AIMC Topic: Machine Learning

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VISAL-A novel learning strategy to address class imbalance.

Neural networks : the official journal of the International Neural Network Society
In the imbalance data scenarios, Deep Neural Networks (DNNs) fail to generalize well on minority classes. In this letter, we propose a simple and effective learning function i.e, Visually Interpretable Space Adjustment Learning (VISAL) to handle the ...

A wholistic view of continual learning with deep neural networks: Forgotten lessons and the bridge to active and open world learning.

Neural networks : the official journal of the International Neural Network Society
Current deep learning methods are regarded as favorable if they empirically perform well on dedicated test sets. This mentality is seamlessly reflected in the resurfacing area of continual learning, where consecutively arriving data is investigated. ...

A domain-agnostic approach for characterization of lifelong learning systems.

Neural networks : the official journal of the International Neural Network Society
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original t...

Continual task learning in natural and artificial agents.

Trends in neurosciences
How do humans and other animals learn new tasks? A wave of brain recording studies has investigated how neural representations change during task learning, with a focus on how tasks can be acquired and coded in ways that minimise mutual interference....

Machine learning using multi-modal data predicts the production of selective laser sintered 3D printed drug products.

International journal of pharmaceutics
Three-dimensional (3D) printing is drastically redefining medicine production, offering digital precision and personalized design opportunities. One emerging 3D printing technology is selective laser sintering (SLS), which is garnering attention for ...

Artificial intelligence in lung cancer diagnosis and prognosis: Current application and future perspective.

Seminars in cancer biology
Lung cancer is one of the malignant tumors with the highest incidence and mortality in the world. The overall five-year survival rate of lung cancer is relatively lower than many leading cancers. Early diagnosis and prognosis of lung cancer are essen...

Implementation of five machine learning methods to predict the 52-week blood glucose level in patients with type 2 diabetes.

Frontiers in endocrinology
OBJECTIVE: For the patients who are suffering from type 2 diabetes, blood glucose level could be affected by multiple factors. An accurate estimation of the trajectory of blood glucose is crucial in clinical decision making. Frequent glucose measurem...

Use of machine learning in pediatric surgical clinical prediction tools: A systematic review.

Journal of pediatric surgery
PURPOSE: Clinical prediction tools (CPTs) are decision-making instruments utilizing patient data to predict specific clinical outcomes, risk-stratify patients, or suggest personalized diagnostic or therapeutic options. Recent advancements in artifici...

Machine Learning Models for Predicting the Outcomes of Surgical Treatment of Colorectal Liver Metastases.

Journal of the American College of Surgeons
BACKGROUND: Surgical intervention remains the cornerstone of a multidisciplinary approach in the treatment of colorectal liver metastases (CLM). Nevertheless, patient outcomes vary greatly. While predictive tools can assist decision-making and patien...