The word sustainable or green supply chain refers to the concept of incorporating sustainable environmental procedures into the traditional supply chain.Green supply chain management gives a chance to revise procedure...The word sustainable or green supply chain refers to the concept of incorporating sustainable environmental procedures into the traditional supply chain.Green supply chain management gives a chance to revise procedures,materials and operational ideas.Choosing the fuzziness of assessing data and the spiritual situations of experts in the decision-making procedure are two important issues.The main contribution of this analysis is to derive the theory of Archimedean Bonferroni mean operator for complex qrung orthopair fuzzy(CQROF)information,called the CQROF Archimedean Bonferroni mean and CQROF weighted Archimedean Bonferroni mean operators which are very valuable,dominant and classical type of aggregation operators used for examining the interrelationship among the finite number of attributes in modern data fusion theory.Inspirational and well-used properties of the initiated theories are also diagnosed with some special cases.Additionally,the theory of extended TODIM tool using the prospect theory based on CQROF information was discovered,which play an essential and critical role in the environment of fuzzy set theory.Finally,a real life problem by computing a green supply chain management based on the initiated CQROF operators was evaluated and fully illustrating the feasibility and efficiency of the diagnosed work with the help of a comparison between existing and prevailing theories.展开更多
Most interesting area is the growing demand of flying-IoT mergers with smart cities.However,aerial vehicles,especially unmanned aerial vehicles(UAVs),have limited capabilities for maintaining node energy efficiency.In...Most interesting area is the growing demand of flying-IoT mergers with smart cities.However,aerial vehicles,especially unmanned aerial vehicles(UAVs),have limited capabilities for maintaining node energy efficiency.In order to communicate effectively,IoT is a key element for smart cities.While improving network performance,routing protocols can be deployed in flying-IoT to improve latency,packet drop rate,packet delivery,power utilization,and average-end-to-end delay.Furthermore,in literature,proposed techniques are verymuch complex which cannot be easily implemented in realworld applications.This issue leads to the development of lightweight energyefficient routing in flying-IoT networks.This paper addresses the energy conservation problem in flying-IoT.This paper presents a novel approach for the internet of flying vehicles using DSDV routing.ISH-DSDV gives the notion of bellman-ford algorithm consisting of routing updates,information broadcasting,and stale method.DSDV shows optimal results in comparison with other contemporary routing protocols.Nomadic mobility model is utilized in the scenario of flying networks to check the performance of routing protocols.展开更多
Detection of epileptic seizures on the basis of Electroencephalogram(EEG)recordings is a challenging task due to the complex,non-stationary and non-linear nature of these biomedical signals.In the existing literature,...Detection of epileptic seizures on the basis of Electroencephalogram(EEG)recordings is a challenging task due to the complex,non-stationary and non-linear nature of these biomedical signals.In the existing literature,a number of automatic epileptic seizure detection methods have been proposed that extract useful features from EEG segments and classify them using machine learning algorithms.Some characterizing features of epileptic and non-epileptic EEG signals overlap;therefore,it requires that analysis of signals must be performed from diverse perspectives.Few studies analyzed these signals in diverse domains to identify distinguishing characteristics of epileptic EEG signals.To pose the challenge mentioned above,in this paper,a fuzzy-based epileptic seizure detection model is proposed that incorporates a novel feature extraction and selection method along with fuzzy classifiers.The proposed work extracts pattern features along with time-domain,frequencydomain,and non-linear analysis of signals.It applies a feature selection strategy on extracted features to get more discriminating features that build fuzzy machine learning classifiers for the detection of epileptic seizures.The empirical evaluation of the proposed model was conducted on the benchmark Bonn EEG dataset.It shows significant accuracy of 98%to 100%for normal vs.ictal classification cases while for three class classification of normal vs.inter-ictal vs.ictal accuracy reaches to above 97.5%.The obtained results for ten classification cases(including normal,seizure or ictal,and seizure-free or inter-ictal classes)prove the superior performance of proposed work as compared to other state-of-the-art counterparts.展开更多
基金Regional Innovation Strategy(RIS)through the National Research Foundation of Korea funded by the Ministry of Education,Grant/Award Number:2021RIS-001(1345341783)Brain Pool program funded by the Ministry of Science and ICT through the National Research Foundation of Korea,Grant/Award Number:NRF-2022H1D3A2A02060097。
文摘The word sustainable or green supply chain refers to the concept of incorporating sustainable environmental procedures into the traditional supply chain.Green supply chain management gives a chance to revise procedures,materials and operational ideas.Choosing the fuzziness of assessing data and the spiritual situations of experts in the decision-making procedure are two important issues.The main contribution of this analysis is to derive the theory of Archimedean Bonferroni mean operator for complex qrung orthopair fuzzy(CQROF)information,called the CQROF Archimedean Bonferroni mean and CQROF weighted Archimedean Bonferroni mean operators which are very valuable,dominant and classical type of aggregation operators used for examining the interrelationship among the finite number of attributes in modern data fusion theory.Inspirational and well-used properties of the initiated theories are also diagnosed with some special cases.Additionally,the theory of extended TODIM tool using the prospect theory based on CQROF information was discovered,which play an essential and critical role in the environment of fuzzy set theory.Finally,a real life problem by computing a green supply chain management based on the initiated CQROF operators was evaluated and fully illustrating the feasibility and efficiency of the diagnosed work with the help of a comparison between existing and prevailing theories.
基金This work was supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(Grant No.NRF-2020R1I1A3074141)the Brain Research Program through the NRF funded by the Ministry of Science,ICT and Future Planning(Grant No.NRF-2019M3C7A1020406),and“Regional Innovation Strategy(RIS)”through the NRF funded by the Ministry of Education.
文摘Most interesting area is the growing demand of flying-IoT mergers with smart cities.However,aerial vehicles,especially unmanned aerial vehicles(UAVs),have limited capabilities for maintaining node energy efficiency.In order to communicate effectively,IoT is a key element for smart cities.While improving network performance,routing protocols can be deployed in flying-IoT to improve latency,packet drop rate,packet delivery,power utilization,and average-end-to-end delay.Furthermore,in literature,proposed techniques are verymuch complex which cannot be easily implemented in realworld applications.This issue leads to the development of lightweight energyefficient routing in flying-IoT networks.This paper addresses the energy conservation problem in flying-IoT.This paper presents a novel approach for the internet of flying vehicles using DSDV routing.ISH-DSDV gives the notion of bellman-ford algorithm consisting of routing updates,information broadcasting,and stale method.DSDV shows optimal results in comparison with other contemporary routing protocols.Nomadic mobility model is utilized in the scenario of flying networks to check the performance of routing protocols.
基金This work was supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(Grant No.NRF-2020R1I1A3074141)the Brain Research Program through the NRF funded by the Ministry of Science,ICT and Future Planning(Grant No.NRF-2019M3C7A1020406),and“Regional Innovation Strategy(RIS)”through the NRF funded by the Ministry of Education.
文摘Detection of epileptic seizures on the basis of Electroencephalogram(EEG)recordings is a challenging task due to the complex,non-stationary and non-linear nature of these biomedical signals.In the existing literature,a number of automatic epileptic seizure detection methods have been proposed that extract useful features from EEG segments and classify them using machine learning algorithms.Some characterizing features of epileptic and non-epileptic EEG signals overlap;therefore,it requires that analysis of signals must be performed from diverse perspectives.Few studies analyzed these signals in diverse domains to identify distinguishing characteristics of epileptic EEG signals.To pose the challenge mentioned above,in this paper,a fuzzy-based epileptic seizure detection model is proposed that incorporates a novel feature extraction and selection method along with fuzzy classifiers.The proposed work extracts pattern features along with time-domain,frequencydomain,and non-linear analysis of signals.It applies a feature selection strategy on extracted features to get more discriminating features that build fuzzy machine learning classifiers for the detection of epileptic seizures.The empirical evaluation of the proposed model was conducted on the benchmark Bonn EEG dataset.It shows significant accuracy of 98%to 100%for normal vs.ictal classification cases while for three class classification of normal vs.inter-ictal vs.ictal accuracy reaches to above 97.5%.The obtained results for ten classification cases(including normal,seizure or ictal,and seizure-free or inter-ictal classes)prove the superior performance of proposed work as compared to other state-of-the-art counterparts.
