AIMC Topic: Neural Networks, Computer

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DeepIDA: Predicting Isoform-Disease Associations by Data Fusion and Deep Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Alternative splicing produces different isoforms from the same gene locus, it is an important mechanism for regulating gene expression and proteome diversity. Although the prediction of gene(ncRNA)-disease associations has been extensively studied, f...

Fuzzy Edge-Detection as a Preprocessing Layer in Deep Neural Networks for Guitar Classification.

Sensors (Basel, Switzerland)
Deep neural networks have demonstrated the capability of solving classification problems using hierarchical models, and fuzzy image preprocessing has proven to be efficient in handling uncertainty found in images. This paper presents the combination ...

DeepACSA: Automatic Segmentation of Cross-Sectional Area in Ultrasound Images of Lower Limb Muscles Using Deep Learning.

Medicine and science in sports and exercise
PURPOSE: Muscle anatomical cross-sectional area (ACSA) can be assessed using ultrasound and images are usually evaluated manually. Here, we present DeepACSA, a deep learning approach to automatically segment ACSA in panoramic ultrasound images of the...

Gumbel-Softmax based Neural Architecture Search for Hierarchical Brain Networks Decomposition.

Medical image analysis
Understanding the brain's functional architecture has been an important topic in the neuroimaging field. A variety of brain network modeling methods have been proposed. Recently, deep neural network-based methods have shown a great advantage in model...

LGLNN: Label Guided Graph Learning-Neural Network for few-shot learning.

Neural networks : the official journal of the International Neural Network Society
Graph Neural Networks (GNNs) have been employed for few-shot learning (FSL) tasks. The aim of GNN based FSL is to transform the few-shot learning problem into a graph node classification or edge labeling tasks, which can thus fully explore the relati...

Cyber-Threat Detection System Using a Hybrid Approach of Transfer Learning and Multi-Model Image Representation.

Sensors (Basel, Switzerland)
Currently, Android apps are easily targeted by malicious network traffic because of their constant network access. These threats have the potential to steal vital information and disrupt the commerce, social system, and banking markets. In this paper...

Water Quality Prediction Based on Multi-Task Learning.

International journal of environmental research and public health
Water pollution seriously endangers people's lives and restricts the sustainable development of the economy. Water quality prediction is essential for early warning and prevention of water pollution. However, the nonlinear characteristics of water qu...

A novel approach for COVID-19 Infection forecasting based on multi-source deep transfer learning.

Computers in biology and medicine
COVID-19 is a contagious disease; so, predicting its future infections in a provincial region requires the consideration of the related data (i.e., rates of infection, mortality and recovery, etc.) over a period of time. Clearly, the COVID-19 data of...

A learning-based, region of interest-tracking algorithm for catheter detection in echocardiography.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Echocardiography (echo) is gaining popularity to guide the catheter during surgical procedures. However, it is difficult to discern the catheter tip in echo even with an acoustically active catheter. An acoustically active catheter is detected for th...

Optimization assisted framework for thyroid detection and classification: A new ensemble technique.

Gene expression patterns : GEP
Ultrasound (US) is an inexpensive and non-invasive technique for capturing the image of the thyroid gland and nearby tissue. The classification and detection of thyroid disorders is still in its infant stage. This study aims to present a new thyroid ...