AIMC Topic: Humans

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Mobile phone-based plasmodium parasites stage detection from Giemsa stained blood smear by convolutional neural networks.

Parasitology research
Plasmodium vivax is a malaria parasite with a broad geographic distribution worldwide. The unique biological characteristics of P. vivax, such as early gametocytogenesis and its latent hypnozoite stage, make it more difficult to control compared to P...

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...

MoleculeFormer is a GCN-transformer architecture for molecular property prediction.

Communications biology
Artificial intelligence is increasingly important in drug discovery, particularly in molecular property prediction. Graph Neural Networks can model molecular structures as graphs, using structural data to predict molecular properties and biological a...

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...

A hybrid bio inspired neural model based on Ropalidia Marginata behavior for multi disease classification.

Scientific reports
Accurate and efficient disease diagnosis remains a critical challenge in the healthcare sector. With the growing availability of biomedical data, machine learning techniques have become invaluable tools for developing intelligent disease detection sy...

Data augmentation alters feature importance in XGBoost for CVD prediction.

Scientific reports
Machine learning models are powerful tools for cardiovascular disease (CVD) prediction, but their performance is often limited by dataset size and class imbalance. While data augmentation techniques can address these issues, their impact on model int...

Explainable ensemble learning for Epstein-Barr virus risk prediction in ulcerative colitis and Crohn's disease using routine biomarkers.

Scientific reports
Epstein-Barr virus (EBV) exacerbates inflammatory bowel disease (IBD) and is challenging to monitor with invasive or costly tests. We investigated whether explainable machine learning can predict EBV infection from routine clinical data in ulcerative...

Spatiotemporal multimodal emotion recognition using Temporal video sequences and pose features for child emotion classification.

Scientific reports
Developmental psychology and affective computing have placed great emphasis on identifying children's emotional cues in recent times. In this study, a novel Spatio-Temporal Multimodal Emotion Recognition Network (ST-MERN) for child emotion classifica...

Deep learning for automatic segmentation of hepatocellular carcinoma in contrast enhanced CT scans.

Scientific reports
Liver cancer represents a significant cause of cancer-related mortality, with hepatocellular carcinoma (HCC) being the most prevalent forms. Computed tomography (CT) serves as the principal imaging modality for the diagnosis of liver tumors, particul...