The development of the recommendatory resource for the adaptive learning of transport philosophy

Kotsyuba, Igor; Shikov, Alexey; Romanov, K. I.; Galperin, Mikhail; Kudriashov, Aleksandr; Zhukova, T. V.

doi:10.1016/j.trpro.2022.06.053

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…The learning rate is maintained for each network weight (parameter) and is adapted separately as learning progresses. The method calculates the individual adaptive learning rates (Pepper et al, 2022), (Kotsyuba et al, 2022) for different parameters of the first and second moment estimates of the gradient.…”

Section: Literature Reviewmentioning

confidence: 99%

Disease Classification on Rice Leaves using DenseNet121, DenseNet169, DenseNet201

Saputra

Hindarto

Santoso

2023

SinkrOn

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Rice is a plant that can grow in the tropics. This plant can produce food that can meet the needs of the people of a country. This plant can grow well if it is cared for properly. If the planting has used good care, such as providing adequate water, adding good fertilizer, it can be ascertained that it will produce a lot of rice fruit after harvesting. This often causes concern if rice growers have given good care but often produce less rice fruit because rice plants are attacked by various diseases. This is what makes the problem, that rice plants are attacked by diseases. Before spraying diseases or pests, farmers should have an understanding of diseases in rice. This makes farmers not wrong in choosing drugs for farmers' rice. It is very vulnerable if farmers do not know about the rice disease. Therefore, it is necessary to observe what types of rice diseases attack rice plants. Observations are not enough just to take pictures with a camera. But it is necessary to carry out further analysis of rice diseases. The presence of information technology is now able to recognize any type. One of the machine learning technologies is able to detect rice diseases. One of these branches of machine learning is deep learning. By using a dataset that focuses on rice disease, the model generated from deep learning training is able to detect rice disease. The purpose of this research is to predict disease in rice leaves using deep learning, namely DenseNet. Training using DenseNet, namely DenseNet121, DenseNet169 and DenseNet201. Accuracy using DenseNet121 reached 91.67%, DenseNet169 reached 90%, and DenseNet201 reached 88.33%. The model training time takes 24 seconds.

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Section: Literature Reviewmentioning

confidence: 99%

Disease Classification on Rice Leaves using DenseNet121, DenseNet169, DenseNet201

Saputra

Hindarto

Santoso

2023

SinkrOn

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“…These are resources where materials on the topic under study are accumulated, but they lack decision support functions during designing of an academic discipline [5].…”

Section: Introductionmentioning

confidence: 99%

The development of the application for support the intellectual analysis of texts of the humanities and socio-economic cycle

et al. 2023

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The article is devoted to the consideration of the problems of didactic design of teaching materials by a teacher of disciplines of the humanities and socio-economic cycle associated with the study of a significant amount of information from various sources, the need to select a number of literary sources taking into account their specifics for different educational tasks and types of educational activities, as well as ranking the most important didactic units for the formation of working programs of disciplines. The possibilities of using automation tools for the analysis of text materials are revealed, which will allow a teacher engaged in the didactic design of a particular discipline to conduct a syntactic analysis of methodological materials, significantly reduce the complexity of this process when developing, making changes and adapting courses of social and humanitarian orientation. As a result of the analysis of the subject area, the authors determined a list of functional requirements, based on which the results of the physical and logical design of the system were presented, including a database model, system architecture, as well as a deployment diagram using an object-oriented modeling language.

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The development of the recommendatory resource for the adaptive learning of transport philosophy

Cited by 2 publications

References 12 publications

Disease Classification on Rice Leaves using DenseNet121, DenseNet169, DenseNet201

Disease Classification on Rice Leaves using DenseNet121, DenseNet169, DenseNet201

The development of the application for support the intellectual analysis of texts of the humanities and socio-economic cycle

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