AIMC Topic: Machine Learning

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The efficacy of F-FDG-PET-based radiomic and deep-learning features using a machine-learning approach to predict the pathological risk subtypes of thymic epithelial tumors.

The British journal of radiology
OBJECTIVE: To examine whether the machine-learning approach using 18-fludeoxyglucose positron emission tomography (F-FDG-PET)-based radiomic and deep-learning features is useful for predicting the pathological risk subtypes of thymic epithelial tumor...

A Three-Stage Dynamic Assessment Framework for Industrial Control System Security Based on a Method of W-HMM.

Sensors (Basel, Switzerland)
Industrial control systems (ICS) are applied in many fields. Due to the development of cloud computing, artificial intelligence, and big data analysis inducing more cyberattacks, ICS always suffers from the risks. If the risks occur during system ope...

MFDroid: A Stacking Ensemble Learning Framework for Android Malware Detection.

Sensors (Basel, Switzerland)
As Android is a popular a mobile operating system, Android malware is on the rise, which poses a great threat to user privacy and security. Considering the poor detection effects of the single feature selection algorithm and the low detection efficie...

Partial Transfer Ensemble Learning Framework: A Method for Intelligent Diagnosis of Rotating Machinery Based on an Incomplete Source Domain.

Sensors (Basel, Switzerland)
Most cross-domain intelligent diagnosis approaches presume that the health states in training datasets are consistent with those in testing. However, it is usually difficult and expensive to collect samples under all failure states during the trainin...

Applications of multivariate analysis and unsupervised machine learning to ToF-SIMS images of organic, bioorganic, and biological systems.

Biointerphases
Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging offers a powerful, label-free method for exploring organic, bioorganic, and biological systems. The technique is capable of very high spatial resolution, while also producing an enormo...

Dark Web Data Classification Using Neural Network.

Computational intelligence and neuroscience
There are several issues associated with Dark Web Structural Patterns mining (including many redundant and irrelevant information), which increases the numerous types of cybercrime like illegal trade, forums, terrorist activity, and illegal online sh...

Research on Keyword Extraction Algorithm in English Text Based on Cluster Analysis.

Computational intelligence and neuroscience
How to facilitate users to quickly and accurately search for the text information they need is a current research hotspot. Text clustering can improve the efficiency of information search and is an effective text retrieval method. Keyword extraction ...

A new person-fit method based on machine learning in CDM in education.

The British journal of mathematical and statistical psychology
Cognitive diagnosis models have become popular in educational assessment and are used to provide more individualized feedback about a student's specific strengths and weaknesses than traditional total scores. However, if the testing data are contamin...

Artificial intelligence in spine care: current applications and future utility.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
PURPOSE: The field of artificial intelligence is ever growing and the applications of machine learning in spine care are continuously advancing. Given the advent of the intelligence-based spine care model, understanding the evolution of computation a...

Epidemiological predictive modeling: lessons learned from the Kuopio ischemic heart disease risk factor study.

Annals of epidemiology
PURPOSE: The use of predictive models in epidemiology is relatively narrow as most of the studies report results of traditional statistical models such as Linear, Logistic, or Cox regressions. In this study, a high-dimensional epidemiological cohort,...