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نتیجه جستجو - artificial intelligence (AI)

تعداد مقالات یافته شده: 169
ردیف عنوان نوع
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 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
4 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
5 AgroLens: A low-cost and green-friendly Smart Farm Architecture to support real-time leaf disease diagnostics
AgroLens: یک معماری مزرعه هوشمند کم‌هزینه و سبز پسند برای پشتیبانی از تشخیص بیماری‌های برگ در زمان واقعی-2022
Agriculture is one of the most significant global economic activities responsible for feeding the world population of 7.75 billion. However, weather conditions and diseases impact production efficiency, reducing economic activity and the food sovereignty of economies worldwide. Thus, computational methods can support disease classification based on an image. This classification requires training Artificial Intelligence (AI) models on high-performance computing resources, usually far from the user domain. State of the art has proposed the concept of Edge Computing (EC), which aims to bring computational resources closer to the domain problem to decrease application latency and improve computational power closer to the client. In addition, EC has become an enabling technology for Smart Farms, and the literature has appropriated EC to support these applications. However, predominantly state-of-the-art architectures are dependent on Internet connectivity and do not allow diverse real-time classification of diseases based on crop leaf on mobile devices. This paper sheds light on a new architecture, AgroLens, built with low-cost and green-friendly devices to support a mobile Smart Farm application, operational even in areas lacking Internet connectivity. Among our main contributions, we highlight the functional evaluation of AgroLens for AI-based real-time classification of diseases based on leaf images, achieving high classification performance using a smartphone. Our results indicate that AgroLens supports the connectivity of thousands of sensors from a smart farm without imposing computational overhead on edge-compute. The AgroLens architecture opens up opportunities and research avenues for deployment and evaluation for large-scale Smart Farm applications with low-cost devices.
keywords: بیماری گیاهی | مزرعه هوشمند | اینترنت اشیا | یادگیری عمیق | سبز پسند| Plant disease | Smart Farm | Internet of Things | Deep learning | Green-friendly
مقاله انگلیسی
6 AI for next generation computing: Emerging trends and future directions
هوش مصنوعی برای محاسبات نسل بعدی: روندهای نوظهور و مسیرهای آینده-2022
Autonomic computing investigates how systems can achieve (user) specified ‘‘control’’ outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for closed and open-loop systems. In practice, complex systems may exhibit a number of concurrent and inter-dependent control loops. Despite research into autonomic models for managing computer resources, ranging from individual resources (e.g., web servers) to a resource ensemble (e.g., multiple resources within a data centre), research into integrating Artificial Intelligence (AI) and Machine Learning (ML) to improve resource autonomy and performance at scale continues to be a fundamental challenge. The integration of AI/ML to achieve such autonomic and self-management of systems can be achieved at different levels of granularity, from full to human-in-the-loop automation. In this article, leading academics, researchers, practitioners, engineers, and scientists in the fields of cloud computing, AI/ML, and quantum computing join to discuss current research and potential future directions for these fields. Further, we discuss challenges and opportunities for leveraging AI and ML in next generation computing for emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing environments.
Keywords: Next generation computing | Artificial intelligence | Cloud computing | Fog computing | Edge computing | Serverless computing | Quantum computing | Machine learning
مقاله انگلیسی
7 Evaluation of corporate requirements for smart manufacturing systems using predictive analytics
ارزیابی الزامات شرکت برای سیستم‌های تولید هوشمند با استفاده از تجزیه و تحلیل پیش‌بینی‌کننده-2022
Smart manufacturing systems (SMS) are one of the most important applications in the Industry 4.0 era, offering numerous advantages over traditional production systems and rapidly being used as a performance-enhancing strategy of manufacturing enterprises. A few of the technologies that must be connected to construct an SMS are the Industrial Internet of Things (IIoT), Big Data, Robotics, Blockchain, 5G Communication, Artificial Intelligence (AI), and many more. SMS is an innovative and popular manufacturing setup that produces increasingly intelligent production systems; yet, designers must adapt to business tastes and requirements. This study employs an analytical and descriptive research technique to identify and assess functional and non-functional, technological, economic, social, and performance evaluation components that are essential to SMS evaluation. A predictive analytics framework, which is a key component of many decision support systems, is used to assess corporate needs as well as proposed and prioritize SMS services.
keywords: صنعت 4.0 | تجزیه و تحلیل پیش بینی کننده | سیستم های تولید هوشمند | اینترنت اشیاء صنعتی | سیستم پشتیبانی تصمیم | Industry4.0 | Predictive analytics | Smart manufacturing systems | Industrial Internet of Things | Decision support system
مقاله انگلیسی
8 Facilitating artificial intelligence powered supply chain analytics through alliance management during the pandemic crises in the B2B context
تسهیل تجزیه و تحلیل های زنجیره تامین مجهز به هوش مصنوعی از طریق مدیریت اتحاد در طول بحران های همه گیر در زمینه B2B-2021
The COVID-19 pandemic has disrupted global supply chains and exposed weak links in the chains far beyond what most people have witnessed in their living memory. The scale of disruption affects every nation and industry, and the sudden and dramatic changes in demand and supply that have occurred during the pandemic crisis clearly differentiate its impact from other crises. Using the dynamic capabilities view, we studied alliance management capability (AMC) and artificial intelligence (AI) driven supply chain analytics capability (AI-SCAC) as dynamic capabilities, under the moderating effect of environmental dynamism. We tested our four research hypotheses using survey data collected from the Indian auto components manufacturing industry. For data analysis we used Warp PLS 7.0 (a variance-based structural equation modelling tool). We found that alliance management capability under the mediating effect of artificial intelligence-powered supply chain analytics capability enhances the operational and financial performance of the organization. Moreover, we also observed that the alliance management capability has a significant effect on artificial intelligence-powered supply chain analytics capability under the moderating effect of environmental dynamism. The results of our study provide a nuanced understanding of the dynamic capabilities and the relational view of organization. Finally, we noted the limitations of our study and provide numerous research directions that may help answer some of the questions that arise from our study.
Keywords: Artificial intelligence | Supply chain analytics | Alliance management | Environmental dynamism | Dynamic capability view
مقاله انگلیسی
9 Computer vision in surgery
بینایی ماشین در جراحی-2021
The fields of computer vision (CV) and artificial intelligence (AI) have undergone rapid advancements in the past decade, many of which have been applied to the analysis of intraoperative video. These advances are driven by wide-spread application of deep learning, which leverages multiple layers of neural networks to teach computers complex tasks. Prior to these advances, applications of AI in the operating room were limited by our relative inability to train computers to accurately understand images with traditional machine learning (ML) techniques. The development and refining of deep neural networks that can now accurately identify objects in images and remember past surgical events has sparked a surge in the applications of CV to analyze intraoperative video and has allowed for the accurate identification of surgical phases (steps) and instruments across a variety of procedures. In some cases, CV can even identify operative phases with accuracy similar to surgeons. Future research will likely expand on this foundation of surgical knowledge using larger video datasets and improved algorithms with greater accuracy and interpretability to create clinically useful AI models that gain widespread adoption and augment the surgeon’s ability to provide safer care for patients everywhere.
مقاله انگلیسی
10 Blockchain-based royalty contract transactions scheme for Industry 4:0 supply-chain management
طرح معاملات قرارداد حق امتیاز مبتنی بر بلاکچین برای مدیریت زنجیره تأمین صنعت 4:0-2021
Industry 4.0-based oil and gas supply-chain (OaG-SC) industry automates and efficiently executes most of the processes by using cloud computing (CC), artificial intelligence (AI), Internet of things (IoT), and industrial Internet of things (IIoT). However, managing various operations in OaG-SC industries is a challenging task due to the involvement of various stakeholders. It includes landowners, Oil and Gas (OaG) company operators, surveyors, local and national level government bodies, financial institutions, and insurance institutions. During mining, OaG company needs to pay incentives as a royalty to the landowners. In the traditional existing schemes, the process of royalty transaction is performed between the OaG company and landowners as per the contract between them before the start of the actual mining process. These contracts can be manipulated by attackers (insiders or outsiders) for their advantages, creating an unreliable and un-trusted royalty transaction. It may increase disputes between both parties. Hence, a reliable, cost-effective, trusted, secure, and tamper-resistant scheme is required to execute royalty contract transactions in the OaG industry. Motivated from these research gaps, in this paper, we propose a blockchain-based scheme, which securely executes the royalty transactions among various stakeholders in OaG industries. We evaluated the performance of the proposed scheme and the smart contracts’ functionalities and compared it with the existing state-of-the-art schemes using various parameters. The results obtained illustrate the superiority of the proposed scheme compared to the existing schemes in the literature.
Keywords: Blockchain | Smart contract | Oil and gas industry | Supply chain management | Royalty
مقاله انگلیسی
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