AIMC Topic: Algorithms

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Benchmarking retrieval-augmented large language models in biomedical NLP: Application, robustness, and self-awareness.

Science advances
To reduce hallucinations in large language models (LLMs), retrieval-augmented LLMs (RALs) retrieve supporting knowledge from external databases. However, their performance on biomedical natural language processing (NLP) tasks remains underexplored. W...

Speech-based respiratory diagnostics: A study on COVID-19 detection with machine learning.

PloS one
Respiratory sound analysis has emerged as a promising approach for detecting and diagnosing respiratory diseases, including COVID-19. This study investigates using OpenSMILE features for COVID-19 detection using vowel speech sounds /a/, /e/, and /o/ ...

Lightweight deep neural networks: Optimization of vehicle classification using ICBAM based on depthwise separable convolutions.

PloS one
Vehicle classification is a core task in intelligent transportation systems, where high demands are placed on both computational efficiency and generalization ability in practical applications. Existing deep learning models often struggle to meet the...

Random rotational embedding Bayesian optimization for human-in-the-loop personalized music generation.

PloS one
Generative deep learning models, such as those used for music generation, can produce a wide variety of results based on perturbations of random points in their latent space. User preferences can be incorporated in the generative process by replacing...

Predicting the risk of postoperative constipation in middle-aged and elderly patients with lower limb fractures using machine learning algorithms.

PloS one
OBJECTIVE: To construct and validate a predictive model for the risk of postoperative constipation in middle-aged and elderly patients with lower limb fractures based on machine learning algorithms, so as to provide decision-making support for clinic...

Structure-enhanced graph meta learning for few-shot gene regulatory network inference.

Genome biology
Inferring gene regulatory networks (GRNs) is essential for understanding biological regulation. Although numerous deep learning approaches have been developed for GRN inference, most require large amounts of labeled data. We present Meta-TGLink, a st...

Embedding-driven dual-branch approach for accurate breast tumor cellularity classification.

Scientific reports
This study proposes a dual-branch framework for precise classification of breast tumor cellularity via histopathological images where it integrates two distinct branches: the Embedding Extraction Branch (embedding-driven) and the Vision Classificatio...

Earlier prediction of Parkinson's disease using cross non-decimated wavelet transform and machine learning algorithm.

Scientific reports
Parkinson's disease (PD) is a brain disorder, that affects a person's body movement causing stiffness, shaking and imbalance. Earlier detection of PD is a challenging task for researchers. In this paper, earlier detection of PD is performed using the...

A lightweight improved YOLOv8 method for intelligent detection of pine wilt disease.

Scientific reports
Pine wood nematode disease (PWD) is one of the most devastating forest diseases worldwide, often described as the "cancer" of pine trees due to its rapid and large-scale lethality. Early and accurate detection of infected trees is essential for inter...

Effects of environment and globalization on the double and triple burdens of infection symptoms among under-five children across low-middle income countries using machine learning algorithms.

Infectious diseases of poverty
BACKGROUND: Childhood infectious diseases and related symptoms, such as fever, cough, and diarrhea among children constitute the leading cause of death in low and middle-income countries (LMICs). We examined the environmental predictors of double and...