Mathematical Foundation of Basic Algorithms of Fuzzy Reasoning

Algorithm of fuzzy reasoning has been successful applied in fuzzy control,but its theoretical foundation of algorithms has not been thoroughly investigated. In this paper,structure of basic algorithms of fuzzy reasoning was studied, its rationality was discussed from the viewpoint of logic and mathematics, and three theorems were proved. These theorems shows that there always exists a mathe-~matical relation (that is, a bounded real function) between the premises and the conclusion for fuzzy reasoning, and in fact various algorithms of fuzzy reasoning are specific forms of this function. Thus these results show that algorithms of fuzzy reasoning are theoretically reliable.

Mining Frequent Sets Using Fuzzy Multiple-Level Association Rules

At present, most of the association rules algorithms are based on the Boolean attribute and single-level association rules mining. But data of the real world has various types, the multi-level and quantitative attributes are got more and more attention. And the most important step is to mine frequent sets. In this paper, we propose an algorithm that is called fuzzy multiple-level association （FMA） rules to mine frequent sets. It is based on the improved Eclat algorithm that is different to many researchers’ proposed algorithms thatused the Apriori algorithm. We analyze quantitative data’s frequent sets by using the fuzzy theory, dividing the hierarchy of concept and softening the boundary of attributes’ values and frequency. In this paper, we use the vertical-style data and the improved Eclat algorithm to describe the proposed method, we use this algorithm to analyze the data of Beijing logistics route. Experiments show that the algorithm has a good performance, it has better effectiveness and high efficiency.

A NEW TECHNIQUE FOR PREDICTING DISTRIBUTION OF TERRESTRIAL VERTEBRATES USING INFERENTIAL MODELING

A new technique for predicting species' geographic distribution is described.The approach involves 3 steps:①setting up geographic base data;②collecting and georeferencing distributional points;③modeling ecological niches using the biodiversity species workshop implementation of the genetic algorithm for rule set prediction (GARP).To illustrate these procedures,an example based on the Brown Eared Pheasant (Crossoptilon mantchuricum) is developed.This technique constitutes a useful tool for assessing geographic distribution for questions of ecology,biogeography,systematics,and conservation biology.

Development of anti-phishing browser based on random forest and rule of extraction framework

Phishing is a technique under Social Engineering attacks which is most widely used to get user sensitive information,such as login credentials and credit and debit card information,etc.It is carried out by a person masquerading as an authentic individual.To protect web users from these attacks,various anti-phishing techniques are developed,but they fail to protect the user from these attacks in various ways.In this paper,we propose a novel technique to identify phishing websites effortlessly on the client side by proposing a novel browser architecture.In this system,we use the rule of extraction framework to extract the properties or features of a website using the URL only.This list consists of 30 different properties of a URL,which will later be used by the Random Forest Classification machine learning model to detect the authenticity of the website.A dataset consisting of 11,055 tuples is used to train the model.These processes are carried out on the client-side with the help of a redesigned browser architecture.Today Researches have come up with machine learning frameworks to detect phishing sites,but they are not in a state to be used by individuals having no technical knowledge.To make sure that these tools are accessible to every individual,we have improvised and introduced detection methods into the browser architecture named as‘Embedded Phishing Detection Browser’(EPDB),which is a novel method to preserve the existing user experience while improving the security.The newly designed browser architecture introduces a special segment to perform phishing detection operations in real-time.We have prototyped this technique to ensure maximum security,better accuracy of 99.36%in the identification of phishing websites in realtime.

Importance of retrieving noun phrases and named entities from digital library content

We present a novel approach for extracting noun phrases in general and named entities in particular from a digital repository of text documents.The problem of coreference resolution has been divided into two subproblems:pronoun resolution and non-pronominal resolution.A rule based-technique was used for pronoun resolution while a learning approach for nonpronominal resolution.For named entity resolution,disambiguation arises mainly due to polysemy and synonymy.The proposed approach fixes both problems with the help of WordNet and the Word Sense Disambiguation tool.The proposed approach,to our knowledge,outperforms several baseline techniques with a higher balanced F-measure,which is harmonic mean of recall and precision.The improvements in the system performance are due to the filtering of antecedents for the anaphor based on several linguistic disagreements,use of a hybrid approach,and increment in the feature vector to include more linguistic details in the learning technique.

Development of anti-phishing browser based on random forest and rule of extraction framework

Phishing is a technique under Social Engineering attacks which is most widely used to get user sensitive information,such as login credentials and credit and debit card information,etc.It is carried out by a person masquerading as an authentic individual.To protect web users from these attacks,various anti-phishing techniques are developed,but they fail to protect the user from these attacks in various ways.In this paper,we propose a novel technique to identify phishing websites effortlessly on the client side by proposing a novel browser architecture.In this system,we use the rule of extraction framework to extract the properties or features of a website using the URL only.This list consists of 30 different properties of a URL,which will later be used by the Random Forest Classification machine learning model to detect the authenticity of the website.A dataset consisting of 11,055 tuples is used to train the model.These processes are carried out on the client-side with the help of a redesigned browser architecture.Today Researches have come up with machine learning frameworks to detect phishing sites,but they are not in a state to be used by individuals having no technical knowledge.To make sure that these tools are accessible to every individual,we have improvised and introduced detection methods into the browser architecture named as‘Embedded Phishing Detection Browser’(EPDB),which is a novel method to preserve the existing user experience while improving the security.The newly designed browser architecture introduces a special segment to perform phishing detection operations in real-time.We have prototyped this technique to ensure maximum security,better accuracy of 99.36% in the identification of phishing websites in realtime.