classification procedure
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2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Hassan W. Kayondo ◽  
Alfred Ssekagiri ◽  
Grace Nabakooza ◽  
Nicholas Bbosa ◽  
Deogratius Ssemwanga ◽  
...  

Abstract Background Host population structure is a key determinant of pathogen and infectious disease transmission patterns. Pathogen phylogenetic trees are useful tools to reveal the population structure underlying an epidemic. Determining whether a population is structured or not is useful in informing the type of phylogenetic methods to be used in a given study. We employ tree statistics derived from phylogenetic trees and machine learning classification techniques to reveal an underlying population structure. Results In this paper, we simulate phylogenetic trees from both structured and non-structured host populations. We compute eight statistics for the simulated trees, which are: the number of cherries; Sackin, Colless and total cophenetic indices; ladder length; maximum depth; maximum width, and width-to-depth ratio. Based on the estimated tree statistics, we classify the simulated trees as from either a non-structured or a structured population using the decision tree (DT), K-nearest neighbor (KNN) and support vector machine (SVM). We incorporate the basic reproductive number ($$R_0$$ R 0 ) in our tree simulation procedure. Sensitivity analysis is done to investigate whether the classifiers are robust to different choice of model parameters and to size of trees. Cross-validated results for area under the curve (AUC) for receiver operating characteristic (ROC) curves yield mean values of over 0.9 for most of the classification models. Conclusions Our classification procedure distinguishes well between trees from structured and non-structured populations using the classifiers, the two-sample Kolmogorov-Smirnov, Cucconi and Podgor-Gastwirth tests and the box plots. SVM models were more robust to changes in model parameters and tree size compared to KNN and DT classifiers. Our classification procedure was applied to real -world data and the structured population was revealed with high accuracy of $$92.3\%$$ 92.3 % using SVM-polynomial classifier.


2021 ◽  
Author(s):  
Mansour Ranjbar

Abstract The concept of malaria elimination is becoming more and more important. Among countries with malaria transmission in 2015, eliminating malaria from 35 countries including those in the Great Mekong has been targeted by 2030. In the journey to elimination through the foci classification procedure, a limited number of “hotspots” among a large number of foci should be precisely defined to be covered by effective controlling measures. There is a common consensus that foci and case classification are fundamental principles of malaria elimination and prevention of reintroduction. However, there are numerous ambiguities and controversies in almost all aspects of foci classifications. These uncertainties result in misclassification that, in turn, wastes lives, time, and money thereby violating value for money principles. New progress in the literature such as ignoring “new potential” foci and using the class of “active foci” instead of the two classes of “new active” and “residual active” is in opposition to the philosophy of foci classification. In this paper, we seek to elaborate the controversies and ambiguities around the concept of foci classification and ultimately suggest some solutions. Some of the ways forward include: (a) foci classification should be done by parasite type; (b) a set of foci classes includes “cleared up”, “new potential”, “new active”, “residual active”, and “residual nonactive”; (c) The number and population of various foci classes should be regularly updated and monitored as the basis for measuring progress toward elimination and it can be considered as the basis for needs assessment and planning response; (d) The coverage and completeness of the controlling interventions by foci classes should be regularly monitored; and (e) The criteria for early detection of outbreaks should be defined. Furthermore, two applicable models for foci classification by parasite have been proposed.


Haematologica ◽  
2021 ◽  
pp. 0-0
Author(s):  
Dianna Hussmann ◽  
Anna Starnawska ◽  
Louise Kristensen ◽  
Iben Daugaard ◽  
Astrid Thomsen ◽  
...  

Currently, no molecular biomarker indexes are used in standard care to make treatment decisions at diagnosis of chronic lymphocytic leukemia (CLL). We used Infinium MethylationEPIC array data from diagnostic blood samples of 114 CLL patients, and developed a patient stratification procedure based on methylation signatures associated with mutation load of the IGHV gene. This procedure allowed us to predict the time to treatment (TTT) with HR 8.34 (95% CI, 4.54-15.30), as opposed to HR 4.35 (95% CI, 2.60-7.28) for IGHV mutation status. Detailed evaluation of 17 discrepant cases between the two classification procedures showed that these cases were incorrectly classified using IGHV status. Moreover, methylation-based classification stratified patients with different overall survival (OS) (HR, 1.82; 95% CI, 1.07-3.09), which was not possible using IGHV status. Furthermore, we assessed the performance of the developed classification procedure using published HumanMethylation450 array data for 159 patients for which TTT, OS and relapse were available. Despite that 450K array methylation data did not contain all biomarkers used in our classification procedure, methylation signatures again stratified patients with significantly better accuracy than IGHV mutation load regarding all available clinical outcomes. Thus, stratification using IGHV-associated methylation signatures may provide improved prognostic power than IGHV mutation status.


2021 ◽  
Vol 15 ◽  
Author(s):  
Omneya Attallah

Childhood medulloblastoma (MB) is a threatening malignant tumor affecting children all over the globe. It is believed to be the foremost common pediatric brain tumor causing death. Early and accurate classification of childhood MB and its classes are of great importance to help doctors choose the suitable treatment and observation plan, avoid tumor progression, and lower death rates. The current gold standard for diagnosing MB is the histopathology of biopsy samples. However, manual analysis of such images is complicated, costly, time-consuming, and highly dependent on the expertise and skills of pathologists, which might cause inaccurate results. This study aims to introduce a reliable computer-assisted pipeline called CoMB-Deep to automatically classify MB and its classes with high accuracy from histopathological images. This key challenge of the study is the lack of childhood MB datasets, especially its four categories (defined by the WHO) and the inadequate related studies. All relevant works were based on either deep learning (DL) or textural analysis feature extractions. Also, such studies employed distinct features to accomplish the classification procedure. Besides, most of them only extracted spatial features. Nevertheless, CoMB-Deep blends the advantages of textural analysis feature extraction techniques and DL approaches. The CoMB-Deep consists of a composite of DL techniques. Initially, it extracts deep spatial features from 10 convolutional neural networks (CNNs). It then performs a feature fusion step using discrete wavelet transform (DWT), a texture analysis method capable of reducing the dimension of fused features. Next, the CoMB-Deep explores the best combination of fused features, enhancing the performance of the classification process using two search strategies. Afterward, it employs two feature selection techniques on the fused feature sets selected in the previous step. A bi-directional long-short term memory (Bi-LSTM) network; a DL-based approach that is utilized for the classification phase. CoMB-Deep maintains two classification categories: binary category for distinguishing between the abnormal and normal cases and multi-class category to identify the subclasses of MB. The results of the CoMB-Deep for both classification categories prove that it is reliable. The results also indicate that the feature sets selected using both search strategies have enhanced the performance of Bi-LSTM compared to individual spatial deep features. CoMB-Deep is compared to related studies to verify its competitiveness, and this comparison confirmed its robustness and outperformance. Hence, CoMB-Deep can help pathologists perform accurate diagnoses, reduce misdiagnosis risks that could occur with manual diagnosis, accelerate the classification procedure, and decrease diagnosis costs.


Author(s):  
E.A. Chernetsova ◽  
◽  
A.V. Gomera ◽  
T.M. Tatarnikova ◽  

A method for processing audio signals of steps of various people in order to effectively separate pseudospectra of signals that can be further subjected to the classification procedure is proposed. The model of a human steps detector was based on the dynamic parameters of the foot position when walking. To record step signals, vibroacoustic sensors were used, sensitive to mechanical and acoustic vibrations in the frequency range from 10 to 1000 Hz. The processing of recorded step signals involved calculating their envelope and then calculating the pseudospectra of the envelope. The procedure for orthogonalization of the pseudospectra of steps for their effective frequency diversity can be used as the basis for the operation of an automated Remote Access Control System. This system can find the application in areas where for one reason or another it is impossible to install another biometric system or is extremely inconvenient, for example, to control access to a room into which the working personnel enter in a special uniform, gloves, and goggles.


2021 ◽  
Vol 881 ◽  
pp. 71-76
Author(s):  
Jian Yang ◽  
Hong Bin Li ◽  
Song Tao Ren ◽  
Peng Gang Jin ◽  
Zan Gao

In order to determine the influence of spheroidization process of Ammonium dinitramide’s hazard grade, the hazardous division of Ammonium dinitramide before and after spheroidization is studied by using hazard classification procedure for combustible and explosive substances and articles standard (WJ20405) and hazard classification method and criterion for combusitible and explosive substances and articles standard (WJ20404). The research results show that spheroidization process can significantly improve the temperature stability of Ammonium dinitramide and significantly reduce friction sensitivity and impact sensitivity of Ammonium dinitramide. So spheroidization process can reduce the hazardous of Ammonium dinitramide and improve the safe character of Ammonium dinitramide.


Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 680
Author(s):  
Stig Uteng ◽  
Eduardo Quevedo ◽  
Gustavo M. Callico ◽  
Irene Castaño ◽  
Gregorio Carretero ◽  
...  

This paper shows new contributions in the detection of skin cancer, where we present the use of a customized hyperspectral system that captures images in the spectral range from 450 to 950 nm. By choosing a 7 × 7 sub-image of each channel in the hyperspectral image (HSI) and then taking the mean and standard deviation of these sub-images, we were able to make fits of the resulting curves. These fitted curves had certain characteristics, which then served as a basis of classification. The most distinct fit was for the melanoma pigmented skin lesions (PSLs), which is also the most aggressive malignant cancer. Furthermore, we were able to classify the other PSLs in malignant and benign classes. This gives us a rather complete classification method for PSLs with a novel perspective of the classification procedure by exploiting the variability of each channel in the HSI.


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