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Using Machine Learning Algorithms to Estimate the Compressive Property of High Strength Fiber Reinforced Concrete
Published 2022“…Using machine learning (ML) techniques, concrete properties prediction is an effective solution to conserve construction time and cost. …”
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State-of-the-art ensemble learning and unsupervised learning in fatigue crack recognition of glass fiber reinforced polyester composite (GFRP) using acoustic emission
Published 2023“…This study evaluates the damage progression on glass fiber reinforced polyester composite specimens using different approaches of machine learning. …”
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Investigation of machine learning models in predicting compressive strength for ultra-high-performance geopolymer concrete: A comparative study
Published 2025“…Overall, the dataset of 128 CS results was used to develop the machine learning (ML) models. …”
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Predicting the rutting parameters of nanosilica/waste denim fiber composite asphalt binders using the response surface methodology and machine learning methods
Published 2023“…The study conducts an extensive investigation using ML algorithms to accurately predict the multiple stress creep recovery (MSCR) rutting parameters for the base and modified asphalt binders. …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method
Published 2024thesis::doctoral thesis -
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Mitigation of Mach Zehnder modulator nonlinearity in millimeter wave radio over fiber system using digital predistortion
Published 2017“…The coefficient computation is performed using recursive prediction error method (RPEM) algorithm which shows a dominant spectral regrowth reduction and in-band distortion reduction with reduced complexity compared to the commonly used slow converging, least mean square algorithm. …”
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Parametric analysis of critical buckling in composite laminate structures under mechanical and thermal loads: a finite element and machine learning approach
Published 2024“…Subsequently, simulation data from numerous cases are utilized to identify the best parameter combination using machine learning algorithms. Various ML techniques such as linear regression, lasso regression, decision tree, random forest, and gradient boosting are employed to assess their accuracy in comparison with finite element results. …”
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Adaptive Mechanism for GA-NN to Enhance Prediction Model
Published 2015“…Data included in the model is MDF properties and its fiber characteristics. The focus of this study is the Multilayer Perceptron NN model, which is reliable to learn from seven inputs fed to the network to produce prediction of three targets. …”
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EstiCal: food calorie image recognition mobile application by using feature descriptor technique / Muhammad Asyraf Suhaimi
Published 2018“…The future work of the project can be done with additional features such as using a hybrid algorithm which is combining algorithms to improvise the feature descriptor or applying a machine learning technique to increase the efficiency of food recognition.…”
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Production and characterization of biochar derived from oil palm wastes, and optimization for zinc adsorption
Published 2015“…The incremental back propagation algorithm demonstrated the best results and which has been used as learning algorithm for ANN in combination with Genetic Algorithm in the optimization. …”
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Lambda-max criteria weight determination in an adaptive neuro-fuzzy inference system / Rosma Mohd Dom, Daud Mohamad and Ajab Bai Akbarally
Published 2012“…A neuro-fuzzy system is a fuzzy system that uses learning algorithms derived from or inspired by neural network theory to determine its parameters (fuzzy sets and fuzzy rules) by processing data samples. …”
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