AIMC Topic: Algorithms

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Migrate demographic group for fair Graph Neural Networks.

Neural networks : the official journal of the International Neural Network Society
Graph Neural networks (GNNs) have been applied in many scenarios due to the superior performance of graph learning. However, fairness is always ignored when designing GNNs. As a consequence, biased information in training data can easily affect vanil...

A Self-Oscillated Organic Synapse for In-Memory Two-Factor Authentication.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Entering the era of AI 2.0, bio-inspired target recognition facilitates life. However, target recognition may suffer from some risks when the target is hijacked. Therefore, it is significantly important to provide an encryption process prior to neuro...

Estimation of wheat protein content and wet gluten content based on fusion of hyperspectral and RGB sensors using machine learning algorithms.

Food chemistry
The protein content (PC) and wet gluten content (WGC) are crucial indicators determining the quality of wheat, playing a pivotal role in evaluating processing and baking performance. Original reflectance (OR), wavelet feature (WF), and color index (C...

Optimizing time prediction and error classification in early melanoma detection using a hybrid RCNN-LSTM model.

Microscopy research and technique
Skin cancer is a terrifying disorder that affects all individuals. Due to the significant increase in the rate of melanoma skin cancer, early detection of skin cancer is now more critical than ever before. Malignant melanoma is one of the most seriou...

Artificial intelligence algorithm accurately assesses oestrogen receptor immunohistochemistry in metastatic breast cancer cytology specimens: A pilot study.

Cytopathology : official journal of the British Society for Clinical Cytology
OBJECTIVE: The Visiopharm artificial intelligence (AI) algorithm for oestrogen receptor (ER) immunohistochemistry (IHC) in whole slide images (WSIs) has been successfully validated in surgical pathology. This study aimed to assess its efficacy in cyt...

Constantly optimized mean teacher for semi-supervised 3D MRI image segmentation.

Medical & biological engineering & computing
The mean teacher model and its variants, as important methods in semi-supervised learning, have demonstrated promising performance in magnetic resonance imaging (MRI) data segmentation. However, the superior performance of teacher model through expon...

Machine learning methods in predicting the risk of malignant transformation of oral potentially malignant disorders: A systematic review.

International journal of medical informatics
BACKGROUND: Oral Potentially Malignant Disorders (OPMDs) refer to a heterogenous group of clinical presentations with heightened rate of malignant transformation. Identification of risk levels in OPMDs is crucial to determine the need for active inte...

Robust Deep Neural Network for Learning in Noisy Multi-Label Food Images.

Sensors (Basel, Switzerland)
Deep networks can facilitate the monitoring of a balanced diet to help prevent various health problems related to eating disorders. Large, diverse, and clean data are essential for learning these types of algorithms. Although data can be collected au...

Heart Rate Variability Monitoring Based on Doppler Radar Using Deep Learning.

Sensors (Basel, Switzerland)
The potential of microwave Doppler radar in non-contact vital sign detection is significant; however, prevailing radar-based heart rate (HR) and heart rate variability (HRV) monitoring technologies often necessitate data lengths surpassing 10 s, lead...

Color-CADx: a deep learning approach for colorectal cancer classification through triple convolutional neural networks and discrete cosine transform.

Scientific reports
Colorectal cancer (CRC) exhibits a significant death rate that consistently impacts human lives worldwide. Histopathological examination is the standard method for CRC diagnosis. However, it is complicated, time-consuming, and subjective. Computer-ai...