AIMC Topic: Algorithms

Clear Filters Showing 15211 to 15220 of 28713 articles

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...

Skin cancer detection from dermoscopic images using deep learning and fuzzy k-means clustering.

Microscopy research and technique
Melanoma skin cancer is the most life-threatening and fatal disease among the family of skin cancer diseases. Modern technological developments and research methodologies made it possible to detect and identify this kind of skin cancer more effective...

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...

A Novel Unsupervised Machine Learning-Based Method for Chatter Detection in the Milling of Thin-Walled Parts.

Sensors (Basel, Switzerland)
Data-driven chatter detection techniques avoid complex physical modeling and provide the basis for industrial applications of cutting process monitoring. Among them, feature extraction is the key step of chatter detection, which can compensate for th...

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...

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, ...

Validation of a Whole Heart Segmentation from Computed Tomography Imaging Using a Deep-Learning Approach.

Journal of cardiovascular translational research
The aim of this study is to develop an automated deep-learning-based whole heart segmentation of ECG-gated computed tomography data. After 21 exclusions, CT acquired before transcatheter aortic valve implantation in 71 patients were reviewed and rand...

Gait Phase Estimation by Using LSTM in IMU-Based Gait Analysis-Proof of Concept.

Sensors (Basel, Switzerland)
Gait phase detection in IMU-based gait analysis has some limitations due to walking style variations and physical impairments of individuals. Therefore, available algorithms may not work properly when the gait data is noisy, or the person rarely reac...

Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer.

International journal of molecular sciences
Early identification of epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations is crucial for selecting a therapeutic strategy for patients with non-small-cell lung cancer (NSCLC). We proposed a machin...

Explainable machine-learning predictions for complications after pediatric congenital heart surgery.

Scientific reports
The quality of treatment and prognosis after pediatric congenital heart surgery remains unsatisfactory. A reliable prediction model for postoperative complications of congenital heart surgery patients is essential to enable prompt initiation of thera...