Journal of Intelligent & Fuzzy Systems - Volume 45, issue 6

Authors: Majumder, Saibal | Kutum, Rintu | Khatua, Debnarayan | Sekh, Arif Ahmed | Kar, Samarjit | Mukerji, Mitali | Prasher, Bhavana

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Abstract: This article has been retracted.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-220990

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9827-9844, 2023

Authors: Cheng, Yafei | Zhao, Bin

Article Type: Research Article

Abstract: In recent years, there are many works about conditional distributivity for aggregation functions, which is closely related to integration theory and utility theory. In this paper, our main idea is to solve conditional distributivity equations from left and right for semi-t-operators over uninorms. One part focuses on these equations involving semi-t-operators over t-norms and obtains some complete characterizations. The other part gives the necessary and sufficient conditions of conditional distributivity for semi-t-operators over uninorms in U max and U min under the condition 0 < U  (x , y ) <1, …which transforms it into the conditional distributivity between t-norms and t-conorms (semi-t-norms and t-conorms, semi-t-conorms and t-norms). Show more

Keywords: Semi-t-operators, t-norms, uninorms, conditional distributivity

DOI: 10.3233/JIFS-230966

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9845-9860, 2023

Authors: Ragu, G. | Ramamoorthy, S.

Article Type: Research Article

Abstract: When a digital adversary or an insider compromised a framework, cloud Forensic examiners can simply lay out the scene of the crime and reconstruct how the event took place using scientific evidence to determine when, why, and how it happened. Be that as it may, computerized proof procurement in a cloud environment is confounded and demonstrated troublesome, Despite modern scientific securing tool compartments. Multi-occupancy, Geo-area, and Administration Level Understanding have added another layer of complexity to obtaining computerized proof from a cloud environment. To moderate these intricacies of proof procurement in the cloud environment, we want a system that can …forensically keep up with the reliability and respectability of proof. In this review, we plan and execute a Blockchain-based Forensic in Cloud (BBFC) structure, utilizing a Cloud Forensic methodology (CFA). The outcomes from our single contextual analysis will exhibit that BBFC will alleviate the difficulties and intricacies looked at by computerized forensic specialists in getting acceptable advanced proof from the cloud biological system. Moreover, a quick exhibition observing the proposed Blockchain based measurable in cloud structure was assessed. BBFC will guarantee dependability, respectability, validness, and non-renouncement of the proof in the cloud. The proposed BBFC framework was also subjected to performance evaluation, considering factors such as latency, bandwidth, energy and resource utilization, and failure points. This evaluation provides insights into the efficiency and effectiveness of the framework in real-world cloud forensic scenarios. Show more

Keywords: Blockchain, cloud computing, forensic data

DOI: 10.3233/JIFS-231072

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9861-9874, 2023

Authors: Chen, Xinquan | Ma, Jianbo | Qiu, Yirou | Liu, Sanming | Xu, Xiaofeng | Bao, Xianglin

Article Type: Research Article

Abstract: The purpose of clustering is to identify distributions and patterns within unlabelled datasets. Since the proposal of the original synchronization clustering (SynC) algorithm in 2010, synchronization clustering has become a significant research direction. This paper proposes a shrinking synchronization clustering (SSynC) algorithm utilizing a linear weighted Vicsek model. SSynC algorithm is developed from SynC algorithm and a more effective synchronization clustering (ESynC) algorithm. Through analysis and comparison, we find that SSynC algorithm demonstrates superior synchronization effect compared to SynC algorithm, which is based on an extensive Kuramoto model. Additionally, it exhibits similar effect to ESynC algorithm, based on a linear …version of Vicsek model. In the simulations, a comparison is conducted between several synchronization clustering algorithms and classical clustering algorithms. Through experiments using some artificial datasets, eight real datasets and three picture datasets, we observe that compared to SynC algorithm, SSynC algorithm not only achieves a better local synchronization effect but also requires fewer iterations and incurs lower time costs. Furthermore, when compared to ESynC algorithm, SSynC algorithm obtains reduced time costs while achieving nearly the same local synchronization effect and the same number of iterations. Extensive comparison experiments with some class clustering algorithms demonstrate the effectiveness of SSynC algorithm. Show more

Keywords: SynC algorithm, Kuramoto model, shrinking synchronization, a linear weighted Vicsek model, near neighbor points

DOI: 10.3233/JIFS-231817

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9875-9897, 2023

Authors: Rida, Zakaria | Boukachour, Hadhoum | Ennaji, Mourad | Machkour, Mustapha

Article Type: Research Article

Abstract: The articulation between machine tutoring and human tutoring remains a productive research within in the context of Intelligent Tutoring Systems (ITS), particularly in the context of e-learning where the dropout rate is high. We explore an innovative approach, the automation of tutoring as it is done in the classroom to respond to the difficulties of the learner. We propose a generic Intelligent Multi-Tutoring System (IMTS) architecture composed of two modules COMES and MAT. The Communication Entry Service (COMES) module manages communications between the IMTS and a Learning Management System (LMS). The module Multi-Agent Tutoring (MAT) is the multi-agent system developed …with JADE, which allows the dynamic adaptation of tutoring (Machine, Peer, Teacher) according to the profile of the learner. We offer a configurable system to customize tutoring to the individual needs of each learner. It can be grafted onto any learning platform, making it multidisciplinary and easy to integrate into existing learning environments. The teacher will be able to devote more time to learners which need greater his intervention.The peer will develop human and relational qualities linked to their know-how, transversal skills sought by recruiters. To validate this architecture, we provide an application and results that integrate the elements of the described model. The results of the experiment prove the feasibility and reliability of our approach. Show more

Keywords: Intelligent tutoring system, multi-agent system, adaptive system, markov, complex system

DOI: 10.3233/JIFS-232319

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9899-9913, 2023

Authors: Liu, Yiyang | Li, Changxian | Cui, Yunxian | Song, Xudong

Article Type: Research Article

Abstract: Intelligent bearing fault diagnosis plays an important role in improving equipment safety and reducing equipment maintenance costs. Noise in the signal can seriously reduce the accuracy of fault diagnosis. To improve the accuracy of fault diagnosis, a novel noise reduction method based on weighted multi-scale morphological filter (WMMF) is proposed. Firstly, Teager energy operator (TEO) is used to amplify the morphological information of the signal. Then, a scale filtering operator using envelope entropy (SFOEE) is proposed to select appropriate scales. At these scales, the noise in the signal can be adequately suppressed. A new weighting method is proposed to integrate …the selected scales to construct the WMMF. Finally, multi-headed self-attention capsule restricted boltzmann network (MSCRBN) is proposed to diagnose bearing faults.The performance of the TEO-SFOEE-WMMF-MSCRBN fault diagnosis method is verified on the CWRU dataset. Compared with existing fault diagnosis methods, this approach achieves 100% identification accuracy. The experimental results indicate that the proposed diagnosis method can effectively resist noise and precisely diagnose bearing faults. Show more

Keywords: Bearing fault diagnosis, mathematical morphological filter, restricted boltzmann machine, capsule network

DOI: 10.3233/JIFS-232737

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9915-9928, 2023

Authors: Cheng, Gang | You, Qinliang | Shi, Lei | Wang, Zhenxue | Luo, Jia | Li, Tianbin

Article Type: Research Article

Abstract: With the rapid development of information science and social networks, the Internet has accumulated various data containing valuable information and topics. The topic model has become one of the primary semantic modeling and classification methods. It has been widely studied in academia and industry. However, most topic models only focus on long texts and often suffer from semantic sparsity problems. The sparse, short text content and irregular data have brought major challenges to the application of topic models in semantic modeling and topic discovery. To overcome these challenges, researchers have explored topic models and achieved excellent results. However, most of …the current topic models are applicable to a specific model task. The majority of current reviews ignore the whole-cycle perspective and framework. It brings great challenges for novices to learn topic models. To deal with the above challenges, we investigate more than a hundred papers on topic models and summarize the research progress on the entire topic model process, including theory, method, datasets, and evaluation indicator. In addition, we also analyzed the statistical data results of the topic model through experiments and introduced its applications in different fields. The paper provides a whole-cycle learning path for novices. It encourages researchers to give more attention to the topic model algorithm and the theory itself without paying extra attention to understanding the relevant datasets, evaluation methods and latest progress. Show more

Keywords: Topic model, text mining, semantic understanding, whole-cycle, topic detection

DOI: 10.3233/JIFS-233551

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9929-9953, 2023

Authors: Lalitha, K. | Murugavalli, S. | Roseline, A. Ameelia

Article Type: Research Article

Abstract: For retrieving the relevant images from the internet, CBIRs (content based image retrievals) techniques are most globally utilized. However, the traditional image retrieval techniques are unable to represent the image features semantically. The CNNs (convolutional neural networks) and DL has made the retrieval task simpler. But, it is not adequate to consider only the finalized aspect vectors from the completely linked layers to fill the semantic gap. In order to alleviate this problem, a novel Hash Based Feature Descriptors (HBFD) method is proposed. In this method, the most significant feature vectors from each block are considered. To reduce the number …of descriptors, pyramid pooling is used. To improve the performance in huge databases, the hash code like function is introduced in each block to represent the descriptors. The proposed method has been evaluated in Oxford 5k, Paris 6k, and UKBench datasets with the accuracy level of 80.6%, 83.9% and 92.14% respectively and demonstrated better recall value than the existing methods. Show more

Keywords: Content-based image retrieval, CNNs, hash based feature descriptor (HBFD), pyramid pooling and hash code

DOI: 10.3233/JIFS-233891

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9955-9964, 2023

Authors: Liao, Tao | Sun, Haojie | Zhang, Shunxiang

Article Type: Research Article

Abstract: The entity-relationship extraction model has a significant influence in relation extraction. The existing model cannot effectively identify the entity-relationship triples in overlapping relationships. It also has the problem of long-distance dependencies between entities. In this paper, an inter span learning for document-level relation extraction model is proposed. Firstly, the model converts input of the BERT pre-training model into word vectors. Secondly, it divides the word vectors to form span sequences by random initial span and uses convolutional neural networks to extract entity information in the span sequences. Dividing the word vector into span sequences can divide the entity pairs that …may have overlapping relationships into the same span sequence, partially solving the overlapping relationship problem. Thirdly, the model uses inter span learning to obtain entity information in different span sequences. It fuses entity type features and uses Softmax regression to achieve entity recognition. Aiming at solving the problem of long-distance dependence between entities, inter span learning can fuse the information in different span sequences. Finally, it fuses text information and relationship type features, and uses Linear Layer to classify relationships. Experiments demonstrate that the model improves the F1-score of the DocRED dataset by 2.74% when compared to the baseline model. Show more

Keywords: Joint extraction, entity relation extraction, span, document-level extraction, neural network

DOI: 10.3233/JIFS-234202

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9965-9977, 2023

Authors: Bai, Shi | Li, Pujie

Article Type: Research Article

Abstract: This paper presents an in-depth study and analysis of oil painting classification and simulation using an improved embedded learning fusion vision perception algorithm. This paper analyzes and models the image quality evaluation problem by simulating the human visual system and extracting quality perception features as the main entry point to improve the prediction accuracy of the overall algorithm. This paper proposes a multi-classification method of CCNN, which uses the similarity measure based on information first to achieve multi-classification of artwork styles and artists, and this part is the main part of this paper. This paper uses the wiki art repository …to construct a dataset of oil paintings, including over 2000 works by 20 artists in 13 styles. CNN achieves an accuracy of 85.75% on the artist classification task, which is far more effective than traditional deep learning networks such as Resnet. Finally, we use the network model of this paper and other network models to train the classification of 3, 4, and 6 categories of art images. The accuracy of art image classification by this paper’s algorithm is higher than that of the current mainstream convolutional neural network models, and the extracted features are more comprehensive and more accurate than traditional art image feature extraction methods, which do not rely on researchers to extract image features. Experiments show that the proposed method can achieve excellent prediction accuracy for both synthetic distorted images and distorted images. Show more

Keywords: Visual perception, embedded learning, oil painting classification, algorithm simulation

DOI: 10.3233/JIFS-234545

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9979-9989, 2023