Journal of Intelligent & Fuzzy Systems - Volume 41, issue 6

Authors: Deva, N. | Felix, A.

Article Type: Research Article

Abstract: Bipolar fuzzy graph is more precise than a fuzzy graph when dealing with imprecision as it is focusing on the positive and negative information of each vertex and edge. Nowadays, researchers have utilized bipolar fuzzy graphs in decision-making problems. Bipolar fuzzy competition graphs aid to compute the competition between the vertices in bipolar fuzzy graphs. To depict the best competitions among the competitions of bipolar fuzzy graphs, the best bipolar fuzzy competition graph can be defined using bipolar fuzzy α-cut and the strength of the competition between the vertices can also be determined. Fuzzy graphs are used well to frame …modelling in real-time problems. In particular, when the real-time scenario is modelled using the bipolar fuzzy graph, it gives more precision and flexibility. At present, researchers have focused on decision-making techniques with bipolar fuzzy graphs. The DEMATEL method is one of the powerful decision-making tools. It effectively analyses the complicated digraphs and matrices. The fuzzy DEMATEL technique can convert the interrelations between factors into an intelligible structural model of the system and divide them into cause and effect groups. Therefore, this study attempts to design the DEMATEL method under the bipolar fuzzy environment. To illustrate this proposed technique, the problem of identifying the best mobile network is taken. With this method, the benefits and drawbacks of networks are measured and a complicated bipolar fuzzy directed graph can be transformed into a viewed structure. Show more

Keywords: DEMATEL, bipolar fuzzy graphs, bipolar fuzzy competition graphs, best bipolar fuzzy competition graphs, α-cut

DOI: 10.3233/JIFS-211112

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7257-7273, 2021

Authors: Xing, Yuping

Article Type: Research Article

Abstract: The recently proposed q -rung orthopair fuzzy set (q -ROFS) whose main feature is that the qth power of membership degree (MD) and the qth power of non-membership degree (NMD) is equal to or less than 1, is a powerful tool to describe uncertainty. The major contribution of this paper lies to investigate power point average (PPA) aggregation operators with q -rung orthopair fuzzy information based on Frank t-conorm and t-norm. Since the existing power average (PA) operators all rely on the traditional distance measures to measure support degree between the input values, it cannot reflect decision makers’ attitude. In …response, this paper introduces firstly a series of distance measures for q -rung orthopair fuzzy numbers (q -ROFNs) based on point operators, from which the corresponding support measures can be obtained. Secondly, based on the proposed point distance measures, new Frank power point average aggregation operators are proposed to aggregate q -rung orthopair fuzzy information. Finally, a novel multiple attribute decision making (MADM) technique is presented based on the proposed Frank power point average aggregation operators. The developed MADM method not only can get more objective information, but also avoid the influence of unduly high or low attribute values on the decision result, providing a new way for decision makers (DMs) under q -rung orthopair fuzzy environment. Show more

Keywords: Multi-attribute decision making, q-Rung orthopair fuzzy set, frank operational laws, q-Rung orthopair fuzzy Frank power point aggregation operators

DOI: 10.3233/JIFS-211152

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7275-7297, 2021

Authors: Talafha, Mohammad | Alkouri, Abd Ulazeez | Alqaraleh, Sahar | Zureigat, Hamzeh | Aljarrah, Anas

Article Type: Research Article

Abstract: Decision-makers (DMs) usually face many obstacles to give the right decision, multiplicity of them highlights a problem to represent a set of potential values to assign a collective membership degree of an object to a set for several DM’s opinions. However, a hesitant fuzzy set (HFS) deals with such problems. The complexity appears in DM’s opinion which can be changed for the same object but with different times/phases. Each of them has a set of potential values in different times/phases of an object. In this paper, the periodicity of hesitant fuzzy information is studied and applied by extending the range …of HFS from [0, 1] to the unit disk in the complex plane to provide more ability for illustrating the full meaning of information to overcome the obstacles in decision making in the mathematical model. Moreover, the advantage of complex hesitant fuzzy set (CHFS) is that the amplitude and phase terms of CHFSs can represent hesitant fuzzy information, some basic operations on CHFS are also presented and we study its properties, in addition, several aggregation operators under CHFS are introduced, also, the relation between CHFS and complex intuitionistic fuzzy sets (CIFS) are presented. Finally, an efficient algorithm with a consistent process and an application in multiple attributes decision-making (MADM) problems are presented to show the effectiveness of the presented approach by using CHFS aggregation operators. Show more

Keywords: Hesitant fuzzy set, complex fuzzy sets, complex intuitionistic fuzzy sets, complex hesitant fuzzy sets, CIF aggregation operators, CHF aggregation operators

DOI: 10.3233/JIFS-211156

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7299-7327, 2021

Authors: Zhang, Yanhan | Tian, Shengwei | Yu, Long | Ren, Yuan | Gao, Zhongyu | Hou, Long

Article Type: Research Article

Abstract: In recent years, the incidence of skin diseases has increased significantly, and some malignant tumors caused by skin diseases have brought great hidden dangers to people’s health. In order to help experts perform lesion measurement and auxiliary diagnosis, automatic segmentation methods are very needed in clinical practice. Deep learning and contextual information extraction methods have been applied to many image segmentation tasks. However, their performance is limited due to insufficient training of a large number of parameters and these parameters sometimes fail to capture long-term dependencies. In addition, due to the many interfering factors of the skin disease image, the …complex boundary and the uncertain size and shape of the lesion, the segmentation of the skin disease image is still a challenging problem. To solve these problems, we propose a long-distance contextual attention network(LCA-Net). By connecting the non-local module and the channel attention (CAM) in parallel to form a non-local operation, the long-term dependence is captured from the two dimensions of space and channel to enhance the network’s ability to extract features of skin diseases. Our method has an average Jaccard index of 0.771 on the ISIC2017 dataset, which represents a 0.6%improvement over the ISIC2017 Challenge Champion model. The average Jaccard index of 5-fold cross-validation on the ISIC2018 dataset is 0.8256. At the same time, we also compared with some advanced methods of image segmentation, the experimental results show our proposed method has a competitive performance. Show more

Keywords: Skin lesion segmentation, attentional mechanism, artificial intelligence, deep learning

DOI: 10.3233/JIFS-211182

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7329-7340, 2021

Authors: Jin, Wenbin | Cui, Wenxia | Wang, Zhenjie

Article Type: Research Article

Abstract: Finite-time synchronization is concerned for the fractional-order complex-valued fuzzy cellular neural networks (FOCVFCNNs) with leakage delay and time-varying delays. Without using the usual complex-valued system decomposition method, this paper designs the different forms of the controllers by using 2-norm. And we construct the appropriate Lyapunov functional and apply inequality analytical techniques, some new sufficient conditions are obtained to ensure finite-time synchronization of the FOCVFCNNs. The upper bound of setting-time function is obtained. Finally, numerical examples are examined to illustrate the effectiveness of the analytical results.

Keywords: Finite-time synchronization, fractional-order, complex-valued, time-varying delays

DOI: 10.3233/JIFS-211183

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7341-7351, 2021

Authors: El-Bably, M.K. | Al-shami, T.M. | Nawar, A.S. | Mhemdi, A.

Article Type: Research Article

Abstract: The main aims of this paper are to show that some results presented in [1 ] are erroneous. To this end, we provide some counterexamples to demonstrate our claim, and give the correct form of the incorrect results in [1 ]. Also, some improvements for the definition of accuracy measure is proposed. Furthermore, we show that the relationships given in the three figures need not be true in general, and determine the conditions under which they are correct. Finally, a medical application in the decision-making of the diagnosis of dengue fever is examined.

Keywords: Rough sets, lower and upper approximations, j-neighborhood, j-adhesion neighborhood, accuracy measure

DOI: 10.3233/JIFS-211198

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7353-7361, 2021

Authors: Sharma, Sonali | Singh, Uday Pratap | Raj, Kuldip

Article Type: Research Article

Abstract: The purpose of this article is to study deferred Cesrào statistical convergence of order (ξ , ω ) associated with a modulus function involving the concept of difference sequences of fuzzy numbers. The study reveals that the statistical convergence of these newly formed sequence spaces behave well for ξ  ≤ ω and convergence is not possible for ξ  > ω . We also define p -deferred Cesàro summability and establish several interesting results. In addition, we provide some examples which explain the validity of the theoretical results and the effectiveness of constructed sequence spaces. Finally, with the help of MATLAB software, …we examine that if the sequence of fuzzy numbers is bounded and deferred Cesàro statistical convergent of order (ξ , ω ) in (Δ , F , f ), then it need not be strongly p -deferred Cesàro summable of order (ξ , ω ) in general for 0 < ξ  ≤ ω  ≤ 1. Show more

Keywords: Statistical convergence, fuzzy sequence, deferred Cesàro mean, modulus function, difference sequence space

DOI: 10.3233/JIFS-211201

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7363-7372, 2021

Authors: Du, Cheng | Ye, Jun

Article Type: Research Article

Abstract: The ambiguity and uncertainty of human cognition about actual engineering problems are very challenging and indispensable issues in the information expression and aggregation. However, existing various cubic (hesitant) concepts may not reasonably represent the hybrid information of both an interval-valued fuzzy value and a fuzzy sequence with identical and/or different fuzzy values, which commonly occurs in engineering fields. To express the hybrid information, this paper first proposes the notion of a cubic fuzzy multi-valued set as a new extension of existing cubic (hesitant) notions and defines operational relations of cubic fuzzy multi-valued elements. To obtain reasonable operations between different fuzzy …sequence lengths in cubic fuzzy multi-valued elements, cubic fuzzy multi-valued elements are transformed into cubic fuzzy-consistency elements based on the average value and consistency degree/level (complement of standard deviation) of a fuzzy sequence in a cubic fuzzy multi-valued element. Next, we present operations of cubic fuzzy-consistency elements and an expected value of a cubic fuzzy-consistency element for ranking cubic fuzzy-consistency elements. Further, we propose a cubic fuzzy-consistency hybrid weighted arithmetic and geometric averaging operator, and then develop a multi-attribute group decision-making model using the cubic fuzzy-consistency hybrid weighted arithmetic and geometric averaging operator and expected value of cubic fuzzy-consistency elements to solve group decision-making problems under the cubic fuzzy multi-valued environment. To reflect the feasibility and effectiveness of the developed group decision-making model, the developed group decision-making model is utilized in an example on the selection problem of slope design schemes regarding an open pit mine in the cubic fuzzy multi-valued environment. Comparative analysis indicates the flexibility and rationality of the developed group decision-making model. Show more

Keywords: Cubic fuzzy multi-valued set, expected value, cubic fuzzy-consistency hybrid weighted arithmetic and geometric averaging operator, group decision making, slope design scheme

DOI: 10.3233/JIFS-211205

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7373-7386, 2021

Authors: Xiao, Huimin | Wang, Meiqi

Article Type: Research Article

Abstract: In this paper, we mainly extended the study of fuzzy matroid related problems to research the fuzzy decision method. Considering the ambiguity of actual event information and evaluation, we chose hesitant fuzzy set as the extended data set. To construct the hesitant fuzzy matroid, we defined the satisfaction function of hesitant fuzzy set combining hesitant fuzzy index entropy and score function, and defined the mapping function of fuzzy matroid through this function. We also defined the algorithm of hesitant fuzzy matroid and proved the theory of rank, basis of hesitant fuzzy matroid.

Keywords: Fuzzy entropy, fuzzy matroid, hesitant fuzzy matroid, rank

DOI: 10.3233/JIFS-211213

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7387-7396, 2021

Authors: Shu, Lei | Huang, Kun | Jiang, Wenhao | Wu, Wenming | Liu, Hongling

Article Type: Research Article

Abstract: It is easy to lead to poor generalization in machine learning tasks using real-world data directly, since such data is usually high-dimensional dimensionality and limited. Through learning the low dimensional representations of high-dimensional data, feature selection can retain useful features for machine learning tasks. Using these useful features effectively trains machine learning models. Hence, it is a challenge for feature selection from high-dimensional data. To address this issue, in this paper, a hybrid approach consisted of an autoencoder and Bayesian methods is proposed for a novel feature selection. Firstly, Bayesian methods are embedded in the proposed autoencoder as a special …hidden layer. This of doing is to increase the precision during selecting non-redundant features. Then, the other hidden layers of the autoencoder are used for non-redundant feature selection. Finally, compared with the mainstream approaches for feature selection, the proposed method outperforms them. We find that the way consisted of autoencoders and probabilistic correction methods is more meaningful than that of stacking architectures or adding constraints to autoencoders as regards feature selection. We also demonstrate that stacked autoencoders are more suitable for large-scale feature selection, however, sparse autoencoders are beneficial for a smaller number of feature selection. We indicate that the value of the proposed method provides a theoretical reference to analyze the optimality of feature selection. Show more

Keywords: Autoencoder, Bayesian method, feature selection, high-dimensional data

DOI: 10.3233/JIFS-211348

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7397-7406, 2021

Authors: Bhuvaneswari, R. | Ganesh Vaidyanathan, S.

Article Type: Research Article

Abstract: Diabetic Retinopathy (DR) is one of the most common diabetic diseases that affect the retina’s blood vessels. Too much of the glucose level in blood leads to blockage of blood vessels in the retina, weakening and damaging the retina. Automatic classification of diabetic retinopathy is a challenging task in medical research. This work proposes a Mixture of Ensemble Classifiers (MEC) to classify and grade diabetic retinopathy images using hierarchical features. We use an ensemble of classifiers such as support vector machine, random forest, and Adaboost classifiers that use the hierarchical feature maps obtained at every pooling layer of a convolutional …neural network (CNN) for training. The feature maps are generated by applying the filters to the output of the previous layer. Lastly, we predict the class label or the grade for the given test diabetic retinopathy image by considering the class labels of all the ensembled classifiers. We have tested our approaches on the E-ophtha dataset for the classification task and the Messidor dataset for the grading task. We achieved an accuracy of 95.8% and 96.2% for the E-ophtha and Messidor datasets, respectively. A comparison among prominent convolutional neural network architectures and the proposed approach is provided. Show more

Keywords: Diabetic retinopathy, convolutional neural network(CNN), feature extraction, ensemble of classifiers

DOI: 10.3233/JIFS-211364

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7407-7419, 2021

Authors: Liang, Pei | Hu, Junhua | Chin, KwaiSang

Article Type: Research Article

Abstract: The use of probabilistic linguistic preference relations (PLPRs) in pairwise comparisons enhances the flexibility of quantitative decision making. To promote the application of probabilistic linguistic term sets (PLTSs) and PLPRs, this paper introduces the consistency and consensus measures and adjustment strategies to guarantee the rationality of preference information utilized in the group decision making process. First of all, a novel entropy-based similarity measure is developed with PLTSs. Hereafter an improved consistency measure is defined on the basis of the proposed similarity measure, and a convergent algorithm is constructed to deal with the consistency improving process. Furthermore, a similarity-based consensus measure …is developed in a given PLPR, and the consensus reaching process is presented to deal with the unacceptable consensus degree. The proposed consistency improving and consensus reaching processes follow a principle of minimum information loss, called a local adjustment strategy. In particular, the presented methods not only overcome the deficiencies in existing studies but also enhance the interpretation and reduce the complexity of the group decision making process. Finally, the proposed consistency measure and improving process, as well as consensus measure and reaching process are verified through a numerical example for the medical plan selection issue. The result and in-depth comparison analysis validate the feasibility and effectiveness of the proposed methods. Show more

Keywords: Group decision making, PLTSs, PLPRs, consistency, consensus

DOI: 10.3233/JIFS-211371

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7421-7445, 2021

Authors: Ergun, Halime

Article Type: Research Article

Abstract: Fiber and vessel structures located in the cross-section are anatomical features that play an important role in identifying tree species. In order to determine the microscopic anatomical structure of these cell types, each cell must be accurately segmented. In this study, a segmentation method is proposed for wood cell images based on deep convolutional neural networks. The network, which was developed by combining two-stage CNN structures, was trained using the Adam optimization algorithm. For evaluation, the method was compared with SegNet and U-Net architectures, trained with the same dataset. The losses in these models trained were compared using IoU (Intersection …over Union), accuracy, and BF-score measurements on the test data. The automatic identification of the cells in the wood images obtained using a microscope will provide a fast, inexpensive, and reliable tool for those working in this field. Show more

Keywords: Image segmentation, fiber-vessel, microscopic wood cells, deep convolutional neural networks

DOI: 10.3233/JIFS-211386

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7447-7456, 2021

Authors: Yu, Xiaobing | Wu, Xuejing | Chen, Hong | Wang, Xuming | Li, Chenliang | Ji, Zhonghui

Article Type: Research Article

Abstract: Social vulnerability assessment is of great significance for risk management and reduction. Carrying out the assessment is beneficial to the sustainability of the development of society and the economy. For this purpose, Jiangsu province in China is taken as the study area to explore the social vulnerability assessment at a city level. A framework has been constructed from three dimensions of demographics, economics, and social security. In our study, a new approach based on the maximizing deviation method and TODIM model is proposed to evaluate social vulnerability in Jiangsu province. For the sake of analysis, we divide 13 cities of …Jiangsu province into three parts, namely the southern part, central part, and northern part, according to the geographical location. As a result, the north part performance of social vulnerability is the worst among the three regions. The average of the northern part has always obviously exceeded the others of Jiangsu province from 2012 to 2017, which indicates that the north part is the most vulnerable to natural hazards. In addition, the performance of the southern part is relatively better than that of the central region. Especially, Suqian has always been at the bottom from 2012 to 2017, which reveals the ability to withstand natural disasters is the most insufficient. Our findings also imply that social vulnerability is related to local economic development to some extent. Show more

Keywords: Social vulnerability, maximizing deviation method, TODIM model, risk management

DOI: 10.3233/JIFS-211428

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7457-7471, 2021

Authors: Chu, Xiaolin | Zhao, Ruijuan

Article Type: Research Article

Abstract: Building carbon emission prediction plays an irreplaceable role in low-carbon economy development, public health protection and environmental sustainability. It is significant to identify influential factors mainly contributed to building emission and predict emission accurately in order to harness the growth from the source. In this paper, 11 influencing factors of building carbon emission are identified and a support vector regression (SVR) prediction model is proposed to forecast building carbon emission considering improvement the prediction accuracy, generalization, and robustness. In the SVR model, parameters are optimized by particle swarm optimization (PSO) algorithm with the aim to improve performance. Cases in Shanghai’s …building sector are adopted to demonstrate practical applications of the proposed PSO-SVR prediction model. The results indicate that the presented prediction system has an outstanding performance in forecasting building carbon emission under multi-criteria evaluation. Furthermore, compared to the results from other four prediction models (e.g., linear regression, decision tree), it is shown that PSO-SVR model can achieve higher accuracy (e.g., improvement average of 1.01% R2 under training subset), better generalization (e.g., improvement average of 19.89% R2 under testing subset), and better robustness (e.g., improvement average of 18.93% R2 under different levels of noise intensity). Show more

Keywords: Building carbon emission, prediction model, support vector regression (SVR), particle swarm optimization (PSO), low-carbon economy development

DOI: 10.3233/JIFS-211435

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7473-7484, 2021

Authors: Wang, Fen | Ali, Zeeshan | Mahmood, Tahir | Zeng, Shouzhen

Article Type: Research Article

Abstract: The Muirhead mean (MM) operators offer a flexible arrangement with its modifiable factors because of Muirhead’s general structure. On the other hand, MM aggregation operators perform a significant role in conveying the magnitude level of options and characteristics. In this manuscript, the complex spherical fuzzy uncertain linguistic set (CSFULS), covering the grade of truth, abstinence, falsity, and their uncertain linguistic terms is proposed to accomplish with awkward and intricate data in actual life dilemmas. Furthermore, by using the MM aggregation operators with the CSFULS, the complex spherical fuzzy uncertain linguistic MM (CSFULMM), complex spherical fuzzy uncertain linguistic weighted MM (CSFULWMM), …complex spherical fuzzy uncertain linguistic dual MM (CSFULDMM), complex spherical fuzzy uncertain linguistic dual weighted MM (CSFULDWMM) operators, and their important results are also elaborated with the help of some remarkable cases. Additionally, multi-attribute decision-making (MADM) based on the Multi-MOORA (Multi-Objective Optimization Based on a Ratio Analysis plus full multiplicative form), and proposed operators are developed. To determine the rationality and reliability of the elaborated approach, some numerical examples are illustrated. Finally, the supremacy and comparative analysis of the elaborated approaches with the help of graphical expressions are also developed. Show more

Keywords: Complex spherical fuzzy uncertain linguistic sets, Muirhead mean Aggregation operators, Dual Muirhead mean Aggregation operators, Multi-attribute decision-making methods.

DOI: 10.3233/JIFS-211455

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7485-7510, 2021

Authors: Li, Chao | Yan, Yeyu | Zhao, Zhongying | Luo, Jun | Zeng, Qingtian

Article Type: Research Article

Abstract: Owing the continuous enrichment of mobile application resources, mobile applications carry almost all user behaviors and preferences. The analysis of user behavior regarding mobile terminals has become an important research direction. The frequency with which users click on mobile applications reflects their preferences to a certain extent. In this study, we propose a mobile application click-frequency prediction model based on heterogeneous information network representation. This model first constructs a heterogeneous information network between users’ mobile devices and mobile applications. To generate a meaningful sequence of network-embedded nodes, we perform a random walk on a specified meta-path. Finally, the prediction of …users’ mobile application click frequency is completed using representation fusion and matrix factorization. Experiments show that our method outperforms other baseline methods in terms of the mean absolute error and root mean square error. Therefore, the application of a heterogeneous information network representation method to the prediction model is effective. This study is significant to the behavior research of mobile terminal users. Show more

Keywords: Heterogeneous information network, network representation learning, prediction algorithm, mobile application

DOI: 10.3233/JIFS-211488

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7511-7526, 2021

Authors: Vo, Tham

Article Type: Research Article

Abstract: Recently, many pre-trained text embedding models have been applied to effectively extract latent features from texts and achieve remarkable performance in various downstream tasks of sentiment analysis domain. However, these pre-trained text embedding models also encounter limitations related to the capability preserving the syntactical structure as well as the global long-range dependent relationships of words. Thus, they might fail to recognize the relevant syntactical features of words as valuable evidences for analyzing sentiment aspects. To overcome these limitations, we proposed a novel deep semantic contextual embedding technique for sentiment analysis, called as: SE4SA. Our proposed SE4SA is a multi-level text …embedding model which enables to jointly exploit the long-range syntactical and sequential representations of texts. Then, these achieved rich semantic textual representations can support to have a better understanding on the sentiment aspects of the given text corpus, thereby resulting the better performance on sentiment analysis task. Extensive experiments in several benchmark datasets demonstrate the effectiveness or our proposed SE4SA model in comparing with recent state-of-the-art model. Show more

Keywords: Sentiment analysis, GCN, BERT, attention, masked language model

DOI: 10.3233/JIFS-211535

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7527-7546, 2021

Authors: Ma, Qihang | Zhang, Jian | Zhang, Jiahao

Article Type: Research Article

Abstract: Local information coding helps capture the fine-grained features of the point cloud. The point cloud coding mechanism should be applicable to the point cloud data in different formats. However, the local features of the point cloud are directly affected by the attributes, size and scale of the object. This paper proposes an Adaptive Locally-Coded point cloud classification and segmentation Network coupled with Genetic Algorithm(ALCN-GA), which can automatically adjust the size of search cube to complete network training. ALCN-GA can adapt to the features of 3D data at different points, whose adjustment mechanism is realized by designing a robust crossover and …mutation strategy. The proposed method is tested on the ModelNet40 dataset and S3DIS dataset. Respectively, the overall accuracy and average accuracy is 89.5% and 86.5% in classification, and overall accuracy and mIoU of segmentation is 80.34% and 51.05%. Compared with PointNet, average accuracy in classification and mIoU of segmentation is improved about 10% and 11% severally. Show more

Keywords: Genetic algorithm, 3D classification, segmentation, deep learning, local coding

DOI: 10.3233/JIFS-211541

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7547-7562, 2021

Authors: Zhang, Jinping | Deng, Xiaoping | Li, Chengdong | Su, Guanqun | Yu, Yulong

Article Type: Research Article

Abstract: Building energy consumption (BEC) prediction often requires constructing a corresponding model for each building based historical data. However, the constructed model for one building is difficult to be reused in other buildings. Recent approaches have shown that cloud-edge collaboration architecture is promising in realizing model reuse. How to complete the reuse of cloud energy consumption prediction models at the edge and reduce the computational cost of the model training is one of the key issues that need to be solved. To handle the above problems, a cloud-edge collaboration based transferring prediction method for BEC is proposed in this paper. Specifically, …a model library stored prediction models for different types of buildings is constructed based the historical energy consumption data and the long short-term memory (LSTM) network in the cloud firstly; then, the similarity measurement strategies of time series with different granularity are given, and the model to be transferred from the model library is matched by analyzing the similarity between observation data uploaded to the cloud and the historical data collected in the cloud; finally, the fine-tuning strategy of the matching prediction model is given, and this model is fine-tuned at the edge to achieve its reuse in concrete application scenarios. Experiments on practical datasets reveal that compared with the prediction model which doesn’t utilize the transfer strategy, the proposed prediction model has better performance according to MAE and RMSE. Experimental results also confirm that the proposed method effectively reduces the computational cost of the network training at the edge. Show more

Keywords: Cloud-edge collaboration, transfer learning, data driven, similarity analysis, energy consumption prediction

DOI: 10.3233/JIFS-211607

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7563-7575, 2021

Authors: Lin, Shaopei | Zhu, Wei

Article Type: Research Article

Abstract: This paper summarizes the relationship of subjective information with artificial intelligence (AI) technology and points out how the role of subjective information and its position in AI. Eventually, the characteristic of digital era is the “softening of the theories and hardening of the experiences”. Subjective information is widely used in digital revolution for transforming the qualitative estimations into quasi-quantitative solutions, such as the empirical methods in decision making for quantitative management, etc., it will be the transferor for realizing it. The theoretical formulation of how subjective information is digitized through “Fuzzy-AI Model” for digital revolution is presented in this paper; …it has becoming a universal problem solver of utilizing AI technology for quantizing the degree uncertainties in decision-making and fuzzy estimation. Besides, the “Big Data” searching will heavily depend on the completeness of its source information, yet “subjective information” approach can directly predict human thinking or the internal law of complicated objective events into an explicit digital form, for the completeness of source information to make the correct and comprehensive “Big Data” prediction possible. Practical case studies are presented. Show more

Keywords: Subjective information, AI application, mathematical operator, fuzzy-AI model, intelligent design

DOI: 10.3233/JIFS-211624

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7577-7587, 2021

Authors: Yihong, Li | Yunpeng, Wang | Tao, Li | Xiaolong, Lan | Han, Song

Article Type: Research Article

Abstract: DBSCAN (density-based spatial clustering of applications with noise) is one of the most widely used density-based clustering algorithms, which can find arbitrary shapes of clusters, determine the number of clusters, and identify noise samples automatically. However, the performance of DBSCAN is significantly limited as it is quite sensitive to the parameters of eps and MinPts . Eps represents the eps-neighborhood and MinPts stands for a minimum number of points. Additionally, a dataset with large variations in densities will probably trap the DBSCAN because its parameters are fixed. In order to overcome these limitations, we propose a new …density-clustering algorithm called GNN-DBSCAN which uses an adaptive Grid to divide the dataset and defines local core samples by using the Nearest Neighbor. With the help of grid, the dataset space will be divided into a finite number of cells. After that, the nearest neighbor lying in every filled cell and adjacent filled cells are defined as the local core samples. Then, GNN-DBSCAN obtains global core samples by enhancing and screening local core samples. In this way, our algorithm can identify higher-quality core samples than DBSCAN. Lastly, give these global core samples and use dynamic radius based on k-nearest neighbors to cluster the datasets. Dynamic radius can overcome the problems of DBSCAN caused by its fixed parameter eps. Therefore, our method can perform better on dataset with large variations in densities. Experiments on synthetic and real-world datasets were conducted. The results indicate that the average Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), Adjusted Mutual Information (AMI) and V-measure of our proposed algorithm outperform the existing algorithm DBSCAN, DPC, ADBSCAN, and HDBSCAN. Show more

Keywords: Density-based clustering algorithm, Grid, The nearest neighbor, DBSCAN

DOI: 10.3233/JIFS-211922

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7589-7601, 2021

Authors: More, Sujeet | Singla, Jimmy

Article Type: Research Article

Abstract: Knee rheumatoid arthritis (RA) is the highly prevalent, chronic, progressive condition in the world. To diagnose this disease in the early stage in detail analysis with magnetic resonance (MR) image is possible. The imaging modality feature allows unbiased assessment of joint space narrowing (JSN), cartilage volume, and other vital features. This provides a fine-grained RA severity evaluation of the knee, contrasted to the benchmark, and generally used Kellgren Lawrence (KL) assessment. In this research, an intelligent system is developed to predict KL grade from the knee dataset. Our approach is based on hybrid deep learning of 50 layers (ResNet50) with …skip connections. The proposed approach also uses Adam optimizer to provide learning linearity in the training stage. Our approach yields KL grade and JSN for femoral and tibial tissue with lateral and medial compartments. Furthermore, the approach also yields area under curve (AUC) of 0.98, accuracy 96.85%, mean absolute error (MAE) 0.015, precision 98.31%, and other commonly used parameters for the existence of radiographic RA progression which is improved than the existing state-of-the-art. Show more

Keywords: Magnetic resonance imaging, ResNet50, MultiResUNet, Sparse aware noise reduction Convolutional neural network (SANR_CNN), Adam optimizer

DOI: 10.3233/JIFS-212015

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7603-7614, 2021