Industrial artificial intelligence in industry 4.0-systematic review, challenges and outlook
The advent of the Industry 4.0 initiative has made it so that manufacturing environments are
becoming more and more dynamic, connected but also inherently more complex, with …
becoming more and more dynamic, connected but also inherently more complex, with …
A survey and analysis of intrusion detection models based on cse-cic-ids2018 big data
JL Leevy, TM Khoshgoftaar - Journal of Big Data, 2020 - Springer
The exponential growth in computer networks and network applications worldwide has been
matched by a surge in cyberattacks. For this reason, datasets such as CSE-CIC-IDS2018 …
matched by a surge in cyberattacks. For this reason, datasets such as CSE-CIC-IDS2018 …
Time series data augmentation for deep learning: A survey
Deep learning performs remarkably well on many time series analysis tasks recently. The
superior performance of deep neural networks relies heavily on a large number of training …
superior performance of deep neural networks relies heavily on a large number of training …
Class-imbalanced dynamic financial distress prediction based on Adaboost-SVM ensemble combined with SMOTE and time weighting
This paper focuses on how to effectively construct dynamic financial distress prediction
models based on class-imbalanced data streams. Two class-imbalanced dynamic financial …
models based on class-imbalanced data streams. Two class-imbalanced dynamic financial …
Accurate prediction of COVID-19 using chest X-ray images through deep feature learning model with SMOTE and machine learning classifiers
ABSTRACT According to the World Health Organization (WHO), the coronavirus (COVID-19)
pandemic is putting even the best healthcare systems across the world under tremendous …
pandemic is putting even the best healthcare systems across the world under tremendous …
Imutube: Automatic extraction of virtual on-body accelerometry from video for human activity recognition
The lack of large-scale, labeled data sets impedes progress in developing robust and
generalized predictive models for on-body sensor-based human activity recognition (HAR) …
generalized predictive models for on-body sensor-based human activity recognition (HAR) …
Data sampling methods to deal with the big data multi-class imbalance problem
The class imbalance problem has been a hot topic in the machine learning community in
recent years. Nowadays, in the time of big data and deep learning, this problem remains in …
recent years. Nowadays, in the time of big data and deep learning, this problem remains in …
Comparative evaluation of machine learning models for groundwater quality assessment
Contamination from pesticides and nitrate in groundwater is a significant threat to water
quality in general and agriculturally intensive regions in particular. Three widely used …
quality in general and agriculturally intensive regions in particular. Three widely used …
Ensemble deep learning models for heart disease classification: A case study from Mexico
Heart diseases are highly ranked among the leading causes of mortality in the world. They
have various types including vascular, ischemic, and hypertensive heart disease. A large …
have various types including vascular, ischemic, and hypertensive heart disease. A large …
Kernel density estimation based sampling for imbalanced class distribution
F Kamalov - Information Sciences, 2020 - Elsevier
Imbalanced response variable distribution is a common occurrence in data science. In fields
such as fraud detection, medical diagnostics, system intrusion detection and many others …
such as fraud detection, medical diagnostics, system intrusion detection and many others …