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  • Open Access

    ARTICLE

    Big Model Strategy for Bridge Structural Health Monitoring Based on Data-Driven, Adaptive Method and Convolutional Neural Network (CNN) Group

    Yadong Xu1, Weixing Hong2, Mohammad Noori3,6,*, Wael A. Altabey4,*, Ahmed Silik5, Nabeel S.D. Farhan2

    Structural Durability & Health Monitoring, Vol.18, No.6, pp. 763-783, 2024, DOI:10.32604/sdhm.2024.053763

    Abstract This study introduces an innovative “Big Model” strategy to enhance Bridge Structural Health Monitoring (SHM) using a Convolutional Neural Network (CNN), time-frequency analysis, and fine element analysis. Leveraging ensemble methods, collaborative learning, and distributed computing, the approach effectively manages the complexity and scale of large-scale bridge data. The CNN employs transfer learning, fine-tuning, and continuous monitoring to optimize models for adaptive and accurate structural health assessments, focusing on extracting meaningful features through time-frequency analysis. By integrating Finite Element Analysis, time-frequency analysis, and CNNs, the strategy provides a comprehensive understanding of bridge health. Utilizing diverse sensor More >

  • Open Access

    ARTICLE

    Quantitative Identification of Delamination Damage in Composite Structure Based on Distributed Optical Fiber Sensors and Model Updating

    Hao Xu1, Jing Wang2, Rubin Zhu2, Alfred Strauss3, Maosen Cao4, Zhanjun Wu1,*

    Structural Durability & Health Monitoring, Vol.18, No.6, pp. 785-803, 2024, DOI:10.32604/sdhm.2024.051393

    Abstract Delamination is a prevalent type of damage in composite laminate structures. Its accumulation degrades structural performance and threatens the safety and integrity of aircraft. This study presents a method for the quantitative identification of delamination identification in composite materials, leveraging distributed optical fiber sensors and a model updating approach. Initially, a numerical analysis is performed to establish a parameterized finite element model of the composite plate. Then, this model subsequently generates a database of strain responses corresponding to damage of varying sizes and locations. The radial basis function neural network surrogate model is then constructed More >

  • Open Access

    ARTICLE

    Experimental Study on the Axial Compression Performance of Bamboo Scrimber Columns Embedded with Steel Reinforcing Bars

    Xueyan Lin1,#, Mingtao Wu2,#, Guodong Li1,*, Nan Guo3, Lidan Mei1

    Structural Durability & Health Monitoring, Vol.18, No.6, pp. 805-833, 2024, DOI:10.32604/sdhm.2024.051033

    Abstract In this paper, a new type of bamboo scrimber column embedded with steel bars (rebars) was proposed, and the compression performance was improved by pre-embedding rebars during the preparation of the columns. The effects of the slenderness ratio and the reinforcement ratio on the axial compression performance of reinforced bamboo scrimber columns were studied by axial compression tests on 28 specimens. The results showed that the increase in the slenderness ratio had a significant negative effect on the axial compression performance of the columns. When the slenderness ratio increased from 19.63 to 51.96, the failure… More >

  • Open Access

    ARTICLE

    Fuzzy Machine Learning-Based Algorithms for Mapping Cumin and Fennel Spices Crop Fields Using Sentinel-2 Satellite Data

    Shilpa Suman1, Abhishek Rawat2,*, Anil Kumar3, S. K. Tiwari4

    Revue Internationale de Géomatique, Vol.33, pp. 363-381, 2024, DOI:10.32604/rig.2024.053981

    Abstract In this study, the impact of the training sample selection method on the performance of fuzzy-based Possibilistic c-means (PCM) and Noise Clustering (NC) classifiers were examined and mapped the cumin and fennel rabi crop. Two training sample selection approaches that have been investigated in this study are “mean” and “individual sample as mean”. Both training sample techniques were applied to the PCM and NC classifiers to classify the two indices approach. Both approaches have been studied to decrease spectral information in temporal data processing. The Modified Soil Adjusted Vegetation Index 2 (MSAVI-2) and Class-Based Sensor… More >

  • Open Access

    RETRACTION

    Retraction: miR-183 modulates cell apoptosis and proliferation in tongue squamous cell carcinoma SCC25 cell line

    Oncology Research Editorial Office

    Oncology Research, Vol.32, No.10, pp. 1691-1692, 2024, DOI:10.32604/or.2024.056908

    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    Efficient Intelligent E-Learning Behavior-Based Analytics of Student’s Performance Using Deep Forest Model

    Raed Alotaibi1, Omar Reyad2,3, Mohamed Esmail Karar4,*

    Computer Systems Science and Engineering, Vol.48, No.5, pp. 1133-1147, 2024, DOI:10.32604/csse.2024.053358

    Abstract E-learning behavior data indicates several students’ activities on the e-learning platform such as the number of accesses to a set of resources and number of participants in lectures. This article proposes a new analytics system to support academic evaluation for students via e-learning activities to overcome the challenges faced by traditional learning environments. The proposed e-learning analytics system includes a new deep forest model. It consists of multistage cascade random forests with minimal hyperparameters compared to traditional deep neural networks. The developed forest model can analyze each student’s activities during the use of an e-learning… More >

  • Open Access

    ARTICLE

    A Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller Model Combined with an Improved Particle Swarm Optimization Method for Fall Detection

    Jyun-Guo Wang*

    Computer Systems Science and Engineering, Vol.48, No.5, pp. 1149-1170, 2024, DOI:10.32604/csse.2024.052931

    Abstract In many Eastern and Western countries, falling birth rates have led to the gradual aging of society. Older adults are often left alone at home or live in a long-term care center, which results in them being susceptible to unsafe events (such as falls) that can have disastrous consequences. However, automatically detecting falls from video data is challenging, and automatic fall detection methods usually require large volumes of training data, which can be difficult to acquire. To address this problem, video kinematic data can be used as training data, thereby avoiding the requirement of creating… More >

  • Open Access

    ARTICLE

    Modern Mobile Malware Detection Framework Using Machine Learning and Random Forest Algorithm

    Mohammad Ababneh*, Ayat Al-Droos, Ammar El-Hassan

    Computer Systems Science and Engineering, Vol.48, No.5, pp. 1171-1191, 2024, DOI:10.32604/csse.2024.052875

    Abstract With the high level of proliferation of connected mobile devices, the risk of intrusion becomes higher. Artificial Intelligence (AI) and Machine Learning (ML) algorithms started to feature in protection software and showed effective results. These algorithms are nonetheless hindered by the lack of rich datasets and compounded by the appearance of new categories of malware such that the race between attackers’ malware, especially with the assistance of Artificial Intelligence tools and protection solutions makes these systems and frameworks lose effectiveness quickly. In this article, we present a framework for mobile malware detection based on a… More >

  • Open Access

    ARTICLE

    Emotion Detection Using ECG Signals and a Lightweight CNN Model

    Amita U. Dessai*, Hassanali G. Virani

    Computer Systems Science and Engineering, Vol.48, No.5, pp. 1193-1211, 2024, DOI:10.32604/csse.2024.052710

    Abstract Emotion recognition is a growing field that has numerous applications in smart healthcare systems and Human-Computer Interaction (HCI). However, physical methods of emotion recognition such as facial expressions, voice, and text data, do not always indicate true emotions, as users can falsify them. Among the physiological methods of emotion detection, Electrocardiogram (ECG) is a reliable and efficient way of detecting emotions. ECG-enabled smart bands have proven effective in collecting emotional data in uncontrolled environments. Researchers use deep machine learning techniques for emotion recognition using ECG signals, but there is a need to develop efficient models… More >

  • Open Access

    ARTICLE

    A Stacking Machine Learning Model for Student Performance Prediction Based on Class Activities in E-Learning

    Mohammad Javad Shayegan*, Rosa Akhtari

    Computer Systems Science and Engineering, Vol.48, No.5, pp. 1251-1272, 2024, DOI:10.32604/csse.2024.052587

    Abstract After the spread of COVID-19, e-learning systems have become crucial tools in educational systems worldwide, spanning all levels of education. This widespread use of e-learning platforms has resulted in the accumulation of vast amounts of valuable data, making it an attractive resource for predicting student performance. In this study, we aimed to predict student performance based on the analysis of data collected from the OULAD and Deeds datasets. The stacking method was employed for modeling in this research. The proposed model utilized weak learners, including nearest neighbor, decision tree, random forest, enhanced gradient, simple Bayes, More >

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