Journal of Intelligent & Fuzzy Systems - Volume 45, issue 5

Authors: Subhashini, R. | Hemalakshmi, G.R. | Rajalakshmi, R. | Chen, Chuang

Article Type: Research Article

Abstract: The quality of sleep plays a crucial role in physical well-being, and individuals are becoming increasingly concerned about sleep quality and its associated health issues. Although various sleep monitoring devices exist, there remains a need for a highly accurate sleep state identification algorithm. To address this, we present a paper that utilizes machine learning techniques to identify human sleep states based on electroencephalogram (EEG) signals collected by an EEG instrument. We propose a model that incorporates two nonlinear characteristic parameters, MSE and PSE, extracted from artificially designed EEG signals as input. Additionally, we employ a Support Vector Machine (SVM) classifier …algorithm to accurately identify sleep states, eliminating uncertainties associated with manually designed feature parameters. Experimental results demonstrate the superior accuracy of our proposed model for sleep state analysis, offering valuable insights for improving sleep quality and addressing related health concerns. Show more

Keywords: Sleeping quality, health, electroencephalograph, support vector machine, machine learning

DOI: 10.3233/JIFS-230765

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8703-8716, 2023

Authors: Zhao, Aiwu | Du, Chuantao | Guan, Hongjun

Article Type: Research Article

Abstract: Based on the double hierarchy linguistic term sets (DHLTS), a novel forecasting model is proposed considering both the internal fluctuation rules and the external correlation of different time series. The innovative aspects of this model consist of: (i) It can expresses more internal fluctuation and external correlation information, providing guarantees for improving the predictive performance of the model. (ii) The equivalent transformation function of DHLTS reduces the fuzzy granularity and improves the prediction accuracy. (iii) The application of similarity measures can extract the closest rules from historical states based on the distance operators of DHLTS. In addition, experiments on TAIEX …considering the impact of the U.S. stock market and other data show that the model has good predictive performance. Show more

Keywords: Fuzzy time series, double hierarchy linguistic term set, forecast, Co-movements of stock markets

DOI: 10.3233/JIFS-230810

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8717-8733, 2023

Authors: Gao, Gengjun | Wang, Yiwen

Article Type: Research Article

Abstract: The rationality of profit distribution affects the stability of the multimodal transport alliance. In the multimodal transport alliance, the participation rate of the carrier and the communication structure of the alliance are important influencing factors of profit distribution results. To get a reasonable profit distribution scheme, this paper constructs a profit distribution model considering the characteristics of multimodal transport, called the Choquet Cloud Gravity Center AT model. Firstly, considering the communication structure of the alliance, the Cloud Gravity Center Average Tree method is used as the base model for profit distribution. Secondly, considering the multimodal transport alliance is a fuzzy …coalition, the profit for each alliance subset in the base model is calculated by the Choquet integral. Then, the profit distribution model considering participation rate and communication structure is obtained. Finally, a numerical example is given to illustrate the applicability of the model, and comparative analysis is conducted to verify the rationality of the model. This study provides a suitable profit-sharing model for multimodal transport alliances, which is conducive to the stable and efficient operation of alliances. Show more

Keywords: Multimodal transport, profit distribution, fuzzy coalition, communication structure, Choquet integral

DOI: 10.3233/JIFS-231370

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8735-8746, 2023

Authors: Cano-Izquierdo, Jose-Manuel | Ibarrola, Julio | Almonacid, Miguel

Article Type: Research Article

Abstract:  Deep-learning (DL) is a new paradigm in the artificial intelligence field associated with learning structures able to connect directly numeric data with high-level patterns or categories. DL seems to be a suitable technique to deal with computationally challenging Brain Computer Interface (BCI) problems. Following DL strategy, a new modular and self-organized architecture to solve BCI problems is proposed. A pattern recognition system to translate the measured signals in order to establish categories representing thoughts, without previous pre-processing, is developed. To achieve an easy interpretability of the system internal functioning, a neuro-fuzzy module and a learning methodology are carried out. The …whole learning process is based on machine learning. The architecture and the learning method are tested on a representative BCI application to detect and classify motor imagery thoughts. Data is gathered with a low-cost device. Results prove the efficiency and adaptability of the proposed DL architecture where the used classification module (S-dFasArt) exhibits a better behaviour compared with the usual classifiers. Additionally, it employs neuro-fuzzy modules which allow to offer results in a rules format. This improves the interpretability with respect to the black-box description. A DL architecture, going from the raw data to the labels, is proposed. The proposed architecture, based on Adaptive Resonance Theory (ART) and Fuzzy ART modules, performs data processing in a self-organized way. It follows the DL paradigm, but at the same time, it allows an interpretation of the operation stages. Therefore this approach could be called Transparent Deep Learning. Show more

Keywords: Transparent deep learning, brain computer interface, neuro-fuzzy modular architecture, s-dFasArt, motor imagery

DOI: 10.3233/JIFS-231387

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8747-8760, 2023

Authors: Buche, Arti | Chandak, M.B.

Article Type: Research Article

Abstract: In the field of finance, deep learning techniques have been extensively researched for predicting stock prices. In this research, we propose a novel approach for predicting stock price movements using a combination of reviews and historical price data for SBI and HDFC stocks. As market volatility is influenced by numerous factors, it is crucial to consider it while predicting stock prices. To capture the interactions between the price and text data effectively, we create a fusion mix and utilize a hybrid information mixing module, designed using BERT and BiLSTM, to extract the multimodal interactions between the time series and semantic …features. The proposed model, the hybrid information mixing module, is based on a multilayer perceptron and achieves high accuracy in predicting price fluctuations in highly volatile stock markets. Future research can extend this approach to include additional data sources and explore other deep learning techniques for better performance. Show more

Keywords: Natural language processing, deep learning, multilayer perceptron, BiLSTM, BERT, Indian stock market

DOI: 10.3233/JIFS-231472

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8761-8773, 2023

Authors: Adaikkan, Kalaivani | Thenmozhi, Durairaj

Article Type: Research Article

Abstract: Social media has become one of the most popular medium of communication and the post may be predominantly unstructured, informal, and frequently misspelled. It has become increasingly common for users to use abusive language in their comments. Detecting offensive language on social media platforms and the presence of such language on the Internet has become a major challenge for modern society. To overcome this challenge, Offensive Language Classification based on the Chaotic Antlion optimization algorithm has been proposed. Initially, the dataset is pre-processed using NLP languages for removing irrelevant data. Consequently, statistical, synthetic, and lexicon features are extracted using various …feature extraction techniques. A Chaotic Antlion Optimization Algorithm is used to select the most relevant features during the feature selection phase. After selecting the features, a Ghost network classifies the input data into four classes namely offensive, non-offensive, swear, and offensive but not offensive. The proposed method was evaluated based on a number of variables, including precision, accuracy, specificity, recall, and F-measure. The best classification accuracy is achieved by the suggested method, which is 99.27% for the SOLID dataset and 98.99% for the OLID dataset. The suggested method outperforms the DCNN, Simple Logistics, and CNN methods in terms of overall accuracy by 4.99%, 8.72%, and 10.4%, respectively. Show more

Keywords: Chaotic Antlion optimization algorithm, detecting offensive language, SOLID dataset, Ghost network, DCNN

DOI: 10.3233/JIFS-232217

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8775-8788, 2023

Authors: Zhang, Xu | Lv, Mingrui | Yuan, Xumei

Article Type: Research Article

Abstract: In order to solve the problems of insufficient uncertainty information measure, inaccuracy of weight calculation and incommensurability of indices in hybrid multi-criteria decision making, this paper introduces the Cloud-CRITIC weight calculation method and Cloud-CRITIC-PDR method, which combine cloud model, CRITIC method and Probabilistic Dominance Relation (PDR). In these two methods, the cloud model is used to characterize uncertainty, the Comprehensive information of CRITIC method has been modified in order to adapt to uncertain situation, the PDR method is used to compare schemes. A case study concerning supplier evaluation is given to demonstrate the merits of the cloud-CRITIC and cloud-CRITIC-PDR. The …effectiveness and superiority of the developed methods are further illustrated through method comparison and sensitivity analysis. These combined methods can be used for dealing with decision-making problems with complex index types and strong data uncertainty, such as supplier evaluation and risk assessment. There are few papers about combining the cloud model, CRITIC method, and PDR method under hybrid indices decision-making situation at present, so this paper can provide a new perspective on hybrid MCDM. Show more

Keywords: Hybrid multi-criteria decision making, cloud model, probabilistic dominance relationship, CRITIC, Gaussian criterion

DOI: 10.3233/JIFS-232605

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8789-8803, 2023

Authors: Guo, Xiao | Feng, Qinrong | Zhao, Lin

Article Type: Research Article

Abstract:  Fuzzy soft set as a tool to deal with uncertainty can effectively handle decision making problems. However, there are many redundant parameters in the decision making process. In order to remove redundant parameters to improve the efficiency of decision making, different parameter reduction algorithms for fuzzy soft sets based on different decision criteria have been proposed. This paper focuses on the problem of parameter reduction of fuzzy soft sets based on choice value criteria. The restrictions of the strict conditions about parameter reduction lead to a very low applicability of some previous algorithms based on choice value criteria. To address …this limitation, we introduce a flexible definition of parameter reduction for fuzzy soft sets. Further a difference-based parameter reduction algorithm for fuzzy soft sets is proposed. Compared with some previous algorithms based on choice value criteria, the proposed algorithm not only has wider applicability, but also can reduce more redundant parameters making the found parameter reduction with a lower cardinality, and it is easier to find the parameter reduction of fuzzy soft sets. Show more

Keywords: Soft set, fuzzy soft set, parameter reduction, difference

DOI: 10.3233/JIFS-232657

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8805-8821, 2023

Authors: Muthazhagan, B. | Ravi, T. | Rajinigirinath, D.

Article Type: Research Article

Abstract: Lung cancer is the prevalent malignancy afflicting both men and women, mostly affects the chain smokers. The lung CT images are examined to identifying the abnormalities, but diagnosing lung cancer with CT images is time-consuming and difficult task. In this work, a novel Sooty-LuCaNet has been proposed in which the best features are selected using sooty tern optimization to reduces computational complexity of neural network. Initially, the denoised CT images are segmented using Grabcut technique to separate the lung nodules by eliminating the background distortions. The deep learning based Shufflenet is used to extract the structural features from the segmented …nodule and the textural features from the enhanced images. Afterwards, the sooty tern optimization (STO) algorithm is applied to select the most relevant features from the extracted features from the ShuffleNet. Finally, the classification process is carried out to differentiate the normal, small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) from the CT images. The experimental findings show the robustness of the proposed Sooty-LuCaNet based on the specific metrics namely sensitivity, accuracy, specificity, recall, precision and F1 score. An average classification accuracy of 99.16% is achieved for detection and classification of lung cancer. Show more

Keywords: Lung cancer, computed tomography, deep learning, Shufflenet, sooty tern optimization algorithm

DOI: 10.3233/JIFS-232875

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8823-8836, 2023

Authors: Ren, Rongrong | Wang, Hailong | Meng, Xinyu | Zhao, Meng

Article Type: Research Article

Abstract: Many businesses and organizations consider group decision making (GDM) to be an important decision-making strategy for dealing with complex decision-making difficulties. Although it is acknowledged that the difference in decision makers’ assessment scales has a significant impact on decision results, how to eliminate the difference in decision makers’ evaluation scales in the decision-making process has not been investigated further. In this research, the non-consensus of MAGDM is studied considering the difference of expert evaluation scale, and an improved two-stage multi-attribute group decision making method (MAGDM) is proposed. The example and comparative analysis of annual bonus allocation in engineering businesses validate …the effectiveness and operability of this system. Simultaneously, the approach is improved to handle the MAGDM problem of tiny samples, and the method’s problem of inadequate information is illustrated by numerical examples. The research presented in this work gives a practicable approach and idea for investigating the eradication of decision-maker evaluation scale disparities in MAGDM, and it demonstrates the importance of decision-maker evaluation scale differences in theoretical research and practical management. Show more

Keywords: Multi-attribute group decision making (MAGDM), expert evaluation scale, relative entropy, massive alternatives, normal distribution

DOI: 10.3233/JIFS-233618

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8837-8858, 2023

Authors: Cho, Seung-Beom | Jeong, Si-Hwa | Yu, Jae-Wook | Choi, Jae-Boong | Kim, Moon Ki

Article Type: Research Article

Abstract: Despite the significant improvements in the detection and diagnosis of plant diseases at an early stage facilitated by deep learning technology, there are challenges associated with the generalization performance of deep learning models. These problems from the differences between in-field and in-lab data, as well as the heterogeneity of training and prediction data features. In the case of tomato leaf diseases, the PlantVillage dataset is widely used and has already demonstrated accuracy of more than 99%. However, using trained model based on this dataset to predict in-field data results in low accuracy due to domain differences and heterogeneous features. In …this paper, we propose a domain adaptation method based on CycleGAN to solve this problem, followed by a preprocessing technique that utilizes both the OpenCV module and a segmentation model based on U-Net for the best generalization performance. The classification accuracy is evaluated by applying the DenseNet121 model trained on the PlantVillage dataset to the images generated by CycleGAN. Our results demonstrate, with an F1-score of 95.6%, that our domain adaptation method between the two domains is effective in mitigating the effect of domain shift. Show more

Keywords: Image processing, leaf classification, deep learning, CycleGAN, domain adaptation, tomato leaf disease

DOI: 10.3233/JIFS-230561

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8859-8870, 2023

Authors: Wang, Ru | Peng, Kexin | Liu, Fang | Li, Shugang

Article Type: Research Article

Abstract: With the increasing of online social behavior, social relationships have an important impact on consumer negative comment behavior (CNCB) on social commerce platforms. Existing studies lack to describe CNCB influenced by social relationships on social commerce platforms from the perspective of well-thought-out planning results, and the proposed structural equation models in previous studies have been difficult to predict CNCB. Hence, this study proposes a new structural equation model (SEM) and artificial neural network (ANN) model to deeply explore and reveal the generation mechanism of CNCB in the context of social commerce platforms based on the theory of planned behavior (TPB). …We regard social support as a moderating effect and construct a consumer negative comment planning behavior model (CNCPBM). The results of the data analysis show CNCPBM is supported. This study provides an important theoretical and practical contribution to CNCB, and offers practical management enlightenment for the managers of social commerce platforms. Show more

Keywords: Social commerce platforms, theory of planned behavior, artificial neural network, social support, negative comment behavior

DOI: 10.3233/JIFS-230563

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8871-8888, 2023

Authors: Wu, Meiqin | Yang, Jindou | Fan, Jianping

Article Type: Research Article

Abstract: With the continuous improvement and development of various decision-making methods, it has led to the widespread use of fuzzy sets and fuzzy numbers. At the same time, the application of decision-making methods in different fuzzy environments has been very effective in addressing the deficiencies in existing research. At present, triangular fuzzy numbers have been widely used in the evaluation aspects of various decision making methods, and the proposed R-number effectively solve the uncertainty involving problems related to future events, but the existing research based on the TOPSIS method in the R-number environment has not yet been clearly applied to the …triangular fuzzy number environment, and the indifference threshold-based attribute ratio analysis (ITARA) method in the fuzzy environment has yet to be extended. Therefore, this paper proposes a fuzzy indifference threshold-based attribute ratio analysis (FITARA) method based on triangular fuzzy numbers for solving the problem of determining attribute weights in the multi-attribute decision-making process. Secondly, the various risks of the decision environment and the impact on future events are considered and R-number are used to solve this puzzle. In addition, the incorporation of risk perception factors in the context of the existing RTOPSIS method considering multiple risk factors and the use of Manhattan distances to optimize the large number of operations in the process of the method resulted in the development of the FITARA-RTOPSIS model. Finally, the proposed FITARA-RTOPSIS method is applied to the problem of siting emergency supplies storage depots, and the effectiveness of the proposed method is verified through comparative analysis. Show more

Keywords: FITARA, R-number, RTOPSIS, Manhattan distance, TFN

DOI: 10.3233/JIFS-232393

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8889-8905, 2023

Authors: Shyama, S. | Iyer, Radha R.

Article Type: Research Article

Abstract: The attractive properties of the hypercube graph such as its diameter, good connectivity, and symmetry have made it a popular topology for the design of multi-computer interconnection networks. Efforts to improve some of these properties have led to the evolution of hypercube variants. Let c be the proper coloring of graph G , where the neighboring vertices will get individual colors. Coloring c is irregular if distinct vertices have distinct color codes and the least number of colors that ought to receive an irregular coloring is the irregular chromatic number, χir  (G ). In this paper, we …discuss the irregular coloring and find the irregular chromatic number for the hypercube graph Q n and some of its variants using binomial coefficients for the Locally twisted cube graph LTQ n , Crossed cube graph CQ n and two types of Fractal cubic network graph FCNG 1  (k ) and FCNG 2  (k ). Show more

Keywords: Irregular coloring, irregular chromatic number, hypercube graph, variants of hypercube graph

DOI: 10.3233/JIFS-232471

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8907-8913, 2023

Authors: Prabhakaran, Sudarsan | Ayyamperumal, Niranjil Kumar

Article Type: Research Article

Abstract: This manuscript proposes an automated artifacts detection and multimodal classification system for human emotion analysis from human physiological signals. First, multimodal physiological data, including the Electrodermal Activity (EDA), electrocardiogram (ECG), Blood Volume Pulse (BVP) and respiration rate signals are collected. Second, a Modified Compressed Sensing-based Decomposition (MCSD) is used to extract the informative Skin Conductance Response (SCR) events of the EDA signal. Third, raw features (edge and sharp variations), statistical and wavelet coefficient features of EDA, ECG, BVP, respiration and SCR signals are obtained. Fourth, the extracted raw features, statistical and wavelet coefficient features from all physiological signals are fed …into the parallel Deep Convolutional Neural Network (DCNN) to reduce the dimensionality of feature space by removing artifacts. Fifth, the fused artifact-free feature vector is obtained for neutral, stress and pleasure emotion classes. Sixth, an artifact-free feature vector is used to train the Random Forest Deep Neural Network (RFDNN) classifier. Then, a trained RFDNN classifier is applied to classify the test signals into different emotion classes. Thus, leveraging the strengths of both RF and DNN algorithms, more comprehensive feature learning using multimodal psychological data is achieved, resulting in robust and accurate classification of human emotional activities. Finally, an extensive experiment using the Wearable Stress and Affect Detection (WESAD) dataset shows that the proposed system outperforms other existing human emotion classification systems using physiological data. Show more

Keywords: Emotional reactivity, physiological signals, modified compressed sensing, motion artifacts, deep convolutional neural network, random forest deep neural network

DOI: 10.3233/JIFS-232662

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8915-8929, 2023

Authors: Zhao, Yiming | Zhao, Hongdong | Zhang, Xuezhi | Liu, Weina

Article Type: Research Article

Abstract: In Intelligent Transport Systems (ITS), vision is the primary mode of perception. However, vehicle images captured by low-cost traffic cameras under challenging weather conditions often suffer from poor resolution and insufficient detail representation. On the other hand, vehicle noise provides complementary auditory features that offer advantages such as environmental adaptability and a large recognition distance. To address these limitations and enhance the accuracy of low-quality traffic surveillance classification and identification, an effective audio-visual feature fusion method is crucial. This paper presents a research study that establishes an Urban Road Vehicle Audio-visual (URVAV) dataset specifically designed for low-quality images and noise …recorded in complex weather conditions. For low-quality vehicle image classification, the paper proposes a simple Convolutional Neural Network (CNN)-based model called Low-quality Vehicle Images Net (LVINet). Additionally, to further enhance classification accuracy, a spatial channel attention-based audio-visual feature fusion method is introduced. This method converts one-dimensional acoustic features into a two-dimensional audio Mel-spectrogram, allowing for the fusion of auditory and visual features. By leveraging the high correlation between these features, the representation of vehicle characteristics is effectively enhanced. Experimental results demonstrate that LVINet achieves a classification accuracy of 93.62% with reduced parameter count compared to existing CNN models. Furthermore, the proposed audio-visual feature fusion method improves classification accuracy by 7.02% and 4.33% when compared to using single audio or visual features alone, respectively. Show more

Keywords: Vehicle classification, feature fusion, convolutional neural network, low-quality images

DOI: 10.3233/JIFS-232812

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8931-8944, 2023

Authors: Chen, Hao | Su, Ze | Xu, Xiangqian

Article Type: Research Article

Abstract: The rapid development of global information technology, especially the emergence and widespread application of the Internet, has enabled information technology to quickly penetrate into various fields of the economy and society. Informatization and networking have become important features of today’s era. However, while people enjoy the tremendous progress brought by information technology to humanity, the openness and security vulnerabilities of computer networks have also made network information security issues increasingly prominent. The invasion of hackers, the continuous generation and spread of computer virus, and the rampant use of rogue software have all caused great economic losses to individuals, enterprises, and …countries. The computer network security evaluation is a multiple-attribute group decision making (MAGDM). Then, the TODIM and TOPSIS method has been established to deal with MAGDM issues. The interval neutrosophic sets (INSs) are established as an effective tool for representing uncertain information during the computer network security evaluation. In this manuscript, the interval neutrosophic number TODIM-TOPSIS (INN-TODIM-TOPSIS) method is established to solve the MAGDM under INSs. Finally, a numerical example study for computer network security evaluation is established to validate the INN-TODIM-TOPSIS method. The main research contribution of this paper is established: (1) the INN-TODIM-TOPSIS method is put up for MAGDM with INSs; (2) the INN-TODIM-TOPSIS method is put up for computer network security evaluation and were compared with existing methods; (3) Through the detailed comparison, it is evident that INN-TODIM-TOPSIS method for computer network security evaluation proposed in this paper are effective. Show more

Keywords: Multiple-attribute group decision making (MAGDM), Interval neutrosophic sets (INSs), TODIM method, TOPSIS method, Computer network security evaluation

DOI: 10.3233/JIFS-233181

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8945-8957, 2023

Authors: Wang, Yixuan | Zhang, Xiaowen

Article Type: Research Article

Abstract: The lean management and innovation capability evaluation of technological small and medium sized enterprises is a classical multi-attributes group decision-making (MAGDM). Recently, the probabilistic hesitant fuzzy sets (PHFSs) have been extended to apply in many fields. However, the existing models don’t evaluate the alternative considering the psychological factors. Thus, in this paper, an extended probabilistic hesitant fuzzy grey relational analysis (PHF-GRA) method is proposed to reduce the restrictions of GRA method by combining with cumulative prospect theory (CPT), considering the psychological preference. In addition, the PHFSs assigns probability values to different degrees of hesitancy, which shows its superiority in complex …environment. At the same time, the weight vectors of each attribute are calculated by the entropy values of different foreground decision elements. Then, probabilistic hesitant fuzzy GRA (PHF-GRA) model based on CPT model is constructed for MAGDM under PHFSs. Finally, a practical example study for lean management and innovation capability evaluation of technological small and medium sized enterprises is constructed to validate the proposed GRA (PHF-GRA) model based on model CPT and some comparative studies are constructed to verify the applicability. Show more

Keywords: Multi-attributes group decision-making (MAGDM), probabilistic hesitant fuzzy sets (PHFSs), grey relational analysis (GRA) method, entropy, lean management and innovation capability evaluation

DOI: 10.3233/JIFS-233403

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8959-8972, 2023

Authors: Wang, Yahui | Chen, Hongchang | Liu, Shuxin | Li, Xing | Hu, Yuxiang

Article Type: Research Article

Abstract: With the continuous escalation of telecommunication fraud modes, telecommunication fraud is becoming more and more concealed and disguised. Existing Graph Neural Networks (GNNs)-based fraud detection methods directly aggregate the neighbor features of target nodes as their own updated features, which preserves the commonality of neighbor features but ignores the differences with target nodes. This makes it difficult to effectively distinguish fraudulent users from normal users. To address this issue, a new model named Feature Difference-aware Graph Neural Network (FDAGNN) is proposed for detecting telecommunication fraud. FDAGNN first calculates the feature differences between target nodes and their neighbors, then adopts GAT …method to aggregate these feature differences, and finally uses GRU approach to fuse the original features of target nodes and the aggregated feature differences as the updated features of target nodes. Extensive experiments on two real-world telecom datasets demonstrate that FDAGNN outperforms seven baseline methods in the majority of metrics, with a maximum improvement of about 5%. Show more

Keywords: Fraud detection, graph neural networks, telecommunication networks, feature fusion

DOI: 10.3233/JIFS-221893

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8973-8988, 2023

Authors: Yuvaraj, S. | Vijay Franklin, J.

Article Type: Research Article

Abstract: The predictions of cognitive emotions are complex due to various cognitive emotion modalities. Deep network model has recently been used with huge cognitive emotion determination. The visual and auditory modalities of cognitive emotion recognition system are proposed. The extraction of powerful features helps obtain the content related to cognitive emotions for different speaking styles. Convolutional neural network (CNN) is utilized for feature extraction from the speech. On the other hand, the visual modality uses the 50 layers of a deep residual network for prediction purpose. Also, extracting features is important as the datasets are sensitive to outliers when trying to …model the content. Here, a long short-term memory network (LSTM) is considered to manage the issue. Then, the proposed Dense Layer Model (DLM) is trained in an E2E manner based on feature correlation that provides better performance than the conventional techniques. The proposed model gives 99% prediction accuracy which is higher to other approaches. Show more

Keywords: Cognitive emotion recognition, deep learning, prediction, visual modality, handcrafted features

DOI: 10.3233/JIFS-230766

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8989-9005, 2023

Authors: Geng, Xiuli | Li, Yiqun | Zhang, Hongliu | He, Jianjia

Article Type: Research Article

Abstract: Product-service system (PSS) has attracted attention of manufacturers to shift from product-providing to solution-providing, which is a marketable set of products and services. The existing researches emphasize the fulfillment of individualized customer requirements through different PSS configurations. The PSS planning phase is of high importance in generating conceptual schemes, which translates customer requirements (CRs) to design requirements (DRs). In this paper, a systematic decision-making approach based on QFD is put forward aiming to configure the PSS design requirements (DRs). To address the uncertainty and hesitancy in QFD modeling, a hesitant fuzzy linguistic term sets (HFLTSs) is applied to elicit the …experts’ linguistic preferences in evaluating the importance of CRs and the relationships between CRs and DRs. To dealing with the group decision-making problems concerning the HFLTSs, the min-upper operator and the max-lower operator assemble the experts’ evaluation results into a linguistic interval, and then the numerical results can be obtained by using the 2-tuple linguistic representation model and the interval preference degree computation. A non-linear 0-1 programming model is proposed to select the target DRs’ specifications for maximizing customer satisfaction under cost constraint. In order to objectively determine the satisfaction degree of each optional specification of DR, the information axiom is introduced to construct the objective function via information content computation. To deal with the qualitative DRs, HFLTSs and information axiom are combined and hesitant information axiom (HIA) is proposed. Finally, a DRs optimization model is established using HIA and the imprecision method. A case study is carried out to demonstrate the effectiveness of the optimal PSS planning approach developed. Show more

Keywords: Product-service system (PSS), design requirement, information axiom, hesitant fuzzy linguistic term set, non-linear programming

DOI: 10.3233/JIFS-231329

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 9007-9028, 2023

Authors: Kai, Wei

Article Type: Research Article

Abstract: In this study, we focus on the analysis of factors influencing the siting decision of coal emergency reserve centers. Specifically, we first draw on the quality function deployment theory in marketing to logically integrate the ideas of this study. On this basis, we adopted an interdisciplinary fuzzy decision-making method, namely the G1-entropy method, to quantitatively evaluate the research of this paper. Thereafter, we constructed a three-level index system based on the characteristics of the coal emergency reserve site selection, and used the G1-entropy value method to calculate the weights of the indicators in the coal emergency reserve center siting decision …index system and obtain the results. Our research findings have found that the three key indicators of coal conventional reserve, emergency coal transportation methods, and emergency response time play a crucial role in the decision-making of coal emergency reserve center location. Therefore, we propose specific countermeasures and suggestions for these three key indicators. Our study can provide support for the government to better select the location of emergency coal reserves, better improve the national energy layout, and provide support for relevant decision makers on how to better reserve coal. The location of the emergency coal reserve center can better play the role of strategic reserve to stabilize the market function, effectively respond to the impact of various events on the energy market, and can make corresponding suggestions to the construction of the national energy security reserve system. Show more

Keywords: Emergency reserve center, site selection decision, quality function deployment theory, G1 method, entropy value method

DOI: 10.3233/JIFS-232299

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 9029-9052, 2023

Authors: Lyu, Aobo | Jiang, Jingjing | Zhou, Liang

Article Type: Research Article

Abstract: Central Bank Digital Currency (CBDC) pledges to realize a vast array of new functionalities, such as frictionless consumer payment and money-transfer systems, as well as precise supervision of money circulation, thereby enabling a number of new financial instruments and monetary policy levers. This study proposes, from a system feedback loop and cybernetics perspective, a Dynamic Issuance Mechanism (DIM) for CBDC that can theoretically enhance the vitality of economic operations. In accordance with this mechanism, the central bank implements dynamic issuance by monitoring cash leakage in real-time, so as to maintain the stability of the amount of money circulating on the …market, thereby boosting the currency turnover rate and financial vitality. To demonstrate the efficacy of the DIM, we employ the Agent-Based Modeling (ABM) tool to develop a macroeconomic simulation model for qualitative analysis that includes four entities: Central Bank, households, firms, and commercial banks. The multi-cycle operation process of the model includes a variety of economic indicators demonstrating that DIM has the potential to boost economic vitality and social production efficiency without exerting an adverse effect on citizens’ incomes, commodity prices, or the stability of the macroeconomic system. Finally, the function principle and potential risks of DIM are explained from a systems perspective, which offers a novel perspective for the functional design of CBDC and highlights that the hierarchical structure is a meaningful domain as the developmental direction. Show more

Keywords: Central bank digital currency, agent-based modeling, dynamic issuance mechanism, system feedback, macroeconomic

DOI: 10.3233/JIFS-221244

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 9053-9067, 2023

Authors: Fang, Jian | Lin, Xiaomei | Wu, Yue | An, Yi | Sun, Haoran

Article Type: Research Article

Abstract: As a deep learning network model, ResNet50 can effectively recognize facial expressions to a certain extent, but there are still problems such as insufficient extraction of local effective feature information and a large number of parameters. In this paper, we take ResNet50 as the basic framework to optimize and improve this network. Firstly, by analyzing the influence mechanism of the attention mechanism module on the network feature information circulation, the optimal embedding position of CBAM (Convolutional Block Attention Module) and SE modules in the ResNet50 network is thus determined to effectively extract local key information, and then the number of …model parameters is effectively reduced by embedding the depth separable module. To validate the performance of the improved ResNet50 model, the recognition accuracy reached 71.72% and 95.72% by ablation experiments using Fer2013 and CK+ datasets, respectively. We then used the trained model to classify the homemade dataset, and the recognition accuracy reached 92.86%. In addition, compared with the current more advanced methods, the improved ResNet50 network model proposed in this paper can maintain a balance between model complexity and recognition ability and can provide a technical reference for facial expression recognition research. Show more

Keywords: ResNet50, SE, CBAM, depth separability, lightweight

DOI: 10.3233/JIFS-230524

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 9069-9081, 2023

Authors: Tan, Simin | Zhang, Ling | Sheng, Yuhong

Article Type: Research Article

Abstract: This paper mainly discusses the extinction and persistent dynamic behavior of infectious diseases with temporary immunity. Considering that the transmission process of infectious diseases is affected by environmental fluctuations, stochastic SIRS models have been proposed, while the outbreak of diseases is sudden and the interference terms that affect disease transmission cannot be qualified as random variables. Liu process is introduced based on uncertainty theory, which is a new branch of mathematics for describing uncertainty phenomena, to describe uncertain disturbances in epidemic transmission. This paper first extends the classic SIRS model from a deterministic framework to an uncertain framework and constructs …an uncertain SIRS infectious disease model with constant input and bilinear incidence. Then, by means of Yao-Chen formula, α-path of uncertain SIRS model and the corresponding ordinary differential equations are obtained to introduce the uncertainty threshold function R 0 * as the basic reproduction number. Moreover, two equilibrium states are derived. A series of numerical examples show that the larger the value of R 0 * , the more difficult it is to control the disease. If R 0 * ≤ 1 , the infectious disease will gradually disappear, while if R 0 * > 1 , the infectious disease will develop into a local epidemic. Show more

Keywords: Uncertainty theory, SIRS epidemic model, basic reproduction number, asymptotic behavior

DOI: 10.3233/JIFS-223439

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 9083-9093, 2023

Authors: Yang, Chun | Sun, Wei | Li, Ningning

Article Type: Research Article

Abstract: In the past decade, people’s life is getting better and better, and the attention to sports competition is also increasing. In the current information age, sports and athletes’ data are very important, especially team football. In college, football coaches can use the data to analyze the situation of college football players and opposing players to better specify the corresponding tactics to win the game. However, at present, most of the data results need to be manually recorded and counted on the spot or after the game. In the process of statistics, Zhou Jing will inevitably have omissions and other problems. …For this problem, a method based on space-time graph convolution. In the process, machine vision and motion recognition methods are combined, and the joint movements of different football players are extracted through the pose estimation method to obtain motion recognition results. To ented the methods on the KTH dataset. The results showed that the football motion recognition using the research method reached 98% on the dataset, which significantly improved the accuracy of nearly 5% over the existing state-of-the-art methods. At the same time, the accuracy rate of football movements was less than 5%. This means that the research method can effectively identify football sports, and can be widely used in other fields, and promote the development of human movement recognition in human-computer interaction and smart city and other fields. Show more

Keywords: Space-time graph convolution, football teaching, motion recognition

DOI: 10.3233/JIFS-230890

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 9095-9108, 2023

Authors: Xie, Canrong | Wang, Jianjun | Wu, Zhiwen | Nie, Shaojun | Hu, Yichan | Huang, Sheng

Article Type: Research Article

Abstract: Machine learning (ML) has been applied in civil engineering to predict the compressive strength of concrete with high accuracy. In this paper, five boosting ensemble algorithms, i.e., XGBoost, AdaBoost, GBDT, LightGBM, and CatBoost, were used to predict the compressive strength of high-performance concrete (HPC). The models were evaluated using performance indicators such as R2 , root mean square error (RMSE), and mean absolute error (MAE). The results showed that the CatBoost model had the highest accuracy with a R2 (0.970) and a RMSE (2.916). The prediction accuracy of the model was increased through hyperparameter optimization, which got a higher …with a R2 (0.975) and a RMSE (2.863). Meanwhile, the SHapley Additive exPlanations (SHAP) method was used to explain the output results of the optimal model (CatBoost), which generated explainable insights that further revealed the complex relationship between the prediction model parameters. The results showed that AGE, W/B, and W/C had the most impact on high-performance concrete compressive strength (HPCCS) prediction, which was similar to the results of sensitivity analysis. This study provided a theoretical basis and technical guidance for developing the mix design of a new high-performance concrete (HPC) system. In the future, the interpretable results of the model output should be iteratively checked and validated in the actual laboratory in order to provide guidance for engineering practice. Show more

Keywords: High-Performance Concrete (HPC), compressive strength, machine learning, boosting algorithms, game theory

DOI: 10.3233/JIFS-231021

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 9109-9122, 2023

Authors: Du, Xianjun | Wu, Hailei

Article Type: Research Article

Abstract: Convolutional neural networks (CNNs) have made significant progress in the field of cloud detection in remote sensing images thanks to their powerful feature representation capabilities. Existing methods typically aggregate low-level features containing details and high-level features containing semantics to make full use of both features to accurately detect cloud regions. However, CNNs are still limited in their ability to reason about the relationships between features, while not being able to model context well. To overcome this problem, this paper designs a novel feature interaction graph convolutional network model that extends the feature fusion process of convolutional neural networks from Euclidean …space to non-Euclidean space. The algorithm consists of three main components: remote sensing image feature extraction, feature interaction graph reasoning, and high-resolution feature recovery. The algorithm constructs a feature interaction graph reasoning (FIGR) module to fully interact with low-level and high-level features and then uses a residual graph convolutional network to infer feature higher-order relationships. The network model effectively alleviates the problem of a semantic divide in the feature fusion process, allowing the aggregated features to fuse valuable details and semantic information. The algorithm is designed to better detect clouds with complex cloud layers in remote sensing images with complex cloud shape, size, thickness, and cloud-snow coexistence. Validated on publicly available 38-Cloud and SPARCS datasets and the paper’s own Landsat-8 cloud detection dataset with higher spatial resolution, the proposed method achieves competitive performance under different evaluation metrics. Code is available at https://github.com/HaiLei-Fly/CloudGraph . Show more

Keywords: Remote sensing image cloud detection, feature interaction, graph convolutional networks, image segmentation, interpretability

DOI: 10.3233/JIFS-223946

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 9123-9139, 2023

Authors: Silva, Victor L. | de Menezes, José Maria P.

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-220232

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 9141-9156, 2023

Authors: yang, Chen | Jinming, Liu | Jian, Mao

Article Type: Research Article

Abstract: The unintentional electromagnetic radiation of digital electronic devices during operation can cause information leakage and threaten the information security of the system. In order to explore the leakage level of important information, it is necessary to separate the electromagnetic leakage signal from the complex environmental electromagnetic wave, so the blind source separation technology is studied.Traditional blind source separation methods are mainly unsupervised learning methods, and their separation results are not as expected. In recent years, deep learning technology based on supervised learning has achieved good results in speech separation and other fields, indicating that it is a feasible method.In order …to solve the problem of separating source signals from mixed electromagnetic radiation signals and reducing noise interference in electromagnetic safety detection. this paper proposes a Deep Focusing U-Net neural network, which makes the model pay more attention to the features at deeper layer. The network is applied to the blind separation of LCD electromagnetic leakage signals, and the good separation performance proves the effectiveness of this method. Show more

Keywords: Blind source separation, Deep Focusing U-Net, Electromagnetic signals

DOI: 10.3233/JIFS-223568

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 9157-9167, 2023

Authors: Fu, Pengbin | Ma, Yuchen | Yang, Huirong

Article Type: Research Article

Abstract: The speaker diarization task pertains to the automated differentiation of speakers within an audio recording, while lacking any prior information regarding the speakers. The introduction of the self-attention mechanism in End-to-End Neural Speaker Diarization (EEND) has elegantly resolved the issue of overlapping speakers. The Transformer model equipped with self-attention mechanism has shown great potential in collecting global information, yielding remarkable outcomes in various tasks. However, the individual speaker characteristics are predominantly reflected in the contextual information, which conventional self-attention would not adequately address. In this study, we propose a hierarchical encoders model to augment the encoders’ acquisition of speaker information …in two distinct ways: (1) Constraining the perceptual field of the self-attentive mechanism with left-right windows or Gaussian weights to highlight contextual information; (2) Utilizing a pre-trained time-delay neural network based speaker embedding extractor to alleviate the shortcomings of speaker feature extraction ability. We evaluate the proposed methods on a simulated dataset of two speakers and a real conversation dataset. The model with the most favorable outcomes among the proposed enhancements achieves a diarization error rate of 7.74% on the simulated dataset and 21.92% on MagicData-RAMC after adaptation. These results compellingly demonstrate the efficacy of the proposed methods. Show more

Keywords: Speaker diarization, contextual information, Gaussian weight, constraint self-attention

DOI: 10.3233/JIFS-230249

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 9169-9180, 2023

Authors: Qin, Ying

Article Type: Research Article

Abstract: English language teaching varies with the universities and faculties for improving student knowledge through adaptability. In improving the adaptability features, multiple practices are blended based on previous outcomes. The outcomes are considered through the accumulated big data for leveraging student performance. This article introduces a Blended Model using Big Data Analytics (BM-BDA) to provide an upgraded teaching environment for different students. This study applied learning analytics and educational big data methods for the early prediction of students’ final academic performance in a blended model for English teaching. The model aims at rectifying the performance inaccuracies observed in the previous sessions …through the pursued teaching methods. Furthermore, the identification is pursued using teaching model classification and its results over students’ performance. The classification is pursued using conventional classifier learning based on different inaccuracies. The inaccuracy in teaching efficiency using the implied model is classified for different types of students for step-by-step model tuning. The tuning is performed by inheriting the successful implications from the other methods. This improves the inclusion and blending of the diverse method to a required level for teaching efficiency. The successful blending method is discarded from the classification process post the outcome verification. This requires intense data analysis using diverse student performance and implied teaching methods. Show more

Keywords: Big data, blended models, classification learning, English teaching

DOI: 10.3233/JIFS-230842

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 9181-9197, 2023