Abduragim Shtanchaev - Academia.edu (original) (raw)
Uploads
Papers by Abduragim Shtanchaev
ArXiv, 2021
For many countries like Russia, Canada, or the USA, a robust and detailed tree species inventory ... more For many countries like Russia, Canada, or the USA, a robust and detailed tree species inventory is essential to manage their forests sustainably. Since one can not apply unmanned aerial vehicle (UAV) imagery-based approaches to large scale forest inventory applications, the utilization of machine learning algorithms on satellite imagery is a rising topic of research. Although satellite imagery quality is relatively low, additional spectral channels provide a sufficient amount of information for tree crown classification tasks. Assuming that tree crowns are detected already, we use embeddings of tree crowns generated by Autoencoders as a data set to train classical Machine Learning algorithms. We compare our Autoencoder (AE) based approach to traditional convolutional neural networks (CNN) end-to-end classifiers.
Ad recall is a commonly used measure of advertising effectiveness. Automatic prediction of advert... more Ad recall is a commonly used measure of advertising effectiveness. Automatic prediction of advertising effectiveness will help to improve video advertising and optimize the process of ad creation. We present a novel multimodal approach to ad recall prediction for video advertising based on viewer’s features and ad features. In our experiment twenty people watched ads (n=100 in total). Ads have ground truth ad recall that was previously obtained in a field study. While people were watching ads, we recorded them with video camera, collected contact photolpletysmography and eye-tracking data, and also asked them to complete questionnaires. From these data we extracted “viewer’s features” –emotional, physiological and behavioral parameters. As well, we had “ad features” – target ratingPoint (TRP) and weighted target rating point (WTRP) metrics. To predict ad recall from these features a range of regression models were tested. Random Gaussian projection with Support Vector Machines howed...
ArXiv, 2021
For many countries like Russia, Canada, or the USA, a robust and detailed tree species inventory ... more For many countries like Russia, Canada, or the USA, a robust and detailed tree species inventory is essential to manage their forests sustainably. Since one can not apply unmanned aerial vehicle (UAV) imagery-based approaches to large scale forest inventory applications, the utilization of machine learning algorithms on satellite imagery is a rising topic of research. Although satellite imagery quality is relatively low, additional spectral channels provide a sufficient amount of information for tree crown classification tasks. Assuming that tree crowns are detected already, we use embeddings of tree crowns generated by Autoencoders as a data set to train classical Machine Learning algorithms. We compare our Autoencoder (AE) based approach to traditional convolutional neural networks (CNN) end-to-end classifiers.
Ad recall is a commonly used measure of advertising effectiveness. Automatic prediction of advert... more Ad recall is a commonly used measure of advertising effectiveness. Automatic prediction of advertising effectiveness will help to improve video advertising and optimize the process of ad creation. We present a novel multimodal approach to ad recall prediction for video advertising based on viewer’s features and ad features. In our experiment twenty people watched ads (n=100 in total). Ads have ground truth ad recall that was previously obtained in a field study. While people were watching ads, we recorded them with video camera, collected contact photolpletysmography and eye-tracking data, and also asked them to complete questionnaires. From these data we extracted “viewer’s features” –emotional, physiological and behavioral parameters. As well, we had “ad features” – target ratingPoint (TRP) and weighted target rating point (WTRP) metrics. To predict ad recall from these features a range of regression models were tested. Random Gaussian projection with Support Vector Machines howed...