Journal of Intelligent & Fuzzy Systems - Volume 40, issue 1

Authors: Shahzadi, Gulfam | Akram, Muhammad

Article Type: Research Article

Abstract: With the rapid increase of COVID-19, mostly people are facing antivirus mask shortages. It is necessary to select a good antivirus mask and make it useful for everyone. For maximize the efficacy of the antivirus masks, we propose a decision support algorithm based on the concept of Fermatean fuzzy soft set (FFS f S ). The basic purpose of this article is to introduce the notion of FFS f S to deal with problems involving uncertainty and complexity corresponding to various parameters. Here, the valuable properties of FFS f S are merged with the Yager …operator to propose four new operators, namely, Fermatean fuzzy soft Yager weighted average (FFS f YWA ), Fermatean fuzzy soft Yager ordered weighted average (FFS f YOWA ), Fermatean fuzzy soft Yager weighted geometric (FFS f YWG ) and Fermatean fuzzy soft Yager ordered weighted geometric (FFS f YOWG ) operators. The fundamental properties of proposed operators are discussed. For the importance of proposed operators, a multi-attribute group decision-making (MAGDM) strategy is presented along with an application for the selection of an antivirus mask over the COVID-19 pandemic. The comparison with existing operators shows that existing operators cannot deal with data involving parametric study but developed operators have the ability to deal decision-making problems using parameterized information. Show more

Keywords: Fermatean fuzzy soft numbers, Yager operators, Aggregation operators, Antivirus mask selection, TOPSIS method

DOI: 10.3233/JIFS-201760

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1401-1416, 2021

Authors: Khan, Jalaluddin | Li, Jian Ping | Haq, Amin Ul | Khan, Ghufran Ahmad | Ahmad, Sultan | Abdullah Alghamdi, Abdulrahman | Golilarz, Noorbakhsh Amiri

Article Type: Research Article

Abstract: The emerging technologies with IoT (Internet of Things) systems are elevated as a prototype and combination of the smart connectivity ecosystem. These ecosystems are appropriately connected in a smart healthcare system which are generating finest monitoring activities among the patients, well-organized diagnosis process, intensive support and care against the traditional healthcare operations. But facilitating these highly technological adaptations, the preserving personal information of the patients are on the risk with data leakage and privacy theft in the current revolution. Concerning secure protection and privacy theft of the patient’s information. We emphasized this paper on secure monitoring with the help of …intelligently recorded summary’s keyframe extraction and applied two rounds lightweight cosine-transform encryption. This article includes firstly, a regimented process of keyframe extraction which is employed to retrieve meaningful frames of image through visual sensor with sending alert (quick notice) to authority. Secondly, employed two rounds of lightweight cosine-transform encryption operation of agreed (detected) keyframes to endure security and safety for the further any kinds of attacks from the adversary. The combined methodology corroborates highly usefulness with engendering appropriate results, little execution of encryption time (0.2277-0.2607), information entropy (7.9996), correlation coefficient (0.0010), robustness (NPCR 99.6383, UACI 33.3516), uniform histogram deviation (R 0.0359, G 0.0492, B 0.0582) and other well adopted secure ideology than any other keyframe or image encryption approaches. Furthermore, this incorporating method can effectively reduce vital communication cost, bandwidth issues, storage, data transmission cost and effective timely judicious analysis over the occurred activities and keep protection by using effective encryption methodology to remain attack free from any attacker or adversary, and provide confidentiality about patient’s privacy in the smart healthcare system. Show more

Keywords: Internet of things, security, privacy, secure surveillance, image encryption

DOI: 10.3233/JIFS-201770

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1417-1442, 2021

Authors: Dinçer, Hasan | Baykal, Elif | Yüksel, Serhat

Article Type: Research Article

Abstract: The study aims to propose a novel model to define the role of spiritual leadership on the ethical climate for the banking industry. There are mainly three different stages in this model. Firstly, the criteria of each factor are selected with correlation coefficients by considering the balanced scorecard (BSC)-based linguistic evaluations. After that, these criteria are weighted by using interval type-2 (IT2) fuzzy decision-making trial and evaluation laboratory (DEMATEL). The third and the final stage aims to rank 5 biggest banks of Turkey which are quoted in İstanbul Stock Exchange. Within this framework, IT2 fuzzy technique for order preference by …similarity to ideal solution (TOPSIS) approach is considered. The findings demonstrate that the spiritual leadership has a significant influence on the ethical climate. Altruistic love is the most important spiritual leadership dimension to improve ethical climate in the organization. On the other side, it is also concluded that private banks in Turkey are the most successful with respect to the ethical climate. The results give an idea that spiritual leader contributes to the improvement of the ties of love and respect among employees. The main reason is that altruistic love improves the judgement and sensitivity competencies of the ethical so that employees tend to be working in a more ethical way. Show more

Keywords: Interval Type-2 fuzzy sets, balanced scorecard, DEMATEL, TOPSIS, ethical climate, spiritual leadership

DOI: 10.3233/JIFS-201840

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1443-1455, 2021

Authors: Ju, Hongmei | Zhao, Ye | Zhang, Yafang

Article Type: Research Article

Abstract: Classification problem is an important research direction in machine learning. Nonparallel support vector machine (NPSVM) is an important classifier used to solve classification problems. It is widely used because of its structural risk minimization principle, kernel trick, and sparsity. When solving multi-class classification problems, NPSVM will encounter the problem of sample noises, low discrimination speed and unrecognized regions, which will affect its performance. In this paper, based on the multi-class NPSVM model, two improvements are made, and a directed acyclic graph fuzzy nonparallel support vector machine (DAG-F-NPSVM) model is established. On the one hand, for the noises that may exist …in the data set, the density information is used to add fuzzy membership to the samples, so that the contribution of each samples to the classification is treated differently. On the other hand, in order to reduce the decision time and solve the problem of unrecognized regions, the theory of directed acyclic graph (DAG) is introduced. Finally, the advantages of the new model in classification accuracy and decision speed is verified through UCI machine learning standard data set experiments. Finally, Friedman test and Bonferroni-Dunn test are used to verify the statistical significance of this new method. Show more

Keywords: Multi-class classification problem, nonparallel support vector machine, fuzzy membership, directed acyclic graph

DOI: 10.3233/JIFS-201847

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1457-1470, 2021

Authors: Mao, Jin | Yang, Lei | Liu, Kai | Du, Jinfu | Cui, Yahui

Article Type: Research Article

Abstract: In the following process, in order to improve the driving safety and road utilization of the adaptive cruise control (ACC) system, a variable time headway spacing strategy was studied. In view of the fact that the variable spacing strategy cannot adapt to the complex and variable deceleration conditions, an improved variable time headway strategy is proposed, which changes with the deceleration time and deceleration of the preceding vehicle. Based on this, the upper controller of adaptive cruise control based on model predictive control is designed, and numerical simulation of the variable time headway spacing strategy is performed, which verifies the …effectiveness of the improved variable time headway strategy. The results show that the spacing strategy proposed in this paper can more smoothly keep up with the preceding vehicle, and improve driving safety, comfort and road utilization. Show more

Keywords: Adaptive cruise control, variable time headway, spacing strategy, model predictive control

DOI: 10.3233/JIFS-202107

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1471-1479, 2021

Authors: Deng, Geng | Xie, Yaoguo | Wang, Xindong | Fu, Qiang

Article Type: Research Article

Abstract: Many classification problems contain shape information from input features, such as monotonic, convex, and concave. In this research, we propose a new classifier, called Shape-Restricted Support Vector Machine (SR-SVM), which takes the component-wise shape information to enhance classification accuracy. There exists vast research literature on monotonic classification covering monotonic or ordinal shapes. Our proposed classifier extends to handle convex and concave types of features, and combinations of these types. While standard SVM uses linear separating hyperplanes, our novel SR-SVM essentially constructs non-parametric and nonlinear separating planes subject to component-wise shape restrictions. We formulate SR-SVM classifier as a convex optimization …problem and solve it using an active-set algorithm. The approach applies basis function expansions on the input and effectively utilizes the standard SVM solver. We illustrate our methodology using simulation and real world examples, and show that SR-SVM improves the classification performance with additional shape information of input. Show more

DOI: 10.3233/JIFS-202155

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1481-1494, 2021

Authors: Wu, Yangxu | Yang, Wanting | Pan, Jinxiao | Chen, Ping

Article Type: Research Article

Abstract: Pavement crack assessment is an important indicator for evaluating road health. However, due to the dark color of the asphalt pavement and the texture characteristics of the pavement, current asphalt pavement crack detection technology cannot meet the requirements of accuracy and efficiency. In this paper, we propose an end-to-end multi-scale full convolutional neural network to achieve the semantic segmentation of cracks in road images by learning the crack characteristics in the complex fine grain background of asphalt pavement. The method uses DenseNet and deconvolution network framework to achieve pixel-level detection and fuses features learned from different scales of convolutional kernels …through a full convolutional network to obtain richer information on multi-scale features, allowing more detailed representation of crack features in high-resolution images. And the back end joins the SVM classifier to achieve crack classification after crack segmentation. Then we create a road test standard data set containing 12 cracks and evaluate it on the data. The experimental results show that the method achieves good segmentation effect for 12 types of cracks, and the crack segmentation for asphalt pavement is better than the most advanced methods. Show more

Keywords: Convolutional neural network (CNN), denseNet, deconvolution network, multi-scale full convolutional

DOI: 10.3233/JIFS-191105

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1495-1508, 2021

Authors: Song, Zekun | Li, Haodong | Shi, Jintang

Article Type: Research Article

Abstract: Service accessibility can be used to describe the travel time of passengers between different nodes, and opportunities to get transportation services in the high-speed railway (HSR) system. Based on the traditional train line planning theory, this paper introduces the transportation service accessibility index, and propose a new nonlinear passenger train line planning model, which aims to maximize the service accessibility, as well as minimize the operational cost of railway company. The model is transformed into a single-objective model, and then we design a harmony search algorithm to solve it. Finally, the model is validated by a numerical example. The results …of this model as well as the scenarios of the single-objective models for minimizing operational costs and maximizing service accessibility are compared. From the perspective of service frequency and accessibility of each nodes, we know that the proposed method can balance conflicts between average speed between large nodes and service frequency of small and medium size nodes in high-speed railway network. Show more

Keywords: High-speed railway, train line plan, service accessibility, harmony search algorithm, multi-objective optimization

DOI: 10.3233/JIFS-191866

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1509-1519, 2021

Authors: Yang, Jian | Han, Jihua | Wu, Tong | Zhang, Hao | Shang, Lixia

Article Type: Research Article

Abstract: The economic development of any country is closely linked with the consumption of energy. Therefore, international policies encourage increasing penetration of renewable energy sources (RES) into the electrical grid in order to reduce CO2 emissions and cover ever-increasing demands. However, high variance of RES complicates their integration into power systems and complicates their transition from central to distributed energy sources. On the other hand, increasing the penetration of RES in electrical networks stimulates the demand for large capacity for energy storage. This paper presents a new approach to optimize the size of on-grid renewable energy systems integrated to pumped …storage system using Salp Swarm Algorithm (SSA). This approach allows the examination of various energy sources and their combination to handle the optimal configuration of the hybrid system. The simulation and optimization process of the studied system have been carried out by MATLAB programming. The impact of the system under study on the grid is examined according to the power exchange values between the system and the grid. Moreover, different scenarios have been introduced for optimal operation. The simulation results indicate that these hybrid systems can reduce power exchange with the grid and ensure that the proposed system is economically and environmentally feasible. Furthermore, the results indicate the technical feasibility of seawater hydroelectric power plants in increasing the capacity of the electric grid to allow for high penetration of RES. Finally, the results showed that the best minimum value of the objective function is 3.9113 and showed that CO2 emission can be reduced about 29.65% per year compared to the conventional power plants. Show more

Keywords: CO2 emission, energy exchange, energy management, renewable energy, hydroelectric pumped storage

DOI: 10.3233/JIFS-192017

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1521-1536, 2021

Authors: Wu, Beng | He, Wei | Wang, Jing | Liang, Huaqing | Chen, Chong

Article Type: Research Article

Abstract: As the environment issue is put on the agenda, air pollution also concerns a lot. Nitrogen oxide (NOx) an is important factor which affects air pollution and is also the main gas emissions of the smoke and waste gas of FCC unit in petrochemical industry. It is important to accurately predict the NOx emission in advance for petrochemical industry to avoid air pollution incidents. In this paper, convolutional neural network (CNN) and long short-term memory (LSTM) are combined to predict the NOx emission in Fluid Catalytic Cracking unit (FCC unit). Convolutional-LSTM (CLSTM) is able to extract the spatial and temporal …features which are essential information in the prediction of the NOx emission. The features in the factors of production which would affect the NOx emission are extracted by CNN which prepares time series data for LSTM. The LSTM layer is connected after CNN to model the irregular trends in time series. CNN, Multi-layer perception (MLP), rand forest (RF), support vector machine (SVM) and LSTM are implemented as baseline models. The results from the proposed CLSTM model showed better performance than all the baseline models. The mean absolute error and root mean square error for CLSTM were calculated with the values of 16.8267 and 23.7089 which are the lowest among all the models. The Pearson correlation coefficient and R2 for the proposed CLSTM model are calculated with the value of 0.9263, 0.8237 which are the highest among all the models. Furthermore, the residual graphs indicate the well matched performance between the observations and the predictions. The study provides a model reference for forecasting the NOx concentration emitted by FCC unit in petrochemical industry. Show more

Keywords: Nitrogen oxides, machine learning, LSTM, CNN

DOI: 10.3233/JIFS-192086

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1537-1545, 2021