AIMC Topic: Machine Learning

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Deep Neural Network Recognition of Rivet Joint Defects in Aircraft Products.

Sensors (Basel, Switzerland)
The mathematical statement of the problem of recognizing rivet joint defects in aircraft products is given. A computational method for the recognition of rivet joint defects in aircraft equipment based on video images of aircraft joints has been prop...

An integrated network representation of multiple cancer-specific data for graph-based machine learning.

NPJ systems biology and applications
Genomic profiles of cancer cells provide valuable information on genetic alterations in cancer. Several recent studies employed these data to predict the response of cancer cell lines to drug treatment. Nonetheless, due to the multifactorial phenotyp...

Classification of non-coding variants with high pathogenic impact.

PLoS genetics
Whole genome sequencing is increasingly used to diagnose medical conditions of genetic origin. While both coding and non-coding DNA variants contribute to a wide range of diseases, most patients who receive a WGS-based diagnosis today harbour a prote...

Machine Learning-Assisted Preoperative Diagnosis of Infection Stones in Urolithiasis Patients.

Journal of endourology
The decision-making of how to treat urinary infection stones was complicated by the difficulty in preoperative diagnosis of these stones. Hence, we developed machine learning (ML) models that can be leveraged to discriminate between infection and no...

An efficient rotational direction heap-based optimization with orthogonal structure for medical diagnosis.

Computers in biology and medicine
The heap-based optimizer (HBO) is an optimization method proposed in recent years that may face local stagnation problems and show slow convergence speed due to the lack of detailed analysis of optimal solutions and a comprehensive search. Therefore,...

MLCPP 2.0: An Updated Cell-penetrating Peptides and Their Uptake Efficiency Predictor.

Journal of molecular biology
Cell-penetrating peptides (CPPs) translocate into the cell as various biologically active conjugates and possess numerous biomedical applications. Several machine learning-based predictors have been proposed in the past, but they mostly focus on iden...

Materials Data toward Machine Learning: Advances and Challenges.

The journal of physical chemistry letters
Machine learning (ML) is believed to have enabled a paradigm shift in materials research, and in practice, ML has demonstrated its power in speeding up the cost-efficient discovery of new materials and autonomizing materials laboratories. In this Per...

Reconstruction of Nuclear Ensemble Approach Electronic Spectra Using Probabilistic Machine Learning.

Journal of chemical theory and computation
The theoretical prediction of molecular electronic spectra by means of quantum mechanical (QM) computations is fundamental to gain a deep insight into many photophysical and photochemical processes. A computational strategy that is attracting signifi...

Machine Learning for Detection of Muscular Activity from Surface EMG Signals.

Sensors (Basel, Switzerland)
BACKGROUND: Muscular-activity timing is useful information that is extractable from surface EMG signals (sEMG). However, a reference method is not available yet. The aim of this study is to investigate the reliability of a novel machine-learning-base...

Cyber Threat Intelligence-Based Malicious URL Detection Model Using Ensemble Learning.

Sensors (Basel, Switzerland)
Web applications have become ubiquitous for many business sectors due to their platform independence and low operation cost. Billions of users are visiting these applications to accomplish their daily tasks. However, many of these applications are ei...