Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (420)
  • Open Access

    REVIEW

    Exploring the Applications of Digital Twin Technology in Enhancing Sustainability in Civil Engineering: A Review

    Jiamin Huang1,2, Ping Wu2,*, Wangxin Li3, Jun Zhang2, Yidong Xu2

    Structural Durability & Health Monitoring, Vol.18, No.5, pp. 577-598, 2024, DOI:10.32604/sdhm.2024.050338

    Abstract With the advent of the big data era and the rise of Industrial Revolution 4.0, digital twins (DTs) have gained significant attention in various industries. DTs offer the opportunity to combine the physical and digital worlds and aid the digital transformation of the civil engineering industry. In this paper, 605 documents obtained from the search were first analysed using CiteSpace for literature visualisation, and an author co-occurrence network, a keyword co-occurrence network, and a keyword clustering set were obtained. Next, through a literature review of 86 papers, this paper summarises the current status of DT More >

  • Open Access

    REVIEW

    A Comprehensive Survey on Deep Learning Multi-Modal Fusion: Methods, Technologies and Applications

    Tianzhe Jiao, Chaopeng Guo, Xiaoyue Feng, Yuming Chen, Jie Song*

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1-35, 2024, DOI:10.32604/cmc.2024.053204

    Abstract Multi-modal fusion technology gradually become a fundamental task in many fields, such as autonomous driving, smart healthcare, sentiment analysis, and human-computer interaction. It is rapidly becoming the dominant research due to its powerful perception and judgment capabilities. Under complex scenes, multi-modal fusion technology utilizes the complementary characteristics of multiple data streams to fuse different data types and achieve more accurate predictions. However, achieving outstanding performance is challenging because of equipment performance limitations, missing information, and data noise. This paper comprehensively reviews existing methods based on multi-modal fusion techniques and completes a detailed and in-depth analysis.… More >

  • Open Access

    REVIEW

    A Review on Characteristics, Extraction Methods and Applications of Renewable Insect Protein

    Adelya Khayrova*, Sergey Lopatin, Valery Varlamov

    Journal of Renewable Materials, Vol.12, No.5, pp. 923-950, 2024, DOI:10.32604/jrm.2024.050033

    Abstract Due to the expected rise in the world population, an increase in the requirements for quality and safety of food and feed is expected, which leads to the growing demand for new sources of sustainable and renewable protein. Insect protein is gaining importance as a renewable material for several reasons, reflecting its potential contributions to sustainability, resource efficiency, and environmental conservation. Some insect species are known to be able to efficiently convert organic waste into high-value products such as protein, requiring less land and water compared to traditional livestock. In addition, insect farming produces fewer… More > Graphic Abstract

    A Review on Characteristics, Extraction Methods and Applications of Renewable Insect Protein

  • Open Access

    ARTICLE

    Reducing the Encrypted Data Size: Healthcare with IoT-Cloud Computing Applications

    Romaissa Kebache1, Abdelkader Laouid1,*, Ahcene Bounceur2, Mostefa Kara1,3, Konstantinos Karampidis4, Giorgos Papadourakis4, Mohammad Hammoudeh2

    Computer Systems Science and Engineering, Vol.48, No.4, pp. 1055-1072, 2024, DOI:10.32604/csse.2024.048738

    Abstract Internet cloud services come at a price, especially when they provide top-tier security measures. The cost incurred by cloud utilization is directly proportional to the storage requirements. Companies are always looking to increase profits and reduce costs while preserving the security of their data by encrypting them. One of the offered solutions is to find an efficient encryption method that can store data in a much smaller space than traditional encryption techniques. This article introduces a novel encryption approach centered on consolidating information into a single ciphertext by implementing Multi-Key Embedded Encryption (MKEE). The effectiveness… More >

  • Open Access

    ARTICLE

    Deep Learning: A Theoretical Framework with Applications in Cyberattack Detection

    Kaveh Heidary*

    Journal on Artificial Intelligence, Vol.6, pp. 153-175, 2024, DOI:10.32604/jai.2024.050563

    Abstract This paper provides a detailed mathematical model governing the operation of feedforward neural networks (FFNN) and derives the backpropagation formulation utilized in the training process. Network protection systems must ensure secure access to the Internet, reliability of network services, consistency of applications, safeguarding of stored information, and data integrity while in transit across networks. The paper reports on the application of neural networks (NN) and deep learning (DL) analytics to the detection of network traffic anomalies, including network intrusions, and the timely prevention and mitigation of cyberattacks. Among the most prevalent cyber threats are R2L,… More >

  • Open Access

    ARTICLE

    Time Parameter Based Low-Energy Data Encryption Method for Mobile Applications

    Li-Woei Chen1, Kun-Lin Tsai2,*, Fang-Yie Leu3, Wen-Cheng Jiang2, Shih-Ting Tseng2

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2779-2794, 2024, DOI:10.32604/cmes.2024.052124

    Abstract Various mobile devices and applications are now used in daily life. These devices require high-speed data processing, low energy consumption, low communication latency, and secure data transmission, especially in 5G and 6G mobile networks. High-security cryptography guarantees that essential data can be transmitted securely; however, it increases energy consumption and reduces data processing speed. Therefore, this study proposes a low-energy data encryption (LEDE) algorithm based on the Advanced Encryption Standard (AES) for improving data transmission security and reducing the energy consumption of encryption in Internet-of-Things (IoT) devices. In the proposed LEDE algorithm, the system time More >

  • Open Access

    ARTICLE

    FDSC-YOLOv8: Advancements in Automated Crack Identification for Enhanced Safety in Underground Engineering

    Rui Wang1, Zhihui Liu2,*, Hongdi Liu3, Baozhong Su4, Chuanyi Ma5

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 3035-3049, 2024, DOI:10.32604/cmes.2024.050806

    Abstract In underground engineering, the detection of structural cracks on tunnel surfaces stands as a pivotal task in ensuring the health and reliability of tunnel structures. However, the dim and dusty environment inherent to underground engineering poses considerable challenges to crack segmentation. This paper proposes a crack segmentation algorithm termed as Focused Detection for Subsurface Cracks YOLOv8 (FDSC-YOLOv8) specifically designed for underground engineering structural surfaces. Firstly, to improve the extraction of multi-layer convolutional features, the fixed convolutional module is replaced with a deformable convolutional module. Secondly, the model’s receptive field is enhanced by introducing a multi-branch More >

  • Open Access

    ARTICLE

    Evaluations of Chris-Jerry Data Using Generalized Progressive Hybrid Strategy and Its Engineering Applications

    Refah Alotaibi1, Hoda Rezk2, Ahmed Elshahhat3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 3073-3103, 2024, DOI:10.32604/cmes.2024.050606

    Abstract A new one-parameter Chris-Jerry distribution, created by mixing exponential and gamma distributions, is discussed in this article in the presence of incomplete lifetime data. We examine a novel generalized progressively hybrid censoring technique that ensures the experiment ends at a predefined period when the model of the test participants has a Chris-Jerry (CJ) distribution. When the indicated censored data is present, Bayes and likelihood estimations are used to explore the CJ parameter and reliability indices, including the hazard rate and reliability functions. We acquire the estimated asymptotic and credible confidence intervals of each unknown quantity. More >

  • Open Access

    REVIEW

    A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications

    Deepak Upreti1, Eunmok Yang2, Hyunil Kim3,*, Changho Seo1,4

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2239-2274, 2024, DOI:10.32604/cmes.2024.048932

    Abstract Federated learning is an innovative machine learning technique that deals with centralized data storage issues while maintaining privacy and security. It involves constructing machine learning models using datasets spread across several data centers, including medical facilities, clinical research facilities, Internet of Things devices, and even mobile devices. The main goal of federated learning is to improve robust models that benefit from the collective knowledge of these disparate datasets without centralizing sensitive information, reducing the risk of data loss, privacy breaches, or data exposure. The application of federated learning in the healthcare industry holds significant promise More >

  • Open Access

    REVIEW

    Applications of Soft Computing Methods in Backbreak Assessment in Surface Mines: A Comprehensive Review

    Mojtaba Yari1,*, Manoj Khandelwal2, Payam Abbasi3, Evangelos I. Koutras4, Danial Jahed Armaghani5,*, Panagiotis G. Asteris4

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2207-2238, 2024, DOI:10.32604/cmes.2024.048071

    Abstract Geo-engineering problems are known for their complexity and high uncertainty levels, requiring precise definitions, past experiences, logical reasoning, mathematical analysis, and practical insight to address them effectively. Soft Computing (SC) methods have gained popularity in engineering disciplines such as mining and civil engineering due to computer hardware and machine learning advancements. Unlike traditional hard computing approaches, SC models use soft values and fuzzy sets to navigate uncertain environments. This study focuses on the application of SC methods to predict backbreak, a common issue in blasting operations within mining and civil projects. Backbreak, which refers to More >

Displaying 1-10 on page 1 of 420. Per Page