H.3. Artificial Intelligence
Hamid Ghaffari; Hemmatollah Pirdashti; Mohammad Reza Kangavari; Sjoerd Boersma
Abstract
An intelligent growth chamber was designed in 2021 to model and optimize rice seedlings' growth. According to this, an experiment was implemented at Sari University of Agricultural Sciences and Natural Resources, Iran, in March, April, and May 2021. The model inputs included radiation, temperature, carbon ...
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An intelligent growth chamber was designed in 2021 to model and optimize rice seedlings' growth. According to this, an experiment was implemented at Sari University of Agricultural Sciences and Natural Resources, Iran, in March, April, and May 2021. The model inputs included radiation, temperature, carbon dioxide, and soil acidity. These growth factors were studied at ambient and incremental levels. The model outputs were seedlings' height, root length, chlorophyll content, CGR, RGR, the leaves number, and the shoot's dry weight. Rice seedlings' growth was modeled using LSTM neural networks and optimized by the Bayesian method. It concluded that the best parameter setting was at epoch=100, learning rate=0.001, and iteration number=500. The best performance during training was obtained when the validation RMSE=0.2884.
M. Ilbeygi; M.R. Kangavari
Abstract
The increasing use of unmanned aerial vehicles (UAVs) or drones in different civil and military operations has attracted attention of many researchers and science communities. One of the most notable challenges in this field is supervising and controlling a group or a team of UAVs by a single user. Thereupon, ...
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The increasing use of unmanned aerial vehicles (UAVs) or drones in different civil and military operations has attracted attention of many researchers and science communities. One of the most notable challenges in this field is supervising and controlling a group or a team of UAVs by a single user. Thereupon, we proposed a new intelligent adaptive interface (IAI) to overcome to this challenge. Our IAI not only is empowered by comprehensive IAI architecture but also has some notable features like: presenting single-display user interface for controlling UAV team, leveraging user cognitive model to deliver right information at the right time, supporting the user by system behavior explanation, guiding and helping the user to choose right decisions. Finally, we examined the IAI with contributing eleven volunteers and in three different scenarios. Obtained results have shown the power of the proposed IAI to reduce workload and to increase user's situation awareness level and as a result to promote mission completion percentage.
F.3.4. Applications
N. Ashrafi Payaman; M.R. Kangavari
Abstract
One solution to process and analysis of massive graphs is summarization. Generating a high quality summary is the main challenge of graph summarization. In the aims of generating a summary with a better quality for a given attributed graph, both structural and attribute similarities must be considered. ...
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One solution to process and analysis of massive graphs is summarization. Generating a high quality summary is the main challenge of graph summarization. In the aims of generating a summary with a better quality for a given attributed graph, both structural and attribute similarities must be considered. There are two measures named density and entropy to evaluate the quality of structural and attribute-based summaries respectively. For an attributed graph, a high quality summary is one that covers both graph structure and its attributes with the user-specified degrees of importance. Recently two methods has been proposed for summarizing a graph based on both graph structure and attribute similarities. In this paper, a new method for hybrid summarization of a given attributed graph has proposed and the quality of the summary generated by this method has compared with the recently proposed method for this purpose. Experimental results showed that our proposed method generates a summary with a better quality.
