Journal of Intelligent & Fuzzy Systems - Volume 46, issue 3

Authors: Xiao, Yanjun | Yin, Shanshan | Ren, Guoqing | Liu, Weiling

Article Type: Research Article

Abstract: The Flexible Job Shop Scheduling Problem (FJSP) is an extension of the classical Job Shop Scheduling Problem (JSP). The research objective of the traditional FJSP mainly considers the completion time, but ignores the energy consumption of the manufacturing system. In this paper, a mathematical model of the energy-efficient flexible job shop scheduling problem is constructed. The optimization objectives are completion time, delay time, and total equipment energy consumption. To solve the model, an improved non-dominated sorting genetic algorithm (CT-NSGA-II) is proposed to obtain the optimal scheduling solution. First, the heuristic rules of GLR were used to generate the initial population …with good quality and diversity. Second, different crossover and variation operators are designed for the process sequencing and equipment selection parts to enhance the diversity of the evolutionary population. The sparsity theory is introduced to find sparse solutions and three neighborhood structures are designed to perform local search on sparse solutions to improve the uniformity of the optimal solution set distribution. Finally, a competitive selection strategy based on the bidding mechanism is proposed for the Pareto optimal solution set to obtain a better scheduling scheme. The experimental results show that the proposed improved algorithm is feasible and effective in the FJSP problem considering energy consumption, and the algorithm has some application value in improving the efficiency of smart shop operation. Show more

Keywords: Flexible job shop scheduling, energy consumption, non-dominated sorting genetic algorithm, sparsity theory, neighborhood search

DOI: 10.3233/JIFS-233337

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5493-5520, 2024

Authors: Gao, Yuan

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Abstract: This article has been retracted.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-234951

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5521-5532, 2024

Authors: Yu, Zhiqiang | Wang, Ting | Liu, Shihu | Tan, Xuewen

Article Type: Research Article

Abstract: As the typical distant language pair, Chinese and Vietnamese vary widely in syntactic structure, which significantly influences the performance of Chinese-Vietnamese machine translation. To address this problem, we present a simple approach with a pre-reordering model for closing syntactic gaps of the Chinese-Vietnamese language pair. Specifically, we first propose an algorithm for recognizing the modifier inverse, one of the most representative syntactic different in Chinese-Vietnamese language pair. Then we pre-train a pre-reordering model based on the former recognition algorithm and incorporate it into the attention-based translation framework for syntactic different reordering. We conduct empirical studies on Chinese-Vietnamese neural machine translation …task, the results show that our approach achieves average improvement of 2.75 BLEU points in translation quality over the baseline model. In addition, the translation fluency can be significantly improved by over 2.44 RIBES points. Show more

Keywords: Neural machine translation, linguistic difference, Chinese-Vietnamese

DOI: 10.3233/JIFS-233762

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5533-5544, 2024

Authors: He, Hongxuan | Wang, Pei | Lu, Jiakuan

Article Type: Research Article

Abstract: Fuzzy β-covering(Fβ-C) plays a key role in processing real-valued data sets and covering plays an important role in the topological spaces. Thus they have attracted much attention. But the relationship between Fβ-C and topology has not been studied. This inspires the research of Fβ-C from the perspective of topology. In this paper, we construct Fβ-C rough continuous and homeomorphism mappings by using Fβ-C operator. We not only obtain some equivalent descriptions of the mappings but also profoundly reveal the relationship of two Fβ-C approximation spaces. We give the classification method of Fβ-C approximation spaces with the help of homeomorphism mapping, …propose a new method to construct topology induced by Fβ-C operator and investigate the properties in the topological spaces further. Finally, we obtain the necessary and sufficient conditions for Fβ-C operators to be topological closure operators. Show more

Keywords: Fuzzy β-covering, Fuzzy β-covering mapping, Fuzzy β-covering operator, Topology

DOI: 10.3233/JIFS-231117

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5545-5553, 2024

Authors: Aurangzeb, Khursheed

Article Type: Research Article

Abstract: Background: Due to rapid progress in the fields of artificial intelligence, machine learning and deep learning, the power grids are transforming into Smart Grids (SG) which are versatile, reliable, intelligent and stable. The power consumption of the energy users is varying throughout the day as well as in different days of the week. Power consumption forecasting is of vital importance for the sustainable management and operation of SG. Methodology: In this work, the aim is to apply clustering for dividing a smart residential community into several group of similar profile energy user, which will be effective for developing …and training representative deep neural network (DNN) models for power load forecasting of users in respective groups. The DNN models is composed of convolutional neural network (CNN) followed by LSTM layers for feature extraction and sequence learning respectively. The DNN For experimentation, the Smart Grid Smart City (SGSC) project database is used and its energy users are grouped into various clusters. Results: The residential community is divided into four groups of customers based on the chosen criterion where Group 1, 2, 3 and 4 contains 14 percent, 22 percent, 19 percent and 45 percent users respectively. Almost half of the population (45 percent) of the considered residential community exhibits less than 23 outliers in their electricity consumption patterns. The rest of the population is divided into three groups, where specialized deep learning models developed and trained for respective groups are able to achieve higher forecasting accuracy. The results of our proposed approach will assist researchers and utility companies by requiring fewer specialized deep-learning models for accurate forecasting of users who belong to various groups of similar-profile energy consumption. Show more

Keywords: Smart community, smart grids, power load forecasting, sustainable systems, outliers, machine learning, deep learning, data analytic, clustering, power consumption, consumption behavior

DOI: 10.3233/JIFS-235873

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5555-5573, 2024

Authors: Natarajan, Ezhilarasan | Augustin, Felix

Article Type: Research Article

Abstract: Tuberculosis (TB) stands as the second leading global infectious cause of death, following closely behind the impact of COVID-19. The standard approach to diagnose TB involves skin tests, but these tests can yield inaccurate results due to limited access to healthcare and insufficient diagnostic resources. To enhance diagnostic accuracy, this study introduces a novel approach employing a Bipolar Fuzzy Utility Matrix Inference System (BFUMIS) and a Bipolar Mamdani Fuzzy Inference System (BMFIS) to assess TB disease levels. By considering factors associated with the causation of TB, the study devises suitable membership functions for bipolar fuzzy sets (BFS) using both triangular …and trapezoidal fuzzy numbers. Using a point factor scale, the study clusters the rules systematically and assesses the level of uncertainty within these grouped rules by utilizing bipolar triangular fuzzy numbers (BTFN). To handle the BTFN, this study proposes converting bipolar triangular fuzzy into bipolar crisp score (CBTFBCS) algorithm as a defuzzification method. The optimal bipolar fuzzy utility sets (BFUS) are determined from the bipolar fuzzy utility matrix to identify patients’ TB disease levels. These sets play a pivotal role in characterizing the severity of TB disease levels in patients. Additionally, rigorous validation of the utility framework is accomplished through measures of bipolar fuzzy satisfactory factors and sensitivity analyses. Furthermore, the study introduces the BMFIS, which presents a novel perspective on the conventional fuzzy inference system. This innovative system integrates the Mamdani fuzzy inference system (MFIS) into a bipolar fuzzy context, enriching the diagnostic process with enhanced insights. To demonstrate the efficacy of the proposed methods, extensive validation is carried out using actual clinical data. The performance metrics used in this validation effectively demonstrate the superiority of the proposed approach. Show more

Keywords: Bipolar triangular fuzzy number, pulmonary tuberculosis, bipolar fuzzy utility matrix, bipolar Mamdani fuzzy inference system, performance measures

DOI: 10.3233/JIFS-233682

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5575-5607, 2024

Authors: Zhang, Xing-Xian | Liu, Wenli | Wang, Xu | Zuo, Wenjin | Wang, Ying-Ming | Sun, Licheng

Article Type: Research Article

Abstract: Efficiency is a relative measure that allows assessment across different ranges. Evaluating the performance of decision-making units (DMUs) from an optimistic perspective yields the best relative efficiency (optimistic efficiency), which establishes an efficiency frontier. Conversely, evaluating from a pessimistic perspective produces the worst relative efficiency (pessimistic efficiency) and creates an inefficiency frontier. This study examines the efficiency of DMUs in two scenarios and proposes models for adjustment coefficient. The pessimistic and optimistic efficiencies are adjusted to the lower and upper bounds of the DMUs based on the adjustment coefficient, enabling determination of efficiency intervals for all DMUs, as well as …evaluation and ranking. A Hurwicz criterion-based approach is introduced and applied to compare and rank the interval efficiencies of DMUs. Two numerical examples are examined using the proposed DEA adjustment coefficient models to demonstrate its potential application and validity. Show more

Keywords: Data envelopment analysis, interval efficiency, adjustment coefficient model, ranking

DOI: 10.3233/JIFS-233051

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5609-5621, 2024

Authors: Pang, Kuo | Lu, Yifan | Xu, Lixian | Yan, Wei | Zou, Li | Lu, Mingyu

Article Type: Research Article

Abstract: The research of object-oriented concept is one of the basic contents of formal concept analysis. To overcome the complexity of computing object-oriented concept, this paper proposes an Object-oriented Concept Acquisition model (OCA) based on attribute topology. The object-oriented attribute topology is first proposed to visualize the coupling relationship between attributes. Second, inspired by rough set theory, object-oriented attribute topology is transformed into rough object-oriented attribute topology. Furthermore, based on the weights of the edges in the rough object-oriented attribute topology, object-oriented concepts are obtained by finding reachable paths. Finally, examples and experiments are used to demonstrate the effectiveness of our …proposed method. Show more

Keywords: Formal concept analysis, object-oriented concept, rough object-oriented attribute topology, object-oriented concept acquisition

DOI: 10.3233/JIFS-233062

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5623-5633, 2024

Authors: Sharmila, V. | Ezhumalai, P.

Article Type: Research Article

Abstract: The global incidence of skin cancer has been rising, resulting in increased mortality and morbidity if left untreated. Accurate diagnosis of skin malignancies is crucial for early intervention through excision. While various innovative medical imaging techniques, such as dermoscopy, have improved the way we examine skin cancers, the progress in medical imaging for identifying skin lesions has not kept pace. Skin lesions exhibit diverse visual features, including variations in size, shape, boundaries, and artifacts, necessitating an efficient image-processing approach to assist dermatologists in decision-making. In this research, we propose an automated skin lesion classifier called GreyNet, which utilizes optimized convolutional …neural networks (CNNs) or shift-invariant networks (SIN). GreyNet comprises three components: (i) a trained fully deep CNN for semantic segmentation, relating input images to manually labeled standard scans; (ii) an enhanced dense CNN with global information exchange and adaptive feature salvaging module to accurately classify each pixel in histopathological scans as benign or malignant; and (iii) a binary grey wolf optimizer (BGWO) to improve the classification process by optimizing the network’s hyperparameters. We evaluate the performance of GreyNet in terms of lesion segmentation and classification on the HAM10000 database. Extensive empirical results demonstrate that GreyNet outperforms existing lesion segmentation methods, achieving improved dice similarity score, volume error, and average processing time of 1.008±0.009, 0.903±0.009%, and 0.079±0.010 s, respectively. Moreover, GreyNet surpasses other skin melanoma classification models, exhibiting improved accuracy, precision, specificity, sensitivity, false negative rate, false positive rate, and Jaccard similarity score (JSS) of 96.5%, 97%, 96.2%, 92.1%, 3.8%, 3%, and 89.5%, respectively. Based on our experimental analysis, we conclude that GreyNet is an efficient tool to aid dermatologists in identifying skin melanoma. Show more

Keywords: Classification, convolution neural networks, optimization, semantic segmentation, skin cancer, super-resolution

DOI: 10.3233/JIFS-232325

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5635-5653, 2024

Authors: Gu, Xiaohong

Article Type: Research Article

Abstract: Hand-drawn is one of the few visual descriptors that can directly represent visual content, and has significant research in the area of computer vision. Aiming at the problem of sparse features in the realm of hand-drawn image retrieval, hand-drawn images, and the easy deformation of hand-drawn images, this paper proposes a feature extraction method of grid resource sharing collaborative algorithm, which can be obtained utilizing precisely extracted semantic characteristics from hand-drawn images through computer multimedia-aided design Efficient and accurate retrieval results. First, the fundamental framework for obtaining semantic features is algorithm; then the attention model mechanism is the grid resource …sharing collaborative introduced in the process of supervised training, and the attention structure block is introduced after the convolutional neural network’s bottom layer. To locate effective semantic features, In order to accomplish high-precision retrieval, the attention structure block combines channel attention structure and spatial attention structure to build the attention structure block. The last feature descriptor is then created by combining various semantic feature levels. The proposed strategy is practical and efficient, as demonstrated by the experimental findings on the comparison database Flickr15k. In addition, in the task of hand-drawn image classification, the proposed attention mechanism greatly improves the classification accuracy. Show more

Keywords: Hand-painted retrieval, grid resource sharing collaborative algorithm, computer-aided, hand-painted classification

DOI: 10.3233/JIFS-233701

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5655-5666, 2024

Authors: Qiao, Jian-min | Li, Wo-yuan | Liu, Lin

Article Type: Research Article

Abstract: In this paper, a two-sided matching decision model based on interval-valued intuitionistic fuzzy environment is proposed to maximize the demand of individual differences. Firstly, the attribute feature table and weight matrix of both agents are constructed, and a formula for calculating the comprehensive advantage in the interval-valued intuitionistic fuzzy environment is given, namely the interval-valued intuitionistic fuzzy comprehensive advantage aggregation operator (IIFCAAO); Secondly, the interval-valued intuitionistic fuzzy decision matrix of both agents is calculated by using the comprehensive advantage aggregation operator, and the interval-valued intuitionistic fuzzy decision matrix is transformed into a score function matrix by using a score function …formula; Thirdly, the two-sided matching model is established to maximize the score; Finally, the scientificity and practicability of the model are verified by an example of college students changing their majors. Show more

Keywords: Interval intuitionistic fuzzy set, interval-valued intuitionistic fuzzy comprehensive advantage aggregation operator (IIFCAAO), two-sided matching

DOI: 10.3233/JIFS-234191

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5667-5676, 2024

Authors: Qi, Ruijuan | Liu, Chang | Zhang, Qiwen | Gu, Lingzi

Article Type: Research Article

Abstract: Business investments are prone to market risks, so pre-analysis is mandatory. The type of risk, its period, sustainability, and economic impact are the analyzable features for preventing loss and downfall. In recent years, mathematical models have been used for representing business cycles and analyzing the impacting risks. This article introduces a Decisive Risk Analytical Model (DRAM) for identifying spur defects in business investments. The proposed risk analytical model exploits the investments, returns, and influencing factors over the various market periods. The risk model is tuned for identifying the influencing factors across various small and large investment periods. The model is …tuned to adapt to different economic periods split into a single financial year. In the process of tuning and training the mathematical analysis model, deep learning is used. The learning paradigm trains the risks and modifying features from expert opinion and previous predictions. Based on these three factors, the risk for the current investment is forecasted. The forecast aids in improving the new investment feasibilities with minimal risks and model modifications. The frequent market status is identified for preventing unnecessary risk-oriented forecasts using the training performed. Therefore, the proposed model is reliable in identifying risks and providing better investment recommendations. Show more

Keywords: Business investment, deep learning, mathematical model, risk analysis

DOI: 10.3233/JIFS-233038

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5677-5693, 2024

Authors: Zhang, Yongcun | Bai, Zhe

Article Type: Research Article

Abstract: Compressive strength (CS) is concrete’s most important mechanical property, as it plays an important role in setting design criteria. Thus, an accurate and early assessment of the CS of concrete can minimize time, labor, and cost. This paper investigated the ability of the Radial Basis Function (RBF) to handle the prediction of CS. The nonlinearities raised from the novel utilized admixtures between the input variables and output CS is tried to be conducted with the RBF model. In order to make a flexible framework combination of the RBF model with the African Vulture Optimization (AVOA) and Salp Swarm Algorithm (SSA) …techniques are considered. The results achieved from the RBF-AVOA model indicated good agreement between the actual and predicted values. The proposed model provides a very accurate HPC compressive strength prediction. In addition, the correlation coefficient R2 is equal to (0.997), and the values of mean absolute error (MAE) (0.1917 MPa), root mean square error (RMSE) (0.937 MPa), and variance account coefficient (VAF) (99.73%) are low. The performance of the RBF-AVOA model, compared to other models, provided the desired advantage and more stable predictions. AVOA plays a key role in modeling results, improving generalization capabilities, avoiding redundant data, and decreasing uncertainty. Show more

Keywords: High-performance concrete, compressive strength, african vulture optimization algorithm, salp swarm algorithm, radial basis function

DOI: 10.3233/JIFS-230907

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5695-5707, 2024

Authors: Shan, Liqian | Zhao, Hui | Feng, Yuhui

Article Type: Research Article

Abstract: Task-oriented collaborative dialogues have become an indispensable form of communication in our daily work and learning, in which participants exchange ideas and share information to advance goals. It is crucial to automatically analyze participants’ contributions and understand these dialogues relative to individuals with limited attention spans. In this paper, seven Discourse Role (DR) labels are designed to describe discourse’s different roles in collaborative dialogues for goal achievement. We collected about 11K discourses from a publicly available dialogue corpus and annotated them with DR tags to construct a dataset named MRDR (Meeting Recorder Discourse Role). In addition, this paper proposes a …novel hierarchical model, STTAHM (Speaker Turn and Topic-Aware Hierarchical Model), for Discourse Role classification. The model is equipped to perceive speaker turn and dialogue topic and can effectively capture the discourse’s local and global semantic information. Experimental results show that our proposed method is effective on the constructed dataset, and the accuracy of Discourse Role classification reaches 86.99%. Show more

Keywords: Task-oriented collaborative dialogue, discourse role, dataset, speaker turn, topic-aware

DOI: 10.3233/JIFS-235263

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5709-5721, 2024

Authors: Liu, Shuanghua

Article Type: Research Article

Abstract: The prediction of shock disturbed systems is always a major challenge in the field of grey prediction. Considering the characteristics of grey buffer operator, this paper proposes a new grey buffer operator based on inverse accumulation, new information priority and logarithmic function to cope with the prediction challenge. In addition, some relevant properties of the new grey buffer operator are discussed in this paper, including adjustment intensity and smoothness. The new grey buffer operator is used to process monotonically increasing sequences, monotonically decreasing sequences and oscillating sequences, respectively. Experimental results show that the proposed buffer operator can effectively improve prediction …accuracy. Show more

Keywords: Grey prediction, grey buffer operator, variable weight coefficient, adjustment intensity, smoothness

DOI: 10.3233/JIFS-230091

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5723-5731, 2024

Authors: Ilakkiya, N. | Rajaram, A.

Article Type: Research Article

Abstract: Different physical objects can be employed in the modern technological environment to facilitate human activity. In order to connect physical objects with the universe of digital using a variety of networks and communication technologies, an IoT, the cutting edges technological and effective solution, is deployed. Mobile ad hoc networks (MANET) interact with the IoTin smart settings, enhancing its user appeal and boosting its commercial viability. The new system of MANET based IoT and IT-network may be created by integrating wireless sensor and MANET with the Internet of Things. A solution like this increases user mobility while lowering network deployment costs. …However, it also raises new, difficult problems in terms of networking considerations. In this, we presented a novel DAG (Directed Acyclic Graph)-Blockchain structure for MANET-IoT security. The network is secured through Multi-Factor PUF (MF-PUF) authentication scheme. With all authorized nodes, the network is segregated into cluster topology. For trusted data transmission, we proposed Jelly Fish Optimization (JFO) algorithm with the consideration of multiple criteria. For deep packet inspection, we proposed a Fully Connected Recurrent Neural Network (FCRNN). Through deep packet inspection, the intrusions are detected and mitigated through blocking system.With help of merged algorithm, the suggested method obtained improved ability in the PDR (Packet Delivery Ratio), production, analysis of time, detection accuracy also security levels. The comparison results clearly indicate that the proposed study outperforms all previous studies in various aspects. Particularly, the suggested methods for cluster creation, data aggregation, routing, encryption, and authentication significantly improve the system of DAG-IDS. Additionally, the planned task exhibits an exceptionally low standard deviation, making the suggested approach highly suitable for a WSN-IoT environment. Show more

Keywords: DAG-blockchain, PUF, trusted routing, RNN, IDS, MANET-IoT

DOI: 10.3233/JIFS-232924

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5733-5752, 2024

Authors: Sui, Duo | Gao, Peng | Fang, Minhang | Lian, Jing | Li, Linhui

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-235246

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5753-5765, 2024

Authors: Shen, Haiyang

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-236234

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5767-5782, 2024

Authors: Zong, Yi | Li, Ying | Pan, Enze | Chen, Simin | Zhang, Jingkuan | Gao, Binbin

Article Type: Research Article

Abstract: Stratifying long-tail customers and identifying high-quality customers with high growth potential are crucial for civil aviation companies to explore new profit growth points. This paper proposes a long-tail customer stratification model based on clustering ensemble to address the problems of insufficient attention to long-tail customers in previous studies and the low accuracy and lack of accuracy testing of single clustering algorithms. First, the Bayesian information criterion is used to determine the optimal number of clusters. Then, an ensemble framework integrating the Gaussian mixture model, spectral clustering, Two step clustering and K-means algorithm is constructed, and the stacking and bagging ensemble …methods are used for the cluster ensemble. Finally, three different indicators are used to evaluate the algorithm performance. Experimental results indicate that compared with single clustering algorithms, the Stacking algorithm increases the silhouette coefficient by 14.77% to 27.11%, the Calinski-Harabasz index by 38.83% to 122.18%, and the Davies-Bouldin Index by 19.38% to 98.04%. This indicates that each clustering has high cohesion and separation, with samples within a category being more closely related and those between categories having clear boundaries. It shows that the Stacking algorithm more accurately stratifies long-tail customers with similar consumption behaviors into different categories, achieving customer stratification. Show more

Keywords: Customer stratification, long tail theory, ensemble learning, stacking algorithm, bagging algorithm

DOI: 10.3233/JIFS-234155

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5783-5799, 2024

Authors: Feng, Chongren | Qin, Jiwei | Zhang, Yuhang

Article Type: Research Article

Abstract: Hypernym discovery aims to distinguish potential hypernyms for a query term. However, existing methods for hypernym discovery suffer from the following problems: (1) traditional unsupervised pattern-based methods suffer from low recall; (2) recent supervised box embedding methods are deficient in identifying specific hypernyms. To cope with the above problems, this paper presents a method for hypernym discovery based on E xtended P atterns and Box E mbeddings (EP-BoxE). Firstly, to acquire more hypernymy relation entity pairs, we identify co-hyponyms of a given term and use their hypernyms as the candidate hypernym set for the given term; Secondly, by analyzing the …text corpus, we find that the language patterns also provide additional information for hypernym discovery, which also solves the deficiency of the box embedding methods in identifying specific hypernyms. Finally, experimentations on two domain-specific datasets reveal that EP-BoxE surpasses the performance of popular methods on the majority of evaluation metrics. Show more

Keywords: Hypernym discovery, pattern-based, box embeddings

DOI: 10.3233/JIFS-235181

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5801-5810, 2024

Authors: Kang, Zhonghui

Article Type: Research Article

Abstract: Intangible cultural heritage can be said to be an important component of tourism resources. With the rapid development of society in today’s era, tourism development and intangible cultural heritage protection have gradually attracted attention from Chinese society, and in recent years, it has attracted high attention from relevant departments of the Chinese government. Tourism development has a “dual” impact on the protection of intangible cultural heritage, with both positive and negative impacts. The risk assessment of intangible cultural heritage tourism development is a MAGDM problems. Recently, the TODIM and GRA technique has been employed to manage MAGDM issues. The interval-valued …Pythagorean fuzzy sets (IVPFSs) are employed as a tool for characterizing uncertain information during the risk assessment of intangible cultural heritage tourism development. In this paper, the interval-valued Pythagorean fuzzy TODIM-GRA (IVPF-TODIM-GRA) technique is construct to manage the MAGDM under IVPFSs. Finally, a numerical case study for risk assessment of intangible cultural heritage tourism development is employed to validate the proposed technique. Show more

Keywords: Multiple-attribute group decision-making (MAGDM), interval-valued pythagorean fuzzy sets (IVPFSs), TODIM technique, GRA technique, intangible cultural heritage tourism development

DOI: 10.3233/JIFS-236937

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5811-5824, 2024

Authors: Chang, Zaibin | Mao, Lingling

Article Type: Research Article

Abstract: Fuzzy complementary β-neighborhoods (FCNs) are used to find information relevant to the target in data mining. Based on FCNs, there are six types of covering-based multigranulation fuzzy rough set (CMFRS) models have been constructed, which can be used to deal with the problem of multi-criteria information systems. These CMFRS models are calculated by set representations. However, it is time-consuming and error-prone when set representations are used to compute these CMFRS models in a large multi-criteria information system. Hence, it is important to present a novel method to compute them quickly, which is our motivation for this paper. In this paper, …we present the matrix representations of six types of CMFRS models on FCNs. Firstly, some new matrices and matrix operations are given in a multi-criteria information system. Then, matrix representations of three types of optimistic CMFRSs on FCNs are proposed. Moreover, matrix approaches are also used for computing three types of pessimistic CMFRSs on FCNs. Finally, some experiments are presented to show the effectiveness of our approaches. Show more

Keywords: Fuzzy rough set, covering, matrix, multigranulation

DOI: 10.3233/JIFS-224323

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5825-5839, 2024

Authors: Wang, Yong | Jiang, Zhipeng | Wang, Yihan | Yang, Chunyu | Zou, Liang

Article Type: Research Article

Abstract: The mining belt conveyor is one of the most important modules in coal mine, whose safety always be threatened by the foreign objects. Although the traditional target detection methods achieve promising results in various computer vision tasks, the performance heavily depends on sufficient labelled data. However, in real-world production scenario, it is difficult to acquire huge number of images with foreign objects. The obtained datasets lacking of capacity and diversity are not suitable for training supervised learning-based foreign objects detection models. To address this concern, we propose a novel method for detecting the foreign objects on the surface of underground …coal conveyor belt via improved GANomaly. The proposed foreign objects detection method employs generative adversarial networks (GAN) with attention gate to capture the distribution of normality in both high-dimensional image space and low-dimensional latent vector space. Only the normal images without foreign object are utilized to adversarially train the proposed network, including a U-shape generator to reconstruct the input image and a discriminator to classify real images from reconstructed ones. Then the combination of the difference between the input and generated images as well as the difference between latent representations are utilized as the anomaly score to evaluate whether the input image contain foreign objects. Experimental results over 707 images from real-world industrial scenarios demonstrate that the proposed method achieves an area under the receiver operating characteristic curve of 0.864 and is superior to the previous GAN-based anomaly detection methods. Show more

Keywords: Generative adversarial networks, anomaly detection, attention, industrial scenarios, mining belt conveyor

DOI: 10.3233/JIFS-230647

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5841-5851, 2024

Authors: Li, Keyuan | Zhang, Qinghua | Xie, Qin | Huang, Shuaishuai

Article Type: Research Article

Abstract: Medical image classification is an essential task in the fields of computer-aided diagnosis and medical image analysis. In recent years, researchers have made extensive work on medical image classification by computer vision techniques. However, most of the current work is based on deep learning methods, which still suffer from expensive hardware resources, long time consuming and a lot of parameters to be optimized. In this paper, a multi-granularity ensemble algorithm for medical image classification based on broad learning system is proposed, which is an end-to-end lightweight model. On the one hand, the proposed method is designed to address the problem …of weak image feature learning ability of broad learning system. The convolution module with fixed weights based on transfer learning is introduced as a feature extractor to extract fusion features of medical images. On the other hand, the multi-granularity ensemble framework is proposed, which learn the fusion features of medical images from fine-grained to coarse-grained respectively, and the prediction results at different granularity levels are integrated by ensemble learning. In this way, the bottom local features can be sufficiently considered, while the global features can also be taken into account. The experimental results show that on the MedMNIST dataset (containing 10 sub-datasets), the proposed method can shorten the training time by tens of times while having similar accuracy to deep convolutional neural networks. On the ChestXRay2017 dataset, the proposed method can achieve an accuracy of 92.5%, and the training time is also significantly better than other methods. Show more

Keywords: Broad learning system(BLS), multi-granularity, ensemble learning, medical image classification

DOI: 10.3233/JIFS-235725

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5853-5867, 2024

Authors: Shen, Hanhan | Zhang, Fu | Pan, Xiaodong | Sun, Xiaofei

Article Type: Research Article

Abstract: As significant carriers of the application of fuzzy set theories, fuzzy systems have been widely used in many fields. However, selecting fuzzifications, fuzzy reasoning engines, and defuzzifications is subjective for Mamdani fuzzy systems, and the fuzzy rule of Takagi-Sugeno-Kang fuzzy systems is less of a linguistic interpretation. Regarding these shortcomings, this paper proposes a fuzzy system based on vague partitions processing information directly from the fuzzy rule base, in which fuzzy rules have explicit semantics. Firstly, the n -dimensional vague partition of the n -dimensional universe is defined based on 1-dimensional vague partitions and the aggregation function, and its properties …are discussed. Based on these, we design the new fuzzy system, and investigate its approximation properties which is the theoretical guarantee for applying the fuzzy system. As an application, we combine the fuzzy system with PID control system to deal with autonomous vehicle path tracking control problems. A series of experiments are constructed, and experimental results indicate that the fuzzy system based on vague partitions makes the fuzzy PID control system strong robustness, and has obvious advantages compared with other traditional fuzzy systems for path tracking control problems. Show more

Keywords: Fuzzy systems, vague partitions, aggregation functions, fuzzy PID control, path tracking

DOI: 10.3233/JIFS-232903

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5869-5892, 2024

Authors: Wang, Chia-Hung | Ye, Qing | Cai, Jiongbiao | Suo, Yifan | Lin, Shengming | Yuan, Jinchen | Wu, Xiaojing

Article Type: Research Article

Abstract: The multi-feature and imbalanced nature of network data has always been a challenge to be overcome in the field of network intrusion detection. The redundant features in data could reduce the overall quality of network data and the accuracy of detection models, because imbalance could lead to a decrease in the detection rate for minority classes. To improve the detection accuracy for imbalanced intrusion data, we develop a data-driven integrated detection method, which utilizes Recursive Feature Elimination (RFE) for feature selection, and screens out features that are conducive to model recognition for improving the overall quality of data analysis. In …this work, we also apply the Adaptive Synthetic Sampling (ADASYN) method to generate the input data close to the original dataset, which aims to eliminate the data imbalance in the studied intrusion detection model. Besides, a novel VGG-ResNet classification algorithm is also proposed via integrating the convolutional block with the output feature map size of 128 from the Visual Geometry Group 16 (VGG16) of the deep learning algorithm and the residual block with output feature map size of 256 from the Residual Network 18 (ResNet18). Based on the numerical results conducted on the well-known NSL-KDD dataset and UNSW-NB15 dataset, it illustrates that our method can achieve the accuracy rates of 86.31% and 82.56% in those two test datasets, respectively. Moreover, it can be found that the present algorithm can achieve a better accuracy and performance in the experiments of comparing our method with several existing algorithms proposed in the recent three years. Show more

Keywords: Artificial Intelligence, Classification Algorithms, Deep Learning Algorithms, Network Intrusion Detection, Multi-class Pattern Classification

DOI: 10.3233/JIFS-234402

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5893-5910, 2024

Authors: Sundar, R. | Purushotham Reddy, M. | Sethy, Abhisek | Selvam, K. | Abidin, Shafiqul | Chakrabarti, Prasun | Nagarjuna, Valeti | Ravuri, Ananda | Selvan, P.

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-237948

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5911-5925, 2024

Authors: Wang, Kai | Bai, Yameng

Article Type: Research Article

Abstract: With the rapid development of science and technology, the flow of information has become more convenient, and society has entered the era of knowledge economy. In this era, technological innovation capability is becoming increasingly important and has become an important weapon for enterprises to survive in fierce competition, especially for technology-based small and medium-sized enterprises. Nowadays, technology-based small and medium-sized enterprises have developed many technological innovation achievements through continuous technological innovation, and have created a large number of high-tech products and services. Technological innovation has been proven to effectively improve the core competitiveness and economic benefits of technology-based small and …medium-sized enterprises. Therefore, evaluating the technological innovation capabilities of technology-based small and medium-sized enterprises has both theoretical and practical significance. The enterprise technological innovation capability evaluation from a low carbon perspective could be deemed as the multiple attribute group decision making (MAGDM) problem. Recently, the evaluation based on distance from average solution (EDAS) technique and cosine similarity measure (CSM) technique has been employed to manage MAGDM issues. The spherical fuzzy sets (SFSs) are used as an efficient tool for portraying uncertain information during the enterprise technological innovation capability evaluation from a low carbon perspective. In this paper, the spherical fuzzy number EDAS based on the CSM (SFN-CSM-EDAS) technique is cultivated to manage the MAGDM under SFSs. Finally, a numerical study for enterprise technological innovation capability evaluation from a low carbon perspective is supplied to validate the proposed technique. The main contributions of this paper are outlined: (1) the EDAS and CSM technique was extended to SFSs; (2) the CRITIC technique is used to derive weight based on CSM technique under SFSs. (3) the SFN-CSM-EDAS technique is founded to manage the MAGDM under SFSs; (4) a numerical case study for enterprise technological innovation capability evaluation from a low carbon perspective and some comparative analysis is supplied to validate the SFN-CSM-EDAS technique. Show more

Keywords: Multiple attribute group decision making (MAGDM), spherical fuzzy sets (SFSs), EDAS technique, CRITIC technique, enterprise technological innovation capability evaluation

DOI: 10.3233/JIFS-236778

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5927-5940, 2024

Authors: Yang, Guangfu | Xiao, Chunyun

Article Type: Research Article

Abstract: The employment of college graduates is related to the overall situation of China’s social development, and the difficulty of employment has become a social problem that cannot be ignored. Through the analysis of the current situation of employment, it is found that the lack of employment guidance in colleges and universities and the lack of employment concept of college students are important factors for the difficulty of college students’ employment, and college counselors play an irreplaceable role in college students’ career planning. Based on the characteristics of college counselors’ work, the paper constructs a career planning evaluation system, hoping to …provide new ideas for counselors’ employment guidance. The college students’ career planning evaluation is a multiple attributes group decision making (MAGDM). Recently, the TODIM and GRA technique has been employed to manage MAGDM. The probabilistic hesitant fuzzy sets (PHFSs) are employed as a useful tool for depicting uncertain information during the college students’ career planning evaluation. In this paper, the probabilistic hesitant fuzzy TODIM-GRA (PHF-TODIM-GRA) technique is built to manage the MAGDM under PHFSs. At last, the numerical example for college students’ career planning evaluation is employed to show the PHF-TODIM-GRA technique. The main contribution of this paper is outlined: (1) the TODIM technique based on GRA technique has been extended to PHFSs based on CRITIC technique; (2) the CRITIC technique is employed to derive weight values under PHFSs. (3) the PHF-TODIM-GRA technique is founded to manage the MAGDM under PHFSs; (4) a numerical case study for college students’ career planning evaluation and some comparative analysis is supplied to validate the proposed PHF-TODIM-GRA technique. Show more

Keywords: Multiple attributes group decision making (MAGDM), probabilistic hesitant fuzzy sets (PHFSs), TODIM technique, GRA technique, career planning evaluation

DOI: 10.3233/JIFS-232606

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5941-5956, 2024

Authors: Yin, Bingquan | Ouyang, Shaojuan | Hou, Yali | Ma, Jizhao

Article Type: Research Article

Abstract: Innovation and entrepreneurship education is an important component of cultivating the comprehensive quality of college students and an important force in promoting economic and social development. Meanwhile, due to changes in the social environment and economic structure, traditional university education is no longer able to meet the needs of contemporary society. Therefore, innovation and reform of innovation and entrepreneurship education for college students are urgent. Innovation and entrepreneurship education for college students needs to keep up with the times, constantly update concepts and techniques, in order to adapt to the ever-changing social and economic environment. The innovation and entrepreneurship education …evaluation in the application-oriented vocational colleges is a multiple-attribute decision-making (MADM) problem. Recently, the TODIM and TOPSIS technique has been used to cope with MADM issues. The Type-2 neutrosophic numbers (T2NNs) are employed as a technique for characterizing uncertain information during the innovation and entrepreneurship education evaluation in the application-oriented vocational colleges. In this paper, the Type-2 neutrosophic number TODIM-TOPSIS (T2NN-TODIM-TOPSIS) technique is implemented to solve the MADM under T2NNs. Finally, a numerical case study for innovation and entrepreneurship education evaluation in the application-oriented vocational colleges and several comparative analysis is implemented to validate the proposed T2NN-TODIM-TOPSIS technique. The main research contribution of this paper is managed: (1) the TODIM and TOPSIS technique was enhanced with T2NNs; (2) Entropy technique is enhanced to manage the weight values with T2NNs. (3) the T2NN-TODIM-TOPSIS technique is founded to manage the MADM with T2NNs; (4) Algorithm framework for innovation and entrepreneurship education evaluation in the application-oriented vocational colleges and several comparative analysis are constructed based on one numerical example to verify the effectiveness of the T2NN-TODIM-TOPSIS technique. Show more

Keywords: Multiple-attribute decision-making (MADM), Type-2 neutrosophic numbers (T2NNs), TODIM technique, TOPSIS technique, innovation and entrepreneurship education evaluation

DOI: 10.3233/JIFS-233811

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5957-5973, 2024

Authors: Yu, Zhao | Ye, Ting

Article Type: Research Article

Abstract: The accurate detection of traffic signs is a critical component of self-driving systems, enabling safe and efficient navigation. In the literature, various methods have been investigated for traffic sign detection, among which deep learning-based approaches have demonstrated superior performance compared to other techniques. This paper justifies the widespread adoption of deep learning due to its ability to provide highly accurate results. However, the current research challenge lies in addressing the need for high accuracy rates and real-time processing requirements. In this study, we propose a convolutional neural network based on the YOLOv8 algorithm to overcome the aforementioned research challenge. Our …approach involves generating a custom dataset with diverse traffic sign images, followed by conducting training, validation, and testing sets to ensure the robustness and generalization of the model. Experimental results and performance evaluation demonstrate the effectiveness of the proposed method. Extensive experiments show that our model achieved remarkable accuracy rates in traffic sign detection, meeting the real-time requirements of the input data. Show more

Keywords: Traffic sign detection, deep learning, YOLOv8 model, self-driving cars, real-time processing

DOI: 10.3233/JIFS-235863

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5975-5984, 2024

Authors: Simin, Wang | Yifei, Kang | Yixuan, Xu | Chunmiao, Ma | Jinyu, Wang | Weiguo, Wu

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-231320

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5985-5999, 2024

Authors: Tong, Mingjia

Article Type: Research Article

Abstract: How to explore the potential value of landscape, realize the organic combination of tourism landscape, enrich landscape elements and enhance tourism experience has become an important topic of tourism landscape planning and design, which is also a practical problem that needs to be solved urgently in the process of tourism landscape development and planning in different regions of China. The tourism landscape planning design scheme evaluation based on the virtual reality technology a typical multi-attribute group decision-making (MAGDM) problem. With the complexity of economic activities, uncertain information has an increasing impact on production activities. However, due to the ambiguity and …uncertainty of human cognition, the factors affecting the risk of things cannot be accurately expressed. Therefore, selecting spherical fuzzy sets (SFSs) can make the expression of information more accurate and complete. On basis of the TODIM method and the PROMETHEE method, in this study, spherical fuzzy number TOMIM-PROMETHEE (SFN-TOMIM-PROMETHEE) method is implemented to solve the MAGDM problem under SFSs. Furthermore, CRITIC method under SFSs is implemented to determine relative weights. Then a numerical example for tourism landscape planning design scheme evaluation based on the virtual reality technology is selected to illustrate the effectiveness and practicality of the method. Finally, the comparative analysis shows that the SFN-TOMIM-PROMETHEE method under SFSs is an effective method to deal with MAGDM problems. The main contribution of this paper is managed: (1) the TODIM and PROMETHEE technique was extended to SFSs; (2) CRITIC technique is employed to manage the weight values under SFSs. (3) the SFN-TOMIM-PROMETHEE technique is founded to manage the MAGDM under IVPFSs; (4) a numerical example for tourism landscape planning design scheme evaluation based on the virtual reality technology and comparison analysis are constructed to verify the feasibility and effectiveness of the SFN-TOMIM-PROMETHEE technique. Show more

Keywords: Multi-attribute group decision-making (MAGDM), TODIM-PROMETHEE method, spherical fuzzy sets, CRITIC method, tourism landscape planning design scheme

DOI: 10.3233/JIFS-233401

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6001-6017, 2024

Authors: Tyagi, Pooja | Singh, Jaspreeti | Gosain, Anjana

Article Type: Research Article

Abstract: The contemporary real-world datasets often suffer from the problem of class imbalance as well as high dimensionality. For combating class imbalance, data resampling is a commonly used approach whereas for tackling high dimensionality feature selection is used. The aforesaid problems have been studied extensively as independent problems in the literature but the possible synergy between them is still not clear. This paper studies the effects of addressing both the issues in conjunction by using a combination of resampling and feature selection techniques on binary-class imbalance classification. In particular, the primary goal of this study is to prioritize the sequence or …pipeline of using these techniques and to analyze the performance of the two opposite pipelines that apply feature selection before or after resampling techniques i.e., F + S or S + F. For this, a comprehensive empirical study is carried out by conducting a total of 34,560 tests on 30 publicly available datasets using a combination of 12 resampling techniques for class imbalance and 12 feature selection methods, evaluating the performance on 4 different classifiers. Through the experiments we conclude that there is no specific pipeline that proves better than the other and both the pipelines should be considered for obtaining the best classification results on high dimensional imbalanced data. Additionally, while using Decision Tree (DT) or Random Forest (RF) as base learner the predominance of S + F over F + S is observed whereas in case of Support Vector Machine (SVM) and Logistic Regression (LR), F + S outperforms S + F in most cases. According to the mean ranking obtained from Friedman test the best combination of resampling and feature selection techniques for DT, SVM, LR and RF are SMOTE + RFE (Synthetic Minority Oversampling Technique and Recursive Feature Elimination), Least Absolute Shrinkage and Selection Operator (LASSO) + SMOTE, SMOTE + Embedded feature selection using RF and SMOTE + RFE respectively. Show more

Keywords: Imbalanced data, feature selection, machine learning, oversampling, undersampling

DOI: 10.3233/JIFS-233511

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6019-6040, 2024

Authors: Yuan, Songlin

Article Type: Research Article

Abstract: Since the dawn of the digital web era, web-based learning resources have become more and more significant in the field of education. To a certain extent, the visual communication design of these resources influences how well students learn. In view of this, the study proposes a deep learning-based approach to visual communication design. Convolutional neural networks are introduced to automatically construct the visual communication interface, a recommendation algorithm is used to develop the system’s recommendation function, and machine translation is used to translate the language description text. The study method’s efficacy was evaluated. According to the experimental results, the research …method’s runtime in a color environment was only about 37.7 seconds at 4k resolution; in a non-color environment, the method’s F1 value was 0.87 at a recommended list length of 35, which was higher than that of other methods; and when it came to the interface solutions in real terms, the research method produced 526 at 30 buttons. The aforementioned findings demonstrate that the suggested approach can successfully increase the visual communication’s design speed and performance in online learning materials and offer a suitable answer to the needs of real-world applications. Show more

Keywords: Visual communication design, convolutional neural networks, transformer, learning resources, teacher forcing

DOI: 10.3233/JIFS-233944

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6041-6052, 2024

Authors: Gou, Hongyuan | Zhang, Xianyong

Article Type: Research Article

Abstract: Multi-granularity rough sets facilitate knowledge-based granular computing, and their compromised models (called CMGRSs) outperform classical optimistic and pessimistic models with extremity. Three-level CMGRSs with statistic-optimization-location effectively process hierarchical granularities with attribute enlargements, and they are worth generalizing for general granularities with arbitrary feature subsets. Thus, three-level CMGRSs on knowledge, approximation, and accuracy are established for arbitrary granularities by using three-way decision (3WD). Corresponding 3WD-CMGRSs adopt statistic-optimization-3WD by adding optimistic and pessimistic bounds to the representative location, so they resort to optimal index sets to acquire the multi-granularity equilibrium and decision systematicness. As a result, multiple CMGRSs emerge within the three-level …and three-way framework, they improve the classical MGRSs and enrich 3WD as well as three-level analysis, and exhibit the good simulation, extension, effectiveness, improvement, and generalization. Firstly at the knowledge level, cardinality statistic-optimization improves previous label statistic-optimization for equilibrium realization, so CMGRSs are improved for hierarchical granularities while 3WD-CMGRSs are proposed for arbitrary granularities. Then at the approximation and accuracy levels, measure statistic-optimization determines optimal index sets, so 3WD-CMGRSs are similarly proposed to complete the simulation and extension. Furthermore, mathematical properties and computational algorithms of relevant models are investigated. Finally, three-level 3WD-CMGRSs are illustrated by table examples and are validated by data experiments. Show more

Keywords: Multi-granularity rough sets, compromised models, statistic-optimal equilibrium, three-way decision, three-level analysis

DOI: 10.3233/JIFS-236063

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6053-6081, 2024

Authors: Zhang, Hao | Sheng, Yuhong

Article Type: Research Article

Abstract: In this study, an innovative approach that combines least square support vector regression (LSSVR) with uncertainty theory to enhance its performance in dealing with low-quality or imprecise data from real-world be proposed. The resulting model, called uncertain least square support vector regression (ULSSVR), incorporates chance constraints and simplified parameter selection, which are critical to handle imprecise observations. A numerical algorithm called the conjugate residual method (CR) is introduced to reduce the computational complexity of the model solution. The experimental results using both small and medium-sized datasets demonstrate the superior performance of ULSSVR in terms of prediction accuracy and generalization ability …compared to other models such as uncertain support vector regression (USVR), uncertain linear lodel, uncertain polynomial model, and uncertain growth models. ULSSVR not only improves prediction accuracy by at least 28.49% but also demonstrates faster computational speed. Overall, ULSSVR presents a promising solution for data science and internet applications where dealing with imprecise and low-quality data is a common challenge. Show more

Keywords: Least square support vector regression, uncertainty theory, conjugate residual method, chance constraint

DOI: 10.3233/JIFS-236849

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6083-6092, 2024

Authors: Shi, Lin

Article Type: Research Article

Abstract: With the improvement of the public’s aesthetic level, product appearance has become an important influencing factor for consumers to make purchasing decisions. Product styling design is based on this market demand, combining the aesthetic and functional aspects of the product to create a personalized product appearance, in order to better attract consumers, improve the competitiveness and added value of the product. Usually, product styling design involves multiple elements such as product form, color, proportion, etc. The quality evaluation of product styling design is a MAGDM problems. Recently, the TODIM and EDAS technique has been employed to manage MAGDM issues. The …interval-valued Pythagorean fuzzy sets (IVPFSs) are employed as a tool for characterizing uncertain information during the quality evaluation of product styling design. In this paper, the interval-valued Pythagorean fuzzy TODIM-EDAS (IVPF-TODIM-EDAS) technique is construct to manage the MAGDM under IVPFSs. Finally, a numerical case study for quality evaluation of product styling design is employed to validate the proposed technique. The main contribution of this paper is managed: (1) the TODIM and EDAS technique was extended to IVPFSs; (2) Entropy technique is employed to manage the weight values under IVPFSs. (3) the IVPF-TODIM-EDAS technique is founded to manage the MAGDM under IVPFSs; (4) Algorithm analysis for quality evaluation of product styling design and comparison analysis are constructed based on one numerical example to verify the feasibility and effectiveness of the IVPF-TODIM-EDAS technique. Show more

Keywords: Multiple-attribute group decision-making (MAGDM), Interval-valued Pythagorean fuzzy sets (IVPFSs), TODIM technique, EDAS technique, product styling design

DOI: 10.3233/JIFS-236947

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6093-6108, 2024

Authors: Weng, Shizhou | Huang, Zhengwei | Lv, Yuejin

Article Type: Research Article

Abstract: In the face of increasingly complex data forms and decision-making problems, the uncertainty of information poses a major challenge to multi-attribute decision-making methods. How to effectively organize information and serve realistic decision-making problems has attracted extensive attention in the academic circles. In view of this, based on the distribution law of random variables, we put forward the basic concept of probability numbers and construct a general framework, including the concepts of type, order, item, isomorphism and isomerism, same domain and same distribution of probability numbers. On this basis, we further define the expectation and variance formula of probability numbers, and …its operation rules are defined for the same type of probability numbers. To compare the dominance and inferiority of probability numbers further accurately, we put forward the concepts of dominance degree and comparability degree of probability numbers, so that decision makers can realize the ranking of probability numbers by calculating the comprehensive dominance degree. In view of the related concepts of probability numbers, we summarize the properties and theorems of probability numbers and prove them. In addition, a probability numbers-based multi-attribute decision-making framework model is proposed to solve the multi-attribute decision-making problem. Decision makers can select appropriate sub-models to construct personalized multi-attribute decision-making methods according to actual needs. At the end of the paper, we apply the method to the multi-attribute decision case of campus express stations evaluation and verify the scientificity and rationality of the evaluation method. The concept of probability numbers and its decision model proposed in this paper extend the concept category of numbers, enrich the multi-attribute decision-making method based on probability numbers, and have certain reference significance for further research of uncertain decision theory and method. Show more

Keywords: Probability numbers, calculation rule, dominance degree, ranking method, multi-attribute decision-making

DOI: 10.3233/JIFS-223565

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6109-6132, 2024

Authors: Jiang, Yanping | Tang, Zhenpeng | Song, Xinchao | Shao, Xinran

Article Type: Research Article

Abstract: There has been widespread and growing concern about parking. This paper attempts to provide decision support for a shared parking system to reduce parking difficulty. We study a many-to-many matching problem between shared private idle parking spaces and their demanders. A novelty is that the demanders are allowed to use different parking spaces successively in parking relocation service support. This can further reintegrate the idle time of the parking spaces and improve their utilization rate. A multi-objective optimization model is constructed to maximize the number of matched demanders, the total priority of the parking spaces, and the total priority of …the demanders. More importantly, the priorities of the parking spaces and the demanders are innovatively considered. Each of the parking spaces and the demanders is given a priority for the matching and the priority of a parking space or a demander will be increased if the parking space or demander rarely gets matched successfully. This helps reduce the withdrawal of parking spaces and the demanders from the parking platform. In addition, an NSGA-II algorithm is designed to solve the model efficiently. Finally, the feasibility of the proposed method is illustrated via an example. Show more

Keywords: Sharing economy, shared private idle parking space, many-to-many matching, parking priority, improved NSGA-II algorithm

DOI: 10.3233/JIFS-223789

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6133-6148, 2024

Authors: Wang, Daiwen | Sun, Jie | Wei, Cuiping

Article Type: Research Article

Abstract: Spent lithium-ion battery (LIB) recycling can create great pollution to the environment. Understanding the safety, environment, technique, and regulation factors’ impact on the recycling process is crucial. Due to the complexity of the relevant factors, and there is a certain degree of correlation and dependence between the factors, the Decision Making Trial and Evaluation Laboratory (DEMATEL) method is used to analyze the factors’ degree of impact in this study. As the experts are ambiguous about some relations between the factors, it is impossible to conduct integrated evaluation. The improved DEMATEL method is proposed in this study to make up the …missing relations. Further, the weights of the factors will be calculated. In the improved DEMATEL method, the numerical scale of a linguistic term set is introduced. Therefore, the numerical scale used by experts can not only be uniform and symmetrical, but can also be non-uniform symmetric, non-uniform asymmetric, etc. Finally, both reusing and recycling companies are included in this study and their factors’ importance weights were analyzed with the fuzzy comprehensive evaluation method. Show more

Keywords: Automotive waste lithium-ion battery recycling, numerical scale function, DEMATEL method, fuzzy comprehensive evaluation

DOI: 10.3233/JIFS-224124

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6149-6169, 2024

Authors: Mohan Raj, K.R. | Katiravan, Jeevaa

Article Type: Research Article

Abstract: Recently, security has been necessary in this computer world due to the fast development of technology and enormous user strength. The different kinds of security mechanisms including the Intrusion Detection System (IDS) were developed by many researchers to confirm the security of the data in the communication process. In general, the IDS are used to detect anomalous nodes, and attacks and increase the security level. Even though, the various disadvantages are available to ensure the data reliability on different kinds of applications. For this purpose, this work proposes a cross-layer IDS that is a combination of the trust-based secure routing …method, attribute selection and classification algorithms. This study introduces a novel attribute selection approach known as the Weighted Genetic Feature Selection Algorithm (WGFSA). This method is designed to identify and prioritize valuable attributes within the context of network, physical, and data link layers. And introduce a deep classifier called the Hyperparameter-Tuned Fuzzy Temporal Convolutional Neural Network (HFT-CNN) for efficient categorization. Additionally, we propose a pioneering secure routing algorithm known as the Fuzzy Logic and Time-Constrained Dynamic Trusted Cross-Layer-Based Secure Routing Algorithm (FCSRA) to ensure the secure transmission of data packets. The effectiveness of the newly developed system is proved by conducting experiments with the network, standard Aegean Wi-Fi intrusion dataset (AWID) and proved superior to other systems in delay, energy consumption, packet delivery rate, and prediction accuracy. Show more

Keywords: Fuzzy temporal logic, intrusion detection systems, trust score, cross-layer, deep learning, attribute selection, secure routing

DOI: 10.3233/JIFS-233275

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6171-6183, 2024

Authors: Wei, Ying | Gong, Kaixin | Chen, Chunfang | Zhu, Xianghong

Article Type: Research Article

Abstract: This research proposes a new method to solve group decision-making(GDM) problems with intuitionistic fuzzy preference relations(IFPRs). First, a new definition of multiplicative consistency of IFPR is presented to address the defects of the existing consistency definitions. Then, two programming models are established to obtain the most optimistic and pessimistic consistent IFPRs and corresponding intuitionistic fuzzy priority weights. Also, in order to improve the accuracy of aggregate information, a new method to determine the weights of decision-makers(DMs) is offered by considering the interaction among DMs. Subsequently, by combining the vagueness and non-vagueness of the aggregated information, a multiplicative consistency definition of …the collective IFPR is provided. Moreover, to simplify the GDM process, a programming model for solving the priority weight is established, which effectively avoids the consistency test and correction of IFPRs. Finally, the values of the proposed method are illustrated by comparative analysis. Show more

Keywords: Group decision-making, interaction, risk preference, intuitionistic fuzzy preference relation, consistency

DOI: 10.3233/JIFS-233543

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6185-6199, 2024

Authors: Hou, Xianyu | Chen, Yumin | Wu, Keshou | Zhou, Ying | Lu, Junwen | Weng, Xuan

Article Type: Research Article

Abstract: Neighborhood granulation is a classical granulation method. Although it is adequate for clustering and classification tasks, its granules are more complex, and the data representation is binary. This paper proposes a new granulation method based on the neighborhood granulation. Firstly, a detailed definition of the granular form is given with fuzzy rough set theory. Then, a modified fuzzy rough discriminant function is proposed based on neighborhood systems. The samples are globally granulated on single features to construct granules and on multiple features to construct granular vectors. Also, a feature selection technique based on the Chi-square, which strikingly reduces the complexity …of the fuzzy rough granular vectors, is introduced to address the disadvantage of the fuzzy rough granular vectors. An ensemble model structure is also proposed in the paper for the mixed nature of fuzzy rough granular vectors. The paper makes a detailed comparison between the fuzzy rough granulation and the neighborhood granulation. The results show that fuzzy rough granulation has higher computational efficiency and classification performance. Finally, a detailed comparison is made between the fuzzy rough granular ensemble model and various classical ensemble algorithms. The final results show that the fuzzy rough granular ensemble model has better robustness and generalization. Show more

Keywords: Granular computing, fuzzy rough granulation, neighborhood granulation, granular ensemble learning, granular selection

DOI: 10.3233/JIFS-234510

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6201-6217, 2024

Authors: Sun, Hong | Zhang, Xianyong

Article Type: Research Article

Abstract: Z-numbers contain fuzzy restrictions, credibility measures, and probability distributions to effectively represent uncertain information. Converting Z-numbers to fuzzy numbers facilitates extensive applications (such as multi-attribute decision-making (MADM)), thus becoming valuable for research purposes. Regarding Z-number conversions, the original method never considers the association probability, while probabilistic strategies offer better informatization. Recently, a probability-driven conversion starts with a linear transformation of the centroid difference between the fuzzy restriction and probabilistic distribution. However, it has the invalidation weakness of edge information due to underlying non-normalization. To improve this probability-linear conversion, a Z-number conversion is proposed by using underlying probability-exponential descriptions, and this …new method is further applied to MADM. At first, the current probability-linear conversion is analyzed based on the initial non-probabilistic conversion, and its intrinsic weakness and correctional improvement are revealed. Then, the novel probability-exponential conversion resorts to an exponential characterization of centroid difference between the restriction and distribution, and it gains information enrichment due to underlying normalization. The refined method preserves the inherent characteristics of Z-numbers more effectively, facilitating their application in subsequent engineering practices. This is especially pertinent in decision-making systems based on expert input and initial value problems. The proposed method for converting Z-numbers aims to minimize information loss in transitions between Z-numbers and classical fuzzy numbers. This approach will be further explored in future research. Furthermore, the probability-exponential conversion induces an ExpTODIM algorithm for MADM, called PE-ExpTODIM. Three Z-number conversions (i.e., the non-probabilistic, probability-linear, and probability-exponential types) and three decision algorithms (i.e., ExpTODIM, EDAS, MOORA) are combined to establish a 3 × 3 framework of Z-number-driven MADM. Finally, the systematical 9 algorithms are applied to the problem of site selection of carbon storage. They are validated by criss-cross contrast analyses and statistical significance tests. Thus, PE-ExpTODIM exhibits the desired optimization. The last technology of statistical testing is original, ingenious, and valuable for MADM. Show more

Keywords: Z-numbers, fuzzy numbers, probability-exponential conversion, multi-attribute decision making, ExpTODIM/EDAS/MOORA

DOI: 10.3233/JIFS-235304

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6219-6233, 2024

Authors: Kavitha, D. | Radha, V.

Article Type: Research Article

Abstract: This work proposes an unique hardware design for a multi-line Refreshable Braille Display (RBD) device using Imprint Punch Head Technology with Optical Character Recognition (OCR) capabilities for learning and reading text in Braille codes. The device uses a microprocessor board to seamlessly integrate stepper/servo motors with OCR algorithms. A thin flexible metal sheet coated with rubber is used as a display surface on which the raised points for Braille codes are repeatedly formed and deformed. The device is designed in such a way that the material used for its construction are low cost which makes them economical and affordable. The …device was evaluated in the lab setup and showed promising results, and had prospects of becoming a vital Assistive Technology for vision impaired people. Show more

Keywords: OCR, refreshable braille display, raspberry pi, text detection

DOI: 10.3233/JIFS-236527

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6235-6248, 2024

Authors: Li, Heng

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-237196

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6249-6263, 2024

Authors: Li, Bo | Lu, TongWei | Min, Feng

Article Type: Research Article

Abstract: 3D point cloud has irregularity and disorder, which pose challenges for point cloud analysis. In the past, the projection or point cloud voxelization methods often used were insufficient in accuracy and speed. In recent years, the methods using Transformer in the NLP field or ResNet in the deep learning field have shown promising results. This article expands these ideas and introduces a novel approach. This paper designs a model AaDR-PointCloud that combines self-attention blocks and deep residual point blocks and operates iteratively to extract point cloud information. The self-attention blocks used in the model are particularly suitable for point cloud …processing because of their order independence. The deep residual point blocks used provide the expression of depth features. The model performs point cloud classification and segmentation tests on two shape classification datasets and an object part segmentation dataset, achieving higher accuracy on these benchmarks. Show more

Keywords: PointCloud, transformer, ResNet, point cloud classification, point cloud part segmentation

DOI: 10.3233/JIFS-231997

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6265-6277, 2024

Authors: Yu, Huan

Article Type: Research Article

Abstract: With the acceleration of urbanization and the significant improvement of people’s living standards, the motorization of urban transportation in China has developed rapidly, and the number of urban motor vehicles has sharply increased. This has also caused a series of problems such as increasingly severe urban road traffic congestion, increased traffic energy consumption, and atmospheric environmental pollution. Unprecedented social and environmental pressures have put forward higher requirements for the development model of urban transportation. Against the backdrop of increasingly severe conflicts between urban transportation and resource environment in China, green transportation with the goal of “meeting maximum demand with minimum …consumption” has gradually received widespread attention from the academic community. The urban green transportation development level evaluation is a classical multiple attribute decision making (MADM). In this paper, we define the triangular Pythagorean fuzzy sets (TPFSs) and investigate the MADM problems under TPFSs. Based on the traditional geometric BM (GBM) operator and generalized weighted GBM (GWGBM) operator, some triangular Pythagorean fuzzy operators are proposed: triangular Pythagorean fuzzy generalized GBM (TPFGGBM) operator and triangular Pythagorean fuzzy generalized WGBM (TPFGWGBM) operator. Accordingly, we have took advantage of these operators to develop some approaches to work out the triangular Pythagorean fuzzy MADM. Ultimately, a practical example for urban green transportation development level evaluation is took advantage of to validate the developed approach, and an influence analysis of the parameter on the final results is been presented to attest its availability and validity. Show more

Keywords: Multiple attribute decision making (MADM), Triangular Pythagorean fuzzy set, geometric BM (GBM) operator, Triangular Pythagorean fuzzy generalized WGBM (TPFGWGBM), green transportation development level

DOI: 10.3233/JIFS-232579

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6279-6297, 2024

Authors: Bolourchi, Pouya | Gholami, Mohammadreza

Article Type: Research Article

Abstract: Alzheimer’s disease (AD) is the most prevalent brain disorder which affects millions of people worldwide. Early detection is crucial for possible treatment. In this regard, machine learning (ML) approaches are widely utilized for AD detection. In this paper, we propose an ML-based method that drastically reduces the dimensionality of features while maintaining the relevant features and boosting the overall performance. To remove irrelevant features, first statistical feature extraction method is applied, and then further reduction among remaining features is applied by utilizing the harmony search method (HSM). The selected features are the most informative features that are fed to the …different classifiers. To test the effectiveness of the proposed method, we deployed three classification techniques including support vector machine (SVM), k -nearest neighbor (k -NN), and decision tree (DT). The experimental results show that the proposed method has a higher performance while decreasing the dimensionality of feature space. To guarantee that the performance of the proposed method is accurate, we applied an ensemble of three classifiers (SVM, KNN, and DT) for classification. The results of the proposed method verify that this method can be successfully deployed for AD detection, due to its high performance and low dimensional features, and can help improve the accuracy and efficiency of Alzheimer’s disease diagnosis. The proposed method demonstrated a significant improvement, achieving high performance in AD/HC classification, with accuracy, sensitivity, specificity, F1 -score, MCC , and Cohen’s Kappa rates reaching 95.5%, 97%, 94%, 95.56%, 0.9104, and 0.9109, respectively. AD/HC classification displayed the highest performance. Additionally, in the more challenging pMCI/sMCI classification, the method achieved an accuracy of 78.50%, sensitivity of 84.00%, specificity of 73.00%, F1 -score of 79.62%, MCC of 0.57, and Cohen’s Kappa of 0.59. Show more

Keywords: Alzheimer’s disease, ensemble of classifiers, harmony search, statistical feature extraction, sMRI

DOI: 10.3233/JIFS-233000

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6299-6312, 2024