Results 51 to 60 of about 914,879 (290)

Retail Demand Forecasting Using Spatial-Temporal Gradient Boosting Methods [PDF]

, 2023
With the significant growth of the e-commerce business, the retail industry is experiencing rapid developments, leading to the explosion of the number of stock-keeping units (SKUs).
Lin, Junyi, Hedenstierna, Carl Philip T, Wang, Jiaxing, Chong, Woon Kian, Дудар, А. М., Жуков, С. О., Zhukov, S. O.   +6 more
core   +1 more source

REGRESSION-BASED PREDICTION OF ANXIETY SEVERITY [PDF]

Carpathian Journal of Electrical Engineering
The present study explores the use of machine learning to predict self-reported anxiety levels based on demographic, behavioral, and physiological data.
Bogdan CHIS, Diana DULF, Madalina MORAR, Eleonora POP   +3 more
doaj   +1 more source

Emerging experimental and computational methods for studying redox‐regulated structural transitions

FEBS Letters, EarlyView.
Redox reactions can reshape proteins and alter how they behave in cells, with important consequences for health and disease. This review explores emerging experimental and computational approaches for discovering these redox‐sensitive protein switches, revealing their structural effects, and predicting their behavior, opening new opportunities to ...
Tasneem Rass, Dana Reichmann, Gábor Erdős   +2 more
wiley   +1 more source

Strength Estimation and Feature Interaction of Carbon Nanotubes-Modified Concrete Using Artificial Intelligence-Based Boosting Ensembles

Buildings
The standard approach for testing ordinary concrete compressive strength (CS) is to cast samples and test them after different curing times. However, testing adds cost and time to projects, and, therefore, construction sites experience delays.
Fei Zhu, Xiangping Wu, Yijun Lu, Jiandong Huang   +3 more
doaj   +1 more source

QUIC Network Traffic Classification Using Ensemble Machine Learning Techniques

Applied Sciences, 2023
The Quick UDP Internet Connections (QUIC) protocol provides advantages over traditional TCP, but its encryption functionality reduces the visibility for operators into network traffic.
Sultan Almuhammadi, Abdullatif Alnajim, Mohammed Ayub   +2 more
doaj   +1 more source

Infinitesimal gradient boosting

Stochastic Processes and their Applications
We define infinitesimal gradient boosting as a limit of the popular tree-based gradient boosting algorithm from machine learning. The limit is considered in the vanishing-learning-rate asymptotic, that is when the learning rate tends to zero and the number of gradient trees is rescaled accordingly.
Dombry, Clément, Duchamps, Jean-Jil
openaire   +5 more sources

Gradient Regularized Budgeted Boosting

CoRR, 2019
As machine learning transitions increasingly towards real world applications controlling the test-time cost of algorithms becomes more and more crucial. Recent work, such as the Greedy Miser and Speedboost, incorporate test-time budget constraints into the training procedure and learn classifiers that provably stay within budget (in expectation ...
Zhixiang Eddie Xu, Matt J. Kusner, Kilian Q. Weinberger, Alice X. Zheng   +3 more
openaire   +2 more sources

Smoothing with Curvature Constraints based on Boosting Techniques [PDF]

, 2006
In many applications it is known that the underlying smooth function is constrained to have a specific form. In the present paper, we propose an estimation method based on the regression spline approach, which allows to include concavity or convexity ...
Gerhard Tutz, Leitenstorfer, Florian, Tutz, Gerhard, Florian Leitenstorfer   +3 more
core   +1 more source

Prospecting the protein design landscape

FEBS Letters, EarlyView.
This review outlines the current state of various protein design approaches. We discuss the current possibilities enabled by recently released tools, highlight future avenues to pursue in protein design, and underscore the crucial role of key databases and resources for successful protein design workflows.
Jakob R. Riccabona, Katharina T. Stonig, Jens Meiler, Clara T. Schoeder, Monica L. Fernández‐Quintero   +4 more
wiley   +1 more source

Ensemble machine learning models for ternary electrolyte viscosity prediction

Chemical Thermodynamics and Thermal Analysis
Accurate viscosity prediction in ternary electrolyte systems is essential for oil and gas drilling, geothermal operations, and brine processing. This comprehensive study compared three boosting-based ensemble machine learning algorithms - Gradient ...
Vinita Sangwan, Rashmi Bhardwaj, Andrés Soto-Bubert, Roberto Acevedo   +3 more
doaj   +1 more source