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2022

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Minimum Dataset And Metadata Guidelines For Soil-Test Correlation And Calibration Research, Nathan A. Slaton, Sarah E. Lyons, Deanna L. Osmond, Sylvie M. Brouder, Steve W. Culman, Gerson L. Drescher, Luciano C. Gatiboni, John Hoben, Peter J.A. Kleinman, Joshua M. Mcgrath, Robert O. Miller, Austin Pearce, Amy L. Shober, John T. Spargo, Jeff J. Volenec Jan 2022

Minimum Dataset And Metadata Guidelines For Soil-Test Correlation And Calibration Research, Nathan A. Slaton, Sarah E. Lyons, Deanna L. Osmond, Sylvie M. Brouder, Steve W. Culman, Gerson L. Drescher, Luciano C. Gatiboni, John Hoben, Peter J.A. Kleinman, Joshua M. Mcgrath, Robert O. Miller, Austin Pearce, Amy L. Shober, John T. Spargo, Jeff J. Volenec

Crop, Soil and Environmental Sciences Faculty Publications and Presentations

Soil-test correlation and calibration data are essential to modern agriculture, and their continued relevance is underscored by the expansion of precision farming and the persistence of sustainable soil management priorities. In support of transparent, science-based fertilizer recommendations, we seek to establish a core set of required and recommended information for soil-test P and K correlation and calibration studies, a minimum dataset, building on previous research. The Fertilizer Recommendation Support Tool (FRST) project team and collaborators are developing a national database that will support a soil-test-based nutrient management decision aid tool. The FRST team includes over 80 scientists from 37 land-grant …

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Big Data Machine Learning Using Apache Spark Mllib, Ziaul Hasan, Hong Jie Xing Hong Jie Xing, M. Idrees Magray M. Idrees Magray Jan 2022

Big Data Machine Learning Using Apache Spark Mllib, Ziaul Hasan, Hong Jie Xing Hong Jie Xing, M. Idrees Magray M. Idrees Magray

Mesopotamian Journal of Big Data

The examination local area has utilized man-made brainpower, and specifically machine learning, in various ways to change various unique and, surprisingly, heterogeneous data sources into excellent realities and information, offering driving capacities to exact example finding. In any case, utilizing machine learning strategies on enormous and convoluted datasets is computationally costly and utilizes a great deal of coherent and actual assets, including central processor, memory, and data record space.In the current study collected the review of different researchers from 2010 to 2022. As how much data produced consistently arrives at quintillions of bytes, it is turning out to be more …

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Big Data Distributed Support Vector Machine, Baby Nirmala, Raed Abueid, Munef Abdullah Ahmed Jan 2022

Big Data Distributed Support Vector Machine, Baby Nirmala, Raed Abueid, Munef Abdullah Ahmed

Mesopotamian Journal of Big Data

Data mining and machine learning (ML) methods are being used more than ever before in cyber security. The use of machine learning (ML) is one of the potential solutions that may be successful against zero day attacks, starting with the categorization of IP traffic and filtering harmful traffic for intrusion detection. In this field, certain published systematic reviews were taken into consideration. Contemporary systematic reviews may incorporate both older and more recent works in the topic of investigation. All of the papers we looked at were thus recent. Data from 2016 to 2021 were utilized in the study. Both security …

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Big Data Processing: A Review, Taufik Gusman, Mohammad Naeemullah, Adeeb Mansoor Qasim Jan 2022

Big Data Processing: A Review, Taufik Gusman, Mohammad Naeemullah, Adeeb Mansoor Qasim

Mesopotamian Journal of Big Data

The processing of "big data," which consists of very vast and complicated datasets, is a fast expanding area. It has been employed in a wide variety of industries and applications, from e-commerce to financial services to transportation, and it has the potential to revolutionise the way organisations functionand make decisions. In this work, we discuss the definitions, characteristics, and challenges of large data processing. We also talk about the ethics of using this technology and the prevalent tools and technologies used for processing large amounts of data. Finally, we consider how big data processing is expected to evolve in the …

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A Survey On Distributed Reinforcement Learning, Maroning Useng, Suleiman Abdulrahman Jan 2022

A Survey On Distributed Reinforcement Learning, Maroning Useng, Suleiman Abdulrahman

Mesopotamian Journal of Big Data

In many settings, reinforcement learning (RL) has proven to be an effective tool for tackling difficult decision-making challenges. Traditional RL algorithms, on the other hand, frequently hit walls when confronted with issues of a sufficiently great scale or complexity. Distributed reinforcement learning (DRL) is a new area of study that hopes to circumvent these restrictions by dividing the learning workload among several computers. In this work, we offer a thorough overview of DRL, discussing its history, difficulties, applications, evaluation, scalability, and outstanding issues. We classify DRL approaches and frameworks and examine their similarities and differences. We also highlight the difficulties …

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Using Neural Networks To Model Complex Mathematical Functions, Abdelfatah Kouidere, Mondher Damak Jan 2022

Using Neural Networks To Model Complex Mathematical Functions, Abdelfatah Kouidere, Mondher Damak

Mesopotamian Journal of Big Data

Accurately modeling highly complex and irregular mathematical functions like fractals, chaos, and turbulence poses longstanding challenges. Traditional physics-based approaches often fail due to analytic intractability and extreme sensitivity. In this work, we pioneer the usage of long short-term memory (LSTM) recurrent neural networks for learning representations of such complex mathematical functions. We train custom-designed deep LSTM architectures on functions including the Lorenz attractor, Mandelbrot set, and Mackey-Glass delay differential equation. The networks achieve excellent quantitative performance across critical evaluation metrics like mean squared error and R-squared. Qualitative visualizations also demonstrate highly precise function replication and generalization. Comparisons to polynomial regression …

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