SMILE: smart monitoring intelligent learning engine. An ontology-based context-aware system for supporting patients subjected to severe emergencies

Remote healthcare has made a revolution in the healthcare domain. However, an important problem this field is facing is supporting patients who are subjected to severe emergencies (as heart attacks) to be both monitored and protected while being at home. In this paper, we present a conceptual framework with the main objectives of: 1) emergency handling through monitoring patients, detecting emergencies and insuring fast emergency responses; 2) preventing an emergency from happening in the first place through protecting patients by organising their lifestyles and habits. To achieve these objectives, we propose a layered middleware. Our context model combines two modelling methods: probabilistic modelling to capture uncertain information and ontology to ease knowledge sharing and reuse. In addition, our system uses a two-level reasoning approach (ontology-based reasoning and Bayesian-based reasoning) to manage both certain and uncertain contextual parameters in an adaptive manner. Bayesian network is learned from ontology. Moreover, to ensure a more sophisticated decision-making for service presentation, influence diagram and analytic hierarchy process are used along with regular probabilistic rules (confidence level) and basic semantic logic rules

Sequential valuation networks for asymmetric decision problems

This paper deals with representation and solution of asymmetric decision problems. We describe a new representation called sequential valuation networks that is a hybrid of Covaliu and Oliver’s sequential decision diagrams and Shenoy’s valuation networks. The solution algorithm is based on the idea of decomposing a large asymmetric problem into smaller sub-problems and then using the fusion algorithm of valuation networks to solve the sub-problems. Sequential valuation networks inherit many of the strengths of sequential decision diagrams and valuation networks while overcoming many of their shortcomings. We illustrate our technique by representing and solving a modified version of Covaliu and Oliver’s [Manage. Sci. 41(12) (1995) 1860] Reactor problem in complete detail

A Framework and System for a Multi-Model Decision Aid for Sustainable Farming Practices

Decision support systems (DSS) for farmers address the need for modeling multiple processes and scenarios that affect farmer decision making. Existing DSS have various drawbacks that stop them from being deployed as decision support tools. This research proposes a multi-model simulation framework that can be used to analyze farm management practices at the crop level, individual farm level and at the community level to show the impact and alternatives for smallholder farming practices. A generic crop growth model is proposed, based on existing equations. We run sensitivity analysis on the model to identify important variables. The outputs from the crop model are utilized in a series of linear programming models to estimate the optimal scheduling of crops. In addition to these models we build a rule-based fuzzy system to replicate existing trends among farmers. Predicting these trends help us in identifying the decision patterns of farmers and help us in conducting scenario analysis to gauge the farmers reactions to external stimuli. The various limitations and assumptions of the models are described, and we conclude with suggestions for improving these models