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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: Qiao, Jian-min | Li, Wo-yuan | Liu, Lin
Article Type: Research Article
Abstract: In this paper, a two-sided matching decision model based on interval-valued intuitionistic fuzzy environment is proposed to maximize the demand of individual differences. Firstly, the attribute feature table and weight matrix of both agents are constructed, and a formula for calculating the comprehensive advantage in the interval-valued intuitionistic fuzzy environment is given, namely the interval-valued intuitionistic fuzzy comprehensive advantage aggregation operator (IIFCAAO); Secondly, the interval-valued intuitionistic fuzzy decision matrix of both agents is calculated by using the comprehensive advantage aggregation operator, and the interval-valued intuitionistic fuzzy decision matrix is transformed into a score function matrix by using a score function …formula; Thirdly, the two-sided matching model is established to maximize the score; Finally, the scientificity and practicability of the model are verified by an example of college students changing their majors. Show more
Keywords: Interval intuitionistic fuzzy set, interval-valued intuitionistic fuzzy comprehensive advantage aggregation operator (IIFCAAO), two-sided matching
DOI: 10.3233/JIFS-234191
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5667-5676, 2024
Authors: Qi, Ruijuan | Liu, Chang | Zhang, Qiwen | Gu, Lingzi
Article Type: Research Article
Abstract: Business investments are prone to market risks, so pre-analysis is mandatory. The type of risk, its period, sustainability, and economic impact are the analyzable features for preventing loss and downfall. In recent years, mathematical models have been used for representing business cycles and analyzing the impacting risks. This article introduces a Decisive Risk Analytical Model (DRAM) for identifying spur defects in business investments. The proposed risk analytical model exploits the investments, returns, and influencing factors over the various market periods. The risk model is tuned for identifying the influencing factors across various small and large investment periods. The model is …tuned to adapt to different economic periods split into a single financial year. In the process of tuning and training the mathematical analysis model, deep learning is used. The learning paradigm trains the risks and modifying features from expert opinion and previous predictions. Based on these three factors, the risk for the current investment is forecasted. The forecast aids in improving the new investment feasibilities with minimal risks and model modifications. The frequent market status is identified for preventing unnecessary risk-oriented forecasts using the training performed. Therefore, the proposed model is reliable in identifying risks and providing better investment recommendations. Show more
Keywords: Business investment, deep learning, mathematical model, risk analysis
DOI: 10.3233/JIFS-233038
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5677-5693, 2024
Authors: Zhang, Yongcun | Bai, Zhe
Article Type: Research Article
Abstract: Compressive strength (CS) is concrete’s most important mechanical property, as it plays an important role in setting design criteria. Thus, an accurate and early assessment of the CS of concrete can minimize time, labor, and cost. This paper investigated the ability of the Radial Basis Function (RBF) to handle the prediction of CS. The nonlinearities raised from the novel utilized admixtures between the input variables and output CS is tried to be conducted with the RBF model. In order to make a flexible framework combination of the RBF model with the African Vulture Optimization (AVOA) and Salp Swarm Algorithm (SSA) …techniques are considered. The results achieved from the RBF-AVOA model indicated good agreement between the actual and predicted values. The proposed model provides a very accurate HPC compressive strength prediction. In addition, the correlation coefficient R2 is equal to (0.997), and the values of mean absolute error (MAE) (0.1917 MPa), root mean square error (RMSE) (0.937 MPa), and variance account coefficient (VAF) (99.73%) are low. The performance of the RBF-AVOA model, compared to other models, provided the desired advantage and more stable predictions. AVOA plays a key role in modeling results, improving generalization capabilities, avoiding redundant data, and decreasing uncertainty. Show more
Keywords: High-performance concrete, compressive strength, african vulture optimization algorithm, salp swarm algorithm, radial basis function
DOI: 10.3233/JIFS-230907
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5695-5707, 2024
Authors: Shan, Liqian | Zhao, Hui | Feng, Yuhui
Article Type: Research Article
Abstract: Task-oriented collaborative dialogues have become an indispensable form of communication in our daily work and learning, in which participants exchange ideas and share information to advance goals. It is crucial to automatically analyze participants’ contributions and understand these dialogues relative to individuals with limited attention spans. In this paper, seven Discourse Role (DR) labels are designed to describe discourse’s different roles in collaborative dialogues for goal achievement. We collected about 11K discourses from a publicly available dialogue corpus and annotated them with DR tags to construct a dataset named MRDR (Meeting Recorder Discourse Role). In addition, this paper proposes a …novel hierarchical model, STTAHM (Speaker Turn and Topic-Aware Hierarchical Model), for Discourse Role classification. The model is equipped to perceive speaker turn and dialogue topic and can effectively capture the discourse’s local and global semantic information. Experimental results show that our proposed method is effective on the constructed dataset, and the accuracy of Discourse Role classification reaches 86.99%. Show more
Keywords: Task-oriented collaborative dialogue, discourse role, dataset, speaker turn, topic-aware
DOI: 10.3233/JIFS-235263
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5709-5721, 2024
Authors: Liu, Shuanghua
Article Type: Research Article
Abstract: The prediction of shock disturbed systems is always a major challenge in the field of grey prediction. Considering the characteristics of grey buffer operator, this paper proposes a new grey buffer operator based on inverse accumulation, new information priority and logarithmic function to cope with the prediction challenge. In addition, some relevant properties of the new grey buffer operator are discussed in this paper, including adjustment intensity and smoothness. The new grey buffer operator is used to process monotonically increasing sequences, monotonically decreasing sequences and oscillating sequences, respectively. Experimental results show that the proposed buffer operator can effectively improve prediction …accuracy. Show more
Keywords: Grey prediction, grey buffer operator, variable weight coefficient, adjustment intensity, smoothness
DOI: 10.3233/JIFS-230091
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5723-5731, 2024
Authors: Ilakkiya, N. | Rajaram, A.
Article Type: Research Article
Abstract: Different physical objects can be employed in the modern technological environment to facilitate human activity. In order to connect physical objects with the universe of digital using a variety of networks and communication technologies, an IoT, the cutting edges technological and effective solution, is deployed. Mobile ad hoc networks (MANET) interact with the IoTin smart settings, enhancing its user appeal and boosting its commercial viability. The new system of MANET based IoT and IT-network may be created by integrating wireless sensor and MANET with the Internet of Things. A solution like this increases user mobility while lowering network deployment costs. …However, it also raises new, difficult problems in terms of networking considerations. In this, we presented a novel DAG (Directed Acyclic Graph)-Blockchain structure for MANET-IoT security. The network is secured through Multi-Factor PUF (MF-PUF) authentication scheme. With all authorized nodes, the network is segregated into cluster topology. For trusted data transmission, we proposed Jelly Fish Optimization (JFO) algorithm with the consideration of multiple criteria. For deep packet inspection, we proposed a Fully Connected Recurrent Neural Network (FCRNN). Through deep packet inspection, the intrusions are detected and mitigated through blocking system.With help of merged algorithm, the suggested method obtained improved ability in the PDR (Packet Delivery Ratio), production, analysis of time, detection accuracy also security levels. The comparison results clearly indicate that the proposed study outperforms all previous studies in various aspects. Particularly, the suggested methods for cluster creation, data aggregation, routing, encryption, and authentication significantly improve the system of DAG-IDS. Additionally, the planned task exhibits an exceptionally low standard deviation, making the suggested approach highly suitable for a WSN-IoT environment. Show more
Keywords: DAG-blockchain, PUF, trusted routing, RNN, IDS, MANET-IoT
DOI: 10.3233/JIFS-232924
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5733-5752, 2024
Authors: Sui, Duo | Gao, Peng | Fang, Minhang | Lian, Jing | Li, Linhui
Article Type: Research Article
Abstract: Aiming at the problems of low precision and low real-time performance when deploying to embedded platforms in existing multi-task networks, this paper proposes a traffic scene multi-task perception network model (ETS_YOLOP) based on feature fusion. Firstly, an Efficient Attention Control Aggregation Network Module (EACAN) is constructed to improve the real-time perception of the model, and the Space Pyramid Pool Fast Convolutional Module (SPPFCSPC) is used at the end of the backbone network to increase the receptive field. Finally, a Multiscale Convolution Transformer Fusion Module (CTFM) is designed in the task branch to better capture global information and rich context information. …The experimental results show that compared with the YOLOP model, the ETS_YOLOP model has a significant improvement in perception accuracy, 156% in real-time performance, 0.4% increase in mAP on the object detection task, 0.5% increase in mIoU on the drivable area segmentation task, and an 11.4% increase in accuracy on the lane detection task. In order to verify the real-time perception of the model on the embedded platform, the ETS_YOLOP model is deployed on the Huawei MDC300F computing platform. Under the condition of the image input size of 640×640, the average frame rate can reach 55FPS, which can realize real-time perception on the embedded platform. Show more
Keywords: Traffic scene, multi-task perception, self-attention, feature fusion, intelligent vehicle
DOI: 10.3233/JIFS-235246
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5753-5765, 2024
Authors: Shen, Haiyang
Article Type: Research Article
Abstract: Mechanical parameters used in many design codes can be achieved by expensive and time-consuming experiments or by non-destructive approaches such as estimative modelling. This investigation proposed Extreme Gradient Boost (XGB) for estimating the slump (SL) and compressive strength (CS) of high-performance concrete (HPC). In addition, to bring the results of the models closer to the experimental data and increase the accuracy, algorithms were combined with the model, including Sunflower Optimizer (SFO) and Jellyfish Search Optimize (JSO). The relevant models have been examined in three frameworks: individual, hybrid, and ensemble-hybrid. For this purpose, several evaluators were provided to determine the errors, …compare, and accuracy of the presented models. The XGFJ model has demonstrated exceptional performance, achieving remarkable results in terms of RMSE (Root Mean Square Error) and R2 (R-squared) values. Specifically, it has attained an exceptionally small RMSE value of 1.785 for CS and 5.183 for SL, indicating the model’s high precision in predicting these parameters. Additionally, it has achieved the biggest R2 values of 0.9960 for CS and 0.9949 for SL. Additionally, it is worth noting that the XGSF model closely matches the performance of the ensemble form of XGFJ, as evident from its R2 values of 0.9956 for CS and 0.9934 for SL. Based on the study, it was observed that using machine learning to anticipate the mechanical characteristics of concrete is valuable and efficient and can be considered an alternative method instead of time-consuming laboratory methods. This research addresses challenges in predicting HPC properties fueled by the need to overcome drawbacks in traditional methods. Costly and time-intensive laboratory experiments prompted the exploration of alternatives, leading to the proposal of XGB combined with optimization algorithms (SFO and JSO). The study aims to enhance prediction accuracy while tackling broader concerns such as construction costs, material efficiency, and environmental impact. The resource-intensive nature of conventional methods, along with inaccuracies due to material variations, serves as a primary challenge. The proposed resolution advocates for a paradigm shift to machine learning, exemplified by the XGFJ model, showcasing exceptional precision and efficiency in predicting HPC properties. Show more
Keywords: High-performance concrete compressive strength and slump, extreme gradient boost, sunflower optimizer, jellyfish search optimizer
DOI: 10.3233/JIFS-236234
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5767-5782, 2024
Authors: Zong, Yi | Li, Ying | Pan, Enze | Chen, Simin | Zhang, Jingkuan | Gao, Binbin
Article Type: Research Article
Abstract: Stratifying long-tail customers and identifying high-quality customers with high growth potential are crucial for civil aviation companies to explore new profit growth points. This paper proposes a long-tail customer stratification model based on clustering ensemble to address the problems of insufficient attention to long-tail customers in previous studies and the low accuracy and lack of accuracy testing of single clustering algorithms. First, the Bayesian information criterion is used to determine the optimal number of clusters. Then, an ensemble framework integrating the Gaussian mixture model, spectral clustering, Two step clustering and K-means algorithm is constructed, and the stacking and bagging ensemble …methods are used for the cluster ensemble. Finally, three different indicators are used to evaluate the algorithm performance. Experimental results indicate that compared with single clustering algorithms, the Stacking algorithm increases the silhouette coefficient by 14.77% to 27.11%, the Calinski-Harabasz index by 38.83% to 122.18%, and the Davies-Bouldin Index by 19.38% to 98.04%. This indicates that each clustering has high cohesion and separation, with samples within a category being more closely related and those between categories having clear boundaries. It shows that the Stacking algorithm more accurately stratifies long-tail customers with similar consumption behaviors into different categories, achieving customer stratification. Show more
Keywords: Customer stratification, long tail theory, ensemble learning, stacking algorithm, bagging algorithm
DOI: 10.3233/JIFS-234155
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5783-5799, 2024
Authors: Feng, Chongren | Qin, Jiwei | Zhang, Yuhang
Article Type: Research Article
Abstract: Hypernym discovery aims to distinguish potential hypernyms for a query term. However, existing methods for hypernym discovery suffer from the following problems: (1) traditional unsupervised pattern-based methods suffer from low recall; (2) recent supervised box embedding methods are deficient in identifying specific hypernyms. To cope with the above problems, this paper presents a method for hypernym discovery based on E xtended P atterns and Box E mbeddings (EP-BoxE). Firstly, to acquire more hypernymy relation entity pairs, we identify co-hyponyms of a given term and use their hypernyms as the candidate hypernym set for the given term; Secondly, by analyzing the …text corpus, we find that the language patterns also provide additional information for hypernym discovery, which also solves the deficiency of the box embedding methods in identifying specific hypernyms. Finally, experimentations on two domain-specific datasets reveal that EP-BoxE surpasses the performance of popular methods on the majority of evaluation metrics. Show more
Keywords: Hypernym discovery, pattern-based, box embeddings
DOI: 10.3233/JIFS-235181
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5801-5810, 2024
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