Artificial Intelligence-Based Deep Learning Approach to Identify the Web-Based Attack DOI

Kavi Chelvy,

Ch. Srividhya,

B. Swathi

et al.

Advances in computational intelligence and robotics book series, Journal Year: 2024, Volume and Issue: unknown, P. 21 - 31

Published: May 28, 2024

The use of cutting-edge technologies like computerised material arrangements, artificial intelligence (AI), and the internet things (IoT) raises possibility netting-located attacks in industrial manufacturing. In settings, instances cyberattacks may corrupt data, disrupt operations, even inflict bodily injury. To identify manufacturing web-based attacks, this study proposes novel deep-learning strategies. It examines effectiveness deep learning models, including convolutional neural networks impacting animate nerve organ systems (CNNs or CNN), reiterating (RNNs), change models classifying identifying typical attack attribute. Regarding detection, anticipated engineer-based structure exhibits superior performance veracity, precision, recall compared to conventional existing knowledge methods.

Language: Английский

FATDNet: A fusion adversarial network for tomato leaf disease segmentation under complex backgrounds DOI

Zaichun Yang,

Lixiang Sun, Zhihuan Liu

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 234, P. 110270 - 110270

Published: March 20, 2025

Language: Английский

Citations

1
MIRNet_ECA: Multi-scale inverted residual attention network used for classification of ripeness level for dragon fruit DOI

Bin Zhang,

Kairan Lou,

Zhanxuan Wang

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127019 - 127019

Published: Feb. 1, 2025

Language: Английский

Citations

0
Dilated Inception U-Net with Attention for Crop Pest Image Segmentation in Real-Field Environment DOI Creative Commons
Chungfeng Zhang, Yun‐Long Zhang, Xinhua Xu

et al.

Smart Agricultural Technology, Journal Year: 2025, Volume and Issue: unknown, P. 100917 - 100917

Published: April 1, 2025

Language: Английский

Citations

0
A two-stage classification scheme for rice leaf diseases based on the PDSwin model for practical application scenarios DOI
Jialiang Zhang, Chunyi Peng,

Haoyi Chen

et al.

The European Physical Journal Special Topics, Journal Year: 2025, Volume and Issue: unknown

Published: April 16, 2025

Language: Английский

Citations

0
FG‐UNet: fine‐grained feature‐guided UNet for segmentation of weeds and crops in UAV images DOI
Jianwu Lin, Xin Zhang, Yongbin Qin

et al.

Pest Management Science, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 17, 2024

Semantic segmentation of weed and crop images is a key component prerequisite for automated management. For weeds in unmanned aerial vehicle (UAV) images, which are usually characterized by small size easily confused with crops at early growth stages, existing semantic models have difficulties to extract sufficiently fine features. This leads their limited performance UAV images.

Language: Английский

Citations

1
Artificial Intelligence-Based Deep Learning Approach to Identify the Web-Based Attack DOI

Kavi Chelvy,

Ch. Srividhya,

B. Swathi

et al.

Advances in computational intelligence and robotics book series, Journal Year: 2024, Volume and Issue: unknown, P. 21 - 31

Published: May 28, 2024

The use of cutting-edge technologies like computerised material arrangements, artificial intelligence (AI), and the internet things (IoT) raises possibility netting-located attacks in industrial manufacturing. In settings, instances cyberattacks may corrupt data, disrupt operations, even inflict bodily injury. To identify manufacturing web-based attacks, this study proposes novel deep-learning strategies. It examines effectiveness deep learning models, including convolutional neural networks impacting animate nerve organ systems (CNNs or CNN), reiterating (RNNs), change models classifying identifying typical attack attribute. Regarding detection, anticipated engineer-based structure exhibits superior performance veracity, precision, recall compared to conventional existing knowledge methods.

Language: Английский

Citations

0