scholarly journals Automatic Morphological Sieving: Comparison between Different Methods, Application to DNA Ploidy Measurements

1999 ◽  
Vol 18 (4) ◽  
pp. 203-210
Author(s):  
Christophe Boudry ◽  
Paulette Herlin ◽  
Benoit Plancoulaine ◽  
Eric Masson ◽  
Abderrahim Elmoataz ◽  
...  
Keyword(s):  
Neural Network ◽  
Breast Carcinoma ◽  
Brain Tumour ◽  
Mathematical Morphology ◽  
False Positive ◽  
Dna Ploidy ◽  
Automatic Classification ◽  
Classification Methods ◽  
Automatic Methods ◽  
Automatic Elimination

The aim of the present study is to propose alternative automatic methods to time consuming interactive sorting of elements for DNA ploidy measurements. One archival brain tumour and two archival breast carcinoma were studied, corresponding to 7120 elements (3764 nuclei, 3356 debris and aggregates). Three automatic classification methods were tested to eliminate debris and aggregates from DNA ploidy measurements (mathematical morphology (MM), multiparametric analysis (MA) and neural network (NN)). Performances were evaluated by reference to interactive sorting. The results obtained for the three methods concerning the percentage of debris and aggregates automatically removed reach 63, 75 and 85% for MM, MA and NN methods, respectively, with false positive rates of 6, 21 and 25%. Information about DNA ploidy abnormalities were globally preserved after automatic elimination of debris and aggregates by MM and MA methods as opposed to NN method, showing that automatic classification methods can offer alternatives to tedious interactive elimination of debris and aggregates, for DNA ploidy measurements of archival tumours.

scholarly journals TRAJECTORY-BASED VISUALIZATION OF MMS POINT CLOUDS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1127-1133
Author(s):  
G. Takahashi ◽  
H. Masuda
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Automatic Classification ◽  
Infrastructure Management ◽  
Classification Methods ◽  
Main Road ◽  
Laser Scanners ◽  
Map Generation ◽  
Automatic Methods ◽  
3D Information

<p><strong>Abstract.</strong> MMSs allow us to obtain detailed 3D information around roads. Especially, LiDAR point clouds can be used for map generation and infrastructure management. For practical uses, however, it is necessary to add labels to a part of the points since various objects can be included in the point clouds. Existing automatic classification methods are not completely error-free, and may incorrectly classify objects. Therefore, even though automatic methods are applied to the point clouds, operators have to verify the labels. While operators classify the point clouds manually, selecting 3D points tasks in 3D views are difficult. In this paper, we propose a new point-cloud image based on the trajectories of MMSs. We call our point-cloud image <i>trajectory-based point-cloud image</i>. Although the image is distorted because it is generated based on rotation angles of laser scanners, we confirmed that most objects can be recognized from point-cloud images by checking main road facilities. We evaluated how efficient the annotation can be done using our method, and the results show that operators could add annotations to point-cloud images more efficiently.</p>

Evaluation of DNA ploidy and S‐phase fraction in fine needle aspirates from breast carcinoma

2021 ◽  
Author(s):  
Shalini Panwar ◽  
Uma Handa ◽  
Manveen Kaur ◽  
Harsh Mohan ◽  
Ashok K Attri
Keyword(s):  
Breast Carcinoma ◽  
Dna Ploidy ◽  
S Phase ◽  
Phase Fraction ◽  
Fine Needle ◽  
Fine Needle Aspirates

An Investigation of the Characteristics of a Bayesian Military Impulse Noise Classifier

2008 ◽  
Author(s):  
Brian Bucci ◽  
Jeffrey Vipperman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bayesian Methods ◽  
False Positive ◽  
Impulse Noise ◽  
False Negative ◽  
Training Data ◽  
Maximum Accuracy ◽  
First Case ◽  
Testing Data

In extension of previous methods to identify military impulse noise in the civilian environmental noise monitoring setting by means of a set of computed scalar metrics input to artificial neural network structures, Bayesian methods are investigated to classify the same dataset. Four interesting cases are identified and analyzed: A) Maximum accuracy achieve on training data, B) Maximum overall accuracy on blind testing data, C) Maximum accuracy on testing data with zero false positive detections, D) Maximum accuracy on testing data with zero false negative rejections. The first case is used to illustrative example and the later three represent actual monitoring modes. All of the cases are compared and contrasted to illuminate respective strengths and weaknesses. Overall accuracies of up to 99.8% are observed with no false negative rejections and accuracies of up to 98.4% are also achieved with no false positive detections.

scholarly journals Klasifikasi Motif Batik Berbasis Kemiripan Ciri dengan Wavelet Transform dan Fuzzy Neural Network

2014 ◽  
Vol 5 (1) ◽  
pp. 361
Author(s):  
A Haris Rangkuti
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Fuzzy Neural Network ◽  
Classification Methods ◽  
Neural Network Classification ◽  
Fuzzy Input ◽  
Fuzzy Neural ◽  
Input Variables

 This paper introduces a classification of the image of the batik process, which is based on the similarity of the characteristics, by combining the method of wavelet transform Daubechies type 2 level 2, to process the characteristic texture consisting of standard deviation, mean and energy as input variables, using the method of Fuzzy Neural Network (FNN). Fuzzyfikasi process will be carried out all input values with five categories: Very Low (VL), Low (L), Medium (M), High (H) and Very High (VH). The result will be a fuzzy input in the process of neural network classification methods. The result will be a fuzzy input in the process of neural network classification methods. For the image to be processed seven types of batik motif is ceplok, kawung, lereng, parang, megamendung, tambal and nitik. The results of the classification process with FNN is rule generation, so for the new image of batik can be immediately known motif types after treatment with FNN classification.  For the degree of precision of this method is 86-92%.

scholarly journals Machine Learning–based Analysis of English Lateral Allophones

2019 ◽  
Vol 29 (2) ◽  
pp. 393-405 ◽  
Author(s):  
Magdalena Piotrowska ◽  
Gražina Korvel ◽  
Bożena Kostek ◽  
Tomasz Ciszewski ◽  
Andrzej Cżyzewski
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Nearest Neighbor ◽  
Native Speakers ◽  
Automatic Evaluation ◽  
Classification Methods ◽  
K Nearest Neighbor ◽  
Self Organizing Maps ◽  
Automatic Methods ◽  
Audio Video

Abstract Automatic classification methods, such as artificial neural networks (ANNs), the k-nearest neighbor (kNN) and self-organizing maps (SOMs), are applied to allophone analysis based on recorded speech. A list of 650 words was created for that purpose, containing positionally and/or contextually conditioned allophones. For each word, a group of 16 native and non-native speakers were audio-video recorded, from which seven native speakers’ and phonology experts’ speech was selected for analyses. For the purpose of the present study, a sub-list of 103 words containing the English alveolar lateral phoneme /l/ was compiled. The list includes ‘dark’ (velarized) allophonic realizations (which occur before a consonant or at the end of the word before silence) and 52 ‘clear’ allophonic realizations (which occur before a vowel), as well as voicing variants. The recorded signals were segmented into allophones and parametrized using a set of descriptors, originating from the MPEG 7 standard, plus dedicated time-based parameters as well as modified MFCC features proposed by the authors. Classification methods such as ANNs, the kNN and the SOM were employed to automatically detect the two types of allophones. Various sets of features were tested to achieve the best performance of the automatic methods. In the final experiment, a selected set of features was used for automatic evaluation of the pronunciation of dark /l/ by non-native speakers.

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