Browsing by Subject "Artificial neural networks"

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    Improving the Robustness of Artificial Neural Networks via Bayesian Approaches
    (2023-08) Zhuang, Jun; Al Hasan, Mohammad; Mukhopadhyay, Snehasis; Mohler, George; Tuceryan, Mihran
    Artificial neural networks (ANNs) have achieved extraordinary performance in various domains in recent years. However, some studies reveal that ANNs may be vulnerable in three aspects: label scarcity, perturbations, and open-set emerging classes. Noisy labeling and self-supervised learning approaches address the label scarcity issues, but most of the work couldn't handle the perturbations. Adversarial training methods, topological denoising methods, and mechanism designing methods aim to mitigate the negative effects caused by perturbations. However, adversarial training methods can barely train a robust model under the circumstance of extensive label scarcity; topological denoising methods are not efficient on dynamic data structures; and mechanism designing methods often depend on heuristic explorations. Detection-based methods devote to identifying novel or anomaly instances for further downstream tasks. Nonetheless, such instances may belong to open-set new emerging classes. To embrace the aforementioned challenges, we address the robustness issues of ANNs from two aspects. First, we propose a series of Bayesian label transition models to improve the robustness of Graph Neural Networks (GNNs) in the presence of label scarcity and perturbations in the graph domain. Second, we propose a new non-exhaustive learning model, named NE-GM-GAN, to handle both open-set problems and class-imbalance issues in network intrusion datasets. Extensive experiments with several datasets demonstrate that our proposed models can effectively improve the robustness of ANNs.
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    Machine Learning and Artificial Intelligence in Surgical Fields
    (Springer, 2020-12) Egert, Melissa; Steward, James E.; Sundaram, Chandru P.; Urology, School of Medicine
    Artificial intelligence (AI) and machine learning (ML) have the potential to improve multiple facets of medical practice, including diagnosis of disease, surgical training, clinical outcomes, and access to healthcare. There have been various applications of this technology to surgical fields. AI and ML have been used to evaluate a surgeon's technical skill. These technologies can detect instrument motion, recognize patterns in video recordings, and track the physical motion, eye movements, and cognitive function of the surgeon. These modalities also aid in the advancement of robotic surgical training. The da Vinci Standard Surgical System developed a recording and playback system to help trainees receive tactical feedback to acquire more precision when operating. ML has shown promise in recognizing and classifying complex patterns on diagnostic images and within pathologic tissue analysis. This allows for more accurate and efficient diagnosis and treatment. Artificial neural networks are able to analyze sets of symptoms in conjunction with labs, imaging, and exam findings to determine the likelihood of a diagnosis or outcome. Telemedicine is another use of ML and AI that uses technology such as voice recognition to deliver health care remotely. Limitations include the need for large data sets to program computers to create the algorithms. There is also the potential for misclassification of data points that do not follow the typical patterns learned by the machine. As more applications of AI and ML are developed for the surgical field, further studies are needed to determine feasibility, efficacy, and cost.