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

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