AIMC Topic: Deep Learning

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A deep learning framework for gender sensitive speech emotion recognition based on MFCC feature selection and SHAP analysis.

Scientific reports
Speech is one of the most efficient methods of communication among humans, inspiring advancements in machine speech processing under Natural Language Processing (NLP). This field aims to enable computers to analyze, comprehend, and generate human lan...

PreMode predicts mode-of-action of missense variants by deep graph representation learning of protein sequence and structural context.

Nature communications
Accurate prediction of the functional impact of missense variants is important for disease gene discovery, clinical genetic diagnostics, therapeutic strategies, and protein engineering. Previous efforts have focused on predicting a binary pathogenici...

Retinal image-based disease classification using hybrid deep architecture with improved image features.

International ophthalmology
OBJECTIVE: Ophthalmologists use retinal fundus imaging as a useful tool to diagnose retinal issues. Recently, research on machine learning has concentrated on disease diagnosis. However, disease detection is less accurate, more likely to be misidenti...

Automated classification of skeletal malocclusion in German orthodontic patients.

Clinical oral investigations
OBJECTIVES: Precisely diagnosing skeletal class is mandatory for correct orthodontic treatment. Artificial intelligence (AI) could increase efficiency during diagnostics and contribute to automated workflows. So far, no AI-driven process can differen...

AI-driven framework for automated detection of kidney stones in CT images: integration of deep learning architectures and transformers.

Biomedical physics & engineering express
. Kidney stones, a prevalent urological condition, associated with acute pain requires prompt and precise diagnosis for optimal therapeutic intervention. While computed tomography (CT) imaging remains the definitive diagnostic modality, manual interp...

A lightweight hybrid DL model for multi-class chest x-ray classification for pulmonary diseases.

Biomedical physics & engineering express
Pulmonary diseases have become one of the main reasons for people's health decline, impacting millions of people worldwide. Rapid advancement of deep learning has significantly impacted medical image analysis by improving diagnostic accuracy and effi...

Leveraging deep learning for the detection of socially desirable tendencies in personnel selection: A proof-of-concept.

PloS one
We propose a deep learning-based method for detecting Socially Desirable Responding (SDR)-the tendency for individuals to distort questionnaire responses to present themselves in a favorable light. Our objective is to showcase that such novel methods...

A lightweight YOLOv8-based model for gastric cancer detection.

Computers in biology and medicine
Recent research on deep learning-based gastric cancer detection has demonstrated high performance, with capabilities comparable to or exceeding those of medical professionals. However, the performance of deep learning models depends on the performanc...

GAN and LSTM-based collaborative tremor classification approach for next generation healthcare system.

Computers in biology and medicine
PURPOSE: To adopt different deep learning (DL) techniques for Essential tremor (ET) and Parkinson's tremor (PST) classification, a collaborative approach to address the misdiagnosis of healthy and PSD patients based on ET and PST's frequency patterns...

A Molecular Representation Learning Model Based on Multidimensional Joint and Cross-Learning for Drug-Drug Interaction Prediction.

Journal of chemical information and modeling
Drug-drug interactions (DDIs) present significant challenges within clinical pharmacology, as they can impact therapeutic outcomes, especially given the growing prevalence of polypharmacy. Traditional methods for the clinical validation of DDIs typic...