AIMC Topic: Deep Learning

Clear Filters Showing 221 to 230 of 28423 articles

A Hybrid Cross-Attentive CNN-BiLSTM-Transformer Network for Dysarthria Severity Classification.

Scientific reports
Dysarthria is a neurological speech disorder characterized by articulatory impairment due to muscle weakness. Objective automated detection and severity classification of dysarthria enables timely intervention and tailored clinical management. Here, ...

Dual-modality fusion for mango disease classification using dynamic attention based ensemble of leaf & fruit images.

Scientific reports
Mango is one of the most beloved fruits and plays an indispensable role in the agricultural economies of many tropical countries like Pakistan, India, and other Southeast Asian countries. Similar to other fruits, mango cultivation is also threatened ...

Towards decoding individual words from non-invasive brain recordings.

Nature communications
While deep learning has enabled the decoding of language from intracranial brain recordings, achieving this with non-invasive recordings remains an open challenge. We introduce a deep learning pipeline to decode individual words from electro- (EEG) a...

Temporal structure of natural language processing in the human brain corresponds to layered hierarchy of large language models.

Nature communications
Large Language Models (LLMs) offer a framework for understanding language processing in the human brain. Unlike traditional models, LLMs represent words and context through layered numerical embeddings. Here, we demonstrate that LLMs' layer hierarchy...

Artificial intelligence tools for the assessment and management of dysphagia: protocol for a scoping review.

BMJ open
INTRODUCTION: Dysphagia, or difficulty in swallowing, significantly impacts the quality of life of the affected individuals. Diagnostic approaches, including video fluoroscopic swallowing studies and flexible endoscopic evaluation of swallowing, are ...

Automated Bone Age Assessment and Adult Height Prediction from Pediatric Hand Radiographs via a Cascaded Deep Learning Framework.

Journal of medical systems
Bone age assessment and adult height prediction are essential for evaluating pediatric growth. Traditional methods rely on manual radiographic interpretation, which is subjective, time-consuming, and prone to inter-observer variability. This study pr...

Light-field deep learning enables high-throughput, scattering-mitigated calcium imaging.

Proceedings of the National Academy of Sciences of the United States of America
Light-field microscopy (LFM) enables high-throughput functional imaging by scanlessly encoding entire volumes in single snapshots. However, LFM's computational burden and vulnerability to scattering limit its application to biological imaging. We pre...

NeuroAgeFusionNet an ensemble deep learning framework integrating CNN, transformers, and GNN for robust brain age estimation using MRI scans.

Scientific reports
Brain age prediction based on anatomical MRI scans, as an essentially new measure in neuroimaging and aging research, provides a crucial marker for the early diagnosis of neurodegenerative diseases, cognitive health appraisal, and biological age pred...

Improving emotional connection of human and machine using Deep Maxout Networks optimized through Modified Water Cycle optimizer.

Scientific reports
The precise identification and understanding of human emotions by computers is crucial for generating natural interactions between humans and machines. This research presents a novel approach for identifying emotions in speech through the integration...

Detect pre-cancerous tongue lesions for early oral cancer diagnosis using deep learning algorithm.

Scientific reports
Precancerous tongue lesion is a prevalent, complex, and highly perilous kind of cancer. The tumour might be in the salivary glands, tonsils, neck, cheek, and mouth. Oral Cancer (OC) is commonly identified in advanced stages due to the limited accurac...