Heart disease is a major public health problem the world over, and accurate measures of risk assessment and appropriate classification of persons are very necessary in the early identification for treatment. This is o...
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computer Aided Design (CAD) and dental industry are going hand-in-hand due to the increasing requirements for digitalizing the process of dental diagnosis and treatment. The crucial step in dental treatment is the ide...
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This research study examines the ways to use sentiment analysis on financial news for corporate strategy making. We examine the impact of sentiment in financial news on corporate decisions (beyond technology) regardin...
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The tourism industry is vital to the world economy and plays a major role in it. The adoption and effects of Intelligent Travel Technology (ITT) for passengers and destinations are the main topics of this study, which...
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Rapid developments in digital technology have expedited the dissemination of information on social media platforms like as Twitter, Facebook, and Weibo. Unverified information can create protests and mislead the publi...
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Knowledge Graph Completion (KGC) aims to predict the missing information in the (head entity)-[relation]-(tail entity) triplet. Deep Neural Networks have achieved significant progress in the relation prediction task. ...
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Numerous advancements in image super resolution in recent years have explored different deep learning approaches and frameworks based on Convolution Neural Network (CNN) to achieve enhanced perceptual results. In this...
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Deep learning breakthroughs have transformed disease prediction and treatment recommendation systems, yet the confidentiality of sensitive medical data remains a major worry. In this paper, we propose a federated lear...
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ISBN:
(纸本)9798350394474
Deep learning breakthroughs have transformed disease prediction and treatment recommendation systems, yet the confidentiality of sensitive medical data remains a major worry. In this paper, we propose a federated learning(FL) methodology that harnesses the capabilities of deep learning models such as Recurrent Neural Networks (RNN), Long ShortTerm Memory Networks (LSTM), Gated Recurrent Units (GRU), and Bidirectional Encoder Representations from Transformers (BERT) to address this challenge. Our approach provides a framework for multiple medical institutions to collaboratively train a deep learning model without sharing patients' private medical records. Leveraging the Flower module, we establish a federated learning architecture comprising decentralized trainers and a global server responsible for aggregating model weights from all trainers. This setup ensures data privacy while enabling the model to learn from the diverse medical records across institutions. The trained model demonstrates significant accuracy in predicting approximately 40 common diseases from rich natural language medical reports. By integrating natural language processing capabilities, our model not only diagnoses ailments but also recommends tailored remedies. Since most of the diseases in our dataset are common and non-severe, users can access these remedies, bypassing the necessity for immediate medical visits, thus saving time and expenses associated with consultations. The proposed methodology offers a viable solution to establish a framework for collaborative learning in healthcare while safeguarding patients' private data- a crucial aspect in today's data driven medical landscape. Additionally, it offers a way to set up a highly accurate disease prediction and remedies recommendation system, empowering users to take proactive measures for their health. BERT+LSTM model which we developed showed an accuracy of 96.5% with centralized training whereas training the model on the FL architecture
Biodiversity is crucial for maintaining ecosystem stability, yet global biodiversity is currently in sharp decline, necessitating urgent protective measures. Wildlife monitoring and conservation, which determine biodi...
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In the era of 6G, cellular networks will no longer be locked into a small set of equipment manufacturers;instead, cellular networks will be disaggregated and support open interfaces. Thus, there is an inherent need fo...
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