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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: Wei, Tao | Yang, Changchun | Zheng, Yanqi | Zhang, Jingxue
Article Type: Research Article
Abstract: Recently, Graph Neural Networks (GNNs) using aggregating neighborhood collaborative information have shown effectiveness in recommendation. However, GNNs-based models suffer from over-smoothing and data sparsity problems. Due to its self-supervised nature, contrastive learning has gained considerable attention in the field of recommendation, aiming at alleviating highly sparse data. Graph contrastive learning models are widely used to learn the consistency of representations by constructing different graph augmentation views. Most current graph augmentation with random perturbation destroy the original graph structure information, which mislead embeddings learning. In this paper, an effective graph contrastive learning paradigm CollaGCL is proposed, which constructs graph augmentation by …using singular value decomposition to preserve crucial structure information. CollaGCL enables perturbed views to effectively capture global collaborative information, mitigating the negative impact of graph structural perturbations. To optimize the contrastive learning task, the extracted meta-knowledge was propagate throughout the original graph to learn reliable embedding representations. The self-information learning between views enhances the semantic information of nodes, thus alleviating the problem of over-smoothing. Experimental results on three real-world datasets demonstrate the significant improvement of CollaGCL over state-of-the-art methods. Show more
Keywords: Self-supervised learning, recommendation, contrastive learning, data augmentation
DOI: 10.3233/JIFS-236497
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Yang, Dianqing | Wang, Wenliang
Article Type: Research Article
Abstract: Unmanned aerial vehicle (UAV) remote-sensing images have a wide range of applications in wildfire monitoring, providing invaluable data for early detection and effective management. This paper proposes an improved few-shot target detection algorithm tailored specifically for wildfire detection. The quality of UAV remote-sensing images is significantly improved by utilizing image enhancement techniques such as Gamma change and Wiener filter, thereby enhancing the accuracy of the detection model. Additionally, ConvNeXt-ECA is used to focus on valid information within the images, which is an improvement of ConvNeXt with the addition of the ECANet attention mechanism. Furthermore, multi-scale feature fusion is performed by …adding a feature pyramid network (FPN) to optimize the extracted small target features. The experimental results demonstrate that the improved algorithm achieves a detection accuracy of 93.2%, surpassing Faster R-CNN by 6.6%. Moreover, the improved algorithm outperforms other target detection algorithms YOLOv8, RT-DETR, YoloX, and SSD by 3.4%, 6.4%, 7.6% and 21.1% respectively. This highlights its superior recognition accuracy and robustness in wildfire detection tasks. Show more
Keywords: Fire target detection, ConvNeXt-ECA, UAV remote-sensing image, feature pyramid network
DOI: 10.3233/JIFS-240531
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Singh, Pratibha | Kushwaha, Alok Kumar Singh | Varshney, Neeraj
Article Type: Research Article
Abstract: Precise video moment retrieval is crucial for enabling users to locate specific moments within a large video corpus. This paper presents Interactive Moment Localization with Multimodal Fusion (IMF-MF), a novel interactive moment localization with multimodal fusion model that leverages the power of self-attention to achieve state-of-the-art performance. IMF-MF effectively integrates query context and multimodal features, including visual and audio information, to accurately localize moments of interest. The model operates in two distinct phases: feature fusion and joint representation learning. The first phase dynamically calculates fusion weights for adapting the combination of multimodal video content, ensuring that the most relevant features …are prioritized. The second phase employs bi-directional attention to tightly couple video and query features into a unified joint representation for moment localization. This joint representation captures long-range dependencies and complex patterns, enabling the model to effectively distinguish between relevant and irrelevant video segments. The effectiveness of IMF-MF is demonstrated through comprehensive evaluations on three benchmark datasets: TVR for closed-world TV episodes and Charades for open-world user-generated videos, DiDeMo dataset, Open-world, diverse video moment retrieval dataset. The empirical results indicate that the proposed approach surpasses existing state-of-the-art methods in terms of retrieval accuracy, as evaluated by metrics like Recall (R1, R5, R10, and R100) and Intersection-of-Union (IoU). The results consistently demonstrate IMF-MF’s superior performance compared to existing state-of-the-art methods, highlighting the benefits of its innovative interactive moment localization approach and the use of self-attention for feature representation and attention modeling. Show more
Keywords: Multimedia data retrieval, query-dependent fusion, ranking system, multimodal retrieval, video segment localization
DOI: 10.3233/JIFS-233071
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Maheswari, M. | Anitha, D. | Sharma, Aditi | Kaur, Kiranpreet | Balamurugan, V. | Garikapati, Bindu | Dineshkumar, R. | Karunakaran, P.
Article Type: Research Article
Abstract: Anomaly detection, a critical aspect of data analysis and cybersecurity, aims to identify unusual patterns that deviate from the expected norm. In this study, we propose a hybrid approach that combines the strengths of Autoencoder neural networks and Multiclass Support Vector Machines (SVM) for robust anomaly detection. The Autoencoder is utilized for feature learning and extraction, capturing intricate patterns in the data, while the Multiclass SVM provides a discriminative classification mechanism to distinguish anomalies from normal patterns. Specifically, the Autoencoder is trained on normal data to acquire a compact and efficient representation of the underlying patterns, with the reconstruction errors …serving as indicative measures of anomalies. Concurrently, a Multiclass SVM is trained to classify instances into multiple classes, including an anomaly class. The anomaly scores from the Autoencoder and the decision function of the Multiclass SVM, along with that of the Random Forest Neural Network (AE-RFNN), are combined, leveraging their complementary strengths. A thresholding mechanism is then employed to classify instances as normal or anomalous based on the combined scores. The performance of the hybrid model is evaluated using standard metrics such as precision, recall, F1-score, and the area under the Receiver Operating Characteristic (ROC) curve. The proposed hybrid anomaly detection approach demonstrates effectiveness in capturing complex patterns and discerning anomalies across diverse datasets. Additionally, the model offers flexibility for adaptation to evolving data distributions. This study contributes to the advancement of anomaly detection methodologies by presenting a hybrid solution that combines feature learning and discriminative classification for improved accuracy and generalization. Show more
Keywords: Anomaly detection, Autoencoder, Multiclass SVM, feature learning, hybrid model, cybersecurity
DOI: 10.3233/JIFS-240028
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Ren, Xinyu | Yang, Wanhe | Yang, Hui
Article Type: Research Article
Abstract: With the increasing demand for tourism, people’s travel modes are more and more diversified, and the tourism recommendation system also arises at the historical juncture. However, the current recommendation system is only recommended for a single user and does not realize the group travel recommendation. To achieve the goal of recommending its preferred attractions for multiple users, the time decay characteristics and Pearson correlation coefficient in Newton’s cooling law are used to obtain the user similarity with spatial distance factor and temporal decay factor and to obtain the score prediction results based on spatiotemporal fusion. In addition, the trust of …user communication is used to recommend, and the weights of the two scoring results are added to obtain the personalized recommendation results of member users. Finally, the study used the fusion strategy to integrate the personalized recommendation results for group preference and obtained the final group travel recommendation list. Therefore, a group travel recommendation model based on spatio-temporal integration factors was constructed. According to the experimental analysis, we can see that the average HR value of the constructed model is 0.8124, and the average NDCG value is 0.7284, which can accurately judge users’ preferences and get the most suitable group travel recommendation results, thus facilitating users to make the next plan for the tourism project. Show more
Keywords: Group recommendation, spatio-temporal fusion, score prediction, fusion strategy
DOI: 10.3233/JIFS-239548
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Shehzadi, Maham | Fahmi, Aliya | Abdeljawad, Thabet | Khan, Aziz
Article Type: Research Article
Abstract: This paper investigates the detailed analysis of linear diophantine fuzzy Aczel-Alsina aggregation operators, enhancing their efficacy and computational efficiency while aggregating fuzzy data by using the fuzzy C-means (FCM) method. The primary goal is to look at the practical uses and theoretical foundations of these operators in the context of fuzzy systems. The aggregation process is optimised using the FCM algorithm, which divides data into clusters iteratively. This reduces computer complexity and enables more dependable aggregation. The mathematical underpinnings of Linear Diophantine Fuzzy Aczel-Alsina aggregation operators are thoroughly examined in this study, along with an explanation of their purpose in …handling imprecise and uncertain data. It also investigates the integration of the FCM method, assessing its impact on simplifying the aggregation procedure, reducing algorithmic complexity, and improving the accuracy of aggregating fuzzy data sets. This work illuminates these operators performance and future directions through extensive computational experiments and empirical analysis. It provides an extensive framework that shows the recommended strategy’s effectiveness and use in a variety of real-world scenarios. We obtain our ultimate outcomes through experimental investigation, which we use to inform future work and research. The purpose of the study is to offer academics and practitioners insights on how to improve information fusion techniques and decision-making processes. Show more
Keywords: Linear diophantine fuzzy set, Aczel-Alsina operational laws, linear diophantine fuzzy Aczel-Alsina aggregation operators, fuzzy C-means algorithm
DOI: 10.3233/JIFS-238716
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-22, 2024
Authors: Chongjuan, Wang
Article Type: Research Article
Abstract: The convergence of visual communication design with unique effects, graphic design, as well as virtual reality, which is becoming progressively more popular, has created a new paradigm for education in recent years. However, emerging evidence indicates that their integration into the world of learning is a somewhat gradual and intricate process. The present research proposes a novel algorithm and a functional model of artificial intelligence technology design to automatically arrange graphic language in visual communication design. In visual communication design, the goal orchestration function used to determine the display size of buffer images is the difference between the minimum and …maximum values of the number of orchestration screens. An ant colony method is used in visual communication design to identify the optimal locations for visuals to be presented, and ASM semantics is used to characterize the visual languages. In order to accomplish the invention and development of a visual communication design style, the suggested algorithm has to be programmed and executed. It employs sequential decision marking to characterize the visual vocabulary and accomplishes automated organization. According to the trial results, visual saturation based on AI technology can reach up to 97%, and the average user satisfaction score is 7.65. It is evident that a creative visual thinking approach can maximize the visual communication design effect and communicate fresh design concepts. Show more
Keywords: Innovation and entrepreneurship, visual communication design (VCD), hybrid optimization, adaptive network-based fuzzy inference system (ANFIS), Statistical analysis, t-test and correlation
DOI: 10.3233/JIFS-235930
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Bakhshi, Mahmood | Ahn, Sun Shin | Jun, Young Bae | Borzooei, Rajab Ali
Article Type: Research Article
Abstract: Some kinds of pseudo valuations such as positive implicative pseudo valuation, (weak) implicative pseudo valuation, and commutative pseudo valuation of various types are introduced. Several examples, properties and characterizations of them are given as well. The relationships between them and the substructures of hyper BCK -algebras are investigated, too. Finally, by giving various examples and theorems, the relationships among the proposed pseudo valuations are investigated and characterized, especially in hyper BCK -algebras with three elements.
Keywords: Hyper BCK -algebra, pseudo valuation, positive implicative pseudo valuation, implicative pseudo valuation, commutative pseudo valuation
DOI: 10.3233/JIFS-233898
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Selvaraj, Sunil Kumar | Bhat Pundikai, Venkatramana
Article Type: Research Article
Abstract: BACKGROUND: The increased depletion of ground water resources poses the risk of higher moisture stress environment for agriculture crops. The rapid increase in the moisture stress situation imposes the need of efficient agricultural research on determining the impact of moisture stress on variety of crops. OBJECTIVE: The prime objective of the proposed work is building an IoT based Plant Phenotyping Device for moisture stress experimental study on variety of crops with deep learning model for stress response detection. METHODS: In this work, IoT technology is used for building a proposed system for conducting …the moisture stress experiments on plants and adopting the image processing and convolution neural network based model for stress prediction. RESULTS: The accuracy of the proposed system was experimentally evaluated and empirical results were satisfactory in maintaining the desired level of moisture stress. Performance analysis of LeNet, AlexNet, customized AlexNet and GoogLeNet CNN models were carried out with hyper-parameters variations on the leaf images. GoogLeNet achieved a better validation accuracy of 96% among other models. The trained GoogLeNet model is used for predicting the moisture stress response and predicted results were matched with manual observation of stress response. SIGNIFICANCE: The affirmative results of proposed system would increases its adoption for in-house precision agriculture and also for conducting various moisture stress experiments on variety of crops. The confirmative detection of moisture stress tolerance level of plant provides knowledge on minimum level of water requirement for plant growth, which in-turn save the water by avoiding excess watering to plants. Show more
Keywords: IoT, sensors, Raspberry Pi, moisture stress, deep learning
DOI: 10.3233/JIFS-236885
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Ashwin, P.V. | Ansal, K.A.
Article Type: Research Article
Abstract: Image classification using polarimetric synthetic aperture radar (Pol-SAR) is becoming more important in image processing for remote sensing applications. However, in the existing techniques, during the feature extraction process, there exist some limitations including laborious endeavour for Pol-SAR image classification, identifying intrinsic features for target recognition is difficult in feature selection, and pixel-level Pol-SAR image classification is difficult for obtaining more precise and coherent interpretation consequences. Hence to overcome these issues, a novel Multifarious Stratification Stratagem in machine learning is proposed to achieve pixel-level Pol-SAR classification. In this proposed model, a novel Scrumptious Integrant Wrenching method is used for efficient …feature extraction. It is compatible with the orientation-sensitive of the Pol-SAR image which increases the variety of intra-layer features. To remove the difficulty in feature selection, a novel Episodicical Proximity Selection method is proposed in which a Split-level parallel feature selection strategy is used to select the best qualities from the extracted features. To tackle the difficulty in classification, an Elastic Net Classifier (ENC) is used that find the coefficient vector for the linear combination of the training sets. This efficiently classified the best features in the Pol-SAR images and improved the proposed system’s accuracy. As a result, the performance measures of the proposed system demonstrate that the accuracy is increased by 99.69%, precision is increased by 98.99%, recall is increased by 98.99%, sensitivity is increased by 98.99%, and F1-score is increased by 98.99% as a response. Show more
Keywords: Feature extraction, feature selection, elastic net classifier, principle component analysis, convolution layer, max-pooling layer
DOI: 10.3233/JIFS-222403
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
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