Improved Machine Learning for Multiclass Fault Classification in Industrial Processes by Khaled Dhibi (16891524)

Sulfur oxidative coupling of methane process development and its modeling via machine learning by Giovanni Scabbia (13751501)

“…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. …”

On the Use of Deep Learning for Video Classification by Atiq ur Rehman (14153391)

Regression estimation for continuous-time functional data processes with missing at random response by Mohamed Chaouch (17983846)

Optimal operation of reverse osmosis desalination process with deep reinforcement learning methods by Arash Golabi (21841484)

Optimizing malicious website prediction: An advanced XGBoost-based machine learning model by Sumaira Hussain (19259669)

Distributed Tree-Based Machine Learning for Short-Term Load Forecasting With Apache Spark by Ameema Zainab (16864263)

“…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.…”

An Empirical Analysis of NMT-Derived Interlingual Embeddings and Their Use in Parallel Sentence Identification by Cristina Espana-Bonet (19720063)

“…</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>…”

Nonlinear analysis of shell structures using image processing and machine learning by M.S. Nashed (16392961)

“…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. …”

Deep learning-based marine big data fusion for ocean environment monitoring: Towards shape optimization and salient objects detection by Sulaiman Khan (12585349)

Assessing the risk of vibration-induced fatigue in process pipework using convolutional neural networks by Ahmed Mohamed (628889)

“…In contrast, vibration data can be efficiently collected using accelerometers and single-channel data loggers, providing a more feasible solution for initial screening. …”

A Comparative Study of Contemporary Learning Paradigms in Bug Report Priority Detection by Eyüp Halit Yilmaz (21400700)

An efficient approach for textual data classification using deep learning by Abdullah Alqahtani (7128143)

“…This paper employs machine and deep learning techniques to classify textual data. Textual data contains much useless information that must be pre-processed. …”

Exploring the potential of onboard energy scavenging subsystems for generating valuable data by Raziq Yaqub (16488878)

RDFFrames: knowledge graph access for machine learning tools by Aisha Mohamed (5152970)

“…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. …”

What do Neural Machine Translation Models Learn about Morphology? by Yonatan Belinkov (18973897)

“…However, little is known about what these models learn about source and target languages during the training process. …”

Self-Supervised Learning Powered by Synthetic Data From Diffusion Models: Application to X-Ray Images by Abdullah Hosseini (22466602)

Collaborative Byzantine Resilient Federated Learning by A. Gouissem (17541396)

Faux-Data Injection Optimization for Accelerating Data-Driven Discovery of Materials by Abdul Wahab Ziaullah (16027076)

“…Among the mechanics of a BO is the use of a machine learning (ML) model that learns about the scope of the problem through data being acquired on the fly. …”

Decoding silent speech: a machine learning perspective on data, methods, and frameworks by Adiba Tabassum Chowdhury (19444792)

“…<p dir="ltr">At the nexus of signal processing and machine learning (ML), silent speech recognition (SSR) has evolved as a game-changing technology that allows for communication without audible voice. …”