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Modeling the Relationship between Rice Yield and Climate Variables Using Statistical and Machine Learning Techniques
Published 2021-01-01“…Rainfall, temperature (minimum and maximum), evaporation, average wind speed (morning and evening), and sunshine hours are the climatic factors considered for modeling. Rice harvest and yield data over the last three decades and monthly climatic data were used to develop the prediction model by applying artificial neural networks (ANNs), support vector machine regression (SVMR), multiple linear regression (MLR), Gaussian process regression (GPR), power regression (PR), and robust regression (RR). …”
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Strength, Hydraulic, and Microstructural Characteristics of Expansive Soils Incorporating Marble Dust and Rice Husk Ash
Published 2021-01-01“…This study evaluates the strength and consolidation characteristics of expansive soils treated with marble dust (MD) and rice husk ash (RHA) through a multitude of laboratory tests, including consistency limits, compaction, uniaxial compression strength (UCS), and consolidation tests. …”
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Comparative Analysis of Gradient Descent Learning Algorithms in Artificial Neural Networks for Forecasting Indonesian Rice Prices
Published 2024-08-01“…Artificial Neural Networks (ANN) are a field of computer science that mimics the way the human brain processes data. …”
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Evaluating 28-Days Performance of Rice Husk Ash Green Concrete under Compression Gleaned from Neural Networks
Published 2023-01-01“…Cement, coarse aggregates, fine aggregates, water, rice husk ash, superplasticizer, and type of sample are used as input parameters to predict CS at 28 days. …”
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Green modification techniques for modulating the properties and starch digestibility of rich-polyphenol low-amylose Riceberry rice (Oryza sativa L.) flour
Published 2025-01-01“…Six green modification techniques, including annealing (ANN), heat moisture treatment (HMT), pregelatinization (Pregel), ultrasound (US), wet-microwave (WM), and dry-microwave (DM), were applied to modulate the properties of rich-polyphenol low-amylose rice flour. …”
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Setting of residue definitions and toxicological reference values for ethiprole
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Integrated Feature Selection of ARIMA with Computational Intelligence Approaches for Food Crop Price Prediction
Published 2018-01-01“…Because an autoregressive integrated moving average (ARIMA) can extract important self-predictor variables with future values that can be calculated, this study incorporates an ARIMA as the FSM for computational intelligence (CI) models to predict three major food crop (i.e., rice, wheat, and corn) prices. Other than the ARIMA, the components of the proposed integrated forecasting models include artificial neural networks (ANNs), support vector regression (SVR), and multivariate adaptive regression splines (MARS). …”
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Using an Artificial Neural Network to Validate and Predict the Physical Properties of Self-Compacting Concrete
Published 2022-01-01“…As a result, the goal of this study is to confirm the various possibilities of using an artificial neural network (ANN) to detect the features of SCC when Portland Pozzolana Cement (PPC) is partially substituted with biowaste such as Bagasse Ash (BA) and Rice Husk Ash (RHA) (RHA). …”
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An Artificial Neural Network Based Prediction of Mechanical and Durability Characteristics of Sustainable Geopolymer Composite
Published 2022-01-01“…This study experimentally investigates the effect of addition of different proportions (0%, 10%, and 20%) of rice husk ash (RHA) and polypropylene (PP) fibers (0%, 0.1%, and 0.3%) on the mechanical and durability characteristics of fly ash (FA)-based geopolymer mortars. …”
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Predicting Nanobinder-Improved Unsaturated Soil Consistency Limits Using Genetic Programming and Artificial Neural Networks
Published 2021-01-01“…Therefore, in this work, genetic programming (GP) and artificial neural network (ANN) have been used to predict the consistency limits, i.e., liquid limits, plastic limit, and plasticity index of unsaturated soil treated with a composite binder known as hybrid cement (HC) made from blending nanostructured quarry fines (NQF) and hydrated-lime-activated nanostructured rice husk ash (HANRHA). …”
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Advancing Horticultural Crop Loss Reduction Through Robotic and AI Technologies: Innovations, Applications, and Practical Implications
Published 2024-01-01“…For instance, Ji et al. in 2007 developed an artificial neural network (ANN)-based system for rice yield prediction in Fujian, China, improving accuracy over traditional models. …”
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Novel banana lectin CAR-T cells to target pancreatic tumors and tumor-associated stroma
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Grand Challenges at the Interface of Engineering and Medicine
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