Results 51 to 60 of about 2,430,587 (138)

AI-Based energy consumption forecasting in smart homes using multi-Algorithm analysis across temporal resolutions

Results in Engineering
The rising demand for electricity necessitates the development of advanced predictive models to enable effective load management and promote sustainable energy usage.
Devanathan B, Jnana Varshitha K, Pavan Kumar L, Pavan Manelllore, Lakshmanan SA, Kayalvizhi S, Suyampulingam A   +6 more
doaj   +1 more source

Efficient seismic fragility analysis method utilizing ground motion clustering and probabilistic machine learning

, 2023
Machine learning (ML) techniques have been recently adopted in engineering practice to define the relationship between seismic intensity measure (IM) and structural damage measure (DM) based on a limited set of numerical simulations.
Feng D. -C., Parisi F., Wu G., Brunesi E., Ding J. -Y.   +4 more
core   +1 more source

Prediction of quality class in face milling of S355J2 steel using meta model based on cutting temperature and surface roughness data

Reviews on Advanced Materials Science
In this study, the effects of cutting parameters on surface roughness and cutting temperature in the face milling process of S355J2 steel were experimentally investigated under different cooling strategies (dry, wet, and air-assisted).
Akdulum Aslan, Siyambaş Yusuf, Ayhan Emre, Çakıroğlu Ramazan, Mavi Ahmet, Erturun İbrahim   +5 more
doaj   +1 more source

Coupling EUR prediction with fracturing optimization: An integrated machine learning framework for shale gas development

Unconventional Resources
As conventional hydrocarbon resources continue to deplete, shale gas has become a key driver of the global energy transition due to its production potential and economic viability. However, significant reservoir heterogeneity and the complex evolution of
Hongjian Chen, Feifei Fang, Pujun Long, Putian Yang, Wei Guo   +4 more
doaj   +1 more source

Integrative Mendelian Randomization and Transcriptome Analysis Unveil Dual Pathways in Prostate Cancer Etiology

International Journal of Clinical Practice, Volume 2025, Issue 1, 2025.
Background: Prostate cancer remains a significant global health challenge, influenced by both genetic and environmental factors. Despite extensive research, the specific metabolic pathways contributing to its pathogenesis are not fully understood. Methods: We obtained significant single nucleotide polymorphisms (SNPs) associated with 1400 metabolites ...
Dongdong Wang, Mingwei Zhan, Pengfei Liu, Yi Yu, Rijian Guan, Dawei Cui   +5 more
wiley   +1 more source

Quantifying Uncertainty in ML‐Derived Atmosphere Remote Sensing: Hourly Surface NO2 Estimation With GEMS

Geophysical Research Letters, Volume 51, Issue 18, 28 September 2024.
Abstract Accurate estimation of nitrogen dioxide (NO2) levels at high spatio‐temporal resolution is crucial for atmospheric research and public health assessments. This study introduces a novel machine learning (ML) framework enhanced with uncertainty quantification (UQ) to predict hourly NO2 concentrations over East Asia.
Qin He, Kai Qin, Jason Blake Cohen, Ding Li, Jhoon Kim   +4 more
wiley   +1 more source

Prediction Skill of Extended Range 2-m Maximum Air Temperature Probabilistic Forecasts Using Machine Learning Post-Processing Methods

, 2020
The extended range temperature prediction is of great importance for public health, energy and agriculture. The two machine learning methods, namely, the neural networks and natural gradient boosting (NGBoost), are applied to improve the prediction ...
Yan Ji, Xiefei Zhi, Ye Tian, Ting Peng, Luying Ji   +4 more
core   +1 more source

A novel two‐stage method to detect non‐technical losses in smart grids

IET Smart Cities, Volume 6, Issue 2, Page 96-111, June 2024.
A novel two‐step process is presented for detecting fraudulent NTLs in smart grids. The first step involves transforming the time‐series data with additional extracted features derived from the publicly available SGCC dataset. The features are extracted after identifying abrupt changes in electricity consumption patterns using the sum of finite ...
Sufian A. Badawi, Maen Takruri, Mahmood G. Al‐Bashayreh, Khouloud Salameh, Jumana Humam, Samar Assaf, Mohammad R. Aziz, Ameera Albadawi, Djamel Guessoum, Isam ElBadawi, Mohammad Al‐Hattab   +10 more
wiley   +1 more source

Algorithm-derived rules list of 45 rules extracted by the algorithm and used in the model with their relative weights.

, 2021
Algorithm-derived rules list of 45 rules extracted by the algorithm and used in the model with their relative weights.
Tobias Schaefer (11565004), Laura Kornhauser (11565001), Dmitry Lesnik (11564998), Clay Marsh (792947), Pierre-François D’Haese (11564992), Victor Finomore (11564995), Peter E. Konrad (11565007), Sally Hodder (11565010), Ali R. Rezai (5056154)   +8 more
core   +1 more source

Exploiting machine learning to tackle peculiar consumption of electricity in power grids: A step towards building green smart cities

IET Generation, Transmission &Distribution, Volume 18, Issue 3, Page 413-445, February 2024.
A comparative analysis of various machine learning algorithms on several data balancing techniques is performed to choose which classifier and balancing method produces the best classification results for the theft detection problem. Abstract The increasing demand for electricity in daily life highlights the need for Smart Cities (SC) to use energy ...
Arshid Ali, Laiq Khan, Nadeem Javaid, Muhammad Aslam, Abdulaziz Aldegheishem, Nabil Alrajeh   +5 more
wiley   +1 more source