A Survey on Trigonometric Measures of Fuzzy Information and Discrimination_Crimson Publishers

A Survey on Trigonometric Measures of Fuzzy Information and Discrimination by DS Hooda in Open Access Biostatistics & Bioinformatics

In the literature of fuzzy information measures, there exist many well-known parametric and non-parametric measures with their own merits and limitations. But our main emphasis is on applications of these information measures to a variety of disciplines. It has been observed that trigonometric measure of fuzzy information measures have their own importance for application point of view particularly to geometry. In present communication the concept of fuzzy information measure is introduced with its generalizations. Some new trigonometric measures of fuzzy information due to various authors are defined and characterized. One generalized measure of fuzzy discrimination by Hooda [1] is proposed and its application in decision making is also studied.

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Breast Cancer Prediction Using Bayesian Logistic Regression_Crimson Publishers

 Breast Cancer Prediction Using Bayesian Logistic Regression by Ashok K Singh in Open Access Biostatistics & Bioinformatics

Prediction of breast cancer based upon several features computed for each subject is a binary classification problem. Several discriminant methods exist for this problem, some of the commonly used methods are: Decision Trees, Random Forest, Neural Network, Support Vector Machine (SVM), and Logistic Regression (LR). Except for Logistic Regression, the other listed methods are predictive in nature; LR yields an explanatory model that can also be used for prediction, and for this reason it is commonly used in many disciplines including clinical research. In this article, we demonstrate the method of Bayesian LR to predict breast cancer using the Wisconsin Diagnosis Breast Cancer (WDBC) data set available at the UCI Machine Learning Repository.

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Maximum Oxygen Uptake Prediction Model Based on Heart Rate Variability Parameters for Young Healthy Adult Males at Rest_Crimson publishers

Maximum Oxygen Uptake Prediction Model Based on Heart Rate Variability Parameters for Young Healthy Adult Males at Rest by Wollner Materko in Open Access Biostatistics & Bioinformatics

The assessment of aerobic fitness through the measurement of maximum oxygen consumption (VO2 max) is an objective parameter that integrates cardiovascular, respiratory and metabolic responses, providing a reliable assessment of exercise capacity and health status, as well as being useful information for exercise training prescription. The purpose of this study was to determine a model for predicting Maximum Oxygen Uptake based on HRV parameters estimated at rest in 70 young physically active adults. After recording the resting tacho gram with a cardio-frequency meter to calculate HRV parameters, a maximal cardiopulmonary incremental test was performed to measure the VO2 max. The model for predicting VO2 max was obtained by stepwise multiple linear regression assuming as independent variables the mean RR interval, pNN50 index, and a proposed cardiac deceleration rate. The models were cross-validated by K-fold method, and the best model accounted for 76% of data variance, with a standard error of estimate 4.40mL·kg-1min-1. In conclusion, the obtained model might be tested as a tool for predicting the aerobic fitness in adult males in rest based on the mean RR interval and the pNN50HRV parameters. Thus, the findings are not only interesting but important in that they can be performed without the need of applying a stress test and extend the HRV applicability in the evaluation of aerobic capacity and athletic performance.

https://crimsonpublishers.com/oabb/fulltext/OABB.000536.php

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Introduction of Improved Maize Variety and Applying its Integrated Stem Borer Management Techniques in Ethiopian Somali Region_Crimson Publishers

Introduction of Improved Maize Variety and Applying its Integrated Stem Borer Management Techniques in Ethiopian Somali Region by Fano Dargo in Open Access Bio statistics & bioinformatics

The stem borer is one of the most destructive pests of maize crop. Research experimentations were carried out on maize to control stem borer. The experiment was 2x3 factorial experiments using randomized complete block design with ten replications. A total of two maize varieties (Melkassa hybrid 130 and local varieties) and three different stem borer management packages (Control, inter-cropping with cowpea and inter-cropping with Cowpea and Sudan Grass) were used. Data was collected on Days to maturity, Plant height, Ear length, Grain yield, Biomass yield, Harvest index, Biomass production rate and Grain yield per day. The results of analysis revealed that the interaction effect of Control mechanisms of stem borer and Maize varieties was significantly (P≤0.05) affected in all agronomic traits recorded except DTM, PH, EL and HI which is not significant. While, they were significantly (P≤0.05) affected by the maize variety. Treatment combination Improved Maize Variety (MH-130) mono-cropping gave the highest grain yield (4.70 t∙ha-1) of all the test treatment combination. While, the minimum grain yield (2.15 t∙ha-1) were observed in treatment combination Local Maize variety Mono-cropping. Grain yield had strong significant positive correlations with plant height (r=0.66**), Ear length (r=0.0.58**), Biomass yield (r=0.76**), harvest index (r=0.0.74**), biological yield rate (r=0.0.73**) and grain yield per day (r=0.96**). Based on objectively measured traits, farmers’ preferred Treatment 4 Improved Maize Variety (MH-130) mono-cropping was ranked the first (1.1), followed by Treatment 5 Improved Maize Variety (MH-130) inter-cropping with Cowpea (2.3) and Treatment 6 Improved Maize Variety (MH-130) inter-cropping with Cowpea and Sudan Grass (2.8). Therefore, it can be concluded that the use of treatment combination 4 Improved Maize Variety (MH-130) mono-cropping could be recommend for optimum yield and yield related traits of maize even though further testing is required to put the recommendation on a strong basis.

https://crimsonpublishers.com/oabb/abstract/OABB.000535.php

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