A New Gridding Technique for High Density Microarray Images Using Intensity Projection Profile of Best Sub Image

As the technologies for the fabrication of high quality microarray advances rapidly, quantification of microarray data becomes a major task. Gridding is the first step in the analysis of microarray images for locating the subarrays and individual spots within each subarray. For accurate gridding of high-density microarray images, in the presence of contamination and background noise, precise calculation of parameters is essential. This paper presents an accurate fully automatic gridding method for locating suarrays and individual spots using the intensity projection profile of the most suitable subimage. The method is capable of processing the image without any user intervention and does not demand any input parameters as many other commercial and academic packages. According to results obtained, the accuracy of our algorithm is between 95-100% for microarray images with coefficient of variation less than two. Experimental results show that the method is capable of gridding microarray images with irregular spots, varying surface intensity distribution and with more than 50% contamination

Inventory management in public sector electrical industry in Kerala

The thesis entitled Inventory Management In Public Sector Electrical Industry In Kerala. Investigations were carried out on inventory management in public sector electrical industry in Kerala and suggest methods to improve their efficiency. Various aspects of inventory management, its scope and need in industry are detailed. The objectives of the present study concentrates to get an overall view of the system of inventory management, assess the positions and levels of inventory. It analyzes the inventory management policies and practices, the organizational set-up for materials by the electrical undertakings. The study examines the liquidity of the electrical undertakings as well as techniques of inventory management in the electrical industry in Kerala. Hypotheses state that the existing organizational systems and practices are inadequate to ensure efficient management of inventories in electrical industry. Introduction of scientific inventory techniques has a favourable effect on the workings of inventory departments. The financial performance of the public sector electrical undertakings is not at all satisfactory on account of the high raw material costs, heavy borrowings and huge interest burdens. The scope of this study is limited to the assessment of savings, in inventories of electrical products due to inventory management. The methodology of the study is to project the cost reduction of the inventory department on the basis of data collected and to validate this projection with the aid of analysis and survey. The limitations of the study is that the data obtained relate to the period 1989-90 and earlier and the current position is not available and uniform norms cannot be applied to evaluate different inventory management organisation.

Development of Hierarchical Clustering Techniques for Gridded Data from Mixed Data Sequences

Knowledge discovery in databases is the non-trivial process of identifying valid, novel potentially useful and ultimately understandable patterns from data. The term Data mining refers to the process which does the exploratory analysis on the data and builds some model on the data. To infer patterns from data, data mining involves different approaches like association rule mining, classification techniques or clustering techniques. Among the many data mining techniques, clustering plays a major role, since it helps to group the related data for assessing properties and drawing conclusions. Most of the clustering algorithms act on a dataset with uniform format, since the similarity or dissimilarity between the data points is a significant factor in finding out the clusters. If a dataset consists of mixed attributes, i.e. a combination of numerical and categorical variables, a preferred approach is to convert different formats into a uniform format. The research study explores the various techniques to convert the mixed data sets to a numerical equivalent, so as to make it equipped for applying the statistical and similar algorithms. The results of clustering mixed category data after conversion to numeric data type have been demonstrated using a crime data set. The thesis also proposes an extension to the well known algorithm for handling mixed data types, to deal with data sets having only categorical data. The proposed conversion has been validated on a data set corresponding to breast cancer. Moreover, another issue with the clustering process is the visualization of output. Different geometric techniques like scatter plot, or projection plots are available, but none of the techniques display the result projecting the whole database but rather demonstrate attribute-pair wise analysis