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

Authors: You, Shuang | Zhou, Yaping

Article Type: Research Article

Abstract: The traffic flow prediction using cellular automata (CA) is a trendy research domain that identified the potential of CA in modelling the traffic flow. CA is a technique, which utilizes the basic units for describing the overall behaviour of complicated systems. The CA model poses a benefit for defining the characteristics of traffic flow. This paper proposes a modified CA model to reveal the prediction of traffic flows at the signalised intersection. Based on the CA model, the traffic density and the average speed are computed for studying the characteristics and spatial evolution of traffic flow in signalised intersection. Moreover, …a CA model with a self-organizing traffic signal system is devised by proposing a new optimization model for controlling the traffic rules. The Sunflower Cat Optimization (SCO) algorithm is employed for efficiently predicting traffic. The SCO is designed by integrating the Sunflower optimization algorithm (SFO) and Cat swarm optimization (CSO) algorithm. Also, the fitness function is devised, which helps to guide the control rules evaluated by traffic simulation using the CA model. Thus, the cellular automaton is optimized using the SCO algorithm for predicting the traffic flows. The proposed Sunflower Cat Optimization-based cellular automata (SCO-CA) outperformed other methods with minimal travel time, distance, average traffic density, and maximal average speed. Show more

Keywords: Traffic flow prediction, signalized intersection, cellular automata, average speed, traffic density

DOI: 10.3233/JIFS-192099

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1547-1566, 2021

Authors: Xin, Xian-Wei | Song, Ji-Hua | Xue, Zhan-Ao | Peng, Wei-Ming

Article Type: Research Article

Abstract: As an important expanded of the classical formal concept, the three-way formal concept analysis integrates more information with the three-way decision theory. However, to the best of our knowledge, few scholars have studied the intuitionistic fuzzy three-way formal concept analysis. This paper proposes an intuitionistic fuzzy three-way formal concept analysis model based on the attribute correlation degree. To achieve this, we comprehensively analyze the composition of attribute correlation degree in the intuitionistic fuzzy environment, and introduce the corresponding calculation methods for different situations, as well as prove the related properties. Furthermore, we investigate the intuitionistic fuzzy three-way concept lattice ((IF3WCL) …of object-induced and attribute-induced. Then, the relationship between the IF3WCL and the positive, negative and boundary domains in the three-way decision are discussed. In addition, considering the final decision problem of boundary objects, the secondary decision strategy of boundary objects is obtained for IF3WCL. Finally, a numerical example of multinational company investment illustrates the effectiveness of the proposed model. In this paper, we systematically study the IF3WCL, and give a quantitative analysis method of formal concept decision along with its connection with three-way decision, which provides new ideas for the related research. Show more

Keywords: Intuitionistic fuzzy, attribute correlation degree, IF3WCL, secondary decision, three-way decision

DOI: 10.3233/JIFS-200002

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1567-1583, 2021

Authors: Zhongzheng, Xiao | Luktarhan, Nurbol

Article Type: Research Article

Abstract: A webshell is a common tool for network intrusion. It has the characteristics of considerable threat and good concealment. An attacker obtains the management authority of web services through the webshell to penetrate and control web applications smoothly. Because webshell and common web page features are almost identical, it can evade detection by traditional firewalls and anti-virus software. Moreover, with the application of various anti-detection feature hiding techniques to the webshell, it is difficult to detect new patterns in time based on the traditional signature matching method. Webshell detection has been proposed based on deep learning. First, a dataset is …opcoded, and the source code and opcode code features are fused. Second, the processed dataset is reduced using the SRNN and an attention mechanism, and the capsule network improves complete predictions for unknown pages. Experiments prove that the algorithm has higher detection efficiency and accuracy than traditional webshell detection methods, and it can also detect new types of webshell with a certain probability. Show more

Keywords: SRNN, Webshell, attention, CapsNet, opcode

DOI: 10.3233/JIFS-200314

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1585-1596, 2021

Authors: Bekmezci, Ilker | Ermis, Murat | Cimen, Egemen Berki

Article Type: Research Article

Abstract: Social network analysis offers an understanding of our modern world, and it affords the ability to represent, analyze and even simulate complex structures. While an unweighted model can be used for online communities, trust or friendship networks should be analyzed with weighted models. To analyze social networks, it is essential to produce realistic social models. However, there are serious differences between social network models and real-life data in terms of their fundamental statistical parameters. In this paper, a genetic algorithm (GA)-based social network improvement method is proposed to produce social networks more similar to real-life data sets. First, it creates …a social model based on existing studies in the literature, and then it improves the model with the proposed GA-based approach based on the similarity of the average degree, the k -nearest neighbor, the clustering coefficient, degree distribution and link overlap. This study can be used to model the structural and statistical properties of large-scale societies more realistically. The performance results show that our approach can reduce the dissimilarity between the created social networks and the real-life data sets in terms of their primary statistical properties. It has been shown that the proposed GA-based approach can be used effectively not only in unweighted networks but also in weighted networks. Show more

Keywords: Genetic algorithm, social network modeling, trust network, online communities

DOI: 10.3233/JIFS-200563

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1597-1608, 2021

Authors: Yang, Jie | Zhou, Wei | Li, Shuai

Article Type: Research Article

Abstract: Vague sets are a further extension of fuzzy sets. In rough set theory, target concept can be characterized by different rough approximation spaces when it is a vague concept. The uncertainty measure of vague sets in rough approximation spaces is an important issue. If the uncertainty measure is not accurate enough, different rough approximation spaces of a vague concept may possess the same result, which makes it impossible to distinguish these approximation spaces for charactering a vague concept strictly. In this paper, this problem will be solved from the perspective of similarity. Firstly, based on the similarity between vague information …granules(VIGs), we proposed an uncertainty measure with strong distinguishing ability called rough vague similarity (RVS). Furthermore, by studying the multi-granularity rough approximations of a vague concept, we reveal the change rules of RVS with the changing granularities and conclude that the RVS between any two rough approximation spaces can degenerate to granularity measure and information measure. Finally, a case study and related experiments are listed to verify that RVS possesses a better performance for reflecting differences among rough approximation spaces for describing a vague concept. Show more

Keywords: Vague sets, uncertainty measure, vague information granule, rough vague similarity, multi-granularity

DOI: 10.3233/JIFS-200611

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1609-1621, 2021

Authors: Iqbal, Shahid | Ullah Khan, Hikmat | Ishfaq, Umar | Alghobiri, Mohammed | Iqbal, Saqib

Article Type: Research Article

Abstract: The social web appears to enrich human lives by providing effective applications for online social interactions. Microblogs are one of the most important applications of the social Web. The Microbloggers who influence the social community users through their content in the form of tweets are known as the influential microbloggers. The identification of such influential microbloggers has vast applications in advertising, online marketing, corporate communication, information dissemination, etc. This paper investigates the problem of identifying influential microbloggers by proposing MIPPLA (Model to identify Influential using Productivity, Popularity and Link Analysis) model which integrates the modules of Productivity and …Popularity . The Productivity module considers a micro-blogger’s activity and the Popularity module identifies a microbloggers influence in an online social community. In addition, we modify the classic PageRank by utilizing the Twitter features such as retweet, mention, and reply for ranking the influential users. The proposed approaches are evaluated using real-world social networks. The results prove that the MIPPLA model efficiently identifies and ranks the top influential users in an effective manner as compared to the existing techniques. Show more

Keywords: Social web, online social networks, microblogs, influential users, big data, data mining

DOI: 10.3233/JIFS-201036

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1623-1637, 2021

Authors: Qahremani, E. | Allahviranloo, T. | Abbasbandy, S. | Ahmady, N.

Article Type: Research Article

Abstract: This paper is concerned with aspects of the analytical fuzzy solutions of the parabolic Volterra partial integro-differential equations under generalized Hukuhara partial differentiability and it consists of two parts. The first part of this paper deals with aspects of background knowledge in fuzzy mathematics, with emphasis on the generalized Hukuhara partial differentiability. The existence and uniqueness of the solutions of the fuzzy Volterra partial integro-differential equations by considering the type of [gH  - p ]-differentiability of solutions are proved in this part. The second part is concerned with the central themes of this paper, using the fuzzy Laplace transform method for …solving the fuzzy parabolic Volterra partial integro-differential equations with emphasis on the type of [gH  - p ]-differentiability of solution. We test the effectiveness of method by solving some fuzzy Volterra partial integro-differential equations of parabolic type. Show more

Keywords: Fuzzy laplace transform, generalized hukuhara partial differentiable, fuzzy parabolic volterra partial integro-differential equation, fuzzy triangular functions

DOI: 10.3233/JIFS-201125

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1639-1654, 2021

Authors: Cheng, Linhai | Zhang, Yu | He, Yingying | Lv, Yuejin

Article Type: Research Article

Abstract: Classical rough set theory (RST) is based on equivalence relations, and does not have an effective mechanism when the attribute value of the objects is uncertain information. However, the information in actual problems is often uncertain, and an accurate or too vague description of the information can no longer fully meet the actual needs. Interval rough number (IRN) can reflect a certain degree of certainty in the uncertainty of the data when describing the uncertainty of the data, and can enable decision makers to make decisions more in line with actual needs according to their risk preferences. However, the current …research on rough set models (RSMs) whose attribute values are interval rough numbers is still very scarce, and they cannot analyze the interval rough number information system (IRNIS) from the perspective of similar relation. therefore, three new interval rough number rough set models (IRNRSMs) based on similar relation are proposed in this paper. Firstly, aiming at the limitations of the existing interval similarity degree (ISD), new interval similarity degree and interval rough number similarity degree (IRNSD) are proposed, and their properties are discussed. Secondly, in the IRNIS, based on the newly proposed IRNSD, three IRNRSMs based on similar class, β -maximal consistent class and β -equivalent class are proposed, and their properties are discussed. And then, the relationships between these three IRNRSMs and those between their corresponding approximation accuracies are researched. Finally, it can be found that the IRNRSM based on the β -equivalent classes has the highest approximation accuracy. Proposing new IRNRSMs based on similar relation is a meaningful contribution to extending the application range of RST. Show more

Keywords: Interval rough number, rough set model, intervals similarity degree, β-equivalent class, approximation accuracy

DOI: 10.3233/JIFS-191096

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1655-1666, 2021

Authors: Sun, Kangjian | Jia, Heming | Li, Yao | Jiang, Zichao

Article Type: Research Article

Abstract: Slime mould algorithm (SMA) is a novel metaheuristic that simulates foraging behavior of slime mould. Regarding its drawbacks and properties, a hybrid optimization (BTβ SMA) based on improved SMA is proposed to produce the higher-quality optimal results. Brownian motion and tournament selection mechanism are introduced into the basic SMA to improve the exploration capability. Moreover, a local search algorithm (Adaptive β -hill climbing, Aβ HC) is hybridized with the improved SMA. It is considered from boosting the exploitation trend. The proposed BTβ SMA algorithm is evaluated in two main phases. Firstly, the two improved hybrid variants (BTβ SMA-1 and BTβ …SMA-2) are compared with the basic SMA algorithm through 16 benchmark functions. Also, the performance of winner is further evaluated through comparisons with 7 state-of-the-art algorithms. The simulation results report fitness and computation time. The convergence curve and boxplot visualize the effects of fitness. The comparison results on the function optimization suggest that BTβ SMA is superior to competitors. Wilcoxon rank-sum test is also employed to investigate the significance of the results. Secondly, the applicability on real-world tasks is proved by solving structure engineering design problems and training multilayer perceptrons. The numerical results indicate the merits of the BTβ SMA algorithm in terms of solution precision. Show more

Keywords: Slime mould algorithm, adaptive β-hill climbing, function optimization, structure engineering design, training multilayer perceptron

DOI: 10.3233/JIFS-201755

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1667-1679, 2021

Authors: Khan, Asif | Li, Jian Ping | Haq, Amin Ul | Memon, Imran | Patel, Sarosh H. | ud. Din, Salah

Article Type: Research Article

Abstract: On-time recovery and treatment of disease is always desirable. The use of Machine learning in health-care has grown very fast to diagnosis the different kinds of diseases in the past few years. In such a diagnosis, past and real-time data are playing very crucial role in using data mining techniques. Still, we are lacking in diagnosing the emotional mental disturbance accurately in the early stages. Thus,the initial diagnosis of depression expressively stances a great problem for both,researchers and clinical professionals. We have addressed the said problem in our proposed work using Pipeline Machine Learning technique where people based on emotional …stages have been effectively classified into different groups in e-healthcare. To implement Hybrid classification, a well known machine learning multi-feature hybrid classifier is used by having the emotional stimulation in form of negative or positive people. In order to improve classification, an Ensemble Learning Algorithm is used which helps in choosing the more suitable features from the available genres-emotion data on online media. Additionally, Hold out validation method has been to split the dataset for training and testing of the predictive model. Further, performance evaluation measures have been applied to check the proposed system evaluation. This study is done on Genres-Tags MovieLens dataset. The experimental results show that applied ensemble method provides optimal classification performance by choosing the best subset of features. The said results proved the excellency of the proposed system which comes from the choosing most related features selected by the Integrated Learning algorithm. Additionally, suggested approach is used to accurately and effectively diagnose the depression in its early stage. It will help in recovery and treatment of depressed people. We conclude that use of the suggested method is highly suitable in all aspects of e-healthcare for depress stimulation. Show more

Keywords: Socialnetworking, human physci, retrieval-ranking, trendprediction, informationretrieval, ML, datascience

DOI: 10.3233/JIFS-201069

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1681-1694, 2021

Authors: Fei, Kaifang | Jiang, Minghui | Zhang, Yadan

Article Type: Research Article

Abstract: In this paper, the matters of dissipativity and finite time synchronization for memristor-based neural networks (MNNs) with mixed time-varying discontinuities are investigated. Firstly, under the framework of extending Filippov differential inclusion theory, several effective new criteria are derived. Then, the global dissipativity of Filippov solution to neural networks is proved by using generalized Halanay inequality and matrix measure method. Secondly, some novel sufficient conditions are introduced to guarantee the finite-time synchronization of the drive-response MNNs based on a simple Lyapunov function and two different feedback controllers. Finally, several numerical examples are given to verify the validity of the theoretical results.

Keywords: Memristor, dissipativity, finite time synchronization, mixed time-varying delayed, neural networks

DOI: 10.3233/JIFS-191397

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1695-1712, 2021

Authors: Kosheleva, Olga | Kreinovich, Vladik

Article Type: Book Review

DOI: 10.3233/JIFS-189471

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1713-1714, 2021

Authors: Kosheleva, Olga | Kreinovich, Vladik

Article Type: Book Review

DOI: 10.3233/JIFS-189472

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1715-1716, 2021

Authors: Kreinovich, Vladik

Article Type: Book Review

DOI: 10.3233/JIFS-189473

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1717-1719, 2021