User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy

Recommender systems have become an integral part of many social networks and extract knowledge from a user's personal and sensitive data both explicitly, with the user's knowledge, and implicitly. This trend has created major privacy concerns as users are mostly unaware of what data and how much data is being used and how securely it is used. In this context, several works have been done to address privacy concerns for usage in online social network data and by recommender systems. This paper surveys the main privacy concerns, measurements and privacy-preserving techniques used in large-scale online social networks and recommender systems. It is based on historical works on security, privacy-preserving, statistical modeling, and datasets to provide an overview of the technical difficulties and problems associated with privacy preserving in online social networks.Comment: 26 pages, IET book chapter on big data recommender system

Environmental and Health Disparities in Appalachian Ohio: Perceptions and Realities

Background. Appalachia is a region of the United States that faces significant environmental and health disparities. Understanding these disparities and the social determinants that contribute to them will help public health practitioners make better decisions. The purpose of this research is two-fold. First, through secondary data analysis, we document environmental and health disparities as well as demographic and economic conditions that may contribute to these disparities between Appalachian and non-Appalachian Ohio. Second, we examine perceptions of environmental health practitioners about the differences in environmental conditions between Appalachian and non-Appalachian Ohio. Methods. We gathered secondary data about economics, health, and the environment from the Ohio Department of Health, Healthy Ohio Community Profiles, the U.S. Environmental Protection Agency, and the U.S. Census. In addition, we conducted an online survey of 76 environmental health professionals across Ohio. Results. The secondary data indicates that there are significant differences between Appalachian and non-Appalachian Ohio in terms of socioeconomic, health, and environmental indicators. In addition, environmental health professionals perceive worse environmental conditions in the Appalachian region and indicate that there are environmental and health disparities found in this part of the state that do not exist elsewhere. Conclusions. The results contribute to understanding environmental and health conditions that contribute to health disparities in the Appalachian region as well as suggest approaches for public health practitioners to reduce these disparities

Prediction of Heart Disease Using Machine Learning Techniques

A potential strategy in the healthcare industry is the prediction of cardiac disease using machine learning algorithms. Worldwide, heart disease continues to be one of the major causes of death, and successful treatment and prevention depend greatly on early identification. Large volumes of patient data may be analyzed using machine learning algorithms to find patterns and risk factors that might lead to the onset of heart disease. These algorithms use supervised learning, unsupervised learning, and ensemble approaches to assess a variety of data sources, including clinical test results, patient demographics, and medical records. Machine-learning algorithms may be trained on historical data from a variety of patients to discover complicated associations and generate precise predictions about a person's risk of acquiring heart disease. Our objective is to create a machine-learning technique that reliably predicts heart disease and is computationally effective. Feature selection is a crucial step in the creation of prediction models as it permits the identification of the most significant risk factors for heart disease. Machine learning methods including logistic regression, support vector machines, decision trees, random forests, and neural networks are often used to predict cardiac disease. By examining extensive patient data, machine learning algorithms show considerable potential in the prediction of cardiac disease. In the battle against heart disease, their capacity to spot patterns and risk factors may result in early identification, individualized therapies, and better patient care