AIMC Topic: Neural Networks, Computer

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End-to-end deep SAE-DNN model for predicting Egyptian buffalo calf sex, weight, and daily milk yield.

Tropical animal health and production
In the present study, a novel stacked Sparse Autoencoder-Deep Neural Network (SAE-DNN) learning prediction model was applied to predict calf sex, weight, and daily milk yield for dairy buffalo. First, SAE stage extracts the unique statistical feature...

Early diagnosis of transient ischemic attack facilitated by SERS-based artificial intelligence sensors.

Mikrochimica acta
Transient ischemic attack (TIA) serves as a critical early warning sign for ischemic stroke. Its timely identification holds significant clinical value in reducing recurrence risk and improving patient prognosis. However, existing detection methods e...

Metaheuristic-optimized generative adversarial network for enhanced sparse-view low-dose CT reconstruction.

Biomedical physics & engineering express
Sparse-view low-dose computed tomography (LDCT) imaging poses difficulties in preserving image quality while reducing radiation exposure. Recent research has focused extensively on artificial intelligence (AI) to reduce artifacts in LDCT. This paper ...

Directed Vectors for Generation of Independent Subspaces in the Bio-inpired Networks.

International journal of neural systems
Machine learning, deep learning and neural networks are extensively developed in many fields, with neural networks playing an important role in a wide variety of applications. However, a sufficient explanation of the structure and functionality of co...

A machine learning-driven early warning system for cryptocaryoniasis in marine aquaculture.

Parasites & vectors
BACKGROUND: Disease outbreaks, particularly cryptocaryoniasis caused by the ciliate Cryptocaryon irritans, pose significant barriers to sustainable marine fish aquaculture, undermining productivity, profitability, and biosecurity. Despite its impact,...

An interpretable geometric graph neural network for enhancing the generalizability of drug-target interaction prediction.

BMC biology
BACKGROUND: Accurate prediction of drug-target interactions (DTIs) is essential for advancing drug discovery. Although numerous computational methods have been proposed, many exhibit limited generalization, particularly when dealing with unseen drugs...

Detection of brain network abnormalities by graph invariants in Alzheimer's disease using MRI images.

Scientific reports
Alzheimer's disease is a major cause of dementia in older adults. It involves gradual changes in brain function that result in cognitive decline, affecting memory, reasoning, and executive skills. The accurate detection of structural abnormalities in...

LightMG-Net: an efficient lightweight deep neural network for multiclass grading of retinal detachment using handcrafted statistical mechanisms.

Scientific reports
Retinal detachment is a severely curable eye condition that becomes a genuine factor for the increased visual acuity worldwide. If neglected, it may result significant visual impairment in individuals aged 60 to 69 years. The successful cure percenta...

High-performance parallel multi-scale attention network with explainable AI for intelligent diagnosis of leaf diseases in agricultural systems.

Scientific reports
Detecting leaf diseases is crucial for ensuring crop health and boosting agricultural productivity. An advanced deep learning-based framework is introduced for cassava and groundnut leaf disease detection, incorporating a suite of innovative techniqu...

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, ...