AIMC Topic: Neural Networks, Computer

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Using Machine Learning and Optical Microscopy Image Analysis of Immunosensors Made on Plasmonic Substrates: Application to Detect the SARS-CoV-2 Virus.

ACS sensors
In this article, we introduce a diagnostic platform comprising an optical microscopy image analysis system coupled with machine learning. Its efficacy is demonstrated in detecting SARS-CoV-2 virus particles at concentrations as low as 1 PFU (plaque-f...

Organic Synaptic Transistors Based on a Semiconductor Heterojunction for Artificial Visual and Neuromorphic Functions.

Nano letters
Visual acuity is the ability of the biological retina to distinguish images. High-sensitivity image acquisition improves the quality of visual perception, making images more recognizable for the visual system. Therefore, developing synaptic phototran...

A Multimodal Pain Sentiment Analysis System Using Ensembled Deep Learning Approaches for IoT-Enabled Healthcare Framework.

Sensors (Basel, Switzerland)
This study introduces a multimodal sentiment analysis system to assess and recognize human pain sentiments within an Internet of Things (IoT)-enabled healthcare framework. This system integrates facial expressions and speech-audio recordings to evalu...

A predictive model for recurrence in patients with borderline ovarian tumor based on neural multi-task logistic regression.

BMC cancer
BACKGROUND: Effective management of patients with borderline ovarian tumor (BOT) requires the timely identification of those at a higher risk of recurrence. Artificial neural networks have been successfully used in many areas of clinical event predic...

A hybrid optimization-enhanced 1D-ResCNN framework for epileptic spike detection in scalp EEG signals.

Scientific reports
In order to detect epileptic spikes, this paper suggests a deep learning architecture that blends 1D residual convolutional neural networks (1D-ResCNN) with a hybrid optimization strategy. The Layer-wise Adaptive Moments (LAMB) and AdamW algorithms h...

Dense convolution-based attention network for Alzheimer's disease classification.

Scientific reports
Recently, deep learning-based medical image classification models have made substantial advancements. However, many existing models prioritize performance at the cost of efficiency, limiting their practicality in clinical use. Traditional Convolution...

Determining human resource management key indicators and their impact on organizational performance using deep reinforcement learning.

Scientific reports
Performance-related indicators are crucial for evaluating and forecasting performance, enhancing decision-making efficiency, and establishing sustainable growth strategies. They motivate individuals and organizations, increase transparency, and accur...

Stacked CNN-based multichannel attention networks for Alzheimer disease detection.

Scientific reports
Alzheimer's Disease (AD) is a progressive condition of a neurological brain disorder recognized by symptoms such as dementia, memory loss, alterations in behaviour, and impaired reasoning abilities. Recently, many researchers have been working to dev...

Efficient Neural Network Classification of Parkinson's Disease and Schizophrenia Using Resting-State EEG Data.

Brain topography
Timely identification of Parkinson's disease and schizophrenia is crucial for the effective management and enhancement of patients' quality of life. The utilization of electroencephalogram (EEG) monitoring applications has proven instrumental in diag...

An 8-point scale lung ultrasound scoring network fusing local detail and global features.

Scientific reports
Manual lung ultrasound (LUS) scoring is influenced by clinicians' subjective interpretation, leading to potential inconsistencies and misdiagnoses due to varying levels of experience. To improve monitoring of pulmonary ventilation and support early d...