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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: Liu, Mingyuan
Article Type: Research Article
Abstract: As virtual reality technology develops, the analysis and processing of video content have become hot spots in the field of computer vision. Video Action Detection aims to locate features in network video, and its research spans many fields, such as computer vision and spatial prediction. In view of the problem of low-efficiency classification models and inaccurate localization of small-scale targets in complex scenes, we propose a novel method to generate candidate intervals for action detection. The action recognition model is adopted to generate the action score sequence on the video time series. We also propose the uncertainty model of the …descending pose detection algorithm. The pre-reaction phase generates a candidate list in the form of concatenated videos containing exactly the same pose to detect action poses that are not identical and of non-maximum duration. Experiments with traditional target detection and multiple deep learning models show that the proposed Non-Maximum Suppression algorithm has a strong ability to extract neural network features. Furthermore, compared with traditional ATSS and Faster R-CNN methods, the detection quality and performance are improved by more than 15.2% and 7.8%, respectively. Our method can fully utilize perception information to improve the quality of decision planning and plays a connecting role between perception fusion and decision planning. Show more
Keywords: Dynamic image processing, spatial feature prediction, uncertainty model, deep neural network
DOI: 10.3233/JIFS-240271
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2024
Authors: Wang, Baoliang | Su, Hongping | Luo, Xiaoqian | Yin, Luqiang
Article Type: Research Article
Abstract: Since the 21st century, network and mobile communication technology are gradually entering the medical and health services field. Combining body area networks, broad-generation mobile communications, and cloud platforms has made various medical applications for large-scale populations possible. The development of digital medical technology, especially digital telemedicine, is increasingly proving to be an important means of significantly reducing the cost of medical care and access, changing the distribution of medical resources, and improving the overall level of care. To observe the effects of traditional Chinese medicine gongfu combined with rehabilitation therapy on mild depression, anxiety, and functional recovery of activities of …daily living (ADL) in patients recovering from stroke, and to provide new treatment methods to improve the function and daily living ability of the group who develop mild depression and anxiety after stroke. In this paper, the digital medical engineering application combining information technology and medical treatment integrates various high-end information technologies such as body domain network and cloud computing to solve the difficulties in the current application one by one, to provide the national people with the system provides “timely”, “local” and “bottomless” remote digital health services to the Chinese people. Show more
Keywords: Cloud technology, digital telemedicine, traditional gongfu, stroke, rehabilitation
DOI: 10.3233/JIFS-238267
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Hussain, Abrar | Zhang, Nan | Ullah, Kifayat | Garg, Harish | Al-Quran, Ashraf | Yin, Shi
Article Type: Research Article
Abstract: The q-rung orthopair fuzzy set (q-ROFS) is a moderate mathematical model, that has diverse capabilities to handle uncertain and ambiguous information of human opinion during the decision analysis process. The Aczel Alsina operations are more flexible and valuable aggregating tools with parameter values ϻ ⩾ 1, reflecting smooth and accurate information by aggregating awkward and redundant information. The theory of the Choquet integral operator is also used to express the interaction between preferences or criteria by incorporating certain values of preferences. The primary features of this article are to derive some dominant mathematical approaches by combining two different theories like Choquet integral …operators and operations of Aczel Alsina tools namely “q-rung orthopair fuzzy Choquet integral Aczel Alsina average” (q-ROFCIAAA), and “q-rung orthopair fuzzy Choquet integral Aczel Alsina geometric” (q-ROFCIAAG) operators. Some special cases and notable characteristics are also demonstrated to show the feasibility of derived approaches. Based on our derived aggregation approaches, a multi-attribute decision-making (MADM) technique aggregates redundant and unpredictable information. In light of developed approaches, a numerical example study to evaluate suitable safety equipment in the construction sector. To reveal the intensity and applicability of derived approaches by contrasting the results of prevailing approaches with currently developed AOs. Show more
Keywords: q-rung orthopair fuzzy values, choquet integral operators, aczel alsina operations, and decision support system
DOI: 10.3233/JIFS-240169
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Saleem, Saima | Khattar, Anuradha | Mehrotra, Monica
Article Type: Research Article
Abstract: Rapidly classifying disaster-related social media (SM) images during a catastrophe event is critical for enhancing disaster response efforts. However, the biggest challenge lies in acquiring labeled data for an ongoing (target) disaster to train supervised learning-based models, given that the labeling process is both time-consuming and costly. In this study, we address this challenge by proposing a new multimodal transfer learning framework for the real-time classification of SM images of the target disaster. The proposed framework is based on Contrastive Language-Image Pretraining (CLIP) model, jointly pretrained on a dataset of image-text pairs via contrastive learning. We propose two distinct methods …to design our classification framework (1) Zero-Shot CLIP: it learns visual representations from images paired with natural language descriptions of classes. By utilizing the vision and language capabilities of CLIP, we extract meaningful features from unlabeled target disaster images and map them to semantically related textual class descriptions, enabling image classification without training on disaster-specific data. (2) Linear-Probe CLIP: it further enhances the performance and involves training a linear classifier on top of the pretrained CLIP model’s features, specifically tailored to the disaster image classification task. By optimizing the linear-probe classifier, we improve the model’s ability to discriminate between different classes and achieve higher performance without the need for labeled data of the target disaster. Both methods are evaluated on a benchmark X (formerly Twitter) dataset comprising images of seven real-world disaster events. The experimental outcomes showcase the efficacy of the proposed methods, with Linear-Probe CLIP achieving a remarkable 7% improvement in average F1-score relative to the state-of-the-art methods. Show more
Keywords: Transfer learning, CLIP, social media, image classification, disaster response
DOI: 10.3233/JIFS-241271
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Wu, Xiongyu | Yan, Yixuan | Zhu, Wenxi | Yang, Nina
Article Type: Research Article
Abstract: BACKGROUND: In recent years, Despite the proven economic growth brought by AI technology globally, the adoption of AI in the construction industry faces obstacles. To better promote the adoption of AI technology in the construction domain, this study, based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT) model, delves into the key factors influencing the adoption of AI technology in the construction industry. By introducing personal-level influencing factors such as AI anxiety and personal innovativeness, the UTAUT model is extended to comprehensively understand users’ attitudes and adoption behaviors towards AI technology. METHODOLOGY: The research …framework is based on the Unified Theory of Acceptance and Use of Technology (UTAUT) with the added constructs of artificial intelligence anxiety and individual Innovativeness. These data were collected through a combination of online and offline surveys, with a total of 258 valid data collected and analyzed using structural equation modeling. RESULTS: The study found that Usage Behavior (UB) in adopting Artificial Intelligence (AI) is positively influenced by several factors. Specifically, Performance Expectancy (PE) (β= 0.266, 95%), Effort Expectancy (EE) (β= 0.262, 95%), and Social Influence (SI) (β= 0.131, 95%) were identified as significant predictors of UB. Additionally, Facilitating Conditions (FC) (β= 0.168, 95%) also positively influenced UB.Moreover, the study explored the moderating effects of Artificial Intelligence Anxiety and Individual Innovativeness on the relationships between Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC) with the Usage Behavior of AI technology. PRACTICAL IMPLICATIONS: This study lie in informing industry stakeholders about the multifaceted dynamics influencing AI adoption. Armed with this knowledge, organizations can make informed decisions, implement effective interventions, and navigate the challenges associated with integrating AI technology into the construction sector. Show more
Keywords: UTAUT, artificial intelligence, construction industry
DOI: 10.3233/JIFS-240798
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Nguyen, Manh Hung | Van Nguyen, Hong | Tran, Van Quan
Article Type: Research Article
Abstract: Forecasting container ship arrival times is challenging, requiring a thorough analysis for accuracy. This study investigates the effectiveness of machine learning (ML) techniques in maritime transportation. Using a dataset of 581 samples with 8 input variables and 1 output variable (arrival time), ML models are constructed. The Pearson correlation matrix reduces input variables to 7 key factors: freight forwarder, dispatch location, loading and discharge ports, post-discharge location, dispatch day of the week, and dispatch week. The ranking of ML performance for predicting the arrival time of container ships can be arranged in descending order as GB-PSO > XGB > RF > RF-PSO > GB > … KNN > SVR. The best ML model, GB-PSO, demonstrates high accuracy in predicting the arrival time of container ships, with R2 = 0.7054, RMSE = 7.4081 days, MAE = 5.1891 days, and MAPE = 0.0993% for the testing dataset. This is a promising research outcome as it seems to be the first time that an approach involving the use of minimal and easily collectible input factors (such as freight forwarder, dispatch time and place, port of loading, post port of discharge, port of discharge) and the combination of a machine learning model has been introduced for predicting the arrival time of container ships. Show more
Keywords: Machine learning, container ships, arrival time, freight forwarder, place of dispatch, port of loading
DOI: 10.3233/JIFS-234552
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: AL-Qadri, Mohammed | Gao, Peiwei | Zhang, Hui | Zhao, Zhiqing | Chen, Lifeng | Cen, Feng | Zhang, Jun
Article Type: Research Article
Abstract: Crack detection in concrete buildings is crucial for assessing structural health, but it poses challenges due to complex backgrounds, real-time requirements, and high accuracy demands. Deep learning techniques, including U-Net and Fully Convolutional Networks (FCN), have shown promise in crack detection. However, they are sensitive to real-world environmental variations, impacting robustness and accuracy. This paper compares the performance of U-Net and FCN for concrete crack detection on bridges using raw images under various conditions. A dataset of 157 images (100 for training, 57 for testing) was used, and the models were evaluated based on Dice similarity coefficient and Jaccard index. …FCN slightly outperformed U-Net in accuracy (94.88% vs. 94.21%), while U-Net had a slight advantage in validation (93.55% vs. 92.99%). These findings provide valuable insights for automated infrastructure maintenance and repair. Show more
Keywords: Cracks detection, concrete buildings, deep learning, U-Net, Fully Convolutional Networks (FCN)
DOI: 10.3233/JIFS-239709
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Parthiban, P. | Vaisakhi, V.S.
Article Type: Research Article
Abstract: Wireless sensor network (WSN) collect and detect data in real time, but their battery life limits their lifetime. The CH selection process increases network overhead and reduces lifetime, but it considers node processing and energy limitations. To solve that problem this research methodology proposed Multi Objective Energy trust - Aware Optimal Clustering and Secure Routing (MOETAOCSR) protocol. At first, the trust factors such as direct and indirect factors are calculated. Thus, the calculated values are given as input to the SDLSTM to detect the malicious node and normal node. Here, the network deployment process is initially carried out and then …the cluster is formed by HWF-FCM. From the clustered sensor nodes, the cluster head is selected using Golden Jackal Siberian Tiger Optimization (GJSTO) approach. Then, the selection of CH the paths are learned by using the Beta Distribution and Scaled Activation Function based Deep Elman Neural Network (BDSAF-DENN) and from the detected paths the optimal paths are selected using the White Shark Optimization (WSO). From the derived path sensed data securely transferred to the BS for further monitoring process using FPCCRSA. The proposed technique is implemented in a MATLAB platform, where its efficiency is assessed using key performance metrics including network lifetime, packet delivery ratio, and delay. Compared to existing models such as EAOCSR, RSA, and Homographic methods, the proposed technique achieves superior results. Specifically, it demonstrates a 0.95 improvement in throughput, 0.8 enhancement in encryption time, and a network lifetime of 7.4. Show more
Keywords: Four point curve cryptographic and rivest shamir adleman (FPCCRSA), Haversine with weighted function based fuzzy c-means clustering (HWF-FCM), wireless sensor network, Cluster head (CH), sigmoid deep long short term memory (SDLSTM)
DOI: 10.3233/JIFS-236739
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Yu, Hongliang | Peng, Zhen | Wu, Zhaoliang | He, Zirui | Huang, Chun
Article Type: Research Article
Abstract: To address the existing shortcomings in the research on the coupling of safety risk factors in subway tunnel construction using the shallow-buried excavation method, this paper conducts a coupled analysis and dynamic simulation of the safety risks associated with this construction method. Firstly, by analyzing the mechanisms and effects of risk coupling in shallow-buried excavation construction of subway tunnels, this study divides the risk system into four risk subsystems (human, material, management, and environment), establishes an evaluation index system for the coupling of safety risks, calculates the comprehensive weight values of the risk indicators using the AHP-entropy weight method, and …constructs a risk coupling degree model by combining the inverse cloud model and efficacy function. Subsequently, based on the principles of system dynamics, a causal relationship diagram and a system dynamics simulation model for the coupling of “human-material” risks in construction are established using Vensim PLE software. Finally, the case study of the underground excavation section of Chengdu Metro Line 2 is employed to perform dynamic simulation using the established model. By adjusting the relevant risk coupling coefficients and simulation duration, the impact of the coupling of various risk factors on the safety risk level of the human-material coupling system is observed. The simulation results demonstrate that: 1) Heterogeneous coupling of human and material risks has a particularly significant effect on the system’s safety risks; 2) Violations by personnel and initial support structure defects are key risk coupling factors. The findings of this study provide new insights for decision-makers to assess the safety risk of shallow-buried excavation construction in subway tunnel. Show more
Keywords: Shallow-buried excavation method, risk coupling, coupling degree model, system dynamics, simulation analysis
DOI: 10.3233/JIFS-239674
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Article Type: Research Article
Abstract: With the rapid development of the building industry, intelligent buildings benefit from its safety, energy saving, environmental protection and integration and other advantages have been widely loved by people, most operators also realize the importance of intelligent buildings to bring people humanized and customized services, and in order to realize the personalized service of the building, multi-modal data fusion is an effective method. On the other hand, in today’s Internet of Things society, many practical applications need to deploy a large number of sensing equipment for data collection and processing, so as to carry out high-quality monitoring of the physical …world, but due to the inherent limitations of these hardware equipment and the influence of factors such as the environment, single mode data often cannot be completely and comprehensively monitored to the physical world’s changing characteristics. In this development context, multi-modal data fusion has become a research hotspot in the field of machine learning. Based on this, this paper proposes a one-stage fast object detection model with multi-level fusion of multi-modal features and end-to-end characteristics for building indoor environment perception, and conducts experimental analysis on the performance of the model. The verification results show that the accuracy of the proposed method is 50.7% and the running speed is 0.107 s, which has better performance than the existing detection methods. Show more
Keywords: Multi-modal data fusion, depth perception, target detection, intelligent building, environmental awareness
DOI: 10.3233/JIFS-241252
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Saleh Mohamed Naser, Naser | Serte, Sertan | Al-Turjman, Fadi
Article Type: Research Article
Abstract: Deep learning has recently made great progress leading to revolutionizing image recognition, speech recognition, and natural language processing tasks that were previously challenging to make using traditional techniques. Image classification offers a lot of potential for architectural design, even though it is rarely used to uncover new techniques. It can be used to determine the client’s preferences and design a building that satisfies those preferences. The different architectural styles based on culture, region, and time are one of the main challenges for image classification in architecture. Hence, it can be challenging for untrained clients to recognize an architectural style, and …sometimes some buildings are made up of various types that are difficult to classify as a single style. This paper investigates the potential of employing state-of-art cutting-edge image classification algorithms in houses classification. In addition, the paper proposes the uses of Shifted Patch Tokenization (SPT) and Locality Self-Attention (LSA) in order to enhance the performance of Vision transformer (ViT) when trained to classify house images with a small dataset, opposed to the regular ViT which requires huge dataset in order to converge. Experimentally, these techniques proved to have a positive impact on the performance of the ViT, which reached 96.85% accuracy when SPT and LSA are employed. Show more
Keywords: Image recognition, house classification, vision transformer, ViT, shifted patch tokenization, locality self-attention
DOI: 10.3233/JIFS-230972
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
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