AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
The proposed novel method allows numerical data to be evaluated as image representations and, accordingly, to be easily utilized in image processing, data augmentation, and similar techniques.
This work proposes a multimodal deep fusion framework with attention for high accurate cardiovascular risk stratification using the integration of medical images and clinical data and demonstrates that this adaptive fusion strategy outperforms simple concatenation baselines.
A hybrid deep learning framework is proposed, which learns discriminative clinical representations and health patterns over time together with Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks.
A deep learning-based method for automatically classifying heart conditions from echocardiography data using the EfficientNetB0 architecture, which has the potential to improve cardiovascular disease prognosis and early detection, thereby increasing the scalability of sophisticated diagnostic capabilities in a variety...
A new solution that allows integrating multiple technologies into the process of disease prediction, which includes machine learning, deep learning, as well as explainable AI, are incorporated into the system, which demonstrates how AI multi-models can easily scale and be explained.
The results of the study reaffirm that the LES-based multimodal framework can provide an accurate, interpretable & computationally efficient diagnosis of early CVD & clinical decision support for clinical decision-making.
Cancer is still an acute issue because of the variability in the characteristics of its diagnosis and possibilities of false negative results in clinical examination. Single architecture deep learning models and machine learning models do lead to their inability to generalize successfully with dissimilar datasets, thus...
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