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نتیجه جستجو - هوش مصنوعی (AI)

تعداد مقالات یافته شده: 13
ردیف عنوان نوع
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 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
3 An IoT-enabled intelligent automobile system for smart cities
یک سیستم خودروی هوشمند مجهز به اینترنت اشیا برای شهرهای هوشمند-2022
In our world of advancing technologies, automobiles are one industry where we can see improved ergonomics and feature progressions. Artificial Intelligence (AI) integrated with Internet of Things (IoT) is the future of most of the cutting-edge applications developed for automobile industry to enhance performance and safety. The objective of this research is to develop a new feature that can enhance the existing technology present in automo- biles at low-cost. We had previously developed a technology known as Smart Accident Precognition System (SAPS) which reduces the rate of accidents in automobile and also enhance the safety of the passengers. Current research advances this technique by inte- grating Google Assistant with the SAPS. The proposed system integrates several embedded devices in the automobiles that monitor various aspects such as speed, distance, safety measures like seatbelt, door locks, airbags, handbrakes etc. The real-time data is stored in the cloud and the vehicle can adapt to various situations from the previous data collected. Also, with the Google Assistant user can lock and unlock, start and stop, alert and do var- ious automated tasks such as low fuel remainder, insurance remainders etc. The proposed IoT enabled real-time vehicle system can detect accidents and adapt to change according to various conditions. Further, with RFID keyless entry authentication the vehicle is secure than ever before. This proposed system is much efficient to the existing systems and will have a great positive impact in the automobile industry and society. © 2020 Elsevier B.V. All rights reserved.
keywords: هوش مصنوعی | سیستم هوشمند خودرو | اینترنت اشیا | شهرهای هوشمند | سیستم هوشمند | Artificial intelligence | Intelligent automobile system | Internet of Things | Smart Cities | Smart System
مقاله انگلیسی
4 Public perceptions on artificial intelligence driven disaster management: Evidence from Sydney, Melbourne and Brisbane
ادراکات عمومی در مورد مدیریت فاجعه مبتنی بر هوش مصنوعی: شواهدی از سیدنی، ملبورن و بریزبن-2021
In recent years, artificial intelligence (AI) is being increasingly utilised in disaster management activities. The public is engaged with AI in various ways in these activities. For instance, crowdsourcing applications developed for disaster management to handle the tasks of collecting data through social media platforms, and increasing disaster awareness through serious gaming applications. Nonetheless, there are limited empirical investigations and understanding on public perceptions concerning AI for disaster management. Bridging this knowledge gap is the justification for this paper. The methodological approach adopted involved: Initially, collecting data through an online survey from residents (n = 605) of three major Australian cities; Then, analysis of the data using statistical modelling. The analysis results revealed that: (a) Younger generations have a greater appreciation of opportunities created by AI-driven applications for disaster management; (b) People with tertiary education have a greater understanding of the benefits of AI in managing the pre- and post-disaster phases, and; (c) Public sector administrative and safety workers, who play a vital role in managing disasters, place a greater value on the contributions by AI in disaster management. The study advocates relevant authorities to consider public perceptions in their efforts in integrating AI in disaster management.
keywords: artificial intelligence (AI) | Disaster management | Disaster preparedness | Disaster response | Disaster recovery | Public perception
مقاله انگلیسی
5 پیامدهای استفاده از هوش مصنوعی در حکمرانی عمومی: بررسی پیشینه نظام‌مند و دستور کار تحقیقاتی
سال انتشار: 2021 - تعداد صفحات فایل pdf انگلیسی: 19 - تعداد صفحات فایل doc فارسی: 72
برای پایه‌گذاری موضوع ویژه‌ای که این مقاله تحقیقاتی معرفی می‌کند، ما 1) مروری نظام‌مند از پیشینه موجود در مورد پیامدهای استفاده از هوش مصنوعی (AI) در حاکمیت عمومی و 2 ) یک دستور کار تحقیقاتی را ارائه می‌کنیم. اول، ارزیابی بر اساس 26 مقاله در مورد این موضوع، تحقیقات اکتشافی، مفهومی، کیفی و عمل محور بسیاری را در مطالعات نشان می‌دهد که پیچیدگی‌های روزافزون استفاده از هوش مصنوعی در دولت را منعکس می‌کند - و پیامدها، فرصت ها و خطرات ناشی از آن برای حاکمیت عمومی نمایان می‌کند. دوم، بر اساس بررسی پیشینه و تجزیه و تحلیل مقالات موجود در این شماره ویژه، ما یک دستور کار تحقیقاتی شامل هشت توصیه مرتبط با فرآیند و هفت توصیه مرتبط با محتوا را پیشنهاد می‌کنیم. از لحاظ فرآیندی، تحقیقات آینده در مورد پیامدهای استفاده از هوش مصنوعی برای حکمرانی عمومی باید به سمت تحقیقات بیشتر متمرکز بر بخش عمومی، تجربی، چند رشته‌ای و توضیحی حرکت کند و در عین حال بیشتر بر اشکال خاص هوش مصنوعی تمرکز کند تا به طور کلی هوش مصنوعی. از نظر محتوا، دستور کار تحقیقاتی ما مستلزم ایجاد مبانی نظری محکم، چند رشته‌ای برای استفاده از هوش مصنوعی برای حکمرانی عمومی، و همچنین بررسی اجرای مؤثر، مشارکت و برنامه‌های ارتباطی برای راهبرد‌های دولت در استفاده از هوش مصنوعی در بخش عمومی است. در نهایت، دستور کار تحقیقاتی خواستار تحقیق در مورد مدیریت خطرات استفاده از هوش مصنوعی در بخش عمومی، حالت‌های حکمرانی ممکن برای استفاده از هوش مصنوعی در بخش عمومی، سنجش عملکرد و تأثیر استفاده از هوش مصنوعی در دولت، و ارزیابی تأثیر مقیاس‌پذیری استفاده از هوش مصنوعی در بخش دولتی است.
کلمات کلیدی : حکمرانی عمومی | هوش مصنوعی | هوش مصنوعی برای دولت | بخش عمومی | دولت دیجیتال | بررسی پیشینه نظام‌مند | دستور کار تحقیق
مقاله ترجمه شده
6 Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review
هوش مصنوعی و مدل های تجاری در چشم انداز اهداف توسعه پایدار: یک مرور ادبیات سیستماتیک-2020
This paper investigates the literary corpus on the role of Artificial Intelligence (AI) in the construction of sustainable business models (SBMs). It provides a quantitative overview of the academic literature that constitutes the field. The paper discusses the relationships between AI and rapid developments in machine learning and sustainable development (SD). Specifically, the aim is to understand whether this branch of computer science can influence production and consumption patterns to achieve sustainable resource management according to Sustainable Development Goals (SDGs) outlined in the UN 2030 Agenda. Moreover, the paper aims to highlight the role of Knowledge Management Systems (KMS) in the cultural drift toward the spread of AI for SBMs. Despite the importance of the topic, there is no comprehensive review of the AI and SBM literature in light of SDGs. Based on a database containing 73 publications in English with publication dates from 1990 to 2019, a bibliometric analysis is conducted. The findings show that the innovation challenge involves ethical, social, economic, and legal aspects. Thus, considering that the development potential of AI is linked to the UN 2030 Agenda for SD, especially to SDG#12, our results also outline the framework of the existing literature on AI and SDGs, especially SDG#12, including AI’s association with the cultural drift (CD) in the SBMs. The paper highlights the key contributions, which are: i) a comprehensive review of the key underlying relationship between AI and SBMs, offering a holistic view as needed, ii) identifying a research gap regarding KMS through AI, and iii) the implications of AI concerning SDG#12. Academic and managerial implications are also discussed regarding KMS in the SBMs, where the AI can represent the vehicle to meet the SDGs allowing for the identification of the cultural change required by enterprises to achieve sustainable goals. Thus, business companies, academic re- search practitioners, and state policy should focus on the further development of the use of AI in SBMs.
Keywords: Artificial Intelligence (AI) | Machine learning sustainability | Cultural drift | Sustainable business models | Knowledge Management System (KMS)
مقاله انگلیسی
7 The use of artificial intelligence (AI) methods in the prediction of thermal comfort in buildings: energy implications of AI-based thermal comfort controls
استفاده از روش های هوش مصنوعی در پیش بینی راحتی حرارتی در ساختمان ها: پیامدهای انرژی کنترل راحتی حرارتی مبتنی بر هوش مصنوعی-2020
Buildings consume about 40 % of globally-produced energy. A notable amount of this energy is used to provide sufficient comfort levels to the building occupants. Moreover, given recent increases in global temperatures as a result of climate change and the associated decrease in comfort levels, providing ade- quate comfort levels in indoor spaces has become increasingly important. However, striking a balance be- tween reducing building energy use and providing adequate comfort levels is a significant challenge. Con- ventional control methods for indoor spaces, such as on/off, proportional-integral (PI), and proportional- integral-derivative (PID) controllers, display significant instabilities and frequently overshoot thermostats, resulting in unnecessary energy use. Additionally, conventional building control methods rarely include comfort regulatory schemes. Consequently, recent research efforts have focused on the use of advanced artificial intelligence (AI) methods to optimize building energy usage while maintaining occupant ther- mal comfort. We present a review of the current AI-based methodologies being used to enhance thermal comfort in indoor spaces. we focus on thermal comfort predictive models using diverse machine learning (ML) algorithms and their deployment in building control systems for energy saving purposes. We then discuss gaps in the existing literature and highlight potential future research directions.
Keywords: Artificial intelligence (AI) | Machine learning (ML) | Comfort control | Predictive modeling | Predictive control
مقاله انگلیسی
8 Developing an Artificial Intelligence (AI) Management System to Improve Product Quality and Production Efficiency in Furniture Manufacture
ایجاد سیستم مدیریت هوش مصنوعی (AI) برای بهبود کیفیت محصول و کارایی تولید در ساخت مبلمان-2020
At present, there are some problems in Chinese furniture production industry, such as low production efficiency, low accuracy, and lack of innovation for products. To resolve those problems, an AI management system is developed to improve the product quality and production efficiency in furniture enterprises in this paper. The AI management system is an organic body consisted of a data management system and an expert system. The model of information transmission and control for furniture manufacture by AI management is developed. It provides technical solutions for the AI application in furniture manufacture.
Key words: artificial intelligence (AI) | management | Furniture
مقاله انگلیسی
9 Detecting abnormal thyroid cartilages on CT using deep learning
تشخیص غضروف غیر طبیعی تیروئید در CT با استفاده از یادگیری عمیق-2019
Purpose: The purpose of this study was to evaluate the performance of a deep learning algorithm in detecting abnormalities of thyroid cartilage from computed tomography (CT) examination. Materials and methods: A database of 515 harmonized thyroid CT examinations was used, of which information regarding cartilage abnormality was provided for 326. The process consisted of determining image abnormality and, from these preprocessed images, finding the best learning algorithm to appropriately characterize thyroid cartilage as normal or abnormal. CT images were cropped to be centered around the cartilage in order to focus on the relevant area. New images were generated from the originals by applying simple transformations in order to augment the database. Characterizations of cartilage abnormalities were made using transfer learning, by using the architecture of a pre-trained neural network called VGG16 and adapting the final layers to a binary classification problem. Results: The best algorithm yielded an area under the receiving operator characteristic curve (AUC) of 0.72 on a sample of 82 thyroid test images. The sensitivity and specificity of the abnormality detection were 83% and 64% at the best threshold, respectively. Applying the model on another independent sample of 189 new thyroid images resulted in an AUC of 0.70. Conclusion: This study demonstrates the feasibility of using a deep learning-based abnormality detection system to evaluate thyroid cartilage from CT examinations. However, although promising results, the model is not yet able to match an expert’s diagnosis.
KEYWORDS : Thyroid cartilage | Artificial intelligence (AI) | Deep learning | Post-mortem computed tomography (CT) | Larynx
مقاله انگلیسی
10 Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy
هوش مصنوعی (AI): چشم اندازهای چند رشته ای در مورد چالش ها ، فرصت ها و دستور کار برای تحقیق ، تمرین و سیاست های نوظهور-2019
As far back as the industrial revolution, significant development in technical innovation has succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision-making, engendering new opportunities for continued innovation. The impact of AI could be significant, with industries ranging from: finance, healthcare, manufacturing, retail, supply chain, logistics and utilities, all potentially disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, realistic assessment of impact, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: business and management, government, public sector, and science and technology. This research offers significant and timely insight to AI technology and its impact on the future of industry and society in general, whilst recognising the societal and industrial influence on pace and direction of AI development.
Keywords: Artificial intelligence | AI | Cognitive computing | Expert systems | Machine learning | Research agenda
مقاله انگلیسی
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