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

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.

Fuzzy-Based Automatic Epileptic Seizure Detection Framework

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.

Improved Sequencing Heuristic DSDV Protocol Using Nomadic Mobility Model for FANETS

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.

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

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 abnormality.We 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.

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

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 sector.WBANs are intrinsically resource-constrained;therefore,they have specific design and development requirements.One such highly desirable requirement is an energy-efficient and reliable Data Aggregation(DA)mechanism for WBANs.The efficient and reliableDAmay ultimately push the network to operate without much human intervention and further extend the network lifetime.The conventional client-serverDAparadigm becomes unsuitable and inefficient for WBANs when a large amount of data is generated in the network.Similarly,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 concern.To tackle the shortcomings of the client-serverDAparadigm,theMobile Agent-Basedmechanismproved to be a more workable solution.In aMobile Agent-Based mechanism,a taskspecific mobile agent(code)traverses to the intended sources to gather data.Thesemobile agents travel on a predefined path called itinerary;however,planning a suitable and reliable itinerary for a mobile agent is also a challenging issue inWBANs.This paper presents a new Mobile Agent-Based DA scheme for WBANs,which is energy-efficient and reliable.Firstly,in the proposed scheme,the network is divided into clusters,and cluster-heads are selected.Secondly,a mobile agent is generated from the base station to collect the required data from cluster heads.In the case,if any fault occurs in the existing itinerary,an alternate itinerary is planned in real-time without compromising the network performance.In 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.