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

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

Authors: Liu, Yu

Article Type: Research Article

Abstract: The “One Belt and One Road” is a major strategic deployment proposed by Chinese President Xi Jinping in 2013, and it is important to study the construction of green financial system under the “One Belt and One Road” initiative, especially the quality assessment of green finance to promote high-quality economic development along the route. At the same time, green finance and green “Belt and Road” have become a hot academic topic in the world. In this study, firstly, on the basis of the existing research on green finance, focusing on the evaluation of economic quality of green finance, we innovatively …draw on the quality function deployment theory in marketing to logically transform our research ideas and propose a combined comprehensive evaluation method based on the hierarchical analysis (AHP) and entropy method in fuzzy mathematical theory, which makes up for the traditional single fuzzy evaluation method’s influence on the evaluation results. This method makes up for the shortcomings of the traditional single fuzzy evaluation method to evaluate the results of subjective or objective weighting results. In this study, we apply this method to the assessment of the quality of green finance development in “One Belt, One Road”, and it is important that we construct a system of “One Belt, One Road” green finance quality assessment indicators, including one primary indicator, four secondary indicators and 12 tertiary indicators. It is worth noting that our indicator system is different from the traditional quality system of financial quality assessment in that we take green factors into account in the construction of the tertiary indicators, and then use this assessment method to calculate and rank the weights (importance) of the 12 tertiary indicators, taking the actual situation in China as an example. More importantly, our study not only extends the academic research results of economic quality evaluation, but also combines quantitative research with qualitative analysis to propose three targeted countermeasures for the development of green finance in the countries along the Belt and Road. This study can also provide theoretical support for the quality assessment of green finance in countries along the Belt and Road, and promote the high-quality development of green finance in countries along the Belt and Road. Show more

Keywords: Green finance, belt and road, financial quality assessment, quality function deployment theory, hierarchical analysis, entropy method

DOI: 10.3233/JIFS-223257

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3077-3095, 2023

Authors: Jeevitha, Kannan | Garg, Harish | Vimala, Jayakumar | Aljuaid, Hanan | Abdel-Aty, Abdel-Haleem

Article Type: Research Article

Abstract: Digital transformation is the significant phenomena in contemporary global environment. All the conventional fuzzy sets are extended by the Linear Diophantine Fuzzy Set (LDFS). LDFS is the most viable adaptable method for decision makers to choose their grade values as it includes reference parameters. The foremost vision is to promote the resilient integration of Linear Diophantine Multi-Fuzzy Set (LDMFS) as a model for constructing decisions in order to identify the appropriate standards for digital transformation. Aggregation Operators are crucial in fuzzy systems for fusing information. To aggregate the LDMF, a number of operators have been devised, such as the Linear …Diophantine Multi-Fuzzy Weighted Geometric Operator (LDMFWGO), Linear Diophantine Multi-Fuzzy Ordered Weighted Geometric Operator (LDMFOWGO), Linear Diophantine Multi-Fuzzy Weighted Averaging Operator (LDMFWGO) and Linear Diophantine Multi-Fuzzy Ordered Weighted Averaging Operator (LDMFOWAO). By integrating preferred aggregating operations, a novel method for MCDM with LDMF data is studied. The best option from the current alternatives can be determined using these operators. Moreover, a comparison of LDMF operators is made. Additionally, the idea of a scoring function for LDF is designed to examine the rank of viable alternaties. Additionally, a novel approach to solving LDMF sets is suggested. The annals on organisational digital transformation is presented as the final section to test the supremacy of the theory. Accurate rankings for digital transformation are provided by the outcome. To exhibit the robustness of the MCDM methodology, a prompt comparative analysis is established between the suggested concept and the currently used approaches. Show more

Keywords: LDMFWG operator, LDMFWA operator, LDMFWA operator, LDMFOWA operator, LDMFOWG operator, score function, MCDM problem

DOI: 10.3233/JIFS-223844

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3097-3107, 2023

Authors: Xiao, Huimin | Yang, Peng | Ma, Xifeng | Wei, Meng

Article Type: Research Article

Abstract: A decision matrix is typically used to express hesitant information when solving multi-attribute decision problems in an uncertain environment. To further investigate the decision problem, this paper takes the property of matrix rank as the starting point, introduces it into the hesitant fuzzy theory, presents the concept of the rank of the hesitant fuzzy decision matrix and discusses the related properties, and then studies the hesitant fuzzy linear relation, obtaining the attribute reduction method based on the hesitant fuzzy linear relation and applying it to the multi-attribute decision making. It adds to the theoretical understanding of the hesitant fuzzy decision …matrix. The aggregation operator first transforms the hesitant fuzzy information into a comprehensive decision matrix, and the row echelon transformation determines the rank of the matrix. Second, the hesitant fuzzy linear relationship is obtained using the rank property. A new hesitant fuzzy matrix is obtained after attribute reduction based on the hesitant fuzzy linear relationship, and the alternatives are sorted using the TOPSIS method. Finally, the effectiveness and superiority of the proposed method are demonstrated through a comparison of actual case analysis and existing methods, and the expected research purpose is met. Show more

Keywords: Hesitant fuzzy set, hesitant fuzzy decision matrix, rank, attribute reduction

DOI: 10.3233/JIFS-224231

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3109-3121, 2023

Authors: Hu, Nanyan | Li, Xuexue | Li, Yufei | Ye, Yicheng | Wu, Menglong

Article Type: Research Article

Abstract: In order to address the issues of emergency decision-making and optimization (EDMO) of fire accidents in colleges, this paper proposes the EDMO way to take into account the synergies among divergent divisions replacements and the psychology of decision makers (DMs) on the basis of the best-worst method (BWM) and VIKOR within an interval 2-tuple linguistic (ITL) surroundings and cumulative prospect theory (CPT). First, DMs use the ITL to evaluate the degree of synergy among replacements from divergent divisions, the language information can be processed accurately and the information loss can be avoided. Then, the multi-alternative amalgamations consisted of divergent divisions …replacements are built. On the grounds of the DMs’ value assignment, the collaborative decision matrix of multi-alternative amalgamations can be gained. And the optimal weight of the evaluation standards can be computed based on the ITL-BWM method. The CPT is extended into VIKOR to think about the effect of the DMs’ psychological behavior on the decision result. Furthermore, the positive and negative utility matrices can be computed through the value function of CPT. On the grounds of the positive and negative utility matrices, the distance from the utility value of multi-alternative amalgamations to the desired right solution of positive and negative utility can be obtained, and the cumulative foreground value function is used to replace the distance among each replacement to the positive and negative right desired solutions, it can avoid ignoring the effect of the correlations among different attributes on the outcome. Furthermore, the model is applied to the example and an analysis of the sensitivity of the factors of the decision-making mechanism coefficient and the weights of synergistic indicators is carried out to prove the validity and stability of the model. Show more

Keywords: Emergency decision-making and optimization, college fire, interval 2-tuple linguistic, best-worst method, VIKOR

DOI: 10.3233/JIFS-224322

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3123-3136, 2023

Authors: Josephin Shermila, P. | Ahilan, A. | Jasmine Gnana Malar, A. | Jothin, R.

Article Type: Research Article

Abstract: Foods are very essential for living beings for providing energy, development and preserve their existence. It plays a vital role in promoting health and preventing illness. Nowadays, many people are suffered from obesity, they tend to maintain their body weight by consuming a sufficient number of calories in their routine life. In this research, a novel Modified Deep Learning-based Food Item Classification (MDEEPFIC) approach has been proposed to categorize the different food items from the dataset with their calorie values. Initially, the images are processed using the sigmoid stretching method to enhance the image quality and remove the noises. Consequently, …the pre-processed images are segmented using Improved Watershed Segmentation (IWS2) algorithm. Recurrent Neural Network (RNN) is used to extract features like shape, size, textures, and color. The extracted features are then normalized using the modified dragonfly technique for same food calorie calculation. Bidirectional Long Short-Term Memory (Bi-LSTM) is utilized to classify food products based on these pertinent aspects. Finally, using food area volume and calorie and nutrition measures based on mass value, the calorie value of the categorized food item is calculated. The efficiency of the proposed method was calculated in terms of specificity, precision, accuracy, and recall F-measure. The proposed method improves the overall accuracy of 4.99%, 8.72%, and 10.4% better than existing Deep Convolution Neural Network (DCNN), Faster Recurrent convolution neural network (FRCNN), Local Variation Segmentation based Support Vector Machine (LSV-SVM) method respectively. Show more

Keywords: Food, bidirectional long short-term memory, improved watershed, recurrent neural network, dragon fly algorithm

DOI: 10.3233/JIFS-230193

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3137-3148, 2023

Authors: Fang, Chang | Chen, Yu | Wang, Yi | Wang, Weizhong | Yu, Qianping

Article Type: Research Article

Abstract: The Fine-Kinney (F-K) model has been broadly employed for evaluating and ranking risk in various fields. The risk scoring information expression and priority ranking are two significant operations for its application. Numerous approaches have been extended to the two operations to improve the performance of conventional Fine-Kinney for risk analysis. Nevertheless, current literature on the F-K framework seldom considers the collective and individual risk attitudes in ranking potential hazards, especially with Fermatean fuzzy-based -risk scoring information. This paper generates a new ranking approach for risk prioritization in F-K to fulfill this gap by integrating the Fermatean fuzzy sets with the …GLDS (gained and lost dominance score) method. First, the Fermatean fuzzy sets-based risk scale is introduced to acquire risk scores. Then, a new collective risk scoring matrix establishment approach based on Fermatean fuzzy Bonferroni mean (BM) operator is built for considering the interactive effects between experts. Next, an extended Fermatean fuzzy GLDS method with CRITIC (Criteria Importance Through Inter-criteria Correlation)is proposed to rank the potential hazards, in which the Fermatean fuzzy CRITIC method is adopted to determine the weights. Especially, this developed weighting method can depict the inter-correlation among risk parameters. Finally, this paper presents a risk evaluation case of professional hazards for construction operations to display the application and advantages of this improved hybrid risk ranking model in the F-K framework. The result reveals that the enhanced framework can effectively rank potential hazards with complex risk information. Show more

Keywords: Fine-Kinney, fermatean fuzzy set, risk prioritization, GLDS approach, CRITIC

DOI: 10.3233/JIFS-230423

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3149-3163, 2023

Authors: Liang, Bingjie | Bi, Jun | Ran, Bin

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-231253

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3165-3179, 2023

Authors: Wang, Ji | Xu, Chunming

Article Type: Research Article

Abstract: This paper explores the issue of patent race in which 5G enterprises invest the patent package in the field of new spectrum to research and develop some core technologies. Based on the comprehensive interaction of expected profit, investment risk, and withdrawal cost, this paper aims to achieve the two objectives of maximizing the profit and minimizing the investment risk for a lagging firm. By numerical experiment analysis, the optimal portfolio strategy of a lagging firm is obtained, followed by the phenomenon in patent race of investment disinvestment. The result shows that the lagging firm can focus on certain self-interested technologies …to realize the leap of key technologies in research and development (R&D) under the high degree selection condition independently. In addition, different initial investment shares affect the portfolio strategy. Show more

Keywords: Patent race, lagging firms, new spectrum, portfolio strategy, R&D

DOI: 10.3233/JIFS-223463

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3181-3200, 2023

Authors: Angel Sajani, J. | Ahilan, A.

Article Type: Research Article

Abstract: Brain diseases is a wide range of disorders and diseases that affect the brain. They can change a person’s behavior, personality, and capacity for thought and function. CT images are more essential than conventional clinical tests for detecting brain hemorrhage accurately. MRI images of the brain can reveal even small abnormalities in the cranial region, helping providers diagnose a wide variety of conditions, ranging from brain stroke, cancers, aneurysms, and Alzheimer’s. This paper proposes a novel Fused dual neural (FDN) network for detecting brain cancer, stroke, aneurysms, and Alzheimer using Brain Medical Images (BMI) the combination of MRI and CT. …In BMI, the adaptive bilateral filter reduces noise artifacts. Google Net is used to extract features from pre-processed MRI images, and Mobile Net is used to extract features from pre-processed CT images. The integration of extracted features from Google Net and Mobile Net is fused by the Wrapper method. Finally, the Deep Belief Network is employed for classifying brain stroke, cancer, Aneurysm, and Alzheimer’s diseases using BMI images. The quantitative analysis of the suggested method is determined using the parameters like specificity, recall, precision, F1 score, and accuracy. The proposed FDN achieves a high classification accuracy rate of 98.19%, 97.68%, 94.31%, and 93.82% for detecting stroke, cancer, Aneurysm, and Alzheimer respectively. The proposed FDN model improves the overall accuracy by 5.35%, 3.14%, 9.48%, 5.33%, and 0.55% better than Faster R-CNN, CNN, Inception-V3, DCNN, and Fine-tuning Network respectively. Show more

Keywords: Brain disease, classification, Google Net, mobile net, deep belief network, deep learning

DOI: 10.3233/JIFS-230090

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3201-3211, 2023

Authors: Ren, Weijia | Du, Yuhong | Sun, Ronglu | Du, Yuqin | Lü, Mubo

Article Type: Research Article

Abstract: To improve the accuracy of decision results in complex fuzzy environments, complex cubic fuzzy sets are studied, which can not only measure the periodicity of decision-making data, but also use interval values and single values to act together on the data. However, the fuzzy sets do not provide a reasonable explanation for some special cases of input arguments. Thus, the power average operator is used to eliminate the influence of extreme input arguments on decision results, and the Maclaurin symmetric mean operator considers the correlation between inputs in this paper. Firstly, we define the operation rules, distance measures, evaluation index …function, and evaluation criteria in a complex cubic q -rung orthopair fuzzy environment. Then, some aggregation operators are proposed to aggregate complex cubic q -rung orthopair fuzzy numbers, and their desirable properties and some special cases are discussed. Next, we use the subjective and objective fusion method to determine the weight of attributes. Further, a multi-attribute decision-making method is established by combining aggregation operator, evaluation function, and weight determination method. Finally, the proposed method is applied to a specific quality evaluation problem, and the effectiveness and practicability of the proposed method are illustrated by other methods and parameter analysis. Show more

Keywords: Complex cubic fuzzy set, aggregation operator, multi-attribute group decision-making, application

DOI: 10.3233/JIFS-230402

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3213-3231, 2023

Authors: Chen, Xu

Article Type: Research Article

Abstract: With the rapid development of China’s construction industry, the competition in the construction industry is becoming increasingly fierce. Enterprises need to continuously improve their competitiveness in the market. Some non-core businesses can be outsourced to professional contractors. At present, contractors have more and more influence on the operation and development of enterprises. Whether it is the construction period or the quality of the project, it will have a greater impact on the operation of the construction project. In the environment of increasingly fierce market competition and increasing project quality requirements, for the construction project contracting enterprises, in order to achieve …the goal of low cost and high quality, it is necessary to select the most suitable contractor on the basis of comprehensive consideration of multiple factors. The construction enterprise contractor selection is a classical multiple attribute group decision making (MAGDM) problem. In recent years, the MAGDM problem has become an important research field in modern decision science. This paper extends the EDAS method to the 2-tuple linguistic Pythagorean fuzzy sets (2TLPFSs). On the basis of the original EDAS method, 2-tuple linguistic Pythagorean fuzzy EDAS (2TLPF-EDAS) is built for MAGDM. Finally, a case study for construction enterprise contractor selection and some comparative analysis with the other methods show that the new method proposed in this paper is effective, reasonable and accurate. Show more

Keywords: Multiple attribute group decision making (MAGDM), 2-tuple linguistic Pythagorean fuzzy sets (2TLPFSs), EDAS method, construction enterprise contractor selection

DOI: 10.3233/JIFS-231063

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3233-3245, 2023

Authors: Suba, M. | Susan, D.

Article Type: Research Article

Abstract: A key component of cognitive radio technology is spectrum sensing, which finds and accesses unused frequency bands to efficiently use the underutilized spectrum. A potential method for spectrum sensing called cyclostationary feature detection (CFD) uses the cyclostationary characteristics of signals to distinguish between the signal and noise. Artificial neural networks (ANNs) have been suggested in recent years as a method for CFD based spectrum detection, which increases detection accuracy and decreases complexity. However, the variable signal to noise ratio (SNR) and noise variance have an impact on the effectiveness of ANNs for CFD-based spectrum sensing. The effectiveness of ANNs for …CFD based spectrum sensing under different SNR and noise variance conditions is evaluated in this work for the determination of threshold value in a dynamic way. We look into how SNR and noise variance affect the precision of probability of detection (Pd) and system complexity. Out analysis show how well ANNs work for CFD based spectrum detection with dynamic threshold value in the presence of changing SNR and noise variation. The findings demonstrate that ANNs may still obtain high Pd values with low SNR and large noise variance while maintaining a modest level of system complexity. According to our research, for a variety of SNR and noise variance situations, ANNs may be a viable option for CFD based spectrum detection in cognitive radio (CR) networks. The proposed approach can significantly improve the detection accuracy and reduce the complexity of the system, thereby enhancing the overall performance of cognitive radio networks. Based on the proposed work, it is determined that MPSK modulation function well with additive white Gaussian noise (AWGN), Rayleigh, and Rician channels up to a lower SNR value of – 30 dB and MQAM supports a lower SNR value of up to – 20 dB. Show more

Keywords: Cyclostationary feature detection, ANN, varying SNR, noise variance, dynamic thresholding, probability of detection

DOI: 10.3233/JIFS-232610

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3247-3257, 2023

Authors: Wei, Lin

Article Type: Research Article

Abstract: Blended teaching, which combines the advantages of face-to-face teaching and online learning, has become an important breakthrough in the current higher education teaching reform and innovation, and the construction of a blended teaching quality evaluation in college English courses system is of great significance and value to ensure the sustainable development of blended teaching activities. The blended teaching quality evaluation in college English courses is regarded as the defined multiple attribute decision making (MADM). In this paper, the Combined Compromise Solution (CoCoSo) method is constructed for MADM under double-valued neutrosophic sets (DVNSs). Then, the double-valued neutrosophic numbers CoCoSo (DVNN-CoCoSo) method …is built for MADM. Finally, a practical numerical example for blended teaching quality evaluation in college English courses is supplied to show the DVNN-CoCoSo method. The main contributions of this constructed paper are: (1) This paper builds the novel MADM method based on CoCoSo decision methods under DVNSs, which extends the classification CoCoSo method. (2) The new MADM method for blended teaching quality evaluation in college English courses based on DVNN-CoCoSo is proposed. Show more

Keywords: MADM, double-valued neutrosophic sets (DVNSs), CoCoSo method, blended teaching quality evaluation

DOI: 10.3233/JIFS-224389

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3259-3266, 2023

Authors: Lei, Yingzi

Article Type: Research Article

Abstract: The goal of internationalization, the actual needs of the new generation of college students, and the innovation of information technology have led to a shift from teaching English for general purposes (EGP) to teaching English courses. The blended teaching effectiveness evaluation of English courses in universities in an output-oriented perspective is viewed as the multiple attribute decision making (MADM). In this paper, Taxonomy method is designed for MADM under interval neutrosophic sets (INSs). Then, the interval neutrosophic numbers Taxonomy (INN-Taxonomy) method is formed to cope with MADM problem. Finally, a numerical decision example for blended teaching effectiveness evaluation of English …courses in universities in an output-oriented perspective is given to demonstrate the INN-Taxonomy method. Show more

Keywords: MADM, interval neutrosophic sets (INSs), taxonomy method, blended teaching effectiveness evaluation

DOI: 10.3233/JIFS-224475

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3267-3277, 2023

Authors: Zhang, Xin | Feng, Tao | Zhang, Shao-Pu

Article Type: Research Article

Abstract: Rough set theory is a powerful tool for handling uncertainty and vagueness in various fields. The hesitant fuzzy rough set, as a generalization of rough sets, can solve more complex problems. However, existing hesitant fuzzy rough sets do not satisfy the inclusive property. To address this issue, a novel hesitant fuzzy rough set model based on dual score functions is proposed. Four generalized hesitant fuzzy rough sets and their discernibility matrices are also presented. Additionally, the lower approximation distribution reductions can be obtained by the discernibility matrix. Meanwhile, hypergraphs provide an accurate description of relationships between multiple objects and offer …a concise operational approach. Then it is discovered that finding the lower approximation distribution reductions of a hesitant fuzzy decision system is equivalent to finding the minimal transversals of its hypergraph. Moreover, an improved algorithm for hesitant fuzzy decision systems based on hypergraphs is presented to accelerate the reduction process. Finally, the proposed algorithm is applied to the hybrid data of Hepatitis C Virus from UCI to demonstrate its feasibility. Show more

Keywords: Attribute reduction, hesitant fuzzy rough set, hypergraph, hesitant fuzzy decision system, approximation operator

DOI: 10.3233/JIFS-230460

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3279-3304, 2023

Authors: Wang, Rong | Rong, Xia

Article Type: Research Article

Abstract: With the rapid development of society, ideological and political education courses have occupied a very important position in various courses in major universities, playing a series of important functions and roles in student quality education, excellent quality cultivation, and other aspects. In the new era, the evaluation and assessment of ideological and political education quality is not only the primary factor to improve the teaching quality of ideological and political education courses in universities, but also an important means to promote the deepening reform of ideological and political education. However, there are many problems in the process of evaluating the …quality of ideological and political education in colleges and universities at present, such as the deviation in understanding the importance of evaluation, the relatively single evaluation method, and the low quality of application of evaluation results. The teaching quality evaluation of ideological and political courses in universities is a classical multiple attribute group decision making (MAGDM). Spherical fuzzy sets (SFSs) provide more free space for decision makers (DMs) to express preference information during the teaching quality evaluation of ideological and political courses in universities. Therefore, this paper we first extend partitioned Maclaurin symmetric mean (PMSM) operator and IOWA operator to SFSs and develop induced spherical fuzzy weighted PMSM (I-SFWPMSM) operator. Subsequently, a new MAGDM method is established based on I-SFWPMSM operator and SFNWG operator under SFSs. Finally, a numerical example for teaching quality evaluation of ideological and political courses in universities is used to illustrate the proposed method. Show more

Keywords: Multiple attribute group decision making (MAGDM), spherical fuzzy sets, I-SFWPMSM operator, teaching quality evaluation

DOI: 10.3233/JIFS-231714

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3305-3319, 2023

Authors: Zhang, Xingfu

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-232839

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3321-3331, 2023

Authors: Huang, Lixun | Sun, Lijun | Chen, Tianfei | Zhang, Qiuwen | Huo, Linlin | Liu, Weihua

Article Type: Research Article

Abstract: Hold-up compensation decelerates the convergence of iterative learning control (ILC) systems with data dropouts and time delays. Only depending on the prior knowledge of both ILC controllers and transmission channels, this paper develops a predictor to calculate the input not received on time due to data dropouts and time delays. First, a controller adopting the proportional learning strategy is considered directly, which is appropriate for objects in ideal communication conditions. After that, two data-receiving equations are given to describe the effect of data dropouts and one-step time delays. Finally, a predictor is designed according to the innovation analysis approach. Since …the prediction uses all historical input at the identical time index in previous iterations, the predicted input is more approximate to the one not received on time than the input held up for compensation. Simulation results show the object with prediction compensation tracks the expected trajectory faster than that with input-hold compensation. Show more

Keywords: Iterative learning control, convergence, input predictor, data dropout, time delay

DOI: 10.3233/JIFS-223074

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3333-3344, 2023

Authors: Jafari, Aya | Al-Mousa, Amjed | Jafar, Iyad

Article Type: Research Article

Abstract: The advent use of smart devices has enabled the emergence of many applications that facilitate user interaction through speech. However, speech reveals private and sensitive information about the user’s identity, posing several security risks. For example, a speaker’s speech can be acquired and used in speech synthesis systems to generate fake speech recordings that can be used to attack that speaker’s verification system. One solution is to anonymize the speaker’s identity from speech before using it. Existing anonymization schemes rely on using a pool of real speakers’ identities for anonymization, which may result in associating a speaker’s speech with an …existing speaker. Hence, this paper investigates the use of Generative Adversarial Networks (GAN) to generate a pool of fake identities that are used for anonymization. Several GAN types were considered for this purpose, and the Conditional Tabular GAN (CTGAN) showed the best performance among all GAN types according to different metrics that measure the naturalness of the anonymized speech and its linguistic content. Show more

Keywords: Speaker anonymization, voice privacy, generative adversarial networks, CTGAN, x-vector

DOI: 10.3233/JIFS-223642

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3345-3359, 2023

Authors: Shen, Xin | Xu, Qianhui | Liu, Qiao | Leibercht, Markus

Article Type: Research Article

Abstract: With the acceleration of technological change and globalization, companies face increasing environmental uncertainty and complexity. The COVID-19 pandemic has severely damaged the global supply chain and aggravated the operational risks of supply chains. Industry and academia have conducted studies on the construction of resilient and integrated supply chains, and to date a bulk of empirical literature has already been accumulated. A notable feature of existing literature is the heterogeneity in the characterization of the relationship between supply chain resilience, supply chain integration, and supply chain performance. In this study meta-analysis and structural equation modeling (MASEM) methods are integrated to construct …a theoretical framework of supply chain resilience, supply chain integration, and supply chain performance. 45 empirical studies (73 effect size data, 2092 samples) are selected from 10,623 papers published over the years 2013 to 2021 to explore the transmission mechanisms, the role of mediator variable, and boundary conditions of the relationship between supply chain resilience and supply chain performance. The results show that supply chain resilience can promote supply chain performance. Moreover, supply chain integration (supplier integration, internal integration, and customer integration) plays a partial mediating role for the impact of supply chain resilience on supply chain performance. Situations and measurement factors such as industry type, national culture (power distance), sampling area, and logistics performance have a certain impact on the relationship, and the usage of different indicators may lead to marked differences in conclusions regarding the relationship. By extracting the conclusions of existing empirical studies, this study proposes new insights into the mechanism of action of supply chain resilience, supply chain integration, and supply chain performance and provides specific suggestions for future supply chain management. Show more

Keywords: Supply chain resilience, supply chain integration, supply chain performance, meta-analysis, structural equation model

DOI: 10.3233/JIFS-220649

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3361-3377, 2023

Authors: Shu, Wenhao | Chen, Ting | Qian, Wenbin | Yan, Zhenchao

Article Type: Research Article

Abstract: Feature selection focuses on selecting important features that can improve the accuracy and simplification of the learning model. Nevertheless, for the ordered data in many real-world applications, most of the existing feature selection algorithms take the single-measure into consideration when selecting candidate features, which may affect the classification performance. Based on the insights obtained, a multi-measure feature selection algorithm is developed for ordered data, which not only considers the certain information by the dominance-based dependence, but also uses the discern information provided by the dominance-based information granularity. Extensive experiments are performed to evaluate the performance of the proposed algorithm on …UCI data sets in terms of the number of selected feature subset and classification accuracy. The experimental results demonstrate that the proposed algorithm not only can find the relevant feature subset but also the classification performance is better than, or comparably well to other feature selection algorithms. Show more

Keywords: Ordered decision system, dominance-based rough set, multi-measure, feature selection

DOI: 10.3233/JIFS-224474

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3379-3392, 2023

Authors: Kalawi, Dana | Cakar, Tarık | Gurul, Binnur

Article Type: Research Article

Abstract: This study aims to investigate the sustainable campus criteria, the variations made or require to be done to become an ecologically sustainable campus. In this context, the major goal of the research is assessing the sustainable campus design principles and indicators, setting the targets and deciding the precedencies with the Fuzzy Multi-Criteria Decision-Making methods (MCDM) for the sustainable campus design at Istanbul Gelisim University. In this study, model-based methods have been used to evaluate the sustainable campus performance of universities. In this respect, the study differs from other studies in the literature. Another difference of this study is that three …different Fuzzy Multi-Criteria Decision-Making methods has been used, these methods are Fuzzy-AHP, Fuzzy-TOPSIS and Fuzzy-ELECTRE. All three have different inference mechanisms. A common solution has been obtained by using the results of these three different Fuzzy-MCDM methods as hybrid dominance and superiority criteria. Here, the Copeland method, which takes the superiority criterion as a reference, has been used in the options where we could not provide the dominance criterion. At the end of this study, a recommendation report has been prepared according to these results. Show more

Keywords: Sustainable campus, fuzzy multicriteria decision making, fuzzy AHP, fuzzy TOPSIS, fuzzy ELECTRE

DOI: 10.3233/JIFS-223778

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3393-3415, 2023

Authors: Sherubha, P. | Jubair Ahmed, L. | Kannan, K.S. | Sasirekha, S.P.

Article Type: Research Article

Abstract: The aggressive form of cancer commonly in breast cells is breast cancer. The highly aggressive form of cancer is frequently created in breast cells. The need for the predictive model to accurately measure the prognosis prediction of breast cancer in the earlier stage is highly recommended. This development of methods for protecting people from fatal diseases by the researchers from the different disciplines who are all working altogether. An accurate breast cancer prognosis prediction is made by using a good predictive model to assist Medical Internet of Things (mIoT). Various advantages such as cancer detection in an earlier stage, medical …expenses related to treatment, and having unwanted treatment gives the accurate prediction attains spare patients. Existing models lie on the uni-modal data such as chosen gene expression to predict the model’s design. Few learning-based predictive models are used in the proposed method to improve breast cancer prognosis prediction from the current data sets. Most of the peculiar benefits of the suggested method rely on the model’s architecture. Here, a novel adaptive boosting model (a-BM) is used to measure the loss function of every individual and intends to reduce the error rate. Various performances metrics are used to evaluate the predictive performance, which provides the model gives a good outcome rather than the previous techniques. Show more

Keywords: Machine learning, breast cancer, prediction rate, loss function, error rate

DOI: 10.3233/JIFS-230086

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3417-3431, 2023

Authors: Kaspar, A. John | Christy, D.K. Sheena | Gloria, D.K. Shirley

Article Type: Research Article

Abstract: A two dimensional language is a collection of two dimensional words, which are rectangular array of symbols made up of finite alphabets. Fuzzy Petri nets are the generalization of classical Petri nets designed to deal with imprecise and ambiguous data which usually occurs in knowledge based systems, image processing, etc. They have been widely used to represent fuzzy production rules and fuzzy rule-based reasoning. In this paper, a new model called array token fuzzy Petri net to generate two dimensional fuzzy regular languages has been introduced. Array token fuzzy Petri nets are used to deal with impreciseness and uncertainties occurring …in two dimensional regular languages. Furthermore, proved that for every two dimensional fuzzy regular grammar there exists an array token fuzzy Petri net that generates the same two dimensional fuzzy regular language and also establish some closure properties of the languages generated by array token fuzzy Petri net. Show more

Keywords: Array grammars, array token petri nets, fuzzy petri nets, fuzzy languages, picture languages

DOI: 10.3233/JIFS-222833

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3433-3443, 2023

Authors: Pandithurai, O. | Urmela, S. | Murugesan, S. | Bharathiraja, N.

Article Type: Research Article

Abstract: The Wireless IoT sensor network (IWSN) has significant potential in industrial settings, but to fully realize its benefits, a robust and scalable computer system is required to handle the continuous influx of data from various applications. In this research study, we propose an IoT sensor-cloud architecture that integrates WSN with cloud technology, providing a unique data analytics framework for highly secure analysis of sensor data. The proposed architecture emphasizes effective interoperability mechanisms in the cloud, and provides an IPv6 extensible enterprise WSN design and simulation technique. To demonstrate the effectiveness of our proposed architecture, we track the pH, resistivity, and …dissolved oxygen levels of industrial effluents that are discharged into water sources. We use AT instructions in conjunction with the HTTP GET technique to gather and upload detector data to the ThingSpeak cloud through a GPRS internet connection, enabling real-time online monitoring and control using IoT functionality. The proposed architecture uses a distributed approach to handle high volumes of incoming data from the IoT sensors, storing the data in a scalable and accessible way for analysis. Real-time analysis is performed using a combination of batch and stream processing frameworks and machine learning algorithms, and the results are visualized using a web-based dashboard that provides real-time updates on key metrics and allows users to explore the data in different ways. Security is a top priority in our proposed architecture, and we use encryption technologies such as SSL/TLS and access control mechanisms such as OAuth2 to ensure the secure transmission and storage of sensitive industrial IoT data. The architecture is designed to be scalable and adaptable to handle a wide range of IoT use cases in industrial settings. The proposed IoT sensor-cloud architecture provides a robust and scalable solution for the collection, analysis, and exchange of significant amounts of IoT sensor information, enabling real-time monitoring and control of critical environmental parameters in industrial settings. Show more

Keywords: WSN, cloud computing, IoT, IoT sensor, industrial case study

DOI: 10.3233/JIFS-224174

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3445-3460, 2023

Authors: Zhu, Xiaorong

Article Type: Research Article

Abstract: The quality management of higher vocational education has become an important part of talent training in colleges and universities. With the increasing demand for social talents, the adaptability of traditional teaching management concepts has gradually emerged. In this case, the innovation and practice of education quality management will become the key research content of higher vocational colleges in the new era in combination with the actual situation of higher vocational colleges and from the perspective of the overall development of talents in vocational colleges. The higher vocational education management quality evaluation is viewed as the multi-attribute decision-making (MADM). In this …paper, the cross-entropy method under The fuzzy number intuitionistic fuzzy sets (FNIFSs) is built based on the traditional cross-entropy method. Firstly, the FNIFSs is introduced. Then, combine the traditional fuzzy cross-entropy method with FNIFSs information, the cross-entropy method is established for MADM under FNIFSs. Finally, a numerical example for higher vocational education management quality evaluation has been given and some comparisons is used to illustrate advantages of cross-entropy method with FNIFSs. Show more

Keywords: Multiple attribute decision making (MADM) problems, fuzzy number intuitionistic fuzzy sets (FNIFSs), cross-entropy method; higher vocational education, management quality evaluation

DOI: 10.3233/JIFS-230094

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3461-3471, 2023

Authors: he, Jia-long | zhang, Xiao-Lin | wang, Yong-Ping | zhang, Huan-Xiang | gao, Lu | xu, En-Hui

Article Type: Research Article

Abstract:  In recent years, contrastive learning has been very successful in unsupervised tasks of representation learning and has received a lot of attention in supervised tasks. In supervised tasks, the discrete nature of natural language makes the construction of sample pairs difficult and the models are poorly robust to adversarial samples, so it remains a challenge to make contrastive learning effective for text classification tasks and to guarantee the robustness of the models. This paper presents a contrastive adversarial learning framework built using data augmentation with labeled insertion data. Specifically,By adding perturbation to the word-embedding matrix, adversarial samples are generated as …positive examples of contrastive learning, and external semantic information is introduced to construct negative examples. Contrastive learning is used to improve the sensitivity and generalization ability of the model, and adversarial training is used to improve robustness, thereby improving the classification accuracy. In addition, the momentum contrast from unsupervised tasks is also introduced into the text classification task to increase the number of sample pairs. Experimental results on several datasets show that the proposed approach outperforms the baseline comparison approach, and in addition some experiments are conducted to verify the effectiveness of the proposed framework under low-resource conditions. Show more

Keywords: Contrastive learning, adversarial training, text classification

DOI: 10.3233/JIFS-230787

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3473-3484, 2023

Authors: Rana, Anurag | Vaidya, Pankaj | Kautish, Sandeep | Kumar, Manoj | Khaitan, Supriya

Article Type: Research Article

Abstract: Parameters related to earthquake origins can be broken down into two broad classes: source location and source dimension. Scientists use distance curves versus average slowness to approximate the epicentre of an earthquake. The shape of curves is the complex function to the epicentral distance, the geological structures of Earth, and the path taken by seismic waves. Brune’s model for source is fitted to the measured seismic wave’s displacement spectrum in order to estimate the source’s size by optimising spectral parameters. The use of ANFIS to determine earthquake magnitude has the potential to significantly alter the playing field. ANFIS can learn …like a person using only the data that has already been collected, which improves predictions without requiring elaborate infrastructure. For this investigation’s FIS development, we used a machine with Python 3x running on a core i5 from the 11th generation and an NVIDIA GEFORCE RTX 3050ti GPU processor. Moreover, the research demonstrates that presuming a large number of inputs to the membership function is not necessarily the best option. The quality of inferences generated from data might vary greatly depending on how that data is organised. Subtractive clustering, which does not necessitate any type of normalisation, can be used for prediction of earthquakes magnitude with a high degree of accuracy. This study has the potential to improve our ability to foresee quakes larger than magnitude 5. A solution is not promised to the practitioner, but the research is expected to lead in the right direction. Using Brune’s source model and high cut-off frequency factor, this article suggests using machine learning techniques and a Brune Based Application (BBA) in Python. Application accept input in the Sesame American Standard Code for Information Interchange Format (SAF). An application calculates the spectral level of low frequency displacement (Ω0 ), the corner frequency at which spectrum decays with a rate of 2(fc ), the cut-off frequency at which spectrum again decays (fmax ), and the rate of decay above fmax on its own (N ). Seismic moment, stress drop, source dimension, etc. have all been estimated using spectral characteristics, and scaling laws. As with the maximum frequency, fmax, its origin can be determined through careful experimentation and study. At some sites, the moment magnitude was 4.7 0.09, and the seismic moment was in the order of (107 0.19) 1023. (dyne.cm). The stress reduction is 76.3 11.5 (bars) and the source-radius is (850.0 38.0) (m). The ANFIS method predicted pretty accurately as the residuals were distributed uniformly near to the centrelines. The ANFIS approach made fairly accurate predictions, as evidenced by the fact that the residuals were distributed consistently close to the centerlines. The R2, RMSE, and MAE indices demonstrate that the ANFIS accuracy level is superior to that of the ANN. Show more

Keywords: Artificial neural networks, brune based application, adaptive neuro fuzzy inference system, source dimension, earthquake occurrence, prediction

DOI: 10.3233/JIFS-224423

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3485-3500, 2023

Authors: Tu, Feng Miao | Wei, Ming Hui | Liu, Jun | Liao, Lu Lu

Article Type: Research Article

Abstract: In steel surface inspection, an accurate steel surface defect identification method is needed to evaluate the impact of defects on structural performance and system maintenance. Traditionally, the recognition accuracy of methods based on handcrafted features is limited, but the system performance can be improved by feature fusion extracted by different methods. Therefore, this research uses the pre-trained convolutional neural network (CNN) combined with transfer learning to extract effective abstract features, and carries out adaptive weighting multimodal fusion of three the abstract features and handcrafted feature sets at the decision-making level, that is, proposes an adaptive weighting multimodal fusion classification system. …The system uses handcrafted features as a supplement to abstract features, and accurately classifies steel surface defects in completely different feature representation spaces. Based on the NEU steel plate surface defect benchmark database, the classification results of feature sets before and after fusion are compared and analyzed. The experimental results show that the classification accuracy of the fusion system is improved by at least 3.4% compared with that before fusion, and the final accuracy rate is 99.0%, which proves the effectiveness of the proposed system. Show more

Keywords: CNN-based features, feature extraction, steel plate surface defect, decision-level fusion

DOI: 10.3233/JIFS-230170

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3501-3512, 2023

Authors: Ye, Kangrui | Jiang, Huiqin | Sadati, Seyed Hossein | Talebi, Ali Asghar

Article Type: Research Article

Abstract: A cubic fuzzy graph is a fuzzy graph that simultaneously supports fuzzy membership and interval-valued fuzzy membership. This simultaneity leads to a better flexibility in modeling problems regarding uncertain variables. The cubic fuzzy graph structure, as a combination of cubic fuzzy graphs and graph structures, shows better capabilities in solving complex problems, especially where there are multiple relationships. Since many problems are a combination of different relationships, as well, applying some operations on them creates new problems; therefore, in this article, some of the most important product operations on cubic fuzzy graph structure have been investigated and some of their …properties have been described. Studies have shown that the product of two strong cubic fuzzy graph structures is not always strong and sometimes special conditions are needed to be met. By calculating the vertex degree in each of the products, a clear image of the comparison between the vertex degrees in the products has been obtained. Also, the relationships between the products have been examined and the investigations have shown that the combination of some product operations with each other leads to other products. At the end, the cubic fuzzy graph structure application in the diagnosis of brain lesions is presented. Show more

Keywords: Cubic fuzzy graph structure, lexicographic max-product, residue product, tensor product

DOI: 10.3233/JIFS-222984

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3513-3538, 2023

Authors: Liu, Wuniu | Li, Zhihui | Li, Yongming

Article Type: Research Article

Abstract:  Multi-objective optimization can be used to address possible conflicting relationships between multiple objectives. However, some objectives have a fuzzy temporal relationship between them, making it difficult to give a common method to portray the fuzzy temporal relationship. To fill this gap, we propose the concept of complex objectives, which can be described by fuzzy temporal logic that includes both temporal and logical operators. Furthermore, we investigated the optimal control problems of complex objectives and developed a fuzzy system called possibilistic decision systems (PDSs) to establish a framework for optimal control. In PDSs, states of fuzzy systems are determined by a …family of variables, and transitions induced by actions between fuzzy states of systems are also fuzzy uncertain and determined by a possibility degree. Importantly, we proved that memoryless strategies are sufficient for optimal control of complex objectives. Finally, the theory presented in this paper is illustrated by a mobile robot simulation. Show more

Keywords: Multi-objective optimization, complex objectives, fuzzy temporal logic, decision systems, possibility theory

DOI: 10.3233/JIFS-221966

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3539-3553, 2023