Artificial Neural Network is a branch of Artificial intelligence, has been accepted as a new technology in computer science. Neural Networks are currently a 'hot' research area in medicine, particularly in the fields of radiology, urology, cardiology, oncology and etc. It has a huge application in many areas such as education, business; medical, engineering and manufacturing .Neural Network plays an important role in a decision support system. In this paper, an attempt has been made to make use of neural networks in the medical field (carcinogenesis (pre-clinical study)). In carcinogenesis, artificial neural networks have been successfully applied to the problems in both pre-clinical and post-clinical diagnosis. The main aim of research in medical diagnostics is to develop more cost-effective and easy-to-use systems, procedures and methods for supporting clinicians. It has been used to analyze demographic data from lung cancer patients with a view to developing diagnostic algorithms that might improve triage practices in the emergency department. For the lung cancer diagnosis problem, the concise rules extracted from the network achieve an high accuracy rate of on the training data set and on the test data set.

Expert system -a branch of Artificial Intelligence is a collection of programs which has the ability to reason, justify and answer the queries in a particular domain as a human expert would do. It can be applied to various fields. Expert system in agriculture is gathering momentum and this paper aims at tackling the control and remedial measures for disease management for the staple food crop of Karnataka -Finger Millets popularly known as Ragi. The introduction section consists of contributions of expert systems in agriculture. The second section explains the process in Integrated Disease Management (IDM).The third section is about knowledge engineering process which consists of knowledge acquisition and knowledge representation. The fourth section is about the application of fuzzy logic in IDM. The fifth section briefs about defuzzification of IDM.

Image Processing refers to the use of algorithm to perform processing on digital image. Microscopic images like some microorganism images contain different type of noises which reduce the quality of the images. Removing noise is a difficult task. Noise removal is an issue of image processing. Images containing noise degrade the quality of the images. Removing noise is an important processing task. After removing noise from the images, the visual effect will not be proper. Image Sharpening in an image is basically a process of extracting high frequency details from the image and then adding this information to the blurred image. This paper presents an approach to a de-noise method for noisy image and a method of sharpening of the noisy image.

A subset of AI is, evolutionary algorithm (EA) which involves evolutionary computation, a generic populationbased meta heuristic optimization algorithm. An EA uses some mechanisms inspired by biological evolution: reproduction, mutation, recombination, and selection. A genetic algorithm (GA) is a search technique used in computing to find exact or approximate solutions to optimization and search problems. Working of a search engine deals with searching for the indexed pages and referring to the related pages within a very short span of. Search engines commonly work through indexing. The paper deals with how a search engine works and how evolutionary algorithms can be used to develop a search engine that feeds on previous user requests to retrieve "alternative" documents that may not be returned by more conventional search engines.

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