AIMC Topic: Algorithms

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FP-GCN: Frequency Pyramid Graph Convolutional Network for Enhancing Pathological Gait Classification.

Sensors (Basel, Switzerland)
Gait, a manifestation of one's walking pattern, intricately reflects the harmonious interplay of various bodily systems, offering valuable insights into an individual's health status. However, the current study has shortcomings in the extraction of t...

Machine learning algorithms using national registry data to predict loss to follow-up during tuberculosis treatment.

BMC public health
BACKGROUND: Identifying patients at increased risk of loss to follow-up (LTFU) is key to developing strategies to optimize the clinical management of tuberculosis (TB). The use of national registry data in prediction models may be a useful tool to in...

Socio-demographic predictors of not having private dental insurance coverage: machine-learning algorithms may help identify the disadvantaged.

BMC public health
BACKGROUND: For accessing dental care in Canada, approximately 62% of the population has employment-based insurance, 6% have some publicly funded coverage, and 32% have to pay out-of pocket. Those with no insurance or public coverage find dental care...

Assessing the efficacy of 2D and 3D CNN algorithms in OCT-based glaucoma detection.

Scientific reports
Glaucoma is a progressive neurodegenerative disease characterized by the gradual degeneration of retinal ganglion cells, leading to irreversible blindness worldwide. Therefore, timely and accurate diagnosis of glaucoma is crucial, enabling early inte...

Assessment of image quality and impact of deep learning-based software in non-contrast head CT scans.

Scientific reports
In this retrospective study, we aimed to assess the objective and subjective image quality of different reconstruction techniques and a deep learning-based software on non-contrast head computed tomography (CT) images. In total, 152 adult head CT sca...

Improved differentiation of cavernous malformation and acute intraparenchymal hemorrhage on CT using an AI algorithm.

Scientific reports
This study aimed to evaluate the utility of an artificial intelligence (AI) algorithm in differentiating between cerebral cavernous malformation (CCM) and acute intraparenchymal hemorrhage (AIH) on brain computed tomography (CT). A retrospective, mul...

Integrating optical and electrical sensing with machine learning for advanced particle characterization.

Biomedical microdevices
Particle classification plays a crucial role in various scientific and technological applications, such as differentiating between bacteria and viruses in healthcare applications or identifying and classifying cancer cells. This technique requires ac...

Transferable deep generative modeling of intrinsically disordered protein conformations.

PLoS computational biology
Intrinsically disordered proteins have dynamic structures through which they play key biological roles. The elucidation of their conformational ensembles is a challenging problem requiring an integrated use of computational and experimental methods. ...

A hybrid feature weighted attention based deep learning approach for an intrusion detection system using the random forest algorithm.

PloS one
Due to the recent advances in the Internet and communication technologies, network systems and data have evolved rapidly. The emergence of new attacks jeopardizes network security and make it really challenging to detect intrusions. Multiple network ...

Optimized encoder-decoder cascaded deep convolutional network for leaf disease image segmentation.

Network (Bristol, England)
Nowadays, Deep Learning (DL) techniques are being used to automate the identification and diagnosis of plant diseases, thereby enhancing global food security and enabling non-experts to detect these diseases. Among many DL techniques, a Deep Encoder-...