Prediction of Methanobacterium using suffix tree is a server designed to know the query sequence related to Methanococus thermophilus and also to execute the maximum length of the string related to Methanococcus. MEGA 4.0 is used to know the conserved sites aligned from 70 sequences related to 16S rRNA nucleotide sequence (Methanococcus thermophilus) from NCBI database. There are 12 strings aligned in all sequences that are highly conserved in the aligned sequences. Ukkonen’s algorithm is used to find the suffix tree for the given patterns (conserved sites). If the query sequence is submitted to the PMST (Prediction of Methanobacterium using Suffix Tree), the results will give the maximum sequence length and the suffix tree based on Ukkonen’s algorithm.

Microarray is one of the most recent and important technology for exploring the genome [1,2]. Image analysis is an important role of microarray experiments, one, which can have a potentially large impact on subsequent analysis such as clustering or the identification of differentially expressed genes [3]. Microarray image grid and spot position determination is a very important step in the analysis of microarray image because it is the first part we need to do for the analysis. By comparing gene expression in normal and abnormal cells, microarrays may be used to identify genes, which are involved in particular diseases. These genes may then be targeted by therapeutic drug. Making this part automated and fast is also important. So we consider this as indeed problem of spot detection and overcome it by using flat structuring element disk-diamond shaped morphological technique, which gives satisfied results. And also we compare the original &resultant image results on the basis of image quality measure [4].

Software reliability is an important aspect of software quality. According to ANSI, it is defined as "the probability of failure-free operation of a computer program in a specified environment for a specified time". One of reliability's distinguishing characteristics is that it is objective, measurable, and can be estimated, whereas much of software quality is subjective criteria. This distinction is especially important in the discipline of SQA. These measured criteria are typically called software metrics. Although software reliability is defined as a probabilistic function, and comes with the notion of time, we must note that, software reliability is different from traditional hardware reliability, and not a direct function of time. Electronic and mechanical parts may become "old" and wear out with time and usage, but software will not rust or wear-out during its life cycle. Software will not change over time unless intentionally changed or upgraded. Neural Network-based Classification Method (NNCM) was used to classify the data using recordset cyclomatic density and design density. The records were preprocessed using normal distribution. The overall error in the classification using NNCM after normal distribution was found to be 0.38%. The reliability of classification with goodness of fit measure results in and forms the subsequent improvement of error classification among the dataset.

Data mining models are extensively used in the field of disease diagnosis. Gene expression data are a main factor for the success of disease diagnosis. With thousands of gene expression data, gene selection is being a big challenge prior to classification. The proposed method incorporates two stages in gene selection. In the first stage pair wise gene selection was performed using a popular statistical technique. In the second stage the gene pairs that achieved 100% Cross Validation (CV) accuracy of those genes selected in first stage were used for classification. The testing results were compared with the single stage method and improvement on the computational burden was also proven to be the best in the proposed two-stage method. The paper also compares the performances of the three different classifiers Support Vector Machines (SVM), K Nearest Neighbour (KNN), Linear Discriminant Analysis (LDA) and promising results have been achieved.

This paper proposes a method for face detection using Modified Fisher Discriminant Analysis (FDA) and Support Vector Machine (SVM). It is a three layer architecture system that identifies all image regions which contain face. The face detection is a preprocessing stage for an automatic face recognition system. At the first stage, the Modified Fisher Linear Discriminant Analysis (FDA) classifies the input pattern into three classes: a face class, undecided class and non-face class. At the next stage, the SVM classifies the undecided class or non face class as either face or non-face class. In the Final stage, FDA-SVM detects the face class if any sub image region falsely judged as non-face class. This system alleviates the problem of false positive rate. The experimental result shows that the proposed approach outperforms some of the existing face detection methods.

MMS is the popular technology that is being misused heavily in the present times. In this paper, we are suggested MMSSEC algorithm to prevent unauthorized users from viewing messages which are not meant for them. This algorithm uses encryption and decryption technique for securing communication.

Plants and Trees are such natural objects which exhibit the property of self similarity. There are many methods developed in computer graphics to generate these self similar objects. Efforts are still going on to formulate new techniques to generate such objects with minimal efforts and time. Parallel string rewriting method has been used to develop fractal formalism in computer graphics. This string rewriting technique can be represented in graphical form through turtle graphics. In this paper it has been investigated and experimented to generate objects like plants and trees using parallel string rewriting formalism.

This paper presents the review of Reconfigurable manufacturing system. That aims at achieving cost effective and rapid system changes needed, by incorporating principle of modularity, integrability and scalability as this new manufacturing system. Reconfigurable manufacturing system promises customized flexibility in a short time, while the other manufacturing system provides generalized flexibility designed for anticipation variations. This paper shows the definition and background of reconfigurable manufacturing system. In this research paper an overview of components of reconfigurable manufacturing system and comparisons of different manufacturing system with their merits and demerits are presented. The capabilities of reconfigurable manufacturing system, challenges of reconfigurable manufacturing system and key role in reconfigurable manufacturing system are explained. The characteristic of reconfigurable manufacturing system are also presented in this research paper.

Metal forming processes are compression-tension processes involving wide spectrum of operations and flow conditions. The result of the process depends on the large number of parameters and their interdependence. The selection and optimization of various parameters is still based on trial and error methods. In this paper the authors presents a compressive study of application of evolutionary strategies to optimize the geometry parameters such as die design and punch design, process parameters such as forming load, blank holder pressure and coefficient of friction, the spring back, hammering sequence etc. Evolutionary algorithms offer many advantages over traditional methods. These are widely used now days for sheet metal industry.

High-throughput image analysis is very important aspect of modern post-genomics and proteomics research. Image optimization technology is the driving force of this revolution and technology, which allows the simultaneous monitoring of expression for thousands of images. The need for accurate and reproducible research has driven the development of robust analysis frameworks for maximizing the information content of cancer pathological data. Image optimization is a powerful tool has multiple applications both in clinical and cellular and molecular biology arenas. Image analysis technology has shown new advancements in the field of biomedical research and diagnosis, it allows studying and understanding tumor activities and interactions in malignancies or diseases; therefore, it has great potential for clinical diagnostics in the future.

Data mining in brain imaging is an emerging field of high importance for providing prognosis, treatment, and a deeper understanding of how the brain functions. Dementia due to Alzheimer’s disease constitutes the fourth most common disorder among the elderly. Early detection of dementia and correct staging of the severity of dementia is critical to select the optional treatment. The present study was designed to classify and categorize brain images of dementia patients into three distinct classes i.e., Normal, Moderately diseased, and Severe. Decision Forest Classifier was employed to classify the various Magnetic Resonance Images (MRIs) of dementia patients. Results of screening the MRIs are organized by classification and finally grouped into the three categories, i.e., Normal, Moderate and Severe. Experimental results obtained indicated that the proposed method performs relatively well with the classification accuracy reaching nearly 99.32% in comparison with the already existing algorithms.