Showing 1 - 19 results of 19 for search 'Anne Rice', query time: 0.09s Refine Results
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    Modeling the Relationship between Rice Yield and Climate Variables Using Statistical and Machine Learning Techniques by Lasini Wickramasinghe, Rukmal Weliwatta, Piyal Ekanayake, Jeevani Jayasinghe

    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 by Fazal E. Jalal, Sultani Mulk, Shazim Ali Memon, Babak Jamhiri, Ahsan Naseem

    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 by Rica Ramadana, Agus Perdana Windarto, Dedi Suhendro

    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 by Sharanjit Singh, Harish Chandra Arora, Aman Kumar, Nishant Raj Kapoor, Kennedy C. Onyelowe, Krishna Kumar, Hardeep Singh Rai

    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 by Tai Van Ngo, Naphatrapi Luangsakul

    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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    Protocol and statistical analysis plan for the PREventing cardiovascular collaPse with Administration of fluid REsuscitation during Induction and Intubation (PREPARE II) randomised... by Li Wang, Tamer Hudali, Shekhar Ghamande, Megan Moore, Bruno Pereira, Christopher J Lindsell, Wesley H Self, Todd W Rice, Matthew W Semler, Kate O’Connor, David B Page, Victor E Ortega, Aaron M Joffe, Swati Gulati, G Patel, Heath D White, Muhammad Ali, Jonathan D Casey, David R Janz, Derek W Russell, Derek J Vonderhaar, Kevin M Dischert, William S Stigler, Emily Adams, Kevin W Gibbs, James M Dargin, A M Joffe, Akram Khan, Simanta Dutta, Janna S Landsperger, Sarah W Robison, Itay Bentov, Joanne M Wozniak, Susan Stempek, Olivia F Krol, Matthew E Prekker, Brian E Driver, Joseph M Brewer, Christopher John Lindsell, David Janz, Stephen P Peters, Rita N Bakhru, Scott Bauer, Christina R Bellinger, Amanda M Brown, Blair Brown, Jerri Brown, Caitlin Bumgarner, Wendy Butcher, Megan Caudle, Arjun B Chatterjee, David J Chodos, Gerardo Corcino, Nathan S Cutler, Travis L Dotson, Daniel C Files, Jonathan L Forbes, John P Gaillard, Katherine A Gershner, Shannon Ginty, Kiadrick R Hood, April Hazelwood, Katherine Hendricks, Kelly Jacobus, Jonathan T Jaffe, Stacy Kay, Chad A Kloefkorn, Jennifer Krall, Margo T Lannan, Cornelia Lane, Cynthia Lanning, Jessica Lyons, William I Mariencheck, Chad R Marion, Matthew A Maslonka, Sara McClintock, Nathaniel M Meier, Matthew C Miles, Peter J Miller, Sophia Mitchell, Wendy C Moore, Katherine Moss, Andrew M Namen, Dustin L Norton, Stella B Ogake, Jill A Ohar, Jessica A Palakshappa, Rodolfo M Pascual, Sandi Pascual, Aaron Pickens, Adam R Schertz, Matt Strong, Alexander O Sy, Braghadheeswar Thyagarajan, Amy Townsend, Russell Worthen, Michael Wlodarski, Charles Yarbrough, Caroline York, Bradley Lloyd, James Dargin, Joanne Wozniak, Christopher Adler, Ahmed Agameya, Michael Colancecco, Daniel Fitelson, Joshua Giaccotto, Gena Han, Louise Kane, Ezra Miller, Timothy Noland, Jaqueline Price, Joseph Plourde, Fraser Mackay, Laura Mahoney, Avignat Patel, Michael Plourde, Zena Saadeh, Sara Shadchehr, Sandeep Somalaraju, Eleanor Summerhill, Ryan Webster, Jordan Winnicki, Ekaterina Yavarovich, Sheetal Gandotra, Anna Altz-Stamm, Cristina Bardita, Mary Clay Boone, Joe W Chiles, Kristina Collins, Abby Drescher, Kevin G Dsouza, Janna Dunn, Stacy Ejem, Josh Gautney, Nicole Harris, Savannah Herder, R Chad Wade, Rutwij Joshi, Daniel Kelmenson, Anne Merrill Mason, Scott R Merriman, Takudzwa Mkorombindo, Jada Nowak, D Sheylan, Lisa Sarratt, Tabitha Stewart, Kadambari Vijaykumar, Gina White, Micah R Whitson, Christopher Barnes, Andrew M Walters, Alejandro C Arroliga, Tasnim Lat, Stephanie Nonas, Milad K Jouzestani, Raya Adi, Chandani Anandkat, Hanae Benchbani, Matthew G Drake, Makrina N Kamel, Ramanpreet Randhawa, Jessica L Tsui

    Published 2020-09-01
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    Integrated Feature Selection of ARIMA with Computational Intelligence Approaches for Food Crop Price Prediction by Yuehjen E. Shao, Jun-Ting Dai

    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 by K. Thirumalai Raja, N. Jayanthi, Jule Leta Tesfaye, N. Nagaprasad, R. Krishnaraj, V. S. Kaushik

    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 by P. Manikandan, K. Selija, V. Vasugi, V. Prem Kumar, L. Natrayan, M. Helen Santhi, G. Senthil Kumaran

    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 by Ahmed M. Ebid, Light I. Nwobia, Kennedy C. Onyelowe, Frank I. Aneke

    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 by H. W. Gammanpila, M. A. Nethmini Sashika, S. V. G. N. Priyadarshani

    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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