Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (29)
  • Open Access

    ARTICLE

    FusionNN: A Semantic Feature Fusion Model Based on Multimodal for Web Anomaly Detection

    Li Wang1,2,3,*, Mingshan Xia1,2,*, Hao Hu1, Jianfang Li1,2, Fengyao Hou1,2, Gang Chen1,2,3

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2991-3006, 2024, DOI:10.32604/cmc.2024.048637

    Abstract With the rapid development of the mobile communication and the Internet, the previous web anomaly detection and identification models were built relying on security experts’ empirical knowledge and attack features. Although this approach can achieve higher detection performance, it requires huge human labor and resources to maintain the feature library. In contrast, semantic feature engineering can dynamically discover new semantic features and optimize feature selection by automatically analyzing the semantic information contained in the data itself, thus reducing dependence on prior knowledge. However, current semantic features still have the problem of semantic expression singularity, as they are extracted from a… More >

  • Open Access

    ARTICLE

    Fusion-Based Deep Learning Model for Automated Forest Fire Detection

    Mesfer Al Duhayyim1, Majdy M. Eltahir2, Ola Abdelgney Omer Ali3, Amani Abdulrahman Albraikan4, Fahd N. Al-Wesabi2, Anwer Mustafa Hilal5,*, Manar Ahmed Hamza5, Mohammed Rizwanullah5

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1355-1371, 2023, DOI:10.32604/cmc.2023.024198

    Abstract Earth resource and environmental monitoring are essential areas that can be used to investigate the environmental conditions and natural resources supporting sustainable policy development, regulatory measures, and their implementation elevating the environment. Large-scale forest fire is considered a major harmful hazard that affects climate change and life over the globe. Therefore, the early identification of forest fires using automated tools is essential to avoid the spread of fire to a large extent. Therefore, this paper focuses on the design of automated forest fire detection using a fusion-based deep learning (AFFD-FDL) model for environmental monitoring. The AFFD-FDL technique involves the design… More >

  • Open Access

    ARTICLE

    Intelligent Fish Behavior Classification Using Modified Invasive Weed Optimization with Ensemble Fusion Model

    B. Keerthi Samhitha*, R. Subhashini

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3125-3142, 2023, DOI:10.32604/iasc.2023.040643

    Abstract Accurate and rapid detection of fish behaviors is critical to perceive health and welfare by allowing farmers to make informed management decisions about recirculating the aquaculture system while decreasing labor. The classic detection approach involves placing sensors on the skin or body of the fish, which may interfere with typical behavior and welfare. The progress of deep learning and computer vision technologies opens up new opportunities to understand the biological basis of this behavior and precisely quantify behaviors that contribute to achieving accurate management in precision farming and higher production efficacy. This study develops an intelligent fish behavior classification using… More >

  • Open Access

    ARTICLE

    Predictive Multimodal Deep Learning-Based Sustainable Renewable and Non-Renewable Energy Utilization

    Abdelwahed Motwakel1,*, Marwa Obayya2, Nadhem Nemri3, Khaled Tarmissi4, Heba Mohsen5, Mohammed Rizwanulla6, Ishfaq Yaseen6, Abu Sarwar Zamani6

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1267-1281, 2023, DOI:10.32604/csse.2023.037735

    Abstract Recently, renewable energy (RE) has become popular due to its benefits, such as being inexpensive, low-carbon, ecologically friendly, steady, and reliable. The RE sources are gradually combined with non-renewable energy (NRE) sources into electric grids to satisfy energy demands. Since energy utilization is highly related to national energy policy, energy prediction using artificial intelligence (AI) and deep learning (DL) based models can be employed for energy prediction on RE and NRE power resources. Predicting energy consumption of RE and NRE sources using effective models becomes necessary. With this motivation, this study presents a new multimodal fusion-based predictive tool for energy… More >

  • Open Access

    ARTICLE

    Leveraging Multimodal Ensemble Fusion-Based Deep Learning for COVID-19 on Chest Radiographs

    Mohamed Yacin Sikkandar1,*, K. Hemalatha2, M. Subashree3, S. Srinivasan4, Seifedine Kadry5,6,7, Jungeun Kim8, Keejun Han9

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 873-889, 2023, DOI:10.32604/csse.2023.035730

    Abstract Recently, COVID-19 has posed a challenging threat to researchers, scientists, healthcare professionals, and administrations over the globe, from its diagnosis to its treatment. The researchers are making persistent efforts to derive probable solutions for managing the pandemic in their areas. One of the widespread and effective ways to detect COVID-19 is to utilize radiological images comprising X-rays and computed tomography (CT) scans. At the same time, the recent advances in machine learning (ML) and deep learning (DL) models show promising results in medical imaging. Particularly, the convolutional neural network (CNN) model can be applied to identifying abnormalities on chest radiographs.… More >

  • Open Access

    ARTICLE

    Deep Learning Based Energy Consumption Prediction on Internet of Things Environment

    S. Balaji*, S. Karthik

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 727-743, 2023, DOI:10.32604/iasc.2023.037409

    Abstract The creation of national energy strategy cannot proceed without accurate projections of future electricity consumption; this is because EC is intimately tied to other forms of energy, such as oil and natural gas. For the purpose of determining and bettering overall energy consumption, there is an urgent requirement for accurate monitoring and calculation of EC at the building level using cutting-edge technology such as data analytics and the internet of things (IoT). Soft computing is a subset of AI that tries to design procedures that are more accurate and reliable, and it has proven to be an effective tool for… More >

  • Open Access

    ARTICLE

    Novel Vegetation Mapping Through Remote Sensing Images Using Deep Meta Fusion Model

    S. Vijayalakshmi*, S. Magesh Kumar

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2915-2931, 2023, DOI:10.32604/iasc.2023.034165

    Abstract Preserving biodiversity and maintaining ecological balance is essential in current environmental conditions. It is challenging to determine vegetation using traditional map classification approaches. The primary issue in detecting vegetation pattern is that it appears with complex spatial structures and similar spectral properties. It is more demandable to determine the multiple spectral analyses for improving the accuracy of vegetation mapping through remotely sensed images. The proposed framework is developed with the idea of ensembling three effective strategies to produce a robust architecture for vegetation mapping. The architecture comprises three approaches, feature-based approach, region-based approach, and texture-based approach for classifying the vegetation… More >

  • Open Access

    ARTICLE

    Identifying Influential Communities Using IID for a Multilayer Networks

    C. Suganthini*, R. Baskaran

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1715-1731, 2023, DOI:10.32604/iasc.2023.034019

    Abstract In online social networks (OSN), they generate several specific user activities daily, corresponding to the billions of data points shared. However, although users exhibit significant interest in social media, they are uninterested in the content, discussions, or opinions available on certain sites. Therefore, this study aims to identify influential communities and understand user behavior across networks in the information diffusion process. Social media platforms, such as Facebook and Twitter, extract data to analyze the information diffusion process, based on which they cascade information among the individuals in the network. Therefore, this study proposes an influential information diffusion model that identifies… More >

  • Open Access

    ARTICLE

    Chaotic Flower Pollination with Deep Learning Based COVID-19 Classification Model

    T. Gopalakrishnan1, Mohamed Yacin Sikkandar2, Raed Abdullah Alharbi3, P. Selvaraj4, Zahraa H. Kareem5, Ahmed Alkhayyat6,*, Ali Hashim Abbas7

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6195-6212, 2023, DOI:10.32604/cmc.2023.033252

    Abstract The Coronavirus Disease (COVID-19) pandemic has exposed the vulnerabilities of medical services across the globe, especially in underdeveloped nations. In the aftermath of the COVID-19 outbreak, a strong demand exists for developing novel computer-assisted diagnostic tools to execute rapid and cost-effective screenings in locations where many screenings cannot be executed using conventional methods. Medical imaging has become a crucial component in the disease diagnosis process, whereas X-rays and Computed Tomography (CT) scan imaging are employed in a deep network to diagnose the diseases. In general, four steps are followed in image-based diagnostics and disease classification processes by making use of… More >

  • Open Access

    ARTICLE

    An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

    Walid El-Shafai1,2, Noha A. El-Hag3, Ahmed Sedik4, Ghada Elbanby5, Fathi E. Abd El-Samie1, Naglaa F. Soliman6, Hussah Nasser AlEisa7,*, Mohammed E. Abdel Samea8

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2905-2925, 2023, DOI:10.32604/cmc.2023.031936

    Abstract Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and therapy. Deep learning provides a high performance for several medical image analysis applications. This paper proposes a deep learning model for the medical image fusion process. This model depends on Convolutional Neural Network (CNN). The basic idea of the proposed model is to extract features from both CT and MR images. Then, an additional process is executed on the extracted features. After that, the fused feature map is reconstructed to obtain the resulting fused image. Finally, the quality of the… More >

Displaying 1-10 on page 1 of 29. Per Page