The Quantum Internet of Things (QIoT) in the healthcare industry holds the promise of transforming patient care, diagnostics, and medical research. Quantum-enhanced sensors, communication, and computation offer unprec...
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The Quantum Internet of Things (QIoT) in the healthcare industry holds the promise of transforming patient care, diagnostics, and medical research. Quantum-enhanced sensors, communication, and computation offer unprecedented capabilities that can revolutionize how healthcare services are delivered and experienced. This paper explores the potential of QIoT in the context of smart healthcare, where interconnected quantum-enabled devices and systems create an ecosystem that enhances data security, enables real-time monitoring, and advances medical knowledge. We delve into the applications of quantum sensors in precise health monitoring, the role of quantum communication in secure telemedicine, and the computational power of quantum computing in drug discovery and personalized medicine. We discuss challenges such as technical feasibility, scalability, and regulatory considerations, along with the emerging trends and opportunities in this transformative field. By examining the intersection of quantum technologies and smart healthcare, this paper aims to shed light on the novel approaches and breakthroughs that could redefine the future of healthcare delivery and patient outcomes. IEEE
Task scheduling, which is important in cloud computing, is one of the most challenging issues in this area. Hence, an efficient and reliable task scheduling approach is needed to produce more efficient resource employ...
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Unmanned aerial vehicles (UAVs) have garnered increasing attention in recent years due to their utilization of artificial intelligence (AI) technologies and automation processes. These vehicles are being developed for...
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The Telecare Medical Information System (TMIS) faces challenges in securely exchanging sensitive health information between TMIS nodes. A Mutual Authenticated Key Agreement (MAKA) scheme is used to eliminate security ...
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Globally, one of the leading causes of mortality is heart disease, necessitating timely along with accurate detection to enhance the results for patients. In the following paper, a generalized approach to heart diseas...
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ISBN:
(纸本)9798350365405
Globally, one of the leading causes of mortality is heart disease, necessitating timely along with accurate detection to enhance the results for patients. In the following paper, a generalized approach to heart disease detection using deep learning is introduced with the view to finding means of utilizing the most cutting-edge machine learning techniques to improve cardiovascular healthcare. The main focus of academic inquiry is directed at utilizing deep learning algorithms to analyze a multitude and variety of medical data, including electrocardiogram images, in order to discover the most effective and precise technique of early heart disease detection and diagnosis. Thus, in this study, a wide range of highly popular state-of-the-art inside deep learning architectures were considered, including but not limited to the neural networks with convolutions. Moreover, the scholars had carefully analyzed and compared above-mentioned architectures using their own method to determine the most suitable one for the detection of cardiac conditions. In the current study, the author applied ECG and electrocardiogram images as their dataset had more varied electrocardiogram images. The dataset's diversity enables the models to capture intricate patterns and achieve robust generalization, essential for real-world deployment. These experimental results demonstrate promising potential for improving heart disease diagnosis and risk assessment, showcasing significantly enhanced performance compared to conventional methods. The study emphasizes the importance of early diagnosis of cardiac disorders in improving patient outcomes and treatment planning. In conclusion, this work provides a thorough investigation of a broad deep learning method for detecting heart disease. By combining diverse medical data, advanced deep learning architectures, and model training techniques, the research showcases the potential of artificial intelligence in revolutionizing cardiovascular healthcare. To an
An authenticated manager must reinforce huge applications and operating systems, keeping information in the cloud while resisting potentially unreliable service providers. This article explores the presence of multipl...
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This research investigates a hybrid approach for predicting movie revenue by integrating machine learning models with sentiment analysis. The growing influence of social media and online discussions offers a valuable ...
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The spread of misinformation and spam on social media has become a critical challenge, undermining information integrity and online security. Addressing this pressing issue, this study introduces an advanced solution ...
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The proliferation of cooking videos on the internet these days necessitates the conversion of these lengthy video contents into concise text recipes. Many online platforms now have a large number of cooking videos, in...
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The proliferation of cooking videos on the internet these days necessitates the conversion of these lengthy video contents into concise text recipes. Many online platforms now have a large number of cooking videos, in which, there is a challenge for viewers to extract comprehensive recipes from lengthy visual content. Effective summary is necessary in order to translate the abundance of culinary knowledge found in videos into text recipes that are easy to read and follow. This will make the cooking process easier for individuals who are searching for precise step by step cooking instructions. Such a system satisfies the needs of a broad spectrum of learners while also improving accessibility and user simplicity. As there is a growing need for easy-to-follow recipes made from cooking videos, researchers are looking on the process of automated summarization using advanced techniques. One such approach is presented in our work, which combines simple image-based models, audio processing, and GPT-based models to create a system that makes it easier to turn long culinary videos into in-depth recipe texts. A systematic workflow is adopted in order to achieve the objective. Initially, Focus is given for frame summary generation which employs a combination of two convolutional neural networks and a GPT-based model. A pre-trained CNN model called Inception-V3 is fine-tuned with food image dataset for dish recognition and another custom-made CNN is built with ingredient images for ingredient recognition. Then a GPT based model is used to combine the results produced by the two CNN models which will give us the frame summary in the desired format. Subsequently, Audio summary generation is tackled by performing Speech-to-text functionality in python. A GPT-based model is then used to generate a summary of the resulting textual representation of audio in our desired format. Finally, to refine the summaries obtained from visual and auditory content, Another GPT-based model is used
Cardiovascular diseases are a major global health challenge, with electrocardiography (ECG) being critical for diagnosis and monitoring. As artificial intelligence and automated ECG diagnostic technologies rapidly adv...
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