An Exploratory Study of the Effectiveness of Mobile Advertising

2017 ◽  
Vol 30 (4) ◽  
pp. 24-38 ◽  
Author(s):  
Jianping Peng ◽  
Juanjuan Qu ◽  
Le Peng ◽  
Jing Quan
Keyword(s):  
Bayesian Network ◽  
Logit Model ◽  
Exploratory Study ◽  
Large Data ◽  
Income Level ◽  
Mobile Advertising ◽  
Bayesian Network Model ◽  
Data Set ◽  
Regional Income ◽  
Advertising Company

This study examines factors related to the effectiveness of mobile advertising. Using a large data set with 115,899 records of ad tap-through from a mobile advertising company in China, the authors identify that the influencing factors for advertisement tap through are application type, mobile operators, scrolling frequency, and regional income level. They use a logit model to analyze how the probability of advertisement tap through is related to the identified factors. The results show that application type, mobile operators, scrolling frequency, and the regional income level are positively correlated with the likelihood whether users would tap on certain types of advertising. In addition, they use the Bayesian network model to estimate the conditional probability for a user to tap on an advertisement in an application after the user already taps on another advertisement in the same application. Based on the findings, the authors propose strategies for mobile advertisers to engage in effective and targeted mobile advertising.

An Exploratory Study of the Effectiveness of Mobile Advertising

2019 ◽  
pp. 140-155
Author(s):  
Jianping Peng ◽  
Juanjuan Qu ◽  
Le Peng ◽  
Jing ("Jim") Quan
Keyword(s):  
Bayesian Network ◽  
Conditional Probability ◽  
Logit Model ◽  
Exploratory Study ◽  
Large Data ◽  
Income Level ◽  
Mobile Advertising ◽  
Data Set ◽  
Regional Income ◽  
Advertising Company

This study examines factors related to the effectiveness of mobile advertising. Using a large data set with 115,899 records of ad tap-through from a mobile advertising company in China, the authors identify that the influencing factors for advertisement tap through are application type, mobile operators, scrolling frequency, and regional income level. They use a logit model to analyze how the probability of advertisement tap through is related to the identified factors. The results show that application type, mobile operators, scrolling frequency, and the regional income level are positively correlated with the likelihood whether users would tap on certain types of advertising. In addition, they use the Bayesian network model to estimate the conditional probability for a user to tap on an advertisement in an application after the user already taps on another advertisement in the same application. Based on the findings, the authors propose strategies for mobile advertisers to engage in effective and targeted mobile advertising.

Using Big Data to Understand Chinese Users' Intentions to Tap Through Mobile Advertisements

2020 ◽  
pp. 116-137
Author(s):  
Jing ("Jim") Quan
Keyword(s):  
Big Data ◽  
Bayesian Network ◽  
Conditional Probability ◽  
Influencing Factors ◽  
Network Model ◽  
Logit Model ◽  
Mobile Advertising ◽  
Data Set ◽  
Regional Income ◽  
Advertising Company

This study examines influencing factors for users' intentions to tap through mobile advertisements. This chapter uses a data set with 115,899 records of ad tap-through from a mobile advertising company in China to fit a logit model to examine how the probability of advertisement tap-through is related to the identified factors. The results show that the influencing variables are application type, mobile operators, scrolling frequency, and the regional income level as they are positively correlated with the likelihood whether users would tap on certain types of advertising. Moreover, a Bayesian network model is used to estimate the conditional probability for a user to tap on an advertisement in an application after the user already taps on another advertisement in the same application. Based on the findings, strategies for mobile advertisers to engage in effective and targeted mobile advertising are proposed.

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

scholarly journals A Unified View of Causal and Non-causal Feature Selection

2021 ◽  
Vol 15 (4) ◽  
pp. 1-46
Author(s):  
Kui Yu ◽  
Lin Liu ◽  
Jiuyong Li
Keyword(s):  
Feature Selection ◽  
Bayesian Network ◽  
Synthetic Data ◽  
Selection Methods ◽  
Bayesian Network Model ◽  
Real World Data ◽  
Feature Sets ◽  
Unified View ◽  
Optimal Feature ◽  
Different Levels

In this article, we aim to develop a unified view of causal and non-causal feature selection methods. The unified view will fill in the gap in the research of the relation between the two types of methods. Based on the Bayesian network framework and information theory, we first show that causal and non-causal feature selection methods share the same objective. That is to find the Markov blanket of a class attribute, the theoretically optimal feature set for classification. We then examine the assumptions made by causal and non-causal feature selection methods when searching for the optimal feature set, and unify the assumptions by mapping them to the restrictions on the structure of the Bayesian network model of the studied problem. We further analyze in detail how the structural assumptions lead to the different levels of approximations employed by the methods in their search, which then result in the approximations in the feature sets found by the methods with respect to the optimal feature set. With the unified view, we can interpret the output of non-causal methods from a causal perspective and derive the error bounds of both types of methods. Finally, we present practical understanding of the relation between causal and non-causal methods using extensive experiments with synthetic data and various types of real-world data.

scholarly journals Correlation between the structure and skin permeability of compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruolan Zeng ◽  
Jiyong Deng ◽  
Limin Dang ◽  
Xinliang Yu
Keyword(s):  
Large Data ◽  
Qsar Model ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Skin Permeability ◽  
Data Set ◽  
Test Set ◽  
Svm Algorithm ◽  
Svm Model ◽  
Toxicity Relationship

AbstractA three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.

scholarly journals Galaxy spin direction distribution in HST and SDSS show similar large-scale asymmetry

2020 ◽  
Vol 37 ◽  
Author(s):  
Lior Shamir
Keyword(s):  
Large Scale ◽  
Spiral Galaxies ◽  
Hubble Space Telescope ◽  
Gravitational Interaction ◽  
Large Data ◽  
Sloan Digital Sky Survey ◽  
Data Sets ◽  
Dipole Axis ◽  
Data Set ◽  
The Asymmetry

Abstract Several recent observations using large data sets of galaxies showed non-random distribution of the spin directions of spiral galaxies, even when the galaxies are too far from each other to have gravitational interaction. Here, a data set of $\sim8.7\cdot10^3$ spiral galaxies imaged by Hubble Space Telescope (HST) is used to test and profile a possible asymmetry between galaxy spin directions. The asymmetry between galaxies with opposite spin directions is compared to the asymmetry of galaxies from the Sloan Digital Sky Survey. The two data sets contain different galaxies at different redshift ranges, and each data set was annotated using a different annotation method. The results show that both data sets show a similar asymmetry in the COSMOS field, which is covered by both telescopes. Fitting the asymmetry of the galaxies to cosine dependence shows a dipole axis with probabilities of $\sim2.8\sigma$ and $\sim7.38\sigma$ in HST and SDSS, respectively. The most likely dipole axis identified in the HST galaxies is at $(\alpha=78^{\rm o},\delta=47^{\rm o})$ and is well within the $1\sigma$ error range compared to the location of the most likely dipole axis in the SDSS galaxies with $z>0.15$ , identified at $(\alpha=71^{\rm o},\delta=61^{\rm o})$ .

Association of Demographic and Disability Characteristics With Total PROMISE Expenditure: Wisconsin PROMISE Findings—Brief Report

2021 ◽  
pp. 003435522098079
Author(s):  
Emre Umucu ◽  
Beatrice Lee ◽  
Veronica Estala-Gutierrez ◽  
Timothy Tansey
Keyword(s):  
Health Care ◽  
Cost Effectiveness ◽  
Treatment Group ◽  
Exploratory Study ◽  
Health Care Expenditure ◽  
Study Data ◽  
Data Set ◽  
Youth With Disabilities ◽  
Total Health Care Expenditure

The purpose of this exploratory study was to examine whether demographic and disability variables predict total health care expenditure of Wisconsin PROMISE. The findings are intended to assist in promoting cost-effectiveness for future similar initiates. This study data were extracted from Wisconsin PROMISE data set. This study had a total of 1,443 youth with disabilities ( Mage = 14.89). The majority of participants were male (69%). Our results indicated that some demographic and disability–related characteristics are associated with total health care expenditure in control with VR case during PROMISE, control without VR case during PROMISE, and treatment group. Overall, findings of the current study suggest demographic and disability variables do assist in predicting total health care expenditure of Wisconsin PROMISE.

scholarly journals The Complete DNA Sequence of the Mitochondrial Genome of a “Living Fossil,” the Coelacanth (Latimeria chalumnae)

Genetics ◽  
1997 ◽  
Vol 146 (3) ◽  
pp. 995-1010 ◽  
Author(s):  
Rafael Zardoya ◽  
Axel Meyer
Keyword(s):  
Mitochondrial Genome ◽  
Tandem Repeats ◽  
Phylogenetic Analyses ◽  
Large Data ◽  
Molecular Data ◽  
Phylogenetic Position ◽  
Data Set ◽  
Living Fossil ◽  
Latimeria Chalumnae ◽  
Relationship Of

The complete nucleotide sequence of the 16,407-bp mitochondrial genome of the coelacanth (Latimeria chalumnae) was determined. The coelacanth mitochondrial genome order is identical to the consensus vertebrate gene order which is also found in all ray-finned fishes, the lungfish, and most tetrapods. Base composition and codon usage also conform to typical vertebrate patterns. The entire mitochondrial genome was PCR-amplified with 24 sets of primers that are expected to amplify homologous regions in other related vertebrate species. Analyses of the control region of the coelacanth mitochondrial genome revealed the existence of four 22-bp tandem repeats close to its 3′ end. The phylogenetic analyses of a large data set combining genes coding for rRNAs, tRNA, and proteins (16,140 characters) confirmed the phylogenetic position of the coelacanth as a lobe-finned fish; it is more closely related to tetrapods than to ray-finned fishes. However, different phylogenetic methods applied to this largest available molecular data set were unable to resolve unambiguously the relationship of the coelacanth to the two other groups of extant lobe-finned fishes, the lungfishes and the tetrapods. Maximum parsimony favored a lungfish/coelacanth or a lungfish/tetrapod sistergroup relationship depending on which transversion:transition weighting is assumed. Neighbor-joining and maximum likelihood supported a lungfish/tetrapod sistergroup relationship.

Sign in / Sign up

Export Citation Format

Share Document