AIMC Topic: Algorithms

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Multi-stage network for single image deblurring based on dual-domain window mamba.

Neural networks : the official journal of the International Neural Network Society
Multi-stage methods have been proven effective and widely used in image deblurring research. These methods, usually designed based on Convolutional Neural Networks (CNNs) or Vision Transformers (ViTs), have limitations, including the inability to cap...

Efficient and anti-interference plastic classification method suitable for one-shot learning based on laser induced breakdown spectroscopy.

Chemosphere
Efficient recycling of plastics is critical for environmental sustainability. In this work, an efficient and anti-interference method for plastic classification based on one-shot learning and laser-induced breakdown spectroscopy (LIBS) was proposed. ...

Machine Learning Interpretation of Optical Spectroscopy Using Peak-Sensitive Logistic Regression.

ACS nano
Optical spectroscopy, a noninvasive molecular sensing technique, offers valuable insights into material characterization, molecule identification, and biosample analysis. Despite the informativeness of high-dimensional optical spectra, their interpre...

Addressing Imbalanced Classification Problems in Drug Discovery and Development Using Random Forest, Support Vector Machine, AutoGluon-Tabular, and H2O AutoML.

Journal of chemical information and modeling
The classification models built on class imbalanced data sets tend to prioritize the accuracy of the majority class, and thus, the minority class generally has a higher misclassification rate. Different techniques are available to address the class i...

Sex classification accuracy through machine learning algorithms - morphometric variables of human ear and nose.

BMC research notes
OBJECTIVE: Sex determination is an important parameter for personal identification in forensic and medico-legal examinations. The study aims at predicting sex accuracy from different parameters of ear and nose by using a novel approach of Machine Lea...

Prediction of postoperative intensive care unit admission with artificial intelligence models in non-small cell lung carcinoma.

European journal of medical research
BACKGROUND: There is no standard practice for intensive care admission after non-small cell lung cancer surgery. In this study, we aimed to determine the need for intensive care admission after non-small cell lung cancer surgery with deep learning mo...

A deep learning approach for blood glucose monitoring and hypoglycemia prediction in glycogen storage disease.

Scientific reports
Glycogen storage disease (GSD) is a group of rare inherited metabolic disorders characterized by abnormal glycogen storage and breakdown. These disorders are caused by mutations in G6PC1, which is essential for proper glucose storage and metabolism. ...

Gated recurrent deep learning approaches to revolutionizing English language learning for personalized instruction and effective instruction.

Scientific reports
Communication is essential for success in today's world, making English language learning (ELL) a crucial skill. Innovative solutions are required to tackle complex language learning issues and meet the various demands of learners. Personalized learn...

An integrated approach of feature selection and machine learning for early detection of breast cancer.

Scientific reports
Breast cancer ranks among the most prevalent cancers in women globally, with its treatment efficacy heavily reliant on the early identification and diagnosis of the disease. The importance of early detection and diagnosis cannot be overstated in enha...

Accurate bladder cancer diagnosis using ensemble deep leaning.

Scientific reports
There are an estimated 1.3 million cases of cancer globally each year, making it one of the most serious types of urinary tract cancer. The methods used today for diagnosing and monitoring bladder cancer are intrusive, costly, and time-consuming. In ...