Self-learning Bayesian Networks in Diagnosis

AbstractThe article presents the main bases of artificial intelligence, probabilistic diagnostic methods, development of the diagnostic database and diagnostic base of knowledge and Bayesian networks as a base of the diagnostic self-learning systems which are commonly used in medicine to recognize diseases on the basis of symptoms. Probabilistic models of diagnostic networks are based on the Bayesian formulas. These formulas let us determine probabilities of causes on the basis of probabilities of results. This is the reason why databases must be created and adequate probabilities determined. Results of this research are then analyzed by means of statistical methods

Multi-Stage Discrete Programming of the Production Line

The paper highlights the problem of multi-stage optimization consisting in determining a timescale of realization orders of objects. Each production stage requires solving a multi-stage discrete programming problem. Production is optimized from the given state of the line to the state in which the line capacity does not allow any further manufacturing. Tools to be replaced in production aggregates are determined by means of heuristic algorithms. Optimization of the manufacturing line is brought to the discrete linear model within each stage