Results 301 to 310 of about 524,719 (351)

Machine learning framework for intelligent aeration control in wastewater treatment plants: Automatic feature engineering based on variation sliding layer.

Water Research, 2023
Intelligent control of wastewater treatment plants (WWTPs) has the potential to reduce energy consumption and greenhouse gas emissions significantly. Machine learning (ML) provides a promising solution to handle the increasing amount and complexity of ...
Yu-Qi Wang, Hong-cheng Wang, Yun-Peng Song, Shi-Qing Zhou, Qiu Li, Bin Liang, Wen-Zong Liu, Yi-Wei Zhao, Ai-Jie Wang   +8 more
semanticscholar   +1 more source

LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers

arXiv.org
Automated feature engineering plays a critical role in improving predictive model performance for tabular learning tasks. Traditional automated feature engineering methods are limited by their reliance on pre-defined transformations within fixed ...
Nikhil Abhyankar, P. Shojaee, Chandan K. Reddy   +2 more
semanticscholar   +1 more source

Enhancing Physics-Informed Neural Networks Through Feature Engineering

Trans. Mach. Learn. Res.
Physics-Informed Neural Networks (PINNs) seek to solve partial differential equations (PDEs) with deep learning. Mainstream approaches that deploy fully-connected multi-layer deep learning architectures require prolonged training to achieve even moderate
Shaghayegh Fazliani, Zachary Frangella, Madeleine Udell   +2 more
semanticscholar   +1 more source

Engineering mechanoreceptor feature selectivity

Neuron, 2023
Touch and proprioception rely on the discriminative abilities of distinct classes of mechanosensory neurons. In this issue of Neuron, two studies1,2 provide evidence that biomechanical mechanisms and ultrastructural cellular specializations are key contributors in defining mechanoreceptor stimulus threshold and selectivity.
openaire   +2 more sources

Intelligent Feature Engineering for Cybersecurity

2019 IEEE International Conference on Big Data (Big Data), 2019
Feature engineering and selection is a critical step in the implementation of any machine learning system. In application areas such as intrusion detection for cybersecurity, this task is made more complicated by the diverse data types and ranges presented in both raw data packets and derived data fields.
Paul Maxwell, Elie Alhajjar, Nathaniel D. Bastian   +2 more
openaire   +1 more source

Deep Residual Principal Component Analysis as Feature Engineering for Industrial Data Analytics

IEEE Transactions on Instrumentation and Measurement
As an efficient feature engineering tool, principal component analysis (PCA) has been widely used for feature extraction in the past decades. While a deep form of PCA has been developed for performance enhancement, the numerical computation problem may ...
Junhua Zheng, Zeyu Yang, Zhiqiang Ge
semanticscholar   +1 more source

Reverse engineering feature models

Proceedings of the 33rd International Conference on Software Engineering, 2011
Feature models describe the common and variable characteristics of a product line. Their advantages are well recognized in product line methods. Unfortunately, creating a feature model for an existing project is time-consuming and requires substantial effort from a modeler. We present procedures for reverse engineering feature models based on a crucial
Steven She, Rafael Lotufo, Thorsten Berger, Andrzej Wasowski, Krzysztof Czarnecki 0001   +4 more
openaire   +1 more source

Feature Selection and Feature Engineering

2019
Feature selection and engineering are important steps in a machine learning pipeline and involves all the techniques adopted to reduce their dimensionality. Most of the time, these steps come after cleaning the dataset.
Hisham El-Amir, Mahmoud Hamdy
openaire   +1 more source

Stock weighted average price prediction based on feature engineering and Lightgbm model

International Conference on Software Development for Enhancing Accessibility and Fighting Info-exclusion
Stock price prediction is essential yet challenging in financial markets, guiding investment decisions for stakeholders. While traditional methods rely on technical and fundamental analysis, the integration of big data and machine learning presents new ...
Hongyi Shui, Xinye Sha, Baizheng Chen, Jiajie Wu   +3 more
semanticscholar   +1 more source

Unified Feature Engineering for Detection of Malicious Entities in Blockchain Networks

IEEE Transactions on Information Forensics and Security
Blockchain technology has been integrated into a wide range of applications in various sectors, such as finance, supply chain, health, and governance.
Jeyakumar Samantha Tharani, Zhé Hóu, Eugene Yugarajah Andrew Charles, Punit Rathore, M. Palaniswami, V. Muthukkumarasamy   +5 more
semanticscholar   +1 more source