Zobrazeno 1 - 10
of 202
pro vyhledávání: '"convolutional block attention module (CBAM)"'
Publikováno v:
Jin'gangshi yu moliao moju gongcheng, Vol 44, Iss 5, Pp 588-598 (2024)
Objectives: With the improvement of production technology, the traditional diamond particle cleanliness detection method can no longer meet the requirements of high precision, high quality and high automation in the diamond industry due to its low ef
Externí odkaz:
https://doaj.org/article/bb9a74c9d06d4de9beb6f83c31214f95
Autor:
Mohammed Zakariah, Abeer Alnuaim
Publikováno v:
Egyptian Informatics Journal, Vol 27, Iss , Pp 100536- (2024)
Human Activity Recognition (HAR) is crucial for the advancement of applications in smart environments, communication, IoT, security, and healthcare monitoring. Convolutional neural networks (CNNs) have made substantial contributions to human activity
Externí odkaz:
https://doaj.org/article/8fff162838174ba6868b28234382bd44
Publikováno v:
Frontiers in Zoology, Vol 21, Iss 1, Pp 1-13 (2024)
Abstract Background Rapid identification and classification of bats are critical for practical applications. However, species identification of bats is a typically detrimental and time-consuming manual task that depends on taxonomists and well-traine
Externí odkaz:
https://doaj.org/article/d71e6a3634454d4d84a85924d1c96dc1
Publikováno v:
IEEE Access, Vol 12, Pp 181997-182009 (2024)
Under high-intensity rail operations, rail tracks endure considerable stresses resulting in various defects such as corrugation and spellings. Failure to effectively detect defects and provide maintenance in time would compromise service reliability
Externí odkaz:
https://doaj.org/article/86b05ba3325f4f2280cd86b06ab9bae2
Publikováno v:
IEEE Access, Vol 12, Pp 172730-172741 (2024)
Corn is a major cereal crop, and accurate monitoring of corn planting areas is crucial for agricultural structural adjustments and ensuring food security. This study proposes an improved HRNet network that utilizes the spectral and spatial features o
Externí odkaz:
https://doaj.org/article/305ebc5afa954c4cbdab2229704eb72e
Revolutionizing Acne Diagnosis With Hybrid Deep Learning Model Integrating CBAM, and Capsule Network
Autor:
Paluri Krishna Veni, Ashish Gupta
Publikováno v:
IEEE Access, Vol 12, Pp 82867-82879 (2024)
Acne is the eighth most prevalent global health concern and affects around 9.4% of the global population. The accurate classification of diverse acne categories is crucial for promptly creating efficient treatment plans. Although various deep learnin
Externí odkaz:
https://doaj.org/article/de31e5535aea429cbb4f6619d6ba4881
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 9099-9109 (2024)
Cage and raft aquaculture (CRA) is vital for the coastal economy and provides high-quality aquatic products. Accurately monitoring large-scale CRA lays the foundation for predicting CRA product yield and mitigating environmental impacts. This study,
Externí odkaz:
https://doaj.org/article/b76ad9143b274fb9aff69492e404b249
Fast Hyperspectral Image Classification with Strong Noise Robustness Based on Minimum Noise Fraction
Publikováno v:
Remote Sensing, Vol 16, Iss 20, p 3782 (2024)
A fast hyperspectral image classification algorithm with strong noise robustness is proposed in this paper, aiming at the hyperspectral image classification problems under noise interference. Based on the Fast 3D Convolutional Neural Network (Fast-3D
Externí odkaz:
https://doaj.org/article/9348afe6efa24c6fb85ea7f6a4144c6b
Publikováno v:
Sensors, Vol 24, Iss 20, p 6768 (2024)
This study proposed an improved full-scale aggregated MobileUNet (FA-MobileUNet) model to achieve more complete detection results of oil spill areas using synthetic aperture radar (SAR) images. The convolutional block attention module (CBAM) in the F
Externí odkaz:
https://doaj.org/article/28035c8d22e346aba18c7402cd03a223