Optimizing Guidance Management through AI for Agricultural Ground and its analysis

The paper introduces "guidance managemet system and it is a completely unique education framework that synergistically combines the guidance management systems and management system-primarily based semi-supervised analysing. Specifically, it integrates the sturdy semi-supervised gaining knowled...

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Bibliographic Details
Published in:	2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) pp. 1080 - 1084
Main Authors:	Tewari, Ramanuj, Qasim, Zahraa Weaam, Amir, Nizar Abdel, Al-Fatlawy, Ramy Riad, Hafid, Athraa, Mezaal, Ali Abdulkareem
Format:	Conference Proceeding
Language:	English
Published:	IEEE 14-05-2024
Subjects:	Agriculture Knowledge engineering Label Propagation Long short term memory management system Construction management system-Regularized Objective Memory management Multi-Label Classification Neural management system Machines Performance evaluation Predictive models Semi-Supervised Learning Stochastic Gradient Descent Stochastic processes Training
Online Access:	Get full text
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Summary:	The paper introduces "guidance managemet system and it is a completely unique education framework that synergistically combines the guidance management systems and management system-primarily based semi-supervised analysing. Specifically, it integrates the sturdy semi-supervised gaining knowledge of method of label propagation with the effective predictive abilities of guidance management systems during various architecture sand agricultural works collectively with feed-in advance networks, convolutional guidance management systems (CNNs), and prolonged quick-term memory (LSTM) recurrent guidance management systems. This integration helps using both labelled and unlabelled facts, enhancing the model's training overall performance and predictive ordinary overall performance. The framework employs a management system-regularised purpose that encourages similar hidden representations for neighbouring nodes, thereby improving generalization and allowing scalable training thru stochastic gradient descent. This approach is especially exceptional in scenarios wherein categorised facts is scarce, making it a robust device for large-scale and complex datasets wherein traditional techniques falter.
DOI:	10.1109/ICACITE60783.2024.10617418