Zobrazeno 1 - 10
of 754
pro vyhledávání: '"Deep convolution neural network"'
Autor:
Yongfei Yu, Yuanjian Yan
Publikováno v:
Systems and Soft Computing, Vol 6, Iss , Pp 200120- (2024)
To solve the problems of the classical color image hybrid noise filtering method, a deep convolutional neural network improved by evolutionary strategy and jump connection is proposed and applied to the filtering noise reduction of color images. Firs
Externí odkaz:
https://doaj.org/article/eaf67dc6bbfc4abfa232012de8d662ea
Publikováno v:
Mathematical Biosciences and Engineering, Vol 21, Iss 4, Pp 5521-5535 (2024)
Early diagnosis of abnormal electrocardiogram (ECG) signals can provide useful information for the prevention and detection of arrhythmia diseases. Due to the similarities in Normal beat (N) and Supraventricular Premature Beat (S) categories and imba
Externí odkaz:
https://doaj.org/article/1beb1662580c44c7868758606d90a8a4
Autor:
Ade Clinton Sitepu, Chuan-Ming Liu
Publikováno v:
IEEE Access, Vol 12, Pp 95517-95528 (2024)
Artificial Intelligence, including machine learning and deep convolutional neural networks (DCNNs), relies on complex algorithms and neural networks to process and analyze data. DCNNs for visual recognition often require access to high-performance ha
Externí odkaz:
https://doaj.org/article/ea69197c5b674e97a0225e84190436fa
Publikováno v:
Journal of Big Data, Vol 10, Iss 1, Pp 1-23 (2023)
Abstract Human Skin cancer is commonly detected visually through clinical screening followed by a dermoscopic examination. However, automated skin lesion classification remains challenging due to the visual similarities between benign and melanoma le
Externí odkaz:
https://doaj.org/article/7e8a33d819a44db6961695510a49e6ce
Publikováno v:
Transport and Telecommunication, Vol 24, Iss 2, Pp 110-119 (2023)
Ego lane detection is one of the key techniques in Ego Lane Analysis System (ELAS) implemented in smart autonomous driving cars for lane detection in roads. This technique has been extensively studied in recent years because of its accurate and robus
Externí odkaz:
https://doaj.org/article/798add6fe5b94bfcafe1048d60c720d4
Publikováno v:
Healthcare Analytics, Vol 4, Iss , Pp 100278- (2023)
COVID-19 is a virus that can cause severe pneumonia, and the severity varies based on the patient's immune system. The rapid spread of the disease can be mitigated through automated detection, addressing the shortage of radiologists in medicine. This
Externí odkaz:
https://doaj.org/article/788806f89b5448a2bf20e4eab28b6c37
Autor:
Xiong Bi, Hongchun Wang
Publikováno v:
Journal of Agricultural Engineering (2023)
Deep convolutional neural network (DCNN) has recently made significant strides in classification and recognition of rice leaf disease. The majority of classification models perform disease image recognitions using a collocation patterns including poo
Externí odkaz:
https://doaj.org/article/b8000f85ed3e46858e73751a0ddf13a2
Publikováno v:
IEEE Access, Vol 11, Pp 125529-125542 (2023)
This paper presents a deep learning-based framework for automating the visual inspection of plastic bottles in an Industry 4.0 context, detecting surface defects to enhance product quality. Our contributions include the acceleration of model developm
Externí odkaz:
https://doaj.org/article/ed5b5d9b4d5945a09050fb2d83315db4
Publikováno v:
IEEE Access, Vol 11, Pp 35567-35578 (2023)
Using a novel Genetic Algorithm-based Compressive Learning (GACL), a compressed domain-learning framework is proposed that is implemented on the Haar wavelet approximation coefficient images of the standard kaggle RGB cat dog dataset with every image
Externí odkaz:
https://doaj.org/article/3028df6d31d84a509f54d2564c1f9910
Publikováno v:
International Journal of Digital Earth, Vol 15, Iss 1, Pp 1101-1124 (2022)
Super-pixel algorithms based on convolutional neural networks with fuzzy C-means clustering are widely used for high-spatial-resolution remote sensing images segmentation. However, this model requires the number of clusters to be set manually, result
Externí odkaz:
https://doaj.org/article/45a02a81335a4590a41ee6d308f2e7c1