Artificial Intelligence
The original dataset SECOM is obtained from the the UC Irvine Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/secom). Then, each
sample is transformed to an image, with each pixel representing a feature. Therefore, image processing mechanisms such as convolutionary neural networks can be utilized for classification.
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AutoScale Dataset
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Along with the increasing use of unmanned aerial vehicles (UAVs), large volumes of aerial videos have been produced. It is unrealistic for humans to screen such big data and understand their contents. Hence methodological research on the automatic understanding of UAV videos is of paramount importance.
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Dataset
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A Traffic Light Controller PETRI_NET (Finite State Machine) Implementation.
An implementation of FSM approach can be followed in systems whose tasks constitute a well-structured list so all states can be easily enumerated. A Traffic light controller represents a relatively complex control function
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The zizania image dataset consists of a total of 4900 zizanias. The quantity of high quality samples is 2648 and defective quality samples is 2252.
There are four classes in the apple image dataset, which are apples with a diameter greater than 90 mm, between 80 mm and 90 mm, less than 80 mm, and diseases and insect pests. The quantity distributionin above categories are 3647 (51.19%), 2464 (34.59%), 558 (7.83%), 455 (6.39%).
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1322 Views
Beijing Building Dataset(BGB) is an elevation satellite image dataset which is integrated by satellite image and aerial photograph for building detection and identification. It contains 2000 images from Google Earth History Map of five different areas in Beijing on November 24th, 2016, and all these images are 512*512 in resolution ratio with a precision of 0.458m. It covers more than 100 km2 geographic areas of Beijing both in suburbs and urban areas.
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I have prepared the dataset for this tutorial which you can download from here. This dataset contains around 3000 handgun images with their bounding box labels distributed in 2 folders: images and labels
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This dataset contains the images used in the paper "Fine-tuning a pre-trained Convolutional Neural Network Model to translate American Sign Language in Real-time".
M. E. Morocho Cayamcela and W. Lim, "Fine-tuning a pre-trained Convolutional Neural Network Model to translate American Sign Language in Real-time," 2019 International Conference on Computing, Networking and Communications (ICNC), Honolulu, HI, USA, 2019, pp. 100-104.
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2135 Views
Visual representations are always better than narrations in accordance to children, for better understanding. This is quite advantageous in learning school lessons and it eventually helps in engaging the children and enhancing their imaginative skills.
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