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

Authors: Qin, Bin

Article Type: Research Article

Abstract: In reality there are always a large number of complex massive databases. The notion of homomorphism may be a mathematical tool for studying data compression in knowledge bases. This paper investigates a knowledge base in dynamic environments and its data compression with homomorphism, where “dynamic” refers to the fact that the involved information systems need to be updated with time due to the inflow of new information. First, the relationships among knowledge bases, information systems and relation information systems are illustrated. Next, the idea of non-incremental algorithm for data compression with homomorphism and the concept of dynamic knowledge base are …introduced. Two incremental algorithms for data compression with homomorphism in dynamic knowledge bases are presented. Finally, an experimental analysis is employed to demonstrate the applications of the non-incremental algorithm and the incremental algorithms for data compression when calculating the knowledge reduction of dynamic knowledge bases. Show more

Keywords: Dynamic knowledge base, knowledge reduction, data compression, homomorphism

DOI: 10.3233/JIFS-210136

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6331-6341, 2021

Authors: He, Yanling | Yao, Chunji

Article Type: Research Article

Abstract: An information system (IS), an important model in the field of artificial intelligence, takes information structure as the basic structure. A fuzzy probabilistic information system (FPIS) is the combination of some fuzzy relations in the same universe that satisfy probability distribution. A FPIS as an IS with fuzzy relations includes three types of uncertainties (i.e., roughness, fuzziness and probability). This paper studies information structures in a FPIS from the perspective of granular computing (GrC). Firstly, two types of information structures in a FPIS are defined by set vectors. Then, equality, dependence and independence between information structures in a FPIS are …proposed, and they are depicted by means of the inclusion degree. Next, information distance between information structures in a FPIS is presented. Finally, entropy measurement for a FPIS is investigated based on the proposed information structures. These results may be helpful for understanding the nature of structures and uncertainty in a FPIS. Show more

Keywords: Fuzzy relation, FPIS, GrC, information structure, dependence, distance, uncertainty, measurement, entropy

DOI: 10.3233/JIFS-210149

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6343-6361, 2021

Authors: Mahmood, Asma | Abbas, Mujahid

Article Type: Research Article

Abstract: A group decision-making process is introduced by utilizing the influence model together with a matrix of interpersonal influences and an opinion matrix. The opinion matrix is constructed with the opinions/advice from one group of experts towards the other. Experts are divided into two groups, one which has more experienced, skilled and qualified persons is known as the group of opinion leaders and the other is known as the group of opinion followers. Sometimes, decision-makers are ordinary agents and their opinion formation is profoundly influenced by opinion leaders. The truthfulness of opinion leaders and the interpersonal influences of decision-makers is also …taken into account. Also, a modified definition of trust score evaluation is presented with the understanding of the fact that the maximum trust which a decision-maker can do upon some opinion leader is his/her truthfulness. On the basis of this definition, a trust score matrix is constructed and the influence model is modified to take into account that matrix. Show more

Keywords: Group decision making, opinion dynamics, trust score evaluations, influence model

DOI: 10.3233/JIFS-210161

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6363-6373, 2021

Authors: Maya, Mario | Yu, Wen | Telesca, Luciano

Article Type: Research Article

Abstract: Neural networks have been successfully applied for modeling time series. However, the results of long-term prediction are not satisfied. In this paper, the modified Meta-Learning is applied to the neural model. The normal Meta-Learning is modified by time-varying learning rates and adding a momentum term to improve convergence speed and robustness property. The stability of the learning process is proven. Finally, two experiments are presented to evaluate the proposed method. The first one shows an improvement in earthquakes prediction in the long-term, and the second one is a classical Benchmark problem. In both experiments, the modified Meta-Learning technique minimizes remarkably …the mean square error index. Show more

Keywords: Meta-learning, neural networks, long-term earthquake prediction

DOI: 10.3233/JIFS-210173

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6375-6388, 2021

Authors: Bhatia, Tanveen Kaur | Kumar, Amit | Appadoo, S.S.

Article Type: Research Article

Abstract: Enayattabr et al. (Journal of Intelligent and Fuzzy Systems 37 (2019) 6865– 6877) claimed that till now no one has proposed an approach to solve interval-valued trapezoidal fuzzy all-pairs shortest path problems (all-pairs shortest path problems in which distance between every two nodes is represented by an interval-valued trapezoidal fuzzy number). Also, to fill this gap, Enayattabr et al. proposed an approach to solve interval-valued trapezoidal fuzzy all-pairs shortest path problems. In this paper, an interval-valued trapezoidal fuzzy shortest path problem is considered to point out that Enayattabr et al.’s approach fails to find correct shortest distance between two fixed nodes. Hence, it …is inappropriate to use Enayattabr et al.’s approach in its present from. Also, the required modifications are suggested to resolve this inappropriateness of Enayattabr et al.’s approach. Show more

Keywords: Interval-valued trapezoidal fuzzy all-pairs shortest path problem, interval-valued trapezoidal fuzzy numbers, signed distance ranking

DOI: 10.3233/JIFS-210182

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6389-6406, 2021

Authors: Narendiranath Babu, T. | Singh, Prabhu Pal | Somesh, M. | Jha, Harshit Kumar | Rama Prabha, D. | Venkatesan, S. | Ramesh Babu, V.

Article Type: Research Article

Abstract: The planetary gearbox works on an epicyclic gear train consisting of sun gear meshed with planets gears and ring gear. It got advantages due to its large torque to weight ratio and reduced vibrations. It is mostly employed in analog clocks, automobile automatic gearbox, Lathe machines, and other heavy industries. Therefore, it was imperative to analyze the various faults occurring in a gearbox. Furthermore, come up with a method so that failures can be avoided at the early stage. It was also a reason why it became the field of intensive research. Moreover, the technology of neural networks emerged recently, …where machine learning models are trained to detect uneven vibrations on their own. This attracted many researchers to perform the study to devise their own methods of prediction. The central concept of fault prediction by the neural network without human beings’ interference inspired this study. Most industries always wanted to know if their operation line is working fine or not. In this study, an attempt was made to apply the method of deep learning on one of the most critical gearboxes because of its components and functionality. A significant part of the study also involved filtering the vibration data obtained while testing. Comparative analysis of the variation of the peak of acceleration was performed for healthy and faulty conditions. Show more

Keywords: Planetary gearbox, neural networks, deep learning

DOI: 10.3233/JIFS-210229

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6407-6427, 2021

Authors: Alagarsamy, Ramachandran | Arunpraksh, R. | Ganapathy, Sannasi | Rajagopal, Aghila | Kavitha, R.J.

Article Type: Research Article

Abstract: Recently, the e-learners are drastically increased from the last two decades. Everything is learnt through internet without help of the tutor as well. For this purpose, the e-learners are required more e-learning applications that are able to supply optimal and satisfied data based on their capability. No content recommendation system is available for recommending suitable contents to the learners. For this purpose, this paper proposes a new semantic and fuzzy aware content recommendation system for retrieving the suitable content for the users. In this content recommendation system, we propose two content pre-processing algorithms namely Target Keyword based Data Pre-processing Algorithm …(TKDPA) and Intelligent Anova-T Residual Algorithm (IAATRA) for selecting the more relevant features from the document. Moreover, a new Fuzzy rule based Similarity Matching algorithm (FRSMA) is proposed and used in this system for finding the similarity between the two terms and also rank them by using the newly proposed Similarity and Temporal aware Weighted Document Ranking Algorithm (STWDRA). In addition, a content clustering process is also incorporated for gathering relevant content. Finally, a new Fuzzy, Target Keyword and Similarity Score based Content Recommendation Algorithm (FTKSCRA) is also proposed for recommending the more relevant content to the learners accurately. The experiments have been conducted for evaluating the proposed content recommendation system and proved as better than the existing recommendation systems in terms of precision, recall, f-measure and prediction accuracy. Show more

Keywords: Fuzzy logic, content ranking, clustering, content recommendation, semantic analysis, fuzzy rules and annova-T

DOI: 10.3233/JIFS-210246

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6429-6441, 2021

Authors: Zhu, Yunwen | Zhang, Wenjun | Zhang, Meixian | Zhang, Ke | Zhu, Yonghua

Article Type: Research Article

Abstract: With the trend of people expressing opinions and emotions via images online, increasing attention has been paid to affective analysis of visual content. Traditional image affective analysis mainly focuses on single-label classification, but an image usually evokes multiple emotions. To this end, emotion distribution learning is proposed to describe emotions more explicitly. However, most current studies ignore the ambiguity included in emotions and the elusive correlations with complex visual features. Considering that emotions evoked by images are delivered through various visual features, and each feature in the image may have multiple emotion attributes, this paper develops a novel model that …extracts multiple features and proposes an enhanced fuzzy k-nearest neighbor (EFKNN) to calculate the fuzzy emotional memberships. Specifically, the multiple visual features are converted into fuzzy emotional memberships of each feature belonging to emotion classes, which can be regarded as an intermediate representation to bridge the affective gap. Then, the fuzzy emotional memberships are fed into a fully connected neural network to learn the relationships between the fuzzy memberships and image emotion distributions. To obtain the fuzzy memberships of test images, a novel sparse learning method is introduced by learning the combination coefficients of test images and training images. Extensive experimental results on several datasets verify the superiority of our proposed approach for emotion distribution learning of images. Show more

Keywords: Image emotion recognition, emotion distribution learning, fuzzy emotional membership, sparse learning

DOI: 10.3233/JIFS-210251

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6443-6460, 2021

Authors: Guo, Dugang | Liu, Jun | Wang, Xuewei

Article Type: Research Article

Abstract: Plant disease is one of the major threats to food security. Accurate diagnosis of plant diseases can benefit the agricultural production. For the purpose of real-time plant disease diagnostics, the deep learning models are employed. In this study, we present an accurate identification method for common diseases of tomatoes based on deep-learning methods. The devising of multi-resolution detector, in line with bounding box generating and assigning, facilitates the feature extracting process of detection. The employment of an dropout and ADAMW (Adaptive moment estimation with decoupled weight decay) optimizer further resolve the overfitting problem. Using the collected images of healthy and …diseased tomatoes, our detector is trained to identify 10 different diseases. Experimental results showed that the disease identification method proposed in this study could accurately and rapidly identify common diseases of tomato with an average accuracy of 85.03%and a recognition speed of 61 frames per second, which was superior to other models under the same conditions and was beneficial for tomato disease control work. Show more

Keywords: Plant diseases, deep learning model, multi-resolution detection layers, bounding box, single shot multibox detector

DOI: 10.3233/JIFS-210262

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6461-6471, 2021

Authors: Mehmood, Arif | Al Ghour, Samer | Ishfaq, Muhammad | Afzal, Farkhanda

Article Type: Research Article

Abstract: In this article, new definition of neutrosophic soft ** b -open set is introduced with the help of neutrosophic soft α -open set and neutrosophic soft β-open set. With the application of this new definition some neutrosophic soft separation axioms and neutrosophic soft other separation axioms are addressed with respect to soft points of the spaces. Suitable examples are provided for the clarification of different results. Soft countability results and its engagements with different other neutrosophic soft results are studied. In continuation, characterization of Bolzano Weirstrass Property with respect to neutrosophic soft results and neutrosophic soft compactness results are inaugurated.

Keywords: Neutrosophic soft set (NSS), neutrosophic soft point, neutrosophic soft ** b-open set and neutrosophic ** b-separation axioms

DOI: 10.3233/JIFS-210306

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6473-6494, 2021

Authors: Li, Chenliang | Yu, Xiaobing | Zhao, Wen-Xuan

Article Type: Research Article

Abstract: In today’s economy, information technology (IT) is vitally important, and the increasing use of the Internet, telecommunications services, and internal IT networks in organizations have led to rapid growth in the demands on big data processing. In general, site selection is a fundamental part of the design of a big data center (BDC), and a poor site decision can affect the sustainability of the facility. To construct a comprehensive assessment framework for a BDC, the following three categories of indicators are determined based on the “Specification for Design of Data Center” in GB50174-2017 of China: economic factors, natural climate environment …factors, and energy resources factors. After explaining the rationality of choosing these indicators in detail, an integrated method that combines the multi-criteria decision-making (MCDM) method and the multi-choice goal programming (MCGP) model is proposed. The proposed approach uses two phases to conduct the decision procedure. First, the preference ranking organization method for enrichment evaluation (PROMETHEE) method is applied to evaluate the economic factors. Then, the evaluation results are added to the MCGP model as one of the goals of multi-objective programming. Second, the remaining five sub-indicators and the evaluation results generated from the first phase are formulated as a complete MCGP model. Finally, an empirical study on the site selection for the BDC is implemented based on the proposed method. The result shows that Guiyang is the most suitable place for locating a BDC in China. Show more

Keywords: Big data center, PROMETHEE, MCGP model, MCDM method

DOI: 10.3233/JIFS-210319

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6495-6515, 2021

Authors: Li, Qiaoyang | Chen, Guiming | Li, Ziqi | Zhang, Yi | Xu, Lingliang

Article Type: Research Article

Abstract: To solve the problems of strong infrared radiation, poor continuous combat capability of the system, serious ablation of the launching device, and environmental pollution of the existing missile launching system, electromagnetic launch system (EMLS) has been studied for missile launch system. Combining the situation that the current research on missile electromagnetic launch system (MEMLS) mainly focuses on the key technical points and the deficiencies in the previous research on MEMLS, this paper establishes an effectiveness prediction model based on GRA-PCA-LSSVM, and discusses the investment efficiency of the system based on DEA. The experimental results prove that the established model is …reasonable, effective and superior, and provides a reference for the further improvement and development of MEMLS. Show more

Keywords: MEMLS, Grey relation analysis (GRA), Principal component analysis (PCA), Least square support vector machine (LSSVM), Data Envelopment Analysis (DEA)

DOI: 10.3233/JIFS-210353

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6517-6526, 2021

Authors: Li, Longmei | Zheng, Tingting | Yin, Wenjing | Wu, Qiuyue

Article Type: Research Article

Abstract:  Entropy and cross-entropy are very vital for information discrimination under complicated Pythagorean fuzzy environment. Firstly, the novel score factors and indeterminacy factors of intuitionistic fuzzy sets (IFSs) are proposed, which are linear transformations of membership functions and non-membership functions. Based on them, the novel entropy measures and cross-entropy measures of an IFS are introduced using Jensen Shannon-divergence (J -divergence). They are more in line with actual fuzzy situations. Then the cases of Pythagorean fuzzy sets (PFSs) are extended. Pythagorean fuzzy entropy, parameterized Pythagorean fuzzy entropy, Pythagorean fuzzy cross-entropy, and weighted Pythagorean fuzzy cross-entropy measures are introduced consecutively based on the …novel score factors, indeterminacy factors and J -divergence. Then some comparative experiments prove the rationality and effectiveness of the novel entropy measures and cross-entropy measures. Additionally, the Pythagorean fuzzy entropy and cross-entropy measures are designed to solve pattern recognition and multiple criteria decision making (MCDM) problems. The numerical examples, by comparing with the existing ones, demonstrate the applicability and efficiency of the newly proposed models. Show more

Keywords: Pythagorean fuzzy entropy, Pythagorean fuzzy cross-entropy, parameterized Pythagorean fuzzy entropy, weighted Pythagorean fuzzy cross-entropy, score factor, indeterminacy factor

DOI: 10.3233/JIFS-210365

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6527-6546, 2021

Authors: Guo, Huijuan | Yao, Ruipu

Article Type: Research Article

Abstract: The symmetry between fuzzy evaluations and crisp numbers provides an effective solution to multiple attribute decision making (MADM) problems under fuzzy environments. Considering the effect of information distribution on decision making, a novel approach to MADM problems under the interval-valued q-rung orthopair fuzzy (Iq-ROF) environments is put forward. Firstly, the clustering method of interval-valued q-rung orthopair fuzzy numbers (Iq-ROFNs) is defined. Secondly, Iq-ROF density weighted arithmetic (Iq-ROFDWA) intermediate operator and Iq-ROF density weighted geometric average (Iq-ROFDWGA) intermediate operator are developed based on the density weighted intermediate operators for crisp numbers. Thirdly, combining the density weighted intermediate operators with the Iq-ROF …weighted aggregation operators, Iq-ROF density aggregation operators including Iq-ROF density weighted arithmetic (Iq-ROFDWAA) aggregation operator and Iq-ROF density weighted geometric (Iq-ROFDWGG) aggregation operator are proposed. Finally, effectiveness of the proposed method is verified through a numerical example. Show more

Keywords: Multiple attribute decision making (MADM), clustering, Iq-ROFDWAA aggregation operator, Iq-ROFDWGG aggregation operator

DOI: 10.3233/JIFS-210376

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6547-6560, 2021

Authors: Muhiuddin, G. | Mahboob, A. | Khan, N. M. | Al-Kadi, D.

Article Type: Research Article

Abstract: In this paper, we introduce new types of fuzzy (m , n )-ideals in ordered semigroups. In fact, the notion of (∈ , ∈ ∨ (κ * , q κ ))-fuzzy (m , n )-ideals of the ordered semigroups is introduced. Further, we present the characterzations of this notion in different ways. Then the (κ * , κ )-lower part of the (∈ , ∈ ∨ (κ * , q κ ))-fuzzy (m , n )-ideals is defined and its associated properties are investigated. After that, (m , n )-regular ordered semigroups are characterized in terms of its (∈ , ∈ ∨ (κ * , q κ …))-fuzzy (m , n )-ideals and their (κ * , κ )-lower parts. Show more

Keywords: Ordered semigroups, fuzzy sets, (∈ , ∈ ∨ (κ*, qκ))-fuzzy (m, n)-ideals, (m, n)-regular ordered semigroups

DOI: 10.3233/JIFS-210378

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6561-6574, 2021

Authors: Shi, Shuo | Huo, Changwei | Guo, Yingchun | Lean, Stephen | Yan, Gang | Yu, Ming

Article Type: Research Article

Abstract: Person re-identification with natural language description is a process of retrieving the corresponding person’s image from an image dataset according to a text description of the person. The key challenge in this cross-modal task is to extract visual and text features and construct loss functions to achieve cross-modal matching between text and image. Firstly, we designed a two-branch network framework for person re-identification with natural language description. In this framework we include the following: a Bi-directional Long Short-Term Memory (Bi-LSTM) network is used to extract text features and a truncated attention mechanism is proposed to select the principal component of …the text features; a MobileNet is used to extract image features. Secondly, we proposed a Cascade Loss Function (CLF), which includes cross-modal matching loss and single modal classification loss, both with relative entropy function, to fully exploit the identity-level information. The experimental results on the CUHK-PEDES dataset demonstrate that our method achieves better results in Top-5 and Top-10 than other current 10 state-of-the-art algorithms. Show more

Keywords: Person re-identification, cross-modal, natural language description, cascade loss function, truncated attention mechanism

DOI: 10.3233/JIFS-210382

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6575-6587, 2021

Authors: Rai, Ashok Kumar | Senthilkumar, Radha | Aruputharaj, Kannan

Article Type: Research Article

Abstract: Face recognition is one of the best applications of computer recognition and recent smart house applications. Therefore, it draws considerable attention from researchers. Several face recognition algorithms have been proposed in the last decade, but these methods did not give the efficient outcome. Therefore, this work introduces a novel constructive training algorithm for smart face recognition in door locking applications. The proposed Framed Recurrent Neural Network with Mutated Dragonfly Search Optimization (FRNN-MDSO) Strategy is applied to face recognition application. The steady preparing system has been utilized where the training designs are adapted steadily and are divided into completely different modules. …The facial feature process works on global and local features. After the feature extraction and selection process, employ the improved classifier followed by the Framed Recurrent Neural Network classification technique. Finally, the face image based on the feature library can be identified. The proposed Framed Recurrent Neural Network with Mutated Dragonfly Search Optimization starts with a single training pattern using Bidirectional Encoder Representations from Transformers (BERT) model. During network training, the Training Data (TD) decrease the Mean Square Error (MSE) while the matching process increases the algorithms generated which are trapped at the local minimum. The training data have been trained to increase the number of input forms (one after the other) until all the forms are selected and trained. An FRNN-MDSO based face recognition system is built, and face recognition is tested using hyperspectral Database parameters. The simulation results indicate that the proposed method acquires the associate grade optimum design of FRNN with MDSO methodology using the present constructive algorithm and prove the proposed FRNN-MDSO method’s effectiveness compared to the conventional architecture methods. Show more

Keywords: Face recognition, Framed Recurrent Neural Network(FRNN), Mutated Dragonfly Search Optimization (MDSO), Mean Square Error (MSE)

DOI: 10.3233/JIFS-210441

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6589-6599, 2021

Authors: Yin, Tao | Mao, Xiaojuan | Wu, Xingtan | Ju, Hengrong | Ding, Weiping | Yang, Xibei

Article Type: Research Article

Abstract: Neighborhood classifier, a common classification method, is applied in pattern recognition and data mining. The neighborhood classifier mainly relies on the majority voting strategy to judge each category. This strategy only considers the number of samples in the neighborhood but ignores the distribution of samples, which leads to a decreased classification accuracy. To overcome the shortcomings and improve the classification performance, D-S evidence theory is applied to represent the evidence information support of other samples in the neighborhood, and the distance between samples in the neighborhood is taken into account. In this paper, a novel attribute reduction method of neighborhood …rough set with a dynamic updating strategy is developed. Different from the traditional heuristic algorithm, the termination threshold of the proposed reduction algorithm is dynamically optimized. Therefore, when the attribute significance is not monotonic, this method can retrieve a better value, in contrast to the traditional method. Moreover, a new classification approach based on D-S evidence theory is proposed. Compared with the classical neighborhood classifier, this method considers the distribution of samples in the neighborhood, and evidence theory is applied to describe the closeness between samples. Finally, datasets from the UCI database are used to indicate that the improved reduction can achieve a lower neighborhood decision error rate than classical heuristic reduction. In addition, the improved classifier acquires higher classification performance in contrast to the traditional neighborhood classifier. This research provides a new direction for improving the accuracy of neighborhood classification. Show more

Keywords: Attribute reduction, D-S evidence theory, neighborhood classification, rough set

DOI: 10.3233/JIFS-210462

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6601-6613, 2021

Authors: Sekar, Aravindkumar | Perumal, Varalakshmi

Article Type: Research Article

Abstract: Automatic road crack detection is a prominent challenging task, in view of that, a novel approach is proposed using multi-tasking Faster-RCNN to detect and classify road cracks. In this present study, we have collected the road images (a dataset of 19300 images) from the Outer Ring Road of Chennai, Tamil Nadu, India. The collected road images were pre-processed using various conventional image processing techniques to identify the ground-truth label of the bounding boxes for the cracks. We present a novel multi-tasking Faster-RCNN based approach using the Global Average Pooling(GAP) and Region of Interest (RoI) Align techniques to detect the road …cracks. The RoI Align is used to avoid quantizing the stride. So that the information loss can be minimized and the bi-linear interpolation can be used to map the proposal to the input image. The resulting features from RoI Align are given as input to the GAP layer which drastically reduces the multi-dimension features into a single feature map. The output of the GAP layer is given to the fully connected layer for classification (softmax) and also to a regression model for predicting the crack location using a bounding box. F1-measure, precision, and recall were used to evaluate the results of classification and detection. The proposed model achieves the accuracy-97.97%, precision-99.12%, and recall-97.25% for classification using the MIT-CHN-ORR dataset. The experimental results show, that the proposed approach outperforms the other state-of-the-art methods. Show more

Keywords: Multi-tasking faster-RCNN, RoI align, road crack detection, road crack classification

DOI: 10.3233/JIFS-210475

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6615-6628, 2021

Authors: Li, Bin | He, Qiyu | Liu, Xiaopeng | Jiang, Yajun | Hu, Zhigang

Article Type: Research Article

Abstract: Person re-identification problem is a valuable computer vision task, which aims at matching pedestrian images of different cameras in a non-overlapping surveillance network. Existing metric learning based methods address this problem by learning a robust distance function. These methods learn a mapping subspace by forcing the distance of the positive pair much smaller than the negative pair by a strict constraint. The metric model is over-fitting to the training dataset. Due to drastic appearance variations, the handcrafted features are weak of representation for person re-identification. To address these problems, we propose a joint distance measure based approach, which learns a …Mahalanobis distance and a Euclidean distance with a novel feature jointly. The novel feature is represented with a dictionary representation based method which considers pedestrian images of different camera views with the same dictionary. The joint distance combine the Mahalanobis distance based on metric learning method with the Euclidean distance based on the novel feature to measure the similarity between matching pairs. Extensive experiments are conducted on the publicly available bench marking datasets VIPeR and CUHK01. The identification results show that the proposed method reaches a good performance than the comparison methods. Show more

Keywords: Person re-identification, metric learning, multi-distance, dictionary representation, Mahalanobis distance

DOI: 10.3233/JIFS-210505

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6629-6639, 2021

Authors: Zhang, Nian | Pan, Qian | Wei, Guiwu

Article Type: Research Article

Abstract: In order to effectively solve the decision-making problems with the diversity of evaluation information, the dynamics of research objects, the limitations of subjective authorization, and the irrational behavior of decision-makers, this paper extends catastrophe progression method to solve hybrid multiple attribute decision-making problems based on regret theory. Firstly, some basic theories are introduced. Secondly, the original catastrophe progression method is extended by using the regret theory, which is employed to solve the multiple attribute decision-making problems with hybrid evaluation information. Finally, a real-life case study of selecting a fresh cold chain logistics service provider is used to verify the practicality …and effectiveness of the proposed method, and a comparative analysis with the TOPSIS method and the sensitivity of the regret avoidance coefficient is analyzed in this article. Show more

Keywords: Regret theory, catastrophe progression, hybrid multi-attribute decision-making

DOI: 10.3233/JIFS-210515

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6641-6654, 2021

Authors: Du, Wen Sheng

Article Type: Research Article

Abstract: Aggregation of q -rung orthopair fuzzy information serves as an important branch of the q -rung orthopair fuzzy set theory, where operations on q -rung orthopair fuzzy values (q -ROFVs) play a crucial role. Recently, aggregation operators on q -ROFVs were established by employing the Einstein operations rather than the algebraic operations. In this paper, we give a further investigation on operations and aggregation operators for q -ROFVs based on the Einstein operational laws. We present the operational principles of Einstein operations over q -ROFVs and compare them with those built on the algebraic operations. The properties of the q …-rung orthopair fuzzy Einstein weighted averaging (q -ROFEWA) operator and q -rung orthopair fuzzy Einstein weighted geometric (q -ROFEWG) operator are investigated in detail, such as idempotency, monotonicity, boundedness, shift-invariance and homogeneity. Then, the developed operators are applied to multiattribute decision making problems under the q -rung orthopair fuzzy environment. Finally, an example for selecting the design scheme for a blockchain-based agricultural product traceability system is presented to illustrate the feasibility and effectiveness of the proposed methods. Show more

Keywords: Aggregation operator, Einstein operation, multiattribute decision making, q-rung orthopair fuzzy Einstein aggregation operator, q-rung orthopair fuzzy value

DOI: 10.3233/JIFS-210548

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6655-6673, 2021

Authors: Wang, Xiaomin | Liu, Yang | Zhou, Rui

Article Type: Research Article

Abstract: A new model named multi-granularity belief interval-valued soft set is introduced in this paper. Some basic properties about it are presented and illustrated. The improved concepts of the soft belief value and soft belief degree are proposed, which provided an easier and better compared horizontally and vertically method among the different objects and different parameters. An algorithm for decision-making problems on multi-granularity belief interval-valued soft set is put forward and its validity is proved by the application of an example. Moreover, the newly proposed algorithm is compared with existing method to indicate its extensive application.

Keywords: Belief interval-valued soft set, soft belief value, soft belief degree, decision making

DOI: 10.3233/JIFS-210565

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6675-6684, 2021

Authors: Cui, Xiaohui | Ying, Yongzhi | Chen, Zhibo

Article Type: Research Article

Abstract: The identification and classification of plant diseases is of great significance to ecological protection and deep learning methods have made a great of progress in the common plant diseases identification for specific plant. While faced with the same plant disease of other plants, due to the insufficient or low quality training data, current deep learning methods will be difficult to identify the diseases effectively and accurately. Inspired by the advantages of GAN in dataset expansion, we propose the CycleGAN based confusion model in this paper. In this paper, GAN framework is improved by adding noise label and learn together during …training stage, which migrates the data of common plant diseases to the plants with insufficient or low quality data. In order to evaluate the quality of the migrated training dataset among different GAN approaches, we introduce the quality indicators of the migration images such as MMD, FID, EMD etc. We compare our model with other GANs model, and the experimental results show that the proposed model obtains better results in the migration process, which make it more effective for the identification of cross species plant diseases. Show more

Keywords: Deep learning, generative adversarial nets, CycleGAN, image translation

DOI: 10.3233/JIFS-210585

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6685-6696, 2021

Authors: Janakiramaiah, B. | Kalyani, G. | Prasad, L.V. Narasimha | Karuna, A. | Krishna, M.

Article Type: Research Article

Abstract: Horticulture crops take a crucial part of the Indian economy by creating employment, supplying raw materials to different food processing industries. Mangoes are one of the major crops in horticulture. General Infections in Mango trees are common by various climatic and fungal infections, which became a cause for reducing the quality and quantity of the mangos. The most common diseases with bacterial infection are anthracnose and Powdery Mildew. In recent years, it has been perceived that different variants of deep learning architectures are proposed for detecting and classifying the problems in the agricultural domain. The Convolutional Neural Network (CNN) based …architectures have performed amazingly well for disease detection in plants but at the same time lacks rotational or spatial invariance. A relatively new neural organization called Capsule Network (CapsNet) addresses these limitations of CNN architectures. Hence, in this work, a variant of CapsNet called Multilevel CapsNet is introduced to characterize the mango leaves tainted by the anthracnose and powdery mildew diseases. The proposed architecture of this work is validated on a dataset of mango leaves collected in the natural environment. The dataset comprises both healthy and contaminated leaf pictures. The test results approved the undeniable level of exactness of the proposed framework for the characterization of mango leaf diseases with an accuracy of 98.5%. The outcomes conceive the higher-order precision of the proposed Multi-level CapsNet model when contrasted with the other classification algorithms such as Support Vector Machine (SVM) and CNNs. Show more

Keywords: Deep learning, disease detection, machine learning, capsule networks, mango leaf diseases, convolutional neural network

DOI: 10.3233/JIFS-210593

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6697-6713, 2021

Authors: Liu, Yikun | Yang, Gongping | Huang, Yuwen | Yin, Yilong

Article Type: Research Article

Abstract: Fruit detection and segmentation is an essential operation of orchard yield estimation, the result of yield estimation directly depends on the speed and accuracy of detection and segmentation. In this work, we propose an effective method based on Mask R-CNN to detect and segment apples under complex environment of orchard. Firstly, the squeeze-and-excitation block is introduced into the ResNet-50 backbone, which can distribute the available computational resources to the most informative feature map in channel-wise. Secondly, the aspect ratio is introduced into the bounding box regression loss, which can promote the regression of bounding boxes by deforming the shape of …bounding boxes to the apple boxes. Finally, we replace the NMS operation in Mask R-CNN by Soft-NMS, which can remove the redundant bounding boxes and obtain the correct detection results reasonably. The experimental result on the Minneapple dataset demonstrates that our method overperform several state-of-the-art on apple detection and segmentation. Show more

Keywords: Apple detection and segmentation, complex background, squeeze-and-excitation block, aspect ratio, soft-NMS

DOI: 10.3233/JIFS-210597

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6715-6725, 2021

Authors: Deng, Shangju | Qin, Jiwei

Article Type: Research Article

Abstract: Tensors have been explored to share latent user-item relations and have been shown to be effective for recommendation. Tensors suffer from sparsity and cold start problems in real recommendation scenarios; therefore, researchers and engineers usually use matrix factorization to address these issues and improve the performance of recommender systems. In this paper, we propose matrix factorization completed multicontext data for tensor-enhanced algorithm a using matrix factorization combined with a multicontext data method for tensor-enhanced recommendation. To take advantage of existing user-item data, we add the context time and trust to enrich the interactive data via matrix factorization. In addition, Our …approach is a high-dimensional tensor framework that further mines the latent relations from the user-item-trust-time tensor to improve recommendation performance. Through extensive experiments on real-world datasets, we demonstrated the superiority of our approach in predicting user preferences. This method is also shown to be able to maintain satisfactory performance even if user-item interactions are sparse. Show more

Keywords: Recommendation system, tensor factorization, similarity, user-project context interaction

DOI: 10.3233/JIFS-210641

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6727-6738, 2021

Authors: Ji, Ying | Jin, Xiaowan | Xu, Zeshui | Qu, Shaojian

Article Type: Research Article

Abstract: In practical multiple attribute decision making (MADM) problems, the interest groups or individuals intentionally set attribute weights to achieve their own benefits. In this case, the rankings of different alternatives are changed strategically, which is called the strategic weight manipulation in MADM. Sometimes, the attribute values are given with imprecise forms. Several theories and methods have been developed to deal with uncertainty, such as probability theory, interval values, intuitionistic fuzzy sets, hesitant fuzzy sets, etc. In this paper, we study the strategic weight manipulation based on the belief degree of uncertainty theory, with uncertain attribute values obeying linear uncertain distributions. …It allows the attribute values to be considered as a whole in the operation process. A series of mixed 0-1 programming models are constructed to set a strategic weight vector for a desired ranking of a particular alternative. Finally, an example based on the assessment of the performance of COVID-19 vaccines illustrates the validity of the proposed models. Comparison analysis shows that, compared to the deterministic case, it is easier to manipulate attribute weights when the attribute values obey the linear uncertain distribution. And a further comparative analysis highlights the performance of different aggregation operators in defending against the strategic manipulation, and highlights the impacts on ranking range under different belief degrees. Show more

Keywords: Multiple attribute decision making, strategic weight manipulation, uncertainty theory, ranking range, belief degree

DOI: 10.3233/JIFS-210650

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6739-6754, 2021

Authors: Wang, Dan | Wang, Jie-Sheng | Wang, Shao-Yan | Xing, Cheng | Li, Xu-Dong

Article Type: Research Article

Abstract: Aiming at predicting the purity of the extract and raffinate components in the simulated moving bed (SMB) chromatographic separation process, a soft-sensor modeling method was proposed by adoptig the hybrid learning algorithm based on an improved particle swarm optimization (PSO) algorithm and the least means squares (LMS) method to optimize the adaptive neural fuzzy inference system (ANFIS) parameters. The hybrid learning algorithm includes a premise parameter learning phase and a conclusion parameter learning phase. In the premise parameter learning stage, the input data space division of the SMB chromatographic separation process and the initialization of the premise parameters are realized …based on the fuzzy C-means (FCM) clustering algorithm. Then, the improved PSO algorithm is used to calculate the excitation intensity and normalized excitation intensity of all the rules for each individual in the population. In the conclusion parameter learning phase, these linear parameters are identified by the LMS method. In order to improve population diversity and convergence accuracy, the population evolution rate function was defined. According to the relationship between population diversity, population fitness function and particle position change, a new adaptive population evolution particle swarm optimization (NAPEPSO) algorithm was proposed. The inertia weight is adaptively adjusted according to the evolution of the population and the change of the particle position, thereby improving the diversity of the particle swarm and the ability of the algorithm to jump out of the local optimal solution. The simulation results show that the proposed soft-sensor model can effectively predict the key economic and technical indicators of the SMB chromatographic separation process so as to meet the real-time and efficient operation of the SMB chromatographic separation process. Show more

Keywords: Keywords: SMB chromatographic separation, soft sensing, adaptive neural fuzzy inference system, PSO algorithm, inertia weight

DOI: 10.3233/JIFS-210663

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6755-6780, 2021

Authors: Zhang, Wen-Ran

Article Type: Research Article

Abstract: The road from bipolar fuzzy sets to equilibrium-based mathematical abstraction is surveyed. A continuing historical debate on bipolarity and isomorphism is outlined. Related literatures are critically reviewed to counter plagiarism, distortion, renaming, and sophistry. Based on the debate, the term “isomorphistry ” is coined. It is concluded that if isomorphism is used correctly it can be helpful in mathematics. If abused it may become isomorphistry—a kind of historical, socially constructed, entrenched, and “noble” hypocrisy hindering major scientific advances. It is shown that isomorphistry can be motivated by “denying” the originality of bipolar fuzzy sets and aimed at “justifying” plagiarism …and distortion. Thus, isomorphistry is sophistry on isomorphism . Some (-,+)-bipolar isomorphistry behaviors are critiqued. YinYang vs. YangYin are distinguished. The geometrical and logical basis of equilibrium-based AI&QI computing machinery is introduced as a new computing paradigm with logically definable causality for mind-body unity. A philosophical joke on sophistry is appended. Show more

Keywords: Bipolar fuzzy sets and mathematics, isomorphistry and plagiarism, YinYang vs. YangYin, equilibrium-based mathematical abstraction, logically definable causality for mind-body unity AI&QI, philosophical joke

DOI: 10.3233/JIFS-210692

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6781-6799, 2021

Authors: Ngo, Quoc-Dung | Nguyen, Huy-Trung | Nguyen, Le-Cuong

Article Type: Research Article

Abstract: Over the last decade, due to exponential growth in IoT devices and weak security mechanisms, the IoT is now facing more security challenges than ever before, especially botnet malware. There are many security solutions in detecting botnet malware on IoT devices. However, detecting IoT botnet malware, particularly multi-architecture botnets, is challenging. This paper proposes a graphically structured feature extraction mechanism integrated with reinforcement learning techniques in multi-architecture IoT botnet detection. We then evaluate the proposed approach using a dataset of 22849 samples, including actual IoT botnet malware, and achieve a detection rate of 98.03 with low time consumption. The proposed …approach also achieves reliable results in detecting the new IoT botnet (has a new architecture-processor) not appearing in the training dataset at 96.69. To promote future research in the field, we share relevant datasets and source code. Show more

Keywords: IoT security, IoT botnet, reinforcement learning, static analysis, PSI-walk

DOI: 10.3233/JIFS-210699

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6801-6814, 2021

Authors: Song, Runze | Liu, Zhaohui | Wang, Chao

Article Type: Research Article

Abstract: As an advanced machine vision task, traffic sign recognition is of great significance to the safe driving of autonomous vehicles. Haze has seriously affected the performance of traffic sign recognition. This paper proposes a dehazing network, including multi-scale residual blocks, which significantly affects the recognition of traffic signs in hazy weather. First, we introduce the idea of residual learning, design the end-to-end multi-scale feature information fusion method. Secondly, the study used subjective visual effects and objective evaluation metrics such as Visibility Index (VI) and Realness Index (RI) based on the characteristics of the real-world environment to compare various traditional dehazing …and deep learning dehazing method with good performance. Finally, this paper combines image dehazing and traffic sign recognition, using the algorithm of this paper to dehaze the traffic sign images under real-world hazy weather. The experiments show that the algorithm in this paper can improve the performance of traffic sign recognition in hazy weather and fulfil the requirements of real-time image processing. It also proves the effectiveness of the reformulated atmospheric scattering model for the dehazing of traffic sign images. Show more

Keywords: Deep learning, image processing, dehazing of real-world, traffic sign recognition, reformulated atmospheric scattering model

DOI: 10.3233/JIFS-210733

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6815-6830, 2021

Authors: Qarehkhani, Azam | Golsorkhtabaramiri, Mehdi | Mohamadi, Hosein | Yadollahzadeh Tabari, Meisam

Article Type: Research Article

Abstract: Directional sensor networks (DSNs) are classified under wireless networks that are largely used to resolve the coverage problem. One of the challenges to DSNs is to provide coverage for all targets in the network and, at the same time, to maximize the lifetime of network. A solution to this problem is the adjustment of the sensors’ sensing ranges. In this approach, each sensor adjusts its own sensing range dynamically to sense the corresponding target(s) and decrease energy consumption as much as possible through forming the best cover sets possible. In the current study, a continuous learning automata-based method is proposed …to form such cover sets. To assess the proposed algorithm’s performance, it was compared to the results obtained from a greedy algorithm and a learning automata algorithm. The obtained results demonstrated the superiority of the proposed algorithm regarding the maximization of the network lifetime. Show more

Keywords: Directional sensor networks, continuous learning automata, target-coverage, cover set formation

DOI: 10.3233/JIFS-210759

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6831-6844, 2021

Authors: Han, Guojiang | Chen, Caikou | Xu, Zhixuan | Zhou, Shengwei

Article Type: Research Article

Abstract: Ensemble learning using a set of deep convolutional neural networks (DCNNs) as weak classifiers has become a powerful tool for face expression. Nevertheless, training a DCNNS-based ensemble is not only time consuming but also gives rise to high redundancy due to the nature of DCNNs. In this paper, a novel DCNNs-based ensemble method, named weighted ensemble with angular feature learning (WDEA), is proposed to improve the computational efficiency and diversity of the ensemble. Specifically, the proposed ensemble consists of four parts including input layer, trunk layers, diversity layers and loss fusion. Among them, the trunk layers which are used to …extract the local features of face images are shared by diversity layers such that the lower-level redundancy can be largely reduced. The independent branches enable the diversity of the ensemble. Rather than the traditional softmax loss, the angular softmax loss is employed to extract more discriminant deep feature representation. Moreover, a novel weighting technique is proposed to enhance the diversity of the ensemble. Extensive experiments were performed on CK+ and AffectNet. Experimental results demonstrate that the proposed WDEA outperforms existing ensemble learning methods on the recogntion rate and computational efficiency. Show more

Keywords: Facial expression recognition, ensemble-based CNN, end to end learning, weight matrix unit

DOI: 10.3233/JIFS-210762

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6845-6857, 2021

Authors: Tao, Yujie | Suo, Chunfeng | Wang, Guijun

Article Type: Research Article

Abstract: Piecewise linear function (PLF) is not only a generalization of univariate segmented linear function in multivariate case, but also an important bridge to study the approximation of continuous function by Mamdani and Takagi-Sugeno fuzzy systems. In this paper, the definitions of the PLF and subdivision are introduced in the hyperplane, the analytic expression of PLF is given by using matrix determinant, and the concept of approximation factor is first proposed by using m -mesh subdivision. Secondly, the vertex coordinates and their changing rules of the n -dimensional small polyhedron are found by dividing a three-dimensional cube, and the algebraic cofactor …and matrix norm of corresponding determinants of piecewise linear functions are given. Finally, according to the method of solving algebraic cofactors and matrix norms, it is proved that the approximation factor has nothing to do with the number of subdivisions, but the approximation accuracy has something to do with the number of subdivisions. Furthermore, the process of a specific binary piecewise linear function approaching a continuous function according to infinite norm in two dimensions space is realized by a practical example, and the validity of PLFs to approximate a continuous function is verified by t -hypothesis test in Statistics. Show more

Keywords: Piecewise linear function, mesh subdivision, approximation factor, Mamdani fuzzy system, matrix norm

DOI: 10.3233/JIFS-210770

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6859-6873, 2021

Authors: Ding, Ling | Chen, Xiaojun | Xiang, Yang

Article Type: Research Article

Abstract: Few-shot text classification aims to learn a classifier from very few labeled text data. Existing studies on this topic mainly adopt prototypical networks and focus on interactive information between support set and query instances to learn generalized class prototypes. However, in the process of encoding, these methods only pay attention to the matching information between support set and query instances, and ignore much useful information about intra-class similarity and inter-class dissimilarity between all support samples. Therefore, in this paper we propose a negative-supervised capsule graph neural network (NSCGNN) which explicitly takes use of the similarity and dissimilarity between samples to …make the text representations of the same type closer with each other and the ones of different types farther away, leading to representative and discriminative class prototypes. We firstly construct a graph to obtain text representations in the form of node capsules, where both intra-cluster similarity and inter-cluster dissimilarity between all samples are explored with information aggregation and negative supervision. Then, in order to induce generalized class prototypes based on those node capsules obtained from graph neural network, the dynamic routing algorithm is utilized in our model. Experimental results demonstrate the effectiveness of our proposed NSCGNN model, which outperforms existing few-shot approaches on three benchmark datasets. Show more

Keywords: Graph neural networks, negative supervision, dynamic routing, few-shot learning

DOI: 10.3233/JIFS-210795

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6875-6887, 2021

Authors: Alshammari, Ibtesam | Parimala, Mani | Jafari, Saeid

Article Type: Research Article

Abstract: Imprecision in the decision-making process is an essential consideration. In order to navigate the imprecise decision-making framework, measuring tools and methods have been developed. Pythagorean fuzzy soft sets are one of the new methods for dealing with imprecision. Pythagorean fuzzy soft topological spaces is an extension of intuitionistic fuzzy soft topological spaces. These sets generalizes intuitionistic fuzzy sets for a broader variety of implementations. This work is a gateway to study such a problem. The concept of Pythagorean fuzzy soft topological spaces(PyFSTS), interior, closure, boundary, neighborhood of Pythagorean fuzzy soft spaces PyFSS, base and subspace of PyFSTSs are presented and …its properties are figured out. We established an algorithm under uncertainty based on PyFSTS for multi-attribute decision-making (MADM) and to validate this algorithm, a numerical example is solved for suitable brand selection. Finally, the benefits, validity, versatility and comparison of our proposed algorithms with current techniques are discussed.The advantage of the proposed work is to detect vagueness with more sizably voluminous valuation space than intuitionistic fuzzy sets. Show more

Keywords: Pythagorean fuzzy soft sets, Pythagorean fuzzy soft topology, Pythagorean fuzzy soft interior and soft closure, multi-attribute decision making

DOI: 10.3233/JIFS-210805

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6889-6897, 2021

Authors: Yuan, Ling | Pan, Zhuwen | Sun, Ping | Wei, Yinzhen | Yu, Haiping

Article Type: Research Article

Abstract: Click-through rate (CTR) prediction, which aims to predict the probability of a user clicking on an ad, is a critical task in online advertising systems. The problem is very challenging since(1) an effective prediction relies on high-order combinatorial features, and(2)the relationship to auxiliary ads that may impact the CTR. In this paper, we propose Deep Context Interaction Network on Attention Mechanism(DCIN-Attention) to process feature interaction and context at the same time. The context includes other ads in the current search page, historically clicked and unclicked ads of the user. Specifically, we use the attention mechanism to learn the interactions between …the target ad and each type of auxiliary ad. The residual network is used to model the feature interactions in the low-dimensional space, and with the multi-head self-attention neural network, high-order feature interactions can be modeled. Experimental results on Avito dataset show that DCIN outperform several existing methods for CTR prediction. Show more

Keywords: Click-through rate, attention mechanism, residual network, feature interaction, context

DOI: 10.3233/JIFS-210830

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6899-6914, 2021

Authors: Tang, Han | Dalin,

Article Type: Research Article

Abstract: Uncertain time series analysis has been developed for studying the imprecise observations. In this paper, we propose a nonlinear model called uncertain max-autoregressive (UMAR) model. The unknown parameters in model are estimated by the least squares estimation. Then the residual analysis is presented. In many cases, there are some outliers in the time series due to short-term change in the underlying process. The UMAR model offers an alternative for detecting outliers in the imprecise observations. Based on the previous theoretical results, the UMAR model is used to forecast the future. Finally, an example suggests that the new proposed time series …model works well compared to the uncertain autoregressive (UAR) model. Show more

Keywords: Uncertain time series analysis, principle of least squares, residual analysis, outlier detection, confidence interval

DOI: 10.3233/JIFS-210848

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6915-6922, 2021

Authors: Maity, Suman | De, Sujit Kumar | Pal, Madhumangal | Mondal, Sankar Prasad

Article Type: Research Article

Abstract: This article deals with an economic order quantity inventory model of imperfect items under non-random uncertain demand. Here we consider the customers screen the imperfect items during the selling period. After a certain period of time, the imperfect items are sold at a discounted price. We split the model into three cases, assuming that the demand rate increases, decreases, and is constant in the discount period. Firstly, we solve the crisp model, and then the model is converted into a fuzzy environment. Here we consider the dense fuzzy, parabolic fuzzy, degree of fuzziness and cloudy fuzzy for a comparative study. …The basic novelty of this paper is that a computer-based algorithm and flow chart have been given for the solution of the proposed model. Finally, sensitivity analysis and graphical illustration have been given to check the validity of the model. Show more

Keywords: Imperfect inventory, dense fuzzy number, parabolic fuzzy number, cloudy fuzzy number, degree of fuzziness, optimization

DOI: 10.3233/JIFS-210856

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6923-6934, 2021

Authors: Dong, Yu | Zhang, Xianquan | Yu, Chunqiang | Tang, Zhenjun | Xia, Guoen

Article Type: Research Article

Abstract: Digital images are easily corrupted by attacks during transmission and most data hiding methods have limitations in resisting cropping and noise attacks. Aiming at this problem, we propose a robust image data hiding method based on multiple backups and pixel bit weight (PBW). Especially multiple backups of every pixel bit are pre-embedded into a cover image according to a reference matrix. Since different pixel bits have different weights, the most significant bits (MSBs) occupy more weights on the secret image than those of the least significant bits (LSBs). Accordingly, some backups of LSBs are substituted by the MSBs to increase …the backups of MSBs so that the quality of the extracted secret image can be improved. Experimental results show that the proposed algorithm is robust to cropping and noise attacks for secret image. Show more

Keywords: Data hiding, anti-cropping, anti-noise, multi-backup data, pixel bit weight, reference matrix

DOI: 10.3233/JIFS-210862

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6935-6948, 2021

Authors: Panityakul, Thammarat | Mahmood, Tahir | Ali, Zeeshan | Aslam, Muhammad

Article Type: Research Article

Abstract: Certain intellectuals have generalized the principle of the fuzzy set (FS), but the theory of complex q-rung orthopair fuzzy set (Cq-ROFS) has received massive attraction from different scholars. The goal of this study is to combine the principle of Heronian mean (HM) operator with Cq-ROFS is to initiate the complex q-rung orthopair fuzzy HM (Cq-ROFHM) operator, complex q-rung orthopair fuzzy weighted HM (Cq-ROFWHM) operator, complex q-rung orthopair fuzzy geometric HM (Cq-ROFGHM) operator, complex q-rung orthopair fuzzy weighted geometric HM (Cq-ROFWGHM) operator, and their flexible and dominant properties. These operators can help to aggregate any number of attributes to determine the …reliability and consistency of the investigated operators. Moreover, there are physical and non-physical threats. Physical threats cause damage to computer systems hardware and infrastructure. Examples include theft, vandalism through to natural disasters. Non-physical threats target the software and data on the computer systems. To manage such sort of troubles, we determine the analyzing and controlling computer security threats based on presented operators under the Cq-ROFS. Finally, to show the reliability and proficiency of the presented approaches, we resolved some numerical examples by using the explored operators. The comparative analysis, advantages, and graphical interpretations of the presented works are also discovered. Show more

Keywords: Complex q-rung orthopair fuzzy sets, heronian mean operators, analyzing and controlling computer security threats

DOI: 10.3233/JIFS-210870

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6949-6981, 2021

Authors: Rubin Bose, S. | Sathiesh Kumar, V.

Article Type: Research Article

Abstract: The real-time perception of hand gestures in a deprived environment is a demanding machine vision task. The hand recognition operations are more strenuous with different illumination conditions and varying backgrounds. Robust recognition and classification are the vital steps to support effective human-machine interaction (HMI), virtual reality, etc. In this paper, the real-time hand action recognition is performed by using an optimized Deep Residual Network model. It incorporates a RetinaNet model for hand detection and a Depthwise Separable Convolutional (DSC) layer for precise hand gesture recognition. The proposed model overcomes the class imbalance problems encountered by the conventional single-stage hand action …recognition algorithms. The integrated DSC layer reduces the computational parameters and enhances the recognition speed. The model utilizes a ResNet-101 CNN architecture as a Feature extractor. The model is trained and evaluated on the MITI-HD dataset and compared with the benchmark datasets (NUSHP-II, Senz-3D). The network achieved a higher Precision and Recall value for an IoU value of 0.5. It is realized that the RetinaNet-DSC model using ResNet-101 backbone network obtained higher Precision (99.21 %for AP0.5 , 96.80%for AP0.75 ) for MITI-HD Dataset. Higher performance metrics are obtained for a value of γ= 2 and α= 0.25. The SGD with a momentum optimizer outperformed the other optimizers (Adam, RMSprop) for the datasets considered in the studies. The prediction time of the optimized deep residual network is 82 ms. Show more

Keywords: Hand gesture recognition, RetinaNet, ResNet-101, CNN, human machine interaction

DOI: 10.3233/JIFS-210875

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6983-6997, 2021

Authors: Liao, Ningna | Gao, Hui | Wei, Guiwu | Chen, Xudong

Article Type: Research Article

Abstract: Facing with a sea of fuzzy information, decision makers always feel it difficult to select the optimal alternatives. Probabilistic hesitant fuzzy sets (PHFs) utilize the possible numbers and the possible membership degrees to describe the behavior of the decision makers. though this environment has been introduced to solve problems using different methods, this circumstance can still be explored by using different method. This paper’ s aim is to develop the MABAC (Multi-Attributive Border Approximation area Comparison) decision-making method which based on cumulative prospect theory (CPT) in probabilistic hesitant fuzzy environment to handle multiple attributes group decision making (MAGDM) problems. Then …the weighting vector of attributes can be calculated by the method of entropy. Then, in order to show the applicability of the proposed method, it is validated by a case study for buying a house. Finally, through comparing the outcome of comparative analysis, we conclude that this designed method is acceptable. Show more

Keywords: Multiple attribute group decision making (MAGDM), probability hesitant fuzzy sets (PHFs), cumulative prospect theory (CPT); MABAC method

DOI: 10.3233/JIFS-210889

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6999-7014, 2021

Authors: Miao, Guoyi | Chen, Yufeng | Liu, Jian | Xu, Jinan | Liu, Mingtong | Feng, Wenhe

Article Type: Research Article

Abstract: The hypotactic structural relation between clauses plays an important role in improving the discourse coherence of document-level translation. However, the standard neural machine translation (NMT) models do not explicitly model the hypotactic relationship between clauses, which usually leads to structurally incorrect translations of long and complex sentences. This problem is particularly noticeable on Chinese-to-English translation task of complex sentences due to the grammatical form distinction between English and Chinese. English is rich in grammatical form (e.g. verb morphological changes and subordinating conjunctions) while Chinese is poor in grammatical form. These linguistic phenomena make it a challenge for NMT to learn …the hypotactic structure knowledge from Chinese as well as the structure alignment between Chinese and English. To address these issues, we propose to model the hypotactic structure for Chinese-to-English complex sentence translation by introducing hypotactic structure knowledge. Specifically, we annotate and build a hypotactic structure aligned parallel corpus that provides rich hypotactic structure knowledge for NMT. Moreover, we further propose a structure-infused neural framework to combine the hypotactic structure knowledge with the NMT model through two integrating strategies. In particular, we introduce a specific structure-aware loss to encourage the NMT model to better learn the structure knowledge. Experimental results on WMT17, WMT18 and WMT19 Chinese-to-English translation tasks demonstrate the effectiveness of the proposed methods. Show more

Keywords: Neural machine translation, hypotactic structure, discourse coherence, structure-infused neural framework

DOI: 10.3233/JIFS-210908

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7015-7029, 2021

Authors: Xiao, Lu | Wei, Guiwu | Guo, Yanfeng | Chen, Xudong

Article Type: Research Article

Abstract: Interval-valued intuitionistic fuzzy set (IVIFS) is a flexible method to deal with uncertainty and fuzziness. For the past few years, extensive researches about the multi-attribute group decision making (MAGDM) problems based on IVIFSs has been extensively studied in many fields. In this study, the Taxonomy method based on IVIFSs (IVIF-Taxonomy) was proposed for MAGDM problems. For the sake of the objectivity of attribute weight, entropy is introduced into the proposed model. The IVIF-Taxonomy method fully considers the weight of the decision makers (DMs) and the homogeneity of the chosen alternatives, making it more realistic. In addition, we apply IVIF-Taxonomy method …to fund selection to verify the validity of IVIF-Taxonomy method. Finally, the trustworthy of IVIF-Taxonomy method is proved by comparing with the aggregate operator, IVIF-TOPSIS method, IVIF-GRA method and modified IVIF-WASPAS method. Show more

Keywords: Multiple attribute group decision making (MAGDM), Interval-valued intuitionistic fuzzy sets, taxonomy method, entropy

DOI: 10.3233/JIFS-210918

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7031-7045, 2021

Authors: Hamad, Aws Hamed | Mahmood, Ali Abdulkareem | Abed, Saad Adnan | Ying, Xu

Article Type: Research Article

Abstract: Word sense disambiguation (WSD) refers to determining the right meaning of a vague word using its context. The WSD intermediately consolidates the performance of final tasks to achieve high accuracy. Mainly, a WSD solution improves the accuracy of text summarisation, information retrieval, and machine translation. This study addresses the WSD by assigning a set of senses to a given text, where the maximum semantic relatedness is obtained. This is achieved by proposing a swarm intelligence method, called firefly algorithm (FA) to find the best possible set of senses. Because of the FA is based on a population of solutions, it …explores the problem space more than exploiting it. Hence, we hybridise the FA with a one-point search algorithm to improve its exploitation capacity. Practically, this hybridisation aims to maximise the semantic relatedness of an eligible set of senses. In this study, the semantic relatedness is measured by proposing a glosses-overlapping method enriched by the notion of information content. To evaluate the proposed method, we have conducted intensive experiments with comparisons to the related works based on benchmark datasets. The obtained results showed that our method is comparable if not superior to the related works. Thus, the proposed method can be considered as an efficient solver for the WSD task. Show more

Keywords: Firefly algorithm, local search, meta-heuristic, semantic relatedness, word sense disambiguation

DOI: 10.3233/JIFS-210934

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7047-7061, 2021

Authors: Gurmani, Shahid Hussain | Chen, Huayou | Bai, Yuhang

Article Type: Research Article

Abstract: As a generalization of linguistic q-rung orthopair fuzzy set (Lq-ROFS), linguistic interval valued q-Rung orthopair fuzzy set (LIVq-ROFS) is a new concept to deal with complex and uncertain decision making problems which Lq-ROFS cannot handle. Due to the lack of information in decision making process, decision makers mostly prefer to give their preferences in interval form rather than a crisp number. In this situations, LIVq-ROFS appears up as a useful tool. In this work, we define operational laws of LIVq-ROFS and prove some properties. Furthermore, we propose the conception of the LIVq-ROF weighted averaging operator and give its formula by …mathematical induction. To compare two or more linguistic interval valued q-Rung orthopair fuzzy numbers (LIVq-ROFNs), the improved form of score function is also given. Considering the powerfulness of LIVq-ROFSs handling ambiguity and complex uncertainty in practical problems, the key innovation of this paper is to develop the linguistic interval-valued q-rung orthopair fuzzy VIKOR model that is significantly different from the existing VIKOR methodology. The computing steps of this newly created model are briefly presented. Finally, the effectiveness of model is verified by an example and through comparative analysis, the superiority of VIKOR method is further illustrated. Show more

Keywords: Linguistic interval-valued q-rung orthopair fuzzy sets, multiple attribute group decision making, aggregation operators, VIKOR model, linguistic variable

DOI: 10.3233/JIFS-210940

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7063-7079, 2021

Authors: Yang, Guotian | Wang, Xiaowei | Wang, Yingnan

Article Type: Research Article

Abstract: This paper develops a fuzzy modeling strategy to study the temperature of different combustion layers in a power plant. First, a new infrared temperature measurement system is developed to measure three layers (bottom, middle and upper) temperature on both sides of the boiler. Then, a fuzzy clustering modeling algorithm is designed based on entropy to determine the structure of the fuzzy model and the corresponding fuzzy memberships of local models. The effect of modeling mismatches are overcome by the use of online identification of parameters. Simulation results show that the effectiveness of the proposed method can be achieved for a …660 MW power plant. Show more

Keywords: Data-driven modeling, combustion layer temperature, multi-model, fuzzy subtractive clustering

DOI: 10.3233/JIFS-210965

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7081-7091, 2021

Authors: Yu, Shujuan | Liu, Danlei | Zhang, Yun | Zhao, Shengmei | Wang, Weigang

Article Type: Research Article

Abstract: As an important branch of Nature Language Processing (NLP), how to extract useful text information and effective long-range associations has always been a bottleneck for text classification. With the great effort of deep learning researchers, deep Convolutional Neural Networks (CNNs) have made remarkable achievements in Computer Vision but still controversial in NLP tasks. In this paper, we propose a novel deep CNN named Deep Pyramid Temporal Convolutional Network (DPTCN) for short text classification, which is mainly consisting of concatenated embedding layer, causal convolution, 1/2 max pooling down-sampling and residual blocks. It is worth mentioning that our work was highly inspired …by two well-designed models: one is temporal convolutional network for sequential modeling; another is deep pyramid CNN for text categorization; as their applicability and pertinence remind us how to build a model in a special domain. In the experiments, we evaluate the proposed model on 7 datasets with 6 models and analyze the impact of three different embedding methods. The results prove that our work is a good attempt to apply word-level deep convolutional network in short text classification. Show more

Keywords: Deep convolution network, causal convolution, shortcut connection, short text classification

DOI: 10.3233/JIFS-210970

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7093-7100, 2021

Authors: Ma, Zong-fang | Liu, Zhe | Luo, Chan | Song, Lin

Article Type: Research Article

Abstract: Classification of incomplete instance is a challenging problem due to the missing features generally cause uncertainty in the classification result. A new evidential classification method of incomplete instance based on adaptive imputation thanks to the framework of evidence theory. Specifically, the missing values of different incomplete instances in test set are adaptively estimated based on Shannon entropy and K -nearest centroid neighbors (KNCNs) technology. The single or multiple edited instances (with estimations) then are classified by the chosen classifier to get single or multiple classification results for the instances with different discounting (weighting) factors, and a new adaptive global fusion …method finally is proposed to unify the different discounted results. The proposed method can well capture the imprecision degree of classification by submitting the instances that are difficult to be classified into a specific class to associate the meta-class and effectively reduce the classification error rates. The effectiveness and robustness of the proposed method has been tested through four experiments with artificial and real datasets. Show more

Keywords: Incomplete instance, evidence theory, classification, missing data, uncertainty

DOI: 10.3233/JIFS-210991

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7101-7115, 2021

Authors: Zeng, Shouzhen | Azam, Amina | Ullah, Kifayat | Ali, Zeeshan | Asif, Awais

Article Type: Research Article

Abstract: T-Spherical fuzzy set (TSFS) is an improved extension in fuzzy set (FS) theory that takes into account four angles of the human judgment under uncertainty about a phenomenon that is membership degree (MD), abstinence degree (AD), non-membership degree (NMD), and refusal degree (RD). The purpose of this manuscript is to introduce and investigate logarithmic aggregation operators (LAOs) in the layout of TSFSs after observing the shortcomings of the previously existing AOs. First, we introduce the notions of logarithmic operations for T-spherical fuzzy numbers (TSFNs) and investigate some of their characteristics. The study is extended to develop T-spherical fuzzy (TSF) logarithmic …AOs using the TSF logarithmic operations. The main theory includes the logarithmic TSF weighted averaging (LTSFWA) operator, and logarithmic TSF weighted geometric (LTSFWG) operator along with the conception of ordered weighted and hybrid AOs. An investigation about the validity of the logarithmic TSF AOs is established by using the induction method and examples are solved to examine the practicality of newly developed operators. Additionally, an algorithm for solving the problem of best production choice is developed using TSF information and logarithmic TSF AOs. An illustrative example is solved based on the proposed algorithm where the impact of the associated parameters is examined. We also did a comparative analysis to examine the advantages of the logarithmic TSF AOs. Show more

Keywords: T-Spherical fuzzy set, logarithmic operations, spherical fuzzy set, multi-attribute decision making methods

DOI: 10.3233/JIFS-211003

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7117-7135, 2021

Authors: Wang, Xiaoyuan | Zhang, Lulu | Wang, Gang | Wang, Quanzheng | He, Guowen

Article Type: Research Article

Abstract: The collision risk of ships is a fuzzy concept, which is the measurement of the likelihood of a collision between ships. Most of existed studies on the risk of multi-ship collision are based on the assessment of two-ship collision risk, and collision risk between the target ship and each interfering ship is calculated respectively, to determine the key avoidance ship. This method is far from the actual situation and has some defects. In open waters, it is of certain reference value when there are fewer ships, but in busy waters, it cannot well represent the risk degree of the target …ship, since it lacks the assessment of the overall risk of the perceived area of the target ship. Based on analysis of complexity of ships group situation, the concept of relative domain was put forward and the model was constructed. On this basis, the relative collision risk was proposed, and the corresponding model was obtained, so as to realize risk assessment. Through the combination of real ship and simulation experiments, the variation trend, stability and sensitivity of the model were verified. The results showed that risk degree of the environment of ships in open and busy waters could be well assessed, and good references for decision-making process of ships collision avoidance could be provided. Show more

Keywords: Ships group situation, unmanned ship, relative domain, relative collision risk

DOI: 10.3233/JIFS-211025

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7137-7150, 2021

Authors: Aaly Kologani, M. | Hoskova-Mayerova, S. | Borzooei, R. A. | Rezaei, G. R.

Article Type: Research Article

Abstract: In this paper, by using the concept of maximal filter of equality algebra, we introduce radical of equality algebra. Then some equivalence definitions of it and some related properties are investigated. Then by using this notion, we introduce the concept of semi-maximal filter and prime-like filter on equality algebras and the relation between them and other filters of equality algebra are investigated. Finally, by using the notion of prime-like filters, we introduce a topology on equality algebra.

Keywords: Equality algebra, maximal filter, radical, semi-maximal filter, prime-like filter

DOI: 10.3233/JIFS-211035

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7151-7165, 2021

Authors: Zhou, Qing | Shi, Xi | Ge, Liang

Article Type: Research Article

Abstract: The early warning of mental disorders is of great importance for the psychological well-being of college students. The accuracy of conventional scaling methods on questionnaires is generally low in predicting mental disorders, as the questionnaires contain much noise, and the processing on the questionnaires is rudimentary. To address this problem, we propose a novel anomaly detection framework on questionnaires, which represents each questionnaire as a document, and applies keyword extraction and machine learning techniques to detect abnormal questionnaires. We also propose a new keyword statistic for the calculation of option significance and three interpretable machine learning models for the calculation …of question significance. Experiments demonstrate the effectiveness of our proposed methods. Show more

Keywords: Mental health, text analysis, interpretability, TF-IDF, Likert scale

DOI: 10.3233/JIFS-211044

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7167-7179, 2021

Authors: Xu, Jie | Lv, Jian | Yang, Hong-Tai | Li, Yan-Lai

Article Type: Research Article

Abstract: The video conferencing software is regarded as a significant tool for social distancing and getting incorporations up and going. Due to the indeterminacy of epidemic evolution and the multiple criteria, this paper proposes a video conferencing software selection method based on hybrid multi-criteria decision making (HMCDM) under risk and cumulative prospect theory (CPT), in which the criteria values are expressed in various mathematical forms (e.g., real numbers, interval numbers, and linguistic terms) and can be changed with natural states of the epidemic. Initially, the detailed description of video conferencing software selection problem under an epidemic are given. Subsequently, a whole …procedure for video conferencing software selection is conducted, the approaches for processing and normalizing the multi-format evaluation values are presented. Furthermore, the expectations provided by DMs under different natural states of the epidemic are considered as the corresponding reference points (RP). Based on this, the matrix of gains and losses is constructed. Then, the prospect values of all criteria and the perceived probabilities of natural states are calculated according to the value function and the weighting function in CPT respectively. Finally, the proposed method is illustrated by an empirical case study, and the comparison analysis and the sensitivity analysis for the loss aversion parameter are conducted to prove the effectiveness and robustness. The results show that considering the psychological characteristics of DMs in selection decision is beneficial to avoid the unacceptable and potential loss risks. This study could provide a useful guideline for managers who intend to select appropriate video conferencing software. Show more

Keywords: Epidemic, video conferencing software selection, cumulative prospect theory, hybrid multi-criteria decision making under risk

DOI: 10.3233/JIFS-211054

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7181-7198, 2021

Authors: Ma, Yanfang | Xu, Weifeng | Wang, Xiaoyu | Li, Zongmin | Lev, Benjamin

Article Type: Research Article

Abstract:  The decreasing resources of the earth and the deterioration of the environment are offering new challenges for handling waste management practices. The establishment of the smart waste bins plays an important role in promoting the development of waste classification and treatment fundamentally. We developed the evaluation system for the location selection problem of smart waste bins. Considering the uncertainty in the location selection of smart waste bins, the probabilistic linguistic term sets (PLTSs) are selected to express the evaluation information. Because of the excellent performance in weight-determing, the best worst method (BWM) is chosen to get the weight of criteria. …While the weighted aggregated sum product assessment (WASPAS) method could handle both the qualitative and quantitative information, which are considered to derive the final ranking of the alternatives. This paper proposed a new group multi-criteria decision making approach integrating the BWM and the WASPAS with probabilistic linguistic information. Finally, in the empirical example, a sensitivity analysis shows that the proposed method is stable, a comparison analysis with PL-TOPSIS, PL-VIKOR, and PL-TODIM reflects its effectiveness and rationality, and the managerial implication verifies its usefulness and practicability, which also give guide to the company, government and resident. Show more

Keywords: Multiple attributes decision making, BWM, WASPAS, probabilistic linguistic term set, smart waste bins

DOI: 10.3233/JIFS-211066

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7199-7218, 2021

Authors: Cui, Xiaoning | Wang, Qicai | Zhang, Rongling | Dai, Jinpeng | Li, Sheng

Article Type: Research Article

Abstract: The compressive strength of concrete can be predicted by machine learning. One thousand thirty samples of concrete compressive strength data were used as the dataset. Machine learning was applied to prediction of concrete compressive strength with seven machine learning algorithms. To improve data utilization and generalization ability of machine learning model, ten data sets were constructed by feature reorganization for data augmentation. Compared with other machine learning models, the XGBoost model based on Boosting tree algorithm had the highest prediction accuracy and the most robust generalization ability. With different multi-feature combination input conditions, the R2 score of the XGBoost …algorithm was 0.9283, the MAE score was 3.4292, the MAPE score was 12.5656, and the RMSE score was 5.2813. The error accumulation curve of the XGBoost algorithm was analyzed. When the compressive strength of concrete is at 5–20MPa, the error contribution rate is higher. When the concrete compressive strength is at 20–40MPa, the prediction result error of the model drops sharply. When the strength reaches 40MPa, the error contribution rate of the model tends to converge and the error contribution rate is stable between 1 and 1.2, which indicates that the model has high prediction accuracy when the compressive strength is higher than 40 MPa. Show more

Keywords: Machine learning, prediction of Compressive strength, feature reorganization, XGBoost, data enhancement

DOI: 10.3233/JIFS-211088

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7219-7228, 2021

Authors: Du, Quan | Feng, Kai | Xu, Chen | Xiao, Tong | Zhu, Jingbo

Article Type: Research Article

Abstract: Recently, many efforts have been devoted to speeding up neural machine translation models. Among them, the non-autoregressive translation (NAT) model is promising because it removes the sequential dependence on the previously generated tokens and parallelizes the generation process of the entire sequence. On the other hand, the autoregressive translation (AT) model in general achieves a higher translation accuracy than the NAT counterpart. Therefore, a natural idea is to fuse the AT and NAT models to seek a trade-off between inference speed and translation quality. This paper proposes an ARF-NAT model (NAT with auxiliary representation fusion) to introduce the merit of …a shallow AT model to an NAT model. Three functions are designed to fuse the auxiliary representation into the decoder of the NAT model. Experimental results show that ARF-NAT outperforms the NAT baseline by 5.26 BLEU scores on the WMT’14 German-English task with a significant speedup (7.58 times) over several strong AT baselines. Show more

Keywords: Neural machine translation, non-autoregressive translation, autoregressive translation, auxiliary representation fusion

DOI: 10.3233/JIFS-211105

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7229-7239, 2021

Authors: Chu, Yongjie | Zhao, Lindu | Ahmad, Touqeer

Article Type: Research Article

Abstract: In this paper, an enhanced discriminative feature learning (EDFL) method is proposed to address single sample per person (SSPP) face recognition. With a separate auxiliary dataset, EDFL integrates Fisher discriminative learning and domain adaptation into a unified framework. The separate auxiliary dataset and the gallery/probe dataset are from two different domains (named source and target domains respectively) and have different data distributions. EDFL is modeled to transfer the discriminative knowledge learned from the source domain to the target domain for classification. Since the gallery set with SSPP contains scarce number of samples, it is hard to accurately represent the data …distribution of the target domain, which hinders the adaptation effect. To overcome this problem, the generalized domain adaption (GDA) method is proposed to realize good overall domain adaptation when one domain contains limited samples. GDA considers the both global and local domain adaptation effect at the same time. Further, to guarantee that the learned domain adaptation components are optimal for discriminative learning, the domain adaptation and Fisher discriminant model learning are unified into a single framework and an efficient algorithm is designed to optimize them. The effectiveness of the proposed approach is demonstrated by extensive evaluation and comparison with some state-of-the-art methods. Show more

Keywords: Single sample per person, domain adaptation, discriminative feature learning, feature selection

DOI: 10.3233/JIFS-211106

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7241-7255, 2021

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