AIMC Topic: Algorithms

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Effect of training sample size, image resolution and epochs on filamentous and floc-forming bacteria classification using machine learning.

Journal of environmental management
Computer vision techniques can expedite the detection of bacterial growth in wastewater treatment plants and alleviate some of the shortcomings associated with traditional detection methods. In recent years, researchers capitalized on this potential ...

Nondestructive detection of cadmium content in oilseed rape leaves under different silicon environments using deep transfer learning and Vis-NIR hyperspectral imaging.

Food chemistry
In this paper, a transfer stack denoising autoencoder (T-SDAE) algorithm is proposed to implement the migration of cadmium (Cd) prediction depth characteristic model of oilseed rape leaves in different silicon environments. Stacked denoising autoenco...

DGEDDGAN: A dual-domain generator and edge-enhanced dual discriminator generative adversarial network for MRI reconstruction.

Magnetic resonance imaging
Magnetic resonance imaging (MRI) as a critical clinical tool in medical imaging, requires a long scan time for producing high-quality MRI images. To accelerate the speed of MRI while reconstructing high-quality images with sharper edges and fewer ali...

Integrated fusion approach for multi-class heart disease classification through ECG and PCG signals with deep hybrid neural networks.

Scientific reports
Detection and classification of cardiovascular diseases are crucial for early diagnosis and prediction of heart-related conditions. Existing methods rely on either electrocardiogram or phonocardiogram signals, resulting in higher false positive rates...

PM concentration prediction using machine learning algorithms: an approach to virtual monitoring stations.

Scientific reports
One of the most important pollutants is PM, which is particularly important to monitor pollutant levels to keep the pollutant concentration under control. In this research, an attempt has been made to predict the concentrations of PM using four Machi...

Position-context additive transformer-based model for classifying text data on social media.

Scientific reports
In recent years, the continuous increase in the growth of text data on social media has been a major reason to rely on the pre-training method to develop new text classification models specially transformer-based models that have proven worthwhile in...

MCNEL: A multi-scale convolutional network and ensemble learning for Alzheimer's disease diagnosis.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Alzheimer's disease (AD) significantly threatens community well-being and healthcare resource allocation due to its high incidence and mortality. Therefore, early detection and intervention are crucial for reducing AD-relate...

FedBM: Stealing knowledge from pre-trained language models for heterogeneous federated learning.

Medical image analysis
Federated learning (FL) has shown great potential in medical image computing since it provides a decentralized learning paradigm that allows multiple clients to train a model collaboratively without privacy leakage. However, current studies have show...

Female autism categorization using CNN based NeuroNet57 and ant colony optimization.

Computers in biology and medicine
Autism identification and classification using biomedical medical image analysis has advanced recently. Research shows autistic females have different phenotypic and age-related brain variations than males. Gender-specific hormones and genes affect a...

Automatic cerebral microbleeds detection from MR images via multi-channel and multi-scale CNNs.

Computers in biology and medicine
BACKGROUND: Computer-aided detection (CAD) systems have been widely used to assist medical professionals in interpreting medical images, aiding in the detection of potential diseases. Despite their usefulness, CAD systems cannot yet fully replace doc...