scholarly journals Experimental comparison of four nonlinear magnetic detection methods and considerations on clinical usability

2020 ◽  
Vol 7 (1) ◽  
pp. 015018
Author(s):  
M M van de Loosdrecht ◽  
L Abelmann ◽  
B ten Haken
Keyword(s):  
Detection Methods ◽  
Experimental Comparison ◽  
Magnetic Detection

scholarly journals Experimental Comparison and Survey of Twelve Time Series Anomaly Detection Algorithms

2021 ◽  
Vol 72 ◽  
pp. 849-899
Author(s):  
Cynthia Freeman ◽  
Jonathan Merriman ◽  
Ian Beaver ◽  
Abdullah Mueen
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Detection Method ◽  
Concept Drift ◽  
Area Under The Curve ◽  
Detection Methods ◽  
Experimental Comparison ◽  
Detection Algorithms ◽  
Extensive Evaluation ◽  
Online Setting

The existence of an anomaly detection method that is optimal for all domains is a myth. Thus, there exists a plethora of anomaly detection methods which increases every year for a wide variety of domains. But a strength can also be a weakness; given this massive library of methods, how can one select the best method for their application? Current literature is focused on creating new anomaly detection methods or large frameworks for experimenting with multiple methods at the same time. However, and especially as the literature continues to expand, an extensive evaluation of every anomaly detection method is simply not feasible. To reduce this evaluation burden, we present guidelines to intelligently choose the optimal anomaly detection methods based on the characteristics the time series displays such as seasonality, trend, level change concept drift, and missing time steps. We provide a comprehensive experimental validation and survey of twelve anomaly detection methods over different time series characteristics to form guidelines based on several metrics: the AUC (Area Under the Curve), windowed F-score, and Numenta Anomaly Benchmark (NAB) scoring model. Applying our methodologies can save time and effort by surfacing the most promising anomaly detection methods instead of experimenting extensively with a rapidly expanding library of anomaly detection methods, especially in an online setting.

scholarly journals The Construction of Machine Translation Model and Its Application in English Grammar Error Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Fei Long
Keyword(s):  
Machine Translation ◽  
Error Detection ◽  
Language Model ◽  
Recall Rate ◽  
Model Construction ◽  
Detection Methods ◽  
Experimental Comparison ◽  
Translation Model ◽  
English Grammar ◽  
Grammatical Errors

In order to solve the problems of low accuracy, recall rate, and F1 value of traditional English grammar error detection methods, a new machine translation model is constructed and applied to English grammar error detection. In the encoder-decoder framework, the machine translation model is constructed through the steps of word vector generation, encoder language model construction, decoder language model construction, word alignment, output module, and so on. On this basis, the machine translation model is trained to detect English grammatical errors through dependency analysis and alternative word generation. Experimental results show that the accuracy, recall rate, and F1 value of the proposed method are higher than those of the experimental comparison method for detecting English grammatical errors such as articles, prepositions, nouns, verbs, and subject-verb agreement, indicating that the proposed method is of high practical value.

An experimental comparison of superpixels detection methods for contour detection

2018 ◽  
Vol 29 (4) ◽  
pp. 677-687 ◽  
Author(s):  
Xuan-Yin Wang ◽  
Chang-Wei Wu ◽  
Ke Xiang ◽  
Sen-Wei Xiang ◽  
Wen Chen
Keyword(s):  
Contour Detection ◽  
Detection Methods ◽  
Experimental Comparison

scholarly journals Asumu Fractional Derivative Applied to Edge Detection on SARS-COV2 Images

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Gustavo Asumu Mboro Nchama ◽  
Leandro Daniel Lau Alfonso ◽  
Roberto Rodríguez Morales ◽  
Ezekiel Nnamere Aneke
Keyword(s):  
Edge Detection ◽  
Training System ◽  
Detection Methods ◽  
Order Derivative ◽  
Experimental Comparison ◽  
Mathematical Methods ◽  
Image Brightness ◽  
Medical Diagnostic ◽  
Image Edge ◽  
Fractional Differential

Edge detection consists of a set of mathematical methods which identifies the points in a digital image where image brightness changes sharply. In the traditional edge detection methods such as the first-order derivative filters, it is easy to lose image information details and the second-order derivative filters are more sensitive to noise. To overcome these problems, the methods based on the fractional differential-order filters have been proposed in the literature. This paper presents the construction and implementation of the Prewitt fractional differential filter in the Asumu definition sense for SARS-COV2 image edge detection. The experiments show that these filters can avoid noise and detect rich edge details. The experimental comparison show that the proposed method outperforms some edge detection methods. In the next paper, we are planning to improve and combine the proposed filters with artificial intelligence algorithm in order to program a training system for SARS-COV2 image classification with the aim of having a supplemental medical diagnostic.

DNA nick end labeling: a reliable marker for apoptosis?

1995 ◽  
Vol 53 ◽  
pp. 962-963
Author(s):  
Anne F. Bushnell ◽  
Sarah Webster ◽  
Lynn S. Perlmutter
Keyword(s):  
Cell Death ◽  
Cultured Cells ◽  
Apoptotic Cell ◽  
Morphological Characteristics ◽  
Detection Methods ◽  
Tissue Preparation ◽  
Preparation Methods ◽  
Dna Laddering ◽  
Disease States ◽  
Reliable Marker

Apoptosis, or programmed cell death, is an important mechanism in development and in diverse disease states. The morphological characteristics of apoptosis were first identified using the electron microscope. Since then, DNA laddering on agarose gels was found to correlate well with apoptotic cell death in cultured cells of dissimilar origins. Recently numerous DNA nick end labeling methods have been developed in an attempt to visualize, at the light microscopic level, the apoptotic cells responsible for DNA laddering.The present studies were designed to compare various tissue processing techniques and staining methods to assess the occurrence of apoptosis in post mortem tissue from Alzheimer's diseased (AD) and control human brains by DNA nick end labeling methods. Three tissue preparation methods and two commercial DNA nick end labeling kits were evaluated: the Apoptag kit from Oncor and the Biotin-21 dUTP 3' end labeling kit from Clontech. The detection methods of the two kits differed in that the Oncor kit used digoxigenin dUTP and anti-digoxigenin-peroxidase and the Clontech used biotinylated dUTP and avidinperoxidase. Both used 3-3' diaminobenzidine (DAB) for final color development.

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