An essential task in supplying the world's food requirement is soil analysis. It is the backbone of agriculture, particularly in developing nations like India, so if data mining methods are used to the fields, spe...
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ISBN:
(纸本)9798331527495
An essential task in supplying the world's food requirement is soil analysis. It is the backbone of agriculture, particularly in developing nations like India, so if data mining methods are used to the fields, specifically to the soils, the pledge-making scenario may be altered, improving cultivations in the process. A significant portion of the decisions made about the majority of problems pertaining to the sector of agriculture include soil analysis. In addition to highlighting several data mining methods and the corresponding work done by various authors in the context of soil analysis, the primary emphasis of this study is on the function that data mining plays in soil analysis in the agricultural area. These data mining methods are quite modern in the soil analysis domain. The study's advancedness lies in its use of sophisticated data mining tools to better understand the characteristics of the soil, its nutrient content, and possible production results. Improving soil management techniques is the primary goal, since it has a direct impact on the agricultural system's sustainability and production. This research makes use of extensive data from soil samples collected from various agricultural locations. Consequently, a number of data mining methods, including cluster analysis, principal component analysis (PCA), and decision trees, will be used in the present study to discover patterns and relationships between crop performance and soil composition. Important soil characteristics, such as pH, the amount of organic matter, nitrogen, and potassium, all affect crop health and productivity. The objective of this study is to identify' soil clusters,' or groups of related prevalent conditions that either support or constrain agricultural output. Additionally, PCA gave me insight into the relevance of soil characteristics that have an impact on crop performance, making them very helpful for focused interventions aimed at improving the soil. It constructed the decision
Big data is regarded as the secret to releasing the following massive surges of economic development. In light of a variety of new apps and platforms that are integrated into our everyday routines, such as smartphones...
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This paper proposes a new Gradient Boosting Component-decision (GBC-D) model, which combines the advantages of Markov Decision process (MDP), gradient lifting tree (GBT) and principal Component analysis (PCA). Applied...
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ISBN:
(数字)9798350389579
ISBN:
(纸本)9798350389586
This paper proposes a new Gradient Boosting Component-decision (GBC-D) model, which combines the advantages of Markov Decision process (MDP), gradient lifting tree (GBT) and principal Component analysis (PCA). Applied to big dataprocessing is based on machine learning in market trend prediction model research. The GBC-D model uses PCA for data reduction and feature extraction, which reduces the computation and preserves the main features of the data. MDP is used to describe the market state transition process and maximize the expected cumulative return by optimizing the decision sequence. GBT is introduced as a forecasting model to gradually improve the forecasting performance to predict market trends more accurately. Combining the advantages of the three methods, the model can effectively deal with the complexity and uncertainty in big data and improve the accuracy and stability of market trend prediction. In the experimental part, we compared the BGC-D with the traditional prediction model. The results show that GBC-D can reduce the prediction error in the complex and changeable market environment, capture the market dynamics more accurately, and improve the overall performance of the system. This fully validates the feasibility and effectiveness of GBC-D architecture in big dataprocessing market trend prediction.
In the current era of booming smart agriculture, chili cultivation is transitioning from traditional models to intelligent approaches. This study deeply analyzed numerous key factors influencing chili growth, includin...
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In developing countries, pavement management systems (PMS) face limitations such as insufficient data, budget constraints, and inadequate analysis tools. To address those limitations, simplified PMS are needed to incr...
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The field of laser-ion acceleration faces significant challenges in handling high-dimensional, computationally intensive problems, often constrained by budgets and available computational power. Reliably achieving hig...
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Brain activity differs according to the state of consciousness. Whole-brain models, typically based on functional magnetic resonance imaging (fMRI) data, provide valuable insight into these changes by utilizing struct...
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Transformer vibration analysis yields critical information with practical implications. Examining vibration variations throughout the aging process of transformers contributes significantly to understanding the rules ...
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Industrial control Systems (ICS) are paramount to the efficient operation of Critical National Infrastructure (CNI) ranging from electricity generation and distribution to manufacturing. However, the growing convergen...
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Industrial control Systems (ICS) are paramount to the efficient operation of Critical National Infrastructure (CNI) ranging from electricity generation and distribution to manufacturing. However, the growing convergence of ICS with Information Technology (IT) systems renders CNI vulnerable to a range of cyber threats. Graph neural networks are being increasingly used for anomaly detection by adding granularity to the detection process. In this paper, we present a comparative study of graph-based deep learning models for ICS anomaly detection. Through the evaluation of four models using three multivariate industrial datasets, we aim to discern the effectiveness of prediction and reconstruction-based graph models in the ICS domain. We investigate data reduction techniques to minimise features needed to represent the window size and examine the representation of sliding window in terms of feature size for time-series analysis. Additionally, we assess the impact of the length of a context window on anomaly detection performance. Our results show that using feature reduction techniques on a longer context window produces better results while having the computational advantages of a shorter window size. Graph autoencoder is the most resilient to feature size reduction by maintaining similar F1 and AUC-PR score regardless of the number of features used to represent a context window. The results also provide insight to the suitability of graph-based models in this domain and offer recommendations for their optimal usage, paving the way for enhanced security and resilience in ICS.
Leprosy, characterized by dermatological manifestations and peripheral nervous system impairment due to Mycobacterium leprae infection in Schwann cells, poses persistent challenges in treatment efficacy. Stem cell the...
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