AIMC Topic: Neural Networks, Computer

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MetaLabelNet: Learning to Generate Soft-Labels From Noisy-Labels.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Real-world datasets commonly have noisy labels, which negatively affects the performance of deep neural networks (DNNs). In order to address this problem, we propose a label noise robust learning algorithm, in which the base classifier is trained on ...

CM-SegNet: A deep learning-based automatic segmentation approach for medical images by combining convolution and multilayer perceptron.

Computers in biology and medicine
Accurate segmentation of lesions in medical images is of great significance for clinical diagnosis and evaluation. The low contrast between lesions and surrounding tissues increases the difficulty of automatic segmentation, while the efficiency of ma...

Intelligent temperature modeling in robotic cortical bone milling process based on teaching-learning-based optimization algorithm.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
Bone milling is one of the most important and sensitive biomechanical processes in the field of medical engineering. This process is used in orthopedic surgery, dentistry, treatment of fractures, and bone biopsy. The use of automatic numerical contro...

A Bearing Fault Classification Framework Based on Image Encoding Techniques and a Convolutional Neural Network under Different Operating Conditions.

Sensors (Basel, Switzerland)
Diagnostics of mechanical problems in manufacturing systems are essential to maintaining safety and minimizing expenditures. In this study, an intelligent fault classification model that combines a signal-to-image encoding technique and a convolution...

Machine Learning-Based Regression Framework to Predict Health Insurance Premiums.

International journal of environmental research and public health
Artificial intelligence (AI) and machine learning (ML) in healthcare are approaches to make people's lives easier by anticipating and diagnosing diseases more swiftly than most medical experts. There is a direct link between the insurer and the polic...

HunCRC: annotated pathological slides to enhance deep learning applications in colorectal cancer screening.

Scientific data
Histopathology is the gold standard method for staging and grading human tumors and provides critical information for the oncoteam's decision making. Highly-trained pathologists are needed for careful microscopic analysis of the slides produced from ...

Comparison of neural basis expansion analysis for interpretable time series (N-BEATS) and recurrent neural networks for heart dysfunction classification.

Physiological measurement
The primary purpose of this work is to analyze the ability of N-BEATS architecture for the problem of prediction and classification of electrocardiogram (ECG) signals. To achieve this, performance comparison with various types of other SotA (state-of...

Automatic ECG classification and label quality in training data.

Physiological measurement
Within the PhysioNet/Computing in Cardiology Challenge 2021, we focused on the design of a machine learning algorithm to identify cardiac abnormalities from electrocardiogram recordings (ECGs) with a various number of leads and to assess the diagnost...

Human Behavior Recognition in Outdoor Sports Based on the Local Error Model and Convolutional Neural Network.

Computational intelligence and neuroscience
With the rapid development of the Internet, various electronic products based on computer vision play an increasingly important role in people's daily lives. As one of the important topics of computer vision, human action recognition has become the m...

Sales Forecast of Marketing Brand Based on BP Neural Network Model.

Computational intelligence and neuroscience
With the advancement of globalization, the market competition among enterprises has become increasingly intense. To win a good market, an enterprise must understand and grasp the laws of the market economy and accordingly predict the future of the ma...