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

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