This paper proposes an alternative detection frame-work for multiple sclerosis (MS) and idiopathic acute transverse myelitis (ATM) within the 6G-enabled Internet of Medical Things (IoMT) environment. The developed fra...
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This study addresses the Testing Facility Location with Constrained Queue Time Problem. This optimization problem focuses on determining the best places to deploy testing sites and their available testers for infectio...
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Task oriented chatbots are a sub-topic related to chatbots, where chatbots will perform certain tasks with specific goals. One part of creating a task-oriented chatbot is doing intent classification. Intent classifica...
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Recommendation Systems (RSs) play a crucial role in assisting users in making decisions and finding their desired items in various domains, such as movies, music, and hotels. However, their complex algorithms often ra...
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We present a technique for information-theoretic optimization of computational imaging systems demonstrated in snapshot 3D microscopy. By directly evaluating measurement quality and decoupling optimization from downst...
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Modern retail businesses face a significant challenge with the inefficiency of manually changing price labels on shelves. This manual process not only consumes valuable time and resources but also increases the likeli...
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ISBN:
(数字)9798350390025
ISBN:
(纸本)9798350390032
Modern retail businesses face a significant challenge with the inefficiency of manually changing price labels on shelves. This manual process not only consumes valuable time and resources but also increases the likelihood of errors, leading to potential inaccuracies in pricing and a less streamlined shopping experience for customers. The proposed solution is the implementation of an Electronic Shelf Label (ESL) system that automatically displays the prices of goods on retail business shelves. This system connects a website (front end and back end) to e-paper via a Wi-Fi network and microcontroller, allowing retail business owners to update prices more easily. Additionally, buyers can search for desired items through the website. The results are the time to send data from the website to the e-paper, namely, to know the performance of the e-paper used. The average time required from 10 attempts to send data from the website to the e-paper is 20.935 seconds. Since the data is connected to the server and will be updated automatically, using this system will be more efficient than manually changing the price label, although it takes time to transfer data from the website to the e-paper.
In general, public or private organizations or companies have used information-based technology as a support to improve business performance to be more effective and efficient in order to achieve a company's busin...
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RSA is an asymmetric encryption algorithm that uses two different keys, a public key to encrypt the plain text and a private key to decrypt the cipher text. Fernet is a symmetric encryption algorithm that uses a singl...
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We present a high-accuracy 3D facial reconstruction system with the following features: real-time 3D facial reconstruction using exposure synchronization multi-camera, feature alignment to quantify facial differences,...
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Digital twin technology has given rise to smart manufacturing in Industry 4.0. Especially with the help of virtual reality, digital twin technology aims to provide an immersive experience by integrating the physical a...
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ISBN:
(数字)9798331507213
ISBN:
(纸本)9798331507220
Digital twin technology has given rise to smart manufacturing in Industry 4.0. Especially with the help of virtual reality, digital twin technology aims to provide an immersive experience by integrating the physical and cyberspace worlds synchronously in real time. One of its key features is the ability to perform predictive maintenance, to help prevent a product from any possible faults in the future. The existing literature has explored the traditional predictive maintenance strategies for forecasting the future state of machines such as simple regression and ARIMA models. However, the potential of machine learning, especially recurrent neural networks (RNNs) is not yet fully investigated for predictive maintenance in digital twin models. The complex nature of industrial operations gives rise to non-linearities in its modeling which can be addressed with the help of neural networks. Therefore, this research aims to investigate the potential of RNNs in the predictive maintenance of an industrial machine use case by performing comparative analysis with simple regression and ARIMA Models. It also highlights the significant improvement RNNs make over the aforementioned strategies. This research also proposes an extension of the existing virtual reality-based digital twin architecture to incorporate automated predictive maintenance of the machine. Moreover, the proposed digital twin architecture acts as a basis for automated predictive maintenance of any product.
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