AIMC Topic: Algorithms

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Prediction Models of Early Childhood Caries Based on Machine Learning Algorithms.

International journal of environmental research and public health
In this study, we developed machine learning-based prediction models for early childhood caries and compared their performances with the traditional regression model. We analyzed the data of 4195 children aged 1-5 years from the Korea National Health...

An Improved Stacked Autoencoder for Metabolomic Data Classification.

Computational intelligence and neuroscience
Naru3 (NR) is a traditional Mongolian medicine with high clinical efficacy and low incidence of side effects. Metabolomics is an approach that can facilitate the development of traditional drugs. However, metabolomic data have a high throughput, spar...

Dose reduction potential of vendor-agnostic deep learning model in comparison with deep learning-based image reconstruction algorithm on CT: a phantom study.

European radiology
OBJECTIVES: To compare the dose reduction potential (DRP) of a vendor-agnostic deep learning model (DLM, ClariCT.AI) with that of a vendor-specific deep learning-based image reconstruction algorithm (DLR, TrueFidelity™).

Non-differentiable saddle points and sub-optimal local minima exist for deep ReLU networks.

Neural networks : the official journal of the International Neural Network Society
Whether sub-optimal local minima and saddle points exist in the highly non-convex loss landscape of deep neural networks has a great impact on the performance of optimization algorithms. Theoretically, we study in this paper the existence of non-diff...

Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems.

Computers in biology and medicine
Prostate cancer (PCa) is one of the most commonly diagnosed cancer and one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020. Artificial Intelligence algorithms have had a huge impact on me...

Classification Framework for Healthy Hairs and Alopecia Areata: A Machine Learning (ML) Approach.

Computational and mathematical methods in medicine
Alopecia areata is defined as an autoimmune disorder that results in hair loss. The latest worldwide statistics have exhibited that alopecia areata has a prevalence of 1 in 1000 and has an incidence of 2%. Machine learning techniques have demonstrate...

Feature-fusion improves MRI single-shot deep learning detection of small brain metastases.

Journal of neuroimaging : official journal of the American Society of Neuroimaging
BACKGROUND AND PURPOSE: To examine whether feature-fusion (FF) method improves single-shot detector's (SSD's) detection of small brain metastases on contrast-enhanced (CE) T1-weighted MRI.

Can Deep Learning Algorithms Help Identify Surgical Workflow and Techniques?

The Journal of surgical research
BACKGROUND: Surgical videos are now being used for performance review and educational purposes; however, broad use is still limited due to time constraints. To make video review more efficient, we implemented Artificial Intelligence (AI) algorithms t...

An interpretable machine learning method for homo-trimeric protein interface residue-residue interaction prediction.

Biophysical chemistry
Protein-protein interaction plays an important role in life activities. A more fine-grained analysis, such as residues and atoms level, will better benefit us to understand the mechanism for inter-protein interaction and drug design. The development ...

Systematic Investigation of Error Distribution in Machine Learning Algorithms Applied to the Quantum-Chemistry QM9 Data Set Using the Bias and Variance Decomposition.

Journal of chemical information and modeling
Most machine learning applications in quantum-chemistry (QC) data sets rely on a single statistical error parameter such as the mean square error (MSE) to evaluate their performance. However, this approach has limitations or can even yield incorrect ...