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 reasoni...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.展开更多
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 attribu...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.展开更多
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 ecologi...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.展开更多
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 ma...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.展开更多
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 subproble...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.展开更多
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 ma...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.展开更多
To ensure the safety of urban rail transit operations and uncover the transmission dynam ics of risk sources,a key risk chain mining method for urban rail transit operation is pro posed.Firstly,the H-Apriori associati...To ensure the safety of urban rail transit operations and uncover the transmission dynam ics of risk sources,a key risk chain mining method for urban rail transit operation is pro posed.Firstly,the H-Apriori association rule algorithm is proposed for the characteristics of low frequency but high riskiness of high hazard degree risk sources in urban rail transit operation,which adds a new hazard degree evaluation index to the traditional Apriori algo rithm and couples with support degree two-dimensionally to mine the strong association rules among risk sources.Secondly,we construct a weighted risk network with risk sources as network nodes and strong association rules as network edges,and propose a key risk chain mining method for urban rail transit operation based on path search theory to mine key risk chains from the weighted risk network.Finally,using the actual urban rail transit operation data of a city in China as an example,a total of 17 key risk chains are mined,and then 5 key risk sources and 8 key chain break locations are obtained by riskiness and fre quency analysis of key risk chains,and control plans are proposed.The research outcomes introduce a novel approach to mining risk chains in urban rail transit operations,shedding light on the propagation mechanisms,triggering probabilities,and degrees of unsafety associated with risk sources.The results not only provide theoretical support but also offer methodological guidance for pinpointing locations of risk chain breaks and refining the control of risk sources.展开更多
文摘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.
基金supported by the Fundamental Research Funds for the Central Universities under Grants No.ZYGX2014J051 and No.ZYGX2014J066Science and Technology Projects in Sichuan Province under Grants No.2015JY0178,No.2016FZ0002,No.2014GZ0109,No.2015KZ002 and No.2015JY0030China Postdoctoral Science Foundation under Grant No.2015M572464
文摘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.
文摘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.
文摘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.
文摘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.
文摘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.
文摘To ensure the safety of urban rail transit operations and uncover the transmission dynam ics of risk sources,a key risk chain mining method for urban rail transit operation is pro posed.Firstly,the H-Apriori association rule algorithm is proposed for the characteristics of low frequency but high riskiness of high hazard degree risk sources in urban rail transit operation,which adds a new hazard degree evaluation index to the traditional Apriori algo rithm and couples with support degree two-dimensionally to mine the strong association rules among risk sources.Secondly,we construct a weighted risk network with risk sources as network nodes and strong association rules as network edges,and propose a key risk chain mining method for urban rail transit operation based on path search theory to mine key risk chains from the weighted risk network.Finally,using the actual urban rail transit operation data of a city in China as an example,a total of 17 key risk chains are mined,and then 5 key risk sources and 8 key chain break locations are obtained by riskiness and fre quency analysis of key risk chains,and control plans are proposed.The research outcomes introduce a novel approach to mining risk chains in urban rail transit operations,shedding light on the propagation mechanisms,triggering probabilities,and degrees of unsafety associated with risk sources.The results not only provide theoretical support but also offer methodological guidance for pinpointing locations of risk chain breaks and refining the control of risk sources.
