Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (2)
  • Open Access


    Towards Sustainable Agricultural Systems: A Lightweight Deep Learning Model for Plant Disease Detection

    Sana Parez1, Naqqash Dilshad2, Turki M. Alanazi3, Jong Weon Lee1,*

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 515-536, 2023, DOI:10.32604/csse.2023.037992

    Abstract A country’s economy heavily depends on agricultural development. However, due to several plant diseases, crop growth rate and quality are highly suffered. Accurate identification of these diseases via a manual procedure is very challenging and time-consuming because of the deficiency of domain experts and low-contrast information. Therefore, the agricultural management system is searching for an automatic early disease detection technique. To this end, an efficient and lightweight Deep Learning (DL)-based framework (E-GreenNet) is proposed to overcome these problems and precisely classify the various diseases. In the end-to-end architecture, a MobileNetV3Small model is utilized as a backbone that generates refined, discriminative,… More >

  • Open Access


    Efficient Deep Learning Framework for Fire Detection in Complex Surveillance Environment

    Naqqash Dilshad1, Taimoor Khan2, JaeSeung Song1,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 749-764, 2023, DOI:10.32604/csse.2023.034475

    Abstract To prevent economic, social, and ecological damage, fire detection and management at an early stage are significant yet challenging. Although computationally complex networks have been developed, attention has been largely focused on improving accuracy, rather than focusing on real-time fire detection. Hence, in this study, the authors present an efficient fire detection framework termed E-FireNet for real-time detection in a complex surveillance environment. The proposed model architecture is inspired by the VGG16 network, with significant modifications including the entire removal of Block-5 and tweaking of the convolutional layers of Block-4. This results in higher performance with a reduced number of… More >

Displaying 1-10 on page 1 of 2. Per Page