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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Xu, Meiling | Fu, Yongqiang | Tian, Boping
Article Type: Research Article
Abstract: The fraud problem has drastically increased with the rapid growth of online lending. Since loan applications, approvals and disbursements are operated online, deceptive borrowers are prone to conceal or falsify information to maliciously obtain loans, while lenders have difficulty in identifying fraud without direct contacts and lack binding force on customers’ loan performance, which results in the frequent occurrence of fraud events. Therefore, it is significant for financial institutions to apply valuable data and competitive technologies for fraud detection to reduce financial losses from loan scams. This paper combines the advantages of statistical methods and ensemble learning algorithms to design …the grouped trees and weighted ensemble algorithm (GTWE), and establishes fraud prediction models for online loans based on mobile application usage behaviors(App behaviors) by logistic regression, extreme gradient boosting (XGBoost), long short-term memory (LSTM) and the GTWE algorithm, respectively. The experimental results show that the fraud prediction model based on the GTWE algorithm achieves outstanding classification effect and stability with satisfactory interpretability. Meanwhile, the fraud probability of customers detected by the fraud prediction model is as high as 84.19%, which indicates that App behaviors have a considerable impact on predicting fraud in online loan application. Show more
Keywords: Fraud prediction, mobile application usage behaviors, extreme gradient boosting, long short-term memory, grouped trees, weighted ensemble algorithm
DOI: 10.3233/JIFS-222405
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7181-7194, 2023
Authors: Zhang, Ruifan | Wang, Hao | Yang, Gongping
Article Type: Research Article
Abstract: Embedding similarity-based methods obtained good results in unsupervised anomaly detection (AD). This kind of method usually used feature vectors from a model pre-trained by ImageNet to calculate scores by measuring the similarity between test samples and normal samples. Ultimately, anomalous regions are localized based on the scores obtained. However, this strategy may lead to a lack of sufficient adaptability of the extracted features to the detection of anomalous patterns for industrial anomaly detection tasks. To alleviate this problem, we design a novel anomaly detection framework, MFFA, which includes a pseudo sample generation (PSG) block, a local-global feature fusion perception (LGFFP) …block and an anomaly map compensation (AMC) block. The PSG block can make the pre-trained model more suitable for real-world anomaly detection tasks by combining the CutPaste augmentation. The LGFFP block aggregates shallow and deep features on different patches and inputs them to CaiT (Class-attention in image Transformers) to guide self-attention, effectively interacting local and global information between different patches, and the AMC block can compensate each other for the two anomaly maps generated by the nearest neighbor search and multivariate Gaussian fitting, improving the accuracy of anomaly detection and localization. In experiments, MVTec AD dataset is used to verify the generalization ability of the proposed method in various real-world applications. It achieves over 99.1% AUROCs in detection and 98.4% AUROCs in localization, respectively. Show more
Keywords: Anomaly detection, pseudo sample, feature fusion, transformer, anomaly map compensation
DOI: 10.3233/JIFS-222595
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7195-7210, 2023
Authors: Jain, Manish | Kumar, Sanjay | Park, Choonkil
Article Type: Research Article
Abstract: The question of relaxing the compatible hypothesis of the pair of mappings in fixed point theory has always been remained an open problem. We address such an open problem raised by Choudhury et al. [4 ] and also explicitly settles the issue of monotone and continuity hypotheses of the involved mappings in coupled coincidence point results. Moreover, we state a gap in an example given in [3 ] and repair it. Application to the dynamic programming problem shows the usability of present work. Finally, we also propose an open problem for further investigation.
Keywords: GV-fuzzy metric space,φ-contractions, Hadɘić type t-norm, mixed monotone property, coupled coincidence point 2010 Mathematics Subject Classification. 47H10, 54H25.
DOI: 10.3233/JIFS-222637
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7211-7223, 2023
Authors: Angel, A. Sheeba | Jayaparvathy, R.
Article Type: Research Article
Abstract: Despite the numerous risks that high-rise buildings face, fire accidents happen most frequently. Studying fire accidents in high-rise buildings is crucial because they can result in harm to people’s health, fatalities, property damage, and pollution. The number of accidental fires in buildings is very large since it is difficult to isolate a single cause and all processes and control measures are not appropriately implemented. This paper proposes a fuzzy-bow tie approach to evaluating the risk of fire accidents by taking into account the various fire sources and effects. The fourteen-floor high-rise residential building is used as a case study for …the proposed fuzzy bow tie approach. The fuzzy fault tree approach estimates that there is a 0.0968% risk of a fire accident occurring in that high-rise building, with a possibility for 9 out of 100 accidental fires annually. The fuzzy event tree model predicts that loss of life and loss of property are the most likely consequences of an accidental fire. Accordingly, mitigation strategies can be developed by building officials and fire safety practitioners. Show more
Keywords: Risk assessment, fire alarm and control system, safety, fault tree, sensitivity analysis
DOI: 10.3233/JIFS-223307
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7225-7242, 2023
Authors: Li, Runya | Pang, Ling
Article Type: Research Article
Abstract: Remote sensing image technology is of great significance for dynamic management and monitoring of ground buildings. In order to improve the data fusion ability of remote sensing image of ground buildings, a data fusion method of remote sensing image of ground buildings based on multi-level fuzzy evaluation is proposed. This method constructs a remote sensing image acquisition model of ground buildings, and uses image enhancement methods to realize the gray information analysis and image enhancement of the remote sensing image rate of ground buildings. Finally, combining the remote sensing image data fusion method and the fuzzy region reconstruction method, it …reconstructs the pixels of the dynamically changed ground buildings. The simulation results show that the remote sensing image data fusion accuracy of ground buildings is good, and the remote sensing feature extraction accuracy of ground buildings is high. The dynamic real-time monitoring of remote sensing image of ground buildings is realized. Show more
Keywords: Multistage fuzzy evaluation, remote sensing image, data fusion, enhancement, building image
DOI: 10.3233/JIFS-223434
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7243-7255, 2023
Authors: Prabhu, Akshatha | Shobha Rani, N. | Basavaraju, H.T.
Article Type: Research Article
Abstract: One of the most essential factors in classifying and qualitatively evaluating mangoes for various industrial uses is weight. To meet grading requirements during industrial processing, this paper presents an orientation-independent weight estimation method for the mango cultivar “Alphonso.” In this study, size and geometry are considered as key variables in estimating weight. Based on the visual fruit geometry, generalized hand-crafted local and global features, and conventional features are calculated and subjected to the proposed feature selection methodology for optimal feature identification. The optimal features are employed in regression analysis to estimate the predicted weight. Four regression models –MLR, Linear SVR, …RBF SVR, and polynomial SVR—are used during the experimental trials. A self-collected mango database with two orientations per sample is obtained using a CCD camera. Three different weight estimation techniques are used in the analysis concerning orientation 1, orientation 2, and combining both orientations. The SVR RBF kernel yields a higher correlation between predicted and actual weights, and experiments demonstrate that orientation 1 is symmetric to orientation 2. By exhibiting a correlation coefficient of R2 = 0.99 with SVR-RBF for weight estimation using both orientations as well as individual orientations, it is observed that the correlation between predicted and estimated weights is nearly identical Show more
Keywords: Mass estimation, computer vision, mango processing, Alphonso mangoes, automated weight estimation
DOI: 10.3233/JIFS-223510
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7257-7275, 2023
Authors: Sahoo, Amit Kumar | Mishra, Sudhansu Kumar | Acharya, Deep Shekhar | Sahu, Sitanshu Sekhar | Paul, Sanchita | Gupta, Vikash Kumar
Article Type: Research Article
Abstract: System identification techniques have proved to be the most effective methodologies for the modeling highly non-linear and system. For the purpose of real-time parameter estimation of a Maglev system, a Teaching Learning Based Optimization (TLBO) for updating the weights of Functional Link Artificial Neural Network (FLANN) model is proposed and implemented in this research. Moreover, we proposed a one & two-Degree of Freedom (one-DOF & two-DOF) Fractional Order PID (FOPID) controller, where the parameters are optimized by using the Teaching Learning Based Optimization (TLBO) and the recently proposed Black Widow Optimization (BWO) algorithm. To investigate the robustness of the proposed …controller, a pulse signal disturbance is added at equal intervals of the output of the identified model of the Maglev system. It is found that the suggested two-DOF FOPID controller with TLBO performs better than its counterpart in terms of both in time domain specifications (i.e., maximum overshoot = 1.2648%, settling time = 1.3884 sec and rise time = 0.8685 sec) and in robustness analysis (i.e., system is sufficiently robust, because the infinity norms of the sensitivity and the complementary sensitivity functions of the system are less than two). The TLBO algorithm has performed better for both identification and optimization of controller parameter due to very less number of algorithmic parameter is as compared to other algorithm. Show more
Keywords: System identification, fractional calculus, FOPID, IOPID, MAGLEV system
DOI: 10.3233/JIFS-222238
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7277-7289, 2023
Authors: Cui, Wanqiu | Wang, Dawei | Feng, Wengang
Article Type: Research Article
Abstract: Image semantic learning techniques are crucial for image understanding and classification. In social networks, image data is widely disseminated thanks to convenient acquisition and intuitive expression characteristics. However, due to the freedom of users to publish information, the image has apparent context dependence and semantic fuzziness, which brings difficulties to image representation learning. Fortunately, social attributes such as hashtags carry rich semantic relations, which can be conducive to understanding the meaning of images. Therefore, this paper proposes a new method named Social Heterogeneous Graph Networks (SHGN) for image semantic learning in social networks. First, a heterogeneous graph is built to …expand image semantic relations by social attributes. Then the consistent semantic space is reconstructed through cross-media feature alignment. Finally, an image semantic extended learning network is designed to capture and integrate the social semantics and visual feature, which obtains a rich semantic representation of images from a social context. The experiments demonstrate that SHGN can achieve efficient image representation, and favorably against many baseline algorithms. Show more
Keywords: Social networks image, representation learning, heterogeneous graph, social semantic aggregation
DOI: 10.3233/JIFS-222981
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7291-7304, 2023
Authors: Manikandan, N.K. | Kavitha, M.
Article Type: Research Article
Abstract: The e-learning is necessary in this fast internet world, especially during this pandemic situation, to continue education without any interruption and it is used reduce the educational cost significantly when reduces the energy loss. Generally, machine learning and deep learning algorithms are used to identify patterns that facilitate learning and help learners understand concepts easily. Many content recommendation systems are available for assisting learners as e-learning applications by providing the required study materials. Despite the fact that existing recommendation systems struggle to provide precise content to e-learners due to the availability of a massive volume of data on the internet …and other repositories. For this purpose, we propose a new content recommendation system for recommending suitable content to learners according to their interests and learning capabilities. The proposed content recommendation system employs a newly proposed semantic-aware hybrid feature optimizer that incorporates new optimization algorithms such as the Enhanced Personalized Best Cuckoo Search Algorithm (EpBestCSA) and the Enhanced Harris Hawks Optimization Algorithm (EHHOA) for selecting suitable features that aid in improving prediction accuracy, as well as a newly proposed Deep Semantic Structure Model (DSSM) that incorporates Artificial Neural Network (ANN) and Convolutional Neural Network (CNN). According to the experimental results, the proposed model outperforms other recommendation systems in terms of precision, recall, f-measure, and prediction accuracy. The ten-fold cross validation is done to test the performance of the proposed methodology. Show more
Keywords: Semantic analysis, hybrid feature optimizer, Cuckoo search, Harris Hawks Optimization, deep semantic structure, and content recommendation system
DOI: 10.3233/JIFS-213422
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7305-7318, 2023
Authors: Ge, Liang | Jia, Yixuan | Li, Qinhong | Ye, Xiaofeng
Article Type: Research Article
Abstract: Traffic speed prediction is a crucial task of the intelligent traffic system. However, due to the highly nonlinear temporal patterns and non-static spatial dependence of traffic data, timely and accurate traffic forecasting remains a challenge. The existing methods usually use a static adjacency matrix to model spatial dependence while ignoring the spatial dynamic characteristics of the road network.Meanwhile, the dynamic influence of different time steps on the prediction target is ignored. Thus, we propose a dynamic multi-graph convolution recurrent neural network (DMGCRNN), which models the dynamic correlations of road networks over time based on various information of road network. Dynamic …correlation is an essential factor for accurate traffic prediction, because it reflects the change of the traffic conditions in real-time. In this model, we design a dynamic graph construction method, which utilizes the local temporal and spatial characteristics of each road segment to construct dynamic graphs. Then, a dynamic multi-graph convolution fusion module is proposed, which considers the dynamic characteristics of spatial correlations and global information to model the dynamic trend of spatial dependence. Moreover, by combing the global context information, temporal attention is provided to capture the dynamic temporal dependence among different time steps. The experimental results from two real-world traffic datasets demonstrate that our method outperforms the state-of-the-art baselines. Show more
Keywords: Traffic speed prediction, dynamic graph construction, Spatio-temporal dependence
DOI: 10.3233/JIFS-222857
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7319-7332, 2023
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