Proceedings ArticleDOI
A Study on Gradient Boosting Algorithms for Development of AI Monitoring and Prediction Systems
Norshakirah Aziz,Emelia Akashah Patah Akhir,Izzatdin Abdul Aziz,Jafreezal Jaafar,Mohd Hilmi Hasan,Ahmad Naufal Che Abas +5 more
TLDR
The proposed work reveals a novel framework called Artificial Intelligence Monitoring 4.0, which is capable of determining the current condition of equipment and provide a predicted mean time before failure occurs, and implemented to produce acceptable accuracy for the monitoring task.Abstract:
Data-driven predictive maintenance for the prediction of machine failure has been widely studied and performed to test machine failures. Predictive maintenance refers to the machine learning method, which utilizes data for identification of potential system malfunction and provides an alert when a system assessed to be prone to breakdown. The proposed work reveals a novel framework called Artificial Intelligence Monitoring 4.0 (AIM 4.0), which is capable of determining the current condition of equipment and provide a predicted mean time before failure occurs. AIM 4.0 utilizes three different ensemble machine learning methods, including Gradient Boost Machine (GBM), Light GBM, and XGBoost for prediction of machine failures. The machine learning methods stated are implemented to produce acceptable accuracy for the monitoring task as well as producing a prediction with a high confidence level.read more
Citations
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Journal ArticleDOI
Research of Machine Learning Algorithms for the Development of Intrusion Detection Systems in 5G Mobile Networks and Beyond
Azamat Imanbayev,Sakhybay Tynymbayev,Roman Odarchenko,Sergiy Gnatyuk,Rat Berdibayev,Alimzhan Baikenov,Nargiz Kaniyeva +6 more
TL;DR: In this paper , the authors proposed to implement an IDS (Intrusion Detection System) machine learning approach into the 5G core architecture to serve as part of the security architecture.
Posted ContentDOI
A Machine Learning-based Diagnosis and Prediction of Diabetes Mellitus Disease
TL;DR: In this paper , machine learning based classifiers are used to detect diabetes in India and Indian Demographic & Health Survey (2019-21) dataset is considered for the analysis. And the results show that the Random Forest has given the maximum classification accuracy, precision, recall, and area under the curve in comparison with other models.
Journal ArticleDOI
Influence of grey wolf optimization feature selection on gradient boosting machine learning techniques for accurate detection of liver tumor
Malik Mustafa Mohammad Jawarneh,J. L. Arias-Gonzáles,Dattatray P. Gandhmal,Rami Qays Malik,Kantilal P. Rane,Batyrkhan Omarov,Cosmena Mahapatra,Md. Shabaz +7 more
TL;DR: In this paper , the importance of feature selection on machine learning algorithms for the early and accurate diagnosis of liver tumors is discussed in a CT scan of the liver, where images are preprocessed by discrete wavelet transform.
Book ChapterDOI
Continuous Authentication of Tablet Users Using Raw Swipe Characteristics: Tree Based Approaches
TL;DR: In this paper , the authors proposed a tree-based framework for continuously authenticating a tablet's user using unprocessed swiping activity data from the user without requiring the data to be preprocessed or scaled and based on single user swipes.
References
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Proceedings Article
LightGBM: a highly efficient gradient boosting decision tree
TL;DR: It is proved that, since the data instances with larger gradients play a more important role in the computation of information gain, GOSS can obtain quite accurate estimation of the information gain with a much smaller data size, and is called LightGBM.
Journal ArticleDOI
A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring
TL;DR: A sequential ensemble credit scoring model based on a variant of gradient boosting machine (i.e., extreme gradient boosting (XGBoost) is proposed, which demonstrates that Bayesian hyper-parameter optimization performs better than random search, grid search, and manual search.
Journal ArticleDOI
Machine learning for email spam filtering: review, approaches and open research problems
Emmanuel Gbenga Dada,Joseph Stephen Bassi,Haruna Chiroma,Shafi’i Muhammad Abdulhamid,Adebayo Olusola Adetunmbi,Opeyemi Emmanuel Ajibuwa +5 more
TL;DR: A systematic review of some of the popular machine learning based email spam filtering approaches and recommended deep leaning and deep adversarial learning as the future techniques that can effectively handle the menace of spam emails.
Journal ArticleDOI
A review on pipeline integrity management utilizing in-line inspection data
Mingjiang Xie,Zhigang Tian +1 more
TL;DR: A comprehensive review on pipeline integrity management based on ILI data is provided, including physics-based models and data-driven methods for predicting defect growth for pipelines with different categories of defects.