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1 |
AI-based computer vision using deep learning in 6G wireless networks
بینایی کامپیوتر مبتنی بر هوش مصنوعی با استفاده از یادگیری عمیق در شبکه های بی سیم 6G-2022 Modern businesses benefit significantly from advances in computer vision technology, one of the
important sectors of artificially intelligent and computer science research. Advanced computer
vision issues like image processing, object recognition, and biometric authentication can benefit
from using deep learning methods. As smart devices and facilities advance rapidly, current net-
works such as 4 G and the forthcoming 5 G networks may not adapt to the rapidly increasing
demand. Classification of images, object classification, and facial recognition software are some
of the most difficult computer vision problems that can be solved using deep learning methods. As
a new paradigm for 6Core network design and analysis, artificial intelligence (AI) has recently
been used. Therefore, in this paper, the 6 G wireless network is used along with Deep Learning to
solve the above challenges by introducing a new methodology named Optimizing Computer
Vision with AI-enabled technology (OCV-AI). This research uses deep learning – efficiency al-
gorithms (DL-EA) for computer vision to address the issues mentioned and improve the system’s
outcome. Therefore, deep learning 6 G proposed frameworks (Dl-6 G) are suggested in this paper
to recognize pattern recognition and intelligent management systems and provide driven meth-
odology planned to be provisioned automatically. For Advanced analytics wise, 6 G networks can
summarize the significant areas for future research and potential solutions, including image
enhancement, machine vision, and access control. keywords: SHG | ارتباطات بی سیم | هوش مصنوعی | فراگیری ماشین | یادگیری عمیق | ارتباطات سیار | 6G | Wireless communication | AI | Machine learning | Deep learning | Mobile communication |
مقاله انگلیسی |
2 |
Computer visual syndrome in graduate students of a private university in Lima, Perú
سندرم بینایی ماشین در دانشجویان تحصیلات تکمیلی یک دانشگاه خصوصی در لیما ، پرو-2021 Background: In recent decades, several studies have found a strong association between prolonged use of video display terminals and ophthalmological symptoms encompassed in the
so-called computer visual syndrome (CVS). Few studies have addressed this syndrome in
graduate students.
Methods: Observational, cross-sectional descriptive study. A total of 106 postgraduate students were surveyed without ophthalmological pathologies. The diagnosis of CVS was made
by means of the questionnaire of Seguí et al. validated in Spanish, which evaluates the
frequency and intensity of 16 ocular symptoms.
Results: The prevalence of CVS among graduate university students was 62.3% (95% CI:
52.3–71.5). It was found that the highest proportion of students with the syndrome was
in the group of older than 40 years old (88.2%) and in the group 21–30 years old (70.0%),
showing statistically significant differences (p = 0.004). According to the device and its time
of use, students who used the mobile phone for 7–10 h a day showed a higher prevalence of
CVS compared to those who used the device for less time (p = 0.030). The business School
had the highest prevalence (75.0%).
Conclusion: Three out of every five graduate students presented CVS with this prevalence
being like reported in other populations. There is a need to investigate possible interventions
that can help reduce this entity.
Keywords: Computer vision syndrome | Vision problems | Postgraduate students |
مقاله انگلیسی |