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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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
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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Mission planning for UAV swarms is an NP-hard combinatorial optimization problem. Genetic algorithms find approximate solutions to this problem within an acceptable computational time. However, in complex real combat ...
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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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Cloud service providers require accurate prediction of cloud workloads to promptly determine resource allocation strategies and enhance resource utilization while meeting Service-Level Agreements. However, existing wo...
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Platooning has been researched for decades but debate about its lasting impact is still ongoing. Meanwhile, adaptive cruise control (ACC) became de facto standard for all new cars as well as for automated driving on t...
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Facial Emotion Recognition (FER) is an important field inc omputer vision that h as significant im plications for human-computer interaction, healthcare, and education. This study aims to tackle the particular difficu...
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This research has been aimed at analyzing the efficiency of utilizing the YOLO models for determining and classifying human activities, primarily YOLOv9. The comparison of YOLO models from v1 to v9 indicates the subst...
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InsightNav redefines desktop interaction by harnessing cutting-edge computer vision and AI technologies to deliver a transformative user experience. Beyond its intuitive gesture-based navigation, InsightNav pioneers t...
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