A systematic review on imbalanced data challenges in machine learning: Applications and solutions
In machine learning, the data imbalance imposes challenges to perform data analytics in
almost all areas of real-world research. The raw primary data often suffers from the skewed …
almost all areas of real-world research. The raw primary data often suffers from the skewed …
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 …
Comparing different resampling methods in predicting students' performance using machine learning techniques
R Ghorbani, R Ghousi - IEEE access, 2020 - ieeexplore.ieee.org
In today's world, due to the advancement of technology, predicting the students' performance
is among the most beneficial and essential research topics. Data Mining is extremely helpful …
is among the most beneficial and essential research topics. Data Mining is extremely helpful …
A systematic literature review and meta-analysis on cross project defect prediction
S Hosseini, B Turhan… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
Background: Cross project defect prediction (CPDP) recently gained considerable attention,
yet there are no systematic efforts to analyse existing empirical evidence. Objective: To …
yet there are no systematic efforts to analyse existing empirical evidence. Objective: To …
Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks
Abstract The novel coronavirus 2019 (COVID-19) is a respiratory syndrome that resembles
pneumonia. The current diagnostic procedure of COVID-19 follows reverse-transcriptase …
pneumonia. The current diagnostic procedure of COVID-19 follows reverse-transcriptase …
An artificial intelligence life cycle: From conception to production
D De Silva, D Alahakoon - Patterns, 2022 - cell.com
This paper presents the" CDAC AI life cycle," a comprehensive life cycle for the design,
development, and deployment of artificial intelligence (AI) systems and solutions. It …
development, and deployment of artificial intelligence (AI) systems and solutions. It …
Benchmarking datasets for anomaly-based network intrusion detection: KDD CUP 99 alternatives
A Divekar, M Parekh, V Savla… - 2018 IEEE 3rd …, 2018 - ieeexplore.ieee.org
Machine Learning has been steadily gaining traction for its use in Anomaly-based Network
Intrusion Detection Systems (A-NIDS). Research into this domain is frequently performed …
Intrusion Detection Systems (A-NIDS). Research into this domain is frequently performed …
Review and analysis of peak tracking techniques for fiber Bragg grating sensors
D Tosi - Sensors, 2017 - mdpi.com
Fiber Bragg Grating (FBG) sensors are among the most popular elements for fiber optic
sensor networks used for the direct measurement of temperature and strain. Modern FBG …
sensor networks used for the direct measurement of temperature and strain. Modern FBG …
Artificial intelligence-based clinical decision support in pediatrics
Abstract Machine learning models may be integrated into clinical decision support (CDS)
systems to identify children at risk of specific diagnoses or clinical deterioration to provide …
systems to identify children at risk of specific diagnoses or clinical deterioration to provide …
Handling imbalanced medical image data: A deep-learning-based one-class classification approach
L Gao, L Zhang, C Liu, S Wu - Artificial intelligence in medicine, 2020 - Elsevier
In clinical settings, a lot of medical image datasets suffer from the imbalance problem which
hampers the detection of outliers (rare health care events), as most classification methods …
hampers the detection of outliers (rare health care events), as most classification methods …