Journal of Intelligent & Fuzzy Systems - Volume 46, issue 4

Authors: Zhang, Jianhua | Liu, Chan | Geng, Na | Zhang, Yixuan | Yang, Liqiang

Article Type: Research Article

Abstract: An improved Ant Colony Optimization (ACO) algorithm, named IACO, is proposed to address the inherent limitation of slow convergence, susceptibility to local optima and excessive number of inflection in traditional ACO when solving path planning problems. To this end, firstly, the search direction number is expanded from 4 or 8 into 32; Secondly, the distance heuristic information is replaced by an area heuristic function, which deviated from the traditional approach that only considers pheromone information between two points; Then, the influence of path angle and number of turns is taken into account in the local pheromone update. Additionally, a reward …and punishment mechanism is employed in the global pheromone update to adjust the pheromone concentrations of different paths; Furthermore, an adaptive update strategy for pheromone volatility factor adaptive is proposed to expand the search range of the algorithm. Finally, simulation experiments are conducted under various scenarios to verify the superiority and effectiveness of the proposed algorithm. Show more

Keywords: Ant colony optimization, mobile robot, path planning, search direction, area-inspired, grid map

DOI: 10.3233/JIFS-238095

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10535-10552, 2024

Authors: Özlü, Şerif | Al-Quran, Ashraf | Riaz, Muhammad

Article Type: Research Article

Abstract: This paper aims to present Bipolar valued probabilistic hesitant fuzzy sets (BVPHFSs) by combining bipolar fuzzy sets and probabilistic hesitant fuzzy sets (PHFSs). PHFSs are a strong version of hesitant fuzzy sets (HFSs) in terms of evaluated as probabilistic of each element. Probabilistic hesitant fuzzy sets (PHFSs) are a set structure that argues that each alternative should be evaluated probabilistically. In this framework, the proposed cluster allows probabilistic evaluation of decision- makers’ opinions as negative. Thus, this case proposes flexibility about selection of an element and aids to overcome with noise channels. Furthermore, some new aggregation operators are discussed called …bipolar valued probabilistic hesitant fuzzy weighted average operator (BVPHFWA), Generalized bipolar valued probabilistic hesitant fuzzy weighted average operator (GBVPHFWA), bipolar valued probabilistic hesitant fuzzy weighted geometric operator (BVPHFWG), Generalized bipolar valued probabilistic hesitant fuzzy weighted geometric operator (GBVPHFWG), bipolar valued probabilistic hesitant fuzzy hybrid weighted arithmetic and geometric operator (BVPHFHWAG) and Generalized bipolar valued probabilistic hesitant fuzzy hybrid weighted arithmetic and geometric (GBVPHFHWAG) and some basic properties are presented. A score function is defined ranking alternatives. Moreover, two different algorithms are put forward with helping to TOPSIS method and by using aggregation operators over BVPHFSs. The validity of proposed operators are analyzed with an example and results are compared in their own. Show more

Keywords: Probabilistic hesitant fuzzy sets, bipolar valued probabilistic hesitant fuzzy sets, generalized hybrid operators, decision-making

DOI: 10.3233/JIFS-238331

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10553-10572, 2024

Authors: Wang, Chishe | Li, Jun | Wang, Jie | Zhao, Weikang

Article Type: Research Article

Abstract: Rapid urbanization has made road construction and maintenance imperative, but detecting road diseases has been time-consuming with limited accuracy. To overcome these challenges, we propose an efficient YOLOv7 road disease detection model. Our approach involves integrating MobilieNetV3 as the backbone feature extraction network to reduce the network’s parameters and computational requirements. Additionally, we introduce the BRA attention module into the spatial pyramid pooling module to eliminate redundant information and enhance the network’s feature representation capability. Moreover, we utilize the F-ReLU activation function in the backbone network, expanding the convolutional layers’ receptive field range. To optimize the model’s boundary loss, we …employ the Wise-IoU loss function, which places more emphasis on the quality of ordinary samples and enhances the overall performance and generalization ability of the network. Experimental results demonstrate that our improved detection algorithm achieves a higher recall rate and mean average precision (mAP) on the public dataset (RDD) and the NJdata dataset in Nanjing’s urban area. Specifically, compared to YOLOv7, our model increases the recall rate and mAP on RDD by 3.3% and 2.6%, respectively. On the NJdata dataset, our model improves the recall rate and mAP by 1.9% and 1.3%, respectively. Furthermore, our model reduces parameter and computational requirements by 30% and 22.5%, respectively, striking a balance between detection accuracy and speed. In conclusion, our road disease detection model presents an effective solution to address the challenges associated with road disease detection in urban areas. It offers improved accuracy, efficiency, and generalization capabilities compared to existing models. Show more

Keywords: Yolov7, lightweight, MobilieNetV3, BRA, F-ReLU, Wise-IoU

DOI: 10.3233/JIFS-239289

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10573-10589, 2024

Authors: Premalatha, G. | Chandramani, Premanand V. | Panimalar, K.

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-210522

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10591-10605, 2024

Authors: Shakkeera, L. | Dhiyanesh, B. | Asha, A. | Kiruthiga, G.

Article Type: Research Article

Abstract: To address this storage issue, we propose a Content-Aware Deduplication Clustering Analysis for Cloud Storage Optimization (CADC-FPRLE) based on a file partitioning running length encoder. At first, preprocessing was done by indexing, counting terms, cleansing, and tokenizing. Further multi-objective clustering points are analysed based on the bisecting divisible partition block, which divides a set of documents. The count terms are filtered from the divisible blocks and make up the count terms content block. Using Content-Aware Multi-Hash Ensemble Clustering (CAMH-EC) to group the similar blocks into clusters. This creates a high-dimensional Euclidean interval to create the number of clusters, and points …are performed randomly to set the initial collection. Then, the Magnitude Vector Space Rate (MVSR) estimates the similarity distance between the groups to select the highest scatter value content for indexing. Finally, the Running Block Parity Encoder (RBPE) generates similarity parity in order to reduce the content to a redundant, singularized file in order to optimise storage. This implementation proves a higher level of storage optimization compared to the previous system than other methods. Show more

Keywords: Data deduplication, semantic analysis, cloud storage, magnitude vector space, cluster analysis, running length encoder

DOI: 10.3233/JIFS-231223

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10607-10619, 2024

Authors: Fan, Lin | Wang, Wenli

Article Type: Research Article

Abstract: The ability, interest, and prior accomplishments of students with varying proficiency levels all impact how they learn English. Exact validation is essential for facilitating efficient evaluation and training models. The research’s innovative significance resides in incorporating personal attributes, progressive appraisal, and Fuzzy Logic-based appraisal in English language learning. The PA2M model, which addresses the shortcomings of existing models, offers a thorough and accurate assessment, enabling personalized recommendations and enhanced teaching tactics for students with varied skill levels. This research proposes the Fuzzy Logic System (FLS)-based Persistent Appraisal Assessment Model (PA2M). Based on the students’ evolving performance and accumulated data, this …model evaluates the students’ English learning capabilities. The model assesses the student’s ability using fuzzification approaches to reduce variations in appraisal verification by linking personal attributes with performance. Mamdani FIS offers a clear and thorough evaluation of student’s English learning capacity within the framework of the appraisal methodology. The inputs are updated utilizing performance and accumulated ability data to improve validation consistently and reduce converge errors. During the fuzzification process, pre-convergence from unavailable appraisal sequences is eliminated. The PA2M approach determines precise improvements and evaluations depending on student ability by merging prior and current data. Several appraisal validations and verifications result in clear fresh suggestions. According to experimental data, the suggested model enhances 9.79% of recommendation rates, 8.79% of appraisal verification, 8.25% of convergence factor, 12.56% error ratio, and verification time with 8.77% over a range of inputs. The PA2M model provides a fresh and useful way to evaluate English learning potential, filling in some gaps in the body of knowledge and practice. Show more

Keywords: Big data, English learning, fuzzy logic system, student ability

DOI: 10.3233/JIFS-232619

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10621-10636, 2024

Authors: Song, Can

Article Type: Research Article

Abstract: The development and utilization of network big data is also accompanied by data theft and destruction, so the monitoring of network security is particularly important. Based on this, the study applies the fuzzy C-mean clustering algorithm to the network security model, however, the algorithm has major defects in discrete data processing and the influence of feature weights. Therefore, the study introduces the concept of local density and optimizes the initial clustering center to solve its sensitive defects as well as empirical limitations; at the same time, the study introduces the adaptive methods of fuzzy indicators and feature weighting, and uses …the concepts such as fuzzy center-of-mass distribution to avoid problems such as the model converging too fast and not being able to handle discrete data. Finally, the study does a simulation analysis of the performance of each module, and the comparison of the overall algorithm with the rest of the models. The experimental results show that in the comparison of the overall algorithm, its false detection rate decreases by 8.57% in the IDS Dataset dataset, compared to the particle swarm algorithm. Therefore, the adaptive weighted fuzzy C-Means algorithm based on local density proposed in the study can effectively improve the network intrusion detection performance. Show more

Keywords: Local density, fuzzy clustering, adaptive, hybrid weighting, network security

DOI: 10.3233/JIFS-235082

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10637-10651, 2024

Authors: Ding, Yahui | Wang, Hongjuan | Liu, Nan | Li, Tong

Article Type: Research Article

Abstract: Traditional Chinese painting (TCP), culturally significant, reflects China’s rich history and aesthetics. In recent years, TCP classification has shown impressive performance, but obtaining accurate annotations for these tasks is time-consuming and expensive, involving professional art experts. To address this challenge, we present a semi-supervised learning (SSL) method for traditional painting classification, achieving exceptional results even with a limited number of labels. To improve global representation learning, we employ the self-attention-based MobileVit model as the backbone network. Furthermore, We present a data augmentation strategy, Random Brushwork Augment (RBA), which integrates brushwork to enhance the performance. Comparative experiments confirm the effectiveness of …TCP-RBA in Chinese painting classification, demonstrating outstanding accuracy of 88.27% on the test dataset, even with only 10 labels, each representing a single class. Show more

Keywords: Traditional chinese paintings, brushwork, semi-supervised learning, image classification

DOI: 10.3233/JIFS-236533

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10653-10663, 2024

Authors: Mahapatra, Rupkumar | Samanta, Sovan | Pal, Madhumangal

Article Type: Research Article

Abstract: The most critical task of a social network is to identify a central node. Numerous methods for determining centrality are documented in the literature. It contributes to online commerce by disseminating news, advertisements and other content via central nodes. Existing methods capture the node’s direct reachability. This study introduces a novel method for quantifying centrality in a fuzzy environment. This measurement takes into account the reachability of nodes and their direct connections. Several critical properties have been demonstrated. A small Facebook network is used to illustrate the issue. Additionally, appropriate tables and graphs present a comparative study with existing methods …for centrality measurement. Show more

Keywords: Fuzzy graph, social network, centrality measure

DOI: 10.3233/JIFS-232602

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10665-10681, 2024

Authors: Cui, Hongzhen | Zhang, Longhao | Zhu, Xiaoyue | Guo, Xiuping | Peng, Yunfeng

Article Type: Research Article

Abstract: Extracting and digitizing drug attributes from medical literature is the first step to build a knowledge computing system for precision disease treatment. In order to build a cardiovascular drug knowledge base, this paper proposes a multi-label text classification method for cardiovascular drug attributes from the Chinese drug guideline. The drug attributes are characterized by a BERT pre-trained model, and a dual-feature extraction structure is proposed based on the BiGRU neural network to capture high-level semantic information. Label categorization of cardiovascular drug attributes, such as indications and mode of administration, is accomplished. The F1 score of 0.8431 was obtained using 5-fold …cross-validation. Comparing KNN and Naïve bayes, and conducting CNN and BiGRU control experiments on the basis of Word2Vec characterization of medication guidelines, the proposed multi-label text classification method is effective and the F1 value is significantly improved. Proved by analysis of ablation and crossover experiments, the proposed method can achieve a high accuracy rate averaged at 0.8339. Show more

Keywords: Multi-label text classification, cardiovascular drug attributes, BERT, BiGRU, dual feature extraction

DOI: 10.3233/JIFS-236115

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10683-10693, 2024