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This project presents a comprehensive knowledge-based system (KBS) focusing on traffic accidents and human diseases. Utilizing an object-oriented methodology, it describes input-output data and pre-processing rules to form a fuzzy rules model. The KBS analyzes risk factors, including street characteristics, operational features, and driver perception for traffic. It incorporates human health factors, including pollution effects like CO and NOx on diseases. This system aims to enhance public health and safety analytics, providing critical insights for mitigating risks.
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Public Health Model(proposition) Technical University of Gdansk Team
General description • Knowledge Based System – idea • Construction • Methodology - Object Oriented Methodology to describe input and output data • Pre - processing • Rules - Rule description of the relations (conditions -> conclusions) • Model- Fuzzy rules model • Tuning – membership function creation • Adaptation – membership function modification
GUI Traffic accident knowledge base Human diseases knowledge base KBS system
Traffic accidents knowledge base (I) Use of OOM to decribe the risk of injuryfactors Street characteristics: lighting of main and secondary street, number of traffic conflicts, visibility and geometry of traffic system, etc Operational characteristics: approximation speed in both, main and secondary street, volume and traffic composition and waiting time at the secondary street, etc Driver perception and motor functions: vision, audition, reflex time, concentration, elevated blood alcohol, etc
Traffic accidents knowledge base (II) Street characteristics: lighting of main and secondary street, number of traffic conflicts. Operational characteristics: approximation speed in both, main and secondary street, volume and traffic composition Driver perception and motor functions: reflex time, concentration. Road types Traffic volume
Traffic accidents knowledge base (III) conditions -> conlusion road types, traffic, volume -> number of traffic accidents
Human diseases knowledge base (I) CO -> Angina - Affects pregnancies, breathing and/or cardiac problems; NOx (Nitrogen oxides)-> Bronchitis - Pneumonia; Pb (Lead)->Affects reproductive, circulatory and nervous systems; HC (hydrocarbons)-> Eyes irritation - Sneeze - Head cold - Cancerous diseases; SOx (Sulphur oxides)-> Asthma - Bronchitis - Coughing.
Human diseases knowledge base (II) Use of OOM to describe human diseases factors CO -> Angina, affects pregnancies NOx (Nitrogen oxides)-> Bronchitis, Pneumonia; Pb (Lead)->Affects reproductive, circulatory and nervous systems; HC (hydrocarbons)-> Cancerous diseases, eyes irritation,; SOx (Sulphur oxides)-> Asthma, Bronchitis Concentration of CO, NOx,, HC, SOx Pb
Human diseases knowledge base(III) Conditions -> conlusion Concentration of CO, NOx, HC, SOx Pb Human diseases
Formal description we assume that the divalent linguistic values will adopt values from the sets trivalent values from the set {0,1,2}or {small, medium, big }
Number of rules • the number of the rules will be as follows (for human diseasesexample): R=r^k=5*5*5=125 r - number of input data k- number of fuzzy sets
INFERENTION Membersip function for y DEFUZZYFICATION Sharp value for y FUZZYFICATION Mebership functions for u1,u2 Ai(u1) u1 y Ck(y) Bj(u2) u 2 Fuzzy modeling
gt P(u1l,u2l,y3l) gt System Model ( gt) ĝt (ĝt) ĝt Adaptation procedures