Polygraph tests have been used for many years as a means of detecting deception, but their accuracy has been the subject of much debate. In recent years, researchers have explored the use of neural networks in polygraph scoring to improve the accuracy of deception detection. The purpose of this scoping review is to offer a comprehensive overview of the existing research on the subject of neural network applications in scoring polygraph tests. A total of 57 relevant papers were identified and analyzed for this review. The papers were examined for their research focus, methodology, results, and conclusions. The scoping review found that neural networks have shown promise in improving the accuracy of polygraph tests, with some studies reporting significant improvements over traditional methods. However, further research is needed to validate these findings and to determine the most effective ways of integrating neural networks into polygraph testing. The scoping review concludes with a discussion of the current state of the field and suggestions for future research directions.

Genetic Algorithms represent a technique of Artificial Intelligence which has developed from the paradigm of biological evolution. They use a population of potential solutions which gradually evolve toward the best solution which satisfies an objective function. By their nature, Genetic Algorithms use random numbers. In a typical algorithm running, a random number generator is used in many occasions, like selection of the best individuals, choosing the parents for crossover and actually applying crossover, and in mutation. Relying on a standard algorithm for random numbers has the advantage of simplicity and easy implementation (for example in embedded applications), but the quality of the random numbers could influence the final results. In this paper we investigate the effect of the random number generator used by a genetic algorithm in finding the optimal solution for two test functions.

The paper proposes an approach using Genetic Algorithm (GA) for development of optimal time profiles of key control variable of Poly-HydroxyButyrate (PHB) production process. Previous work on modeling and simulation of PHB process showed that it is a highly nonlinear process that needs special controllers based on human experience, as such fuzzy logic controller proved to be a good choice. Fuzzy controllers are not totally replaced, due to the specific process knowledge that they contain. The achieved results are compared with previously proposed knowledge-based approach to the same optimal control task.