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Alzheimer's Disease (AD) is a neurodegenerative irreversible brain disorder that gradually wipes out the memory, thinking skills and eventually the ability to carry out day-to-day tasks. The amount of AD patients is rapidly increasing due to several lifestyle changes that affect biological functions. Detection of AD at its early stages helps in the treatment of patients. In this paper, a predictive and preventive model that uses biomarkers such as the amyloid-beta protein is proposed to detect, predict, and prevent AD onset. A Convolution Neural Network (CNN) based model is developed to predict AD at its early stages. The results obtained proved that the proposed model outperforms the traditional Machine Learning (ML) algorithms such as Logistic Regression, Support Vector Machine, Decision Tree Classifier, and K Nearest Neighbor algorithms.

Health digital GIS map provides a great solution for medical geographical distribution to efficiently explore diseases and health services. In Sudan, tuberculosis disease is expanding in different areas, which requires a digital GIS map to collect information about the patients and support medical institutions by geographical distribution based on health services, drug supply, and consumption. This paper developed a health digital GIS map to provide a fair geographical distribution of tuberculosis health centers and control the drug supply according to medical reports. The proposed approach extracts the unfair distribution of medicine, as some centers receive medicine but do not receive patients, while others receive a large number of patients but limited amounts of medicine. The analysis results show that there is a defect in some states representing the distribution of tuberculosis centers. In the Northern State, there are 15 tuberculosis centers distributed over all localities, serving about 84 tuberculosis-infected patients only.

One of the big challenges of our modern life is to find the right items or contents on the Internet and particularly in social media. One way of addressing the information overload problem in social media is to predict the future trends and popularity of online items. The popularity of an item can be measured by its attractiveness, i.e., the number of times it is being used. This popularity prediction can be translated to a link prediction and ranking problem, which aims to predict the link gain of the items in a user-item interaction network. User-item interactions in an online environment can be modelled as a bipartite network, where a link represents an event, reflecting a user buys or collects an item. Popularity prediction problem in temporal bipartite networks is of great interest to researchers. In this study, we propose a heuristic based model which only consider nodes collective link gain in a recent past time window of time as well as total link gain. To evaluate our model's efficiency, we tested them on co-evolving social media items. We also evaluated the models' performance on five information retrieval metrics (i.e., Area Under the Receiver Operating Characteristic, Kendall's rank correlation tau, Precision, Novelty, and temporal novelty). The proposed model does not need hyper-parameter learning, which makes it the best choice for highly temporal and data streaming scenarios.INDEX TERMS Temporal bipartite networks, ranking, popularity prediction.

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