Applying Explainable Artificial Intelligence to Develop a Model for Predicting the Supply and Demand of Teachers by Region

Among various methods to improve educational conditions, efforts are being made to reduce the number of students per teacher. However, for policy decisions it is necessary to reflect multiple factors such as changes in the number of students over time and local requirements. Time-series analysis-based statistical models have been used as a method to inform policy decisions. However, the existing statistical models are linear and the accuracy of their predictions is inferior. Also, since there are both internal and external factors that influence the number of students and thus the prediction of the number of required teachers, it is necessary to develop a model that reflects this. Therefore, in this study, an artificial intelligence model based on machine learning was developed using the XGBoost technique, and feature importance, partial dependence plot, and Shap Value were used to increase the model's explanatory potential. The model showed a performance of less than 0.03 RMSE, and it was confirmed that among several factors the economically active population had the most significant effect on the number of teachers. Through this study, it was possible to examine the applicability of an artificial intelligence model with improved explanatory possibilities in predicting the number of teachers

Association of Health Risk Perception and Physical Activity among Adolescents

The current study was to identify health risk perceptions and perception bias in adolescents. Moreover, the study investigated the relationship of risk perceptions with physical activity. A total of 625 adolescents (314 male and 311 female) were voluntarily participated from the Nowon district, geographically located in northern Seoul. In order to measure health risk perceptions a Korean version of self-other risk judgments profile and leisure time exercise questionnaire were used. Results indicated that the study participants, regardless of gender and age, tend to underestimate their vulnerability to the majority of health risk events. The finding revealed that there were significant differences in perception bias toward to health risks by gender and the physical activity level. Furthermore, it is revealed that risk perceptions are directly associated with physical activity participation. The significance of this study lies in the fact that it made a unique contribution to the existing knowledge in exercise and health psychology on relationship between risk perceptions and physical activity.El presente estudio tuvo como objetivo identificar las percepciones del riesgo para la salud y el sesgo de percepción en adolescentes. Además, el estudio investigó la relación entre las percepciones de riesgo y la actividad física. De forma voluntaria, participaron un total de 625 adolescentes (314 hombres y 311 mujeres) del distrito de Nowon, geográficamente ubicado en el norte de Seúl. Con el fin de medir las percepciones de riesgo sobre la salud se utilizó una versión coreana del perfil de juicios de riesgo self-other y el cuestionario de ejercicio de tiempo libre. Los resultados indicaron que los participantes del estudio, independientemente del sexo y la edad, tienden a subestimar su vulnerabilidad a la mayoría de los. El hallazgo reveló que había diferencias significativas en el sesgo de percepción hacia los riesgos de salud por género y el nivel de actividad física. Además, se revela que las percepciones de riesgo están directamente asociadas con la participación en la actividad física. La importancia de este estudio reside en el hecho de que hizo una contribución única al conocimiento existente en el ejercicio y la psicología de la salud sobre la relación entre las percepciones de riesgo y la actividad física

Coastal Resilience Decision Making with Machine Learning

Our research aims to understand how social data can be integrated with climate data using machine learning for coastal resilience decisions. Although data analytics techniques have been adapted for decision models, incorporating unstructured data is a challenge. We adapt a design science research approach to develop a coastal resilience decision model that can accommodate various sets of climate criteria and social attributes to help us understand coastal risks in communities vulnerable to coastal hazards. We collected social data from environmental groups and individuals and conducted an exploratory social media data analysis on coastal resilience in the greater Boston, U.S., area. We employ non-negative matrix factorization (NMF), a topic modeling technique, to extract human-interpretable topics from a preliminary dataset of 131 documents from 50 different accounts. The outcomes of this research can help community members and policy makers understand and develop robust sustainability and climate focused decisions

Coastal Resilience with Social Data Analytics: A Design Science Approach

We adapt a design science approach (DSR) for coastal resilience and climate justice using big data analytics. Our big data and machine learning based artifact can accommodate various sets of social attributes to understand coastal risks for vulnerable communities. We analyzed social data from communities vulnerable to coastal hazards by incorporating machine learning (ML) to assess coastal community needs and demands. In addition, we developed a user interface that provides data selection and weighting functionalities. We extend IS literature in design science research and ML techniques to further our understanding of coastal resilience in vulnerable communities. The outcomes of this research can help community members and policy makers understand and develop robust sustainability and climate focused decisions using a coastal resilience decision approach

A Comparison of Network Clustering Algorithms in Keyword Network Analysis: A Case Study with Geography Conference Presentations

The keyword network analysis has been used for summarizing research trends, and network clustering algorithms play important roles in identifying major research themes. In this paper, we performed a comparative analysis of network clustering algorithms to find out their performances, effectiveness, and impact on cluster themes. The AAG (American Association for Geographers) conference datasets were used in this research. We evaluated seven algorithms with modularity, processing time, and cluster members. The Louvain algorithm showed the best performance in terms of modularity and processing time, followed by the Fast Greedy algorithm. Examining cluster members also showed very coherent connections among cluster members. This study may help researchers to choose a suitable network clustering algorithm and understand geography research trends and topical fields

What Geographers Research: An Analysis of Geography Topics, Clusters, and Trends Using a Keyword Network Analysis Approach and the 2000-2019 AAG Conference Presentations

The spectrum of geographic research topics is very broad, and several thousands of research projects are presented at AAG annual conferences. This research aims at analyzing geography research topics, clusters, and trends using conference presentation data. We analyzed the 2000-2019 AAG conference presentations with keyword network analysis methods. The most frequently used keywords during the 20-year span were GIS, followed by Remote Sensing, Climate Change, Urban, China, Education, Political Ecology, Migration, Gender, and Agriculture. Results showed that geographic research has focused on six major clusters during 2000-2019: GIS, Urban, Climate Change, Political Ecology, People, and Education. About 68.6 percent of keywords were about the GIS, People, and Urban issues. The GIS keyword showed very strong connections with Remote Sensing, Urban, Spatial, Education, Climate Change, and Health. Over the 2015-2019 period, big data analysis and artificial intelligence became popular as emerging fields. This research also shows that the keyword network analysis is an effective method to summarize research trends in geography using conference presentation data. To some fellow geographers, the findings in this research may also cast meaningful insights into what geography is and where it is heading

Security analysis and enhancements of an improved multi-factor biometric authentication scheme

Many remote user authentication schemes have been designed and developed to establish secure and authorized communication between a user and server over an insecure channel. By employing a secure remote user authentication scheme, a user and server can authenticate each other and utilize advanced services. In 2015, Cao and Ge demonstrated that An's scheme is also vulnerable to several attacks and does not provide user anonymity. They also proposed an improved multi-factor biometric authentication scheme. However, we review and cryptanalyze Cao and Ge's scheme and demonstrate that their scheme fails in correctness and providing user anonymity and is vulnerable to ID guessing attack and server masquerading attack. To overcome these drawbacks, we propose a security-improved authentication scheme that provides a dynamic ID mechanism and better security functionalities. Then, we show that our proposed scheme is secure against various attacks and prove the security of the proposed scheme using BAN Logic.111Ysciescopu