Macroeconomic Determinants of Urbanization in Pakistan

Urbanization refers to the migration of rural people to urban centers in search of better jobs. Urbanization and growth go together; no country has ever reached middle-income status without a significant population shift into cities. Urbanization has strong association with unemployment, economic growth, poverty, infrastructure, crimes, health, socio-economic conditions and education. In 2050, most of the urban population of the world will be concentrated in Asia (52%) and in Africa (21%). A simple and modest model provided reasonable results. Increase in literacy is a decisive factor that has significant impact on increasing urban population. Per capita GDP growth also positively influences the urban population. Age-structure is too an important determinant of migration and urbanization. It is an open secret that generally young persons have gone abroad. Specification and diagnostics test supported the model which reveals appropriateness and statistical soundness of the model. Serious heed is paid to delimitation of cities to make them manageable and governable. Agriculture is provided sufficient resources to discourage migration to cities

Multi-Modal Data Analysis Based Game Player Experience Modeling Using LSTM-DNN

Game player modeling is a paradigm of computational models to exploit players’ behavior and experience using game and player analytics. Player modeling refers to descriptions of players based on frameworks of data derived from the interaction of a player’s behavior within the game as well as the player’s experience with the game. Player behavior focuses on dynamic and static information gathered at the time of gameplay. Player experience concerns the association of the human player during gameplay, which is based on cognitive and affective physiological measurements collected from sensors mounted on the player’s body or in the player’s surroundings. In this paper, player experience modeling is studied based on the board puzzle game “Candy Crush Saga” using cognitive data of players accessed by physiological and peripheral devices. Long Short-Term Memory-based Deep Neural Network (LSTM-DNN) is used to predict players’ effective states in terms of valence, arousal, dominance, and liking by employing the concept of transfer learning. Transfer learning focuses on gaining knowledge while solving one problem and using the same knowledge to solve different but related problems. The homogeneous transfer learning approach has not been implemented in the game domain before, and this novel study opens a new research area for the game industry where the main challenge is predicting the significance of innovative games for entertainment and players’ engagement. Relevant not only from a player’s point of view, it is also a benchmark study for game developers who have been facing problems of “cold start” for innovative games that strengthen the game industrial economy