H.6.3.2. Feature evaluation and selection
Farhad Abedinzadeh Torghabeh; Yeganeh Modaresnia; Seyyed Abed Hosseini
Abstract
Various data analysis research has recently become necessary in to find and select relevant features without class labels using Unsupervised Feature Selection (UFS) approaches. Despite the fact that several open-source toolboxes provide feature selection techniques to reduce redundant features, data ...
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Various data analysis research has recently become necessary in to find and select relevant features without class labels using Unsupervised Feature Selection (UFS) approaches. Despite the fact that several open-source toolboxes provide feature selection techniques to reduce redundant features, data dimensionality, and computation costs, these approaches require programming knowledge, which limits their popularity and has not adequately addressed unlabeled real-world data. Automatic UFS Toolbox (Auto-UFSTool) for MATLAB, proposed in this study, is a user-friendly and fully-automatic toolbox that utilizes several UFS approaches from the most recent research. It is a collection of 25 robust UFS approaches, most of which were developed within the last five years. Therefore, a clear and systematic comparison of competing methods is feasible without requiring a single line of code. Even users without any previous programming experience may utilize the actual implementation by the Graphical User Interface (GUI). It also provides the opportunity to evaluate the feature selection results and generate graphs that facilitate the comparison of subsets of varying sizes. It is freely accessible in the MATLAB File Exchange repository and includes scripts and source code for each technique. The link to this toolbox is freely available to the general public on: bit.ly/AutoUFSTool
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.
