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"Prediction and evaluation of energy and exergy efficiencies of a nanof" by Yuanlei Si, Frantisek Brumercik et al.

The Photovoltaic thermal (PVT) collector performance is numerically investigated considering the effect of using needle fins in the serpentine channel with Nanofluid (NF). The influence of increasing the nanoparticle concentration (ฯ†) and Reynolds number (Re) on the energy and exergy features of the PVT device is examined. A comparison is made between the hydrothermal characteristics of the PVT with the finned and plain serpentine channels. The utilization of needle fins improves the thermal ef...
Random Forest Machine Learning Eedle Fin Photovoltaic Thermal Andom Forest Technique Serpentine Channel
Source: uow.edu.au

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MyJournals.org - Science - 'Using random-forest multiple imputation to address bias of self-reported anthropometric measures, hypertension and hypercholesterolemia in the Belgian health interview survey' (BMC Medical Research Methodology)

MyJournals.org - Science - Using random-forest multiple imputation to address bias of self-reported anthropometric measures, hypertension and hypercholesterolemia in the Belgian health interview survey (BMC Medical Research Methodology)
Research Methodology Random Forest Self Reported

"Text classification based on machine learning for Tibetan social netwo" by Hui Lv, Fenfang Li et al.

Social network technologies have gained widespread attention in many fields. However, the research on Tibetan Social Network (TSN) is limited to the sentiment analysis of micro-blogs, and few researchers focus on text classification and data mining in TSN. It cannot meet the social needs of the majority of Tibetans and the text information they really care about. In this paper, we investigate and compare different models that we adopted for the classification of Tibetan text. Machine learning mo...
Tibetan Social Network Convolutional Neural Networks Naive Bayesian Random Forest Support Vector Machine Tibetan Social
Source: uow.edu.au

"SedimentNet — a 1D-CNN machine learning model for prediction of hydrod" by Muhammad Zain Bin Riaz, Umair Iqbal et al.

In natural free surface flows, sediment particles in the surface layer of a sediment bed are moved and entrained by the fluctuating hydrodynamic forces, such as lift and drag, exerted by the overlying flow. Accurate prediction of near-bed hydrodynamic forces in rapidly varied flows is vital for coastal sediment transport and morphodynamics. Directly measured hydrodynamic forces within the rapidly varied flows over rough bed layer have been limited by previous spatial averaging shear force studie...
Random Forest Multilayer Perceptron Support Vector Regressor Nearest Neighbour 1d Cnn Artificial Intelligence Ai
Source: uow.edu.au

"Multi-Layer Efficient Data Classification Methods for Enterprise Busin" by Yazeed Alzahrani, Jun Shen et al.

The efficient maintenance and classification of huge amounts of data is a big challenge for the websites which provide services for online businesses. Many of the websites provide multiple services for the customer. In the present work, we have compared various machine learning-based classification methods for the efficient distribution of data. To effectively categorize the data, the Enterprise Interface (El) layer is suggested between the application layer and the physical layer. Methods based...
Enterprise Interface Global Clustering Naive Bayesian Decision Tree Random Forest Support Vector Machine
Source: uow.edu.au

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