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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: Sun, Haobin | Chen, Bingsan | Zhang, Wenshui | Wei, Songma | Lian, Changwei
Article Type: Research Article
Abstract: In the process of production, the label on the product provides the basic product information. Due to the complex text contained on the product labels, the high accuracy recognition for online production labels has always been a challenging problem. To address this issue, a more effective method for complex text detection by improving the convolutional recurrent neural network has been proposed to enhance the recognition accuracy of complex text. Firstly, the SE-DenseNet feature extraction network has been introduced for feature extraction, aiming to improve the model’s depth and feature extraction capacity. Then, the Bi-GRU network is utilized to learn and …model the hidden states and spatial features extracted by SE-DenseNet, anticipate preliminary sequence results, reduce model parameters, and improve the model’s calculation performance. Finally, the CTC network is employed for transcription to convert each feature sequence prediction output by Bi-GRU into a label sequence, achieving complex text recognition. Experimental results on the SVT, IIIT-5K, ICDAR2013 public dataset, and a self-built dataset demonstrate that the proposed model achieves superior outcomes on both public and self-built datasets. Remarkably, the model exhibits the highest recognition accuracy of 93.2% on the ICDAR2013 public dataset, demonstrating its potential to support complex text recognition for online production labels. Show more
Keywords: Online production labels, complex text recognition, SE-DenseNet, Bi-GRU
DOI: 10.3233/JIFS-234748
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Lv, Zhangwei
Article Type: Research Article
Abstract: In the context of China’s cultural and tourism industry, cultural equipment plays a critical role in cultural dissemination, especially in remote areas with harsh road conditions and unique environmental factors. However, the efficiency and stability of manual analysis are significantly challenged by these conditions and the vast yet sparsely collected monitoring data. This study aims to develop a method for extracting valuable information from monitoring data to assess the health status of cultural equipment. We introduce a deep learning-based algorithm that leverages convolutional neural networks (CNNs) to extract local features from multidimensional monitoring indicators and long short-term memory (LSTM) networks …to capture time series features, facilitating the classification of cultural equipment’s health status. The algorithm’s effectiveness is demonstrated through simulation results, highlighting its practicality and applicability in real-world scenarios. This research not only provides a novel approach for cultural equipment health assessment but also contributes significantly to the field by addressing the challenges of data analysis in complex environments, underscoring the importance of technological advancements in preserving cultural heritage. Show more
Keywords: Environmental evaluation, convolutional neural network, long short term memory, health status
DOI: 10.3233/JIFS-241607
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Shamma, Aashitha L. | Vekkot, Susmitha | Gupta, Deepa | Zakariah, Mohammed | Alotaibi, Yousef Ajami
Article Type: Research Article
Abstract: This paper investigates the potential of COVID-19 detection using cough, breathing, and voice patterns. Speech-based features, such as MFCC, zero crossing rate, spectral centroid, spectral bandwidth, and chroma STFT are extracted from audio recordings and evaluated for their effectiveness in identifying COVID-19 cases from Coswara dataset. The explainable AI SHAP tool is employed which identified MFCC, zero crossing rate, and spectral bandwidth as the most influential features. Data augmentation techniques like random sampling, SMOTE, Tomek, and Edited Nearest Neighbours (ENN), are applied to improve the performance of various machine learning models used viz. Naive Bayes, K-nearest neighbours, support vector machines, …XGBoost, and Random Forest. Selecting the top 20 features achieves an accuracy of 73%, a precision of 74%, a recall of 94%, and an F1-score of 83% using the Random Forest model with the Tomek sampling technique. These findings demonstrate that a carefully selected subset of features can achieve comparable performance to the entire feature set while maintaining a high recall rate. The success of the Tomek undersampling technique highlights the ability of model to handle sparse clinical data and predict COVID-19 and associated diseases using speech-based features. Show more
Keywords: Covid-19, MFCC, spectral bandwidth, zero crossing rate, SHAP tool, Tomek
DOI: 10.3233/JIFS-219387
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Zou, Chao | Zhu, Jiwei | Cao, Jiawei | Wang, Xin | Mei, Zhenyu | Zhou, Kui
Article Type: Research Article
Abstract: Prefabricated buildings (PBs) are a new type of building construction, which are less time-consuming and cause low environmental pollution and resource consumption. They play an important role in industrialized construction and clean production and have gained worldwide attention. However, the high construction costs have become a major obstacle to their popularity and application. This study investigates the factors influencing construction costs of PBs in China using a systematic literature review (SLR), fuzzy interpretive structure modeling (fuzzy ISM), and the Matrice d’Impacts croises-multiplication appliqué an classment (MICMAC) technique. First, 32 influencing factors were identified from the SLR. Second, out of which …16 critical factors were selected and mapped in a hierarchical model through semi-structured interview screening, and the MICMAC technique was used to classify the cost-influencing factors of PBs into different categories. The results revealed that all identified factors played pivotal roles in various capacities and influenced the cost of PB construction. This study may assist administrators and policymakers in better understanding the factors that influence the costs of PBs construction to manage and reduce them. Show more
Keywords: Prefabricated buildings, construction costs, critical factors, fuzzy ISM, MICMAC technique
DOI: 10.3233/JIFS-240206
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Ding, Zongchao
Article Type: Research Article
Abstract: The networks have achieved good results by using sparse connections, weight sharing, pooling, and establishing their own localized receptive fields. This work aims to improve the Space Invariant Artificial Neural Network approach and raise its recognition accuracy and convergence rate. Incorporating the continuous neural architecture into the Space Invariant Artificial Neural Network is the first step toward simultaneously learning the deep features of an image. Second, the skip convolution layer of ResNet serves as the foundation for developing a new residual module named QuickCut3-ResNet. A dual evaluation model is then developed to achieve the combined evaluation of the convolutional and …complete connection process. Ultimately, the best network parameters of the Space Invariant Artificial Neural Network are determined after simulation experiments are used to examine the impact of various network parameters on the network performance. Results from experiments demonstrate that the Space Invariant Artificial Neural Network technique described in this research can learn the image’s varied characteristics, which enhances the Space Invariant Artificial Neural Network’s capacity to recognize images and extract features accurately. Show more
Keywords: Artificial intelligence, big data, space invariant artificial neural network, image recognition, QuickCut3-ResNet
DOI: 10.3233/JIFS-239538
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Wang, Zhimin | Li, Boquan
Article Type: Research Article
Abstract: This paper introduces an expert system to decision-making. The expert system is linguistic summarization combined with prioritized operators. In the practical decision-making problems, the information of attributes is linguistic type and needs to be converted into numerical type. The validity of the linguistic summarization is recorded as the attribute value. We discuss how to calculate the validity of the linguistic summarization, and present three prioritized operators. Then the three prioritized operators are used to aggregate the attribute values. Finally, a practical example is given. In addition, we conduct a comparative analysis between the expert system method and another multi-attribute decision-making …method by using a measure of specificity, and conclude that the expert system method is better. Show more
Keywords: Expert system, decision-making, linguistic summarization, prioritized operators, comparative analysis
DOI: 10.3233/JIFS-238556
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Lv, Fangxing | Liu, Wenfeng | Yang, Yuzhen | Gao, Yaling | Bao, Longqing
Article Type: Research Article
Abstract: The automatic generation of natural language is a complex and essential task in text processing. This study proposes a novel approach to address this fundamental problem by leveraging an improved version of DST_BERT, a model that converts input text into a vector representation. Our key contribution lies in the joint optimization of two models, NLU (Natural Language Under-standing) and NLG (Natural Language Generation), which enables us to obtain variable representations within a hidden space. This integration enhances the capabilities of both NLU and NLG in generating coherent and contextually appropriate language. The NLU and NLG …models are seamlessly integrated with the hidden variable space, forming a generative representation model. To assess the effectiveness of our proposed approach, we conducted extensive experiments on the E2E and Weather datasets. The results highlight the state-of-the-art performance achieved by our model in generating natural language. Show more
Keywords: Natural language generation, natural language understanding, text summarization
DOI: 10.3233/JIFS-232981
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2024
Authors: Yang, Fan | Zhou, Qing | Su, Renbin | Xiong, Weihong
Article Type: Research Article
Abstract: Molecular graph representation learning has been widely applied in various domains such as drug design. It leverages deep learning techniques to transform molecular graphs into numerical vectors. Graph Transformer architecture is commonly used for molecular graph representation learning. Nevertheless, existing methods based on the Graph Transformer fail to fully exploit the topological structural information of the molecular graphs, leading to information loss for molecular representation. To solve this problem, we propose a novel molecular graph representation learning method called MTS-Net (Molecular Topological Structure-Network), which combines both global and local topological structure of a molecule. In global topological representation, the molecule …graph is first transformed into a tree structure and then encoded by employing a hash algorithm for tree. In local topological representation, paths between atom pairs are transcoded and incorporated into the calculation of the Transformer attention coefficients. Moreover, MTS-Net has intuitive interpretability for identifying key structures within molecules. Experiments on eight molecular property prediction datasets show that MTS-Net achieves optimal results in three out of five classification tasks, the average accuracy is 0.85, and all three regression tasks. Show more
Keywords: Molecular representation, graph structure, graph transformer, property prediction
DOI: 10.3233/JIFS-236788
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Veeraiah, D. | Sai Kumar, S. | Ganiya, Rajendra Kumar | Rao, Katta Subba | Nageswara Rao, J. | Manjith, Ramaswamy | Rajaram, A.
Article Type: Research Article
Abstract: Medical image fusion plays a crucial role in accurate medical diagnostics by combining images from various modalities. To address this need, we propose an AI model for efficient medical image fusion using multiple modalities. Our approach utilizes a Siamese convolutional neural network to construct a weight map based on pixel movement information extracted from multimodality medical images. We leverage medical picture pyramids to incorporate multiscale techniques, enhancing reliability beyond human visual intuition. Additionally, we dynamically adjust the fusion mode based on local comparisons of deconstructed coefficients. Evaluation metrics including F1-score, recall, accuracy, and precision are computed to assess performance, yielding …impressive results: an F1-score of 0.8551 and a mutual information (MI) value of 2.8059. Experimental results demonstrate the superiority of our method, achieving a remarkable 99.61% accuracy in targeted experiments. Moreover, the Structural Similarity Index (SSIM) of our approach is 0.8551. Compared to state-of-the-art approaches, our model excels in medical picture classification, providing accurate diagnosis through high-quality fused images. This research advances medical image fusion techniques, offering a robust solution for precise medical diagnostics across various modalities. Show more
Keywords: Multimodal medical image fusion, image classification, siamese CNN, LSTM, genetic algorithm
DOI: 10.3233/JIFS-240018
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Huang, Rongbing | Hanif, Muhammad Farhan | Aleem, Aqsa | Siddiqui, Muhammad Kamran | Hanif, Muhammad Faisal | Hussain, Mazhar
Article Type: Research Article
Abstract: The triangular γ-graphyne structure is highlighted in particular, as it is a new configuration with possible applications in medicine. We shed light on this structure’s special qualities and potential uses in healthcare by computing several topological indices linked to it through computational research. Furthermore, we use Shannon’s entropy measure to express the information content of the connection-based topological indices in tandem. This method offers a thorough comprehension of the intricate features and structural properties of the triangular γ-graphyne structure. A logarithmic regression model is built to establish a quantifiable relationship between the computed indices and entropy. The SPSS program was …used in the development of this model, allowing for a thorough examination of the relationship between structural features and informational entropy. A regression model based on triangular graphyne topological indices is used as a predictive tool for entropy estimation. Show more
Keywords: Connection number (CN), triangular γ-graphyne, line graph, logarithmic regression model, Shannon entropy
DOI: 10.3233/JIFS-240356
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
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