Results 61 to 70 of about 102,701 (252)
Annals of Gastroenterological Surgery, EarlyView.ABSTRACT Aim Bile acids accumulation in hepatocytes causes liver damage and contributes to the development of hepatocellular carcinoma. However, the association between serum bile acid levels and postoperative intrahepatic recurrence in hepatocellular carcinoma remains unclear.
Tomoaki Bekki +9 more
wiley +1 more source
A Study Concerning Soft Computing Approaches for Stock Price Forecasting
Axioms, 2019 Financial time-series are well known for their non-linearity and non-stationarity nature. The application of conventional econometric models in prediction can incur significant errors.
Chao Shi, Xiaosheng Zhuang
doaj +1 more source
Advanced Intelligent Discovery, EarlyView.In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley +1 more source
Forecasting of Rice Harvest Results Using SVR Modeling Techniques
Jambura Journal of MathematicsForecasting is an activity that predicts future values {}{}by utilizing existing track record data. The object of this study is rice plants because they are the primary food source for the Indonesian people.
Devie Rosa Anamisa +7 more
doaj +1 more source
A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction
Advanced Intelligent Discovery, EarlyView.This study develops six machine learning models (k‐nearest neighbors, support vector regression, decision tree, random forest, CatBoost, and backpropagation neural network) to predict SiNx deposition rates in plasma‐enhanced chemical vapor deposition using hybrid production and simulation data.
Yuxuan Zhai +8 more
wiley +1 more source
Compositional optimization of hard-magnetic phases with machine-learning models
, 2018 Machine Learning (ML) plays an increasingly important role in the discovery and design of new materials. In this paper, we demonstrate the potential of ML for materials research using hard-magnetic phases as an illustrative case. We build kernel-based ML
Elsässer, Christian +4 more
core +1 more source
A Generalized Framework for Data‐Efficient and Extrapolative Materials Discovery for Gas Separation
Advanced Intelligent Discovery, EarlyView.This study introduces an iterative supervised machine learning framework for metal‐organic framework (MOF) discovery. The approach identifies over 97% of the best performing candidates while using less than 10% of available data. It generalizes across diverse MOF databases and gas separation scenarios.
Varad Daoo, Jayant K. Singh
wiley +1 more source
Generating Spatial Distribution and Forecasting the Rainfall by Suitable ML Models-A Case Study of Aiyar River Basin, Tiruchirappalli District [PDF]
MATEC Web of ConferencesRainfall plays a prominent role in managing of water resources. The accurate prediction of rainfall is the greatest challenge in the field of hydrologic studies. The prediction of rainfall is necessary to overcome natural disasters like flood and drought.
Natarajan Surendar +1 more
doaj +1 more source
Jambura Journal of Mathematics, 2020 Sulawesi Tengah memiliki tujuh bandara sebagai akses transportasi udara keluar atau masuk. Jumlah penumpang berangkat menggunakan transportasi udara melalui ketujuh bandara tersebut mengalami fluktuasi setiap bulannya.
Drajat Indra Purnama +1 more
doaj +1 more source
Which Surrogate Works for Empirical Performance Modelling? A Case Study with Differential Evolution
, 2019 It is not uncommon that meta-heuristic algorithms contain some intrinsic parameters, the optimal configuration of which is crucial for achieving their peak performance. However, evaluating the effectiveness of a configuration is expensive, as it involves
Li, Ke, Tan, Kay Chen, Xiang, Zilin
core +1 more source