AIMC Topic: Algorithms

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AI optimization algorithms enhance higher education management and personalized teaching through empirical analysis.

Scientific reports
This research investigates the application of artificial intelligence (AI) optimization algorithms in higher education management and personalized teaching. Through a comprehensive literature review, theoretical analysis, and empirical study, the pot...

Two-tier nature inspired optimization-driven ensemble of deep learning models for effective autism spectrum disorder diagnosis in disabled persons.

Scientific reports
Autism spectrum disorder (ASD) includes a varied set of neuropsychiatric illnesses. This disorder is described by a definite grade of loss in social communication, academic functioning, personal contact, and limited and repetitive behaviours. Individ...

Employing artificial bee and ant colony optimization in machine learning techniques as a cognitive neuroscience tool.

Scientific reports
Higher education is essential because it exposes students to a variety of areas. The academic performance of IT students is crucial and might fail if it isn't documented to identify the features influencing them, as well as their strengths and shortc...

Using machine learning to simultaneously quantify multiple cognitive components of episodic memory.

Nature communications
Why do we remember some events but forget others? Previous studies attempting to decode successful vs. unsuccessful brain states to investigate this question have met with limited success, potentially due, in part, to assessing episodic memory as a u...

Brain tumor intelligent diagnosis based on Auto-Encoder and U-Net feature extraction.

PloS one
Preoperative classification of brain tumors is critical to developing personalized treatment plans, however existing classification methods rely on manual intervention and often have problems with efficiency and accuracy, which may lead to misdiagnos...

BSA-Seg: A Bi-level sparse attention network combining narrow band loss for multi-target medical image segmentation.

Neural networks : the official journal of the International Neural Network Society
Segmentation of multiple targets of varying sizes within medical images is of significant importance for the diagnosis of disease and pathological research. Transformer-based methods are emerging in the medical image segmentation, leveraging the powe...

TCDE-Net: An unsupervised dual-encoder network for 3D brain medical image registration.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Medical image registration is a critical task in aligning medical images from different time points, modalities, or individuals, essential for accurate diagnosis and treatment planning. Despite significant progress in deep learning-based registration...

Evaluation and comparison of machine learning algorithms for predicting discharge against medical advice in injured inpatients.

Surgery
BACKGROUND: Whether the application of machine learning algorithms offers an advantage over logistic regression in forecasting discharge against medical advice occurrences needs to be evaluated.

Which approach better predicts diabetes: Traditional econometric methods or machine learning? Evidence from a cross-sectional study in South Korea.

Computers in biology and medicine
To prevent chronic disease from getting worse, it is important to detect and predict it at an early stage. Therefore, the accuracy of the prediction is particularly important. To investigate the accuracy of different methods, this study compares the ...

Revisiting low-homophily for graph-based fraud detection.

Neural networks : the official journal of the International Neural Network Society
The openness of Internet stimulates a large number of fraud behaviors which have become a huge threat. Graph-based fraud detectors have attracted extensive interest since the abundant structure information of graph data has proved effective. Conventi...