Journal of Intelligent & Fuzzy Systems - Volume 40, issue 3

Authors: Wang, Zicheng | Chen, Huayou | Zhu, Jiaming | Ding, Zhenni

Article Type: Research Article

Abstract: Faced with the rapid update of nonlinear and irregular big data from the environmental monitoring system, both the public and managers urgently need reliable methods to predict possible air pollutions in the future. Therefore, a multi-scale deep learning (MDL) and optimal combination ensemble (OCE) approach for hourly air quality index (AQI) forecasting is proposed in this paper, named MDL-OCE model. Before normal modeling, all original data are preprocessed through missing data filling and outlier testing to ensure smooth computation. Due to the complexity of such big data, slope-based ensemble empirical mode decomposition (EEMD) is adopted to decompose the time series …of AQI and meteorological conditions into a finite number of simple intrinsic mode function (IMF) components and one residue component. Then, to unify the number of components of different variables, the fine-to-coarse (FC) technique is used to reconstruct all components into high frequency component (HF), low frequency component (LF), and trend component (TC). For purpose of extracting the underlying relationship between AQI and meteorological conditions, the three components are respectively trained and predicted by different deep learning architectures (stacked sparse autoencoder (SSAE)) with a multilayer perceptron (MLP). The corresponding forecasting results of three components are merged by OCE method to better achieve the ultimate AQI forecasting outputs. The empirical results clearly demonstrate that our proposed MDL-OCE model outperforms other advanced benchmark models in terms of forecasting performances in all cases. Show more

Keywords: AQI forecasting, multi-scale deep learning, optimal combination ensemble, meteorological conditions, big data

DOI: 10.3233/JIFS-202481

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5483-5500, 2021

Authors: Sharma, Shalini | Kumar, Naresh | Kaswan, Kuldeep Singh

Article Type: Research Article

Abstract: Big data requires new technologies and tools to process, analyze and interpret the vast amount of high-speed heterogeneous information. A simple mistake in processing software, error in data, and malfunctioning in hardware results in inaccurate analysis, compromised results, and inadequate performance. Thus, measures concerning reliability play an important role in determining the quality of Big data. Literature related to Big data software reliability was critically examined in this paper to investigate: the type of mathematical model developed, the influence of external factors, the type of data sets used, and methods employed to evaluate model parameters while determining the system reliability …or component reliability of the software. Since the environmental conditions and input variables differ for each model due to varied platforms it is difficult to analyze which method gives the better prediction using the same set of data. Thus, paper summarizes some of the Big data techniques and common reliability models and compared them based on interdependencies, estimation function, parameter evaluation method, mean value function, etc. Visualization is also included in the study to represent the Big data reliability distribution, classification, analysis, and technical comparison. This study helps in choosing and developing an appropriate model for the reliability prediction of Big data software. Show more

Keywords: Reliability models, Big data, stochastic equation, hazard rate, jump diffusion

DOI: 10.3233/JIFS-202503

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5501-5516, 2021

Authors: Kişi, Ömer

Article Type: Research Article

Abstract: We investigate the concepts of pointwise and uniform I θ -convergence and type of convergence lying between mentioned convergence methods, that is, equi-ideally lacunary convergence of sequences of fuzzy valued functions and acquire several results. We give the lacunary ideal form of Egorov’s theorem for sequences of fuzzy valued measurable functions defined on a finite measure space ( X , M , μ ) . We also introduce the concept of I θ -convergence in measure for sequences of fuzzy valued functions and proved some …significant results. Show more

Keywords: Pointwise convergence, uniformly convergence, ideal convergence, lacunary convergence, fuzzy-valued function

DOI: 10.3233/JIFS-202624

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5517-5526, 2021

Authors: Zhao, Baohua | Sung, Tien-Wen | Zhang, Xin

Article Type: Research Article

Abstract: The artificial bee colony (ABC) algorithm is one of the classical bioinspired swarm-based intelligence algorithms that has strong search ability, because of its special search mechanism, but its development ability is slightly insufficient and its convergence speed is slow. In view of its weak development ability and slow convergence speed, this paper proposes the QABC algorithm in which a new search equation is based on the idea of quasi-affine transformation, which greatly improves the cooperative ability between particles and enhances its exploitability. During the process of location updating, the convergence speed is accelerated by updating multiple dimensions instead of one …dimension. Finally, in the overall search framework, a collaborative search matrix is introduced to update the position of particles. The collaborative search matrix is transformed from the lower triangular matrix, which not only ensures the randomness of the search, but also ensures its balance and integrity. To evaluate the performance of the QABC algorithm, CEC2013 test set and CEC2014 test set are used in the experiment. After comparing with the conventional ABC algorithm and some famous ABC variants, QABC algorithm is proved to be superior in efficiency, development ability, and robustness. Show more

Keywords: Artificial bee colony algorithm, bioinspired swarm intelligence, optimization, quasi-affine transformation, collaborative search matrix

DOI: 10.3233/JIFS-202712

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5527-5544, 2021

Authors: Zulqarnain, Rana Muhammad | Xin, Xiao Long | Garg, Harish | Khan, Waseem Asghar

Article Type: Research Article

Abstract: The Pythagorean fuzzy soft sets (PFSS) is a parametrized family and one of the appropriate extensions of the Pythagorean fuzzy sets (PFS). It’s also a generalization of intuitionistic fuzzy soft sets, used to accurately assess deficiencies, uncertainties, and anxiety in evaluation. The most important advantage of PFSS over existing sets is that the PFS family is considered a parametric tool. The PFSS can accommodate more uncertainty comparative to the intuitionistic fuzzy soft sets, this is the most important strategy to explain fuzzy information in the decision-making process. The main objective of the present research is to progress some operational laws …along with their corresponding aggregation operators in a Pythagorean fuzzy soft environment. In this article, we introduce Pythagorean fuzzy soft weighted averaging (PFSWA) and Pythagorean fuzzy soft weighted geometric (PFSWG) operators and discuss their desirable characteristics. Also, develop a decision-making technique based on the proposed operators. Through the developed methodology, a technique for solving decision-making concerns is planned. Moreover, an application of the projected methods is presented for green supplier selection in green supply chain management (GSCM). A comparative analysis with the advantages, effectiveness, flexibility, and numerous existing studies demonstrates the effectiveness of this method. Show more

Keywords: Pythagorean fuzzy sets, Pythagorean fuzzy soft sets, PFSWA operator, PFSWG operator, GSCM

DOI: 10.3233/JIFS-202781

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5545-5563, 2021

Authors: Sfiris, D.S.

Article Type: Research Article

Abstract: This paper deals with improving the approximation capability of fuzzy systems. Fuzzy negations produced via conical sections are a promising methodology towards better fuzzy implications in fuzzy rules. The linguistic variables and the fuzzy rules are induced automatically following a fuzzy equivalence relation. The uncertainty of linear or nonlinear systems is thus dealt with. In this study, the clustering is optimized without human intervention, but also the best inference mechanism for a particular dataset is prescribed. It has been found that clustering based on fuzzy equivalence relation and fuzzy inference via conical sections leads to remarkably accurate approximations. A fuzzy …rule based system with fewer control parameters is proposed. An application on telecom data shows the use of the methodology, its applicability to a real problem and its performance compared to other alternatives in terms of quality. Show more

Keywords: Fuzzy inference, fuzzy negation, rule based systems, fuzzy clustering, fuzzy equivalence relation

DOI: 10.3233/JIFS-192029

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5565-5581, 2021

Authors: Jia, Heming | Peng, Xiaoxu

Article Type: Research Article

Abstract: With the advent of the information age, people have higher requirements for basic algorithms. Meta-heuristic algorithms have received wide attention as a high-level strategy to study and generate fully optimized solutions to data-driven optimization problems. Using the advantage of equilibrium optimizer (EO) with better balance mode, combined with the strategy of memetic algorithm, different proportion of temperature is introduced in different stages. That is, EO and thermal exchange optimization (TEO) are fused to obtain a new highly balanced optimizer (HEO). While keeping the guiding strategy and memory mode unchanged of EO, the accuracy of optimization is greatly improved. 14 well-known …benchmark functions and 7 selective algorithms were used for HEO evaluation comparison experiments. On the basis of the fitness function curve, the optimal solution and other experimental data are tested statistically. The experimental results show that the improved algorithm has high accuracy and stability, but at the cost of running a little more time. Application testing of complex engineering problems is also one of the main purposes of algorithm design. In this paper, three typical engineering design problems (three truss, welded beam and rolling bearing design) are tested and the experimental results show that this algorithm has certain competitiveness and superiority in classical engineering design. Show more

Keywords: Equilibrium optimizer, thermal exchange optimization, memetic algorithm, benchmark functions, engineering design problems

DOI: 10.3233/JIFS-200101

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5583-5594, 2021

Authors: Ni, Na | Zhu, Yuanguo

Article Type: Research Article

Abstract: Bacteria foraging optimization (BFO) algorithm is easy to fall into the local optimal solution and slow in convergence. In this paper, we have come up with a self-adaptive bacterial foraging algorithm based on estimation of distribution to overcome the mentioned shortages. First, in the chemotactic operator, the swimming step size of bacterium is adaptively adjusted by its fitness value and bacteria move in a random direction. Second, the bacteria obtain the probability of replication based on the fitness value. We choose half of the population for replication by the roulette wheel method. Finally, the possibility of elimination-dispersal is adjusted by …the fitness value. Selected bacteria are dispersed to the new locations produced by BOX-Muller formula. Compared with some relative heuristic algorithms on finding the optimal value of ten benchmark functions, the proposed algorithm shows higher convergence speed and accuracy. Show more

Keywords: Bacteria foraging optimization algorithm, self-adaptive, estimation of distribution, benchmark function

DOI: 10.3233/JIFS-200439

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5595-5607, 2021

Authors: Huang, Hui-Yu | Lin, Chih-Hung

Article Type: Research Article

Abstract: Inpainting is a technique to enhance digital videos. Based on the spatiotemporal domain, we herein propose a video inpainting method to repair the removal objects in the videos. The method consists of an adaptive foreground model, the motion rate estimation of objects, and a repairing scheme. Initially, the adaptive foreground model based on the background subtraction method is developed. The model is used to estimate the motion rate for each moving object in the frame. According to the estimated motion rate, the model specifies an adaptive interval between the forwarding reference frame and backward reference frame to obtain the useful …information and to repair the removal objects. The remaining un-repaired areas are filled using an exemplar-based inpainting technique with color variance. The results show that the proposed method can produce visually pleasing results. Additionally, it reduces the inpainting time and provides efficient computing. Show more

Keywords: Video inpainting, image inpainting, exemplar-based inpainting, spatiotemporal domain

DOI: 10.3233/JIFS-200542

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5609-5622, 2021

Authors: Zhang, Yu | Yu, Zhengtao | Mao, Cunli | Huang, Yuxin | Gao, Shengxiang

Article Type: Research Article

Abstract: Correlation analysis of law-related news is a task to of dividing news into law-related or law-unrelated news, which is the basis of public opinion analysis. Public opinion news consists of the title and the body. The title describes the theme of the news, and the body describes the content of the news. They are equally important and interdependent in the analysis of lawrelated news. Therefore, we make full use of the dependence between the title and the body and propose a learning method that combines the bidirectional attention flow of the title and the body. This method encodes the title …and the body respectively by using a bidirectional gated recurrent unit (BiGRU) to obtain the word-level feature matrix of the title and the word-level feature matrix of the body. Then it further extracts the law relevant key features from the body feature matrix, to obtain the word-level feature representation of the body. Finally, we combine the word-level feature representation of the title and the body to build bidirectional attention flow. In this way, the information of the two is fully integrated and interacted to improve the accuracy of the legal correlation analysis of news. To verify the validity of the method in this paper, we conducted experiments on the analysis of law-related news. The results show that our method has achieved good results. Compared with the baseline method, the F1 values of our method is increased by 2.2%, which strongly proves that the interaction between title and body has a good supporting effect on news text classification. Show more

Keywords: Law-related news, public opinion analysis, title combined body, bidirectional attention flow

DOI: 10.3233/JIFS-201162

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5623-5635, 2021

Authors: Sun, Rui | Han, Meng | Zhang, Chunyan | Shen, Mingyao | Du, Shiyu

Article Type: Research Article

Abstract: High utility itemset mining (HUIM) with negative utility is an emerging data mining task. However, the setting of the minimum utility threshold is always a challenge when mining high utility itemsets (HUIs) with negative items. Although the top-k HUIM method is very common, this method can only mine itemsets with positive items, and the problem of missing itemsets occurs when mining itemsets with negative items. To solve this problem, we first propose an effective algorithm called THN (Top-k High Utility Itemset Mining with Negative Utility). It proposes a strategy for automatically increasing the minimum utility threshold. In order to solve …the problem of multiple scans of the database, it uses transaction merging and dataset projection technology. It uses a redefined sub-tree utility value and a redefined local utility value to prune the search space. Experimental results on real datasets show that THN is efficient in terms of runtime and memory usage, and has excellent scalability. Moreover, experiments show that THN performs particularly well on dense datasets. Show more

Keywords: Utility mining, high utility itemsets mining, top-k high utility itemsets, negative utility

DOI: 10.3233/JIFS-201357

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5637-5652, 2021

Authors: Sun, Xiaofei | Li, Jianming | Ma, Jialiang | Xu, Huiqing | Chen, Bin | Zhang, Yuefei | Feng, Tao

Article Type: Research Article

Abstract: Chromosome visualization has been used in human chromosome analysis and is a crucial step in clinical diagnosis and drug development. An important step in chromosome visualization is the extraction of chromosomes from chromosome images obtained by light microscopy. Chromosomes often overlap in a complex and variable manner, resulting in significant challenges in chromosome segmentation. The process of chromosome visualization requires manual intervention and is tedious. A method based on a neural network is proposed for the automatic segmentation of overlapping chromosome images to speed up the workflow of visualizing chromosomes. Three improved dilated convolutions are used in the chromosome image …segmentation models based on U-Net. The proposed models successfully segment overlapping chromosomes in two publicly available overlapping chromosome data sets. Our models have better performance than existing overlapping chromosome segmentation methods based on U-Net. In summary, it is demonstrated that the improved dilated convolutions can be used for the automatic segmentation of overlapping chromosome images. The proposed improved dilated convolutions have a stable performance improvement, can be easily extended to the segmentation of multiple overlapping chromosomes, and are suitable as general neural network operations to replace standard convolutions in any network. Show more

Keywords: Overlapping chromosomes, image segmentation, improved dilated convolution, artificial intelligence, light microscopy

DOI: 10.3233/JIFS-201466

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5653-5668, 2021

Authors: Jingni, Guo | Junxiang, Xu | Zhenggang, He

Article Type: Research Article

Abstract: The construction of the Sichuan-Tibet railway is encountered with some problems such as complicated geological conditions, bad climate, active plate movement, and sensitive ecological environment. Therefore, scientific and reasonable site selection is an essential guarantee for the smooth construction of the Sichuan-Tibet railway. Through constructing weighted scoring function and intuitionistic fuzzy similarity model and researching the dynamic intuitionistic fuzzy multi-attribute decision-making method considering time factor, the location decision of client-supplied goods and materials support center for Sichuan-Tibet railway construction can be complete, and the research theories and methods of location problem worldwide can be analyzed. Given the route direction and …engineering construction of the Ya’an-Linzhi section of the Sichuan-Tibet railway, this paper aims to set up seven client-supplied goods and materials support centers as alternative site selection schemes, which integrates six factors (transportation, geological conditions, climate environment, site selection characteristics, engineering construction, and communication conditions) and constructs 12 index systems for client-supplied goods and materials support center location selection. Combining with the index system, the intuitionistic fuzzy decision-making matrices for four periods are established. Besides, using a dynamic intuitionistic multi-attribute decision-making method, the weighted results of similarity decision-making matrices are compared, and the location schemes of client-supplied goods and materials support centers are sequenced. The results demonstrate that Linzhi is the best site selection scheme for the construction client-supplied goods and materials support center of Ya’an to Linzhi section of the Sichuan-Tibet Railway, providing reference significance for supporting the construction of the Sichuan-Tibet Railway Project. Show more

Keywords: Sichuan-tibet railway, client-supplied goods and materials, location decision, dynamic multiple attribute decision, intuitionistic fuzzy set, similarity degree

DOI: 10.3233/JIFS-201572

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5669-5679, 2021

Authors: Imran, | Ahmad, Shabir | Kim, Do Hyeun

Article Type: Research Article

Abstract: Mountains are attraction spots for tourists, and tourism contributes to the country’s gross domestic product. Mountains have many benefits such as biodiversity, tourism, and the supplication of food, to name a few. However, there are challenges to protect mountain lives from hazards such as fire caused by tourist activities in mountains. The in-time fire detection and notification to the authorities have always been the central point in literature studies, and different studies have been carried out to optimize the notification time. In this paper, we model the fire detection and notification as a real-time internet of things application and uses …task orchestration and task scheduling mechanism to provide scalability along with optimal latency. The proposed fire detection and prediction mechanism detect mountain fire at the earliest stage and provide predictive analysis to prevent damage to mountain life and tourists. The architecture is based on microservice-based IoT task orchestration mechanism and device virtualization, which is not only lightweight but also handles a single problem in parallel chunks, thus optimizes the latency. The in-time information about the fire is used for predictive analysis and notified to safety authorities which helps them to make a more informed decisions to minimize the damage caused by mountain fire. The performance of the proposed mechanism is evaluated in terms of different measures such as RMSE, MAPE, MSE, and MAPE. The proposed work approaches the fire detection and notification as a collection of tasks, and thus those tasks are selected for deployment which are guaranteed to be executed and have minimum latency. This idea of pre-planing the latency and task execution is the first attempt to the best of the authors’ knowledge. Show more

Keywords: Internet of things, fire safety, fire detection, fire notification, predictive analysis, microservices, fire tracking, virtual objects

DOI: 10.3233/JIFS-201614

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5681-5696, 2021

Authors: Ding, Xiong | Lu, Yan

Article Type: Research Article

Abstract: In order to solve some optimization problems with many local optimal solutions, a microbial dynamic optimization (MDO) algorithm is proposed by the kinetic theory of hybrid food chain microorganism cultivation with time delay. In this algorithm, it is assumed that multiple microbial populations are cultivated in a culture system. The growth of microbial populations is not only affected by the flow of culture fluid injected into the culture system, the concentration of nutrients and harmful substances, but also by the interaction between the populations. The influence of culture medium which is injected regularly will suddenly increase the concentration of nutrients …and toxic substances, it will suddenly increase the impact on the population. These characteristics are used to construct absorption operators, grabbing operators, hybrid operators, and toxin operators; the global optimal solution of the optimization problem can be quickly solved by these operators and the population growth changes. The simulation experiment results show that the MDO algorithm has certain advantages for solving optimization problems with higher dimensions. Show more

Keywords: Swarm intelligence optimization algorithm, microbial culture kinetics, microbial population, microbial dynamics optimization (MDO)

DOI: 10.3233/JIFS-201828

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5697-5713, 2021

Authors: Sun, Hongchang | wang, Yadong | Niu, Lanqiang | Zhou, Fengyu | Li, Heng

Article Type: Research Article

Abstract: Building energy consumption (BEC) prediction is very important for energy management and conservation. This paper presents a short-term energy consumption prediction method that integrates the Fuzzy Rough Set (FRS) theory and the Long Short-Term Memory (LSTM) model, and is thus named FRS-LSTM. This method can find the most directly related factors from the complex and diverse factors influencing the energy consumption, which improves the prediction accuracy and efficiency. First, the FRS is used to reduce the redundancy of the input features by the attribute reduction of the factors affecting the energy consumption forecasting, and solves the data loss problem caused …by the data discretization of a classical rough set. Then, the final attribute set after reduction is taken as the input of the LSTM networks to obtain the final prediction results. To validate the effectiveness of the proposed model, this study used the actual data of a public building to predict the building’s energy consumption, and compared the proposed model with the LSTM, Levenberg-Marquardt Back Propagation (LM-BP), and Support Vector Regression (SVR) models. The experimental results reveal that the presented FRS-LSTM model achieves higher prediction accuracy compared with other comparative models. Show more

Keywords: Short-term energy consumption prediction, fuzzy rough set, long short-term memory, QuickReduct, public buildings

DOI: 10.3233/JIFS-201857

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5715-5729, 2021

Authors: Tong, Mingyu | Duan, Huiming | Luo, Xilin

Article Type: Research Article

Abstract: In view of the uncertainties in short-time traffic flows and the multimode correlation of traffic flow data, a grey prediction model for short-time traffic flows based on tensor decomposition is proposed. First, traffic flow data are expressed as tensors based on the multimode characteristics of traffic flow data, and the principle of the tensor decomposition algorithm is introduced. Second, the Verhulst model is a classic grey prediction model that can effectively predict saturated S-type data, but traffic flow data do not have saturated S-type data. Therefore, the tensor decomposition algorithm is applied to the Verhulst model, and then, the Verhulst …model of the tensor decomposition algorithm is established. Finally, the new model is applied to short-term traffic flow prediction, and an instance analysis shows that the model can deeply excavate the multimode correlation of traffic flow data. At the same time, the effect of the new model is superior to five other grey prediction models. The predicted results can provide intelligent transportation system planning, control and optimization with reliable real-time dynamic information in a timely manner. Show more

Keywords: Intelligent transportation, short-term traffic flow forecasting, grey model, tensor decomposition

DOI: 10.3233/JIFS-201873

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5731-5741, 2021

Authors: Guo, Yiming | Zhang, Hui | Xia, Zhijie | Dong, Chang | Zhang, Zhisheng | Zhou, Yifan | Sun, Han

Article Type: Research Article

Abstract: The rolling bearing is the crucial component in the rotating machinery. The degradation process monitoring and remaining useful life prediction of the bearing are necessary for the condition-based maintenance. The commonly used deep learning methods use the raw or processed time domain data as the input. However, the feature extracted by these approaches is insufficient and incomprehensive. To tackle this problem, this paper proposed an improved Deep Convolution Neural Network with the dual-channel input from the time and frequency domain in parallel. The proposed methodology consists of two stages: the incipient failure identification and the degradation process fitting. To verify …the effectiveness of the method, the IEEE PHM 2012 dataset is adopted to compare the proposed method and other commonly used approaches. The results show that the improved Deep Convolution Neural Network can effectively describe the degradation process for the rolling bearing. Show more

Keywords: Rolling bearing, Deep Convolution Neural Network, remaining useful life prediction, dual-channel input

DOI: 10.3233/JIFS-201965

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5743-5751, 2021

Authors: Kreinovich, Vladik

Article Type: Book Review

DOI: 10.3233/JIFS-189730

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5753-5755, 2021

Authors: Kreinovich, Vladik

Article Type: Book Review

DOI: 10.3233/JIFS-189731

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5757-5758, 2021