AIMC Topic: Algorithms

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X-scPAE: An explainable deep learning model for embryonic lineage allocation prediction based on single-cell transcriptomics revealing key genes in embryonic cell development.

Computers in biology and medicine
In single-cell transcriptomics research, accurately predicting cell lineage allocation and identifying differences between lineages are crucial for understanding cell differentiation processes and reducing early pregnancy miscarriages in humans. This...

Gabor-modulated depth separable convolution for retinal vessel segmentation in fundus images.

Computers in biology and medicine
BACKGROUND: In diabetic retinopathy, precise segmentation of retinal vessels is essential for accurate diagnosis and effective disease management. This task is particularly challenging due to the varying sizes of vessels, their bifurcations, and the ...

Augmented Intelligence-Based Interference Pattern Analysis (AI-IPA) in Concentric Needle Electromyography.

Muscle & nerve
INTRODUCTION/AIMS: To add objectivity to the routine needle electromyography examination, we describe an "Augmented Intelligence" based interference pattern (IP) analysis method that mimics the subjective assessment by quantifying IP fullness, discre...

Self-supervised machine learning methods for protein design improve sampling but not the identification of high-fitness variants.

Science advances
Machine learning (ML) is changing the world of computational protein design, with data-driven methods surpassing biophysical-based methods in experimental success. However, they are most often reported as case studies, lack integration and standardiz...

A metabolic fingerprint of ovarian cancer: a novel diagnostic strategy employing plasma EV-based metabolomics and machine learning algorithms.

Journal of ovarian research
Ovarian cancer (OC) is the third most common malignant tumor of women and is accompanied by an alteration of systemic metabolism. A liquid biopsy that captures and detects tumor-related biomarkers in body fluids has great potential for OC diagnosis. ...

A multi-classification deep neural network for cancer type identification from high-dimension, small-sample and imbalanced gene microarray data.

Scientific reports
Gene microarray technology provides an efficient way to diagnose cancer. However, microarray gene expression data face the challenges of high-dimension, small-sample, and multi-class imbalance. The coupling of these challenges leads to inaccurate res...

Automated grading of oleaster fruit using deep learning.

Scientific reports
The agriculture sector is crucial to many economies, particularly in developing regions, with post-harvest technology emerging as a key growth area. The oleaster, valued for its nutritional and medicinal properties, has traditionally been graded manu...

Apriori algorithm based prediction of students' mental health risks in the context of artificial intelligence.

Frontiers in public health
INTRODUCTION: The increasing prevalence of mental health challenges among college students necessitates innovative approaches to early identification and intervention. This study investigates the application of artificial intelligence (AI) techniques...

A novel deep learning-based framework with particle swarm optimisation for intrusion detection in computer networks.

PloS one
Intrusion detection plays a significant role in the provision of information security. The most critical element is the ability to precisely identify different types of intrusions into the network. However, the detection of intrusions poses a importa...

Hybrid attention-CNN model for classification of gait abnormalities using EMG scalogram images.

Journal of medical engineering & technology
This research aimed to develop an algorithm for classifying scalogram images generated from electromyography data of patients with Rheumatoid Arthritis and Prolapsed Intervertebral Disc. Electromyography is valuable for assessing muscle function and ...