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From the perspective of the medical device development community, widespread adoption of RFA has led to a maturing of the market and created...
Andre Khalil’s Computational Modeling, Analysis of Imagery and Numerical Experiments (CompuMAINE) lab has used image analysis techniques to solve a range of problems at every scale, from cancer cells to galaxies. And he has only scratched the surface of its potential. Since 2008, over $1 million in funding has supported CompuMAINE research using image analysis […]
Cyber-attacks and intrusions have become the major obstacles to the adoption of the Industrial Internet of Things (IIoT) in critical industries. Imbalanced data distribution is a common problem in IIoT environments that negatively influence machine learning-based intrusion detection systems. To address this issue, we introduce EvolCostDeep, a hybrid model of stacked auto-encoders (SAE) and convolutional neural networks (CNNs) with a new cost-dependent loss function. The loss function aims to optimize the model’s parameters, where the costs are determined using an evolutionary algorithm. The ...
Edge intelligence is an emerging technology that integrates edge computing and deep learning to bring AI to the network’s edge. It has gained wide attention for its lower network latency and better privacy preservation abilities. However, the inference of deep neural networks is computationally demanding and results in poor real-time performance, making it challenging for resource-constrained edge devices. In this paper, we propose a hierarchical deep learning model based on TreeNet to reduce the computational cost for edge devices. Based on the similarity of the classification categories, w...
A fascinating new preprint reports the results of examining the VOC composition from individual cell cultures derived from human oral cancers, using an experimental setup employing an insect brain with attached antenna.