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Random Forest Bagging and X‐Means Clustered Antipattern Detection from SQL Query Log for Accessing Secure Mobile Data
Published 2021“…During this process, the input patterns are categorized into different clusters. …”
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Sulfur oxidative coupling of methane process development and its modeling via machine learning
Published 2022“…The outcomes of the simulated process were used to design a data-driven modeling approach, based on machine learning methods, and to evaluate its interpolation accuracy. …”
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Modelling fatigue uncertainty by means of nonconstant variance neural networks
Published 2022“…First, we model the fatigue life of cover‐plated beams under constant amplitude loading, and then we model the relationship between random vibration velocity and equivalent stress in process pipework. The two case studies demonstrate that PNNs with nonconstant variance can model the distribution of the data while also considering the variability of both distribution parameters (mean and standard deviation). …”
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Distributed Tree-Based Machine Learning for Short-Term Load Forecasting With Apache Spark
Published 2021“…One thousand distribution transformers' real data from Spain for three years are used to demonstrate the performance of the proposed methodology with a trade-off between accuracy and processing time.…”
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An Empirical Analysis of NMT-Derived Interlingual Embeddings and Their Use in Parallel Sentence Identification
Published 2017“…</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Journal of Selected Topics in Signal Processing<br>License:<a href="https://creativecommons.org/licenses/by/4.0/" rel="noreferrer" target="_blank"> https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/jstsp.2017.2764273" target="_blank">https://dx.doi.org/10.1109/jstsp.2017.2764273</a></p>…”
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Nonlinear analysis of shell structures using image processing and machine learning
Published 2023“…The proposed approach can be significantly more efficient than training a machine learning algorithm using the raw numerical data. To evaluate the proposed method, two different structures are assessed where the training data is created using nonlinear finite element analysis. …”
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Deep learning-based marine big data fusion for ocean environment monitoring: Towards shape optimization and salient objects detection
Published 2023“…<h3>Objective</h3><p dir="ltr">During the last few years, underwater object detection and marine resource utilization have gained significant attention from researchers and become active research hotspots in underwater image processing and analysis domains. This research study presents a data fusion-based method for underwater salient object detection and ocean environment monitoring by utilizing a deep model.…”
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Assessing the risk of vibration-induced fatigue in process pipework using convolutional neural networks
Published 2025“…In contrast, vibration data can be efficiently collected using accelerometers and single-channel data loggers, providing a more feasible solution for initial screening. …”
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An efficient approach for textual data classification using deep learning
Published 2022“…This paper employs machine and deep learning techniques to classify textual data. Textual data contains much useless information that must be pre-processed. …”
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RDFFrames: knowledge graph access for machine learning tools
Published 2021“…Machine learning tools work on data in tabular format and process it using an imperative programming style, while SPARQL is declarative and has as its basic operation matching graph patterns to RDF triples. …”
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What do Neural Machine Translation Models Learn about Morphology?
Published 2017“…However, little is known about what these models learn about source and target languages during the training process. …”
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