AIMC Topic: Neural Networks, Computer

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Flemboda artificial intelligence: hybrid fuzzy-convolutional neural network for efficient chromosome abnormality classification.

Molecular genetics and genomics : MGG
Chromosomal abnormality detection is a fundamental task in clinical genetics, as accurate identification of structural and numerical defects is essential for reliable diagnosis and treatment planning. However, many existing learning-based approaches ...

Automated 3D segmentation of human vagus nerve fascicles and epineurium from micro-computed tomography images using anatomy-aware neural networks.

Journal of neural engineering
Objective.Precise segmentation and quantification of nerve morphology from imaging data are critical for designing effective and selective peripheral nerve stimulation (PNS) therapies. However, prior studies on nerve morphology segmentation suffer fr...

Implementation of reconfigurable logic-in memory in a cultured neuronal network with a crossbar structure.

Lab on a chip
The concept of logical neural networks, proposed by McCulloch and Pitts, along with Hebb's postulate of learning-specifically, spike-timing-dependent plasticity (STDP), has had a substantial influence on the development of brain-inspired computing re...

FetCAT: Cross-attention fusion of transformer-CNN architecture for fetal brain plane classification with explainability using motion-degraded MRI.

PloS one
Fetal brain magnetic resonance imaging (MRI) has been recognized as a vital diagnostic tool for identifying neurological anomalies during pregnancy. Accurate classification of fetal MRI planes is essential for effective prenatal neurological assessme...

Semantic code clone detection using hybrid intermediate representations and BiLSTM networks.

PloS one
Semantic code clone detection plays an essential role in software maintenance and quality assurance, as it helps uncover fragments of code that express the same logic even when their syntax has been altered or deliberately obfuscated. In this study, ...

Improving micromorphological analysis with CNN-based segmentation of flint/obsidian, bone and charcoal.

PloS one
The quantification and identification of components in archaeological micromorphology remain subjective and challenging, particularly for early-career researchers. To address this, we developed a deep learning tool for the automatic segmentation of t...

Improved one-dimensional residual network high-voltage DC diagnosis for high-precision fault identification.

PloS one
High-Voltage Direct Current (HVDC) transmission systems require fast and reliable fault diagnosis to ensure secure and stable operation. However, existing methods, including conventional Convolutional Neural Networks (CNNs), often suffer from limited...

Deep learning-based no-reference image quality assessment framework for Cryptosporidium spp. and Giardia spp.

PloS one
Image Quality Assessment (IQA) plays a critical role in image-based decision-making systems, especially in domains requiring high diagnostic precision. Effective feature information is a prerequisite for the high performance of machine learning metho...

Deep learning detection and classification of fungal and non-fungal calcifications on paranasal sinus CT imaging.

PloS one
This study aimed to develop and evaluate a deep learning algorithm for detecting and classifying intrasinus calcifications on paranasal sinus (PNS) computed tomography (CT) for the diagnosis of fungal sinusitis and differentiation of fungal and non-f...

Robot steering-angle prediction lightweight network based non-local attention and lane guidance.

PloS one
Predicting the steering angle of robots is a core challenge in autonomous navigation. This paper proposes a novel end-to-end prediction network that integrates non-local attention and lane line guidance mechanisms to significantly reduce computationa...