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1 |
Intelligent authentication of 5G healthcare devices: A survey
احراز هویت هوشمند دستگاه های مراقبت بهداشتی 5G: یک مرور-2022 The dynamic nature of wireless links and the mobility of devices connected to the Internet of
Things (IoT) over fifth-generation (5G) networks (IoT-5G), on the one hand, empowers pervasive
healthcare applications. On the other hand, it allows eavesdroppers and other illegitimate
actors to access secret information. Due to the poor time efficiency and high computational
complexity of conventional cryptographic methods and the heterogeneous technologies used,
it is easy to compromise the authentication of lightweight wearable and healthcare devices.
Therefore, intelligent authentication, which relies on artificial intelligence (AI), and sufficient
network resources are extremely important for securing healthcare devices connected to IoT-
5G. This survey considers intelligent authentication and includes a comprehensive overview of
intelligent authentication mechanisms for securing IoT-5G devices deployed in the healthcare
domain. First, it presents a detailed, thoughtful, and state-of-the-art review of IoT-5G, healthcare
technologies, tools, applications, research trends, challenges, opportunities, and solutions. We
selected 20 technical articles from those surveyed based on their strong overlaps with IoT,
5G, healthcare, device authentication, and AI. Second, IoT-5G device authentication, radiofrequency fingerprinting, and mutual authentication are reviewed, characterized, clustered,
and classified. Third, the review envisions that AI can be used to integrate the attributes
of the physical layer and 5G networks to empower intelligent healthcare devices. Moreover,
methods for developing intelligent authentication models using AI are presented. Finally, the
future outlook and recommendations are introduced for IoT-5G healthcare applications, and
recommendations for further research are presented as well. The remarkable contributions and
relevance of this survey may assist the research community in understanding the research gaps
and the research opportunities relating to the intelligent authentication of IoT-5G healthcare
devices.
keywords: اینترنت اشیا (IoT) | امنیت اینترنت اشیا | احراز هویت دستگاه | هوش مصنوعی | امنیت مراقبت های بهداشتی | شبکه های 5g | InternetofThings(IoT) | InternetofThingssecurity | Deviceauthentication | Artificialintelligence | Healthcaresecurity | 5Gnetworks |
مقاله انگلیسی |
2 |
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 |
3 |
Predicting social media engagement with computer vision: An examination of food marketing on Instagram
پیشبینی تعامل رسانههای اجتماعی با بینایی رایانه: بررسی بازاریابی مواد غذایی در اینستاگرام-2022 In a crowded social media marketplace, restaurants often try to stand out by showcasing elaborate “Insta-
grammable” foods. Using an image classification machine learning algorithm (Google Vision AI) on restaurants’
Instagram posts, this study analyzes how the visual characteristics of product offerings (i.e., their food) relate to
social media engagement. Results demonstrate that food images that are more confidently evaluated by Google
Vision AI (a proxy for food typicality) are positively associated with engagement (likes and comments). A follow-
up experiment shows that exposure to typical-appearing foods elevates positive affect, suggesting they are easier
to mentally process, which drives engagement. Therefore, contrary to conventional social media practices and
food industry trends, the more typical a food appears, the more social media engagement it receives. Using
Google Vision AI to identify what product offerings receive engagement presents an accessible method for
marketers to understand their industry and inform their social media marketing strategies. keywords: بازاریابی از طریق رسانه های اجتماعی | تعامل با مصرف کننده | یادگیری ماشین | غذا | روان بودن پردازش | هوش مصنوعی گوگل ویژن | Social media marketing | Consumer engagement | Machine learning | Food | Processing fluency | Google Vision AI |
مقاله انگلیسی |
4 |
Computer vision for anatomical analysis of equipment in civil infrastructure projects: Theorizing the development of regression-based deep neural networks
چشم انداز کامپیوتری برای تجزیه و تحلیل آناتومیکی تجهیزات در پروژه های زیرساختی عمرانی: نظریه پردازی توسعه شبکه های عصبی عمیق مبتنی بر رگرسیون-2022 There is high demand for heavy equipment in civil infrastructure projects and their performance is a determinant
of the successful delivery of site operations. Although manufacturers provide equipment performance hand-
books, additional monitoring mechanisms are required to depart from measuring performance on the sole basis
of unit cost for moved materials. Vision-based tracking and pose estimation can facilitate site performance
monitoring. This research develops several regression-based deep neural networks (DNNs) to monitor equipment
with the aim of ensuring safety, productivity, sustainability and quality of equipment operations. Annotated
image libraries are used to train and test several backbone architectures. Experimental results reveal the pre-
cision of DNNs with depthwise separable convolutions and computational efficiency of DNNs with channel
shuffle. This research provides scientific utility by developing a method for equipment pose estimation with the
ability to detect anatomical angles and critical keypoints. The practical utility of this study is the provision of
potentials to influence current practice of articulated machinery monitoring in projects. keywords: هوش مصنوعی (AI) | سیستم های فیزیکی سایبری | معیارهای ارزیابی خطا | طراحی و آزمایش تجربی | تخمین ژست کامل بدن | صنعت و ساخت 4.0 | الگوریتم های یادگیری ماشین | معماری های ستون فقرات شبکه | Artificial intelligence (AI) | Cyber physical systems | Error evaluation metrics | Experimental design and testing | Full body pose estimation | Industry and construction 4.0 | Machine learning algorithms | Network backbone architectures |
مقاله انگلیسی |
5 |
A combined real-time intelligent fire detection and forecasting approach through cameras based on computer vision method
یک رویکرد تشخیص و پیشبینی حریق هوشمند ترکیبی در زمان واقعی از طریق دوربینها بر اساس روش بینایی کامپیوتری-2022 Fire is one of the most common hazards in the process industry. Until today, most fire alarms have had very
limited functionality. Normally, only a simple alarm is triggered without any specific information about the fire
circumstances provided, not to mention fire forecasting. In this paper, a combined real-time intelligent fire
detection and forecasting approach through cameras is discussed with extracting and predicting fire development
characteristics. Three parameters (fire spread position, fire spread speed and flame width) are used to charac-
terize the fire development. Two neural networks are established, i.e., the Region-Convolutional Neural Network
(RCNN) for fire characteristic extraction through fire detection and the Residual Network (ResNet) for fire
forecasting. By designing 12 sets of cable fire experiments with different fire developing conditions, the accu-
racies of fire parameters extraction and forecasting are evaluated. Results show that the mean relative error
(MRE) of extraction by RCNN for the three parameters are around 4–13%, 6–20% and 11–37%, respectively.
Meanwhile, the MRE of forecasting by ResNet for the three parameters are around 4–13%, 11–33% and 12–48%,
respectively. It confirms that the proposed approach can provide a feasible solution for quantifying fire devel-
opment and improve industrial fire safety, e.g., forecasting the fire development trends, assessing the severity of
accidents, estimating the accident losses in real time and guiding the fire fighting and rescue tactics. keywords: ایمنی آتش سوزی صنعتی | تشخیص حریق | پیش بینی آتش سوزی | تجزیه و تحلیل آتش سوزی | هوش مصنوعی | Industrial fire safety | Fire detection | Fire forecasting | Fire analysis | Artificial intelligence |
مقاله انگلیسی |
6 |
Performance analysis of machine learning algorithm of detection and classification of brain tumor using computer vision
تحلیل عملکرد الگوریتم یادگیری ماشین تشخیص و طبقه بندی تومور مغزی با استفاده از بینایی کامپیوتر-2022 Brain tumor is one of the undesirables, uncontrolled growth of cells in all age groups. Classification of tumors
depends no its origin and degree of its aggressiveness, it also helps the physician for proper diagnosis and
treatment plan. This research demonstrates the analysis of various state-of-art techniques in Machine Learning
such as Logistic, Multilayer Perceptron, Decision Tree, Naive Bayes classifier and Support Vector Machine for
classification of tumors as Benign and Malignant and the Discreet wavelet transform for feature extraction on the
synthetic data that is available data on the internet source OASIS and ADNI. The research also reveals that the
Logistic Regression and the Multilayer Perceptron gives the highest accuracy of 90%. It mimics the human
reasoning that learns, memorizes and is capable of reasoning and performing parallel computations. In future
many more AI techniques can be trained to classify the multimodal MRI Brain scan to more than two classes of
tumors. keywords: هوش مصنوعی | ام آر آی | رگرسیون لجستیک | پرسپترون چند لایه | Artificial Intelligence | MRI | Logistic regression | OASIS | Multilayer Perceptron |
مقاله انگلیسی |
7 |
Artificial intelligence versus natural selection: Using computer vision techniques to classify bees and bee mimics
هوش مصنوعی در مقابل انتخاب طبیعی: استفاده از تکنیکهای بینایی کامپیوتری برای طبقهبندی زنبورها و تقلیدهای زنبور عسل-2022 Many groups of stingless insects have independently evolved mimicry of bees to fool would-be predators. To investigate this mimicry, we trained artificial intelligence (AI) algorithms—specifically, computer vision—to classify citizen scientist images of bees, bumble bees, and diverse bee mimics. For detecting bees and bumble bees, our models achieved accuracies of and , respectively. As a proxy for a natural predator, our models were poorest in detecting bee mimics that exhibit both aggressive and defensive mimicry. Using the explainable AI method of class activation maps, we validated that our models learn from appropriate components within the image, which in turn provided anatomical insights. Our t-SNE plot yielded perfect within-group clustering, as well as between-group clustering that grossly replicated the phylogeny. Ultimately, the transdisciplinary approaches herein can enhance global citizen science efforts as well as investigations of mimicry and morphology of bees and other insects.
keywords: Artificial intelligence | Bioinformatics | Computing methodology | Entomology | Zoology |
مقاله انگلیسی |
8 |
Animal biometric assessment using non-invasive computer vision and machine learning are good predictors of dairy cows age and welfare: The future of automated veterinary support systems
ارزیابی بیومتریک حیوانات با استفاده از بینایی کامپیوتری غیرتهاجمی و یادگیری ماشینی پیشبینیکننده خوبی برای سن و رفاه گاوهای شیری هستند: آینده سیستمهای پشتیبانی خودکار دامپزشکی-2022 Digitally extracted biometrics from visible videos of farm animals could be used to automatically assess animal
welfare, contributing to the future of automated veterinary support systems. This study proposed using non-
invasive video acquisition and biometric analysis of dairy cows in a robotic dairy farm (RDF) located at the
Dookie campus, The University of Melbourne, Australia. Data extracted from dairy cows were used to develop
two machine learning models: a biometrics regression model (Model 1) targeting (i) somatic cell count, (ii)
weight, (iii) rumination, and (iv) feed intake and a classification model (Model 2) mapping features from dairy
cow’s face to predict animal age. Results showed that Model 1 achieved a high correlation coefficient (R = 0.96),
slope (b = 0.96), and performance, and Model 2 had high accuracy (98%), low error (2%), and high performance
without signs of under or overfitting. Models developed in this study can be used in parallel with other models to
assess milk productivity, quality traits, and welfare for RDF and conventional dairy farms. keywords: هوش مصنوعی | فیزیولوژی گاو | ماستیت | بیومتریک حیوانات | سنجش از راه دور برد کوتاه | Artificial intelligence | Cows physiology | Mastitis | Animal biometrics | Short range remote sensing |
مقاله انگلیسی |
9 |
Quantum Kernels for Real-World Predictions Based on Electronic Health Records
هستههای کوانتومی برای پیشبینیهای دنیای واقعی بر اساس پروندههای سلامت الکترونیکی-2022 Research on near-term quantum machine learning has explored how classical machine learning
algorithms endowed with access to quantum kernels (similarity measures) can outperform their purely
classical counterparts. Although theoretical work has shown a provable advantage on synthetic data sets,
no work done to date has studied empirically whether the quantum advantage is attainable and with what
data. In this article, we report the first systematic investigation of empirical quantum advantage (EQA) in
healthcare and life sciences and propose an end-to-end framework to study EQA. We selected electronic
health records data subsets and created a configuration space of 5–20 features and 200–300 training samples.
For each configuration coordinate, we trained classical support vector machine models based on radial basis
function kernels and quantum models with custom kernels using an IBM quantum computer, making this
one of the largest quantum machine learning experiments to date. We empirically identified regimes where
quantum kernels could provide an advantage and introduced a terrain ruggedness index, a metric to help
quantitatively estimate how the accuracy of a given model will perform. The generalizable framework introduced here represents a key step toward a priori identification of data sets where quantum advantage could
exist.
INDEX TERMS: Artificial intelligence | digital health | electronic health records (EHR) | empirical quantum advantage (EQA) | machine learning | quantum kernels | real-world data | small data sets | support vector machines (SVM). |
مقاله انگلیسی |
10 |
محلیسازی دقیق سطح خط مبتنی بر تطبیق نقشه با استفاده از دوربین و GPS کمهزینه
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 14 در سیستم های خودران یا وسایل نقلیه خودران(وسایل بدون راننده) (AVs) ، محلی سازی دقیق سطح خط، برای انجام مانورهای پیچیده رانندگی ضروری است. معمولاً روشهای کلاسیک مبتنی بر GNSS از دقت کافی برای محلیسازی در سطح خط و پشتیبانی از مانورهای AV برخوردار نیستند. محلی سازی مبتنی بر LiDAR قابلیت ارائه محلی سازی دقیق را دارد. با این حال، یکی از مسائل مهمی که مانع تبدیل کاربرد گسترده این نوع راه حل می شود، قیمت LiDAR است. بنابراین، در این پژوهش راهحلی کمهزینه برای محلیسازی سطح خط و برای دستیابی به محلیسازی با دقت بالا در سطح خط با استفاده از سیستم مبتنی بر دید و GPS کمهزینه پیشنهاد شد. آزمایشها در دنیای واقعی و زمان واقعی اثبات می کند که روش پیشنهادی در محلیسازی سطح خط دقت مطلوبی داشته و عملکرد بهتری نسبت به راهحلهای مبتنی بر فقط GPS ، ارائه داده است.
کلیدواژه: رانندگی خودران | محلی سازی سطح خط | تشخیص خط | GNSS| GPS| تطبیق نقشه |
مقاله ترجمه شده |