2022
DOI: 10.1007/s11063-021-10679-4
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Multiview Objects Recognition Using Deep Learning-Based Wrap-CNN with Voting Scheme

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Cited by 70 publications
(12 citation statements)
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“…Network energy dissipation can be achieved by selecting a sleep schedule that is based on the distance of each hop. TDMA scheduling and an asynchronous duty cycling system can be used to avoid idle node energy consumption by using uneven clustering to improve energy efficiency [18]. The nodes are constantly awakened by the synchronous protocol for the specified period.…”
Section: Related Workmentioning
confidence: 99%
“…Network energy dissipation can be achieved by selecting a sleep schedule that is based on the distance of each hop. TDMA scheduling and an asynchronous duty cycling system can be used to avoid idle node energy consumption by using uneven clustering to improve energy efficiency [18]. The nodes are constantly awakened by the synchronous protocol for the specified period.…”
Section: Related Workmentioning
confidence: 99%
“…As a result, it is evident that computer-based techniques and machine learning algorithms significantly positively influence on data mining techniques, logistic analysis, and accessibility choices 18 . Additionally, with straightforward data management, robots and computers may do the same work in less time 19 .…”
Section: Introductionmentioning
confidence: 99%
“…Deep learning approaches have recently gained popularity because of their ability to beat prior state-of-the-art techniques in various tasks and the amount of complex data from various sources (e.g., visual, auditory, medical, social, and sensor) ( 14 ). Deep learning has made significant advances in a wide range of computer vision tasks, including object recognition ( 15 ), motion tracking ( 16 ), and medical image classification and detection ( 17 , 18 ). Classification of brain tumors for medical specialists is an important field where computer vision and deep learning techniques work together and bring prosperity to patients with non-invasive diagnosis of brain tumors using MRI.…”
Section: Introductionmentioning
confidence: 99%