AIMC Topic: Humans

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FDCN-C: A deep learning model based on frequency enhancement, deformable convolution network, and crop module for electroencephalography motor imagery classification.

PloS one
Motor imagery (MI)-electroencephalography (EEG) decoding plays an important role in brain-computer interface (BCI), which enables motor-disabled patients to communicate with external world via manipulating smart equipment. Currently, deep learning (D...

Boosted Harris Hawks Shuffled Shepherd Optimization Augmented Deep Learning based motor imagery classification for brain computer interface.

PloS one
Motor imagery (MI) classification has been commonly employed in making brain-computer interfaces (BCI) to manage the outside tools as a substitute neural muscular path. Effectual MI classification in BCI improves communication and mobility for people...

MERIT: Accurate Prediction of Multi Ligand-binding Residues with Hybrid Deep Transformer Network, Evolutionary Couplings and Transfer Learning.

Journal of molecular biology
Multi-ligand binding residues (MLBRs) are amino acids in protein sequences that interact with multiple different ligands that include proteins, peptides, nucleic acids, and a variety of small molecules. MLBRs are implicated in a number of cellular fu...

Developing a method for predicting DNA nucleosomal sequences using deep learning.

Technology and health care : official journal of the European Society for Engineering and Medicine
BackgroundDeep learning excels at processing raw data because it automatically extracts and classifies high-level features. Despite biology's low popularity in data analysis, incorporating computer technology can improve biological research.Objective...

Parkinson's disease prediction using improved crayfish optimization based hybrid deep learning.

Technology and health care : official journal of the European Society for Engineering and Medicine
BackgroundPredicting the course of Parkinson's disease is essential for prompt diagnosis and treatment, which may enhance patient outcomes.ObjectiveThis study presents a novel method for Parkinson's disease prediction using freely accessible resource...

Continual learning in the presence of repetition.

Neural networks : the official journal of the International Neural Network Society
Continual learning (CL) provides a framework for training models in ever-evolving environments. Although re-occurrence of previously seen objects or tasks is common in real-world problems, the concept of repetition in the data stream is not often con...

Burnout crisis in Chinese radiology: will artificial intelligence help?

European radiology
OBJECTIVES: To assess the correlation between the use of artificial intelligence (AI) software and burnout in the radiology departments of hospitals in China.

TIRADS-based artificial intelligence systems for ultrasound images of thyroid nodules: protocol for a systematic review.

Journal of ultrasound
PURPOSE: The thyroid imaging reporting and data system (TIRADS) was developed as a standard global term to describe thyroid nodule risk features, aiming to address issues such as variability and low reproducibility in nodule feature detection and int...

Moral enhancement and cheapened achievement: Psychedelics, virtual reality and AI.

Bioethics
A prominent critique of cognitive or athletic enhancement claims that certain performance-improving drugs or technologies may 'cheapen' resulting achievements. Considerably less attention has been paid to the impact of enhancement on the value of mor...

Advances in diagnosis and prognosis of bacteraemia, bloodstream infection, and sepsis using machine learning: A comprehensive living literature review.

Artificial intelligence in medicine
BACKGROUND: Blood-related infections are a significant concern in healthcare. They can lead to serious medical complications and even death if not promptly diagnosed and treated. Throughout time, medical research has sought to identify clinical facto...