PythonFly ashArtificial neural networksPurpose\nUtilization of sustainable materials is a global demand in the construction industry. Hence, this study aims to integrate waste management and artificial intelligence by developing an artificial neural network (ANN) model to predict the compressive strength ...
Notably, the AR model was recommended due to its simplicity in terms of input parameters while maintaining reliability and accurate prediction. In another study, Kouadri et al. [28] used a machine learning model to predict the water quality index (WQI) in Illizi, Southeast Algeria, particularly...
They found that their model outperformed the other advanced parametric models used in the study. Further, Ma et al. [20] pointed out that accuracy is very important in short term traffic flow prediction. They proposed a 2-dimensional prediction method using Kalman filtering for historic traffic ...
and an ANN model. In order to determine the best and optimum machine learning model in predicting the aforementioned water quality parameters, the outcomes of the models were compared. This study also able to contribute in monitoring the water quality of Langat River as there are less to none ...
In this paper, we present the development, implementation and use of an artificial neural network (ANN) based flow law for a GrC15 alloy under high temperature thermomechanical solicitations. The flow law modeling by ANN shows a significant superiority in terms of model prediction quality compared...
15 BackdoorDM: A Comprehensive Benchmark for Backdoor Learning in Diffusion Model Weilin Lin, Nanjun Zhou, Yanyun Wang, Jianze Li, Hui Xiong, Li Liu 2025-02-19 arXiv …, 2025 https://github.com/linweiii/BackdoorDM http://arxiv.org/abs/2502.11798v1 16 BoT: Breaking Long Thought Pr...
This study primarily employs Grasshopper to conduct modeling and simulation while integrating external Python libraries to facilitate optimization. The experimental procedure entails three stages, as illustrated in Fig. 2: the creation of training data, training and testing of an ANN model, and multi-...
Short contigs, small plasmids, low quality assemblies, or merged metagenomic reads should be analyzed using Prodigal's algorithms for low quality/coverage assemblies (i.e. contigs <20,000 bp) and inclusion of partial gene prediction. If the low sequence quality option is selected, RGI uses Prodi...
Afterwards, SPSS and Python were used for the analysis. The significant factors affecting the quality performance of building construction projects in Rwanda were revealed and the model with best prediction was identified to be a feed forward neural network of one hidden layer and three hidden nodes...
Artificial neural networks,Predictive models,Data models,Cryptography,Stock markets,Programming profession,PythonThe stock market is one of the best channels for financial development that requires a high accuracy prediction of the trades. This subject needs some technical skills and experience to achieve ...