# Generative Adversarial Networks in Business and Social Science
## 3 Statistics on GAN Publications
### Knowledge Areas and GANs
\begin{table}
\begin{tabular}{l r r} \hline \hline
**Research Area** & \# & \% \\ \hline Computer Science & 10,974 & 58,940 \\ Engineering & 8418 & 45.212 \\ Imaging Science/Photographic Technology & 2368 & 12.718 \\ Telecommunications & 1983 & 10.650 \\ Radiology/Nuclear Medicine/Medical Imaging & 1182 & 6.348 \\ Remote Sensing & 897 & 4.818 \\ Optics & 868 & 4.662 \\ Physics & 854 & 4.587 \\ Chemistry & 667 & 3.582 \\ Instruments/Instrumentation & 674 & 3.620 \\ Automation/Control Systems & 631 & 3.389 \\ Materials Science & 507 & 2.723 \\ Geology & 444 & 2.385 \\ Mathematics & 404 & 2.170 \\ Science/Technology/Other Topics & 404 & 2.170 \\ Mathematical/Computational Biology & 389 & 2.089 \\ Environmental Sciences/Ecology & 361 & 1.939 \\ Acoustics & 351 & 1.885 \\ Neurosciences/Neurology & 329 & 1.767 \\ Geochemistry/Geophysics & 336 & 1.805 \\ Robotics & 283 & 1.520 \\ Medical Informatics & 245 & 1.316 \\ Transportation & 235 & 1.262 \\ Energy Fuels & 212 & 1.139 \\ Operations Research/Management Science & 189 & 1.015 \\ Others (90 areas) & 2283 & 12.261 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Publications according to research areas. differential equations, expanding the capabilities and applications of GANs in the mathematical domain [95; 144; 145; 146; 147; 148]. In the field of Science, Technology and other Topics, advances have been made in image generation techniques, enhancements in the quality and stability of GANs, as well as in the manipulation and control of features in image synthesis. These advances have propelled the field of computer vision and machine learning [28; 59; 60; 63; 149]. In Mathematical/Computational Biology, advances have been made in unsupervised learning techniques, synthetic data generation, molecular modelling, and analysis of biological images, thereby opening new perspectives in mathematical and biological research [21; 150; 151]. The application of GANs in Environmental Sciences and Ecology has developed innovative solutions for species distribution modelling, land cover prediction, environmental data analysis, and water quality monitoring, thereby enhancing our understanding and management of ecosystems and the environment [152; 153]. Also, in the field of Acoustics, GANs have contributed significant solutions in areas such as audio synthesis, speech enhancement, sound simulation, and speaker verification, thereby enhancing the quality and robustness of acoustic signal processing [71; 154; 155; 156; 157; 158]. In the field of Neurosciences and Neurology, GANs have achieved advancements in diagnostic techniques, synthetic data generation, analysis of brain signals, and the study of neurological diseases, thereby providing new insights and tools for the field of neuroscience and neurology [159; 160; 161]. When studying GANs in Geochemistry and Geophysics, significant advancements have been achieved in techniques for the estimation of petrophysical properties, seismic data inversion, interpretation of geophysical data, and modelling of subsurface structures. These advances enhance our understanding and analytical capabilities in exploring and characterising natural resources and geological processes [162; 163; 164; 165; 166]. In the field of Robotics, GANs have been applied in areas such as synthetic data generation, reinforcement learning enhancement, robotic perception, terrain classification, and robotic grasp improvement, thereby enhancing the capabilities and applications of robots in various environments and tasks [167; 168; 169; 170; 171]. In the field of Medical Informatics, studies on GANs have developed innovative solutions in areas such as the generation of synthetic medical images, anomaly detection, incomplete image reconstruction, and data synthesis to enhance the analysis and interpretation of medical images, thereby significantly impacting disease diagnosis and treatment [38; 172; 173; 174; 175].