This research proposes a model for sensitive data detection and protection in IoT, based on deep learning and optimization-enabled secure encryption. By combining deep learning-based sensitive data detection and
The differentiation of neural stem cells (NSCs) into neurons is proposed to be critical in devising potential cell-based therapeutic strategies for central nervous system (CNS) diseases, however, the determination and prediction of differentiation is com
Urban activities, particularly vehicle traffic, are contributing significantly to environmental pollution with detrimental effects on public health. The ability to anticipate air quality in advance is critical for public authorities and the general publi
Recently, deep learning-based approaches have presented the state-of-the-art performance in image classification, segmentation, object detection and tracking tasks. Due to their self-learning and generalization ability over large amounts of data, deep learning recently has also gained great interest ...
Deep-Learning-Based-Anomaly-Detection Anomaly Detection: The process of detectingdata instances that significantly deviate from the majority of the whole dataset. Contributed by Chunyang Zhang. 🤝 Looking for Collaborators | 寻找协作者 This is an open-source repository for Deep-Learning-Based Anomaly...
[56]. Transfer learning on 3D CT scans using ResNet architectures is also conducted [57]. A machine-learning algorithm-based method is also designed and evaluated for coronavirus detection [58,59]. 3.2. Novel architectures COVID-Net [60] utilizes a new CNN architecture for detecting COVID ...
Significance is placed on performance metrics, including accuracy, stability, and robustness, in evaluating these deep learning-based authentication systems. The challenges and limitations that deep learning approaches must surmount when dealing with real-world biometric data in the context of biometric ...
data [27,28]. It is still in continuous development regarding novel performance for several ML tasks [22,29,30,31] and has simplified the improvement of many learning fields [32,33], such as image super-resolution [34], object detection [35,36], and image recognition [30,37]. Recently...
Not Suitable for Imbalanced Data Sensitive to extreme predicted probabilities 3.2. Kullback-Leibler Divergence (KL Divergence) Loss Kullback-Leibler Divergence (KL Divergence) Loss, also known as KL Loss, is a mathematical measure used in machine learning and statistics to quantify the difference betwee...
we use the deep learning-based emotion detection method to detect the emotion (SSE, SEE) in the speech and text parts of the interviewees' responses in the subjective question bank, and based on the "7-38-55" rule of Professor Albert McLabin, we initially obtain the objective and subjecti...