AIMC Topic: Neural Networks, Computer

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A hybrid learning approach for MRI-based detection of alzheimer's disease stages using dual CNNs and ensemble classifier.

Scientific reports
Alzheimer's Disease (AD) and related dementias are significant global health issues characterized by progressive cognitive decline and memory loss. Computer-aided systems can help physicians in the early and accurate detection of AD, enabling timely ...

Insight mechanism of ANN model for denitrification in spouted bed bioreactor.

Scientific reports
Numerous technologies have been developed to remove nitrate from wastewater due to its significant health and environmental impacts. In the present study, an isolate of Pseudomonas syringae was utilized to investigate the denitrification rate using i...

AI-driven smart agriculture using hybrid transformer-CNN for real time disease detection in sustainable farming.

Scientific reports
Plant diseases pose a significant threat to global food security, with severe implications for agricultural productivity. Early and accurate detection of these diseases is crucial, yet it remains a challenging task, significantly impacting crop yield...

Analyzing crises in global financial indices using Recurrent Neural Network based Autoencoder.

PloS one
In this study, we present a novel approach to analyzing financial crises of the global stock market by leveraging a modified Autoencoder model based on Recurrent Neural Network (RNN-AE). We analyze time series data from 24 global stock markets betwee...

Finger drawing on smartphone screens enables early Parkinson's disease detection through hybrid 1D-CNN and BiGRU deep learning architecture.

PloS one
BACKGROUND: Parkinson's disease (PD), a progressive neurodegenerative disorder prevalent in aging populations, manifests clinically through characteristic motor impairments including bradykinesia, rigidity, and resting tremor. Early detection and tim...

Artificial intelligence-based action recognition and skill assessment in robotic cardiac surgery simulation: a feasibility study.

Journal of robotic surgery
To create a deep neural network capable of recognizing basic surgical actions and categorizing surgeons based on their skills using video data only. Nineteen surgeons with varying levels of robotic experience performed three wet lab tasks on a porcin...

Early breast cancer detection via infrared thermography using a CNN enhanced with particle swarm optimization.

Scientific reports
Breast cancer remains the most prevalent cause of cancer-related mortality among women worldwide, with an estimated incidence exceeding 500,000 new cases annually. Timely diagnosis is vital for enhancing therapeutic outcomes and increasing survival p...

MTA-Net: Multi-scale triplet attention-aware network for multiclass skin lesion classification.

Computers in biology and medicine
Multiclass classification of skin lesions plays a crucial role in computer-aided skin cancer diagnosis and still remains challenging due to the high similarity between different classes and large variations within the same classes. Additionally, the ...

Comparative machine learning analysis for predicting organ tropism in breast cancer and identifying key gene signatures.

Computers in biology and medicine
BACKGROUND: Breast cancer metastasis (BCM) metastasizes preferentially to certain organs. Important genetic markers can be used for early detection and treatment. Machine learning (ML) can efficiently handle gene expression data to enhance metastasis...

A preprocessing method based on 3D U-Net for abdomen segmentation.

Computers in biology and medicine
Deep learning methods have made significant progress in the field of biomedical automatic segmentation but still open to developments, especially due to the insufficient use of preprocessing methods. In this study, a pre-processing step is proposed b...