AIMC Topic: Neural Networks, Computer

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A predictive model developed to classify Leishmania promastigotes at two distinct life stages using MALDI-TOF mass spectrometry.

Archives of microbiology
Investigating the molecular differences between procyclic (non-infective) and metacyclic (infective) promastigotes is essential for understanding the Leishmania life cycle in the sandfly vector and may aid in identifying molecular markers specific to...

Motor unit number estimation based on convolutional neural network.

Journal of neural engineering
. The compound muscle action potential (CMAP) scan contains a muscle's detailed stimulus-activation information and thereby can be used for motor unit number estimation (MUNE). Due to the challenges in accurately obtaining the motor unit numbers from...

Learning and spiking dynamics in brain-like nanoscale networks.

Nanoscale horizons
Neuromorphic approaches to computation are driven by both the low-power operation of the biological brain and ever-increasing energy consumption of modern computing systems. Percolating networks of nanoparticles are promising candidates for self-asse...

CQ-CNN: A lightweight hybrid classical-quantum convolutional neural network for Alzheimer's disease detection using 3D structural brain MRI.

PloS one
The automatic detection of Alzheimer's disease (AD) using 3D volumetric MRI data is a complex, multi-domain challenge that has traditionally been addressed by training classical convolutional neural networks (CNNs). With the rise of quantum computing...

Harnessing interpretable novel combination of GloVe embedding with deep CNN-BiLSTM neural network for fake news detection.

PloS one
The important issue of fake news to society is how it affects how society runs in terms of decision-making and public perception. Hence, this study is a comparative analysis of innovative hybrid deep learning models and embedding techniques focusing ...

Analysis and optimization of student learning paths based on CRNN and sequential data.

PloS one
The analysis and optimization of student learning paths have become increasingly critical in modern education, as they enable personalized learning experiences and improved academic outcomes. However, existing approaches often struggle to effectively...

Image aesthetic quality assessment: A method based on deep convolutional capsule network.

PloS one
Image aesthetics assessment (IAA) has become a hot research area in recent years due to its extensive application potential. However, existing IAA methods often overlook the importance of spatial information in evaluating image aesthetics. To address...

Analysis and prediction of the axial compression properties of desert sand concrete with steel tube restraint based on an improved BP neural network model.

PloS one
Accurate analysis and prediction of axial compression are important for ensuring the construction quality and safety of desert sand recycled aggregate concrete confined by steel tubes. In this study, the axial compressive strength and elastic modulus...

Enhanced ribbon quality in roller compaction process by mitigating splitting through a machine-learning framework.

International journal of pharmaceutics
Ribbon splitting, a phenomenon that can occur during the roller compaction operation used in dry granulation processes, can lead to compromised granule uniformity, poor tabletability, and ultimately, off-specification tablet production. Despite its i...

Investigating the use of physics informed neural networks for dam-break scenarios.

PloS one
The real-time forecasting of flood dynamics is a long-standing challenge traditionally addressed through numerical solutions of the Shallow Water Equations (SWEs). Numerical solutions of realistic flow problems using numerical schemes are often hinde...