Blockchain technology‑based FinTech banking sector involvement using adaptive neuro‑fuzzy‑based K‑nearest neighbors algorithm

The study aims to investigate the financial technology(FinTech)factors influencing Chinese banking performance.Financial expectations and global realities may be changed by FinTech’s multidimensional scope,which is lacking in the traditional financial sector.The use of technology to automate financial services is becoming more important for economic organizations and industries because the digital age has seen a period of transition in terms of consumers and personalization.The future of FinTech will be shaped by technologies like the Internet of Things,blockchain,and artificial intelligence.The involvement of these platforms in financial services is a major concern for global business growth.FinTech is becoming more popular with customers because of such benefits.FinTech has driven a fundamental change within the financial services industry,placing the client at the center of everything.Protection has become a primary focus since data are a component of FinTech transactions.The task of consolidating research reports for consensus is very manual,as there is no standardized format.Although existing research has proposed certain methods,they have certain drawbacks in FinTech payment systems(including cryptocurrencies),credit markets(including peer-to-peer lending),and insurance systems.This paper implements blockchainbased financial technology for the banking sector to overcome these transition issues.In this study,we have proposed an adaptive neuro-fuzzy-based K-nearest neighbors’algorithm.The chaotic improved foraging optimization algorithm is used to optimize the proposed method.The rolling window autoregressive lag modeling approach analyzes FinTech growth.The proposed algorithm is compared with existing approaches to demonstrate its efficiency.The findings showed that it achieved 91%accuracy,90%privacy,96%robustness,and 25%cyber-risk performance.Compared with traditional approaches,the recommended strategy will be more convenient,safe,and effective in the transition period.

Anomaly Detection of Complex Networks Based on Intuitionistic Fuzzy Set Ensemble

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.

Recognizing Expression Variant and Occluded Face Images Based on Nested HMM and Fuzzy Rule Based Approach

The face recognition with expression and occlusion variation becomes the greatest challenge in biometric applications to recognize people. The proposed work concentrates on recognizing occlusion and seven kinds of expression variations such as neutral, surprise, happy, sad, fear, disgust and angry. During enrollment process, principle component analysis (PCA) detects facial regions on the input image. The detected facial region is converted into fuzzy domain data to make decision during recognition process. The Haar wavelet transform extracts features from the detected facial regions. The Nested Hidden markov model is employed to train these features and each feature of face image is considered as states in a Markov chain to perform learning among the features. The maximum likelihood for the input image was estimated by using Baum Welch algorithm and these features were kept on database. During recognition process, the expression and occlusion varied face image is taken as the test image and maximum likelihood for test image is found by following same procedure done in enrollment process. The matching score between maximum likelihood of input image and test image is computed and it is utilized by fuzzy rule based method to decide whether the test image belongs to authorized or unauthorized. The proposed work was tested among several expression varied and occluded face images of JAFFE and AR datasets respectively.

A Model of Cellular Automata for the Fuzzy Control of Aphids

Pesticides are substances used to prevent, destroy or mitigate any pest. We have adopted in this paper the Cellular Automata model to study the dispersion of the aphids in the block of citric trees using the pesticides (chemical control) and the biological agent (biological control). The main purpose of this research is the development of a simple and specific methodology to study Citrus Sudden Death (CSD). CSD is a disease that has affected sweet orange trees grafted on Rangpur lime in the state of S?o Paulo-Brazil. Some studies suggest that this disease has been caused by a virus and it is transmitted by insects known as aphids (vector). The ladybug was selected among the most known enemies of aphids in citrus in Brazil. In order to elaborate a predator-prey type of model to study the interaction between aphids (preys) and ladybugs (predators) in citriculture we have used a fuzzy rule-based system (FRBS). The states of the variables of the system (inputs) are the density of preys and the density of predators and their variations are the outputs. Therefore we take into account the effect of the wind in the space covered by the aphid, since the wind is important for the flight of the aphid as described in Peixoto et al. (2008) [1]. After, we used a FRBS to establish the relationship between the quantity of pesticides and the density of the preys. The simulations have been performed and have been compared between blocks with the presence of both aphids and ladybugs without the use of pesticides and the presence of them with the use of these ones using the Cellular Automata model. Numerical simulations allow us to foresee the behavior of the system, hence creating a spectrum of possibilities and proposing control techniques for different initial scenarios.

Fuzzy prediction and experimental verification of road surface cleaning rate by pure waterjets

The cleaning parameters affecting cleaning rate using pure waterjets to clean road surface was researched. A mathematical model for predicting cleaning rate was established using fuzzy mathematical method. A fuzzy rule base character-izing the relationship between input and output parameters was built through experiments. The prediction of cleaning rate was achieved under the condition of given input parameters by rule-based fuzzy reasoning. The prediction results were analyzed through experimental verification.

Hybrid approach for fuzzy system design

A hybrid approach for fuzzy system design based on clustering and a kind of neurofuzzy networks is proposed. An unsupervised clustering technique is firstly used to determine the number of if-then fuzzy rules and generate an initial fuzzy rule base from the given input-output data. Then, a class of neurofuzzy networks is constructed and its weights are tuned so that the obtained fuzzy rule base has a high accuracy. Finally, two examples of function approximation problems are given to illustrate the effectiveness of the proposed approach.

Adaptive Fuzzy Logic Controller for Harmonics Mitigation Using Particle Swarm Optimization

An excessive use of non-linear devices in industry results in current harmonics that degrades the power quality with an unfavorable effect on power system performance.In this research,a novel control techniquebased Hybrid-Active Power-Filter(HAPF)is implemented for reactive power compensation and harmonic current component for balanced load by improving the Power-Factor(PF)and Total–Hormonic Distortion(THD)and the performance of a system.This work proposed a soft-computing technique based on Particle Swarm-Optimization(PSO)and Adaptive Fuzzy technique to avoid the phase delays caused by conventional control methods.Moreover,the control algorithms are implemented for an instantaneous reactive and active current(Id-Iq)and power theory(Pq0)in SIMULINK.To prevent the degradation effect of disturbances on the system’s performance,PS0-PI is applied in the inner loop which generate a required dc link-voltage.Additionally,a comparative analysis of both techniques has been presented to evaluate and validate the performance under balanced load conditions.The presented result concludes that the Adaptive Fuzzy PI controller performs better due to the non-linearity and robustness of the system.Therefore,the gains taken from a tuning of the PSO based PI controller optimized with Fuzzy Logic Controller(FLC)are optimal that will detect reactive power and harmonics much faster and accurately.The proposed hybrid technique minimizes distortion by selecting appropriate switching pulses for VSI(Voltage Source Inverter),and thus the simulation has been taken in SIMULINK/MATLAB.The proposed technique gives better tracking performance and robustness for reactive power compensation and harmonics mitigation.As a result of the comparison,it can be concluded that the PSO-basedAdaptive Fuzzy PI system produces accurate results with the lower THD and a power factor closer to unity than other techniques.

II-Type Products of L-Fuzzy Filters

W. Gahler has proposed the concept of L-fuzzy filters and discussed the product of L-fuzzy filters. This note is devoted to the discussion of another type product, we called it I I-type product of L-fuzzy filters.

Integral Performance Criteria Based Analysis of Load Frequency Control in Bilateral Based Market

Performance index based analysis is made to examine and highlight the effective application of Particle Swarm Optimization (PSO) to optimize the Proportional Integral gains for Load Frequency Control (LFC) in a restructured power system that operates under Bilateral based policy scheme. Various Integral Performance Criteria measures are taken as fitness function in PSO and are compared using overshoot, settling time and frequency and tie-line power deviation following a step load perturbation (SLP). The motivation for using different fitness technique in PSO is to show the behavior of the controller for a wide range of system parameters and load changes. Error based analysis with parametric uncertainties and load changes are tested on a two-area restructured power system. The results of the proposed PSO based controller show the better performance compared to the classical Ziegler-Nichols (Z-N) tuned PI and Fuzzy Rule based PI controller.

Soft Computing: Fuzzy Logic Approach in Wireless Sensors Networks

The recent growth of communication and sensor technology results in the enlargement of a new attractive and challenging area-wireless sensor networks (WSNs). A network comprising of several minute wireless sensor nodes which are organized in a dense manner is called as a Wireless Sensor Network (WSN). Every node estimates the state of its surroundings in this network. The estimated results are then converted into the signal form in order to determine the features related to this technique after the processing of the signals. It’s high computational environment with limited and controlled broadcast range, processing, as well as limited energy. The embedded soft computing approach in wireless sensor networks is suggested. This approach means a grouping of embedded fuzzy logic and neural networks models for information processing in complex environment with unsure, rough, fuzzy measuring data. It is generalization of soft computing concept for the embedded, distributed, adaptive systems.

Small Signal Stability Analysis of DFIG Fed Wind Power System Using a Robust Fuzzy Logic Controller

This paper focuses on the small signal stability analysis of Doubly-Fed Induction Generator (DFIG) fed wind power system under three modes of operation. The system stability is affected by the influence of electromechanical oscillations, which can be damped using Power System Stabilizer (PSS). A detailed modeling of DFIG fed wind system including controller has been carried out. The damping controller is designed using fuzzy logic to damp the oscillatory modes for stability. The robust performance of the system with controllers has been evaluated using eigen value analysis and time domain simulations under various disturbances and wind speeds. The effectiveness of the proposed fuzzy based PSS is compared with the performance of conventional PSS implemented in the wind system.

FG-SMOTE:Fuzzy-based Gaussian synthetic minority oversampling with deep belief networks classifier for skewed class distribution

Purpose-Adequate resources for learning and training the data are an important constraint to develop an efficient classifier with outstanding performance.The data usually follows a biased distribution of classes that reflects an unequal distribution of classes within a dataset.This issue is known as the imbalance problem,which is one of the most common issues occurring in real-time applications.Learning of imbalanced datasets is a ubiquitous challenge in the field of data mining.Imbalanced data degrades the performance of the classifier by producing inaccurate results.Design/methodology/approach-In the proposed work,a novel fuzzy-based Gaussian synthetic minority oversampling(FG-SMOTE)algorithm is proposed to process the imbalanced data.The mechanism of the Gaussian SMOTE technique is based on finding the nearest neighbour concept to balance the ratio between minority and majority class datasets.The ratio of the datasets belonging to the minority and majority class is balanced using a fuzzy-based Levenshtein distance measure technique.Findings-The performance and the accuracy of the proposed algorithm is evaluated using the deep belief networks classifier and the results showed the efficiency of the fuzzy-based Gaussian SMOTE technique achieved an AUC:93.7%.F1 Score Prediction:94.2%,Geometric Mean Score:93.6%predicted from confusion matrix.Research limitations/implications-The proposed research still retains some of the challenges that need to be focused such as application FG-SMOTE to multiclass imbalanced dataset and to evaluate dataset imbalance problem in a distributed environment.Originality/value-The proposed algorithm fundamentally solves the data imbalance issues and challenges involved in handling the imbalanced data.FG-SMOTE has aided in balancing minority and majority class datasets.

Properties of Measure-based Fuzzy Logic

Measure based fuzzy logic, which is constructed on the basis of eight axioms, is a seemingly powerful fuzzy logic. It possesses several remarkable properties. (1) It is an extended Boolean logic, satisfying all the properties of Boolean algebra, including the law of excluded middle and the law of contradiction. (2) It is conditional. Conditional membership functions play an important role in this logic. (3) The negation operator is not independently defined with the conjunction and disjunction operators, but on the contrary, it is derived from them. (4) Zadehs fuzzy logic is included in it as a particular case. (5) It gives more hints to the relationship between fuzzy logic and probability logic.

Improved Kernel Possibilistic Fuzzy Clustering Algorithm Based on Invasive Weed Optimization

Fuzzy c-means(FCM) clustering algorithm is sensitive to noise points and outlier data, and the possibilistic fuzzy c-means(PFCM) clustering algorithm overcomes the problem well, but PFCM clustering algorithm has some problems: it is still sensitive to initial clustering centers and the clustering results are not good when the tested datasets with noise are very unequal. An improved kernel possibilistic fuzzy c-means algorithm based on invasive weed optimization(IWO-KPFCM) is proposed in this paper. This algorithm first uses invasive weed optimization(IWO) algorithm to seek the optimal solution as the initial clustering centers, and introduces kernel method to make the input data from the sample space map into the high-dimensional feature space. Then, the sample variance is introduced in the objection function to measure the compact degree of data. Finally, the improved algorithm is used to cluster data. The simulation results of the University of California-Irvine(UCI) data sets and artificial data sets show that the proposed algorithm has stronger ability to resist noise, higher cluster accuracy and faster convergence speed than the PFCM algorithm.

Pattern recognition of acoustic sea-bed profiling records (An expert system based on the fuzzy set theory)

An expert system based on the fuzzy set theory has been developed for geological interpretation of Acoustic Seabed Profiling Records(ASPR). After successively extracting each state of several main pattern characteristics shown on the ASPRs, the similarities between this pattern characteristic-state set and the standard ones corresponding to different geological categories of marine sediments are computed respectively By comparillg these values of sidrilarities, the conclusion of geological classification to the ASPR can be derived.

Fuzzy-Based Dynamic Distributed Queue Scheduling for Packet Switched Networks

Addressing the problem of queue scheduling for the packet-switched system is a vital aspect of congestion control. In this paper, the fuzzy logic based decision method is adopted for queue scheduling in order to enforce some level of control for traffic of different quality of service requirements using predetermined values. The fuzzy scheduler proposed in this paper takes into account the dynamic nature of the Internet traffic with respect to its time-varying packet arrival process that affects the network states and performance. Three queues are defined, viz low, medium and high priority queues. The choice of prioritizing packets influences how queues are served. The fuzzy scheduler not only utilizes queue priority in the queue scheduling scheme, but also considers packet drop susceptibility and queue limit. Through simulation it is shown that the fuzzy scheduler is more appropriate for the dynamic nature of Internet traffic in a packet-switched system as compared with some existing queue scheduling methods. Results show that the scheduling strategy of the proposed fuzzy scheduler reduces packet drop, provides good link utilization and minimizes queue delay as compared with the priority queuing （PQ）, first-in-first-out （FIFO）, and weighted fair queuing （WFQ）.

Chosen Ciphertext Secure Fuzzy Identity Based Encryption without ROM

Two new constructions of chosen-ciphertext secure fuzzy identity-based encryption （fuzzy-IBE） schemes without random oracle are proposed. The first scheme combines the modification of chosen-plaintext secure Sahai and Waters＇ ＂large universe＂ construction and authenticated symmetric encryption, and uses con- sistency checking to handle with ill-formed ciphertexts to achieve chosen-ciphertext security in the selective ID model. The second scheme improves the efficiency of first scheme by eliminating consistency checking. This improved scheme is more efficient than existing chosen-ciphertext secure fuzzy-IBE scheme in the standard model.

Dynamic multicast sharing protection algorithm based on fuzzy game in multi-domain optical network

Aiming at the problem of dynamic multicast service protection in multi-domain optical network, this paper proposes a dynamic multicast sharing protection algorithm based on fuzzy game in multi-domain optical network. The algorithm uses the minimum cost spanning tree strategy and fuzzy game theory. First, it virtualizes two planes to calculate the multicast tree and the multicast protection tree respectively. Then, it performs a fuzzy game to form a cooperative alliance to optimize the path composition of each multicast tree. Finally, it generates a pair of optimal multicast work tree and multicast protection tree for dynamic multicast services. The time complexity of the algorithm is O(k3 m2 n), where n represents the number of nodes in the networks, k represents the number of dynamic multicast requests, and m represents the number of destination nodes for each multicast request. The experimental results show that the proposed algorithm reduces significantly the blocking rate of dynamic multicast services, and improves the utilization of optical network resources within a certain number of dynamic multicast request ranges.

SHM-based F-AHP bridge rating system with application to Tsing Ma Bridge

This paper aims at developing a structural health monitoring(SHM)-based bridge rating method for bridge inspection of long-span cable-supported bridges.The fuzzy based analytic hierarchy approach is employed,and the hierarchical structure for synthetic rating of each structural component of the bridge is proposed.The criticality and vulnerability analyses are performed largely based on the field measurement data from the SHM system installed in the bridge to offer relatively accurate condition evaluation of the bridge and to reduce uncertainties involved in the existing rating method.The procedures for determining relative weighs and fuzzy synthetic ratings for both criticality and vulnerability are then suggested.The fuzzy synthetic decisions for inspection are made in consideration of the synthetic ratings of all structural components.The SHM-based bridge rating method is finally applied to the Tsing Ma suspension bridge in Hong Kong as a case study.The results show that the proposed method is feasible and it can be used in practice for longspan cable-supported bridges with SHM system.