greater than 0.90 for crabs less than 135 mm carapace width, but then declined rapidly until they were near zero for crabs exceeding 160 mm carapace width. This conclusion was not substantively affected by choices of a survival ratio ranging from 0.4 to 1.0.

Does intense fishing on males impair mating success of female Dungeness crabs

1997 ◽  
Vol 54 (3) ◽  
pp. 655-669 ◽  
Author(s):  
D G Hankin ◽  
T H Butler ◽  
P W Wild ◽  
Q -L Xue
Keyword(s):  
Mating Success ◽  
Egg Production ◽  
Carapace Width ◽  
Minimum Size ◽  
Cancer Magister ◽  
Worst Case ◽  
Mature Adult ◽  
Commercial Harvest ◽  
Size Limits ◽  
Dungeness Crabs

Commercial capture of female Dungeness crabs, Cancer magister, is prohibited and minimum size limits for commercial harvest of male crabs are designed to allow most males to mate at least once before capture. Annual exploitation rates often exceed 90%, however, and the resulting scarcity of large males might reduce mating success among large females. We present new data regarding (i) sizes of male and female crabs collected in premating embraces, (ii) carapace width frequencies of female Dungeness crabs, (iii) presence of sperm plugs and sperm, and (iv) fecundity. Minimum carapace width of hard-shelled mating males typically exceeds postmolt carapace width of soft-shelled females (i), but female Dungeness crabs exceeding the minimum legal size of males usually account for less than 5% of mature adult female crabs (ii), and sublegal-sized males actively participate in mating (i). Remnants of sperm plugs, definitive indicators of mating, were found in 97.5% of recently molted large females (iii), suggesting that virtually all molting females mate regardless of size. On the basis of (ii) and (iv), hypothetical worst-case calculations, assuming that no large females could find mates, suggest that total egg production would be reduced by no more than 2-25% among molting female crabs.

Movements of Adult Female Dungeness Crabs (Cancer magister) In Northern California Based on Tag Recoveries

1985 ◽  
Vol 42 (5) ◽  
pp. 919-926 ◽  
Author(s):  
Nancy Diamond ◽  
David G. Hankin
Keyword(s):  
Adult Female ◽  
Fishing Effort ◽  
Nonparametric Tests ◽  
Northern California ◽  
Cancer Magister ◽  
Directed Movement ◽  
Location Data ◽  
Movement Data ◽  
Large Numbers ◽  
Dungeness Crabs

Of 11072 adult female Dungeness crabs (Cancer magister) tagged and released in northern California, 463 were recovered with useful location data that could be used for analyses of crab movement patterns. Although qualitative analyses of movement data suggested possible directed northward movement during winter months, application of two nonparametric tests of movement directionality (the Rayleigh test and Moore's test) failed to support significant directed movement during winter. Large numbers of tagged crabs were recovered inshore of release in shallow sandy areas during spring months, but valid statistical analyses of spring movement data were ruled out by concentration of fishing effort in shallow waters during spring. Nevertheless, recovery of large numbers of tagged females in inshore areas during spring is entirely consistent with an hypothesis of spring inshore movement of females for molting, mating, and later extrusion of egg masses. This hypothesis can be constructed on the basis of information independent of tag recovery data. The most striking finding was that 46% of all recovered crabs were recaptured within 2 km of original release sites; many of these crabs had been at large more than 1 yr. Adult female Dungeness crabs appear to constitute extremely localized stocks in northern California.

Exploitation and Mortality of Male Dungeness Crabs (Cancer magister) Near Tofino, British Colombia

1989 ◽  
Vol 46 (9) ◽  
pp. 1609-1614 ◽  
Author(s):  
Barry D. Smith ◽  
Glen S. Jamieson
Keyword(s):  
Frequency Analysis ◽  
Carapace Width ◽  
Fishing Mortality ◽  
Cancer Magister ◽  
Commercial Catch ◽  
Small Component ◽  
Size Frequency ◽  
British Colombia ◽  
Intense Exploitation ◽  
Dungeness Crabs

Male Dungeness crabs (Cancer magister) were sampled by traps and monitored by tagging as they moulted and entered the fishery near Tofino, British Columbia, from April 1985 until March 1987. Males first recruited to the fishery after moulting from the μ = 129-mm CW (carapace width) to the μ = 156-mm CW normal instar. Sublegalsized males (< 154 mm notch-to-notch CW) in the μ = 156-mm CW instar (≈ 42% of this instar) were found to have a high annual natural mortality (M = 2.9–4.5), with < 10% surviving to legal size. Legal-sized males experienced high annual fishing mortality (F = 5.1–6.9), so consequently a small component of the commercial catch consisted of males in larger instars. Size frequency analysis, which measured the percent exploitation of the μ = 156-mm CW instar, indicated that legal-sized males remained in relatively low abundance during this year-round fishery because of intense exploitation. Mark–recovery data and size frequency analysis also indicated this intense fishery was sustained throughout most of the year by a protracted moulting season. Consequently, we observed prolonged periods with a high percentage of less desirable soft-shelled males in the commercial catch.

Growth and reproductive dynamics of adult female Dungeness crabs (Cancer magister) in northern California

1989 ◽  
Vol 46 (1) ◽  
pp. 94-108 ◽  
Author(s):  
D. G. Hankin ◽  
N. Diamond ◽  
M. S. Mohr ◽  
J. Ianelli
Keyword(s):  
Adult Female ◽  
Northern California ◽  
Cancer Magister ◽  
Dungeness Crabs

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

A Study on Multi Class Classification from Breast Cancer Images using Ensemble Network and Transfer Learning

2020 ◽  
Vol 14 ◽  
Author(s):  
Lahari Tipirneni ◽  
Rizwan Patan
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Disease Diagnosis ◽  
Feature Descriptors ◽  
Histopathological Images ◽  
Viable Approach ◽  
Multi Class Classification

Abstract:: Millions of deaths all over the world are caused by breast cancer every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increases the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. The current paper presents an ensemble Convolutional neural network for multi class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from publicly available BreakHis dataset and classified between 8 classes. The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification.

Diel Activity of an Estuarine Population of Dungeness Crabs, Cancer Magister, in Relation to Feeding and Environmental Factors

1984 ◽  
Vol 4 (3) ◽  
pp. 390-403 ◽  
Author(s):  
Bradley G. Stevens ◽  
David A. Armstrong ◽  
James C. Hoeman
Keyword(s):  
Environmental Factors ◽  
Diel Activity ◽  
Cancer Magister ◽  
Dungeness Crabs

scholarly journals Improving Land Cover Classification Using Genetic Programming for Feature Construction

2021 ◽  
Vol 13 (9) ◽  
pp. 1623
Author(s):  
João E. Batista ◽  
Ana I. R. Cabral ◽  
Maria J. P. Vasconcelos ◽  
Leonardo Vanneschi ◽  
Sara Silva
Keyword(s):  
Land Cover ◽  
Genetic Programming ◽  
Satellite Images ◽  
State Of The Art ◽  
Binary Classification ◽  
Feature Construction ◽  
Classification Problems ◽  
Construction Methods ◽  
Box Models

Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.

scholarly journals Monotonic Functions Method and Its Application to Staging of Patients with Prostate Cancer According to Pretreatment Data

2021 ◽  
Vol 11 (9) ◽  
pp. 3836
Author(s):  
Valeri Gitis ◽  
Alexander Derendyaev ◽  
Konstantin Petrov ◽  
Eugene Yurkov ◽  
Sergey Pirogov ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Learning Algorithm ◽  
Binary Classification ◽  
Preoperative Staging ◽  
Threshold Value ◽  
Logical Function ◽  
Adequate Treatment ◽  
Monotonic Functions ◽  
Selection Of

Prostate cancer is the second most frequent malignancy (after lung cancer). Preoperative staging of PCa is the basis for the selection of adequate treatment tactics. In particular, an urgent problem is the classification of indolent and aggressive forms of PCa in patients with the initial stages of the tumor process. To solve this problem, we propose to use a new binary classification machine-learning method. The proposed method of monotonic functions uses a model in which the disease’s form is determined by the severity of the patient’s condition. It is assumed that the patient’s condition is the easier, the less the deviation of the indicators from the normal values inherent in healthy people. This assumption means that the severity (form) of the disease can be represented by monotonic functions from the values of the deviation of the patient’s indicators beyond the normal range. The method is used to solve the problem of classifying patients with indolent and aggressive forms of prostate cancer according to pretreatment data. The learning algorithm is nonparametric. At the same time, it allows an explanation of the classification results in the form of a logical function. To do this, you should indicate to the algorithm either the threshold value of the probability of successful classification of patients with an indolent form of PCa, or the threshold value of the probability of misclassification of patients with an aggressive form of PCa disease. The examples of logical rules given in the article show that they are quite simple and can be easily interpreted in terms of preoperative indicators of the form of the disease.

Deep Learning-Based Binary Classification of ADHD Using Resting State MR Images

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Vikas Khullar ◽  
Karuna Salgotra ◽  
Harjit Pal Singh ◽  
Davinder Pal Sharma
Keyword(s):  
Deep Learning ◽  
Resting State ◽  
Binary Classification ◽  
Mr Images
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