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Case-Based Reasoning (CBR) has been applied in many different medical applications. Due to the complexities and the diversities of this domain, most medical CBR systems become hybrid. Besides, the case adaptation process in CBR is often a challenging issue as it is traditionally carried out manually by domain experts. In this paper, a new hybrid case-based reasoning approach for medical diagnosis systems is proposed to improve the accuracy of the retrieval-only CBR systems. The approach integrates case-based reasoning and rule-based reasoning, and also applies the adaptation process automatically by exploiting adaptation rules. Both adaptation rules and reasoning rules are generated from the case-base. After solving a new case, the case-base is expanded, and both adaptation and reasoning rules are updated. To evaluate the proposed approach, a prototype was implemented and experimented to diagnose breast cancer and thyroid diseases. The final results show that the proposed approach increases the diagnosing accuracy of the retrieval-only CBR systems, and provides a reliable accuracy comparing to the current breast cancer and thyroid diagnosis systems.

Advances in location acquisition and mobile technologies led to the addition of the location dimension to Social Networks (SNs) and to the emergence of a newer class called Location-Based Social Networks (LBSNs). While LBSNs are richer in their model and functions than SNs, they fail so far to attract as many users as SNs. On the other hand, SNs have large amounts of geo-tagged media that are under-utilized. In this paper, we propose an Interest-Aware Location-Based Recommender system (IALBR), which combines the advantages of both LBSNs and SNs, in order to provide interest-aware location-based recommendations. This recommender system is proposed as an extension to LBSNs. It is novel in: (1) utilizing the geo-content in both LBSNs and SNs; (2) ranking the recommendations based on a novel scoring method that maps to the user interests. It also works for passive users who are not active content contributors to the LBSN. This feature is critical to increase the number of LBSN users. Moreover, it helps with reducing the cold start problem, which is a common problem facing the new users of recommender systems who get random unsatisfying recommendations. This is due to the lack of user interest awareness, which is reliant on user history in most of the recommenders. We evaluated our system with a large-scale real dataset collected from foursquare with respect to precision, recall and the f-measure. We also compared the results with a ground truth system using metrics like the normalized discounted cumulative gain and the mean absolute error. The results confirm that the proposed IALBR generates more efficient recommendations than baselines in terms of interest awareness.

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