Ensemble learning for anomaly detection of data structured into a complex network has been barely studied due to the inconsistent performance of complex network characteristics and the lack of inherent objective funct...Ensemble learning for anomaly detection of data structured into a complex network has been barely studied due to the inconsistent performance of complex network characteristics and the lack of inherent objective function. We propose the intuitionistic fuzzy set（IFS）-based anomaly detection, a new two-phase ensemble method for anomaly detection based on IFS, and apply it to the abnormal behavior detection problem in temporal complex networks.Firstly, it constructs the IFS of a single network characteristic, which quantifies the degree of membership,non-membership and hesitation of each network characteristic to the defined linguistic variables so that makes the unuseful or noise characteristics become part of the detection. To build an objective intuitionistic fuzzy relationship, we propose a Gaussian distribution-based membership function which gives a variable hesitation degree. Then, for the fuzzification of multiple network characteristics, the intuitionistic fuzzy weighted geometric operator is adopted to fuse multiple IFSs and to avoid the inconsistence of multiple characteristics. Finally, the score function and precision function are used to sort the fused IFS. Finally, we carry out extensive experiments on several complex network datasets for anomaly detection, and the results demonstrate the superiority of our method to state-of-the-art approaches, validating the effectiveness of our method.展开更多
Time-stamped data is fast and constantly growing and it contains significant information thanks to the quick development ofmanagement platforms and systems based on the Internet and cutting-edge information communicat...Time-stamped data is fast and constantly growing and it contains significant information thanks to the quick development ofmanagement platforms and systems based on the Internet and cutting-edge information communication technologies.Mining the time series data including time series prediction has many practical applications.Many new techniques were developed for use with various types of time series data in the prediction problem.Among those,this work suggests a unique strategy to enhance predicting quality on time-series datasets that the timecycle matters by fusing deep learning methods with fuzzy theory.In order to increase forecasting accuracy on such type of time-series data,this study proposes integrating deep learning approaches with fuzzy logic.Particularly,it combines the long short-termmemory network with the complex fuzzy set theory to create an innovative complex fuzzy long short-term memory model(CFLSTM).The proposed model adds a meaningful representation of the time cycle element thanks to a complex fuzzy set to advance the deep learning long short-term memory(LSTM)technique to have greater power for processing time series data.Experiments on standard common data sets and real-world data sets published in the UCI Machine Learning Repository demonstrated the proposedmodel’s utility compared to other well-known forecasting models.The results of the comparisons supported the applicability of our proposed strategy for forecasting time series data.展开更多
A novel model termed a bipolar complex fuzzy N-soft set(BCFN-SS)is initiated for tackling information that involves positive and negative aspects,the second dimension,and parameterised grading simultaneously.The theor...A novel model termed a bipolar complex fuzzy N-soft set(BCFN-SS)is initiated for tackling information that involves positive and negative aspects,the second dimension,and parameterised grading simultaneously.The theory of BCFN-SS is the generalisation of two various theories,that is,bipolar complex fuzzy(BCF)and N-SS.The invented model of BCFN-SS helps decision-makers to cope with the genuine-life dilemmas containing BCF information along with parameterised grading at the same time.Further,various algebraic operations,including the usual type of union,intersection,complements,and a few others types,are invented.Certain primary operational laws for BCFNSS are also invented.Moreover,a technique for order preference by similarity to the ideal solution(TOPSIS)approach is devised in the setting of BCFN-SS for managing strategic decision-making(DM)dilemmas containing BCFN-SS information.Keeping in mind the usefulness and benefits of the TOPSIS approach,two various types of TOPSIS approaches in the environment of BCFN-SS are devised and then a numerical example for exposing the usefulness of the devised TOPSIS approach is interpreted.To disclose the prominence and benefits of the devised work,the devised approaches with numerous prevailing work are compared.展开更多
Supply chain management is an essential part of an organisation's sustainable programme.Understanding the concentration of natural environment,public,and economic influence and feasibility of your suppliers and pu...Supply chain management is an essential part of an organisation's sustainable programme.Understanding the concentration of natural environment,public,and economic influence and feasibility of your suppliers and purchasers is becoming progressively familiar as all industries are moving towards a massive sustainable potential.To handle such sort of developments in supply chain management the involvement of fuzzy settings and their generalisations is playing an important role.Keeping in mind this role,the aim of this study is to analyse the role and involvement of complex q-rung orthopair normal fuzzy(CQRONF)information in supply chain management.The major impact of this theory is to analyse the notion of confidence CQRONF weighted averaging,confidence CQRONF ordered weighted averaging,confidence CQRONF hybrid averaging,confidence CQRONF weighted geometric,confidence CQRONF ordered weighted geometric,confidence CQRONF hybrid geometric operators and try to diagnose various properties and results.Furthermore,with the help of the CRITIC and VIKOR models,we diagnosed the novel theory of the CQRONF-CRITIC-VIKOR model to check the sensitivity analysis of the initiated method.Moreover,in the availability of diagnosed operators,we constructed a multi-attribute decision-making tool for finding a beneficial sustainable supplier to handle complex dilemmas.Finally,the initiated operator's efficiency is proved by comparative analysis.展开更多
The evaluation of the electricity market is crucial for fostering market construction and development.An accurate assessment of the electricity market reveals developmental trends,identifies operational issues,and con...The evaluation of the electricity market is crucial for fostering market construction and development.An accurate assessment of the electricity market reveals developmental trends,identifies operational issues,and contributes to stable and healthy market growth.This study investigated the characteristics of electricity markets in different provinces and synthesized a comprehensive set of evaluation indicators to assess market effectiveness.The evaluation framework,comprising nine indicators organized into two tiers,was constructed based on three aspects:market design,market efficiency,and developmental coordination.Furthermore,a novel fuzzy multi-criteria decision-making evaluation model for electricity market performance was developed based on the Fuzzy-BWM and fuzzy COPRAS methodologies.This model aimed to ensure both accuracy and comprehensiveness in market operation assessment.Subsequently,empirical analyses were conducted on four typical provincial-level electricity markets in China.The results indicate that Guangdong’s electricity market performed best because of its effective balance of stakeholder interests and adherence to contractual integrity principles.Zhejiang and Shandong ranked second and third,respectively,whereas Sichuan exhibited the poorest market performance.Sichuan’s electricity market must be improved in terms of market design,such that market players can obtain a fairly competitive environment.The sensitivity analysis of the constructed indicators verified the effectiveness of the evaluation model proposed in this study.Finally,policy recommendations were proposed to facilitate the sustainable development of China’s electricity markets with the objective of transforming them into efficient and secure markets adaptable to the evolution of novel power systems.展开更多
The photovoltaic grid-connected inverter is an important interface between the photovoltaic power generation system and power grid.Its high-quality operation is directly related to the output power quality of the powe...The photovoltaic grid-connected inverter is an important interface between the photovoltaic power generation system and power grid.Its high-quality operation is directly related to the output power quality of the power grid.In order to further optimize the control effect of the quasi-Z source grid-connected photovoltaic inverter,a fuzzy proportional complex integral control(PCI)method is proposed for the current internal loop control.This method can eliminate the steady-state error,and has the characteristic of zero steady-state error adjustment for the AC disturbance signal of a specific frequency.The inductance-capacitance-inductance(LCL)filter is adopted in the grid-connected circuit,and the feedback capacitive current is taken as the control variable of the inner loop to form the active damping control method,which can not only effectively suppress the resonance of the LCL circuit,but also significantly inhibit the high-order harmonics in the grid-connected current.Finally,a system simulation model is built in MATLAB/Simulink to verify the superiority and effectiveness of the proposed method.展开更多
Purpose-Gathering,analyzing and securing electronic data from various digital devices for use in legal or investigative procedures is the key process of computer forensics.Information retrieved from servers,hard drive...Purpose-Gathering,analyzing and securing electronic data from various digital devices for use in legal or investigative procedures is the key process of computer forensics.Information retrieved from servers,hard drives,cellphones,tablets and other devices is all included in this.This article tackles the challenging problem of how to prioritize different kinds of computer forensics and figure out which kind is most useful in cases of cybercrime,fraud,theft of intellectual property,harassment and espionage.Design/methodology/approach-Therefore,we first introduce enhanced versions of Hamacher power aggregation operators(AOs)within the framework of bipolar complex fuzzy(BCF)sets.These include BCF Hamacher power averaging(BCFHPA),BCF Hamacher power-weighted averaging(BCFHPWA),BCF Hamacher power-ordered weighted averaging(BCFHPOWA),BCF Hamacher power geometric(BCFHPG),BCF Hamacher power-weighted geometric(BCFHPWG)and BCF Hamacher power-ordered-weighted geometric(BCFHPOWG)operators.Employing the devised AOs,we devise a technique of decision-making(DM)for dealing with DM dilemmas with the BCF set(BCFS).Findings-We prioritize different types of computer forensic by taking artificial data in a numerical example and getting the finest computer forensic.Further,by this example,we reveal the applicability of the proposed theory.This work provides a more elaborate and versatile procedure for classifying computer forensics with dual aspects of criteria and extra fuzzy information.It allows for better and less biased DM in the more intricate digital investigations,which may lead to better DM and time-saving in real-life forensic scenarios.To demonstrate the significance and impression of the devised operators and techniques of DM,they are compared with existing ones.Originality/value-This research is the first to combine Hamacher and power AOs in BCFS for computer forensics DM.It presents new operators and a DM approach that is not encountered in the existing literature and is specifically designed to deal with the challenges and risks associated with the classification of computer forensics.The framework’s capacity to accommodate bipolar criteria and extra fuzzy information is a major development in the field of digital forensics and decision science.展开更多
This paper introduces the concept of semi-continuity of complex fuzzy functions, and discusses some of their elementary properties, such as the sum of two complex fuzzy functions of type I upper (lower) semi-continui...This paper introduces the concept of semi-continuity of complex fuzzy functions, and discusses some of their elementary properties, such as the sum of two complex fuzzy functions of type I upper (lower) semi-continuity is type I upper (lower) semi-continuous, and the opposite of complex fuzzy functions of type I upper (lower) semi-continuity is type I lower (upper) semi-continuous. Based on some assumptions on two complex fuzzy functions of type I upper (lower) semi-continuity, it is shown that their product is type I upper (lower) semi-continuous. The paper also investigates the convergence of complex fuzzy functions. In particular, sign theorem, boundedness theorem, and Cauchy's criterion for convergence are kept. In this paper the metrics introduced by Zhang Guangquan was used. This paper gives a contribution to the study of complex fuzzy functions, and extends the corresponding work of Zhang Guangquan.展开更多
Land suitability analysis(LSA)is an evaluation method that measures the degree to which land is suitable for certain land use.The primary aims of this study are to identify potentially viable agricultural land in the ...Land suitability analysis(LSA)is an evaluation method that measures the degree to which land is suitable for certain land use.The primary aims of this study are to identify potentially viable agricultural land in the Gangarampur subdivision(West Bengal)using Multiple Criteria Decision Making(MCDM)and machine learning procedures and to evaluate the efficacy of the employed methodologies.The Analytic Hierarchy Process(AHP)model was used to assign relative weights to the fifteen various criteria in this suitability analysis,and then the Fuzzy Complex Proportional Assessment(FCOPRAS)model was applied using the AHP’s normalised pairwise comparison matrix,whereas the Waikato Environment for Knowledge Analysis(Weka)Software was used to apply machine learning algorithms to the field data.The Random Forest(RF)model,on the other hand,is a better fit for the locational study of soil potential.According to the RF findings,areas of 14.67 per cent(15368.46 ha)are excellent(ZONE Ⅴ)for growing crops,approximately 22.30 per cent(23367.9 ha)are highly suitable(ZONE Ⅳ),and 23.63 per cent(24762.12 ha)are moderately suitable(ZONE Ⅲ)for cultivation,respectively.The numbers for FCOPRAS are roughly 15.39%(16130.52 ha),22.54%(23620.65 ha),and 19.79%(20733.26 ha).The Receiver Operating Characteristic(ROC)curve and accuracy measurements of the results indicate the high accuracy of the applied models,with Random Forest and FCOPRAS being the most popular and effective techniques.This study will make an important contribution to evaluations of soil fertility and site suitability.This will help local government officials,academics,and farmers scientifically use the land.展开更多
OBJECTIVE:To establish a quantification model of Traditional Chinese Medicine(TCM)syndromes by sampling patients undergoing idiopathic precocious puberty(IPP)and early puberty.METHODS:A questionnaire for classifying a...OBJECTIVE:To establish a quantification model of Traditional Chinese Medicine(TCM)syndromes by sampling patients undergoing idiopathic precocious puberty(IPP)and early puberty.METHODS:A questionnaire for classifying and quantifying TCM syndromes was designed and administered.All the results were analyzed;the relationship between 3 types of syndrome and 47symptoms were summated.Meanwhile,the frequency distribution of each symptom or sign was aggregated.Fuzzy mathematics was used to develop a quantification model ofTCM syndromes.RESULTS:We found that precocious puberty had 3types of syndrome,including hyperactivity of fire due to Yin deficiency(Syndrome I),depressed liver Qi transforming into fire(Syndrome II),and end retention of damp heat(Syndrome III).In the IPP group,Syndrome I was the most common principal syndrome(100%).Forty-six patients(43.81%)werediagnosed with Syndrome I accompanied by Syndrome II and 11(10.48%)were diagnosed with Syndrome I accompanied by Syndrome III.In the early puberty group,Syndrome I was also the main syndrome(98.39%).The degrees of most symptoms were mild to moderate.Reddened tongue was the most common tongue manifestation(62.86%prevalence)in the IPP group.The most common pulse manifestations were slippery pulse,thread pulse,and taut pulse.The Asymptotic Normalization Coefficient(ANC)method was used to quantify the TCM syndromes in 167 cases.Diagnostic accuracy rate reached 91%,comparable to expert diagnosis.CONCLUSION:We find that there are 3 types of syndrome in the IPP group and in the early puberty group.Syndrome I(hyperactivity of fire due to Yin deficiency)is the main syndrome in the two groups.ANC may be an appropriate for quantification model ofTCM syndromes.展开更多
Purpose-This research focuses on a very important research question of determining the appropriate feature selection methods for software defect prediction.The study is centered on the creation of a new method that wo...Purpose-This research focuses on a very important research question of determining the appropriate feature selection methods for software defect prediction.The study is centered on the creation of a new method that would enable the identification of both positive and negative selection criteria and the handling of ambiguous information in the decision-making process.Design/methodology/approach-To do so,we develop an improved method by extending the WASPAS assessment in the context of bipolar complex fuzzy sets,which leads to the bipolar complex fuzzy WASPAS method.The approach also uses Einstein operators to increase the accuracy of aggregation and manage complicated decision-making parameters.The methodology is designed for the processing of multi-criteria decision-making problems where criteria have positive and negative polarities as well as other ambiguous information.Findings-It is also shown that the proposed methodology outperforms the traditional weighted sum or product models when assessing feature selection methods.The incorporation of bipolar complex fuzzy sets with WASPAS improves the assessment of selection criteria by taking into account both positive and negative aspects of the criteria,which contributes to more accurate feature selection for software defect prediction.We investigate a case study related to the identification of feature selection techniques for software defect prediction by using the bipolar complex fuzzy WASPAS methodology.We compare the proposed methodology with certain prevailing ones to reveal the supremacy and the requirements of the proposed theory.Originality/value-This research offers the first integrated framework for handling bipolarity and uncertainty in feature selection for software defect prediction.The combination of Einstein operators with bipolar complex fuzzy sets improves the DM process,which will be useful for software engineers and help them select the best feature selection techniques.This work also helps to enhance the overall performance of software defect prediction systems.展开更多
基金Supported by the National Natural Science Foundation of China under Grant No 61671142the Fundamental Research Funds for the Central Universities under Grant No 02190022117021
文摘Ensemble learning for anomaly detection of data structured into a complex network has been barely studied due to the inconsistent performance of complex network characteristics and the lack of inherent objective function. We propose the intuitionistic fuzzy set（IFS）-based anomaly detection, a new two-phase ensemble method for anomaly detection based on IFS, and apply it to the abnormal behavior detection problem in temporal complex networks.Firstly, it constructs the IFS of a single network characteristic, which quantifies the degree of membership,non-membership and hesitation of each network characteristic to the defined linguistic variables so that makes the unuseful or noise characteristics become part of the detection. To build an objective intuitionistic fuzzy relationship, we propose a Gaussian distribution-based membership function which gives a variable hesitation degree. Then, for the fuzzification of multiple network characteristics, the intuitionistic fuzzy weighted geometric operator is adopted to fuse multiple IFSs and to avoid the inconsistence of multiple characteristics. Finally, the score function and precision function are used to sort the fused IFS. Finally, we carry out extensive experiments on several complex network datasets for anomaly detection, and the results demonstrate the superiority of our method to state-of-the-art approaches, validating the effectiveness of our method.
基金funded by the Research Project:THTETN.05/23-24,Vietnam Academy of Science and Technology.
文摘Time-stamped data is fast and constantly growing and it contains significant information thanks to the quick development ofmanagement platforms and systems based on the Internet and cutting-edge information communication technologies.Mining the time series data including time series prediction has many practical applications.Many new techniques were developed for use with various types of time series data in the prediction problem.Among those,this work suggests a unique strategy to enhance predicting quality on time-series datasets that the timecycle matters by fusing deep learning methods with fuzzy theory.In order to increase forecasting accuracy on such type of time-series data,this study proposes integrating deep learning approaches with fuzzy logic.Particularly,it combines the long short-termmemory network with the complex fuzzy set theory to create an innovative complex fuzzy long short-term memory model(CFLSTM).The proposed model adds a meaningful representation of the time cycle element thanks to a complex fuzzy set to advance the deep learning long short-term memory(LSTM)technique to have greater power for processing time series data.Experiments on standard common data sets and real-world data sets published in the UCI Machine Learning Repository demonstrated the proposedmodel’s utility compared to other well-known forecasting models.The results of the comparisons supported the applicability of our proposed strategy for forecasting time series data.
文摘A novel model termed a bipolar complex fuzzy N-soft set(BCFN-SS)is initiated for tackling information that involves positive and negative aspects,the second dimension,and parameterised grading simultaneously.The theory of BCFN-SS is the generalisation of two various theories,that is,bipolar complex fuzzy(BCF)and N-SS.The invented model of BCFN-SS helps decision-makers to cope with the genuine-life dilemmas containing BCF information along with parameterised grading at the same time.Further,various algebraic operations,including the usual type of union,intersection,complements,and a few others types,are invented.Certain primary operational laws for BCFNSS are also invented.Moreover,a technique for order preference by similarity to the ideal solution(TOPSIS)approach is devised in the setting of BCFN-SS for managing strategic decision-making(DM)dilemmas containing BCFN-SS information.Keeping in mind the usefulness and benefits of the TOPSIS approach,two various types of TOPSIS approaches in the environment of BCFN-SS are devised and then a numerical example for exposing the usefulness of the devised TOPSIS approach is interpreted.To disclose the prominence and benefits of the devised work,the devised approaches with numerous prevailing work are compared.
文摘Supply chain management is an essential part of an organisation's sustainable programme.Understanding the concentration of natural environment,public,and economic influence and feasibility of your suppliers and purchasers is becoming progressively familiar as all industries are moving towards a massive sustainable potential.To handle such sort of developments in supply chain management the involvement of fuzzy settings and their generalisations is playing an important role.Keeping in mind this role,the aim of this study is to analyse the role and involvement of complex q-rung orthopair normal fuzzy(CQRONF)information in supply chain management.The major impact of this theory is to analyse the notion of confidence CQRONF weighted averaging,confidence CQRONF ordered weighted averaging,confidence CQRONF hybrid averaging,confidence CQRONF weighted geometric,confidence CQRONF ordered weighted geometric,confidence CQRONF hybrid geometric operators and try to diagnose various properties and results.Furthermore,with the help of the CRITIC and VIKOR models,we diagnosed the novel theory of the CQRONF-CRITIC-VIKOR model to check the sensitivity analysis of the initiated method.Moreover,in the availability of diagnosed operators,we constructed a multi-attribute decision-making tool for finding a beneficial sustainable supplier to handle complex dilemmas.Finally,the initiated operator's efficiency is proved by comparative analysis.
文摘The evaluation of the electricity market is crucial for fostering market construction and development.An accurate assessment of the electricity market reveals developmental trends,identifies operational issues,and contributes to stable and healthy market growth.This study investigated the characteristics of electricity markets in different provinces and synthesized a comprehensive set of evaluation indicators to assess market effectiveness.The evaluation framework,comprising nine indicators organized into two tiers,was constructed based on three aspects:market design,market efficiency,and developmental coordination.Furthermore,a novel fuzzy multi-criteria decision-making evaluation model for electricity market performance was developed based on the Fuzzy-BWM and fuzzy COPRAS methodologies.This model aimed to ensure both accuracy and comprehensiveness in market operation assessment.Subsequently,empirical analyses were conducted on four typical provincial-level electricity markets in China.The results indicate that Guangdong’s electricity market performed best because of its effective balance of stakeholder interests and adherence to contractual integrity principles.Zhejiang and Shandong ranked second and third,respectively,whereas Sichuan exhibited the poorest market performance.Sichuan’s electricity market must be improved in terms of market design,such that market players can obtain a fairly competitive environment.The sensitivity analysis of the constructed indicators verified the effectiveness of the evaluation model proposed in this study.Finally,policy recommendations were proposed to facilitate the sustainable development of China’s electricity markets with the objective of transforming them into efficient and secure markets adaptable to the evolution of novel power systems.
基金the Foundation of a Hundred Youth Talents Training Program of Lanzhou Jiaotong University under Grant No.2018-103the Colleges and University Scientific Research Funds of Gansu Province under Grant No.2017A-026。
文摘The photovoltaic grid-connected inverter is an important interface between the photovoltaic power generation system and power grid.Its high-quality operation is directly related to the output power quality of the power grid.In order to further optimize the control effect of the quasi-Z source grid-connected photovoltaic inverter,a fuzzy proportional complex integral control(PCI)method is proposed for the current internal loop control.This method can eliminate the steady-state error,and has the characteristic of zero steady-state error adjustment for the AC disturbance signal of a specific frequency.The inductance-capacitance-inductance(LCL)filter is adopted in the grid-connected circuit,and the feedback capacitive current is taken as the control variable of the inner loop to form the active damping control method,which can not only effectively suppress the resonance of the LCL circuit,but also significantly inhibit the high-order harmonics in the grid-connected current.Finally,a system simulation model is built in MATLAB/Simulink to verify the superiority and effectiveness of the proposed method.
基金funded by the Ningbo Natural Science Foundation(No:2023J101).
文摘Purpose-Gathering,analyzing and securing electronic data from various digital devices for use in legal or investigative procedures is the key process of computer forensics.Information retrieved from servers,hard drives,cellphones,tablets and other devices is all included in this.This article tackles the challenging problem of how to prioritize different kinds of computer forensics and figure out which kind is most useful in cases of cybercrime,fraud,theft of intellectual property,harassment and espionage.Design/methodology/approach-Therefore,we first introduce enhanced versions of Hamacher power aggregation operators(AOs)within the framework of bipolar complex fuzzy(BCF)sets.These include BCF Hamacher power averaging(BCFHPA),BCF Hamacher power-weighted averaging(BCFHPWA),BCF Hamacher power-ordered weighted averaging(BCFHPOWA),BCF Hamacher power geometric(BCFHPG),BCF Hamacher power-weighted geometric(BCFHPWG)and BCF Hamacher power-ordered-weighted geometric(BCFHPOWG)operators.Employing the devised AOs,we devise a technique of decision-making(DM)for dealing with DM dilemmas with the BCF set(BCFS).Findings-We prioritize different types of computer forensic by taking artificial data in a numerical example and getting the finest computer forensic.Further,by this example,we reveal the applicability of the proposed theory.This work provides a more elaborate and versatile procedure for classifying computer forensics with dual aspects of criteria and extra fuzzy information.It allows for better and less biased DM in the more intricate digital investigations,which may lead to better DM and time-saving in real-life forensic scenarios.To demonstrate the significance and impression of the devised operators and techniques of DM,they are compared with existing ones.Originality/value-This research is the first to combine Hamacher and power AOs in BCFS for computer forensics DM.It presents new operators and a DM approach that is not encountered in the existing literature and is specifically designed to deal with the challenges and risks associated with the classification of computer forensics.The framework’s capacity to accommodate bipolar criteria and extra fuzzy information is a major development in the field of digital forensics and decision science.
基金Supported by the National Natural Science Foundationof China ( No. 10 2 710 35 ) and the MultidiscilineScientific Research Fund of Harbin Institute ofTechnology ( HIT.MD. 2 0 0 0 . 2 1)
文摘This paper introduces the concept of semi-continuity of complex fuzzy functions, and discusses some of their elementary properties, such as the sum of two complex fuzzy functions of type I upper (lower) semi-continuity is type I upper (lower) semi-continuous, and the opposite of complex fuzzy functions of type I upper (lower) semi-continuity is type I lower (upper) semi-continuous. Based on some assumptions on two complex fuzzy functions of type I upper (lower) semi-continuity, it is shown that their product is type I upper (lower) semi-continuous. The paper also investigates the convergence of complex fuzzy functions. In particular, sign theorem, boundedness theorem, and Cauchy's criterion for convergence are kept. In this paper the metrics introduced by Zhang Guangquan was used. This paper gives a contribution to the study of complex fuzzy functions, and extends the corresponding work of Zhang Guangquan.
文摘Land suitability analysis(LSA)is an evaluation method that measures the degree to which land is suitable for certain land use.The primary aims of this study are to identify potentially viable agricultural land in the Gangarampur subdivision(West Bengal)using Multiple Criteria Decision Making(MCDM)and machine learning procedures and to evaluate the efficacy of the employed methodologies.The Analytic Hierarchy Process(AHP)model was used to assign relative weights to the fifteen various criteria in this suitability analysis,and then the Fuzzy Complex Proportional Assessment(FCOPRAS)model was applied using the AHP’s normalised pairwise comparison matrix,whereas the Waikato Environment for Knowledge Analysis(Weka)Software was used to apply machine learning algorithms to the field data.The Random Forest(RF)model,on the other hand,is a better fit for the locational study of soil potential.According to the RF findings,areas of 14.67 per cent(15368.46 ha)are excellent(ZONE Ⅴ)for growing crops,approximately 22.30 per cent(23367.9 ha)are highly suitable(ZONE Ⅳ),and 23.63 per cent(24762.12 ha)are moderately suitable(ZONE Ⅲ)for cultivation,respectively.The numbers for FCOPRAS are roughly 15.39%(16130.52 ha),22.54%(23620.65 ha),and 19.79%(20733.26 ha).The Receiver Operating Characteristic(ROC)curve and accuracy measurements of the results indicate the high accuracy of the applied models,with Random Forest and FCOPRAS being the most popular and effective techniques.This study will make an important contribution to evaluations of soil fertility and site suitability.This will help local government officials,academics,and farmers scientifically use the land.
基金Supported by the National Natural Science Foundation of China(No.81072841)the Shanghai Science and Technology Research Grant Program(No.09dZ1971600)State Key Clinical Department of TCM pediatrics
文摘OBJECTIVE:To establish a quantification model of Traditional Chinese Medicine(TCM)syndromes by sampling patients undergoing idiopathic precocious puberty(IPP)and early puberty.METHODS:A questionnaire for classifying and quantifying TCM syndromes was designed and administered.All the results were analyzed;the relationship between 3 types of syndrome and 47symptoms were summated.Meanwhile,the frequency distribution of each symptom or sign was aggregated.Fuzzy mathematics was used to develop a quantification model ofTCM syndromes.RESULTS:We found that precocious puberty had 3types of syndrome,including hyperactivity of fire due to Yin deficiency(Syndrome I),depressed liver Qi transforming into fire(Syndrome II),and end retention of damp heat(Syndrome III).In the IPP group,Syndrome I was the most common principal syndrome(100%).Forty-six patients(43.81%)werediagnosed with Syndrome I accompanied by Syndrome II and 11(10.48%)were diagnosed with Syndrome I accompanied by Syndrome III.In the early puberty group,Syndrome I was also the main syndrome(98.39%).The degrees of most symptoms were mild to moderate.Reddened tongue was the most common tongue manifestation(62.86%prevalence)in the IPP group.The most common pulse manifestations were slippery pulse,thread pulse,and taut pulse.The Asymptotic Normalization Coefficient(ANC)method was used to quantify the TCM syndromes in 167 cases.Diagnostic accuracy rate reached 91%,comparable to expert diagnosis.CONCLUSION:We find that there are 3 types of syndrome in the IPP group and in the early puberty group.Syndrome I(hyperactivity of fire due to Yin deficiency)is the main syndrome in the two groups.ANC may be an appropriate for quantification model ofTCM syndromes.
文摘Purpose-This research focuses on a very important research question of determining the appropriate feature selection methods for software defect prediction.The study is centered on the creation of a new method that would enable the identification of both positive and negative selection criteria and the handling of ambiguous information in the decision-making process.Design/methodology/approach-To do so,we develop an improved method by extending the WASPAS assessment in the context of bipolar complex fuzzy sets,which leads to the bipolar complex fuzzy WASPAS method.The approach also uses Einstein operators to increase the accuracy of aggregation and manage complicated decision-making parameters.The methodology is designed for the processing of multi-criteria decision-making problems where criteria have positive and negative polarities as well as other ambiguous information.Findings-It is also shown that the proposed methodology outperforms the traditional weighted sum or product models when assessing feature selection methods.The incorporation of bipolar complex fuzzy sets with WASPAS improves the assessment of selection criteria by taking into account both positive and negative aspects of the criteria,which contributes to more accurate feature selection for software defect prediction.We investigate a case study related to the identification of feature selection techniques for software defect prediction by using the bipolar complex fuzzy WASPAS methodology.We compare the proposed methodology with certain prevailing ones to reveal the supremacy and the requirements of the proposed theory.Originality/value-This research offers the first integrated framework for handling bipolarity and uncertainty in feature selection for software defect prediction.The combination of Einstein operators with bipolar complex fuzzy sets improves the DM process,which will be useful for software engineers and help them select the best feature selection techniques.This work also helps to enhance the overall performance of software defect prediction systems.
