AIMC Topic: Machine Learning

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CTTGAN: Traffic Data Synthesizing Scheme Based on Conditional GAN.

Sensors (Basel, Switzerland)
Most machine learning algorithms only have a good recognition rate on balanced datasets. However, in the field of malicious traffic identification, benign traffic on the network is far greater than malicious traffic, and the network traffic dataset i...

WLAN RSS-Based Fingerprinting for Indoor Localization: A Machine Learning Inspired Bag-of-Features Approach.

Sensors (Basel, Switzerland)
Location-based services have permeated Smart academic institutions, enhancing the quality of higher education. Position information of people and objects can predict different potential requirements and provide relevant services to meet those needs. ...

A Narrative Literature Review of Natural Language Processing Applied to the Occupational Exposome.

International journal of environmental research and public health
UNLABELLED: The evolution of the Exposome concept revolutionised the research in exposure assessment and epidemiology by introducing the need for a more holistic approach on the exploration of the relationship between the environment and disease. At ...

Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force.

Scientific reports
An intelligent sensing framework using Machine Learning (ML) and Deep Learning (DL) architectures to precisely quantify dielectrophoretic force invoked on microparticles in a textile electrode-based DEP sensing device is reported. The prediction accu...

A Generative Adversarial Network Based a Rolling Bearing Data Generation Method Towards Fault Diagnosis.

Computational intelligence and neuroscience
As a new generative model, the generative adversarial network (GAN) has great potential in the accuracy and efficiency of generating pseudoreal data. Nowadays, bearing fault diagnosis based on machine learning usually needs sufficient data. If enough...

When proxy-driven learning is no better than random: The consequences of representational incompleteness.

PloS one
Machine learning is widely used for personalisation, that is, to tune systems with the aim of adapting their behaviour to the responses of humans. This tuning relies on quantified features that capture the human actions, and also on objective functio...

Psychological improvement in Employee Productivity by Maintaining Attendance System using Machine Learning Behavior.

Journal of community psychology
For a very long time, researchers, educationists, practitioners, and psychologists have tried to conduct extensive research on employee productivity at the workplace. It was firmly believed that positive traits of the employees positively affect the ...

A unified parameter model based on machine learning for describing microbial transport in porous media.

The Science of the total environment
The transport and retention of microorganisms are typically described using attachment/detachment and straining/liberation models. However, the parameters in the models varied significantly, posing a significant challenge to describe microbial transp...

Predicting recurrence and recurrence-free survival in high-grade endometrial cancer using machine learning.

Journal of surgical oncology
OBJECTIVE: To develop machine-learning models to predict recurrence and time-to-recurrence in high-grade endometrial cancer (HGEC) following surgery and tailored adjuvant treatment.

Automated evaluation of rheumatoid arthritis from hand radiographs using Machine Learning and deep learning techniques.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
The aim and objectives of the study are as follows: (i) to implement automated patch-based classification of hand X-ray images using modified pre-trained convolutional neural network (CNN) models; (ii) to develop a customized CNN model for automated ...