Results 51 to 60 of about 35,527 (280)

ecGBMsub: an integrative stacking ensemble model framework based on eccDNA molecular profiling for improving IDH wild-type glioblastoma molecular subtype classification

Frontiers in Pharmacology
IDH wild-type glioblastoma (GBM) intrinsic subtypes have been linked to different molecular landscapes and outcomes. Accurate prediction of molecular subtypes of GBM is very important to guide clinical diagnosis and treatment. Leveraging machine learning
Zesheng Li, Cheng Wei, Zhenyu Zhang, Lei Han   +3 more
doaj   +1 more source

Stacking for machine learning redshifts applied to SDSS galaxies

, 2016
We present an analysis of a general machine learning technique called 'stacking' for the estimation of photometric redshifts. Stacking techniques can feed the photometric redshift estimate, as output by a base algorithm, back into the same algorithm as ...
Hoyle, Ben, Paech, Kerstin, Rau, Markus Michael, Seitz, Stella, Weller, Jochen, Zitlau, Roman   +5 more
core   +1 more source

Effective Approaches to Attention-based Neural Machine Translation [PDF]

, 2015
An attentional mechanism has lately been used to improve neural machine translation (NMT) by selectively focusing on parts of the source sentence during translation.
Luong, Minh-Thang, Manning, Christopher D., Pham, Hieu   +2 more
core   +3 more sources

In Materia Shaping of Randomness with a Standard Complementary Metal‐Oxide‐Semiconductor Transistor for Task‐Adaptive Entropy Generation

Advanced Functional Materials, EarlyView.
This study establishes a materials‐driven framework for entropy generation within standard CMOS technology. By electrically rebalancing gate‐oxide traps and Si‐channel defects in foundry‐fabricated FDSOI transistors, the work realizes in‐materia control of temporal correlation – achieving task adaptive entropy optimization for reinforcement learning ...
Been Kwak, Ryun‐Han Koo, Youngchan Cho, Changhyeon Han, Dongbin Kim, Soi Jeong, Yunho Shin, Joonhyeok Choi, Jiseong Im, Jonghyun Ko, Jong‐Ho Lee, Jangsaeng Kim, Youngho Kang, Wonjun Shin, Daewoong Kwon   +14 more
wiley   +1 more source

Tree-based Intelligent Intrusion Detection System in Internet of Vehicles

, 2019
The use of autonomous vehicles (AVs) is a promising technology in Intelligent Transportation Systems (ITSs) to improve safety and driving efficiency. Vehicle-to-everything (V2X) technology enables communication among vehicles and other infrastructures ...
Hamieh, Ismail, Moubayed, Abdallah, Shami, Abdallah, Yang, Li   +3 more
core   +1 more source

Comparative Analysis of Voting and Stacking Ensemble Learning for Heart Disease Prediction: A Machine Learning Approach

Jurnal Teknologi Informatika & Komputer
Heart disease remains a leading cause of mortality worldwide, necessitating the development of accurate predictive models for early diagnosis and intervention. This study investigates the effectiveness of ensemble learning approaches, particularly Voting
Gregorius Airlangga
doaj   +1 more source

Day-Ahead Forecast of Photovoltaic Power Based on a Novel Stacking Ensemble Method

IEEE Access, 2023
Accurate prediction of photovoltaic (PV) power is the prerequisite for the safe and stable operation of the power grid with high penetration of PV. Despite various machine learning models for forecasting PV power have been developed, their accuracies are
Luyao Liu, Qie Sun, Ronald Wennersten, Zhigang Chen   +3 more
doaj   +1 more source

Toward Scalable Solutions for Silver‐Based Gas Diffusion Electrode Fabrication for the Electrochemical Conversion of CO2 – A Perspective

Advanced Functional Materials, EarlyView.
In this study, the preparation techniques for silver‐based gas diffusion electrodes used for the electrochemical reduction of carbon dioxide (eCO2R) are systematically reviewed and compared with respect to their scalability. In addition, physics‐based and data‐driven modeling approaches are discussed, and a perspective is given on how modeling can aid ...
Simon Emken, K. Ashoke Raman, Alexander Bauer, Violeta Karyofylli, Eva Jodat, André Karl, Rüdiger‐A. Eichel   +6 more
wiley   +1 more source

A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics

, 2013
The combination of multiple classifiers using ensemble methods is increasingly important for making progress in a variety of difficult prediction problems. We present a comparative analysis of several ensemble methods through two case studies in genomics,
Pandey, Gaurav, Whalen, Sean
core   +1 more source

A novel improved model for building energy consumption prediction based on model integration [PDF]

, 2020
Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems.
Feng, W, Lu, S, Wang, R
core