Modeling, diagnostics, optimization, and control of internal combustion engines via modern machine learning techniques: A review and future directions
A critical review of the existing Internal Combustion Engine (ICE) modeling, optimization,
diagnosis, and control challenges and the promising state-of-the-art Machine Learning (ML) …
diagnosis, and control challenges and the promising state-of-the-art Machine Learning (ML) …
Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools
Background Brain cancer is a destructive and life-threatening disease that imposes
immense negative effects on patients' lives. Therefore, the detection of brain tumors at an …
immense negative effects on patients' lives. Therefore, the detection of brain tumors at an …
Scaffold-gs: Structured 3d gaussians for view-adaptive rendering
Neural rendering methods have significantly advanced photo-realistic 3D scene rendering
in various academic and industrial applications. The recent 3D Gaussian Splatting method …
in various academic and industrial applications. The recent 3D Gaussian Splatting method …
Personalized recommendation system based on collaborative filtering for IoT scenarios
Z Cui, X Xu, XUE Fei, X Cai, Y Cao… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
Recommendation technology is an important part of the Internet of Things (IoT) services,
which can provide better service for users and help users get information anytime …
which can provide better service for users and help users get information anytime …
Machine learning and deep learning in smart manufacturing: The smart grid paradigm
Industry 4.0 is the new industrial revolution. By connecting every machine and activity
through network sensors to the Internet, a huge amount of data is generated. Machine …
through network sensors to the Internet, a huge amount of data is generated. Machine …
Machine learning based workload prediction in cloud computing
As a widely used IT service, more and more companies shift their services to cloud
datacenters. It is important for cloud service providers (CSPs) to provide cloud service …
datacenters. It is important for cloud service providers (CSPs) to provide cloud service …
Unsupervised time-series representation learning with iterative bilinear temporal-spectral fusion
Unsupervised/self-supervised time series representation learning is a challenging problem
because of its complex dynamics and sparse annotations. Existing works mainly adopt the …
because of its complex dynamics and sparse annotations. Existing works mainly adopt the …
[HTML][HTML] How much can k-means be improved by using better initialization and repeats?
P Fränti, S Sieranoja - Pattern Recognition, 2019 - Elsevier
In this paper, we study what are the most important factors that deteriorate the performance
of the k-means algorithm, and how much this deterioration can be overcome either by using …
of the k-means algorithm, and how much this deterioration can be overcome either by using …
Development of a YOLO-V3-based model for detecting defects on steel strip surface
X Kou, S Liu, K Cheng, Y Qian - Measurement, 2021 - Elsevier
During steel strip production, mechanical forces and environmental factors cause surface
defects of the steel strip. Therefore, detection of such defects is key to the production of high …
defects of the steel strip. Therefore, detection of such defects is key to the production of high …
K-means properties on six clustering benchmark datasets
P Fränti, S Sieranoja - Applied intelligence, 2018 - Springer
This paper has two contributions. First, we introduce a clustering basic benchmark. Second,
we study the performance of k-means using this benchmark. Specifically, we measure how …
we study the performance of k-means using this benchmark. Specifically, we measure how …