Journal of Intelligent & Fuzzy Systems - Volume 45, issue 2

Authors: Jin, Feifei | Zhu, Yajun | Zhang, Yixiao | Guo, Shuyan | Liu, Jinpei | Zhou, Ligang

Article Type: Research Article

Abstract: Interval type-2 trapezoidal fuzzy (IT2TrF) number is a powerful tool to depict fuzzy information. Information measures methods have received more and more attention in recent years as they play an important role in decision-making theory. A new multi-attribute decision-making (MADM) method supported by IT2TrF information measures is investigated in this paper under the IT2TrF information environment. Firstly, three axiomatic definitions of IT2TrF information measures are introduced, which include information entropy, similarity measure and cross-entropy. Secondly, with the help of the exponential function, we formulate some information measure formulas, which are followed by the proofs that the exponential entropy, exponential similarity …measure and exponential cross-entropy fit the three axiomatic definitions. Subsequently, a novel IT2TrF MADM method is designed, in which the IT2TrF exponential entropy and cross-entropy are utilized to generate the attribute weights, the IT2TrF exponential similarity measure is employed to obtain the closeness degree of the ideal solution and derive the most satisfying solution. Lastly, we provide a numerical example of corporate investment to demonstrate the applicability and feasibility of the proposed MADM method. The robustness and merits of the developed MADM method are highlighted by the comparative analysis. Show more

Keywords: Multi-attribute decision-making, interval type-2 trapezoidal fuzzy numbers, information measures, exponential function

DOI: 10.3233/JIFS-230310

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2319-2330, 2023

Authors: Yuan, Yuxia | Zhang, Yachao

Article Type: Research Article

Abstract: Background: Image semantic segmentation can be understood as the allocation of a predefined category label to each pixel in the image to achieve the region segmentation of the image. Different categories in the image are identified with different colors. While achieving pixel classification, the position information of pixel points of different categories in the image is retained. Purpose: Due to the influence of background and complex environment, the traditional semantic segmentation methods have low accuracy. To alleviate the above problems, this paper proposes a new real-time image semantic segmentation framework based on a lightweight deep convolutional encoder-decoder architecture …for robotic environment sensing. Methodology: This new framework is divided into three stages: encoding stage, decoding stage and dimension reduction stage. In the coding stage, a cross-layer feature map fusion (CLFMF) method is proposed to improve the effect of feature extraction. In the decoding stage, a new lightweight decoder (LD) structure is designed to reduce the number of convolutional layers to speed up model training and prediction. In the dimension reduction stage, the convolution dimension reduction method (CDR) is presented to connect the encoder and decoder layer by layer to enhance the decoder effect. Results: Compared with other state-of-the-art image semantic segmentation methods, we conduct comparison experiments on datasets Cityscapes, SUN RGB-D, CamVid, KITTI. The Category iIoU combined with the proposed method is more than 70%, and the Category IoU is as high as 89.7%. Conclusion: The results reflect that the new method can achieve the better semantic segmentation effect. Show more

Keywords: Image semantic segmentation, lightweight deep convolutional encoder-decoder architecture, cross-layer feature map fusion, convolution dimension reduction method, robotic environment sensing

DOI: 10.3233/JIFS-222221

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2331-2345, 2023

Authors: Hu, Zhexian

Article Type: Research Article

Abstract: The motivation for this paper is to consider that in recent years, the concept of metaverse, as the latest and most popular concept in the world, has been widely applied and studied in various industries, including economic management, art design, education and teaching. However, the academic and scientific circles have not reached a consensus on whether to define the metaverse as a technology or an intelligent scene. We believe that the metaverse should be a key concept and emerging theory for constructing the future wisdom field. Therefore, in this study, our research objective is to focus on the visual art …evaluation in digital works, and propose a visual art quality evaluation method in future metaverse digital works. This method is based on the quality function deployment theory and fuzzy mathematics theory in marketing. The second core point of this study is to build a field framework for the visual art evaluation of future digital works based on the metaverse by combing the current international and domestic understanding of the concept of metaverse. In addition, taking visual art quality evaluation as the research object, we have constructed a visual art quality evaluation index system for digital works under the background of metaverse. The index system is composed of one first-class index, three second-class indexes and nine third-class indexes. At the same time, we proposed a new fuzzy mathematics evaluation method in the research, called G1 entropy method. This algorithm combines subjective weighting method: G1 method and objective weighting method: entropy method as an important method of quality evaluation, and carries out the final rating through the combination weight of G1 entropy method. This study makes up for the concept of the future metaverse, introduces the gaps in the theory of visual art evaluation of future digital works, innovates the analysis of new concepts and the improvement of old methods, builds a new scene of organic combination of new technology and traditional visual art, and provides new ideas for the improvement of art quality at home and abroad in the future. In general, we sorted out the contributions of this research, including the following three aspects: (1) we constructed the metaverse field structure of digital works. By analyzing the current international and domestic research literature on the application of metaverse technology, especially the concept of metaverse in art scenes, we proposed to construct the field structure of online visual art after introducing the concept of metaverse, including blockchain technology, artificial intelligence technology Interaction technology and Internet of things technology as the four characteristics; (2) Method theoretical contribution: we further take the visual art quality evaluation as the research object, construct the index system of visual art quality evaluation of digital works under the background of metaverse, and propose an evaluation method of G1 entropy method, which is actually a method of subjective weighting by experts; (3) We use the method proposed in (2) to complete the calculation and ranking of the importance of 9 indicators in a practical case, and give some countermeasures for the calculation results of the importance of indicators. In conclusion, this study has realized the construction of new application scenarios of concepts and the new improvement of methods, and can provide theoretical and practical case experience support for the quality improvement of international and domestic metaverse visual art. Show more

Keywords: Metaverse, visual art, field architecture, quality function deployment, G1 entropy method

DOI: 10.3233/JIFS-223376

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2347-2365, 2023

Authors: Davvaz, Bijan | Chinram, Ronnason | Lekkoksung, Somsak | Lekkoksung, Nareupanat

Article Type: Research Article

Abstract: Ideals play an essential part in studying ordered semigroups. There are several generalizations of ideals that are used to investigate ordered semigroups. It is known that (m , n )-ideals are an abstraction of bi-ideals, and n -interior ideals are an abstraction of interior ideals. This paper introduces a generality of (m , n )-ideals and n -interior ideals, so-called (α, β)-fuzzy (m , n )-ideals and (α, β)-fuzzy n -interior ideals. Furthermore, we discuss our current notions with those that already exist. We examine connections between (m , n )- (resp., n -interior) ideals and (α, β)-fuzzy (m , …n )- (resp., n -interior) ideals. A characterization of (α, β)-fuzzy (m , n )- (resp., n -interior) ideals, by a particular product, in ordered semigroups is provided. We demonstrate that our results generalize the known results through specific settings. Show more

Keywords: Ordered semigroup, (α, β)-fuzzy (m, n)-ideal, (α, β)-fuzzy n-ideal, bi-ideal, interior-ideal

DOI: 10.3233/JIFS-224255

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2367-2380, 2023

Authors: Zhou, Bin | Chen, Jieshi | Zhang, Yang | Yang, Shanglei | Lu, Hao

Article Type: Research Article

Abstract: In the laser spiral welding (LSW) process, the welding parameters have a significant impact on the weld quality. In this paper, experiments were conducted and experimental data were collected on galvanized steel sheets using the LSW process, and mathematical models were developed using response surface methodology (RSM) and genetic algorithm (GA) to verify the specific effects of each process parameter on the weld and to perform process optimization. Laser power, welding speed, gap and focal length were selected as the influencing factors, and melt depth, melt width and concave as the output results. In the RSM model we found that …the laser power was positively correlated with the weld depth and width, while the welding speed was inversely correlated with the weld depth and width, the gap was positively correlated with the amount of concave, and the focal length had no significant effect on the weld. In the GA model we use a large amount of experimental data for BP neural network training and iterative optimization using a genetic algorithm. Validation experiments were conducted on two models, and the results indicated that the two models had higher accuracy in predicting the welding depth and width compared to predicting the concave. The GA model had an 8% increase in tensile strength and a 25% increase in plasticity of the weld joint obtained from the optimal process compared to the RSM model. The GA model has higher accuracy in optimizing the LSW process. Show more

Keywords: Laser spiral welding, response surface methodology, genetic algorithm, process optimization, mechanical property

DOI: 10.3233/JIFS-224448

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2381-2392, 2023

Authors: Deng, Guannan | Zhang, Mei | Meng, Xiangqi | Yuan, Jiaming

Article Type: Research Article

Abstract: In this paper, we establish the matching relation between implication operator and aggregation operator, which provides a new solution for the design and construction of multi-rule fuzzy inference system. Firstly, according to the definition and monotonicity of implication operator, a new classification method of implication operator is proposed, and then the fuzzy inference process using different implication operators is classified. Then, dynamic maximum aggregation operator and dynamic minimum aggregation operator are proposed. Based on the compositional rule of inference (CRI) method, a matching method and basis of implication operator and aggregation operator for fuzzy inference systems is given and illustrated …with examples. Finally, the applicability of the proposed method in this paper is further illustrated by comparing the method with existing methods in the literature and using the nearness degree as an evaluation index. Show more

Keywords: Multi-rule fuzzy inference systems, classification of fuzzy implication, aggregation operators, nearness degree

DOI: 10.3233/JIFS-230866

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2393-2408, 2023

Authors: Shi, Bo | Lv, Bin

Article Type: Research Article

Abstract: To improve the recognition accuracy of badminton players’ swing movements, this study proposes a single inertial sensor based method for badminton swing movement recognition. This article proposes a badminton racket-mounted data gathering system with a single inertial sensor and proposes a real-time motion data flow-based window segmentation technique to capture motion data. On this basis, a two-layer classifier recognition model based on C4.5 Decision Tree (C4.5 T) algorithm and Random Forest (RF) method is constructed to recognize swing technical actions. Using the C4.5 T to identify the swing style of athletes; The RF method is used to recognize the swing …technical action. The final experiment showed that the method studied achieved a recognition accuracy of 95.36% for six common swing movements. The proposed model has good application prospects in the recognition of badminton swing movements. However, due to the limitations of the experimental conditions, the recognition effect of this method on more complex swing movements needs to be further verified. Show more

Keywords: Single inertial sensor, the swinging movement of badminton, action recognition, random forest

DOI: 10.3233/JIFS-231409

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2409-2418, 2023

Authors: Huang, Jingcao | Guo, Bin | Dian, Songyi

Article Type: Research Article

Abstract: Hydropower station is vital for the stable growth of the national economy. How to timely warn the possible faults of hydropower stations has become an increasingly popular research topic. The traditional detection model is difficult to detect the small abnormal changes in the data, and these abnormal changes are often the precursor of faults. To improve the sensitivity of the traditional detection model, this study introduced a weight factor into the traditional LSTM detection model. By using the correction mechanism, the LSTM correction model makes the prediction model never deviate from the normal track following the appearance of abnormal data. …This ensures that the model can generate large residuals after abnormal data occur so that we can detect these abnormal data in time. Finally, this paper puts forward two factors related to equipment health and integrates these two factors to form a health index. The results show that the LSTM correction model based on the health index can not only detect small changes that cannot be detected by traditional detection models but also knows the wear and tear of equipment during operation based on the changes in health indicators. Show more

Keywords: Hydropower station, LSTM, correction mechanism, anomaly detection, health factors

DOI: 10.3233/JIFS-223461

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2419-2436, 2023

Authors: Yu, Ping | Wang, Haotian | Cao, Jie

Article Type: Research Article

Abstract: In order to address the timing problem, invalid data problem and deep feature extraction problem in the current deep learning based aero-engine remaining life prediction, a remaining life prediction method based on time-series residual neural networks is proposed. This method uses a combination of temporal feature extraction layer and deep feature extraction layer to build the network model. First, the temporal feature extraction layer with multi-head structure is used to extract rich temporal features; then, the spatial attention mechanism is applied to improve the weights of important data; finally, the deep feature extraction layer is used to process the deep …features of the data. To verify the effectiveness of the proposed method, experiments are conducted on the C-MAPSS dataset provided by NASA. The experimental results show that the method proposed in this paper can make accurate predictions of the remaining service life under different sub-datasets and has outstanding performance advantages in comparison with other outstanding networks. Show more

Keywords: Time sequential resnet, temporal feature extraction layer, spatial attention module, deep feature extraction layer, remaining useful life Introduction

DOI: 10.3233/JIFS-223971

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2437-2448, 2023

Authors: Ramasamy, Uma | Santhoshkumar, Sundar

Article Type: Research Article

Abstract: A machine learning model intends to produce a secure model with low bias and variance. Finding the optimal machine learning model for a dataset is a challenging task. A suitable machine learning model is yet to be specified for the Arthritis Profile Data dataset. Autoimmune disease is widely spread all over the world. Some autoimmune arthritis diseases are Rheumatoid Arthritis, Psoriatic Arthritis, Juvenile Arthritis, etc. These diseases come under both categories autoimmune and inflammatory diseases. The proposed work is designed to suggest the best machine learning model with the highest observed accuracy for the Arthritis Profile Data. Many authors do …not compare newly created datasets with previously used datasets. This can lead to inaccurate results due to the lack of reliable comparison. Additionally, it can prevent researchers from detecting potential bias in the data. Comparing datasets can help to identify and address any potential issues and improve the accuracy of the results. It is important to review existing datasets before beginning a new project to ensure the accuracy of the results. This article is the first study on the topic that analysis the accuracy behavior of each machine learning model concerning the Arthritis Profile Data and various benchmark disease datasets with different hold-out and k-fold cross-validation methods. The study concludes with a glimpse of whether dataset and feature size affect model prediction accuracy and proffers a machine learning model for the Arthritis Profile Data. The proposed research explores base learning classification algorithms and ensemble methods such as Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Random Forest, and Extreme Gradient Boosting from machine learning. Our empirical evidence clearly states XGBoost ensemble technique shows the highest accuracy for the Arthritis Profile Data. Show more

Keywords: Bias, variance, hold-out, cross-validation, autoimmune arthritis disease, machine learning, ensemble method

DOI: 10.3233/JIFS-224115

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2449-2463, 2023

Authors: Guo, Wei | Zhang, Chuchen

Article Type: Research Article

Abstract: The expansive growth of information on the Internet has led to new developments in computer vision technology and image processing techniques. Since stone inscriptions are subject to erosion and polishing by the external environment for years, it is difficult to extract image and text information. In this study, the fuzzy control theory is combined with edge detection technology for image edge detection. Firstly, a suitable fuzzy rule and affiliation function are set, then a fuzzy control system is used to extract and detect the image edge information, and then a fuzzy logic rule-based edge detection algorithm is proposed to detect …the inscription images. To test the performance of the algorithm, the detection effect of the image is first analyzed from a subjective perspective. The experimental results show that the proposed algorithm has better edge detection for both inscription and lena images, with better noise suppression without excessive distortion, and clearer inscription images. The proposed algorithm has the lowest MSE value of 41.26 when the detection object is the lena image b, and the highest PSNR value of 33.84 when the detection object is the lena image a. The proposed algorithm has the lowest MSE value of 41.26 when the detection object is the lena image b, and the highest PSNR value of 41.26 when the detection object is the lena image b. The proposed algorithm has the highest PSNR value of 33.84 when the detection object is the lena image b. In summary, the analysis of both subjective and objective indicators shows that the inscription image processing algorithm used in this paper has better processing effect, and the processed images become clearer with less distortion, which is helpful for both inscription image and text extraction. Show more

Keywords: Fuzzy logic, inscription picture, EA, picture processing technology

DOI: 10.3233/JIFS-230218

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2465-2475, 2023

Authors: Yang, Long-Hao | Ye, Fei-Fei | Wang, Ying-Ming | Huang, Yan | Hu, Haibo

Article Type: Research Article

Abstract: Performance evaluation is one of the most important standards to measure the competitiveness and productivity of enterprises. Although existing studies could obtain the specific values of enterprises performance based on historical data, they usually failed to effectively evaluate enterprises performance in the consideration of different indicators. Meanwhile, as the characteristics of existing performance evaluation models are uneven, how to choose a reasonable data envelopment analysis (DEA) model for enterprises performance evaluation must be considered. Therefore, a new ensemble model on the basis of homogeneous, heterogeneous, and hybrid efficiency evaluation together with the evidential reasoning (ER) approach is proposed in this …study for enterprises performance evaluation, so called the ER-based ensemble model. The ER-based ensemble model can overcome the inconsistency results caused by the application of different indicators and different DEA models. In case study, 40 state-own holding enterprises in China are selected and all these enterprises are evaluated and ranked using the integrated efficiency obtained from the ER-based ensemble model. Comparative analysis demonstrates that the ER-based model is better than some traditional efficiency evaluation models in enterprises performance evaluation and performance ranking. Show more

Keywords: Data envelopment analysis, efficiency evaluation, efficiency ensemble, enterprise performance, evidential reasoning

DOI: 10.3233/JIFS-230247

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2477-2495, 2023

Authors: Zhou, Jiaqi | Wu, Tingming | Yu, Xiaobing | Wang, Xuming

Article Type: Research Article

Abstract: Accurate and reliable prediction of PM2.5 concentrations is the basis for appropriate warning measures, and a single prediction model is often ineffective. In this paper, we propose a novel decomposition-and-ensemble model to predict the concentration of PM2.5 . The model utilizes Ensemble Empirical Mode Decomposition (EEMD) to decompose PM2.5 series, Support Vector Regression (SVR) to predict each Intrinsic Mode Function (IMF), and a hybrid algorithm based on Differential Evolution (DE) and Grey Wolf Optimizer (GWO) to optimize SVR parameters. The proposed prediction model EEMD-SVR-DEGWO is employed to forecast the concentration of PM2.5 in Guangzhou, Wuhan, and Chongqing of …China. Compared with six prediction models, the proposed EEMD-SVR-DEGWO is a reliable predictor and has achieved competitive results. Show more

Keywords: PM2.5, prediction, decomposition-and-ensemble, support vector regression

DOI: 10.3233/JIFS-230343

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2497-2512, 2023

Authors: Yi, Weiguo | Ma, Bin | Zhang, Heng | Ma, Siwei

Article Type: Research Article

Abstract:  Compared with other traditional community discovery algorithms, density peak clustering algorithm is more efficient in getting network structures through clustering. However, DPC needs to contain the distance information between all nodes as sources, so it cannot directly processing the complex network represented by the adjacency matrix. DPC introduces truncation distance when calculating the local density of nodes, which is usually set as a fixed value according to experience, and lacks self-adaptability for different network structures. A feasible solution to those problems is to combined rough set theory and kernel fuzzy similarity measures. In this work, we present overlapping community detection …algorithm based on improved rough entropy fusion density peak. The algorithm applied rough set theory to attribute reduction of massive high-dimensional data. Another algorithm defines the similarity of sample points by the inner product between two vectors on the basis of fuzzy partition matrix. Finally, a community detection algorithm based on rough entropy and kernel fuzzy density peaks clustering (CDRKD) has proposed by combining the two algorithms above, we perform an extensive set of experiments to verify the effectiveness and feasibility of the algorithm. Show more

Keywords: Overlapping community detection, rough neighborhood mutual information entropy, density peaks clustering, kernel fuzzy similarity measure

DOI: 10.3233/JIFS-230614

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2513-2527, 2023

Authors: Xu, Li | Bai, Jinniu

Article Type: Research Article

Abstract: Brain cancer is one of the most deadly forms of cancer today, and its timely and accurate diagnosis can significantly impact the patient’s quality of life. A computerized tomography scan (CT) and magnetic resonance imaging (MRI) of the brain is required to diagnose this condition. In the past, several methods have been proposed as a means of diagnosing brain tumors through the use of medical images. However, due to the similarity between tumor tissue and other brain tissues, these methods have not proven to be accurate. A novel method for diagnosing brain tumors using MRI and CT scan images is …presented in this paper. An architecture based on deep learning is used to extract the distinguishing characteristics of brain tissue from tumors. The use of fusion images allows for more accurate detection of tumor types. In comparison with other approaches, the proposed method has demonstrated superior results. Show more

Keywords: Deep learning, brain tumor, visual geometry group, CT scan, MRI images

DOI: 10.3233/JIFS-230850

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2529-2536, 2023

Authors: Garg, Harish | Ünver, Mehmet | Aydoğan, Büşra | Olgun, Murat

Article Type: Research Article

Abstract: As an extension of the concepts of fuzzy set and intuitionistic fuzzy set, the concept of Pythagorean fuzzy set better models some real life problems. Distance, entropy, and similarity measures between Pythagorean fuzzy sets play important roles in decision making. In this paper, we give a new entropy measure for Pythagorean fuzzy sets via the Sugeno integral that uses fuzzy measures to model the interaction between criteria. Moreover, we provide a theoretical approach to construct a similarity measure based on entropies. Combining this theoretical approach with the proposed entropy, we define a distance measure that considers the interaction between criteria. …Finally, using the proposed distance measure, we provide an extended Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for multi-criteria decision making and apply the proposed technique to a real life problem from the literature. Finally, a comparative analysis is conducted to compare the results of this paper with those of previous studies in the literature. Show more

Keywords: Pythagorean fuzzy set, entropy measure, distance measure, extended TOPSIS, medical diagnosis

DOI: 10.3233/JIFS-231454

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2537-2549, 2023

Authors: Wang, Tengfei | Shi, Peng

Article Type: Research Article

Abstract: In this paper, the problems of expressing and fusing multi-channel uncertain digital information is studied. The concept of a special high-dimensional fuzzy number called multi-level linear fuzzy ellipsoid number is given, and a method of constructing such high dimensional fuzzy number to express multi-channel uncertain digital information is established. Then a calculation formula of the centroid of multistage linear fuzzy ellipsoid number is deduced. And then, as an application example of multi-channel uncertain digital information fusion, a specific example is given to show ranking some objects which are characterized by multi-channel uncertain digital information by using the obtained results and …the concept of fuzzy order on high dimensional fuzzy number space. Show more

Keywords: Fuzzy numbers, fuzzy ellipsoid numbers, multistage linear fuzzy ellipsoid number, constructing fuzzy numbers, expressing multi-channel uncertain digital information, multi-channel uncertain digital information fusion

DOI: 10.3233/JIFS-222761

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2551-2563, 2023

Authors: Memon, Abdul Sami | Laghari, J.A. | Bhayo, Muhammad Akram | Khokhar, Suhail | Chandio, Sadullah | Memon, Muhammad Saleem

Article Type: Research Article

Abstract: In the modern power system, the use of renewable energy sources is increasing rapidly, which makes the system more sensitive. Therefore, it requires effective controllers to operate within the allowable ranges. The existing techniques based on cascaded controllers implemented so far for load frequency control have the advantage of improving the system response. However, this makes the system a more complex and time-consuming process. This makes the system more straightforward, makes it easy to optimize PID parameters, and provides results in acceptable ranges. This paper attempts to solve the load frequency control (LFC) problem in an interconnected hybrid power system …with a classical PID controller employing the tunicate swarm algorithm (TSA). This algorithm is used for two areas of an interconnected hybrid power system: thermal, hydro, nuclear, and wind. The PID controller parameters are optimized by tunicate swarm algorithm using integral time absolute error (ITAE) based objective function. To show the robustness of the proposed TSA algorithm, a sensitivity analysis is performed for four case studies ranging from 20% to 30% load increments and decrements. The performance of the proposed TSA algorithm has been compared with the well-known optimization algorithms, particle swarm optimization (PSO), artificial bee colony (ABC), and arithmetic optimization algorithm (AOA) in terms of overshoot, undershoot, and settling time. The simulation results show that the proposed TSA has better optimization capability than PSO, ABC, and AOA in terms of overshoot, undershoot, and settling time. Show more

Keywords: Tunicate Swarm based Automatic generation control, hybrid power system, TSA based Optimized PID controller, Interconnected power system, multi-area power system.

DOI: 10.3233/JIFS-223227

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2565-2578, 2023

Authors: Leng, Hongyong | Shao, Jinxin | Zhang, Zhe | Qian, Yurong | Ma, Mengnan | Li, Zichen

Article Type: Research Article

Abstract: To address the problem that single-channel neural networks cannot fully extract text semantic features in traditional user portrait construction methods, this paper proposes a dual-channel user portrait model based on DPCNN-BIGRU and attention mechanism. The model first uses Bidirectional Encoder Representation from Transformers(Bert) and CK-means+ to obtain the fusion vector of semantic features and topic features, and then feeds the vector into Deep Pyramid Convolutional Neural Networks (DPCNN) and Bidirectional Gated Recurrent Unit (BiGRU). Deep features and global features of the text are obtained simultaneously, and then weights are assigned by the attention mechanism. Finally, the output features of the …dual channels are fused and classified. It is tested on the Sogou user portrait datasets, and the experimental results prove that the dual-channel model outperforms the baseline model. Show more

Keywords: User profile, BERT, canopy, K-means, text classification

DOI: 10.3233/JIFS-224532

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2579-2591, 2023

Authors: Fathy, E. | Ammar, E. | Helmy, M.A.

Article Type: Research Article

Abstract: Due to the importance of the multi-level fully rough interval linear programming (MLFRILP) problem to address a wide range of management and optimization challenges in practical applications, such as policymaking, supply chain management, energy management, and so on, few researchers have specifically discussed this point. This paper presents an easy and systematic roadmap of studies of the currently available literature on rough multi-level programming problems and improvements related to group procedures in seven basic categories for future researchers and also introduces the concept of multi-level fully rough interval optimization. We start remodeling the problem into its sixteen crisp linear programming …LP problems using the interval method and slice sum method. All crisp LPs can be reduced to four crisp LPs. In addition, three different optimization techniques were used to solve the complex multi-level linear programming issues. A numerical example is also provided to further clarify each strategy. Finally, we have a comparison of the methods used for solving the MLFRILP problem. Show more

Keywords: Constraint method, interval arithmetic, interactive approach, fuzzy approach, rough interval programming, slice sum method

DOI: 10.3233/JIFS-230057

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2593-2610, 2023

Authors: Zhang, Zhaojun | Lu, Jiawei | Xu, Zhaoxiong | Xu, Tao

Article Type: Research Article

Abstract: To solve the problems of the ant colony optimization (ACO), such as slow convergence speed, easy to fall into local extremum and deadlock in path planning, this paper proposed an improved ACO, which was hybridized by PSO based on logistic chaotic mapping, called hybrid ant colony optimization (HACO). According to the number of obstacles around the next feasible node, HACO distributes the initial pheromones unevenly to avoid the ant getting stuck in deadlock. According to the orientation of the next node selected by the ant, the heuristic information is adaptively adjusted to guide the ant to the direction of the …target position. When updating the pheromone, the local and global search mechanism of the particle swarm optimization is used to improve the pheromone update rule and accelerate convergence speed. Finally, the grid method is used to construct the environment map, and simulation experiments are conducted in different environments. The experimental results verify the effectiveness and feasibility of the improved algorithm. Show more

Keywords: ant colony optimization, path planning, grid method, pheromone update

DOI: 10.3233/JIFS-231280

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2611-2623, 2023

Authors: Sundarakumar, M.R. | Salangai Nayagi, D. | Vinodhini, V. | VinayagaPriya, S. | Marimuthu, M. | Basheer, Shajahan | Santhakumar , D. | Johny Renoald, A.

Article Type: Research Article

Abstract: Improving data processing in big data is a delicate procedure in our current digital era due to the massive amounts of data created by humans and machines in daily life. Handling this data, creating a repository for storage, and retrieving photos from internet platforms is a difficult issue for businesses and industries. Currently, clusters have been constructed for many types of data, such as text, documents, audio, and video files, but the extraction time and accuracy during data processing remain stressful. Hadoop Distributed File System (HDFS) is a system that provides a large storage area in big data for managing …large datasets, although the accuracy level is not as high as desired. Furthermore, query optimization was used to produce low latency and high throughput outcomes. To address these concerns, this study proposes a novel technique for query optimization termed the Enhanced Salp Swarm Algorithm (ESSA) in conjunction with the Modified K-Means Algorithm (MKM) for cluster construction. The process is separated into two stages: data collection and organization, followed by data extraction from the repository. Finally, numerous experiments with assessments were carried out, and the outcomes were compared. This strategy provides a more efficient method for enhancing data processing speed in a big data environment while maintaining an accuracy level of 98% while processing large amounts of data. Show more

Keywords: Hadoop distributed file system, latency, throughput, query optimization, hash algorithms clustering

DOI: 10.3233/JIFS-231389

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2625-2640, 2023

Authors: Chandana Mani, R.K. | Kamalakannan, J.

Article Type: Research Article

Abstract: Breast cancer (BC) is categorized as the most widespread cancer among women throughout the world. The earlier analysis of BC assists to increase the survival rate of the disease. BC diagnosis on histopathology images (HIS) is a tedious process that includes recognizing cancerous regions within the microscopic image of breast tissue. There are various methods to discovering BC on HSI, namely deep learning (DL) based methods, classical image processing techniques, and machine learning (ML) based methods. The major problems in BC diagnosis on HSI are the larger size of images and the high degree of variability in the appearance of …tumorous regions. With this motivation, this study develops a computer-aided diagnosis using a white shark optimizer with attention-based deep learning for the breast cancer classification (WSO-ABDLBCC) model. The presented WSO-ABDLBCC technique performs accurate classification the breast cancer using DL techniques. In the WSO-ABDLBCC technique, the Guided filtering (GF) based noise removal is applied to improve the image quality. Next, the Faster SqueezeNet model with WSO-based hyperparameter tuning performs the feature vector generation process. Finally, the classification of histopathological images takes place using attention-based bidirectional long short-term memory (ABiLSTM). A detailed experimental validation of the WSO-ABDLBCC occurs utilizing the benchmark Breakhis database. The proposed model achieved an accuracy of 95.2%. The experimental outcomes portrayed that the WSO-ABDLBCC technique accomplishes improved performance compared to other existing models. Show more

Keywords: Breast cancer, computer-aided diagnosis, histopathological images, deep learning, white shark optimizer

DOI: 10.3233/JIFS-231776

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2641-2655, 2023

Authors: Prajitha, C. | Sridhar, K.P. | Baskar, S.

Article Type: Research Article

Abstract: Electrocardiogram (ECG) signal analyses can enhance human life in various ways, from detecting and treating heart illness to controlling the lives of cardiac-diseased people. ECG analysis has become crucial in medical studies for accurately detecting cardiovascular diseases (CVDs). Cardiac Arrhythmia is one of the major life-threatening diseases. Analyzing ECG signals is the easiest way to detect Arrhythmia. Different noises often corrupt the ECG signals, like power line interference, electromyographic (EMG) noise, and electrode motion artifact noise. Such noises make it difficult to identify the various peaks in the ECG signal for arrhythmia classification. To overcome such problems, Noise Removal-based Thresholding …(NRT) framework has been introduced to remove noises from ECG signals and accurately classify Arrhythmia. Discrete Wavelet transform reduces noise from ECG signals in the pre-processing stage. The noise-removed signal is segmented by K-means clustering for R-peak detection by finding all local maximum points from the signal. The signal features are extracted by Burg’s method to obtain good frequency resolution and quick integration for short-time signals in the form of a cumulative distribution function. All features collected from R-peak are fed to the Iterative Convolutional Neural Network (ICNN) and classified the arrhythmia types based on the alignment of a few variables to work well with the Euclidean distance metric. The NRT framework is evaluated based on the data obtained from the MIT-BIH Arrhythmia dataset and achieves the Accuracy of 99.45 %, Positive Prediction of 98.92%, F1-Score of 98.95%, SNR of 35 dB, MSE of 0.001, RMSE of 0.002 Show more

Keywords: K-means clustering, Iterative Convolutional Neural Network, arrhythmia classification, R-peak

DOI: 10.3233/JIFS-223719

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2657-2668, 2023

Authors: Huang, Jr-Jen | Yang, Cheng-Ying | Lin, Yi-Nan | Shen, Victor R.L. | Lin, Chia-Tsai | Shen, Frank H.C.

Article Type: Research Article

Abstract: Human faces have been naturally viewed as a central part in each image. One interesting task is to classify each face into different categories based on the emotion shown in the facial expression. In addition, an awareness of emotion during work on a project and how affective states are presented in the communication style might help system developers work more effectively, thus improving the performance of a collaborative team. Currently, the feasibility and portability of emotion recognition in the platform with Raspberry PI are insufficient. Hereby, a novel emotion recognition system in real time using the edge computing platform with …deep learning has been implemented successfully. The feature values of objects are calculated by a high computing processor on the embedded platform. When an object with the matching features is detected, it is drawn as a rectangular bounding box and the results are displayed on the screen. In the proposed system, it first annotates the image datasets and saves them in the corresponding input data format for model training. Thus, the You Only Look Once (YOLOv5) model has been employed for training because it is a state-of-the-art object detection system. In other words, a fast and accurate emotion recognition is the main benefits of choosing YOLOv5 model. Then, the correctly trained YOLOv5 model file is loaded into an edge computing platform; and the feature values of objects are analyzed by a high computing processor. Finally, the experimental results show that the promising mean Average Precision (mAP), 92.6%, and recognition speed in Frames Per Second (FPS), 40, are obtained, which outperforms other existing systems. Show more

Keywords: Deep learning, emotion recognition, high computing platform, face recognition, image recognition, object detection

DOI: 10.3233/JIFS-223801

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2669-2683, 2023

Authors: Arivalagan, Divya | Bhoopathy Began, K. | Ewins Pon Pushpa, S. | Rajendran, Kiruthiga

Article Type: Research Article

Abstract: Fingerprints are widely used as effective personal authentication systems, because they constitute unique, robust, and risk-free evidence. Fingerprinting techniques refer to biometric procedures used for identifying individuals based on their physical characteristics. A fingerprint image contains ridges and valleys forming a directionally-oriented pattern. The robustness of the fingerprint authentication technique determines the quality of the fingerprint image. This study proposed an intelligent 12-layered Convolutional Neural Network (CNN) model using Deep learning (DL) for gender determination based on fingerprints. Further, the study compared the performance of this model to existing state-of-the-art methods. The primary goal of this study was to reduce …the number of comparisons within a large database obtained from automatic fingerprint recognition systems. The classification process was found to be swifter and more accurate when analysis of the DL algorithm was performed. With reference to the criteria of precision, recall, and accuracy evaluation during classification, this proposed 12-layered CNN model outperformed the Residual Neural Network with 50 Layers (ResNet-50) and Dense Convolutional Network with 201 Layers (DenseNet-201) models. The accuracies obtained were 97.0%, 95.8%, 98.0%, and 96.8% for female-left, female-right, male-left, and male-right classes respectively, while achieving an overall accuracy of 94.0%. Show more

Keywords: Fingerprint image, intelligent system, authentication, convolutional neural network, deep learning algorithm, precision, recall, accuracy, DenseNet201, ResNet-50

DOI: 10.3233/JIFS-224284

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2685-2706, 2023

Authors: Zhang, Linzi | Shi, Yong

Article Type: Research Article

Abstract: Classical supply chain finance (SCF) primarily focuses on the financial service among all upstream and downstream supply chain participants. Due to the continuously deteriorating of the ecological environment, an environmental-friendly SCF system is urgently needed. In this paper, we propose a novel SCF design scheme with environmental concerns, i.e., green supply chain finance (GSCF), consider the financing channels both from banks and from consumers, and design a bi-objective optimization model that depicts the trade-off between the benefit and the emission. Further, an improved normalized normal constraint (INNC) Pareto method is developed to address the optimal financing strategy of the bi-objective …model. We then conduct a numerical case of a Taiwanese steel firm to verify the effectiveness and efficiency of our method. Results show that our model provides a portfolio of optimal solutions on Pareto frontier which can be applied as an effective decision support system when designing a GSCF. Furthermore, the sensitivity analysis also presents the impact of environmental investment cost, technological ratio of companies and the interest rate of trade credit on the optimal configuration of the GSCF. Show more

Keywords: Green supply chain finance, Multi-objective optimization, Network design, Pareto frontiers, Trade credit

DOI: 10.3233/JIFS-230270

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2707-2721, 2023

Authors: Al-Jamaan, Rawabe | Ykhlef, Mourad | Alothaim, Abdulrahman

Article Type: Research Article

Abstract: Social networks like Twitter are extremely popular and widely used, which has increased interest in studying the information posted there. One such analytical application is extracting location information of users for real-time monitoring of the objects and events of interest, such as political and social events, disease surveillance, natural calamities, and crime prevention. Identifying geographic location is a nontrivial task, as user profiles contain outdated and inaccurate location information. Furthermore, extracting geographical information from Arabic tweets is challenging since they contain many nonstandard data (dialects), complex structures, abbreviations, grammatical and spelling mistakes, etc. This study focuses on the localization of …Saudi Arabian users who tweet in Arabic. This study proposes a convolutional neural network-based deep learning model to predict a Twitter user’s region-level location using user profiles, text texts, place attachments, and historical tweets. The model was evaluated empirically on a dataset of 95,739 tweets written in Arabic and produced by 4,331 users from Saudi Arabia cities. Regarding classification accuracy, the proposed CNN model outperformed machine learning classifiers such as NB, LR, and SVM with a 60% accuracy on the test set. This study is the first of its kind, aimed at localizing Saudi users based on their tweets. Show more

Keywords: Convolutional neural network, location estimation, machine learning, natural language processing, Twitter

DOI: 10.3233/JIFS-230518

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2723-2734, 2023

Authors: Özer, N. Ceyda | Tuzkaya, U. Rıfat

Article Type: Research Article

Abstract: City logistics approaches and modeling struggles have a significant role in urban areas in increasing the efficiency of logistics operations and reducing traffic jams and their environmental effects. By developing an effective distribution network for cities, it is possible to compete with the changing world and satisfy flexible customer requirements. In this study, as a real-world case, a city logistics model for Istanbul metropolitan area is designed using multi-objective linear programming that considers the different objectives of the stakeholders in cities by integrating the fuzzy Choquet integral technique in a multi-level distribution network for the automotive spare part industry. This …paper makes decisions regarding the amount of product flowing among the echelons, the amount of stock to be kept in the warehouses, and the product delays allowed. While minimizing the transportation cost, holding cost and emission levels during these decisions, the study also aims to maximize the service quality in the warehouses. The model is applied to a logistics network of fifty demand points and thirty time periods which can be considered a middle or large-scale problem. In the model, it is also decided to transport the products with electric or fuel vehicles. In the transport sector, electric vehicles are the key to meet future needs for social, health and other human services. The results are discussed under different scenarios. This research allows the use of such a model in making strategic decisions for the distribution network design in big cities. Show more

Keywords: Fuzzy Choquet integral, electric vehicles, multi-criteria decision making, city logistics, mathematical modeling

DOI: 10.3233/JIFS-223282

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2735-2752, 2023

Authors: Yang, Jun | Qiao, Linke | Li, Changjiang | Wu, Xing

Article Type: Research Article

Abstract: Roof collapse is the most frequent production accident in the mine production process, which seriously threatens the efficient and safe production of the mine. Therefore, it is urgent to carry out practical research on the roof collapse tendency of the roadway. After searching and analyzing the relevant documents, the primary influencing factors of roof collapse risk based on AHP are determined, namely engineering geology, rock mass support, construction management and natural environment. After refining the main influencing factors, the evaluation factor set is obtained, the fuzzy comprehensive evaluation relationship matrix is established, and the fuzzy comprehensive evaluation model of roof …collapse risk is obtained. Finally, the quantitative evaluation of no collapse risk, weak collapse risk, medium collapse risk and high collapse risk is carried out. Taking a metal mine as an example, the risk of roof collapse of its C11 haulage roadway is selected for fuzzy evaluation. The evaluation result is high collapse risk, which is consistent with the evaluation result of the current specification, indicating that the model can be used for mine roof collapse risk evaluation. This method of estimating roof collapse has been applied on-site, which is consistent with the actual situation and has achieved good results. It has guiding significance for predicting the stability of tunnels and supporting operations. Show more

Keywords: Analytic hierarchy process, risk assessment, roof collapse, fuzzy theory

DOI: 10.3233/JIFS-224146

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2753-2762, 2023

Authors: Liu, Pingqing | Wang, Hongjun | Wei, Guiwu

Article Type: Research Article

Abstract: Generalized hesitant fuzzy numbers (GHFNs) can reflect the real situation of the event, in which we may encounter limited known values and known values of the set of the degree of doubt, as a quantitative approximation of uncertainty or quantification of linguistic expressions. The score function and weighting method of GHFNs are of great significance in multi-attribute decision-making (MADM) problems. In different ambiguous environments, many scholars have proposed score functions and entropy measures for different fuzzy sets. Firstly, the existed score function of GHFNs was analyzed in detail and a new score function of GHFNs was established by combining previous …references. Secondly, a combined weighting method is built based on the minimum identification information principle by fusing GHF entropy and Method based on the Removal Effects of Criteria (MEREC). Thirdly, a novel GHF MADM method (GHF-EDAS) is built by extending evaluation based on distance from average solution (EDAS) method to the GHF environment to solve the issue that the decision attribute information is GHFNs. Finally, the validity and usefulness of the technique are verified by applying the GHF-EDAS technique to energy projects selection and comparing with the existing GHF-MADM method, the practicability and effectiveness of the model are verified, which offer a new way to solve the MADM problem of GHFNs. Show more

Keywords: Multi-attribute decision-making (MADM), generalized hesitant fuzzy numbers (GHFNs), EDAS method, MEREC method, energy project selection

DOI: 10.3233/JIFS-230105

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2763-2779, 2023

Authors: Chen, Mengxing | Dou, Jun | Fan, Yali | Song, Yan

Article Type: Research Article

Abstract:  Self-training semi-supervised classification has grown in popularity as a research topic. However, when faced with several challenges including outliers, imbalanced class, and incomplete data in reality, the traditional self-training semi-supervised methods might adversely damage the classification accuracy. In this research, we develop a two-step robust semi-supervised self-training classification algorithm that works with imbalanced and incomplete data. The proposed method varies from traditional self-training semi-supervised methods in three major ways: (1) The method in this paper does not necessitate the balance and complete assumption in traditional semi-supervised self-training methods, since it can complete and rebalance the dataset simultaneously. (2) This method …is compatible with many classifiers, so it can handle multi-classification and non-linear classification cases. (3) The classifier in this paper is resistant to outliers during semi-supervised classification. Furthermore, several numerical simulations were performed in this research to illustrate the quality of our method to synthesized data, as well as multiple experiments to demonstrate our method superior classification performance on various real datasets. Show more

Keywords: Semi-supervised classification, robust classification, fuzzy information decomposition, imbalanced class, incomplete data

DOI: 10.3233/JIFS-230658

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2781-2797, 2023

Authors: Hu, Guanghua

Article Type: Research Article

Abstract: Classroom teaching is an important link related to the quality of teaching and talent cultivation. In the implementation of classroom teaching, we should fully attach importance to the main position of students, the role of educational technology and information technology in teaching activities, make use of the latest educational ideas and educational concepts, and combine the actual situation of college English teaching and college English teaching in China, and attach importance to foreign language teaching theories and practices at home and abroad, Establish monitoring indicators and monitoring system for college English teaching quality. Under the guidance of effective monitoring indicators, …teachers’ teaching concepts can be updated and improved in real time to achieve better teaching results. At the same time, the quality assurance and monitoring system of college English teaching can be continuously improved to make it more perfect. The English classroom teaching quality evaluation could be deemed as a classic multiple attribute group decision making (MAGDM) problem. Spherical fuzzy sets (SFSs) can excavate the uncertainty and fuzziness in MAGDM more effectively and deeply. This article we first present a novel score function to compare spherical fuzzy numbers (SFNs) more directly and efficiently. Then, on basis of evaluation based on distance from average solution (EDAS), a novel spherical fuzzy EDAS (SF-EDAS) method is built for dealing with MAGDM. Moreover, when the attribute weights are completely unknown, the MEthod based on the Removal Effects of Criteria (MEREC) is extended to spherical fuzzy environment (SFE) to reasonably acquire the attribute weights. Finally, SF-EDAS approach is used for English classroom teaching quality evaluation to prove practicability of the developed method and compare SF-EDAS method with existing methods to further demonstrate its legitimacy and superiority. Show more

Keywords: Multiple attribute group decision making, spherical fuzzy sets, EDAS, MEREC, English classroom teaching quality evaluation

DOI: 10.3233/JIFS-230962

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2799-2811, 2023

Authors: Du, Kanghua | Du, Yuming

Article Type: Research Article

Abstract: At present, there is generally little research on intangible assets of sports events, and most of the research content focuses on general research on the meaning and content of intangible assets of sports events, the operation status of regional sports intangible assets, and development strategies of sports intangible assets. From the perspective of research results, only attention has been paid to the research on process management and process control of intangible assets of sports events, However, there is a lack of systematic research on the performance evaluation of intangible assets operation and management. It is necessary to build a scientific …and reasonable performance evaluation system for intangible assets operation and management of sports events based on the content of intangible assets in sports events. The performance evaluation of intangible assets operation and management in sports events is multiple attribute decision making (MADM). Recently, the TODIM and VIKOR method has been used to cope with MADM issues. The double-valued neutrosophic sets (DVNSs) are used as a tool for characterizing uncertain information during the performance evaluation of intangible assets operation and management in sports events. In this manuscript, the double-valued neutrosophic number TODIM-VIKOR (DVNN-TODIM-VIKOR) method is built to solve the MADM under DVNSs. In the end, a numerical case study for performance evaluation of intangible assets operation and management in sports events is given to validate the proposed method. Show more

Keywords: Multiple attribute decision making (MADM), double-valued neutrosophic sets (DVNSs), TODIM, VIKOR, performance evaluation, intangible assets operation and management

DOI: 10.3233/JIFS-231467

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2813-2822, 2023

Authors: Han, Yongguang | Xu, Xinrui

Article Type: Research Article

Abstract: As an important way to cultivate talents, school-enterprise cooperation is highly praised by the educational and economic circles of all countries. Vocational education spans enterprises and schools, and is an education of cross-border innovation. Vocational schools should make the people they cultivate meet the needs of the market and enterprises, and carry out in-depth school-enterprise cooperation is one of the effective ways. The school-enterprise cooperation between vocational colleges and enterprises makes vocational education more targeted and practical, and plays an important role in the training of skilled talents. It is not only an inevitable requirement for enterprise development and participation …in competition, but also an important direction of China’s vocational education reform, and is the fundamental outlet for the development of China’s vocational education. The performance evaluation of school-enterprise cooperation in vocational colleges is classical multiple-attribute group decision-making (MAGDM) issues. Recently, the TODIM and VIKOR method has been used to solve MAGDM issues. The 2-tuple linguistic Pythagorean fuzzy sets (2TLPFSs) are used as a tool for characterizing uncertain information during the performance evaluation of school-enterprise cooperation in vocational colleges. In this manuscript, we design the 2-tuple linguistic Pythagorean fuzzy TODIM-VIKOR(2TLPF-TODIM-VIKOR) method to solve the MAGDM under 2TLPFSs. In the end, a numerical case study for performance evaluation of school-enterprise cooperation in vocational colleges is given to validate the proposed method. Show more

Keywords: Multiple attribute group decision making (MAGDM), 2-tuple linguistic Pythagorean fuzzy sets (2TLPFNs), TODIM-VIKOR method, performance evaluation, school-enterprise cooperation

DOI: 10.3233/JIFS-231575

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2823-2834, 2023

Authors: Wang, Rong | Rong, Xia

Article Type: Research Article

Abstract: Mental health education (MHE) is an indispensable and fundamental content for schools to cultivate high-quality, high-standard, and high-quality talents, which has attracted sufficient attention and widespread attention in the educational community. Due to the impact of various factors such as society, family, and oneself, the overall psychological quality of Chinese college students is poor, their thinking is relatively childish, they cannot calmly face new problems and situations in life, and they feel vague and confused about life, ideals, and goals Confusion and other psychological barriers are becoming more and more common. If effective psychological crisis intervention is not available, it …will seriously affect their learning and personality development, and is not conducive to talent growth. In recent years, Huaibei Vocational and Technical College has placed psychological quality education at the top of its list, providing good psychological quality training for college students, improving their psychological endurance, eliminating negative attitudes and psychological barriers, and promoting their mental health and sustainable development. The quality evaluation of MHE of college students is a classical multiple attribute decision making (MADM) problems. Recently, the TODIM and VIKOR method has been used to cope with MAGDM issues. The hesitant triangular fuzzy sets (HTFSs) are used as a tool for characterizing uncertain information during the quality evaluation of MHE of college students. In this manuscript, the hesitant triangular fuzzy TODIM-VIKOR (HTF-TODIM-VIKOR) method is built to solve the MADM under HTFSs. In the end, a numerical case study for quality evaluation of MHE of college students is given to validate the proposed method. Show more

Keywords: Multiple attribute group decision making (MAGDM), hesitant triangular fuzzy sets (HTFSs), TODIM, VIKOR, quality evaluation of MHE

DOI: 10.3233/JIFS-231719

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2835-2845, 2023

Authors: Madhu, D. | Vasuhi, S.

Article Type: Research Article

Abstract: The role of communication technologies has become increasingly vital in various fields such as industrial communication, surveillance and monitoring, healthcare, and data communication, leading to a surge in demand for these technologies in recent years. In this research paper, the LEMARS model is proposed as a novel and robust lightweight encryption assisted Man-in-The-Middle (MITM) attack-resilient steganography model for secure satellite imagery services. The proposed solution combines lightweight encryption and attack-resilient embedding to achieve higher attack-resilience with low computational cost and optimal reconstruction quality. The use of Feistel architecture-driven substitution and permutation-based block-cipher encryption and heuristic-driven pixel adjustment method for MITM-Adaptive …B-Channel Embedding results in higher imperceptibility and superior attack-resilience under uncertain channels. The strategic amalgamation of FSPN-BCE, L2D-IWT, and HD-OMABE enabled attack-resilient steganography for secure satellite communication. The performance of LEMARS has been evaluated using Peak Signal-to-Noise Ratio (PSNR), entropy, Non-Parametric Change Rate (NPCR), Unified Average Change Intensity (UACI), and regular and singular block pattern changes. LEMARS achieved a PSNR value of 58.02 dB, an entropy value of 6.15 dB, an NPCR value of 99.84%, a UACI value of 33.70, and exhibited minimal block pattern changes. These results indicate that the proposed model achieved higher attack-resilience with low computational cost and optimal reconstruction quality. The research aimed to improve encryption and embedding methods to achieve an MITM-attack resilient embedding solution for secure satellite imagery services. The proposed LEMARS model exhibited superiority over other state-of-the-art methods, affirming the robustness of the proposed LEMARS model for real-time multimedia data security purposes, including remote sensing, satellite imagery services, telemedicine, and EHR. In conclusion, the LEMARS model offers an optimal solution for multimedia data security, combining lightweight encryption and attack-resilient embedding to achieve higher attack-resilience with low computational cost and optimal reconstruction quality. Show more

Keywords: Steganography, lightweight encryption, heuristic-driven pixel adjustment, MITMattack resilient cipher embedding

DOI: 10.3233/JIFS-223329

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2847-2869, 2023

Authors: Rong, Xianghong

Article Type: Research Article

Abstract: At the stage of college education, English is a very important subject, which is directly related to the enhancement of college students’ English literacy and the improvement of their English level. From the perspective of mobile internet, the construction and application of college English intelligent classroom teaching mode can promote the reform and improvement of traditional English teaching mode. By giving full play to students’ subjectivity, English teaching can be carried out in a modern teaching classroom environment, which is conducive to strengthening students’ comprehensive English ability, which is also very important and meaningful for building an efficient college English …classroom. The college English smart classroom (CESC) teaching quality evaluation is a classical multiple attribute decision making (MADM). In this paper, we define the triangular Pythagorean fuzzy set (TPFS) and investigate the multiple attribute decision making problems under it. Accordingly, we take advantage of traditional Bonferroni mean (BM) operator to develop some triangular Pythagorean fuzzy information operators: triangular Pythagorean fuzzy Bonferroni mean (TPFBM) operator and triangular Pythagorean fuzzy weighted Bonferroni mean (TPFWBM) operator. The dominating natures of these operators are researched. Accordingly, the TPFWBM operator is built for triangular Pythagorean fuzzy MADM. Ultimately, a practical example for CESC teaching quality evaluation is took advantage of to validate the developed approach. Show more

Keywords: Multiple attribute decision making (MADM), Triangular Pythagorean fuzzy set, Bonferroni mean (BM) operator, triangular Pythagorean fuzzy weighted Bonferroni mean (TPFWBM) operator, CESC, teaching quality evaluation

DOI: 10.3233/JIFS-232002

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2871-2886, 2023

Authors: Zhang, Jiarui

Article Type: Research Article

Abstract: The design of the emergency communication vehicle is centered around the overall layout, which is characterized by the fact that the vehicle chassis is mostly purchased parts and is equipped with an insulated and insulated box, mainly used for instrument operation and equipment placement. The emergency communication vehicle cabin environment includes: cabin seats; seat cover; control cabinet, including operation keyboard, display screen, etc; The ceiling and carpet that make up the interior space of the car; Storage box for storage equipment; tool cabinet; cabin control system facilities; Fire prevention and extinguishing facilities in the cabin; various handrails, coat racks, short …ladders, lighting facilities, etc. inside the car. The design of emergency communication vehicle interior environment involves multiple disciplines, and the selection of design schemes requires scientific and reasonable evaluation methods to select reasonable schemes for optimization design. The evaluation of emergency communication vehicle cabin’s internal environment design is classical multiple attribute decision making (MADM) problems. In this article, based on bidirectional projection and grey relational projection measure, we shall propose some projection models with q-rung orthopair fuzzy sets (q-ROFSs). First of all, the definition of q-ROFSs is introduced. Furthermore, some projection models with q-ROFSs are proposed based on the bidirectional projection and grey relational projection model. Based on developed weighted projection models, the MADM model is established and all computing steps are simply depicted. Finally, a numerical example for evaluation of emergency communication vehicle cabin’s internal environment design is given to illustrate this new model and some comparisons are conducted to illustrate advantages of the new built method. Show more

Keywords: MADM problems, q-rung orthopair fuzzy sets (q-ROFSs), projection model, emergency communication vehicle, cabin’s internal environment design

DOI: 10.3233/JIFS-232198

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2887-2898, 2023

Authors: Oh, Ju-Mok

Article Type: Research Article

Abstract: In this paper, we present the notion of commutative coquantale frames as logical relational systems. We introduce an approach to study these frames through the utilization of distance functions in place of the commonly used fuzzy partially orders. We show that a commutative coquantale frame can be regarded as an algebraic structure that induces a commutative coquantale, while a commutative coquantale with q -distance function leads to the development of a commutative coquantale frame. Moreover, we provide several examples pertaining to these notions.

Keywords: Commutative coquantales, commutative coquantale frames, distance functions, Alexandrov topologies

DOI: 10.3233/JIFS-223367

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2899-2917, 2023

Authors: Li, Zhao | Sun, Weiyi | Li, Dongze

Article Type: Research Article

Abstract: With the implementation of China’s “two-carbon” target strategy, the status of renewable energy in the power system is gradually improving. In order to improve the carbon emission control level of power grid, it is necessary to predict the peak carbon emission of power grid. A prediction method of grid carbon emission peak based on energy elasticity coefficient is proposed. A time series model of grid carbon emission samples was constructed, and a combination of combinatorial sorting and machine learning was used to reconstruct the time series of grid carbon emission peaks. Based on the results of time series reconstruction, feature …extraction and classification training of grid carbon emission peak are carried out, and the measurement error correction of grid carbon emission under the constraint of energy consumption elasticity is realized. Combined with the association rule mining method, the fusion processing of grid carbon emission peak samples under the elastic constraint of energy consumption is realized. Through characteristic detection and fusion processing of energy consumption elasticity coefficient, the reconstituted carbon emission samples of power grid constrained by energy consumption elasticity were reconstructed, and the peak carbon emission of power grid was predicted. The simulation results show that this method has high accuracy and convergence in predicting the peak carbon emission of power grid, and improves the error correction ability in the prediction process. Show more

Keywords: Energy consumption, elastic coefficient, power grid, carbon emissions, peak value prediction, association rule mining

DOI: 10.3233/JIFS-224599

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2919-2930, 2023

Authors: Lei, Chang

Article Type: Research Article

Abstract: Cloud computing has emerged as one of the most promising technologies for meeting rising computing needs. However, high-performance computing systems are more likely to fail due to the proliferation of components and servers. If a sub-system fails, the entire system may not be functional. In this regard, the occurrence of faults is tolerable using an efficient fault-tolerant method. Since cloud computing involves storing data on a remote network, system failures and congestion are the most common causes of faults. The paper presents a new approach to adopting a fault-tolerant mechanism that adaptively monitors health to detect faults, handles faults using …a migration technique, and avoids network congestion. With the advantage of the Ant Colony Optimization (ACO) algorithm and active clustering, the load is distributed evenly in data centers. Simulation results indicate that our algorithm outperforms previous algorithms regarding total execution time and imbalance degree up to 10% and 17%, respectively. Show more

Keywords: Cloud computing, fault tolerance, load balancing, energy efficiency

DOI: 10.3233/JIFS-230102

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2931-2948, 2023

Authors: Long, Xiaoqing | Gao, Fei

Article Type: Research Article

Abstract:  Cooperative attack with unmanned aerial vehicles (UAVs) plays a critical role in modern military warfare. To achieve multi-swarm cooperative attack with obstacle avoidance of formation, this paper proposes a cooperative control strategy that integrates flight control and autonomous marshaling. Firstly, an improved dynamics model with virtual leader-following mode is constructed to achieve obstacle avoidance of the formation. And an improved interference fluid dynamic system (IIFDS) is applied to improve path selectivity during multi-swarm attack. Secondly, a two-layer attack framework based on distributed swarm coordinated trajectory tracking with heading angle constraints is designed to achieve autonomous clustering of the UAVs and …target striking. Finally, the proposed improved dynamics model is compared with the particle swarm optimization (PSO) algorithm and artificial potential field (APF) method in terms of obstacle avoidance of formation to demonstrate its superiority, which can obtain better benefits. Furthermore, two simulations of multi-swarm cooperative attack are conducted to validate the effectiveness of the control strategy. The proposed method expands the application of UAVs attack with obstacle avoidance of formation and provides a valuable reference for modern military operations. Show more

Keywords: Multi-swarm cooperative attack, autonomous marshalling, coordinated trajectory tracking, IIFDS

DOI: 10.3233/JIFS-231180

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2949-2965, 2023

Authors: Wang, Chu | Wang, Jian

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-231449

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2967-2977, 2023

Authors: Kala, R. | Deepa, P.

Article Type: Research Article

Abstract: Brain tumor is an anomalous growth of brain cells. Segmentation of brain tumors is currently the most important surgical and pharmaceutical procedure. However, manually segmenting the brain tumor is a challenging task due to the complex structure of brain. In recent years, artificial intelligence techniques with the fuzzy logic have shown better results in the field of medicine. In this work, a novel deep learning classification network with fuzzy hexagonal membership function (DLC-FHMF) model has been proposed for accurately segmenting brain tumors. The different MRI modalities namely T1, T1-c, T2 and Flair images are preprocessed using a fuzzy hexagonal trilateral …and median filter to eliminate the Rician noise. Afterwards, the DLC-FHMF model is used for segmenting the tumor portion by using the multimodal composition of MRI as input. The fuzzy weights are determined with hexagonal membership functions and convoluted with the corresponding MRI images. The quantitative examination is carried out using the performance metrics namely accuracy, specificity, precision, sensitivity, incorrect segmentation, under-segmentation, and over-segmentation. In addition to the above metrics, the pre-processing metrics include PSNR, RMSE, and SSIM. The experimental fallout portrayals that the proposed DLC-FHMF approach attains a better accuracy range of 99% for detecting brain tumors using the BRATS 2013 dataset. The proposed DLC-FHMF model improves the overall accuracy by 15.1%, 11.1%, 3.0%, 21.2% and 0.5% better than ANN, SVM, NB, DNN and DAE respectively. Show more

Keywords: Brain tumor, magnetic resonance image, fuzzy logic, deep learning, segmentation

DOI: 10.3233/JIFS-221990

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2979-2992, 2023

Authors: Zhang, Yongliang | Lu, Yang | Zhu, Wuqiang | Wei, Xing | Wei, Zhen

Article Type: Research Article

Abstract: Deep learning has dominated the research field of traffic sign detection, but the traffic sign detection algorithms based on deep learning have difficulty in solving the two tasks of localization and classification simultaneously when performing traffic sign detection on realistic and complex traffic scene images, and the images or the types of traffic signs provided by the public dataset used by the relevant algorithm cannot meet the situations encountered in realistic traffic scenes.To solve the above problems, this paper creates a new road traffic sign dataset, and based on the YOLOv4 algorithm, designs a multi-size feature extraction module and an …enhanced feature fusion module to improve the algorithm’s ability to locate and classify traffic signs simultaneously, in view of the complexity of realistic traffic scene images and the large variation of traffic sign sizes in the images. The experimental results on the newly created dataset show that the improved algorithm achieves 83.63% mean Average Precision (mAP), which is higher than several major object detection algorithms based on deep learning for the same type of task at present. The newly created dataset in this paper is publicly available at https://github.com/zhang1018/Traffic-sign-dataset-for-public . Show more

Keywords: Traffic sign detection and recognition, traffic sign datasets, autonomous driving, convolutional neural networks, intelligent traffic system

DOI: 10.3233/JIFS-210838

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2993-3004, 2023

Authors: Jayashree, P. | Laila, K. | Amuthan, Aara

Article Type: Research Article

Abstract: The large flux of online products in today’s world makes business reviews a valuable source for consumers for making sound decisions before making online purchases. Reviews are useful for readers in learning more about the product and gauge its quality. Fake reviews and reviewers form the bulk of the review corpus, making review spamming an open research challenge. These spam reviews require detection to nullify their contribution to product recommendations. In the past, researchers and communities have taken spam detection problems as a matter of serious concern. Yet, for all that, there is space for the performance of exploration on …large-scale complex datasets. The work contributes towards robust feature selection with derived features that provide more details on malicious reviews and spammers. Ensemble and other standard machine learning techniques are trained and evaluated over optimal feature sets. In addition, the Metapath-based Graph Convolution Network (M-GCN) framework is proposed, which is an implicit knowledge extraction method to automatically capture the complex semantic meaning of reviews from the heterogeneous network. It makes analysis of triplet (users, reviews, and products) relationships in e-commerce sites through examination of Top-n feature sets in a mutually reinforcing manner. The proposed model is demonstrated on Yelp and Amazon benchmark datasets for evaluation of efficacy and it is shown outperforming state-of-the-art techniques with and without graph-utilization, providing an accuracy of 96% in the prediction task. Show more

Keywords: Spam review detection, feature sets derivation, machine learning, Metapath, graph convolution network

DOI: 10.3233/JIFS-223136

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3005-3023, 2023

Authors: Jadhav, Ranjana S. | Dhore, Manikrao Laxmanrao

Article Type: Research Article

Abstract: Transliteration is phonetically translating a language’s words into an international or non-native screenplay. The machine translation process now plays an essential role in scholarly research. The most crucial complement criterion of the English translation system is preserving the phonetic qualities of the language specification after English translation in the chosen language. However, a suitable bilingual text corpus is necessary for statistical models to attain improved transliteration accuracy. Marathi-to-English direct machine translation is done through a cross-language information retrieval system using the CNN classifier model in this proposed research. The proposed method considers a sequence labelling issue brought on by the …split transliteration units used in the process. All half-consonant clusters in the Devanagari script are effectively mapped as half-consonant “a” s and labelled using the Modified Intermediate Phonetic Code (MIPC). After generating the phonetic units for each feature in the base and aim languages, the weight is assigned to a phonetic unit in both languages, and individual phonetic unit probabilities are computed. If the probability is zero, then segments are established and recalculated for each segment based on the target phonetic unit location in the word. Therefore, the proposed approach classifies the required phonetic unit with a high accuracy rate. Show more

Keywords: Machine Transliteration, phonetic unit, Devanagari, syllabification, N-grams, CNN classifier model

DOI: 10.3233/JIFS-223591

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3025-3037, 2023

Authors: Tang, Bingsong | Li, Nan

Article Type: Research Article

Abstract: Disputes are inevitable in construction. Tremendous losses caused by dispute are so amazing that professionals try to figure out how to manage it. It is a practical way to study the dispute problem from the perspective of governance theory. In this study, the paper intends to investigate the characteristics of contractual governance for disputes. Based on governance theory, the framework of contractual governance for dispute is constituted of governance structure (GS) and governance mechanism (GM). The flexibility of GS and GMs are all explored so as to better draft the contracts. By a multiple cases study, a new conceptual model …instructing governance picture for construction disputes was proposed which was mainly inspired from literature. The cases study shows that the GS determination is rigidly drafted and executed while the mostly GMs are flexibly designed. The rigid GS has an advantage to a stable foundation and the flexible GMs are apt to coordinate the disputes. Show more

Keywords: Contract flexibility, dispute resolution, governance theory, rigid governance, flexible governance

DOI: 10.3233/JIFS-224227

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3039-3051, 2023

Authors: Hu, Shan | Jiang, Weitao | Rong, Lingda | Hu, Shixuan | Zhong, Xiaoying | Wei, Yaxin

Article Type: Research Article

Abstract: Accessible products play an essential role in the lives of people with disabilities. This paper aims to identify key user satisfaction with accessible products factors affecting the use of accessible products by people with disabilities that influence user satisfaction. The extended model incorporates the essential elements of the TAM, TPB, and PR models and user satisfaction as an external variable. Data were collected from 339 users of accessible products. Structural equation modeling was used to identify significant variables in this study. SEM considered “behavioral intention” to be the most important among them. This study generated design strategies based on significant …factors analyzed in the findings and validated the design cases using the PSSUQ questionnaire, which showed that users had better user satisfaction when using accessible products with the new design strategies. Show more

Keywords: Accessibility, user satisfaction, structural equation modeling (SEM), technology acceptance model (TAM), theory of planned behavior (TPB), theory of perceived risk (PR)

DOI: 10.3233/JIFS-231121

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3053-3075, 2023