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Supervised Machine Learning

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Research on the Guidance of Youth Labor Education Based on the "Combination of Education and Production Labor" Program Based on the Deep Learning Model.

Computational intelligence and neuroscience
At present, there is a lack of research on Marx's idea of "combining education and productive labor" and its guiding significance for youth labor education, and no effective teaching model has been formed. In response to this problem, this study prop...

Clustering of trauma patients based on longitudinal data and the application of machine learning to predict recovery.

Scientific reports
Predicting recovery after trauma is important to provide patients a perspective on their estimated future health, to engage in shared decision making and target interventions to relevant patient groups. In the present study, several unsupervised tech...

Active deep learning from a noisy teacher for semi-supervised 3D image segmentation: Application to COVID-19 pneumonia infection in CT.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Supervised deep learning has become a standard approach to solving medical image segmentation tasks. However, serious difficulties in attaining pixel-level annotations for sufficiently large volumetric datasets in real-life applications have highligh...

Robust image hashing for content identification through contrastive self-supervised learning.

Neural networks : the official journal of the International Neural Network Society
Content identification systems are an essential technology for many applications. These systems identify query multimedia items using a database of known identities. A hash-based system uses a perceptual hashing function that generates a hash value i...

Evaluation of two semi-supervised learning methods and their combination for automatic classification of bone marrow cells.

Scientific reports
Differential bone marrow (BM) cell counting is an important test for the diagnosis of various hematological diseases. However, it is difficult to accurately classify BM cells due to non-uniformity and the lack of reproducibility of differential count...

Temporal Coding in Spiking Neural Networks With Alpha Synaptic Function: Learning With Backpropagation.

IEEE transactions on neural networks and learning systems
The timing of individual neuronal spikes is essential for biological brains to make fast responses to sensory stimuli. However, conventional artificial neural networks lack the intrinsic temporal coding ability present in biological networks. We prop...

Affinity Attention Graph Neural Network for Weakly Supervised Semantic Segmentation.

IEEE transactions on pattern analysis and machine intelligence
Weakly supervised semantic segmentation is receiving great attention due to its low human annotation cost. In this paper, we aim to tackle bounding box supervised semantic segmentation, i.e., training accurate semantic segmentation models using bound...

Surface similarity parameter: A new machine learning loss metric for oscillatory spatio-temporal data.

Neural networks : the official journal of the International Neural Network Society
Supervised machine learning approaches require the formulation of a loss functional to be minimized in the training phase. Sequential data are ubiquitous across many fields of research, and are often treated with Euclidean distance-based loss functio...

Application of supervised machine learning algorithms to predict the risk of hidden blood loss during the perioperative period in thoracolumbar burst fracture patients complicated with neurological compromise.

Frontiers in public health
BACKGROUND: Machine learning (ML) is a type of artificial intelligence (AI) and has been utilized in clinical research and practice to construct high-performing prediction models. Hidden blood loss (HBL) is prevalent during the perioperative period o...

Survival Prediction of Children Undergoing Hematopoietic Stem Cell Transplantation Using Different Machine Learning Classifiers by Performing Chi-Square Test and Hyperparameter Optimization: A Retrospective Analysis.

Computational and mathematical methods in medicine
Bone marrow transplant (BMT) is an effective surgical treatment for bone marrow-related disorders. However, several associated risk factors can impair long-term survival after BMT. Machine learning (ML) technologies have been proven useful in surviva...