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Article Citation - WoS: 7Citation - Scopus: 13An Expert System for the Diagnosis of Sexually Transmitted Diseases - Esstd(Ios Press, 2017) Thompson, Temitope; Sowunmi, Olaperi; Misra, Sanjay; Fernandez-Sanz, Luis; Crawford, Broderick; Soto, RicardoOver 93 million people get ill with sexually transmitted diseases in sub-Saharan Africa. However, research has shown that people with sexually transmitted diseases find it difficult to share their problem with a physician due to societal discrimination in Africa. Due to this problem, we have implemented a medical expert system for diagnosing sexually transmitted diseases (ESSTD) that maintains the anonymity of the individuals. The patients diagnose themselves by answering questions provided by the system. This paper presents the design and development of the system. Forward chaining rules were used to implement the knowledge base and the system is easily accessible on mobile platforms. The Java Expert System Shell was used for its inference engine and the system was validated by domain experts. It is useful because it helps to maintain anonymity for patients with STD.Conference Object Citation - WoS: 2Citation - Scopus: 3Autonomous Tuning for Constraint Programming Via Artificial Bee Colony Optimization(Springer-verlag Berlin, 2015) Soto, Ricardo; Crawford, Broderick; Mella, Felipe; Flores, Javier; Galleguillos, Cristian; Misra, Sanjay; Paredes, FernandoConstraint Programming allows the resolution of complex problems, mainly combinatorial ones. These problems are defined by a set of variables that are subject to a domain of possible values and a set of constraints. The resolution of these problems is carried out by a constraint satisfaction solver which explores a search tree of potential solutions. This exploration is controlled by the enumeration strategy, which is responsible for choosing the order in which variables and values are selected to generate the potential solution. Autonomous Search provides the ability to the solver to self-tune its enumeration strategy in order to select the most appropriate one for each part of the search tree. This self-tuning process is commonly supported by an optimizer which attempts to maximize the quality of the search process, that is, to accelerate the resolution. In this work, we present a new optimizer for self-tuning in constraint programming based on artificial bee colonies. We report encouraging results where our autonomous tuning approach clearly improves the performance of the resolution process.
