**4. Conclusion**

Artificial intelligence is built along human cognition i.e., learning and retrieving. A network is expected to recognize a previously learned pattern even when some noise is involved. Associative memory is desirable in building multimedia database. Here optimization plays a significant role as it deals with finding the solution which satisfies a given set of constraints. The purpose of AI will be to create admissible model to the human brain. The idea to produce some computational structures similar to neurons or neuron system and connections between them to form neural networks. Application of AI is vast and in this chapter an attempt is made to sensitize the reader with few basic prerequisites to start exploring AI using mathematics for advancements in forensic sciences. The topics involve maybe studied in details to implement the procedures. This will pave the way to understanding and exploring new search methods.

A variety of search mechanisms are employed to problems which include blind search, searching in extent and Heuristic search. A basic introduction to graph theory and probability with examples of implementation in game of 'chess' or 'Go' motivates the reader to have an insight to the applications. For example, the study of Bayesian networks as a direct acyclic connected graph, with probability distribution associated with each node that defines a mutual relationship between nodes and edges.

Modelling data can be done using algorithms mentioned could use MATLAB techniques. For example if the knowledge of how different parameters influence the energy load is known then we might use statistics or curve fitting tools to model the data with linear or nonlinear regression. If the number of variables is more, the underlying system is particularly complex, or the governing equations are unknown, then we could use machine learning techniques such as decision trees or neural networks. Use of Neural fitting app (Filion).

Further a deeper conceptualization aims to modify the existing research in the benefit of society as a whole. Future research in IoT forensic analysis is having wide scope as different algorithms could be modified incorporating data from relevant inputs which could lead to capturing of the limitations for the protype generated.

To conclude, the prerequisites of the chapter would enhance the reader to work with the underlying concepts in dept to advance the research in the field of image processing and forensic sciences. In future study, researchers may undertake improvement of the algorithms of forensic image processing for an enhanced AI development which is not possible without the basic understanding of some key procedures as discussed. Although the topics may be of interest to readers not limited to forensic sciences as they are applied in various engineering fields [26–33].

*Mathematical Basics as a Prerequisite to Artificial Intelligence in Forensic Analysis DOI: http://dx.doi.org/10.5772/intechopen.108416*
