Applications And Potentials Of Artificial Neural Networks In Plant Tissue Culture

In a broad sense, intelligence is something, which deals with the ability to grasp, analyze a task and then reach for a logical conclusion upon which an action can be initiated. Over the years, many researchers have been attempting to create a non-biological entity that can match human level performance. Such attempts have manifested in the emergence of a cognitive approach termed as artificial intelligence (AI). There are many ways in which artificial intelligence can be manoeuvred to execute its function. Computers can be programmed to provide a platform for a coherent approach for executing a particular task. Complex mathematical functions can be deciphered and logical theorems can be deduced by the use of symbolic artificial intelligence. But symbolic artificial intelligence neither could decrypt a digitized image nor could deduce a signal from imperfect data, and has difficulty in adapting things to a change in a specified process. Many problems do exist which cannot be elucidated by simple stepwise algorithm or a precise formulae, particularly when the data is too complex or noisy. Such problems require a sort of connectionism or in other words a network approach. It is possible to interconnect many mathematical functions, all of which perform a dedicated task of processing the data into a desired form of meaningful output. The data can be forwarded through valued connection routes. The conduction strength of the routes, which regulates the movement of data processing can act as a sort of memory and can be very useful in adapting to process changes. Function wise, such network approach is exactly the reverse of symbolic AI. The strength of neural network analysis lies in its ability to generalize distorted and partially occluded patterns and potential for parallel processing. However, they encounter difficulty in formal reasoning and rule following. The results of applying such network technology have been found to be astounding and phenomenal with a relatively modest effort. Biological processes are incomprehensible in terms of their behaviour with respect to time. It is a well-recognized fact that the genetic and environmental factors are the key effectors, which contribute to their functioning. These two factors have a very high degree of variability in and among themselves ultimately manifesting in a wide spectrum of biological developments that are non-deterministic and non-linear in nature. Such