Classification with Fuzzification Optimization Combining Fuzzy Information Systems and Type-2 Fuzzy Inference

In this research, we introduce a classification procedure based on rule induction and fuzzy reasoning. The classifier generalizes attribute information to handle uncertainty, which often occurs in real data. To induce fuzzy rules, we define the corresponding fuzzy information system. A transformation of the derived rules into interval type-2 fuzzy rules is provided as well. The fuzzification applied is optimized with respect to the footprint of uncertainty of the corresponding type-2 fuzzy sets. The classification process is related to a Mamdani type fuzzy inference. The method proposed was evaluated by the F-score measure on benchmark data.

Values and Consequences: Frames for Korean Reunification

Using the results of detailed surveys conducted by National Institute for Korean Unification in 2018 and 2019 with representative national samples of South Koreans, we argue that value-derived rules and consequences of policies are useful for conceptualizing different types of attitudes toward Korean unification.

A General Principle of Isomorphism: Determining Inverses

The problem of determining inverses for maps in commutative diagrams arising in various problems of a new paradigm in algebraic system theory based on a single principle—the general principle of isomorphism is considered. Based on the previously formulated and proven theorem of realization, the rules for determining the inverses for typical cases of specifying commutative diagrams are derived. Simple examples of calculating the matrix maps inverses, which illustrate both the derived rules and the principle of relativity in algebra based on the theorem of realization, are given. The examples also illustrate the emergence of new properties (emergence) in maps in commutative diagrams modeling (realizing) the corresponding systems.

Data mining on Chinese train accidents to derive associated rules

The costs of fatalities and injuries from train accidents have a great impact on society. As part of our effort to understand the characteristics of past train accidents, this paper presents an analysis of significant train accidents occurring in China from 1954 to 2014. Rough set theory and associated rules approaches are applied in analyzing the collected data. The results show that although most derived rules are unique, some rules are worth noting. Collision accidents generally lead to more casualties than derailment accidents, and the most frequent cause of accidents is human error. Additionally, most train accidents occur during summer. These findings can provide railway leaders with lessons and rules learned from past accidents, thus facilitating the establishment of a safer railway operation environment in China.

Specificity Principles in RNA-Guided Targeting

ABSTRACTRNA-guided nucleases (RGNs) provide sequence-specific gene regulation through base-pairing interactions between a small RNA guide and target RNA or DNA. RGN systems, which include CRISPR-Cas9 and RNA interference (RNAi), hold tremendous promise as programmable tools for engineering and therapeutic purposes. However, pervasive targeting of sequences that closely resemble the intended target has remained a major challenge, limiting the reliability and interpretation of RGN activity and the range of possible applications. Efforts to reduce off-target activity and enhance RGN specificity have led to a collection of empirically derived rules, which often paradoxically include decreased binding affinity of the RNA-guided nuclease to its target. Here we demonstrate that simple kinetic considerations of the targeting reaction can explain these and other literature observations. The kinetic models described provide a foundation for understanding RGN systems and a necessary physical and functional framework for their rational engineering.

WordNet and Cosine Similarity based Classifier of Exam Questions using Bloom’s Taxonomy

Assessment usually plays an indispensable role in the education and it is the prime indicator of student learning achievement. Exam questions are the main form of assessment used in learning. Setting appropriate exam questions to achieve the desired outcome of the course is a challenging work for the examiner. Therefore this research is mainly focused to categorize the exam questions automatically into its learning levels using Bloom’s taxonomy. Natural Language Processing (NLP) techniques such as tokenization, stop word removal, lemmatization and tagging were used before generating the rule set to be used for this classification. WordNet similarity algorithms with NLTK and cosine similarity algorithm were developed to generate a unique set of rules to identify the question category and the weight for each exam question according to Bloom’s taxonomy. These derived rules make it easy to analyze the exam questions. Evaluators can redesign their exam papers based on the outcome of the evaluation process. A sample of examination questions of the Department of Computing and Information Systems, Wayamba University, Sri Lanka was used for the evaluation; weight assignment was done based on the total value generated from both WordNet algorithm and the cosine algorithm. Identified question categories were confirmed by a domain expert. The generated rule set indicated over 70% accuracy.

The Analysis of the Stability of Elastic Bending Simply Supported Beam Steel Artifacts under High Temperature

This paper applicate steel bending member in-plane is bending performance calculation formula that at the normal temperature steel structure under the action of uniformly distributed load and derived rules of calculation method of in-plane bending performance that steel bending member under the high temperature , the steel bending member in the practical example should be analyzed by the finite element software ANSYS, through the analysis and verify the accuracy of the calculation method that the steel structure bending member under uniform distributed load in-plane bending performance