AIMC Topic: Machine Learning

Clear Filters Showing 18081 to 18090 of 34417 articles

Ensemble Learning Based on Policy Optimization Neural Networks for Capability Assessment.

Sensors (Basel, Switzerland)
Capability assessment plays a crucial role in the demonstration and construction of equipment. To improve the accuracy and stability of capability assessment, we study the neural network learning algorithms in the field of capability assessment and i...

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...

Uncovering cancer vulnerabilities by machine learning prediction of synthetic lethality.

Molecular cancer
BACKGROUND: Synthetic lethality describes a genetic interaction between two perturbations, leading to cell death, whereas neither event alone has a significant effect on cell viability. This concept can be exploited to specifically target tumor cells...

Hybrid Gradient Descent Grey Wolf Optimizer for Optimal Feature Selection.

BioMed research international
Feature selection is the process of decreasing the number of features in a dataset by removing redundant, irrelevant, and randomly class-corrected data features. By applying feature selection on large and highly dimensional datasets, the redundant fe...

Risk prediction of diabetic nephropathy using machine learning techniques: A pilot study with secondary data.

Diabetes & metabolic syndrome
AIMS: This research work presented a comparative study of machine learning (ML), including two objectives: (i) determination of the risk factors of diabetic nephropathy (DN) based on principal component analysis (PCA) via different cutoffs; (ii) pred...

Fused CNN-LSTM deep learning emotion recognition model using electroencephalography signals.

The International journal of neuroscience
The traditional machine learning-based emotion recognition models have shown effective performance for classifying Electroencephalography (EEG) based emotions. The different machine learning algorithms outperform the various EEG based emotion models...

In silico screening of ssDNA aptamer against Escherichia coli O157:H7: A machine learning and the Pseudo K-tuple nucleotide composition based approach.

Computational biology and chemistry
This study was planned to in silico screening of ssDNA aptamer against Escherichia coli O157:H7 by combination of machine learning and the PseKNC approach. For this, firstly a total numbers of 47 validated ssDNA aptamers as well as 498 random DNA seq...

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...

Artificial intelligence-based hybrid deep learning models for image classification: The first narrative review.

Computers in biology and medicine
BACKGROUND: Artificial intelligence (AI) has served humanity in many applications since its inception. Currently, it dominates the imaging field-in particular, image classification. The task of image classification became much easier with machine lea...

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 ...