Results 41 to 50 of about 95,729 (269)

Makine Öğrenmesi Yaklaşımı Kullanılarak Tavuk Etinden İzole Edilen Listeria’ya Karşı Ozonun Antibakteriyel Etkisinin Tahmin Edilmesi

Turkish Journal of Agriculture: Food Science and Technology
Bu çalışmada, ozon gazının Listeria spp. (tavuk işletmeleri ve tavuk etlerinden izole edilen) üzerine antibakteriyel etkilerini tahmin etmek amacıyla %99.99 doğruluk oranına sahip bir XGBoost tabanlı tahmin modeli geliştirilmiştir. Makine öğrenimi süreci
Bülent Zorlugenç, Sema Atasever, Feyza Kıroğlu Zorlugenç   +2 more
doaj   +1 more source

Relative Humidity Prediction using XGBoost Machine Learning Model, Case Study: Bajgah Climatological Station, Iran [PDF]

مهندسی منابع آب
given the prevalence of available data for only these two parameters in many parts of the country, various scenarios involving these parameters were studied. The best scenario for predicting relative humidity was obtained using the XGBoost model.
Reza Piraei, Ali Mohammadi, Seied Hosein Afzali   +2 more
doaj   +1 more source

Compact Multi-Class Boosted Trees

, 2017
Gradient boosted decision trees are a popular machine learning technique, in part because of their ability to give good accuracy with small models. We describe two extensions to the standard tree boosting algorithm designed to increase this advantage ...
Colthurst, Thomas, Haykal, Salem, Hendry, Gilbert, Ponomareva, Natalia, Radpour, Soroush   +4 more
core   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Innovative Machine Learning Approach for Distinguishing Rheumatoid Arthritis and Osteoarthritis: Integrating Shapely Additive Explanations and Dendrograms

Journal of Scientific Innovation in Medicine
Background: Arthritis is a major healthcare issue and accurate diagnosis is important to treatment. Objective: The study aimed to identify and intuitively visualize feature importance of factors associated with osteoarthritis versus rheumatoid arthritis ...
Alexander A. Huang, Samuel Y. Huang
doaj   +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

Influence-Balanced XGBoost: Improving XGBoost for Imbalanced Data Using Influence Functions

IEEE Access
Decision tree boosting algorithms, such as XGBoost, have demonstrated superior predictive performance on tabular data for supervised learning compared to neural networks. However, recent studies on loss functions for imbalanced data have primarily focused on deep learning.
Akiyoshi Sutou, Jinfang Wang
openaire   +2 more sources

Precise Control of Drug Release in Machine Learning‐Designed Antibody‐Eluting Implants for Postoperative Scarring Inhibition in Glaucoma

Advanced Healthcare Materials, EarlyView.
We developed a micro‐sized, biocompatible implant for postoperative sustained delivery of anti‐fibrotic antibodies in glaucoma surgery. Machine learning‐guided optimization of polymer composition, implant geometry, and porosity enabled precise control of drug release.
Mengqi Qin, Wenbing Jiang, Kai Xin Thong, Zeynep Ulker, Brihitejas Patel, Cynthia Yu‐Wai‐Man   +5 more
wiley   +1 more source

Short-term Demand Forecasting for Online Car-hailing Services using Recurrent Neural Networks

, 2019
Short-term traffic flow prediction is one of the crucial issues in intelligent transportation system, which is an important part of smart cities. Accurate predictions can enable both the drivers and the passengers to make better decisions about their ...
Bahrak, Behnam, Mahini, Hamid, Nejadettehad, Alireza   +2 more
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