Academic literature on the topic 'Adaptive Decision Table'

In this chapter, the author describes the different categories of pixel value differencing (PVD) techniques and their performances. The main goal in PVD technique is to find the pixel value difference. If it is large, hide the larger number of bits; otherwise, hide the lesser number of bits. The traditional PVD techniques operates on 2, 4, and 8 pixel blocks to calculate the pixel value difference and then take the embedding decision. The traditional PVD techniques use a range table to decide the embedding capacity in a block. The adaptive PVD techniques do not use any range table and calculate the embedding capacity dynamically for every block based on the pixel values of that block. Most of the traditional PVD techniques are attacked by pixel difference histogram (PDH) analysis, but adaptive PVD techniques are tolerant to PDH analysis.

This chapter provides a review of new hybrid methods that deal with the continuous local and global optimization problems for constrained industrial production planning problems. In this chapter, details about all types of optimization methods and approaches for the local and global optimization are highlighted. Altogether there are eight famous methods in hybrid evolutionary optimization. In this research, the hybridization between evolutionary algorithms and other heuristic approaches such as simulated annealing, line search, pattern search, and mesh adaptive direct search are adopted. A particular evolutionary computation approach of genetic algorithm is used in this hybridization process. An intelligent performance analysis table is suggested in this chapter which is significantly important for decision makers and implementers in the industrial engineering of production planning. A brief summary on the conclusions of the main contributions and achievements in this chapter as well as future research directions are highlighted.

In this chapter, three meta-heuristic optimization techniques have been utilized to solve the fuzzy programming problems in industrial production systems. This chapter outlines an introduction to real-world industrial problem for product-mix selection involving eight variables and 21 constraints with fuzzy technological coefficients and thereafter, a formulation for an optimization approach to solve the problem. This problem occurs in production planning in which a decision maker plays a pivotal role in making decision under fuzzy environment. Decision-maker should be aware of his/her level of satisfaction as well as degree of fuzziness while making the product-mix decision. Genetic algorithms, pattern search, and mesh adaptive direct search methods have been employed to solve the large scale problems in real world industrial sector. The results for these techniques have been investigated thoroughly in the form of 2D, 3D plots, and tables. The industrial production planning problem which was illustrated in this chapter was solved successfully by these three meta-heuristic methods. The results are analyzed along with the optimal profit function (objective or fitness function) with level of satisfaction, decision variables, vagueness factor, and computational time (CPU).