AIMC Topic: Neural Networks, Computer

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Balanced Convolutional Neural Networks for Pneumoconiosis Detection.

International journal of environmental research and public health
Pneumoconiosis remains one of the most common and harmful occupational diseases in China, leading to huge economic losses to society with its high prevalence and costly treatment. Diagnosis of pneumoconiosis still strongly depends on the experience o...

Automated description of the mandible shape by deep learning.

International journal of computer assisted radiology and surgery
PURPOSE: The shape of the mandible has been analyzed in a variety of fields, whether to diagnose conditions like osteoporosis or osteomyelitis, in forensics, to estimate biological information such as age, gender, and race or in orthognathic surgery....

Predicting phenotypes from genetic, environment, management, and historical data using CNNs.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Convolutional Neural Networks (CNNs) can perform similarly or better than standard genomic prediction methods when sufficient genetic, environmental, and management data are provided. Predicting phenotypes from genetic (G), environmental (E), and man...

LwF-ECG: Learning-without-forgetting approach for electrocardiogram heartbeat classification based on memory with task selector.

Computers in biology and medicine
Most existing Electrocardiogram (ECG) classification methods assume that all arrhythmia classes are known during the training phase. In this paper, the problem of learning several successive tasks is addressed, where, in each new task, there are new ...

MedmeshCNN - Enabling meshcnn for medical surface models.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: MeshCNN is a recently proposed Deep Learning framework that drew attention due to its direct operation on irregular, non-uniform 3D meshes. It outperformed state-of-the-art methods in classification and segmentation tasks of...

Deep Convolutional Neural Networks Help Scoring Tandem Mass Spectrometry Data in Database-Searching Approaches.

Journal of proteome research
Spectrum annotation is a challenging task due to the presence of unexpected peptide fragmentation ions as well as the inaccuracy of the detectors of the spectrometers. We present a deep convolutional neural network, called Slider, which learns an opt...

Traffic Accident Data Generation Based on Improved Generative Adversarial Networks.

Sensors (Basel, Switzerland)
For urban traffic, traffic accidents are the most direct and serious risk to people's lives, and rapid recognition and warning of traffic accidents is an important remedy to reduce their harmful effects. However, research scholars are often confronte...

Optimization of a Sports Activity Development Model Using Artificial Intelligence under New Curriculum Reform.

International journal of environmental research and public health
The recent curriculum reform in China puts forward higher requirements for the development of physical education. In order to further improve students' physical quality and motor skills, the traditional model was improved to address the lack of accur...

Deep neural networks using a single neuron: folded-in-time architecture using feedback-modulated delay loops.

Nature communications
Deep neural networks are among the most widely applied machine learning tools showing outstanding performance in a broad range of tasks. We present a method for folding a deep neural network of arbitrary size into a single neuron with multiple time-d...

Identifying novel transcript biomarkers for hepatocellular carcinoma (HCC) using RNA-Seq datasets and machine learning.

BMC cancer
BACKGROUND: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death in the world owing to limitations in its prognosis. The current prognosis approaches include radiological examination and detection of serum biomarkers, however, ...