Forward Propagation Neural Network Weighting Analysis as a Model Estimation Method

Zhangirov, T. R.; Krevchik, Angelina B.; Zuev, Daniel V.; Liss, A. A.; Ekalo, A.; Grigoryeva, Nataliya Yu.

doi:10.1109/neuront53022.2021.9472858

Cited by 1 publication

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the case of forward propagation, the data flow is unidirectional in the sense of the output direction.in addition, no feedback is available which make the accuracy measurements difficult as a way of evaluating the model [17].…”

Section: B Forward Propagationmentioning

confidence: 99%

Deep Learning for Combined Water Quality Testing and Crop Recommendation

Alkhudaydi¹,

Albalawi²,

Alanazi³

et al. 2023

IJACSA

View full text Add to dashboard Cite

The field of agriculture and its specifics has been gaining more attention nowadays due to the limited present resources and the continuously increasing need for food. In fact, agriculture has benefited greatly from the advancements of artificial intelligence, namely, Machine Learning (ML). In order to make the most of a crop field, one must initially plan on what crop is best for planting in this particular field, and whether it will provide the necessary yield. Additionally, it's very important to constantly monitor the quality of soil and water for irrigation of the selected crop. In this paper, we are going to rely on Machine Learning and data analysis to decide the type of crop that we will use, and the quality of soil and water. To do so, certain parameters must be taken into consideration. For choosing the crop, parameters such as sun exposure, humidity, soil pH, and soil moisture will be taken into consideration. On the other hand, water pH, electric conductivity, content of minerals such as chloride, calcium, and magnesium are among the parameters taken into consideration for water quality classification. After acquiring datasets for crop and water potability, we implemented a deep learning model in order to predict these two features. Upon training, our neural network model achieved 97% accuracy for crop recommendation and 96% for water quality prediction. However, the SVM model achieves 96% for crop recommendation and 92% for water quality prediction.

show abstract

Section: B Forward Propagationmentioning

confidence: 99%