13,910 research outputs found
Evaluation of IoT-Based Computational Intelligence Tools for DNA Sequence Analysis in Bioinformatics
In contemporary age, Computational Intelligence (CI) performs an essential
role in the interpretation of big biological data considering that it could
provide all of the molecular biology and DNA sequencing computations. For this
purpose, many researchers have attempted to implement different tools in this
field and have competed aggressively. Hence, determining the best of them among
the enormous number of available tools is not an easy task, selecting the one
which accomplishes big data in the concise time and with no error can
significantly improve the scientist's contribution in the bioinformatics field.
This study uses different analysis and methods such as Fuzzy, Dempster-Shafer,
Murphy and Entropy Shannon to provide the most significant and reliable
evaluation of IoT-based computational intelligence tools for DNA sequence
analysis. The outcomes of this study can be advantageous to the bioinformatics
community, researchers and experts in big biological data
Algorithms to Detect and Rectify Multiplicative and Ordinal Inconsistencies of Fuzzy Preference Relations
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Consistency, multiplicative and ordinal, of fuzzy preference relations (FPRs) is investigated. The geometric consistency index (GCI) approximated thresholds are extended to measure the degree of consistency for an FPR. For inconsistent FPRs, two algorithms are devised (1) to find the multiplicative inconsistent elements, and (2) to detect the ordinal inconsistent elements. An integrated algorithm is proposed to improve simultaneously the ordinal and multiplicative consistencies. Some examples, comparative analysis, and simulation experiments are provided to demonstrate the effectiveness of the proposed methods
Measuring Technical Efficiency of Dairy Farms with Imprecise Data: A Fuzzy Data Envelopment Analysis Approach
This article integrates fuzzy set theory in Data Envelopment Analysis (DEA) framework to compute technical efficiency scores when input and output data are imprecise. The underlying assumption in convectional DEA is that inputs and outputs data are measured with precision. However, production agriculture takes place in an uncertain environment and, in some situations, input and output data may be imprecise. We present an approach of measuring efficiency when data is known to lie within specified intervals and empirically illustrate this approach using a group of 34 dairy producers in Pennsylvania. Compared to the convectional DEA scores that are point estimates, the computed fuzzy efficiency scores allow the decision maker to trace the performance of a decision-making unit at different possibility levels.fuzzy set theory, Data Envelopment Analysis, membership function, α-cut level, technical efficiency, Farm Management, Production Economics, Productivity Analysis, Research Methods/ Statistical Methods, Risk and Uncertainty, D24, Q12, C02, C44, C61,
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