A new classification technique based on hybrid fuzzy soft set theory and supervised fuzzy c-means

Recent advances in information technology have led to significant changes in today‟s world. The generating and collecting data have been increasing rapidly. Popular use of the World Wide Web (www) as a global information system led to a tremendous amount of information, and this can be in the form of text document. This explosive growth has generated an urgent need for new techniques and automated tools that can assist us in transforming the data into more useful information and knowledge. Data mining was born for these requirements. One of the essential processes contained in the data mining is classification, which can be used to classify such text documents and utilize it in many daily useful applications. There are many classification methods, such as Bayesian, K-Nearest Neighbor, Rocchio, SVM classifier, and Soft Set Theory used to classify text document. Although those methods are quite successful, but accuracy and efficiency are still outstanding for text classification problem. This study is to propose a new approach on classification problem based on hybrid fuzzy soft set theory and supervised fuzzy c-means. It is called Hybrid Fuzzy Classifier (HFC). The HFC used the fuzzy soft set as data representation and then using the supervised fuzzy c-mean as classifier. To evaluate the performance of HFC, two well-known datasets are used i.e., 20 Newsgroups and Reuters-21578, and compared it with the performance of classic fuzzy soft set classifiers and classic text classifiers. The results show that the HFC outperforms up to 50.42% better as compared to classic fuzzy soft set classifier and up to 0.50% better as compare classic text classifier

Three dimensional finite element modeling, when drilling of Ti-6Al-4V

Finite element modeling (FEM) is widely used to optimize machining processes, to predict and analyze the cutting force, cutting temperature and other related responses. Most of the FEM studies were conducted under the two dimensional orthogonal cutting. Drilling process, which involves oblique cutting is not suitable for orthogonal cutting modelling. Therefore, an attempt to simulate a three dimensional simulation of the drilling process is required. A commercially available software called DEFORM is used to accomplish the task. The value of thrust force from the simulation is compared with the experimental results and they are both in a good agreement. Comparison of the drill temperature at TC1 and TC2 are within an error margin of 12%

Grammatical Functions and Possibilistic Reasoning for the Extraction and Representation of Semantic Knowledge in Text Documents

This study seeks to explore and develop innovative methods for the extraction of semantic knowledge from unlabelled written English documents and the representation of this knowledge using a formal mathematical expression to facilitate its use in practical applications. The first method developed in this research focuses on semantic information extraction. To perform this task, the study introduces a natural language processing (NLP) method designed to extract information-rich keywords from English sentences. The method involves initially learning a set of rules that guide the extraction of keywords from parts of sentences. Once this learning stage is completed, the method can be used to extract the keywords from complete sentences by pairing these sentences to the most similar sequence of rules. The key innovation in this method is the use of a part-of-speech hierarchy. By raising words to increasingly general grammatical categories in this hierarchy, the system can compare rules, compute the degree of similarity between them, and learn new rules. The second method developed in this study addresses the problem of knowledge representation. This method processes triplets of keywords through several successive steps to represent information contained in the triplets using possibility distributions. These distributions represent the possibility of a topic given a particular triplet of keywords. Using this methodology, the information contained in the natural language triplets can be quantified and represented in a mathematical format, which can be easily used in a number of applications, such as document classifiers. In further extensions to the research, a theoretical justification and mathematical development for both methods are provided, and examples are given to illustrate these notions. Sample applications are also developed based on these methods, and the experimental results generated through these implementations are expounded and thoroughly analyzed to confirm that the methods are reliable in practice

A Fuzzy Similarity Approach in Text Classification Task

We present a fuzzy similarity approach to solve a text categorization problem. The effectiveness of various fuzzy conjunction and disjunction operators used in fuzzy similarity formula and several document representations were evaluated using test sets from three text document collections. Based on empirical results obtained from using these collections, a special case of the fuzzy similarity formula performs very well