Journal of Intelligent & Fuzzy Systems - Volume 45, issue 4

Authors: Chen, Liuxin | Wang, Yutai | Liu, Jinyuan

Article Type: Research Article

Abstract: In the emergency decision-making process, decision-makers usually cannot give rational evaluations, and existing decision-making methods do not adequately consider the risk attitude of decision-makers either. To solve these problems, a combined method based on the prospect theory and the multi-attributive ideal-real comparative analysis (MAIRCA) method is put forward in the picture fuzzy environment. Firstly, the optimal aggregation (OA) model is proposed to obtain the ideal evaluations with the least disagreement among decision-makers. Regarding the evaluations as reference systems, the OA-based prospect theory is put forward, which could calculate the prospect matrix more reasonably. Secondly, considering the prospect matrix and alternative …preference, the improved MAIRCA method is proposed, which overcomes the defects of theory and has the better ranking ability. Then, the OA-based prospect theory-MAIRCA method is further put forward to effectively complete the decision-making process with risk attitudes. Finally, an illustrative example of earthquake emergency assessment and a series of comparative experiments are presented. The analyses of results show that the proposed method has great guiding significance in the field of emergency decision-making management. Show more

Keywords: Picture fuzzy set, optimal aggregation model, prospect theory, MAIRCA method

DOI: 10.3233/JIFS-223279

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5493-5507, 2023

Authors: Gautam, Devendra | Dixit, Anurag | Banda, Latha | Goyal, S.B. | Verma, Chaman | Kumar, Manoj

Article Type: Research Article

Abstract: In recent generations of the digital world medical data in Recommender Systems. Health Care Recommender System (HCRS) analyses the medical data and then predicts the user’s or patient’s illness. Nowadays, healthcare data is used by various users or patients in recommendation systems which are useful for everyone. Analysing and predicting medical data provides awareness to users and these data predictions may be enriched using various techniques of RS. Machine learning techniques are used to make sure that health data is reliable and of high quality. In every RS the issues are targeted such as scalability, sparsity and cold start problems. …In many social networking applications, these issues are resolved using ML algorithms. However, there is a significant gap between IT systems and medical diagnosis. The fuzzy genetic method is used in HCRS in order to bridge the gap between IT and healthcare applications. Through the use of the mutation and crossover operators, a real-value genetic method is used in this to compute similarity. With the user’s extra personalized information, fuzzy rules are later generated for the database. The Hybrid fuzzy-genetic method, also known as this situation, combines both techniques to improve recommendation quality. Utilizing this method will improve the quality of the recommendation process by discovering the most precise similarity measures among different users. Six factors are subjected to fuzzification, including age, gender, employment, height, weight, and region. Genre-interesting measure weights are then used, including Very Light, Light, Average, Heavy, and Very Heavy. Finally, the evaluation metrics used MAE and RMSE to evaluate the recommendation accuracy which showed the best results in comparison with baseline approaches such as Convolutional Neural Networks and Restricted Boltzman Machine. Show more

Keywords: Recommender system, confidentiality, deep learning, convolutional neural networks, fuzzy logics

DOI: 10.3233/JIFS-224257

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5509-5522, 2023

Authors: Qian, Zhuoyi | Guo, Peng | Wang, Yifan | Xiao, Fangcheng

Article Type: Research Article

Abstract: Self-driving cars are expected to replace human drivers shortly, bringing significant benefits to society. However, they have faced opposition from various organizations that argue it is challenging to respond to instances involving unavoidable personal injury. In situations involving deadly collisions, self-driving cars must make decisions that balance life and death. This paper investigates the ethical and moral decision-making challenges for self-driving cars from an algorithmic perspective. To address this issue, we introduce the accident-prioritized replay mechanism to the Deep Q-Networks (DQN) algorithm based on early humanities research. The mechanism quantifies a reward function that takes priority into account. RGB (red, …green, blue) images obtained by the camera installed in front of the self-driving cars are fed into the Xception network for training. To evaluate our approach, we compare it to the conventional DQN algorithm. The simulation results indicate that the Rawlsian DQN algorithm has superior stability and interpretability in decision-making. Furthermore, the majority of respondents to our survey accept the final decision made by our algorithm. Our experiment demonstrates that it is possible to incorporate ethical considerations into self-driving car decision-making, providing a solution for rational decision-making in emergency and dilemma circumstances. Show more

Keywords: Rawlsian maximin principle, carla simulator, depth-wise separable convolution, deep Q-network, ethical decision-making

DOI: 10.3233/JIFS-224553

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5523-5540, 2023

Authors: Shan, Yuxiang | Lu, Hailiang | Lou, Weidong

Article Type: Research Article

Abstract: Exploiting dynamic spatial and temporal features of location information for robot modeling is of great importance in many real applications. It has gained increasing attention in the era of the Internet of Things (IoT). However, successful modeling and accurate localization for robot in indoor environment is still a challenge, where the environment factors are complex and unpredictable, such as signal noise, obstacles and spare fingerprints. Existing studies usually employ data driven and learning based models to capture spatial and temporal features for robot location estimation, modeling dynamics of robot and make robot decision. However, the modeling and localization performance is …not satisfied. In this paper, to address above challenges, a novel deep learning framework called multi-faceted deep learning based dynamics modeling and robot localization learning (DMLoc) method is proposed. Specifically, a localization attention module is designed to capture the features from original fingerprints and optimized fingerprints information. Then, a multi-faceted localization module is proposed, which integrates extraction model and optimized model with long short-term memory (LSTM) and gate recurrent unit (GRU). Moreover, a multi-feature fusion layer is designed to fuse the extracted features and generate localization results. Extensive simulation results show the efficiency of the proposed DMLoc. Show more

Keywords: Robot localization, dynamics modeling, learning-based robot decision

DOI: 10.3233/JIFS-230895

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5541-5550, 2023

Authors: Jeyabalan, Saranya Devi | Yesudhas, Nancy Jane | Sathyanarayanan, Jayashree | Harichandran, Khanna Nehemiah

Article Type: Research Article

Abstract: Coronavirus disease 2019 (Covid-19) is a contagious pandemic illness characterized by severe acute respiratory syndrome. The daily rise of Covid-19 instances and fatalities has resulted in worldwide lockdowns, quarantines and social distancing. Researchers have been working incredibly to develop precisely focused strategies to warfare the Covid-19 pandemic. This study aims to develop a cyclical learning rate optimized stacked generalization computational models (CLR-SGCM) for predicting Covid-19 pandemic outbreaks. Stacked generalization framework performs hierarchical two-phase prediction. In the first phase, deep learning models namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) and statistical model Auto Regressive Integrated Moving Average (ARIMA) …are used as sub models to create pooled datasets (PDS). Cyclical learning rate (CLR) optimizer is used to enhance learning rate of ensemble deep learning models namely LSTM and GRU. In the second phase, meta learner is trained on dataset PDS using four different regression algorithms such as linear regression, polynomial regression, lasso regression and ridge regression to perform the final predictions. Time series data from India, Brazil, and the United States were utilized to forecast the Covid-19 pandemic outbreak. According to experimental finding, the presented stacking ensemble model outpaces the individual learners in terms of accuracy and error rate. Show more

Keywords: Covid-19, forecasting, time series prediction, stacked generalization, CLR optimization, deep learning models

DOI: 10.3233/JIFS-231229

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5551-5566, 2023

Authors: Ge, Hongping | Liu, Huaying | Luo, Yun

Article Type: Research Article

Abstract: Aiming at the troubles of difficult extraction of fault features and low fault recognition rate in rotating equipment fault detection approach, a new technique for intelligent diagnosis based on modified hierarchical diversity entropy (MHDE) and extension theory (ET) is proposed in the thesis. Firstly, MHDE employs to comprehensively describe the fault information of the given signals. Secondly, the MHDE feature sets are regarded as the characteristic parameters of the extension matter element model, and the matter element model in various states is established. Finally, the testing datasets are fed into the matter element model for each operating conditions, and the …correlation function is used to compute the comprehensive correlation between the testing datasets and the various conditions of the rotating machinery, so as to realize the qualitative and quantitative identification of the testing datasets. The reliability and superiority of the proposed new approach is validated by real experimental datasets on various rotating machinery types. The analysis results show that the proposed novel technology can effectively excavate the feature information and accurately identify various fault conditions of rotating machinery. In addition, compared with other combined model technology in the paper, the proposed intelligent fault diagnosis technology has better classification performance. Show more

Keywords: Rotating machinery, modified hierarchical diversity entropy, extension theory, correlation function, fault diagnosis technology

DOI: 10.3233/JIFS-231363

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5567-5586, 2023

Authors: Peng, Peng | Ni, Zhiwei | Zhu, Xuhui | Chen, Qian

Article Type: Research Article

Abstract: A framework for spatial crowdsourcing task allocation based on centralized differential privacy is proposed for addressing the problem of worker’s location privacy leakage. Firstly, by combining two stages of differential privacy noise addition and clustering matching, a spatial crowdsourcing worker dataset with high differential privacy protection can be obtained; Secondly, the dynamic problem of spatial crowdsourcing task allocation is transformed into a static combinatorial optimization problem by dividing the spatiotemporal units and the “delay matching” strategy; Finally, the improved discrete glowworm swarm optimization algorithm is used to calculate the results of spatial crowdsourcing task allocation. It has been demonstrated that, …compared to the direct differential privacy noise-adding assignment method and the discrete glowworm swarm optimization assignment method, the proposed method achieves better task assignment results, with the total travel distance reduced by 12.42% and 3.56%, respectively, and the task assignment success rate increased by 11.75% and 3.34%, respectively. Show more

Keywords: Differential privacy, k-means clustering, space crowdsourcing, task allocation, the glowworm swarm optimization algorithm

DOI: 10.3233/JIFS-230734

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5587-5600, 2023

Authors: Zhu, Xuemin | Liu, Sheng | Zhu, Xuelin | You, Xiaoming

Article Type: Research Article

Abstract: An enhancing sparrow optimization algorithm with hybrid multi-strategy (EGLTA-SSA) is proposed, to improve the defects of the sparrow search algorithm (SSA), which is easy to fall into local optimum. Firstly, the elite backward learning strategy is introduced to initialize the sparrow population, to generate high-quality initial solutions. Secondly, the leader position is updated by fusing multi-strategy mechanisms. On one hand, the high distributivity of arithmetic optimization algorithm operators are used to deflate the target position, and enhance the ability of SSA to jump out of the local optimum. On the other hand, the leader position is perturbed by adopting the …golden levy flight method and the t-distribution perturbation strategy to improve the shortcoming of SSA in the late iteration when the population diversity decreases. Further, a probability factor is added for random selection to achieve more effective communication among leaders. Finally, to verify the effectiveness of EGLTA-SSA, CEC2005 and CEC2019 functions are tested and compared with state-of-the-art algorithms, and the experimental results show that EGLTA-SSA has a better performance in terms of convergence rate and stability. EGLTA-SSA is also successfully applied to three practical engineering problems, and the results demonstrate the superior performance of EGLTA-SSA in solving project optimization problems. Show more

Keywords: Sparrow optimization algorithm (SSA), arithmetic optimization algorithm, golden levy flight distribution, t-distribution perturbation, engineering design problems

DOI: 10.3233/JIFS-231114

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5601-5632, 2023

Authors: Bai, Xiaojun | Pan, Zhaofeng | Meng, Gong | Wang, Shenhang | Fu, Yanfang

Article Type: Research Article

Abstract: Hard disk is the main storage device for cloud service, and there always contain massive disks deployed in a data center. Disk failure occur frequently in data center, which may lead to data loss and other disasters, so there have urgent needs for a failure prediction method of hard disk so as to ensure service reliability. This paper proposes a temporal prediction model based on LSTM. Firstly, the SMART data of the disk is analyzed, and the Pearson correlation coefficient is used to analyze the correlation between the monitoring time series data of the faulty disk and the normal disk, …and the monitoring index with the lowest correlation is selected as the fault feature; secondly, for the problem of serious imbalance of positive and negative samples in the SMART dataset, the SMOTEENN algorithm is introduced for oversampling to obtain a balanced dataset of positive and negative samples. The proposed method improves accuracy by 8.268% and F1-score by 8.657% compared to the conventional method. Show more

Keywords: Hard disk drives, failure prediction, association analysis, long-short term memory, SSA

DOI: 10.3233/JIFS-231268

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5633-5645, 2023

Authors: Suo, Jiafeng | Han, Dongchen | Zhao, Hui

Article Type: Research Article

Abstract: In the entity extraction task, there are some complex extraction problems, such as nested entity, entity boundary recognition, context ambiguity, and multi-instance entity recognition. Entity nesting is an important challenge in relational extraction. The main reason of entity nesting problem is that the boundary information between entities is not clear. In order to solve the entity nesting problem at the fragment level, while preserving the relationship between fragments with the same characteristics and improving efficiency, we proposed a brand new fragment annotation method. On the basis of traditional fragment annotation method, combined with pointer annotation method, we designed an annotation …method of "ergodic enumeration + group mapping". On the basis of this method, an entity extraction model is designed: Span-Extraction Based Entity Extraction Model (LMA). Our model underwent a series of validations in the English data sets New York Times(NYT) and WEBNLG, showing significant improvements over the baseline model F1. It can effectively alleviate the above problems. Show more

Keywords: Entity extraction, relational extraction, nested entity, context ambiguity

DOI: 10.3233/JIFS-231766

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5647-5657, 2023

Authors: Jia, Lijuan | Hou, Fang

Article Type: Research Article

Abstract: The evaluation of physical education teaching effectiveness is an important component of physical education teaching, and plays a multifaceted role in the process of physical education teaching. The information provided by it can control and regulate the progress of physical education teaching activities as a whole, ensuring that physical education teaching activities develop towards predetermined goals. With the development of the popularization of physical education, people’s requirements for the quality of physical education continue to improve, and the role and position of evaluation in teaching has become increasingly evident. Evaluation of physical education teaching effectiveness has become an indispensable process …in teaching activities. The college physical education teaching effect evaluation can be regarded as a multiple attribute decision making (MADM). Thus, this paper collected information in probabilistic hesitant fuzzy sets (PHFSs) and using CRITIC method to obtain the unknown weight among attributes. Further, a novel probabilistic hesitant fuzzy QUALIFLEX (PHF-QUALIFLEX) method was constructed for MADM. Finally, a numerical case for college physical education teaching effect evaluation was illustrated with this proposed model and other methods were utilized to compare with PHF-QUALIFLEX method to verify the feasibility and applicability. Show more

Keywords: Multiple attributes decision making (MADM), probabilistic hesitant fuzzy sets (PHFSs), QUALIFLEX method, CRITIC method, teaching effect evaluation

DOI: 10.3233/JIFS-231769

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5659-5670, 2023

Authors: Zhang, Yunlai

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-232226

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5671-5683, 2023

Authors: Shen, Haiyang | Huo, Kui | Qiao, Xin | Li, Chongzhi

Article Type: Research Article

Abstract: In order to solve the problems with the traditional aircraft target type recognition algorithm, such as difficulty in feature selection, weak generalization ability, slow recognition speed, and low recognition accuracy, this paper put forward a new method that could detect and recognize aircraft targets in aerial images quickly and accurately. The aircraft targets in the images were detected rapidly and located through YOLOv3-tiny, and after image denoising, shadow detection, and positioning, then we used the Sobel operator to calculate the edge gradient of the target; the image of the aircraft target was segmented by using the region growth method, and …then the principal component analysis (PCA)was used to obtain the central axis of the aircraft target. The projected distance from the edge contour to the central axis was sampled at equal intervals along the direction of the central axis, and its ratio to the length of the central axis was calculated to construct the feature vector. Finally, the Spearman rank correlation method was used to match the feature vectors to realize the recognition of the aircraft type. Experiments showed that the proposed method had strong adaptability and small computation and could quickly detect and accurately recognize aircraft targets in aerial images. Show more

Keywords: Deep learning, aircraft identification, principal component analysis, spearman rank correlation

DOI: 10.3233/JIFS-232239

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5685-5696, 2023

Authors: Gong, Meng

Article Type: Research Article

Abstract: With the increasing maturity and widespread application of computer multimedia technology, many universities have attempted to use multimedia technology for English teaching in order to solve some of the difficulties and contradictions faced in current college English teaching practices. Practice has proven that multimedia teaching of college English not only increases the amount of information in classroom teaching, but also improves the effectiveness of classroom teaching. At the same time, due to deviations in understanding, lack of conditions, and improper operation in work, the normal functioning of multimedia teaching is also restricted, which affects the effectiveness of multimedia teaching in …college English. How to carry out multimedia teaching of college English is indeed an important topic that needs further research. The fuzzy comprehensive evaluation of multimedia teaching effectiveness in college English is a classical multiple attribute decision making (MADM) problems. Recently, the TODIM and GRA method has been used to cope with MADM issues. The double-valued neutrosophic sets (DVNSs) are used as a tool for characterizing uncertain information during the fuzzy comprehensive evaluation of multimedia teaching effectiveness in college English. In this manuscript, the double-valued neutrosophic number Exponential TODIM-GRA (DVNN-ExpTODIM-GRA) method is built to solve the MADM under DVNSs. In the end, a numerical case study for fuzzy comprehensive evaluation of multimedia teaching effectiveness in college English is given to validate the proposed method. Show more

Keywords: Multiple attribute decision making (MADM), double-valued neutrosophic sets (DVNSs), ExpTODIM method, GRA method, multimedia teaching effectiveness

DOI: 10.3233/JIFS-233116

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5697-5707, 2023

Authors: Zhang, Qianhong | Pan, Bairong | Ouyang, Miao | Lin, Fubiao

Article Type: Research Article

Abstract: The article is concerned with large time behavior of solution to second-order fractal difference equation with positive fuzzy parameters x n + 1 = A + x n B + x n - 1 ,     n = 1 , 2 , ⋯ , here the initial values x i  (i  = -1, 0) and the parameters A , B are positive fuzzy numbers. Utilizing a generalization of division (g-division) of fuzzy numbers, one presents large time behaviors of positive fuzzy solution including persistence, boundedness, …global convergence. Moreover, two numerical examples verify the effectiveness of the qualitative analysis. Show more

Keywords: Second- order fractal difference equation, g-division, large time behavior, positive fuzzy parameter

DOI: 10.3233/JIFS-224099

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5709-5721, 2023

Authors: Xu, Huajie | Zhou, Yanping | Chen, Huiying | Kou, Yuanyuan

Article Type: Research Article

Abstract: In the era of the knowledge economy, how integrating into the network of collaborative innovation and promoting technology sharing has become the key to enhancing the competitiveness of enterprises. It is well known that inter-organizational trust is essential to technology sharing. Firstly, this paper discussed how inter-organizational trust plays a role in technology-sharing behavior. Secondly, based on “organization is bounded rational”, we established an evolutionary game model to analyze the influencing factors of technology sharing. Finally, we used the numerical simulation method to verify the model. Research shows that affective trust facilitates technology acquisition and cognitive trust facilitates technology sharing. …The synergetic benefit distribution coefficient influences the evolutionary equilibrium strategy of technology sharing, and there is an optimal synergistic benefit distribution coefficient that maximizes the willingness of both enterprises to share technology. Technology transfer cost and technology leakage risk negatively affect technology-sharing behavior. The degree of technology complementarity, trust coefficient, incentive coefficient, and the ability of shared technologies to transform into synergistic benefits positively influence technology-sharing behavior. The research provides a new way to solve the practical problem of collaborative innovation technology sharing among enterprises. Show more

Keywords: Collaborative innovation, inter-organizational trust, technology sharing, technology acquisition, evolutionary game

DOI: 10.3233/JIFS-231898

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5723-5738, 2023

Authors: Huang, Peixin | Luo, Qifang | Wei, Yuanfei | Zhou, Yongquan

Article Type: Research Article

Abstract: Data clustering is a machine learning method for unsupervised learning that is popular in the two areas of data analysis and data mining. The objective is to partition a given dataset into distinct clusters, aiming to maximize the similarity among data objects within the same cluster. In this paper, an improved honey badger algorithm called DELHBA is proposed to solve the clustering problem. In DELHBA, to boost the population’s diversity and the performance of global search, the differential evolution method is incorporated into algorithm’s initial step. Secondly, the equilibrium pooling technique is included to assist the standard honey badger algorithm …(HBA) break free of the local optimum. Finally, the updated honey badger population individuals are updated with Levy flight strategy to produce more potential solutions. Ten famous benchmark test datasets are utilized to evaluate the efficiency of the DELHBA algorithm and to contrast it with twelve of the current most used swarm intelligence algorithms and k-means. Additionally, DELHBA algorithm’s performance is assessed using the Wilcoxon rank sum test and Friedman’s test. The experimental results show that DELHBA has better clustering accuracy, convergence speed and stability compared with other algorithms, demonstrating its superiority in solving clustering problems. Show more

Keywords: Cluster analysis, k-means, equilibrium honey badger algorithm, differential evolution, metaheuristic optimization

DOI: 10.3233/JIFS-231922

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5739-5763, 2023

Authors: Yang, Taoli | Li, Jinjin | Li, Zhaowen | Zhou, Yinfeng | Feng, Danlu

Article Type: Research Article

Abstract: Knowledge and learning assessment is a popular topic. In existing models for constructing the knowledge structure of an individual, it is often considered whether an individual has mastered the skills to solve the corresponding item. However, the relationship between the number of skills an individual has mastered and the item is ignored. It is not reasonable to explain the phenomenon that individuals solve the same item but have different knowledge structures behind it. This paper introduces the concept of skill inclusion degree and constructs the variable precision α-models to delineate knowledge structures. The skill inclusion degree takes into account an …individual’s mastery of the number of skills assigned to each item. Firstly, the concept of the skill inclusion degree is given, and some of its properties are discussed. Then, the variable precision α-model is constructed. Moreover, the relationship between knowledge structures delineated via the variable precision α-models by a skill map is studied, and the algorithm of knowledge structures delineated via these models by a skill map is designed. Finally, the experimental results on a real dataset demonstrate the feasibility and effectiveness of the proposed algorithm. Show more

Keywords: Knowledge structure, skill map, skill inclusion degree, disjunctive model, conjunctive model, variable precision α-model

DOI: 10.3233/JIFS-222149

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5765-5781, 2023

Authors: Li, Kun | Tian, Shengwei | Yu, Long | Zhou, Tiejun | Wang, Bo | Wang, Fun

Article Type: Research Article

Abstract: In recent years multimodal sentiment analysis (MSA) has been devoted to developing effective fusion mechanisms and has made advances, however, there are several challenges that have not been addressed adequately: the models make insufficient use of important information (inter-modal relevance and independence information) resulting in additional noise, and the traditional ternary symmetric architecture cannot well solve the problem of uneven distribution of task-related information among modalities. Thus, we propose Mutual Information Maximization and Feature Space Separation and Bi-Bimodal Modality Fusion (MFSBF)framework which effectively alleviates these problems. To alleviate the problem of underutilization of important information among modalities, a mutual information …maximization module and a feature space separation module have been designed. The mutual information module maximizes the mutual information between two modalities to retain more relevance (modality-invariant) information, while the feature separation module separates fusion features to prevent the loss of independence(modality-specific) information during the fusion process. As different modalities contribute differently to the model, a bimodal fusion architecture is used, which involves the fusion of two bimodal pairs. The architecture focuses more on the modality that contains more task-ralated information and alleviates the problem of uneven distribution of useful information among modalities. The experiment results of our model on two publicly available datasets (CUM-MOSI and CUM-MOSEI) achieved better or comparable results than previous models, which demonstrate the efficacy of our method. Show more

Keywords: Multimodal sentiment analysis, mutual information, feature separation, modality fusion

DOI: 10.3233/JIFS-222189

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5783-5793, 2023

Authors: Xie, Ying | Hu, Fanchao | Liu, Xuewei | Zhai, Lirong

Article Type: Research Article

Abstract: In the actual production process, time-varying and nonlinear problems are numerous important problems to be considered, in view of these problems, a process monitoring approach based on locally weighted probabilistic kernel principal component analysis (LWPKPCA) is proposed. First, the method selects the normal process data with a high similarity to the test samples as training data of the local model, and continuously updates the local model according to the test samples to build an accurate time-varying model. Second, by weighting the data of different importance, the role of data similar to test samples in the modeling process is strengthened. Third, …the LWPKPCA model is applied to process monitoring, the monitoring indicators are established in a high-dimensional space and used to detect faults. Finally, on the basis of LWPKPCA, the penicillin fermentation process (PFP) is taken to evaluate the monitoring performance of the proposed methods. According to the comparison of the experiment results, the detection rate and accuracy rate of the LWPKPCA method is considerably better than those of probabilistic principal component analysis and probabilistic kernel principal component analysis methods. The results demonstrate that the proposed method is suitable for processing time-varying data with nonlinear characteristics, and the LWPKPCA process monitoring method is effective for improving the performance of fault detection. Show more

Keywords: Locally weighted probabilistic kernel principal component analysis, process monitoring, fault detection

DOI: 10.3233/JIFS-224383

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5795-5805, 2023

Authors: Liu, Baokai | He, Fengjie | Du, Shiqiang | Li, Jiacheng | Liu, Wenjie

Article Type: Research Article

Abstract: Small object detection has important application value in the fields of autonomous driving and drone scene analysis. As one of the most advanced object detection algorithms, YOLOv3 suffers some challenges when detecting small objects, such as the problem of detection failure of small objects and occluded objects. To solve these problems, an improved YOLOv3 algorithm for small object detection is proposed. In the proposed method, the dilated convolutions mish (DCM) module is introduced into the backbone network of YOLOv3 to improve the feature expression ability by fusing the feature maps of different receptive fields. In the neck network of YOLOv3, …the convolutional block attention module (CBAM) and multi-scale fusion module are introduced to select the important information for small object detection in the shallow network, suppress the uncritical information, and use the fusion module to fuse the feature maps of different scales, so as to improve the detection accuracy of the algorithm. In addition, the Soft-NMS and Complete-IOU (ClOU) strategies are applied to candidate frame screening, which improves the accuracy of the algorithm for the detection of occluded objects. The experimental results on MS COCO2017, VOC2007, VOC2012 datasets and the ablation experiments on MS COCO2017 datasets demonstrate the effectiveness of the proposed method.The experimental results show that the proposed method achieves better accuracy in small object detection than the original YOLOv3 model. Show more

Keywords: Small object detection, Dilated convolutions mish, Fusion module, Soft-NMS

DOI: 10.3233/JIFS-224530

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5807-5819, 2023

Authors: Jiang, Minghua | Wang, Yulin | Yu, Feng | Peng, Tao | Hu, Xinrong

Article Type: Research Article

Abstract: Forest fires can pose a serious threat to the survival of living organisms, and wildfire detection technology can effectively reduce the occurrence of large forest fires and detect them faster. However, the unpredictable and diverse appearance of smoke and fire, as well as interference from objects that resemble smoke and fire, can lead to the overlooking of small objects and detection of false positives that resemble the objects in the detection results. In this work, we propose UAV-FDN, a forest fire detection network based on the perspective of an unmanned aerial vehicle (UAV). It performs real-time wildfire detection of various …forest fire scenarios from the perspective of UAVs. The main concepts of the framework are as follows: 1) The framework proposes an efficient attention module that combines channel and spatial dimension information to improve the accuracy and efficiency of model detection under complex backgrounds. 2) It also introduces an improved multi-scale fusion module that enhances the network’s ability to learn objects details and semantic features, thus reducing the chances of small objects being false negative during inspection and false positive issues. 3) Finally, the framework incorporates a multi-head structure and a new loss function, which aid in boosting the network’s updating speed and convergence, enabling better adaptation to different objects scales. Experimental results demonstrate that the UAV-FDN achieves high performance in terms of average precision (AP), precision, recall, and mean average precision (mAP). Show more

Keywords: Forest fire, wildfire detection, unmanned aerial vehicle, deep learning, attention mechanism, multi-scale feature fusion

DOI: 10.3233/JIFS-231550

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5821-5836, 2023

Authors: Guo, An | Sun, Kaiqiong | Wang, Meng

Article Type: Research Article

Abstract: While deep learning based object detection methods have achieved high accuracy in fruit detection, they rely on large labeled datasets to train the model and assume that the training and test samples come from the same domain. This paper proposes a cross-domain fruit detection method with image and feature alignments. It first converts the source domain image into the target domain through an attention-guided generative adversarial network to achieve the image-level alignment. Then, the knowledge distillation with mean teacher model is fused in the yolov5 network to achieve the feature alignment between the source and target domains. A contextual aggregation …module similar to a self-attention mechanism is added to the detection network to improve the cross-domain feature learning by learning global features. A source domain (orange) and two target domain (tomato and apple) datasets are used for the evaluation of the proposed method. The recognition accuracy on the tomato and apple datasets are 87.2% and 89.9%, respectively, with an improvement of 10.3% and 2.4%, respectively, compared to existing methods on the same datasets. Show more

Keywords: Domain adaptation, deep learning, knowledge distillation, fruit detection

DOI: 10.3233/JIFS-232104

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5837-5851, 2023

Authors: Liu, Junhui | Li, Guozhu | Gao, Chen

Article Type: Research Article

Abstract: In this study, we are concerned with the optimization of fuzzy clustering (Fuzzy C-Means) on the basis of a collection of distributed datasets without violating data confidentiality and security. The optimization of fuzzy clusters is realized using the differential evolution algorithm in a federated learning environment. Fuzzy clustering plays an important role in revealing the underlying structure of a given dataset. However, traditional iterative method is easy to get stuck at local optimum. With the growing concerning on data confidentiality and security, how to reveal the underlying structure of the data that are stored locally across different sites is becoming …an urgent problem. In order to overcome these two obstacles, we propose a federated differential evolution algorithm to realize fuzzy clustering. We augment the well-known differential evolution algorithm such that it can work in a federated learning environment to ensure local data privacy. The design practice of the federated differential evolution is elaborated on by highlighting its effectiveness in finding the optimal fuzzy clusters on the basis of distributed datasets. The performance of the proposed method is compared with traditional fuzzy clustering algorithm. Experimental studies completed on a series of real-world datasets coming from machine learning repository are reported to demonstrate the superiority of the proposed algorithm. Show more

Keywords: Differential evolution, horizontal federated learning, fuzzy clustering, global optimization

DOI: 10.3233/JIFS-232709

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5853-5860, 2023

Authors: Wang, Yajun

Article Type: Research Article

Abstract: In order to improve the detection accuracy of high-voltage dense channel satellite image, a satellite target detection algorithm based on deep learning is proposed. The convolution neural network is selected to extract the feature map of high-voltage dense channel satellite image, and the extracted feature map is input into the optimized deformation convolution neural network. The value of each sampling point and the corresponding position authority of block convolution kernel are weighted by using the regular region sampling feature map. The feature map output by the convolution operation of pooling layer is used to obtain the depth features of the …same dimension. The depth feature is input into the full connection layer to obtain the full connection feature of candidate target area, and the target detection in high-voltage dense channel satellite image is realized. The experimental results show that the target detection accuracy of the method is higher than 99% and the false alarm rate and false alarm rate are lower than 1.4%. Show more

Keywords: Deep learning, high voltage dense channel, satellite, target detection algorithm, convolution neural network, regular region

DOI: 10.3233/JIFS-223936

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5861-5869, 2023

Authors: Che, Gaofeng | Yu, Zhen

Article Type: Research Article

Abstract: In this work, the output-feedback fault-tolerant tacking control issue for underactuated autonomous underwater vehicle (AUV) with actuators faults is investigated. Firstly, an output-feedback error tacking system is constructed based on the theoretical model of underactuated AUV with actuators faults. Then, an adaptive dynamic programming (ADP) based fault-tolerant control controller is developed. In our proposed control scheme, a neural-network observer is designed to approximate the system states with actuators faults. An online policy iteration algorithm is designed with critic network and action network in order to improve the tracking accuracy. Based on Lyapunov stability theorem, the stability of the error tracking …system is guaranteed by the proposed controller. At last, the simulation results show that the underactuated AUV achieves better tracking performance. Show more

Keywords: Adaptive dynamic programming (ADP), fault-tolerant tracking control, actuators faults, neural network observer, autonomous underwater vehicle (AUV)

DOI: 10.3233/JIFS-223976

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5871-5883, 2023

Authors: Xu, Fei | Wang, Peng | Xu, Huimin

Article Type: Research Article

Abstract:  Deep convolutional neural networks (DCNNs) have shown remarkable performance in image classification tasks in recent years. In the network structure of DPRN, as the network depth increases, the number of convolutional kernels also increases linearly or nonlinearly. On the one hand, in the DPRN block, the size of the receptive field is only 3 × 3, which results in insufficient network ability to extract feature map information of different filter sizes. On the other hand, the number of convolution kernels in the second 1x1 convolution will be multiplied by a coefficient relative to the first convolution, which can cause overfitting to some …extent. In order to overcome these weaknesses, we introduce the inception-like structure on the basis of the DPRN network which is called by pyramid inceptional residual networks (PIRN). In addition, we also discuss the performance of PIRN network with squeeze and excitation (SE) mechanism and regularization term. Furthermore, some results in network performance are discussed when adding a stochastic depth networkto the PIRN model. Compared to DPRN, PIRN achieved better results on the CIFAR10, CIFAR100, and Mini-ImageNet datasets. In the case of using zero-padding, the multiplicative PIRN with SE mechanism achieves the best result of 95.01% on the CIFAR10 dataset. Meanwhile, on the CIFAR100 and Mini-ImageNet datasets, the additive PIRN network with a network depth of 92 achieves the best results of 76.06% and 65.86%, respectively. According to the experimental results, our method has achieved better accuray than that of DPRN with same network settings which demonstrate its effectiveness in generalization ability. Show more

Keywords: Convolution neural network, Deep pyramidal residual network, Squeeze and excitation mechanism, Pyramidal inceptional residual network, L2 regularization

DOI: 10.3233/JIFS-230569

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5885-5906, 2023

Authors: Zhang, Dong | Liu, Jinzhu | Liu, Duo | Li, Guanyu

Article Type: Research Article

Abstract: Knowledge graphs exhibit a typical hierarchical structure and find extensive applications in various artificial intelligence domains. However, large-scale knowledge graphs need to be completed, which limits the performance of knowledge graphs in downstream tasks. Knowledge graph embedding methods have emerged as a primary solution to enhance knowledge graph completeness. These methods aim to represent entities and relations as low-dimensional vectors, focusing on handling relation patterns and multi-relation types. Researchers need to pay more attention to the crucial feature of hierarchical relationships in real-world knowledge graphs. We propose a novel knowledge graph embedding model called H ierarchy-Aware P aired R elation …Vectors Knowledge Graph E mbedding (HPRE) to bridge this gap. By leveraging the power of 2D coordinates, HPRE adeptly model relation patterns, multi-relation types, and hierarchical features in the knowledge graph. Specifically, HPRE employs paired relation vectors to capture the distinct characteristics of head and tail entities, facilitating a better fit for relational patterns and multi-relation scenarios. Additionally, HPRE employs angular coordinates to differentiate entities at various levels of the hierarchy, effectively representing the hierarchical nature of the knowledge graph. The experimental results show that the HPRE model can effectively learn the hierarchical features of the knowledge graph and achieve state-of-the-art experimental results on multiple real-world datasets for the link prediction task. Show more

Keywords: Knowledge graph completion, link prediction, knowledge graph embedding, knowledge graph representation

DOI: 10.3233/JIFS-230982

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5907-5926, 2023

Authors: Wang, Hejin | He, Mingzhao | Zeng, Chengli | Qian, Lei | Wang, Jun | Pan, Wu

Article Type: Research Article

Abstract: Immersive virtual reality technology has been widely used in teaching and learning scenarios because of its unique visual and interactive experiences that bring learners a sense of immersive reality. However, how to better apply immersive virtual reality technology to learning environments to promote learning effectiveness is a direction that has been studied and explored by many scholars. Although a growing number of studies have concluded that immersive virtual reality technology can enhance learners’ attention in teaching and learning, few studies have directly linked both learning behaviors and attention to investigate the differences in behavioral performance across attention. In this study, …attention data monitored by EEG physiological brainwaves and a large number of videos recorded during learning were used to explore the differences in the sequence of high attention behaviors across performance levels in an immersive virtual reality environment using behavioral data mining techniques. The results found that there was a strong correlation between attention and performance in immersive virtual reality, that thinking and looking may be more conducive to learners’ concentration, and that high concentration behaviors in the high-performing group accompanied the test and appeared after the monitoring, while the action continued to be repeated after the high concentration behaviors in the low-performing group. Based on this, this study provides a reference method for the analysis of the learning process in this environment, and provides a theoretical basis and practical guidance for the improvement of participants’ attention and learning effectiveness. Show more

Keywords: Immersive virtual reality, EEG feedback, learning behaviour, data mining

DOI: 10.3233/JIFS-231383

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5927-5938, 2023

Authors: Chen, Fu

Article Type: Research Article

Abstract: How to guarantee the quality of college physical education (PE) teaching and reverse the declining trend of college students’ physique year by year has become a hot topic for the research of higher education and school PE workers. The quality assurance of higher education in China should give full play to the role of colleges in teaching quality assurance activities, constantly improve the level of school running and improve the efficiency of school running. Because colleges themselves are the main body of higher education and teaching activities, they have the most power, qualification and responsibility to explain the quality of …higher education. The classroom teaching quality (CTQ) evaluation of college badminton training is regarded as multi-attribute decision-making (MADM). The 2-tuple linguistic neutrosophic sets (2TLNSs) which the truth-membership, indeterminacy-membership and the falsity-membership are assessed by using the 2-tuple linguistic term sets is an appropriate form to express the indeterminate decision-making information in the classroom teaching quality (CTQ) evaluation of college badminton training. In this paper, the Hamy mean (HM) and the power average (PA) are connected with 2-tuple linguistic neutrosophic sets (2TLNSs) to propose the 2-tuple linguistic neutrosophic numbers weighted power HM (2TLNWPHM) operator. Then, use the 2TLNWPHM operator to handle MADM with 2TLNS. Finally, taking the CTQ evaluation of college badminton training as an example, the proposed method is explained. The main contributions of this study are summarized: the establishment of the 2TLNWPHM operator; (2) The 2TLNWPHM operator was developed to handle MADM with 2TLNS; (3) Through the empirical application of the CTQ evaluation of badminton training in universities, the proposed method is validated; (4) Some comparative studies have shown the rationality of the 2TLNWPHM operator. Show more

Keywords: Multi-attribute decision making (MADM), neutrosophic numbers, 2-tuple linguistic neutrosophic sets (2TLNSs), 2TLNWPHM operator, CTQ evaluation

DOI: 10.3233/JIFS-231731

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5939-5953, 2023

Authors: Chen, Haoying

Article Type: Research Article

Abstract: Big data is changing our lives and the way we understand the world, as well as the operational patterns of business and social organizations. Fully understanding the value of data and knowing how to use big data to provide a basis for business decision-making has gradually become the most basic thinking that business organizations should possess in the era of big data. Under the thinking mode of data-driven decision-making, many information science researchers have discussed the model, architecture, operation mechanism and other aspects of big data competitive intelligence system. At the same time, more and more enterprises, such as IBM, …Amazon, Google, Microsoft, Wal Mart, etc., have begun to attach importance to the development and construction of big data competitive intelligence software systems, and have achieved certain results. The enterprise competitive intelligence system evaluation in the context of big data is regarded as multi-attribute decision-making (MADM). In this paper, the Hamy mean (HM) and the power average (PA) are connected with 2-tuple linguistic neutrosophic sets (2TLNSs) to propose the 2-tuple linguistic neutrosophic numbers power HM (2TLNPHM) operator. Then, use the 2TLNPHM operator to handle MADM with 2TLNS. Finally, taking the enterprise competitive intelligence system evaluation in the context of big data as an example, the proposed method is explained. The main contributions of this study are summarized: the establishment of the 2TLNPHM operator; (2) The 2TLNPHM operator was developed to handle MADM with 2TLNS; (3) Through the empirical application of the enterprise competitive intelligence system evaluation, the proposed method is validated; (4) Some comparative studies have shown the rationality of the 2TLNPHM operator. Show more

Keywords: Multi-attribute decision making (MADM), neutrosophic numbers, 2-tuple linguistic neutrosophic sets (2TLNSs), 2TLNPHM operator, enterprise competitive intelligence system evaluation

DOI: 10.3233/JIFS-231768

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5955-5970, 2023

Authors: Wu, Huiyong | Yang, Tongtong | Wu, Harris | Li, Hongkun | Zhou, Ziwei

Article Type: Research Article

Abstract: Good air quality is one of the prerequisites for stable urban economic growth and sustainable development. Air quality is influenced by a range of environmental elements. In this study, seven common air pollutants and six kinds of meteorological data in a major city in China are studied. In this urban setting, the air quality index will be estimated based on a Long Short-term Memory (LSTM)model. To improve prediction accuracy, the Random Forest (RF) method is adopted to choose important features and pass them to the LSTM model as input, an improved sparrow search algorithm (ISSA) is used to optimize the …hyperparameters of the LSTM model. According to the experimental findings, the RF-ISSA-LSTM model demonstrates superior accuracy compared to both the basic LSTM model and the ISSA-LSTM fusion model. Show more

Keywords: Sustainable development, long short-term memory, sparrow search algorithm, random forest, air quality index

DOI: 10.3233/JIFS-232308

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5971-5985, 2023

Authors: Prabakaran, S. | Mary Praveena, S.

Article Type: Research Article

Abstract: Osteosarcomas are a type of bone tumour that can develop anywhere in the bone but most typically do so around the metaphyseal growth plates at the ends of long bones. Death rates can be lowered by early detection. Manual osteosarcoma identification can be difficult and requires specialised knowledge. With the aid of contemporary technology, medical photographs may now be automatically analysed and categorised, enabling quicker and more effective data processing. This paper proposes a novel hyperparameter-tuned deep learning (DL) approach for predicting osteosarcoma on histology images with effective feature selection mechanism which aims to improve the prediction accuracy of the …classification system for bone tumor detection. The proposed system mainly consists of ‘6’ phases: data collection, preprocessing, segmentation, feature extraction, feature selection, and classification. Firstly, the dataset of histology images is gathered from openly available sources. Then Median Filtering (MEF) is utilized as the preprocessing step that enhances the quality of the input images for accurate prediction by eliminating unwanted information from them. Afterwards, the pre-processed image was segmented using Harmonic Mean-based Otsu Thresholding (HMOTH) approach to obtain the tumor-affected regions from the pre-processed data. Then the features from the segmented tumor portions are extracted using the Self-Attention Mechanism-based MobileNet (SAMMNet) model. A Van der Corput sequence and Adaptive Inertia Weight included Reptile Search Optimization Algorithm (VARSOA) is used to select the more relevant features from the extracted features. Finally, a Hyperparameter-Tuned Deep Elman Neural Network (HTDENN) is utilized to diagnose and classify osteosarcoma, in which the hyperparameters of the neural network are obtained optimally using the VARSOA. The proposed HTDENN attains the higher accuracy of 0.9531 for the maximum of 200 epochs, whereas the existing DENN, MLP, RF, and SVM attains the accuracies of 0.9492, 0.9427, 0.9413, and 0.9387. Likewise, the proposed model attains the better results for precision (0.9511), f-measure (0.9423), sensitivity (0.9345) and specificity (0.9711) than the existing approaches for the maximum of 200 epochs. Simulation outcomes proved that the proposed model outperforms existing research frameworks for osteosarcoma prediction and classification. Show more

Keywords: Deep Elman Neural Network, osteosarcoma diagnosis, histology images, median filter, convolutional neural network

DOI: 10.3233/JIFS-233484

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5987-6003, 2023

Authors: Ullah, Sami | Kashif, Muhammad | Aslam, Muhammad | Haider, Gulfam | AlAita, Abdulrahman | Saleem, Muhammad

Article Type: Research Article

Abstract: The application of classical statistical methods is not feasible given the presence of imprecise, fuzzy, uncertain, or undetermined observations in the underlying dataset. This is due to the existence of uncertainties pervading every aspect of real-life situations, which cannot always be accurately addressed by classical statistical approaches. In order to tackle this problem, a new methodology known as neutrosophic analysis of variance (NANOVA) has been developed as an extension of classical approaches to analyze datasets with uncertainty. The proposed approach can be applied regardless of the number of factors and replications. Moreover, NANOVA introduces a novel matrix-based approach to derive …the F_N-test in an uncertain environment. To assess the effectiveness of NANOVA, various real datasets have been employed, and research findings on single- and two-factor NANOVAs with measures of indeterminacy have been presented. According to our comparisons, NANOVA provides a more informative, efficient, flexible, and reliable approach to deal with uncertainties than classical statistical methods. Therefore, there is a need to go beyond conventional statistical techniques and adopt advanced methodologies that can effectively handle uncertainties. Show more

Keywords: Imprecise data, classical statistics, interval statistics, analysis of variance, F-test

DOI: 10.3233/JIFS-223636

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6005-6017, 2023

Authors: Patidar, Ritu | Patel, Sachin

Article Type: Research Article

Abstract: Many people have been severely affected by the COVID-19 outbreak, which has left them anxious, terrified, and other difficult feelings. Since the introduction of coronavirus vaccinations, people’s emotional spectrum has broadened and become more sophisticated.We want to observe and interpret their sentiments using deep learning techniques in this work. The most efficient way to convey one’s thoughts and feelings right now is via social media, and using Twitter may help one better understand what is popular and what is going through other people’s minds. Analyzing and visualization of data play a vital role in Data Science; as customers over e-commerce …increase, feedback/reviews shared by them increase significantly, and decisions by a new customer to buy a product or not rely on these reviews; reviews might falsely be displayed which may be involving in controlling if any products demand and supply so, reviews analyzing and visualizationto understand they are genuinely playing an important role over e-commerce nowadays. Our primary objective in conducting this study was to understand better the various perspectives individuals held on the vaccination process and reviews of products purchased online. As shown by the presented study, analysis and visualization approaches may be used to facilitate rapid and easy comprehension of e-commerce data, despite its high dimensionality.All correlation and non-correlation factors were mapped and examined, providing a comprehensive picture of the proposed data and its connection to other parameters.The proposed work provides an overview of sentiment observations across arguments and the relationships between parameters; it opens the door for modeling to extract some decision-making insights from the data, which can be used to improve the efficiency of application areas like product quality and customer satisfaction. Show more

Keywords: E-commerceproduct, COVID-19 vaccines, NLTK, CNN model, XLnet model, TextBlob

DOI: 10.3233/JIFS-230662

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6019-6034, 2023

Authors: Fan, Jianping | Tian, Ge | Wu, Meiqin

Article Type: Research Article

Abstract: Cross-efficiency in data envelopment analysis is widely used in production as an evaluation method that includes input and output indicators and allows for self-evaluation and mutual evaluation of decision making units (DMUs). However, as the application scenarios continue to expand, the traditional methods gradually fail to meet the needs. Many researchers have proposed improved methods and made great progress in weight determination, but the existing studies still have shortcomings in considering the psychological behavior of decision makers (DMs) and there is still relatively little research on cross-efficiency in fuzzy environments. In this paper, we proposed a method to apply CRITIC …to determine weights and introduce both prospect theory and regret theory into the evaluation method of cross-efficiency to obtain the prospect cross-efficiency matrix and regret cross-efficiency matrix respectively, and then applied the Pythagorean hesitant fuzzy operator to aggregate them to achieve the ranking of DMUs through the fraction function. This largely takes into account the subjective preference and regret avoidance psychology of DMs. The applicability of this paper’s method is also verified through an example of shopping for a new energy vehicle. Finally, the effectiveness of this paper’s method is verified by comparing three traditional methods with this paper’s method, which provides an effective method for considering risk preferences in the decision-making process. Show more

Keywords: Data envelopment analysis, cross-efficiency, CRITIC, prospect theory, regret theory, Pythagorean hesitant fuzzy set

DOI: 10.3233/JIFS-231371

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6035-6045, 2023

Authors: Ismail, Isaudin | Abd Mutalip, Fatin Noor Najihah | Jacob, Kavikumar

Article Type: Research Article

Abstract: The Copula concept has long been used in many applications, especially in the financial field. This concept was first used in 1959 by Sklar in his mathematical work and greatly assisted in the applications of financial and insurance areas. The copula functions have been widely used in dependence modeling. In this study, we look at how the copula began to develop from a basic form to a more advanced form through studies that previous researchers have made. Throughout this study, we find various types of the copula, and each exhibits its own characteristics lying under two main families, Elliptical and …Archimedean copulas. Our findings suggest that copula is vital in solving problems in statistical dependence measures and joint marginal distribution functions. This comprehensive study served as a review paper on the development of copulas from their initial existence to their latest evolution. Show more

Keywords: Copula, financial field, decision-making, insurance, marginal distribution

DOI: 10.3233/JIFS-223481

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6047-6062, 2023

Authors: Yu, Zhongliang

Article Type: Research Article

Abstract: The aerospace target tracking is difficult to achieve due to the dataset is intrinsically rare and expensive, and the complex space background, and the large changes of the target in the size. Meta-learning can better train a model when the data sample is insufficient, and tackle the conventional challenges of deep learning, including the data and the fundamental issue of generalization. Meta-learning can quickly generalize a tracker for new task via a few adapt. In order to solve the strenuous problem of object tracking in aerospace, we proposed an aerospace dataset and an information fusion based meta-learning tacker, and named …as IF-Mtracker. Our method mainly focuses on reducing conflicts between tasks and save more task information for a better meta learning initial tracker. Our method was a plug-and-play algorithms, which can employ to other optimization based meta-learning algorithm. We verify IF-Mtracker on the OTB and UAV dataset, which obtain state of the art accuracy than some classical tracking method. Finally, we test our proposed method on the Aerospace tracking dataset, the experiment result is also better than some classical tracking method. Show more

Keywords: Aerospace tracking dataset, meta learning, information fusion, aerospace tracking dataset

DOI: 10.3233/JIFS-230265

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6063-6075, 2023

Authors: Ramaswamy, Srividhya Lakshmi | Chinnappan, Jayakumar

Article Type: Research Article

Abstract: The deep learning revolution in the current decade has transformed the artificial intelligence industry. Eventually, deep learning techniques have become essential for many computational modeling tasks. Nevertheless, deep neural models provide a high degree of automation for natural language processing (NLP) applications. Deep neural models are extensively used to decode public reviews subjective to specific products, services, and other social activities. Further, to improve sentiment classification accuracy, several neural architectures have been developed. Convolutional neural networks (CNN) and Long-short term memory (LSTM) are the popular deep models employed in ensemble architectures for sentiment classification tasks. This review article extensively compares …the competence of CNN and LSTM-based ensemble models to improve the sentiment accuracy for online review datasets. Further, this article also provides an empirical study on various ensemble models concerning the position of LSTM and CNN for efficient sentiment classification. This empirical study provides deep learning researchers with insights into building effective multilayer LSTM and CNN models for many sentiment analysis tasks. Show more

Keywords: Sentiment analysis, convolutional neural network, long-short term memory, multilayer ensemble architectures, review dataset

DOI: 10.3233/JIFS-230917

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6077-6105, 2023

Authors: Jhansi Rani, Challapalli | Devarakonda, Nagaraju

Article Type: Research Article

Abstract: The study addresses the challenges of human action recognition and analysis in computer vision, with a focus on classifying Indian dance forms. The complexity of these dance styles, including variations in body postures and hand gestures, makes classification difficult. Deep learning models require large datasets for good performance, so standard data augmentation techniques are used to increase model generalizability. The study proposes the Indian Classical Dance Generative Adversarial Network (ICD-GAN) for augmentation and the quantum-based Convolutional Neural Network (QCNN) for classification. The research consists of three phases: traditional augmentation, GAN-based augmentation, and a combination of both. The proposed QCNN is …introduced to reduce computational time. Different GAN variants DC-GAN, CGAN, MFCGAN are employed for augmentation, while transfer learning-based CNN models VGG-16, VGG-19, MobileNet-v2, ResNet-50, and new QCNN are implemented for classification. The study demonstrates that GAN-based augmentation outperforms traditional methods, and QCNN reduces computational complexity while improving prediction accuracy. The proposed method achieves a precision rate of 98.7% as validated through qualitative and quantitative analysis. It provides a more effective and efficient approach compared to existing methods for Indian dance form classification. Show more

Keywords: Quantum convolution neural network, data augmentation, generative adversarial network, Indian classical dance, transfer learning

DOI: 10.3233/JIFS-231183

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6107-6125, 2023

Authors: Zhang, Chaoqin | Li, Ting | Yin, Yifeng | Ma, Jiangtao | Gan, Yong | Zhang, Yanhua | Qiao, Yaqiong

Article Type: Research Article

Abstract: With the continuous development of knowledge graph completion (KGC) technology, the problem of few-shot knowledge graph completion (FKGC) is becoming increasingly prominent. Traditional methods for KGC are not effective in addressing this problem due to the lack of sufficient data samples. Therefore, completing the task of knowledge graph with few-shot data has become an urgent issue that needs to be addressed and solved. This paper first presents a concise introduction to FKGC, which covers relevant definitions and highlights the advantages of FKGC techniques. We then categorize FKGC methods into meta-learning-based, metric-based, and graph neural network-based methods, and analyze the unique …characteristics of each model. We also introduced the research on FKGC in a specific domain - Temporal Knowledge Graph Completion (TKGC). Subsequently, we summarized the commonly used datasets and evaluation metrics in existing methods and evaluated the completion performance of different models in TKGC. Finally, we presented the challenges faced by FKGC and provided directions for future research. Show more

Keywords: Knowledge graph, few-shot learning, knowledge graph completion, temporal knowledge graph completion

DOI: 10.3233/JIFS-232260

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6127-6143, 2023

Authors: Marimuthu, M. | Mohanraj, G. | Karthikeyan, D. | Vidyabharathi, D.

Article Type: Research Article

Abstract: Web browsers have become an integral part of our daily lives, granting us access to vast information and services. However, this convenience significantly risks personal information and data security. One common source of this risk is browser extensions, which users often employ to add new features to their browsers. Unfortunately, these extensions can also pose a security threat, as malicious ones may access and steal sensitive information such as passwords, credit card details, and personal data. The vulnerability of web browsers to malicious extensions is a significant challenge that effectively tackles through robust defence mechanisms. To address this concern, Secure …Vault – API is proposed and designed to safeguard confidential web page content from malicious extensions. The Web Crypto API provides cryptographic functions that protect data during transmission and storage. The Secure Vault encompasses a Sentinel extension responsible for parsing the web page’s Document Object Model (DOM) content and querying for all “vault” elements. The extension then verifies that the DOM content has not been tampered with by any malicious extension by calculating the SHA512 hash value of the concatenated vault elements as a string, with no whitespace between them. With its encryption, hashing, and isolation techniques, the Secure Vault effectively protects confidential web page content from malicious extensions. It provides a secure environment for storing and processing sensitive data, reducing the risk of data breaches caused by malicious extensions. The proposed approach offers significant advantages over existing strategies in terms of protecting confidential web page content from malicious extensions. This not only improves the efficiency and effectiveness of the browser extensions but also ensures compatibility, interoperability and performance across different web browsers with respect to the load time of HTML elements. Users can browse the web and carry out sensitive transactions with peace of mind, knowing their data is safeguarded against theft or manipulation by malicious extensions. Show more

Keywords: Browser security, chrome extensions, secure browsing, Web Crypto API, malicious extension

DOI: 10.3233/JIFS-233122

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6145-6160, 2023

Authors: Sundarakumar, M.R. | Sharma, Ravi | Fathima, S.K. | Gokul Rajan, V. | Dhayanithi, J. | Marimuthu, M. | Mohanraj, G. | Sharma, Aditi | Johny Renoald, A.

Article Type: Research Article

Abstract: For large data, data mining methods were used on a Hadoop-based distributed infrastructure, using map reduction paradigm approaches for rapid data processing. Though data mining approaches are established methodologies, the Apriori algorithm provides a specific strategy for increasing data processing performance in big data analytics by applying map reduction. Apriori property is used to increase the efficiency of level-wise creation of frequent itemsets by minimizing the search area. A frequent itemset’s subsets must also be frequent (Apriori property). If an itemset is rarely, then all of its supersets are infrequent as well. We refined the apriori approach by varying the …degree of order in locating frequent item sets in large clusters using map reduction programming. Fixed Pass Combined Counting (FPC) and Dynamic Pass Combined Counting (DPC) is a classical algorithm which are used for data processing from the huge datasets but their accuracy is not up to the mark. In this article, updated Apriori algorithms such as multiplied-fixed-pass combined counting (MFPC) and average time-based dynamic combined counting (ATDFC) are used to successfully achieve data processing speed. The proposed approaches are based on traditional Apriori core notions in data mining and will be used in the map-reduce multi-pass phase by ignoring pruning in some passes. The optimized-MFPC and optimized-ATDFC map-reduce framework model algorithms were also presented. The results of the experiments reveal that MFPC and ATDFC are more efficient in terms of execution time than previously outmoded approaches such as Fixed Pass Combined Counting (FPC) and Dynamic Pass Combined Counting (DPC). In a Hadoop multi-node cluster, this paradigm accelerates data processing on big data sets. Previous techniques were stated in terms of reducing execution time by 60–80% through the use of several passes. Because of the omitted trimming operation in data pre-processing, our proposed new approaches will save up to 84–90% of that time. Show more

Keywords: Algorithms, pruning, data mining, hadoop cluster, map reduce

DOI: 10.3233/JIFS-232048

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6161-6177, 2023

Authors: Zhang, Hang | Liu, Yongli | Chao, Hao

Article Type: Research Article

Abstract: The density peak clustering algorithm (DPC) quickly divides each cluster based on high-density peak points and shows better clustering performance. In order to address the issue that the local density is constrained by the preset cut-off distance in DPC and the Euclidean distance cannot capture the possible correlation between different features, a DPC algorithm based on improved dung beetle optimization (IDBO) and Mahalanobis metric is proposed, called IDBO-MDDPC. The IDBO algorithm enhances the ball dung beetle individual by incorporating nonlinear dynamic factors to increase the search and development capabilities of the algorithm and by incorporating an adaptive cosine wave inertial …weight strategy to more precisely determine the optimal position of the thief dung beetle in order to improve the convergence speed and accuracy of the algorithm. The IDBO algorithm is simulated on eight benchmark functions, and the results demonstrate that it is superior to other comparison algorithms in terms of convergence speed and accuracy. In the DPC algorithm, the Mahalanobis metric is used to capture the correlation between features to improve clustering performance. The IDBO algorithm is integrated with the DPC algorithm, and the F-Measure evaluation index is used to design the objective function so that the optimal value of the cut-off distance can be automatically selected. In order to evaluate the efficiency of the algorithm, three sets of artificially synthesized datasets and five sets of UCI standard datasets were chosen for studies. Experimental results show that the IDBO-MDDPC algorithm can automatically determine a better cut-off distance value and ensure higher clustering accuracy. Show more

Keywords: Density peak clustering, nonlinear dynamic factor, adaptive cosine wave inertia weight, mahalanobis metric

DOI: 10.3233/JIFS-232334

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6179-6191, 2023

Authors: Cheng, Chen | Li, Bixin | Chen, Dong

Article Type: Research Article

Abstract: Intelligent Traffic Management System (ITMS) is a complex and intelligent cyber-physical system (CPS) with multi-subsystem interaction, which plays a significant role in traffic safety. However, the quality evaluation requirements of ITMS, particularly its running quality, cannot be satisfied by the current quality evaluation metrics. Moreover, the present ITMS evaluation techniques are arbitrary. The effectiveness of road traffic is impacted because ITMS quality cannot be adequately assured. To fill this gap, this paper proposes a quality evaluation (QE) methodology based on the ITMS business data flow. First, the ITMS QE dimension extraction process was introduced to describe the ITMS architecture and …activities; then the new evaluation indexes including intelligence, complexity and interactivity were proposed and an ITMS QE model was established; further through the measurement of metrics elements, the quality score of the indicators were calculated; finally a prototype tool was developed to verify the efficacy and practicability of the method. The results showed that the proposed method has the advantages of accurate problem tracking and decrease decision-making uncertainty. This is applicable to the ITMS QE in various operational scenarios. Show more

Keywords: Intelligent traffic management system, complex system, multi-system interaction, quality evaluation

DOI: 10.3233/JIFS-230182

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6193-6208, 2023

Authors: Saini, Monika | Maan, Vijay Singh | Kumar, Ashish | Saini, Dinesh Kumar

Article Type: Research Article

Abstract: Cloud infrastructure provides a real time computing environment to customers and had wide applicability in healthcare, medical facilities, business, and several other areas. Most of the health data recorded and saved on the cloud. But the cloud infrastructure is configured using several components and that makes it a complex structure. And the high value of availability and reliability is essential for satisfactory operation of such systems. So, the present study is conducted with the prominent objective of assessing the optimum availability of the cloud infrastructure. For this purpose, a novel stochastic model is proposed and optimized using dragonfly algorithm (DA) …and Grey Wolf optimization (GWO) algorithms. The Markovian approach is employed to develop the Chapman-Kolmogorov differential difference equations associate with the system. It is considered that all failure and repair rates are exponentially distributed. The repairs are perfect. The numerical results are derived to highlight the importance of the study and identify the best algorithm. The system attains its optimum availability 0.9998649 at population size 120 with iteration 700 by GWO. It is revealed that grey wolf optimization algorithm performed better than the Dragonfly algorithm in assessing the availability, best fitted parametric values and execution time. Show more

Keywords: Availability, cloud infrastructure, dragonfly algorithm, grey wolf optimization algorithm, markov process

DOI: 10.3233/JIFS-231513

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6209-6227, 2023

Authors: Liao, Yi | Ning, Kuangfeng

Article Type: Research Article

Abstract: Multi-source online transfer learning uses the tagged data from multiple source domains to enhance the classification performance of the target domain. For unbalanced data sets, a multi-source online transfer learning algorithm that can oversample in the feature spaces of the source domain and the target domain is proposed. The algorithm consists of two parts: oversampling multiple source domains and oversampling online target domains. In the oversampling phase of the source domain, oversampling is performed in the feature space of the support vector machine (SVM) to generate minority samples. New samples are obtained by amplifying the original Gram matrix through neighborhood …information in the source domain feature space. In the oversampling phase of the online target domain, minority samples from the current batch search for k-nearest neighbors in the feature space from multiple batches that have already arrived, and use the generated new samples and the original samples in the current batch to train the target domain function together. The samples from the source domain and the target domain are mapped to the same feature space through the kernel function for oversampling, and the corresponding decision function is trained using the data from the source domain and the target domain with relatively balanced class distribution, so as to improve the overall performance of the algorithm. Comprehensive experiments were conducted on four real datasets, and compared to other baseline algorithms on the Office Home dataset, the accuracy improved by 0.0311 and the G-mean value improved by 0.0702. Show more

Keywords: Multi-source transfer learning, online learning, imbalanced data, support vector machine (SVM), k-nearest neighbor

DOI: 10.3233/JIFS-232627

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6229-6245, 2023

Authors: Zhao, Zhengwei | Yang, Genteng | Li, Zhaowen | Yu, Guangji

Article Type: Research Article

Abstract: Outlier detection is an important topic in data mining. An information system (IS) is a database that shows relationships between objects and attributes. A real-valued information system (RVIS) is an IS whose information values are real numbers. People often encounter missing values during data processing. A RVIS with the miss values is an incomplete real-valued information system (IRVIS). Due to the presence of the missing values, the distance between two information values is difficult to determine, so the existing outlier detection rarely considered an IS with the miss values. This paper investigates outlier detection for an IRVIS via rough set …theory and granular computing. Firstly, the distance between two information values on each attribute of an IRVIS is introduced, and the parameter λ to control the distance is given. Then, the tolerance relation on the object set is defined according to the distance, and the tolerance class is obtained, which is regarded as an information granule. After then, λ-lower and λ-upper approximations in an IRVIS are put forward. Next, the outlier factor of every object in an IRVIS is presented. Finally, outlier detection method for IRVIS via rough set theory and granular computing is proposed, and the corresponding algorithms is designed. Through the experiments, the proposed method is compared with other methods. The experimental results show that the designed algorithm is more effective than some existing algorithms in an IRVIS. It is worth mentioning that for comprehensive comparison, ROC curve and AUC value are used to illustrate the advantages of the proposed method. Show more

Keywords: RST, GrC, IRVIS, outlier detection, outlier factor

DOI: 10.3233/JIFS-230737

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6247-6271, 2023

Authors: Fan, Jianping | Chai, Mingxuan | Wu, Meiqin

Article Type: Research Article

Abstract: The competition in the new energy vehicle industry has intensified with the rapid development of the industry. In order to create innovative products, many businesses are now seeking cooperation with their supply chain members. Previous research on the new energy vehicle supply chain has mainly focused on government policies, supply chain retailers and with consumer gaming issues. This manuscript examines the problem of cooperation decisions between members of the new energy vehicle supply chain, namely a battery manufacturer and vehicle producer. The benefits of the two members are analyzed by constructing two models, one with non-incentives and the other with …government incentives. The model uses the triangular fuzzy number (TFN) instead of parameters in numerical calculations, taking complete account of the influence of uncertain environmental factors and using the triangular structured element method. The numerical examples result that government incentives positively promote cooperation between the two players, but the incentives should be as equal as possible. Finally, we aim to encourage supply chain members to cooperate and promote the development of the new energy vehicle industry. This study has positive implications for future supply chain member cooperation issues. Show more

Keywords: Energy vehicle supply chain, triangular fuzzy number (TFN), nash equilibrium, triangular structured element method

DOI: 10.3233/JIFS-231521

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6273-6287, 2023

Authors: Princy Magdaline, P. | Ganesh Babu, T.R.

Article Type: Research Article

Abstract:  Computed tomography (CT) scan pictures are routinely employed in the automatic identification and classification of lung cancer. The texture distribution of lung nodules can vary widely over the CT scan space and requires accurate detection. The evaluation of discriminative information in this volume can tremendously aid the classification process. A convolutional neural network, the Attention Gate Residual U-Net model, and KNN classifiers are utilized to detect lung cancer. The dataset of 1097 computed tomography (CT) images utilized in this study was obtained from the Iraq-Oncology Teaching Hospital/National Centre for Cancer Diseases (IQ-OTH/NCCD) to segment and classify lung tumors from CT …images using the novel Attention Gate Residual U-Net model, i.e., AGResU-Net and CNN architecture. The initial step is applying CNN to detect normal, benign, and malignant patients in CT images. Second, use AGResU-Net to partition lung tumour areas. In the third section of the project, a KNN classifier is used to determine if an instance is malignant or benign. In the initial phase, CNN was proposed to classify three distinct regions. Three optimization strategies are used in this work: Adam, RMSP, and SGDM. The classifier’s accuracy is 97%, 85%, and 82%, respectively. When compared to the RMSP optimizer, the Adams optimizer predicts probability rates more accurately. In the second phase, AGResU-Net is used for schematic segmentation of the tumor region. In the third phase, a KNN classifier is used to classify benign and malignant tumor from the segmented tumor regions. A new segmentation of the lung tumor model is proposed. In this developed algorithm, the labelled classified data set and the segmented tumor output result provide the same accuracy. The study results demonstrate high tumour classification accuracy and high probability of detection in benign and malignant cases. Show more

Keywords: Lung cancer, CT images, convolutional neural network, AGResU-Net, KNN

DOI: 10.3233/JIFS-233787

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6289-6302, 2023