Journal of Intelligent & Fuzzy Systems - Volume 45, issue 3

Authors: Zheng, Zhangqi | Liu, Yongshan | Zhang, Bing | Ren, Jiadong | Zong, Yongsheng | Wang, Qian | Yang, Xiaolei | Liu, Qian

Article Type: Research Article

Abstract: A software defect is a common cyberspace security problem, leading to information theft, system crashes, and other network hazards. Software security is a fundamental challenge for cyberspace security defense. However, when researching software defects, the defective code in the software is small compared with the overall code, leading to data imbalance problems in predicting software vulnerabilities. This study proposes a heterogeneous integration algorithm based on imbalance rate threshold drift for the data imbalance problem and for predicting software defects. First, the Decision Tree-based integration algorithm was designed following sample perturbation. Moreover, the Support Vector Machine (SVM)-based integration algorithm was designed …based on attribute perturbation. Following the heterogeneous integration algorithm, the primary classifier was trained by sample diversity and model structure diversity. Second, we combined the integration algorithms of two base classifiers to form a heterogeneous integration model. The imbalance rate was designed to achieve threshold transfer and obtain software defect prediction results. Finally, the NASA-MDP and Juliet datasets were used to verify the heterogeneous integration algorithm’s validity, correctness, and generalization based on the Decision Tree and SVM. Show more

Keywords: Software defect, imbalance rate, heterogeneous, integration, threshold shift

DOI: 10.3233/JIFS-224457

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4807-4824, 2023

Authors: Jin, Xiu | Li, He | Hou, Yuting

Article Type: Research Article

Abstract: Emerging markets, such as the Chinese financial market, are occasionally subject to extreme risk events that result in investor losses during the investment process. To address the challenge of investment selection amidst market fluctuations, considering the fuzzy uncertainty and tail risk compensation based on the asymmetric perspective, we propose to use the lower VaR ratio and the upper VaR ratio as investment objectives to construct a multi-period credibilistic portfolio selection model. The study reveals that the cumulative returns and terminal wealth of the constructed model surpassed those of the benchmark models, delivering greater social and economic welfare to investors. During …extreme events, investors could promptly adjust their portfolio structure to achieve higher investment returns. Investors who prefer the lower VaR ratio tend to make conservative investment decisions and allocate a higher proportion to defensive assets, such as bonds and risk-free assets. Conversely, investors who favor the upper VaR ratio are inclined to adopt aggressive investment strategies and allocate a larger proportion to high-risk stocks. The findings demonstrate that the proposed model offers differentiated investment decisions, and the research conclusions serve as valuable references for investors engaged in multi-period asset allocation and risk management. Show more

Keywords: Lower VaR ratio, upper VaR ratio, multi-period portfolio selection, generalized fuzzy numbers, credibility measure

DOI: 10.3233/JIFS-224517

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4825-4845, 2023

Authors: Onat Bulak, Fatma | Bozkurt, Hacer

Article Type: Research Article

Abstract: In this study, we define soft quasilinear functionals on soft normed quasilinear spaces and we examine some of its qualities. By using the soft quasilinear operator defined in [6 ] we specify and prove some theorem related to the continuity and boundedness of soft quasilinear operators and functionals. Furthermore, we give some examples in favor of the soft quasilinear functionals.

Keywords: Soft set, soft quasilinear space, soft normed quasilinear space, soft quasilinear operator, soft quasilinear functional

DOI: 10.3233/JIFS-230035

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4847-4856, 2023

Authors: Zhang, Hengshan | Wang, Yun | Chen, Tianhua

Article Type: Research Article

Abstract: Methods on the basis of the consensus reaching process are prevalent in Group Decision Making (GDM), which typically forces some evaluators to revise initial opinions in order to reach group consensus without being able to precisely reflect original viewpoints. Furthermore, in case the correct opinion is embedded in the hand of the minority, existing methods may not reach the correct consensus. With the aim to tackle these observations, a novel approach of the Positive and Negative Prediction Selection Rate (PNPSR) is proposed on the basis of the Pythagorean Fuzzy Preference Relation (PFPR) which enables to present individuals’ opinions in a …pairwise manner using the linguistic preference relation. The PFPR expressed opinions then serve as input for the computation of the proposed PNPSR, the minimum of which is subsequently selected as the correct one. Finally, the full ranking of the alternatives can be calculated through the proposed iterative algorithm. In the process, the evaluators’ original opinions are not required to modify, and the correct result can be achieved when the minority evaluators provide the correct opinions. Experimental results demonstrate the efficacy of the proposed approach in comparison with two state-of-the-art methods. Show more

Keywords: Group decision making, Pythagorean fuzzy preference relation, positive and negative prediction selection rate, consensus measure, consensus reaching process

DOI: 10.3233/JIFS-230395

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4857-4870, 2023

Authors: Hu, Hongqiang | Zhai, Ce | Chu, Yunxia | Feng, Jiu | Shi, Jianfeng | Liu, Xuning | Zhang, Genshan

Article Type: Research Article

Abstract: The prediction of coal and gas outburst is very necessary for the prevention of gas disaster, so an outburst prediction model coupled with feature extraction and feature weighting using optimized classifier is proposed. First, Pearson correlation coefficient(PCC) and symmetric uncertainty(SU) are employed to measure the effective information in outburst sample data. Second, Kernel principal component analysis(KPCA) and linear discriminant analysis(LDA) methods are used to extract the exiting discriminate information, and the extracted linear and nonlinear feature information can effectively reflect significant information of outburst influencing factors. Third, the combination of gradient boost decision tree(GBDT) and grey relation analysis(GRA) is used …to weight and fuse the extracted linear and nonlinear feature components, then form a new feature set as important discriminant information. Forth, the weighted and fused features of the coal and gas outburst influencing factors are used as the input of support vector machine(SVM) classifier with optimized parameters, it can classify outburst states, and the achieved classification accuracy can obtain 95%. Finally, the proposed model and the existing outburst classification models in literatures are used to predict outburst, then the experiment results verify the effectiveness of the proposed model and conclude that the performance of the proposed predication model are significant than present outburst prediction models. Show more

Keywords: Coal and gas outburst, KPCA, LDA, GBDT, GRA, SVM

DOI: 10.3233/JIFS-222979

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4871-4884, 2023

Authors: Liu, Weiling | Xu, Jinliang | Ren, Guoqing | Xiao, Yanjun

Article Type: Research Article

Abstract: Due to the dynamic nature of work conditions in the manufacturing plant, it is difficult to obtain accurate information on process processing time and energy consumption, affecting the implementation of scheduling solutions. The fuzzy flexible job shop scheduling problem with uncertain production parameters has not yet been well studied. In this paper, a scheduling optimization model with the objectives of maximum completion time, production cost and delivery satisfaction loss is developed using fuzzy triangular numbers to characterize the time parameters, and an improved quantum particle swarm algorithm is proposed to solve it. The innovations of this paper lie in designing …a neighborhood search strategy based on machine code variation for deep search; using cross-maintaining the diversity of elite individuals, and combining it with a simulated annealing strategy for local search. Based on giving full play to the global search capability of the quantum particle swarm algorithm, the comprehensive search capability of the algorithm is enhanced by improving the average optimal position of particles. In addition, a gray target decision model is introduced to make the optimal decision on the scheduling scheme by comprehensively considering the fuzzy production cost. Finally, simulation experiments are conducted for test and engineering cases and compared with various advanced algorithms. The experimental results show that the proposed algorithm significantly outperforms the compared ones regarding convergence speed and precision in optimal-searching. The method provides a more reliable solution to the problem and has some application value. Show more

Keywords: Fuzzy flexible job shop scheduling, PSO, QPSO, simulated annealing, local search

DOI: 10.3233/JIFS-231640

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4885-4905, 2023

Authors: Yang, Juan

Article Type: Research Article

Abstract: In order to improve the accuracy of English online course teaching effect evaluation results, a paper proposed an English online course teaching effect evaluation method based on ResNet algorithm. The effect of College English online teaching was evaluated from five aspects: pre-class preparation, teaching content, basic skills, ability training, and teaching methods. Each evaluation item was set with seven levels of scoring standards. An evaluation model of the classroom teaching effect was constructed based on convolutional neural network according to the internal relationship between facial expression recognition and classroom teaching effect evaluation. The problem of network depth deepening affecting the …accuracy of evaluation in convolutional neural network models was innovatively solved by utilizing the ResNet algorithm. The evaluation of the effectiveness of English online course teaching was achieved. The experimental results showed that this method could effectively improve the effect of English online course teaching evaluation and improve the teaching quality of English online courses. Show more

Keywords: ResNet algorithm, English online teaching, teaching evaluation, face recognition, convolutional neural network

DOI: 10.3233/JIFS-230048

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4907-4916, 2023

Authors: Xu, Wan | Zhang, Yuhao | Yu, Leitao | Zhang, Tingting | Cheng, Zhao

Article Type: Research Article

Abstract: In order to solve the problem that the traditional DWA algorithm cannot have both safety and speed because of the fixed parameters in the complex environment with many obstacles, a parameter adaptive DWA algorithm (PA-DWA) is proposed to improve the robot running speed on the premise of ensuring safety. Firstly, the velocity sampling space is optimized by the current pose of the mobile robot, and a criterion of environment complexity is proposed. Secondly, a parameter-adaptive method is presented to optimize the trajectory evaluation function. When the environment complexity is greater than a certain threshold, the minimum distance between the mobile …robot and the obstacle is taken as the input, and the weight of the velocity parameter is adjusted according to the real-time obstacle information dynamically. The current velocity of the mobile robot is used as input to dynamically adjust the weight of the direction angle parameter. In the Matlab simulation, the total time consumption of PA-DWA is reduced by 47.08% in the static obstacle environment and 39.09% in the dynamic obstacle environment. In Gazebo physical simulation experiment, the total time of PA-DWA was reduced by 26.63% in the case of dynamic obstacles. The experimental results show that PA-DWA can significantly reduce the total time of the robot under the premise of ensuring safety. Show more

Keywords: Speed sampling space, parameter adaptation, DWA, local path planning

DOI: 10.3233/JIFS-221837

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4917-4933, 2023

Authors: Huang, Haojian | Liu, Zhe | Han, Xue | Yang, Xiangli | Liu, Lusi

Article Type: Research Article

Abstract: Dempster-Shafer theory (DST) has attracted widespread attention in many domains owing to its powerful advantages in managing uncertain and imprecise information. Nevertheless, counterintuitive results may be generated once Dempster’s rule faces highly conflicting pieces of evidence. In order to handle this flaw, a new belief logarithmic similarity measure ( BLSM ) based on DST is proposed in this paper. Moreover, we further present an enhanced belief logarithmic similarity measure ( EBLSM ) to consider the internal discrepancy of subsets. In parallel, we prove that EBLSM satisfies several desirable properties, …like bounded, symmetry and non-degeneracy. Finally, a new multi-source data fusion method based on EBLSM is well devised. Through its best performance in two application cases, specifically those pertaining to fault diagnosis and target recognition respectively, the rationality and effectiveness of the proposed method is sufficiently displayed. Show more

Keywords: Dempster-Shafer theory, basic belief assignment, logarithmic similarity measure, belief entropy, data fusion

DOI: 10.3233/JIFS-230207

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4935-4947, 2023

Authors: Park, Choonkil | Rehman, Noor | Ali, Abbas | Alahmadi, Reham A. | Khalaf, Mohammed M. | Hila, Kostaq

Article Type: Research Article

Abstract: In clasical logic, it is possible to combine the uniary negation operator ¬ with any other binary operator in order to generate the other binary operators. In this paper, we introduce the concept of (N ∗ , O , N , G )-implication derived from non associative structures, overlap function O , grouping function G and two different fuzzy negations N ∗ and N are used for the generalization of the implication p  → q  ≡ ¬ [p  ∧ ¬ (¬ p  ∨ q )] . We show that (N ∗ , O , N , G )-implication are fuzzy implication without any restricted …conditions. Further, we also study that some properties of (N ∗ , O , N , G )-implication that are necessary for the development of this paper. The key contribution of this paper is to introduced the concept of circledcircG ,N -compositions on (N ∗ , O , N , G )-implications. If ( N 1 ∗ , O ( 1 ) , N 1 , G ( 1 ) ) - or ( N 2 ∗ , O ( 2 ) , N 2 , G ( 2 ) ) -implications constructed from the tuples ( N 1 ∗ , O ( 1 ) , N 1 , G ( 1 ) ) or ( N 2 ∗ , O ( 2 ) , N 2 , G ( 2 ) ) satisfy a certain property P , we now investigate whether circledcircG ,N -composition of ( N 1 ∗ , O ( 1 ) , N 1 , G ( 1 ) ) - and ( N 2 ∗ , O ( 2 ) , N 2 , G ( 2 ) ) -implications satisfies the same property or not. If not, then we attempt to characterise those implications ( N 1 ∗ , O ( 1 ) , N 1 , G ( 1 ) ) -, ( N 2 ∗ , O ( 2 ) , N 2 , G ( 2 ) ) -implications satisfying the property P such that circledcircG ,N -composition of ( M 1 ∗ , O ( 1 ) , M 1 , G ( 1 ) ) - and ( M 2 ∗ , O ( 2 ) , M 2 , G ( 2 ) ) -implications also satisfies the same property. Further, we introduced sup-circledcircO -composition of (N ∗ , O , N , G )-implications constructed from tuples (N ∗ , O , N , G ) . Subsequently, we show that under which condition sup-circledcircO -composition of (N ∗ , O , N , G )-implications are fuzzy implication. We also study the intersections between families of fuzzy implications, including R O -implications (residual implication), (G , N )-implications, QL -implications, D -implications and (N ∗ , O , N , G )-implications. Show more

Keywords: Overlape function, grouping function, fuzzy implication, fuzzy negation

DOI: 10.3233/JIFS-222878

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4949-4977, 2023

Authors: Wang, Jing

Article Type: Research Article

Abstract: The traditional text-image confrontation model utilizes a convolutional network in the discriminator to extract image features, yet this fails to involve the spatial relationship between underlying objects, resulting in a poor-quality generated image. To remedy this, a capsule network is proposed to improve the model. The convolutional network in the discriminator is replaced with a capsule network, thereby improving the robustness of the images. Through experiments on the Oxford-102 and CUB datasets, it has been found that the new model can effectively improve the quality of generated text-image. The FID value of the generated flower image decreased by 14.49%, and …the FID value of the generated bird image decreased by 9.64%. Additionally, the Inception Score of images generated on the Oxford-102 and CUB datasets increased by 22.60% and 26.28%, respectively, indicating that the improved model generated richer and more meaningful image features. Show more

Keywords: Generating images, capsule network, generation adversarial network, convolutional network, robustness

DOI: 10.3233/JIFS-223741

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4979-4989, 2023

Authors: Li, Yuqi

Article Type: Research Article

Abstract: The method based on entropy was used for the Bayesian optimization. Based on compelling information theory, Entropy Search (ES) and Predictive Entropy Search (PES) maximized information about the unknown function when the loss function reaches the maximum value. However, both methods were plagued by complicated calculations for estimating entropy. The most important motivation of this article is to improve and modularize the entropy search itself, making this method more flexible and effective for model adaptation. After the initial optimization and pruning module processing, a reasonable initial configuration for the complex model was successfully established, further reducing the space required for …secondary optimization hyper-parameter search. The advantage of this method is that, on the one hand, the basic method of Bayesian optimization is used to get the best result of the iteration, while ensuring that the algorithm has theoretical boundedness. On the other hand, through the maximum entropy, the information features of the original space and data set are retained as much as possible to reduce the loss of information due to the initialization process, so as to improve the precision of the secondary optimization of the model. Further, a new algorithm framework is proposed, integrating MES and Sequential Model-Based Optimization (SMBO). With MES as the final module of the whole optimization process, a more accurate and reasonable algorithmic model was built, which lays a solid mathematical basis for the final empirical analysis. Show more

Keywords: Bayesian optimization, SMBO, hyperparameter optimization, entropy search

DOI: 10.3233/JIFS-230470

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4991-5006, 2023

Authors: Chen, Chuen-Jyh | Huang, Chieh-Ni | Yang, Shih-Ming

Article Type: Research Article

Abstract: Weather forecasts are essential to aviation safety. Unreliable forecasts not only cause problems to pilots and air traffic controllers, but also lead to aviation accidents and incidents. To enhance the forecast accuracy, an integrated model comprising a convolutional neural network (CNN) and long short-term memory (LSTM) network is developed to achieve improved weather visibility forecasting. In this model, the CNN acts as the precursor of the LSTM network and classifies weather images to increase the visibility forecasting accuracy achieved with the LSTM network. For a dataset with 1500 weather images, the training, validation, and testing accuracy achieved with the integrated …model is 100.00%, 97.33%, and 97.67%, respectively. On a numerical dataset of 10 weather features over 10 years, the RMSE and MAPE of an LSTM forecast can be reduced by multiple linear regression from RMSE 12.02 to 11.91 and 44.46% to 39.02%, respectively, and further by the Pearson’s correlation coefficients to 10.12 and 36.77%, respectively. By using CNN result as precursor to LSTM, the visibility forecast by integrating both can decrease the RMSE and MAPE to 2.68 and 13.41%, respectively. The integration by deep learning is shown an effective, accurate aviation weather forecast. Show more

Keywords: Aviation weather, convolutional neural network, long short-term memory network, weather forecasting

DOI: 10.3233/JIFS-230483

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5007-5020, 2023

Authors: Wang, Yuxian | Li, Zhaowen | Zhang, Jie | Yu, Guangji

Article Type: Research Article

Abstract: Gene selection is an important research topic in data mining. A gene decision space means a real-valued decision information system (RVDIS) where objects, conditional attributes and information values are cells, genes and gene expression values, respectively. This paper explores gene selection in a gene decision space based on information entropy and considers its application for gene expression data classification. In the first place, the distance between two cells in a given decision subspace is constructed. In the next place, the binary relations induced by this decision subspace are defined. After that, some information entropy for a gene decision space are …investigated. Lastly, several gene selection algorithms in a gene decision space are presented by using the presented information entropy. The presented algorithms are applied to gene expression data classifications. Multiple publicly available gene expression datasets are employed to evaluate the gene selection performances of the proposed algorithms, while two commonly-used classifiers, KNN and CART, are utilized to obtain 10 fold cross validation accuracy of classification (ACC ). The classification results demonstrated that the proposed algorithms can lower significantly the number genes selected, achieve the higher ACC , and outperform the other competing methods, such as raw data, Fisher, tSNE, PCA, FMIFRFS and DNEAR, with respect to gene number and ACC . Show more

Keywords: Gene expression data, Gene decision space, Gene selection, Uncertainty measurement

DOI: 10.3233/JIFS-231569

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5021-5044, 2023

Authors: Zhang, Yihao | Chen, Ruizhen | Hu, Jiahao | Zhang, Guangjian | Zhu, Junlin | Liao, Weiwen

Article Type: Research Article

Abstract: The key to sequential recommendation modeling is to capture dynamic users’ interests. Existing sequential recommendation methods (e.g., self-attention mechanism) have achieved extraordinary success in modeling users’ interests. However, these models ignore that users have different levels of preferences for different aspects of items, failing to capture users’ most concerning aspects. In addition, they are highly dependent on the quality of training data, which may lead to overfitting of the model when the training data is insufficient. To address the above issues, we propose a novel sequence-aware model (Multi-Aspect Features of Items for Time-Ordered Sequential Recommendation, MFITSRec), which combines the features …of items with user behavior sequences to learn more complex item-item and item-attribute relationships. Moreover, the model uses a self-attention network based on an absolute time relationship, which can better represent the changes in users’ interests and capture users’ preferences for particular aspects of items. Extensive experiments on five datasets demonstrate that our model outperforms various baseline models. In particular, the model’s prediction accuracy has been significantly improved on sparse datasets. Show more

Keywords: Sequential recommendation, multi-aspect preferences of users, data sparsity, absolute time relationship

DOI: 10.3233/JIFS-230274

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5045-5061, 2023

Authors: Wen, Haolan | Chen, Yu | Wang, Weizhong | Ding, Ling

Article Type: Research Article

Abstract: Sustainable food consumption and production (SFCP) has become increasingly significant for creating new value, reducing costs, and reducing greenhouse gas emissions. However, there are some challenges and barriers to implementing SFCP in practice. Moreover, current methods for prioritizing barriers to SFCP seldom consider the behavioral preference of experts and interactions among factors, especially with q-Rung orthopair fuzzy set (q-ROFS)-based information. Thus, this study aims to construct a hybrid q-ROFS-based framework for ranking these barriers. First, the q-ROFS is introduced to express the experts’ uncertain information. Then, the q-ROF- CRITIC (CRiteria importance through intercriteria correlation) method is utilized to determine criteria …weights considering the interrelations among barriers. Next, the q-ROF generalized TODIM method is built to rank the barriers to SFCP by considering the impact of experts’ behavioral preferences. Finally, a numerical case of barriers analysis for SFCP is organized to display the application procedures of the constructed ranking method. The result indicates that the top-priority set is education and culture (a 4 ), with the most significant overall dominance value (0.839). Further, a comparison exploration is given to demonstrate the preponderances of the present barriers ranking method. The outcomes demonstrate that the proposed ranking method can provide a synthetic and reliable framework to handle the prioritizing issue for the barriers to SFCP within a complex and uncertain context. Show more

Keywords: Sustainable food consumption and production, q-Rung orthopair fuzzy set, generalized TODIM method, CRITIC approach

DOI: 10.3233/JIFS-230526

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5063-5074, 2023

Authors: IssanRaj, R. | Visalakshi, S.

Article Type: Research Article

Abstract: Triple Diode Solar Cell Module (TDSCM) circuit with nine parameters for various environmental circumstances represents the behavior and practical performance of solar cell.The precise extraction of photovoltaic (PV) module parameters is essential for optimising the energy conversion efficiency of PV systems. Usually the equations describing solar panels are implicit in nature, and parameter extraction has been very complicated. The solar cell is mathematically modelled with nonlinear I-V (Current – Voltage) characteristics behavior, and it cannot be directly determined from the PV’s datasheet due to the lack of data offered by the PV manufacturers. On the basis of the technical datasheet …of the photovoltaic module (PV), only four equations can be obtained in single diode, double diode, and triple diode parameters. To be implemented with fifth equation, many researchers have been done with multiple approximations and it becomes with low accuracy, complexity of computation, convergence problem. To resolve these issues, a new multi-objective optimization (GA) genetic algorithm method is prescribed to frame the fifth equation using the Boole rules implemented with the curved area concept. The proposed Boole’s rule based model offers superior non-linearity performance and high precision modelling, and the error shows a significant reduction when compared to the single and double diode approaches used in the existing approach. The effectiveness of the proposed I-V curve characteristics efficiency was improved by the implementation of the proposed Boole’s rule with RMSE error 0.000034. Show more

Keywords: Photovoltaic cell model, solar cell modelling, multi objective genetic algorithm, triple diode model, boole’s rule

DOI: 10.3233/JIFS-230663

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5075-5092, 2023

Authors: Zhu, Cunxin | Huang, Xuhong | Chen, Yanyi | Tang, Shengping | Zhao, Nan | Xiao, Weihao

Article Type: Research Article

Abstract: Chinese couplet is one of the important forms of expression in Chinese and even world literature, with its own unique charm and beauty. In order to meet the needs of users who only need one image to obtain corresponding couplets, realize the function of computer automatically writing couplets with images, and improve the literary expression ability of couplets to images, this paper proposes an image based intelligent generative model of couplets. The model first outputs corresponding descriptions based on image extraction features, and then extracts keywords through an improved hybrid algorithm according to the descriptions. Then, based on the keywords, …the Chinese GPT-2 model automatically expands the first line of a couplet, and finally generates the second line of a couplet from the first line of a couplet through the encoding and decoding framework. Through experiments, it has been shown that the generated couplets of the model meet the requirements for image description, and the effectiveness of the model has been confirmed by manual evaluation results. Show more

Keywords: Chinese couplet, image description, keyword extraction, Encoding and decoding framework

DOI: 10.3233/JIFS-231155

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5093-5105, 2023

Authors: Segura Dorado, Jhon | Anacona Mopan, Yesid Ediver | Solis Pino, Andrés Felipe | Paz Orozco, Helmer

Article Type: Research Article

Abstract: Colombia exhibits a considerable incidence rate of natural disasters because of its location within the intertropical zone, which exposes it to various meteorological and geological phenomena, including the Nevado del Huila volcano. The identification of suitable areas for the installation of temporary shelters is critical for managing these disasters. However, the task of identifying such locations is a complex problem that involves multiple criteria. This study uses a fuzzy systems approach to identify suitable sites for establishing temporary shelters in the Paez municipality during natural disasters, considering the essential criteria determined by experts through pairwise comparisons. The study results indicate …that responsiveness is the most significant criterion, followed by area profile. Using these criteria, it identified a specific locality in the Paez municipality as suitable for establishing temporary shelters during natural disasters caused by volcanic phenomena. The findings were compared with those obtained from existing scientific literature and validated by experts in natural disasters. The methodological process described in this study provides a valuable tool for public entities to make informed decisions concerning natural disasters in indigenous territories caused by volcanic phenomena. Show more

Keywords: Location temporary shelters, multiple criteria decisions making, analytic network process

DOI: 10.3233/JIFS-231453

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5107-5121, 2023

Authors: Sakthi, K. | Nirmal Kumar, P.

Article Type: Research Article

Abstract: Rapid technological advances and network progress has occurred in recent decades, as has the global growth of services via the Internet. Consequently, piracy has become more prevalent, and many modern systems have been infiltrated, making it vital to build information security tools to identify new threats. An intrusion detection system (IDS) is a critical information security technology that detects network fluctuations with the help of machine learning (ML) and deep learning (DL) approaches. However, conventional techniques could be more effective in dealing with advanced attacks. So, this paper proposes an efficient DL approach for network intrusion detection (NID) using an …optimal weight-based deep neural network (OWDNN). The network traffic data was initially collected from three openly available datasets: NSL-KDD, CSE-CIC-IDS2018 and UNSW-NB15. Then preprocessing was carried out on the collected data based on missing values imputation, one-hot encoding, and normalization. After that, the data under-sampling process is performed using the butterfly-optimized k-means clustering (BOKMC) algorithm to balance the unbalanced dataset. The relevant features from the balanced dataset are selected using inception version 3 with multi-head attention (IV3MHA) mechanism to reduce the computation burden of the classifier. After that, the dimensionality of the selected feature is reduced based on principal component analysis (PCA). Finally, the classification is done using OWDNN, which classifies the network traffic as normal and anomalous. Experiments on NSL-KDD, CSE-CIC-IDS2018 and UNSW-NB15 datasets show that the OWDNN performs better than the other ID methods. Show more

Keywords: Intrusion detection system, deep learning, dimensionality reduction, butterfly optimization, k-means clustering, inception v3, multi head attention, deep neural network

DOI: 10.3233/JIFS-231758

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5123-5140, 2023

Authors: Wajahat, Ahsan | He, Jingsha | Zhu, Nafei | Mahmood, Tariq | Nazir, Ahsan | Pathan, Muhammad Salman | Qureshi, Sirajuddin | Ullah, Faheem

Article Type: Research Article

Abstract: Positive developments in smartphone usage have led to an increase in malicious attacks, particularly targeting Android mobile devices. Android has been a primary target for malware exploiting security vulnerabilities due to the presence of critical applications, such as banking applications. Several machine learning-based models for mobile malware detection have been developed recently, but significant research is needed to achieve optimal efficiency and performance. The proliferation of Android devices and the increasing threat of mobile malware have made it imperative to develop effective methods for detecting malicious apps. This study proposes a robust hybrid deep learning-based approach for detecting and predicting …Android malware that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). It also presents a creative machine learning-based strategy for dealing with unbalanced datasets, which can mislead the training algorithm during classification. The proposed strategy helps to improve method performance and mitigate over- and under-fitting concerns. The proposed model effectively detects Android malware. It extracts both temporal and spatial features from the dataset. A well-known Drebin dataset was used to train and evaluate the efficacy of all creative frameworks regarding the accuracy, sensitivity, MAE, RMSE, and AUC. The empirical finding proclaims the projected hybrid ConvLSTM model achieved remarkable performance with an accuracy of 0.99, a sensitivity of 0.99, and an AUC of 0.99. The proposed model outperforms standard machine learning-based algorithms in detecting malicious apps and provides a promising framework for real-time Android malware detection. Show more

Keywords: Android malware detection, deep learning, CNN, LSTM, Drebin dataset

DOI: 10.3233/JIFS-231969

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5141-5157, 2023

Authors: Zhou, Qiaozhen | Wang, Fang | Zhao, Xuanyu | Hu, Kai | Zhang, Yujian | Shan, Xin | Lin, Xin | Zhang, Yupeng | Shan, Ke | Zhang, Kailiang

Article Type: Research Article

Abstract: Resistive random access memory (RRAM) has lots of advantages that make it a promising candidate for ultra-high-density memory applications and neuromorphic computing. However, challenges such as high forming voltage, low endurance, and poor uniformity have hampered the development and application of RRAM. To improve the uniformity of the resistive memory, this paper systematically investigates the HfOx -based RRAM by embedding nanoparticles. In this paper, the HfOx -Based RRAM with and without tungsten nanoparticles (W NPs) is fabricated by magnetron sputtering, UV lithography, and stripping. Comparing the various resistive switching behaviors of the two devices, it can be observed that the …W NPs device exhibits lower switching voltage (including a 69.87% reduction in Vforming and a reduction in Vset /Vreset from 1.4 V/-1.36 to 0.7 V/-1.0 V), more stable cycling endurance (>105 cycles), and higher uniformity. A potential switching mechanism is considered based on the XPS analysis and the research on the fitting of HRS and LRS: Embedding W NPs can improve the device performance by inducing and controlling the conductive filaments (CFs) size and paths. This thesis has implications for the performance enhancement and development of resistive memory. Show more

Keywords: Resistive random access memory (RRAM), HfOx, embedding W nanoparticles, uniformity, conduction mechanism

DOI: 10.3233/JIFS-232028

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5159-5167, 2023

Authors: Li, Zhenjiang | Zhang, Qianxue

Article Type: Research Article

Abstract: The writer identification task infers the writer by analyzing the texture, structure, and other representative features of the handwriting. Inspired by the attention mechanism, an end-to-end writer identification model is proposed in this paper, which combines both global features and local features. The Vision Transformer is used as the backbone network, and the Convolutional block attention module (CBAM) is introduced to enhance the ability of global feature awareness of the model. The proposed method is evaluated on two public data sets, IAM and CVL respectively. In the task of word-level writer identification, the accuracy rates in two data sets were …90.1% and 92.3% respectively. In the task of page-level writer identification, the accuracy rates were 98.6% and 99.5%, as a state-of-the-art performance. Show more

Keywords: Biometrics, writer identification, computer vision, neural network, vision transformer

DOI: 10.3233/JIFS-232134

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5169-5179, 2023

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

Article Type: Research Article

Abstract: In recent years, it has been shown that deep learning methods have excellent performance in establishing spatio-temporal correlations for traffic speed prediction. However, due to the complexity of deep learning models, most of them use only short-term historical data in the time dimension, which limits their effectiveness in handling long-term information. We propose a new model, the Multi-feature Two-stage Attention Convolution Network (MTA-CN), to address this issue. The MTA-CN intercepts longer single-feature historical data, converts them into shorter multi-feature data with multiple time period features, and uses the most recent past point as the main feature. Furthermore, two-stage attention mechanisms …are introduced to capture the importance of different time period features and time steps, and a Temporal Graph Convolutional Network (T-GCN) is used instead of traditional recurrent neural networks. Experimental results on both the Los Angeles Expressway (Los-loop) and Shen-zhen Luohu District Taxi (Sz-taxi) datasets demonstrate that the proposed model outperforms several baseline models in terms of prediction accuracy. Show more

Keywords: Traffic speed prediction, attentional mechanisms, temporal dependence, spatial dependence, graph convolutional network

DOI: 10.3233/JIFS-231133

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5181-5196, 2023

Article Type: Retraction

DOI: 10.3233/JIFS-219329

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5197-5197, 2023