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A novel extended Portuguese of Interactive and Multi-Criteria Decision Making and Archimedean Bonferroni mean operators based on prospect theory to select green supplier with complex q-rung orthopair fuzzy information

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CAAI Transactions on Intelligence Technology 2023年第1期8卷 177-191页

作者： Zeeshan Ali Tahir Mahmood jeonghwan gwak Naeem Jan Department of Mathematics&Statistics International Islamic University IslamabadIslamabadPakistan Department of Software Korea National University of TransportationChungjuKorea Department of Biomedical Engineering Korea National University of TransportationChungjuKorea Department of AI Robotics Engineering Korea National University of TransportationChungjuKorea Department of IT&Energy Convergence(BK21 FOUR) Korea National University of TransportationChungjuKorea

The word sustainable or green supply chain refers to the concept of incorporating sustainable environmental procedures into the traditional supply *** supply chain management gives a chance to revise procedures,materials and operational *** the fuzziness of assessing data and the spiritual situations of experts in the decision-making procedure are two important *** 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 *** and well-used properties of the initiated theories are also diagnosed with some special ***,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 ***,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.

关键词： fuzzy set fuzzy sets

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Recurrent Autoencoder Ensembles for Brake Operating Unit Anomaly Detection on Metro Vehicles

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Computers, Materials & Continua 2022年第10期73卷 1-14页

作者： Jaeyong Kang Chul-Su Kim Jeong Won Kang jeonghwan gwak Department of Software Korea National University of TransportationChungju27469Korea School of Railroad Engineering Korea National University of TransportationUiwang16106Korea Graduate School of Transportation Korea National University of TransportationUiwang16106Korea Department of AI Robotics Engineering Korea National University of TransportationChungju27469Korea Department of Biomedical Engineering Korea National University of TransportationChungju27469Korea Department of IT Convergence(Brain Korea PLUS 21) Korea National University of TransportationChungju27469Korea

The anomaly detection of the brake operating unit (BOU) in thebrake systems on metro vehicle is critical for the safety and reliability ofthe trains. On the other hand, current periodic inspection and maintenanceare unable to detect anomalies in an early stage. Also, building an accurateand stable system for detecting anomalies is extremely difficult. Therefore,we present an efficient model that use an ensemble of recurrent autoencodersto accurately detect the BOU abnormalities of metro trains. This is the firstproposal to employ an ensemble deep learning technique to detect BOUabnormalities in metro train braking systems. One of the anomalous caseson metro vehicles is the case when the air cylinder (AC) pressures are less thanthe brake cylinder (BC) pressures in certain parts where the brake pressuresincrease before coming to a halt. Hence, in this work, we first extract the dataof BC and AC pressures. Then, the extracted data of BC and AC pressuresare divided into multiple subsequences that are used as an input for bothbi-directional long short-term memory (biLSTM) and bi-directional gatedrecurrent unit (biGRU) autoencoders. The biLSTM and biGRU autoencodersare trained using training dataset that only contains normal subsequences. Fordetecting abnormalities from test dataset which consists of abnormal subsequences, the mean absolute errors (MAEs) between original subsequences andreconstructed subsequences from both biLSTM and biGRU autoencoders arecalculated. As an ensemble step, the total error is calculated by averaging twoMAEs from biLSTM and biGRU autoencoders. The subsequence with totalerror greater than a pre-defined threshold value is considered an *** carried out the experiments using the BOU dataset on metro vehiclesin South Korea. Experimental results demonstrate that the ensemble modelshows better performance than other autoencoder-based models, which showsthe effectiveness of our ensemble model for detecting BOU anomalies onmetro trains.

关键词： Anomaly detection brake operating unit deep learning machine learning signal processing

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Fuzzy-Based Automatic Epileptic Seizure Detection Framework

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Computers, Materials & Continua 2022年第3期70卷 5601-5630页

作者： Aayesha Muhammad Bilal Qureshi Muhammad Afzaal Muhammad Shuaib Qureshi jeonghwan gwak Shaheed Zulfikar Ali Bhutto Institute of Science and Technology IslamabadPakistan Department of Computer Science&IT University of Lakki MarwatKPKPakistan Stockholm University StockholmSweden Department of Computer Science School of Arts and SciencesUniversity of Central AsiaKyrgyz Republic Department of Software Korea National University of TransportationChungju27469Korea Department of Biomedical Engineering Korea National University of TransportationChungju27469Korea Department of AI Robotics Engineering Korea National University of TransportationChungju27469Korea Department of IT&Energy Convergence(BK21 FOUR) Korea National University of TransportationChungju27469Korea

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 *** 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 *** characterizing features of epileptic and non-epileptic EEG signals overlap;therefore,it requires that analysis of signals must be performed from diverse *** studies analyzed these signals in diverse domains to identify distinguishing characteristics of epileptic EEG *** 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 *** proposed work extracts pattern features along with time-domain,frequencydomain,and non-linear analysis of *** applies a feature selection strategy on extracted features to get more discriminating features that build fuzzy machine learning classifiers for the detection of epileptic *** empirical evaluation of the proposed model was conducted on the benchmark Bonn EEG *** shows significant accuracy of 98%to 100%for normal *** classification cases while for three class classification of normal ***-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.

关键词： Medical image processing electroencephalography machine learning fuzzy system models seizure detection epileptic seizure virtualization

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Improved Sequencing Heuristic DSDV Protocol Using Nomadic Mobility Model for FANETS

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Computers, Materials & Continua 2022年第2期70卷 3653-3666页

作者： Inam Ullah Khan Muhammad Abul Hassan Muhammad Fayaz jeonghwan gwak Muhammad Adnan Aziz Department of Electronic Engineering Isra University(SEAS)Islamabad44000Pakistan Department of Computing and Technology Abasyn University Peshawar25000Pakistan Deparment of Computer Engineering Jeju National UniversityJejuKorea Department of Software Korea National University of TransportationChungju27469Korea Department of Biomedical Engineering Korea National University of TransportationChungju27469Korea Department of AI Robotics Engineering Korea National University of TransportationChungju27469Korea Department of IT&Energy Convergence(BK21 FOUR) Korea National University of TransportationChungju27469Korea

Most interesting area is the growing demand of flying-IoT mergers with smart ***,aerial vehicles,especially unmanned aerial vehicles(UAVs),have limited capabilities for maintaining node energy *** order to communicate effectively,IoT is a key element for smart *** 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 ***,in literature,proposed techniques are verymuch complex which cannot be easily implemented in realworld *** issue leads to the development of lightweight energyefficient routing in flying-IoT *** paper addresses the energy conservation problem in *** paper presents a novel approach for the internet of flying vehicles using DSDV ***-DSDV gives the notion of bellman-ford algorithm consisting of routing updates,information broadcasting,and stale *** shows optimal results in comparison with other contemporary routing *** mobility model is utilized in the scenario of flying networks to check the performance of routing protocols.

关键词： Flying-IoT DSR smart cities UAV’s FANETS

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An Energy-Efficient Mobile Agent-Based Data Aggregation Scheme for Wireless Body Area Networks

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Computers, Materials & Continua 2022年第3期70卷 5929-5948页

作者： Gulzar Mehmood Muhammad Zahid Khan Muhammad Fayaz Mohammad Faisal Haseeb Ur Rahman jeonghwan gwak Department of Computer Science&Information Technology University of MalakandChakdara Dir Lower23021Pakistan Department of Computer Science University of Central AsiaNarynKyrgyzstan Department of Software Korea National University of TransportationChungju27469Korea Department of Biomedical Engineering Korea National University of TransportationChungju27469Korea Department of AI Robotics Engineering Korea National University of TransportationChungju27469Korea Department of IT and Energy Convergence(BK21 FOUR) Korea National University of TransportationChungju27469Korea

Due to the advancement in wireless technology and miniaturization,Wireless Body Area Networks(WBANs)have gained enormous popularity,having various applications,especially in the healthcare *** are intrinsically resource-constrained;therefore,they have specific design and development *** such highly desirable requirement is an energy-efficient and reliable Data Aggregation(DA)mechanism for *** efficient and reliableDAmay ultimately push the network to operate without much human intervention and further extend the network *** conventional client-serverDAparadigm becomes unsuitable and inefficient for WBANs when a large amount of data is generated in the ***,in most of the healthcare applications(patient’s critical conditions),it is highly important and required to send data as soon as possible;therefore,reliable data aggregation in WBANs is of great *** tackle the shortcomings of the client-serverDAparadigm,theMobile Agent-Basedmechanismproved to be a more workable *** aMobile Agent-Based mechanism,a taskspecific mobile agent(code)traverses to the intended sources to gather *** agents travel on a predefined path called itinerary;however,planning a suitable and reliable itinerary for a mobile agent is also a challenging issue *** paper presents a new Mobile Agent-Based DA scheme for WBANs,which is energy-efficient and ***,in the proposed scheme,the network is divided into clusters,and cluster-heads are ***,a mobile agent is generated from the base station to collect the required data from cluster *** the case,if any fault occurs in the existing itinerary,an alternate itinerary is planned in real-time without compromising the network *** our simulation-based validation,we have found that the proposed system delivers significantly improved fault-tolerance and reliability with energy-efficiency and extended network lifetime in WBANs.

关键词： WBANs mobile-agent data aggregation reliability

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