AIMC Topic: Neural Networks, Computer

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Cytopathological quantification of NORs using artificial intelligence to oral cancer screening.

Brazilian oral research
Oral squamous cell carcinoma (OSCC) remains the most prevalent neoplasm of the head and neck. In recent decades, the incidence and prevalence of OSCC have not significantly changed, highlighting the critical need to develop and implement new risk ass...

OS-DETR: End-to-end brain tumor detection framework based on orthogonal channel shuffle networks.

PloS one
OrthoNets use the Gram-Schmidt process to achieve orthogonality among filters but do not impose constraints on the internal orthogonality of individual filters. To reduce the risk of overfitting, especially in scenarios with limited data such as medi...

iProtDNA-SMOTE: Enhancing protein-DNA binding sites prediction through imbalanced graph neural networks.

PloS one
Protein-DNA interactions play a crucial role in cellular biology, essential for maintaining life processes and regulating cellular functions. We propose a method called iProtDNA-SMOTE, which utilizes non-equilibrium graph neural networks along with p...

Accurate total consumer price index forecasting with data augmentation, multivariate features, and sentiment analysis: A case study in Korea.

PloS one
The Consumer Price Index (CPI) is a key economic indicator used by policymakers worldwide to monitor inflation and guide monetary policy decisions. In Korea, the CPI significantly impacts decisions on interest rates, fiscal policy frameworks, and the...

CCCNet: Criss-cross attention enhanced cross layer refinement network for lane detection in complex scenarios.

PloS one
Lane detection plays a crucial role in autonomous driving systems by enabling vehicles to comprehend road structure and ensure safe navigation. However, the current performance of lane line detection models, such as CCNet, exhibits limitations in han...

Deep learning-based fine-grained assessment of aneurysm wall characteristics using 4D-CT angiography.

PeerJ
PURPOSE: This study proposes a novel deep learning-based approach for aneurysm wall characteristics, including thin-walled (TW) and hyperplastic-remodeling (HR) regions.

Integrating temporal convolutional networks with metaheuristic optimization for accurate software defect prediction.

PloS one
The increasing importance of deep learning in software development has greatly improved software quality by enabling the efficient identification of defects, a persistent challenge throughout the software development lifecycle. This study seeks to de...

A super resolution generative adversarial networks and partition-based adaptive filtering technique for detect and remove flickers in digital color images.

PloS one
Eliminating flickering from digital images captured by cameras equipped with a rolling shutter is of paramount importance in computer vision applications. The ripple effect observed in an individual image is a consequence of the non-synchronized expo...

HCAP: Hybrid cyber attack prediction model for securing healthcare applications.

PloS one
The rapid development and integration of interconnected healthcare devices and communication networks within the Internet of Medical Things (IoMT) have significantly enhanced healthcare services. However, this growth has also introduced new vulnerabi...

DASNeRF: depth consistency optimization, adaptive sampling, and hierarchical structural fusion for sparse view neural radiance fields.

PloS one
To address the challenges of significant detail loss in Neural Radiance Fields (NeRF) under sparse-view input conditions, this paper proposes the DASNeRF framework. DASNeRF aims to generate high-detail novel views from a limited number of input viewp...