AIMC Topic: Algorithms

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Neural network surrogate and projected gradient descent for fast and reliable finite element model calibration: A case study on an intervertebral disc.

Computers in biology and medicine
Accurate calibration of finite element (FE) models is essential across various biomechanical applications, including human intervertebral discs (IVDs), to ensure their reliability and use in diagnosing and planning treatments. However, traditional ca...

Deep learning model to identify and validate hypotension endotypes in surgical and critically ill patients.

British journal of anaesthesia
BACKGROUND: Hypotension is associated with organ injury and death in surgical and critically ill patients. In clinical practice, treating hypotension remains challenging because it can be caused by various underlying haemodynamic alterations. We aime...

Developing machine-learning-based amyloidogenicity predictors with Cross-Beta DB.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: The importance of protein amyloidogenesis, associated with various diseases and functional roles, has driven the creation of computational predictors of amyloidogenicity. The accuracy of these predictors, particularly those utilizing ar...

Enhancing meteorological data reliability: An explainable deep learning method for anomaly detection.

Journal of environmental management
Accurate meteorological observation data is of great importance to human production activities. Meteorological observation systems have been advancing toward automation, intelligence, and informatization. Yet, instrumental malfunctions and unstable s...

Artificial Intelligence Models May Aid in Predicting Lymph Node Metastasis in Patients with T1 Colorectal Cancer.

Gut and liver
BACKGROUND/AIMS: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predict...

Open-set deep learning-enabled single-cell Raman spectroscopy for rapid identification of airborne pathogens in real-world environments.

Science advances
Pathogenic bioaerosols are critical for outbreaks of airborne disease; however, rapidly and accurately identifying pathogens directly from complex air environments remains highly challenging. We present an advanced method that combines open-set deep ...

Utilizing Feature Selection Techniques for AI-Driven Tumor Subtype Classification: Enhancing Precision in Cancer Diagnostics.

Biomolecules
Cancer's heterogeneity presents significant challenges in accurate diagnosis and effective treatment, including the complexity of identifying tumor subtypes and their diverse biological behaviors. This review examines how feature selection techniques...

Effective BCDNet-based breast cancer classification model using hybrid deep learning with VGG16-based optimal feature extraction.

BMC medical imaging
PROBLEM: Breast cancer is a leading cause of death among women, and early detection is crucial for improving survival rates. The manual breast cancer diagnosis utilizes more time and is subjective. Also, the previous CAD models mostly depend on manma...

Clustering-based binary Grey Wolf Optimisation model with 6LDCNNet for prediction of heart disease using patient data.

Scientific reports
In recent years, the healthcare data system has expanded rapidly, allowing for the identification of important health trends and facilitating targeted preventative care. Heart disease remains a leading cause of death in developed countries, often lea...

An intelligent attention based deep convoluted learning (IADCL) model for smart healthcare security.

Scientific reports
In recent times, there has been rapid growth of technologies that have enabled smart infrastructures-IoT-powered smart grids, cities, and healthcare systems. But these resource-constrained IoT devices cannot be protected by existing security mechanis...