Journal of Intelligent & Fuzzy Systems - Volume 43, issue 5

Authors: Yang, Jing | Su, Wei

Article Type: Research Article

Abstract: Interval-valued neutrosophic set (IVNS) plays an important role in dealing with imprecise judgment information. For a multi-attribute decision making problem, the information of alternatives under different attributes is given in the form of interval valued neutrosophic number(IVNN). The objective of the presented paper is to develop a multiple-attribute decision making (MADM) method under interval-valued neutrosophic sets(IVNSs) using the new similarity measurement. The similarity measurement of IVNSs has always been a research hotspot. A new similarity measurement of IVNSs is first proposed in this paper based on Chebyshev distance. The proposed method enriches the existing similarity measurement methods. It can be …applied to not only IVNSs, but also single-valued neutrosophic sets(SVNSs). The influence of each attribute on the decision-making result can be described by the weight. How to formulate the weight scientifically is vital as well. In this paper, the objective weight is calculated by normalizing the grey correlation coefficient obtained by a score function which can be applied to IVNSs. The objective weight is then combined with the subjective one by considering an adjustment factor with the weighted summation method. The adjustment factor is determined by the importance of subjective weight. Finally, an example is used to illustrate the comparison results of the proposed algorithm and other three ones. The comparison shows that the proposed algorithm is effective and can identify the optimal scheme quickly. Show more

Keywords: Fuzzy multi-attribute decision making, similarity measure, chebyshev distance, interval-valued neutrosophic sets

DOI: 10.3233/JIFS-220534

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6549-6559, 2022

Authors: Tan, Guimei | Yu, Xichang

Article Type: Research Article

Abstract: As an important tool to measure the degree of difficulty of predicting the realization of an uncertain set, entropy theory of uncertain set has been investigated by many scholars. In order to measure the uncertainty associated with some uncertain sets, this paper first proposes the arc entropy for an uncertain set. Then a computational arc entropy formula via inverse membership function is introduced to calculate the arc entropy more quickly, and some properties of arc entropy are studied. Furthermore, some applications are also provided to illustrate the superiority of the arc entropy.

Keywords: Uncertainty theory, uncertain set, arc entropy, portfolio selection

DOI: 10.3233/JIFS-220564

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6561-6574, 2022

Authors: Tang, Jianfei | Zhao, Hui

Article Type: Research Article

Abstract: The focus of a large amount of research on malware detection is currently working on proposing and improving neural network structures, but with the constant updates of Android, the proposed detection methods are more like a race against time. Through the analysis of these methods, we found that the basic processes of these detection methods are roughly the same, and these methods rely on professional reverse engineering tools for malware analysis and feature extraction. These tools generally have problems such as high time-space cost consumption, difficulty in achieving concurrent analysis of a large number of Apk, and the output results …are not convenient for feature extraction. Is it possible to propose a general malware detection process implementation platform that optimizes each process of existing malware detection methods while being able to efficiently extract various features on malware datasets with a large number of APK? To solve this problem, we propose an automated platform, AmandaSystem, that highly integrates the various processes of deep learning-based malware detection methods. At the same time, the problem of over privilege due to the openness of Android system and thus the problem of excessive privileges has always required the accurate construction of mapping relationships between privileges and API calls, while the current methods based on function call graphs suffer from inefficiency and low accuracy. To solve this problem, we propose a new bottom-up static analysis method based on AmandaSystem to achieve an efficient and complete tool for mapping relationships between Android permissions and API calls, PerApTool. Finally, we conducted tests on three publicly available malware datasets, CICMalAnal2017, CIC-AAGM2017, and CIC-InvesAndMal2019, to evaluate the performance of AmandaSystem in terms of time efficiency of APK parsing, space occupancy, and comprehensiveness of extracted features, respectively, compared with existing methods were compared. Show more

Keywords: Cybersecurity, android malware analysis, static analysis, dynamic analysis, least privilege

DOI: 10.3233/JIFS-220567

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6575-6589, 2022

Authors: Cypto, J. | Karthikeyan, P.

Article Type: Research Article

Abstract: With the growth in vehicular traffic, there is a greater risk of road accidents. Over speeding, intoxicated driving, driver distractions, red-light runners, ignoring safety equipment such as seat belts and helmets, non-adherence to lane driving, and improper overtaking are the leading causes of accidents. Speed violation, in particular, has a significant influence on today’s transportation. Also, detecting this speed violation and punishing this violator are more time-consuming tasks. For that reason, a novel automatic speed violation detection in traffic based on Deep learning is proposed in this paper. This proposed method is separated into two working modules: object detection and …license plate recognition. The object detection module uses the most efficient PP YOLO neural networks. It utilizes open ALPR (Automatic License Plate Recognition) for the vehicle’s number plate identification, which passes the traffic above maximum speed. With the number plate details, the authorities can take action against the rule violator with less time and effort. The simulation results show that the proposed automatic speed violation detection system also has an accuracy rate of 98.8% for speed violation detection and 99.3% for license plate number identification, demonstrating that the approach described in this work has a higher performance in terms of accuracy. Furthermore, the proposed technique was compared to recent existing results. Show more

Keywords: Speed violation, intoxicated driving, deep learning, PP YOLO, object detection, license plate recognition

DOI: 10.3233/JIFS-220577

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6591-6606, 2022

Authors: Shi, Xuecheng | Lin, Zhichao | Zhou, Ligang | Bao, Hengjia

Article Type: Research Article

Abstract: Linguistic q-rung orthopair fuzzy numbers (Lq-ROFNs) are an effective tool for representing fuzzy linguistic information, and they can obtain a wider expression scope than linguistic intuitionistic fuzzy numbers and linguistic Pythagorean fuzzy numbers by increasing the value of parameter q . In this paper, we propose a new similarity measure called the grey similarity degree between any two Lq-ROFNs based on the concept of the grey correlation degree. Considering the significance of determining unknown weights, we also propose a grey correlation method to determine each expert’s weight under different alternatives and attributes, and we construct an optimization model to determine …incompletely known attribute weights. Furthermore, an approach to linguistic q-rung orthopair fuzzy multiple-attribute group decision making is proposed that combines the grey similarity degree with the PROMETHEE II method. Finally, a numerical example is given to illustrate the effectiveness of the proposed method, and a sensitivity analysis and comparison analysis are also performed. Show more

Keywords: Linguistic q-rung orthopair fuzzy numbers, grey correlation degree, grey similarity degree, PROMETHEE, group decision making

DOI: 10.3233/JIFS-220579

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6607-6625, 2022

Authors: An, Qing | Tang, Ruoli | Li, Xueyan | Zhang, Xiaodi | Li, Xin

Article Type: Research Article

Abstract: In order to optimally control the marine hybrid power system (HPS) under increasingly complex regulation constraints or hardware constraints, an efficient power-flow scheduling model and optimization algorithm are of great importance. This work focuses on the optimal power-flow scheduling of marine HPS, especially on the efficiency improvement of the penalty functions for satisfying complex constraints. To be specific, an optimal operation model of marine HPS is discussed, and the complex model constraints are described as various penalty functions. Secondly, a novel optimization algorithm, namely adaptive multi-context cooperatively coevolving differential evolution algorithm with random topology and mutated context vector (AMCCDE - rt - mcv ) …is developed to optimize the aforementioned model. In order to ensure the satisfaction of the complex model constraints, the detailed forms for penalty functions are researched and the optimal parameters for penalty functions are comprehensively compared, analysed and tested by a set of numerical experiments. Finally, the developed methodologies are tested by simulation experiments. Experimental results show that the damping factor, exponent parameter and punish strength constant effect the efficiency of penalty functions a lot, and the developed penalty functions can effectively satisfy all the model constraints with fast response speed. With the integration of penalty functions, the developed methodology can obtain promising performance on the optimal scheduling of the evaluated marine HPS. Show more

Keywords: Hybrid power system, optimal energy management, penalty function, optimization algorithm, differential evolution

DOI: 10.3233/JIFS-220645

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6627-6649, 2022

Authors: Suresh, K. | Jagatheeswari, P.

Article Type: Research Article

Abstract: Renewable energy has seen a substantial increase in deployment as an alternative to traditional power sources. However, two fundamental constraints exist that preclude widespread adoption: the availability of the generated power and the expense of the equipment. One of the most critical difficulties with this sort of hybrid system is to appropriately design the Hybrid Renewable Energy System (HRES) elements so that they fulfill all load requirements while requiring the least amount of investment and running expenditures. This research proposes a novel technique for evaluating the optimal smart grid linking Hybrid Renewable Energy (Solar photovoltaic and wind) with battery, to …increase profitability, dependability, and feasibility. A multiobjective function is suggested and constructed to be optimized utilizing two optimization algorithms: Enhanced Particle Swarm Optimization (EPSO) and Harris Hawks Optimization (HHO) algorithm with Fuzzy-Extreme Learning Machine (ELM). The primary goal for the HRES is to operate optimally to reduce the cost of energy generat ion through hourly day-ahead. Here, the Fuzzy-ELM is utilized to predict the required load of the smart grid-connected system and hybrid EPSO-HHO, which are introduced to solve the problem of HRES economic analysis. Finally, the suggested EPSO-EHO method is implemented in the MATLAB software, and its performance comparison is made with other existing methods such as PSO, WOA, and HHO. The simulation result shows that the cost of the newly suggested EPSO-HHO technique-based Hybrid Renewable Energy System is less than PSO, WOA, and HHO by 4.89 %, 4.51 %, and 4.05 %, respectively. Show more

Keywords: Harris Hawks’ Optimization, economic analysis, renewable energy sources, Extreme Learning Machine, smart grid

DOI: 10.3233/JIFS-220726

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6651-6662, 2022

Authors: Durmaz, Nida | Budak, Ayşenur

Article Type: Research Article

Abstract: This study aims to define the adoption barriers to Industry 4.0 for sustainable supply chain and define their causalities and, dependencies, hierarchical levels of these barriers. Firstly, a framework for critical barriers to Industry 4.0 for sustainable supply chain management is created with literature review and experts for the first time. Then an integrated approach of Grey DEMATEL – ANP is proposed to analyze the adoption barriers to Industry 4.0 in sustainable supply chain management. The proposed method determines the cause-effect relationship among barriers, the strength of interactions, and the relative weights of critical barriers to Industry 4.0 in a …sustainable supply chain. The results show that uncertainty about economic benefits, resistance to change, and lack of infrastructure and tools for Industry 4.0 in the Sustainable supply chain are crucial barriers to implementing Industry 4.0 technologies in SSC. This study can help decision-makers and managers define the barriers and provide the theoretical guideline to implement Industry 4.0 technologies across the sustainable supply chain successfully. Show more

Keywords: Sustainable supply chain management, Grey DEMATEL, ANP, Industry 4.0 adoption barriers

DOI: 10.3233/JIFS-220732

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6663-6682, 2022

Authors: Jiang, Ruiyang

Article Type: Research Article

Abstract: The Pile motion seems to be one of the most critical in pile failure that requires appraisal before installing piles. The variables to estimate the Pile Settlement parameter, there are several methods. Among existing theoretical ways to investigate the pile movement mathematically, most studies have tried to model the piles’ settlement overloading period using artificial intelligence. Thus, this research has used the Artificial Neural Network to have the actual status of pile motion vertically over the loading periods dynamically and statically. Therefore, the present research has utilized the Radial Basis Function Neural Network joint with Equilibrium Optimizer Algorithm and Grasshopper …Optimization Algorithm to figure out the optimum number of neurons within the hidden layer. Kuala Lumpur’s Klang Valley Mass Rapid Transit transportation network, Malaysia, opted to model the piles’ settlement and earth properties via the proposed hybrid RBF-GOA and RBF-EOA frameworks. By modeling both frameworks, the error index of RMSE for RBF-GOA and HRBF-EOA were gained to 0.6312 and 0.5947, respectively. However, the VAF indicator showed identical results of the rates 96.98 and 97.33, respectively. Overly, the RBF-EOA represented better than RBF-GOA by little efficiency. Show more

Keywords: Pile in rock, settlement, prediction, radial basis function, equilibrium optimizer algorithm, grasshopper optimization, R-value correlation

DOI: 10.3233/JIFS-220741

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6683-6695, 2022

Authors: Kalaichelvi, V. | Vimala Devi, P. | Meenakshi, P. | Swaminathan, S. | Suganya, S.

Article Type: Research Article

Abstract: The billions of bits of information are transferred each second through the internet every day. The information may be text, image, audio or video etc, accordingly, we need some protection mechanism while sharing confidential data. Generally, RSA algorithm is used for encrypting the Secret images. However, the security provided by Elliptic Curve Cryptography (ECC) is higher with lower sized key than the RSA algorithm. So, this article proposes an extended Elliptic Curve encryption approach for encrypting the secret images. In this system, the secret image is partitioned into three color image planes such as Red, Green and Blue. By applying …Radix-64 encoding and Mapping table, these planes are converted into elliptic curve points and then these points are encrypted using ECC algorithm. Again, these points are applied to the Radix-64 decoding and the mapping table to get ciphered-image. At last, the key parameters such as a, b, p and Generator point (G) are embedded in the last four pixel positions of the ciphered-image. In order to get the original secret image, the recipient must extract these key parameters from the encrypted image and then apply the remaining processes to the encrypted image in the opposite order. Experimental results tested using MATLAB R2021b and it shows that the NPCR and UACI values are 99.54% and 28.73 % and better quality feature is attained since the entropy value is almost closer to eight. So, the proposed image encryption has robust capacity to fight against the differential attack. Show more

Keywords: ECC, Radix-64 conversion, image encryption, image decryption, security

DOI: 10.3233/JIFS-220767

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6697-6708, 2022