CALCULATION OF THE MINIMUM ENERGY VALUES OF THE THEORETICAL AND EXPERIMENTAL DATA BELONGING TO CANDIDATE SCIENCE TEACHERS ON THE SUBJECT OF PROCEDURAL KNOWLEDGE OF ELECTRICITY

In this study, we calculate the minimum energy values of candidate science teachers’ knowledge on the subject of electricity using 11 open-ended questions to measure their procedural knowledge. The goal is to enhance the teaching processes of candidate teachers by calculating the minimum amounts of energy that they consume, do not consume, and are expected to consume in the process of converting data into knowledge. It is important to know the energy that the people in the training process are spending or willing to spend, especially in getting information and measurement-evaluation. This energy will be calculated by information theories. In these calculations, energy equality of a biological unit will be used. The "bit" value in the energy calculations of the information theories will be determined by the VDOIHI statistical method. We find that candidate teachers’ energy consumption is focused on success, and that they should consume more energy in independent variables to ensure the permanence of this success by converting knowledge into understanding. Efficiency is of primary importance in energy planning, and can be enhanced in problem solving techniques by developing methods in accordance with energy plans that prescribe the volume of energy to be consumed in independent variables.  Article visualizations

Generalizations of rough sets via topology

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.In this paper, we present a general framework for the study of rough sets using topological approaches. First,we introduce several concepts and properties of τ−R-open sets. After that, we used topology to generalize the basic rough set concepts and study their properties. It’s application in data reduction and decision analysis is investigated. Finally, a simple example is adopted to demonstrate the effectiveness of the proposed models

不完全な情報システムのためのラフ集合モデルと知識獲得

国立大学法人長岡技術科学大

Pharmacovigilance Decision Support : The value of Disproportionality Analysis Signal Detection Methods, the development and testing of Covariability Techniques, and the importance of Ontology

The cost of adverse drug reactions to society in the form of deaths, chronic illness, foetal malformation, and many other effects is quite significant. For example, in the United States of America, adverse reactions to prescribed drugs is around the fourth leading cause of death. The reporting of adverse drug reactions is spontaneous and voluntary in Australia. Many methods that have been used for the analysis of adverse drug reaction data, mostly using a statistical approach as a basis for clinical analysis in drug safety surveillance decision support. This thesis examines new approaches that may be used in the analysis of drug safety data. These methods differ significantly from the statistical methods in that they utilize co variability methods of association to define drug-reaction relationships. Co variability algorithms were developed in collaboration with Musa Mammadov to discover drugs associated with adverse reactions and possible drug-drug interactions. This method uses the system organ class (SOC) classification in the Australian Adverse Drug Reaction Advisory Committee (ADRAC) data to stratify reactions. The text categorization algorithm BoosTexter was found to work with the same drug safety data and its performance and modus operandi was compared to our algorithms. These alternative methods were compared to a standard disproportionality analysis methods for signal detection in drug safety data including the Bayesean mulit-item gamma Poisson shrinker (MGPS), which was found to have a problem with similar reaction terms in a report and innocent by-stander drugs. A classification of drug terms was made using the anatomical-therapeutic-chemical classification (ATC) codes. This reduced the number of drug variables from 5081 drug terms to 14 main drug classes. The ATC classification is structured into a hierarchy of five levels. Exploitation of the ATC hierarchy allows the drug safety data to be stratified in such a way as to make them accessible to powerful existing tools. A data mining method that uses association rules, which groups them on the basis of content, was used as a basis for applying the ATC and SOC ontologies to ADRAC data. This allows different views of these associations (even very rare ones). A signal detection method was developed using these association rules, which also incorporates critical reaction terms.Doctor of Philosoph