scholarly journals Use of principal component analysis in conjunction with soft computing methods for investigating total sediment load transferability from laboratory to field scale

2013 ◽  
Vol 45 (4-5) ◽  
pp. 540-550 ◽  
Author(s):  
Gokmen Tayfur ◽  
Yashar Karimi
Keyword(s):  
Principal Component Analysis ◽  
Soft Computing ◽  
Laboratory Data ◽  
Principal Component ◽  
Component Analysis ◽  
Empirical Methods ◽  
Field Scale ◽  
Dimensionless Parameters ◽  
Optimal Values ◽  
Total Sediment

This study quantitatively investigates the generalization from laboratory scale to field scale using the soft computing (expert) and the empirical methods. Principal component analysis is utilized to form the input vector for the expert methods. Five main dimensionless parameters are used in the input vector of artificial neural networks (ANN), calibrated with laboratory data, to predict field total sediment loads. In addition, nonlinear equations are constructed based upon the same dimensionless parameters. The optimal values of the exponents and constants of the equations are obtained by the genetic algorithm (GA) method using the laboratory data. The performance of the so-developed ANN and GA based models are compared against the field data and those of the existing empirical methods, namely Bagnold, Ackers and White, and Van Rijn. The results show that ANN outperforms the empirical methods. The results also show that the expert models, calibrated with laboratory data, are capable of predicting field total loads and thus proving their transferability capability. The transferability is also investigated by a newly proposed equation which is based on the Bagnold approach. The optimal values of the coefficients of this equation are obtained by the GA. The performance of the proposed equation is found to be very efficient.

scholarly journals Coronavirus Disease Predictor: An RNA-Seq based pipeline for dimension reduction and prediction of COVID-19

2021 ◽  
Vol 2089 (1) ◽  
pp. 012025
Author(s):  
Naiyar Iqbal ◽  
Pradeep Kumar
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Dimension Reduction ◽  
Soft Computing ◽  
Principal Component ◽  
Component Analysis ◽  
Formal Concept ◽  
Genome Wide Association Studies ◽  
Rna Seq ◽  
Soft Computing Techniques

Abstract SARS CoV-2, the novel coronavirus behind the COVID-19 infection, has caused destruction around the world with human life, detecting a range of complexity which has knocked medical care specialists to investigate new innovative solutions and diagnosis strategies. The soft computing-based approach has assumed a significant role in resolving complex issues, and numerous societies have been shifted to implement and convert these innovations in response to the encounters created by the COVID-19 pandemic. To perform genome-wide association studies using RNA-Seq of COVID-19 and identify gene biomarkers, classification, and prediction using soft computing techniques of Coronavirus disease studies to fight this emergency pandemic in the epidemiological domain, and disease prognosis. The RNA-Seq profiles of both healthy and COVID-19 positive patients’ samples were considered. We have proposed an integrated pipeline from bioinformatics in-silico phase for-omic profile data processing to dimension reduction using various prominent techniques such as formal concept analysis and principal component analysis followed by machine learning phase for prediction of the disease. In this experimental research, we have applied different eminent machine learning techniques to implement an effective integrated model using Classifier Subset Evaluator (CSE) followed by principal component analysis (PCA) for dimension reduction to select the highly significant features and then to do the classification and prediction of Coronavirus disease, different eminent classifiers have been applied on the selected features. In this analysis, the Hoeffding Tree model found the topmost performance classifier with a classification accuracy of 99.21% as well as sensitivity and specificity of 99% and 100% respectively.

scholarly journals Soft Computing Based Ground Target Recognition

2020 ◽  
Vol 8 (6) ◽  
pp. 5598-5603
Keyword(s):  
Principal Component Analysis ◽  
Soft Computing ◽  
High Speed ◽  
Target Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Automatic Target Recognition ◽  
Kernel Functions ◽  
Support Vector

Target recognition from the data obtained from radars poses great challenge to manual analysis of the target with high speed and accuracy. So to overcome this challenge automatic target recognition system is developed using soft computing machine learning tool. The problem becomes more complex when the images are clicked from various angles. An automated classification scheme is proposed in this paper. Principal Component Analysis is used for feature extraction and to reduce the high dominions in the images data. It is known that principal component analysis is widely used from in various fields like space science. Support vector machine is used as a tool. All major kernel functions are applied to gain the maximum accuracy. This framework is evaluated and found effective as compared to results than other methods.

A German version of the Intermittent Claudication Questionnaire (ICQ): cultural adaptation and validation

VASA ◽  
2012 ◽  
Vol 41 (5) ◽  
pp. 333-342 ◽  
Author(s):  
Kirchberger ◽  
Finger ◽  
Müller-Bühl
Keyword(s):  
Principal Component Analysis ◽  
Intermittent Claudication ◽  
Completion Time ◽  
Short Form ◽  
Principal Component ◽  
Component Analysis ◽  
German Version ◽  
Average Completion Time ◽  
Sf 36 ◽  
Related Quality

Background: The Intermittent Claudication Questionnaire (ICQ) is a short questionnaire for the assessment of health-related quality of life (HRQOL) in patients with intermittent claudication (IC). The objective of this study was to translate the ICQ into German and to investigate the psychometric properties of the German ICQ version in patients with IC. Patients and methods: The original English version was translated using a forward-backward method. The resulting German version was reviewed by the author of the original version and an experienced clinician. Finally, it was tested for clarity with 5 German patients with IC. A sample of 81 patients were administered the German ICQ. The sample consisted of 58.0 % male patients with a median age of 71 years and a median IC duration of 36 months. Test of feasibility included completeness of questionnaires, completion time, and ratings of clarity, length and relevance. Reliability was assessed through a retest in 13 patients at 14 days, and analysis of Cronbach’s alpha for internal consistency. Construct validity was investigated using principal component analysis. Concurrent validity was assessed by correlating the ICQ scores with the Short Form 36 Health Survey (SF-36) as well as clinical measures. Results: The ICQ was completely filled in by 73 subjects (90.1 %) with an average completion time of 6.3 minutes. Cronbach’s alpha coefficient reached 0.75. Intra-class correlation for test-retest reliability was r = 0.88. Principal component analysis resulted in a 3 factor solution. The first factor explained 51.5 of the total variation and all items had loadings of at least 0.65 on it. The ICQ was significantly associated with the SF-36 and treadmill-walking distances whereas no association was found for resting ABPI. Conclusions: The German version of the ICQ demonstrated good feasibility, satisfactory reliability and good validity. Responsiveness should be investigated in further validation studies.

Multifactorial analysis of clinical laboratory signs, the levels of IL-17A, IL-23, IL-33, IL-35, and specific antibodies in the serum of patients with Lyme borreliosis without erythema migrans

2020 ◽  
Vol 4 (11) ◽  
pp. 676-681
Author(s):  
V.V. Sapozhnikova ◽  
◽  
A.L. Bondarenko ◽  
Keyword(s):  
Principal Component Analysis ◽  
Lyme Borreliosis ◽  
Clinical Laboratory ◽  
Acute Infection ◽  
Erythema Migrans ◽  
Principal Component ◽  
Component Analysis ◽  
Control Group ◽  
Multifactorial Analysis ◽  
Specific Antibodies

Aim: to determine the association between clinical laboratory parameters, the production of cytokines (IL-17A, -23, -33, -35), and specific IgM and IgG in the serum of patients with Lyme borreliosis without erythema migrans. Patients and Methods: complete blood count, the concentrations of IL-17A, -23, -33, -35, and the levels of specific IgM and IgG were measured during acute infection and convalescence (n=30). The control group included age- and sex-matched healthy individuals (n=30). Statistical analysis was performed using the StatSoft Statistica v 10.0 software (parametric and non-parametric methods and multifactorial analysis, i.e., principal component analysis). Results: most (80%) patients with Lyme borreliosis without erythema migrans are the people of working age. In most patients, the combination of the specific antibodies against Borrelia afzelii and Borrelia garinii (76.7%) and severe intoxication and inflammatory process (100%) were detected. Moderate and severe disease associated with meningism was diagnosed in 90% and 10%, respectively. The mean duration of hectic period was 8.3±1.27 days. Abnormal ECG was reported in 40% of patients, i.e., conduction abnormalities in 20%, sinus bradycardia in 16.7%,and sinus tachycardia in 3.3%. The clinical laboratory signs of hepatitis without jaundice were identified in 26.7%. During treatment, the significant reduction in band and segmented neutrophil counts as well as the significant increase in platelet count were revealed compared to these parameters at admission. Abnormal cytokine levels (i.e., the increase in IL-17A, -23, -33 and the deficiency of IL-35) were detected. Conclusions: multifactorial analysis has demonstrated that the severity of immunological abnormalities in patients with Lyme borreliosis without erythema migrans is associated with fever, cardiac and liver disorders, the high levels of IL-23 and IL-33, and the lack of IL-35 and specific IgM and IgG. KEYWORDS: tick-borne borreliosis, Lyme disease without erythema migrans, clinical laboratory signs, cytokines, specific antibodies, multifactorial analysis, principal component analysis. FOR CITATION: Sapozhnikova V.V., Bondarenko A.L. Multifactorial analysis of clinical laboratory signs, the levels of IL-17A, IL-23, IL-33, IL-35, and specific antibodies in the serum of patients with Lyme borreliosis without erythema migrans. Russian Medical Inquiry. 2020;4(11):676–681. DOI: 10.32364/2587-6821-2020-4-11-676-681.

Oxypropylation of Brazilian Pine-Fruit Shell Evaluated by Principal Component Analysis

2018 ◽  
Vol 6 (7) ◽  
pp. 715-723 ◽  
Author(s):  
Stephany C. de Rezende ◽  
Jo鉶 A. Pinto ◽  
Isabel P. Fernandes ◽  
Fernanda V. Leimann and Maria-Filomena Barreiro
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis
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