scholarly journals Multimodal Ad Recall Prediction Based on Viewer’s and Ad Features

2020 ◽  
Author(s):  
Mariya Malygina ◽  
Abduragim Shtanchaev ◽  
Marina Churikova ◽  
Olga Perepelkina
Keyword(s):  
Regression Models ◽  
Ground Truth ◽  
Video Camera ◽  
Advertising Effectiveness ◽  
Support Vector ◽  
Multimodal Approach ◽  
Video Advertising ◽  
Vector Machines ◽  
Behavioral Parameters ◽  
Ad Recall

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 the best performance (MAE=0.09, R=0.6).

scholarly journals Regression Models for Symbolic Interval-Valued Variables

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 429
Author(s):  
Jose Emmanuel Chacón ◽  
Oldemar Rodríguez
Keyword(s):  
Regression Models ◽  
Mean Squared Error ◽  
Nearest Neighbors ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
R Language ◽  
Squared Error ◽  
Vector Machines ◽  
Synthetic Datasets ◽  
Interval Valued

This paper presents new approaches to fit regression models for symbolic internal-valued variables, which are shown to improve and extend the center method suggested by Billard and Diday and the center and range method proposed by Lima-Neto, E.A.and De Carvalho, F.A.T. Like the previously mentioned methods, the proposed regression models consider the midpoints and half of the length of the intervals as additional variables. We considered various methods to fit the regression models, including tree-based models, K-nearest neighbors, support vector machines, and neural networks. The approaches proposed in this paper were applied to a real dataset and to synthetic datasets generated with linear and nonlinear relations. For an evaluation of the methods, the root-mean-squared error and the correlation coefficient were used. The methods presented herein are available in the the RSDA package written in the R language, which can be installed from CRAN.

scholarly journals LAND COVER CLASSIFICATION FROM FULL-WAVEFORM LIDAR DATA BASED ON SUPPORT VECTOR MACHINES

2016 ◽  
Vol XLI-B3 ◽  
pp. 447-452
Author(s):  
M. Zhou ◽  
C. R. Li ◽  
L. Ma ◽  
H. C. Guan
Keyword(s):  
Support Vector Machines ◽  
Land Cover ◽  
Prediction Model ◽  
Image Data ◽  
Ground Truth ◽  
Land Cover Classification ◽  
Training Data ◽  
Support Vector ◽  
Vector Machines ◽  
Waveform Lidar

In this study, a land cover classification method based on multi-class Support Vector Machines (SVM) is presented to predict the types of land cover in Miyun area. The obtained backscattered full-waveforms were processed following a workflow of waveform pre-processing, waveform decomposition and feature extraction. The extracted features, which consist of distance, intensity, Full Width at Half Maximum (FWHM) and back scattering cross-section, were corrected and used as attributes for training data to generate the SVM prediction model. The SVM prediction model was applied to predict the types of land cover in Miyun area as ground, trees, buildings and farmland. The classification results of these four types of land covers were obtained based on the ground truth information according to the CCD image data of Miyun area. It showed that the proposed classification algorithm achieved an overall classification accuracy of 90.63%. In order to better explain the SVM classification results, the classification results of SVM method were compared with that of Artificial Neural Networks (ANNs) method and it showed that SVM method could achieve better classification results.

scholarly journals Dual possibilistic regression models of support vector machines and application in power load forecasting

2020 ◽  
Vol 16 (5) ◽  
pp. 155014772092163
Author(s):  
Xianfei Yang ◽  
Xiang Yu ◽  
Hui Lu
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machines ◽  
Quadratic Programming ◽  
Regression Models ◽  
Load Forecasting ◽  
Interval Data ◽  
Support Vector ◽  
Power Load ◽  
Vector Machines ◽  
Power Load Forecasting

Power load forecasting is an important guarantee of safe, stable, and economic operation of power systems. It is appropriate to use interval data to represent fuzzy information in power load forecasting. The dual possibilistic regression models approximate the observed interval data from the outside and inside directions, respectively, which can estimate the inherent uncertainty existing in the given fuzzy phenomenon well. In this article, efficient dual possibilistic regression models of support vector machines based on solving a group of quadratic programming problems are proposed. And each quadratic programming problem containing fewer optimization variables makes the training speed of the proposed approach fast. Compared with other interval regression approaches based on support vector machines, such as quadratic loss support vector machine approach and two smaller quadratic programming problem support vector machine approach, the proposed approach is more efficient on several artificial datasets and power load dataset.

scholarly journals LAND COVER CLASSIFICATION FROM FULL-WAVEFORM LIDAR DATA BASED ON SUPPORT VECTOR MACHINES

2016 ◽  
Vol XLI-B3 ◽  
pp. 447-452 ◽  
Author(s):  
M. Zhou ◽  
C. R. Li ◽  
L. Ma ◽  
H. C. Guan
Keyword(s):  
Support Vector Machines ◽  
Land Cover ◽  
Prediction Model ◽  
Image Data ◽  
Ground Truth ◽  
Land Cover Classification ◽  
Training Data ◽  
Support Vector ◽  
Vector Machines ◽  
Waveform Lidar

In this study, a land cover classification method based on multi-class Support Vector Machines (SVM) is presented to predict the types of land cover in Miyun area. The obtained backscattered full-waveforms were processed following a workflow of waveform pre-processing, waveform decomposition and feature extraction. The extracted features, which consist of distance, intensity, Full Width at Half Maximum (FWHM) and back scattering cross-section, were corrected and used as attributes for training data to generate the SVM prediction model. The SVM prediction model was applied to predict the types of land cover in Miyun area as ground, trees, buildings and farmland. The classification results of these four types of land covers were obtained based on the ground truth information according to the CCD image data of Miyun area. It showed that the proposed classification algorithm achieved an overall classification accuracy of 90.63%. In order to better explain the SVM classification results, the classification results of SVM method were compared with that of Artificial Neural Networks (ANNs) method and it showed that SVM method could achieve better classification results.

Regression Models Using Pattern Search Assisted Least Square Support Vector Machines

2005 ◽  
Vol 83 (8) ◽  
pp. 1030-1037 ◽  
Author(s):  
N.S. Patil ◽  
P.S. Shelokar ◽  
V.K. Jayaraman ◽  
B.D. Kulkarni
Keyword(s):  
Support Vector Machines ◽  
Regression Models ◽  
Pattern Search ◽  
Least Square ◽  
Support Vector ◽  
Vector Machines

scholarly journals Optimizing Biodiesel Production from Waste Cooking Oil Using Genetic Algorithm-Based Support Vector Machines

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2995 ◽  
Author(s):  
Marina Corral Bobadilla ◽  
Roberto Fernández Martínez ◽  
Rubén Lostado Lorza ◽  
Fátima Somovilla Gómez ◽  
Eliseo Vergara González
Keyword(s):  
Support Vector Machines ◽  
Vegetable Oils ◽  
Regression Models ◽  
Waste Cooking Oil ◽  
Biodiesel Production ◽  
Support Vector ◽  
Alkaline Catalyst ◽  
Heating Value ◽  
Cooking Oil ◽  
Vector Machines

The ever increasing fuel demands and the limitations of oil reserves have motivated research of renewable and sustainable energy resources to replace, even partially, fossil fuels, which are having a serious environmental impact on global warming and climate change, excessive greenhouse emissions and deforestation. For this reason, an alternative, renewable and biodegradable combustible like biodiesel is necessary. For this purpose, waste cooking oil is a potential replacement for vegetable oils in the production of biodiesel. Direct transesterification of vegetable oils was undertaken to synthesize the biodiesel. Several variables controlled the process. The alkaline catalyst that is used, typically sodium hydroxide (NaOH) or potassium hydroxide (KOH), increases the solubility and speeds up the reaction. Therefore, the methodology that this study suggests for improving the biodiesel production is based on computing techniques for prediction and optimization of these process dimensions. The method builds and selects a group of regression models that predict several properties of biodiesel samples (viscosity turbidity, density, high heating value and yield) based on various attributes of the transesterification process (dosage of catalyst, molar ratio, mixing speed, mixing time, temperature, humidity and impurities). In order to develop it, a Box-Behnken type of Design of Experiment (DoE) was designed that considered the variables that were previously mentioned. Then, using this DoE, biodiesel production features were decided by conducting lab experiments to complete a dataset with real production properties. Subsequently, using this dataset, a group of regression models—linear regression and support vector machines (using linear kernel, polynomial kernel and radial basic function kernel)—were constructed to predict the studied properties of biodiesel and to obtain a better understanding of the process. Finally, several biodiesel optimization scenarios were reached through the application of genetic algorithms to the regression models obtained with greater precision. In this way, it was possible to identify the best combinations of variables, both independent and dependent. These scenarios were based mainly on a desire to improve the biodiesel yield by obtaining a higher heating value, while decreasing the viscosity, density and turbidity. These conditions were achieved when the dosage of catalyst was approximately 1 wt %.

Tuning support vector machines regression models improves prediction accuracy of soil properties in MIR spectroscopy

Geoderma ◽  
2020 ◽  
Vol 365 ◽  
pp. 114227 ◽  
Author(s):  
Leonardo Deiss ◽  
Andrew J. Margenot ◽  
Steve W. Culman ◽  
M. Scott Demyan
Keyword(s):  
Support Vector Machines ◽  
Soil Properties ◽  
Prediction Accuracy ◽  
Regression Models ◽  
Support Vector ◽  
Mir Spectroscopy ◽  
Vector Machines

Efficient and Private Scoring of Decision Trees, Support Vector Machines and Logistic Regression Models Based on Pre-Computation

2019 ◽  
Vol 16 (2) ◽  
pp. 217-230 ◽  
Author(s):  
Martine De Cock ◽  
Rafael Dowsley ◽  
Caleb Horst ◽  
Raj Katti ◽  
Anderson C. A. Nascimento ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Support Vector Machines ◽  
Decision Trees ◽  
Regression Models ◽  
Support Vector ◽  
Logistic Regression Models ◽  
Vector Machines

scholarly journals Automatic Artifact Recognition and Correction for Electrodermal Activity in Uncontrolled Environments

2021 ◽  
Author(s):  
Jose Llanes-Jurado ◽  
Lucía Amalia Carrasco-Ribelles ◽  
Mariano Alcañiz ◽  
Javier Marín-Morales
Keyword(s):  
Neural Networks ◽  
Intelligent Systems ◽  
Electrodermal Activity ◽  
Time Windows ◽  
Ground Truth ◽  
Linear Interpolation ◽  
Support Vector ◽  
Emotional States ◽  
Vector Machines ◽  
High Degree

Abstract Scholars are increasingly using electrodermal activity (EDA) to assess cognitive-emotional states in laboratory environments, while recent applications have recorded EDA in uncontrolled settings, such as daily-life and virtual reality (VR) contexts, in which users can freely walk and move their hands. However, these records can be affected by major artifacts stemming from movements that can obscure valuable information. Previous work has analyzed signal correction methods to improve the quality of the signal or proposed artifact recognition models based on time windows. Despite these efforts, the correction of EDA signals in uncontrolled environments is still limited, and no existing research has used a signal manually corrected by an expert as a benchmark. This work investigates different machine learning and deep learning architectures, including support vector machines, recurrent neural networks (RNNs), and convolutional neural networks, for the automatic artifact recognition of EDA signals. The data from 44 subjects during an immersive VR task were collected and cleaned by two experts as ground truth. The best model, which used an RNN fed with the raw signal, recognized 72% of the artifacts and had an accuracy of 87%. An automatic correction was performed on the detected artifacts through a combination of linear interpolation and a high degree polynomial. The evaluation of this correction showed that the automatically and manually corrected signals did not present differences in terms of phasic components, while both showed differences to the raw signal. This work provides a tool to automatically correct artifacts of EDA signals which can be used in uncontrolled conditions, allowing for the development of intelligent systems based on EDA monitoring without human intervention.

scholarly journals Application of texture features and machine learning methods to grains segmentation in rock material images

2020 ◽  
Author(s):  
Karolina Nurzynska ◽  
Sebastian Iwaszenko
Keyword(s):  
Principal Component ◽  
Texture Features ◽  
Feature Space ◽  
Ground Truth ◽  
Support Vector ◽  
Difference Matrix ◽  
Nearest Neighbours ◽  
Vector Machines ◽  
Grey Tone ◽  
Increase In Accuracy

The segmentation of rock grains on images depicting bulk rock materials is considered. The rocks material images are transformed by selected texture operators, to obtain a set of features describing them. The first order features, second-order features, run-length matrix, grey tone difference matrix, and Laws' energies are used for that purpose. The features are classified using k-nearest neighbours, support vector machines, and artificial neural networks classifiers. The results show that the border of rocks grains can be determined with above 70% accuracy. The multi-texture approach was also investigated, leading to an increase in accuracy to over 77% for early-fusion of features. Attempts were made to reduce feature space dimensionality by manually picking features as well as by use of the principal component analysis. The outcomes showed a significant decrease in accuracy. The obtained results have been visually compared with the ground truth. The observed compliance can be considered satisfactory.

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