Search

Pattern matching algorithm plays a vital role for searching and analyzing patterns in computational biology. The functional and structural relationship among different DNA databases is performed by different algorithms.The similarities among the biological sequences determined by determined bymatchingalgorithms, is a major research area.This paper surveys the performance of Naïve exactmatching, and Knuth-Morris-Pratt (KMP) algorithms. The main process of this study is to find the matched DNA sequence from the database using these pattern matching algorithms. This study determines more efficient pattern matching algorithm by analyzing the matching result. Among those three algorithms, Boyer Moore algorithm is faster and efficient for matching large DNA sequence. It has no needless comparison. It works sub linearly on best case. Comparison result also demonstrates that the matching values of natural genomes are higher than virus genomes.

Data mining is a sum of process to find anomalies, patterns, correlations which can assist banks to look for hidden patterns in a group and discover unknown relationship in the data. In this competitive world, governments, private companies, large organizations and all businesses, predict their future plans using various methods of data mining. Banks are an integral part of a country’s economy, contributing to both people and governments. In case of money transaction bank is the most essential media in our country. In this circumstance, some vested peoples gratify their evil task. These individual acts make a big issue for our country’s economy. In this paper, we have discussed about the comparative study on several data mining classification techniques that are generally used to classify suspicious transactions, included Naïve Bayes, MLP (is the particular feed forward method of ANN), sequential minimal optimization (SMO) and decision tree based J48 & random forest algorithm. we have found the random forest algorithm performance is better than others to classify banking transactions.

The Biometric recognition is the study of identifying individuals based on their unique physiological or behavioral characteristics, includes iris, face, fingerprint, retina, vein, hand geometry, hand writing, human gait, signature, keystrokes and voice. Among the biometrics, an iris has unique structure and it remains stable over a person life time. So that iris recognition is regarded as the most accurate and reliable biometric recognition system. In this paper, we proposed a technique that uses Principal Component Analysis (PCA) based on Discrete Wavelet Transformation (DWT) for selecting feature of iris templates to increase the efficiency of iris recognition. Basically, the idea of DWT is to convert the iris image into four frequency band. We are using one frequency band instead of four and applying PCA for further feature extraction. Experiments with iris images from the CASIA database present good results, showing that the proposed combination strategy of feature extraction is suitable for increasing accuracy of iris recognition.

Lao PDR and Cambodia have been transitioning to a market-oriented policy regime. Both are agrarian economies with agriculture contributing about one-third of the gross domestic product (GDP). This study assessed their prospects of achieving the first Millennium Development Goal (MDG 1, extreme poverty eradication) and the centrality of agricultural growth in achieving this goal. As these are macro relationships, richer insights on determinants of poverty were obtained by detailed analyses of recent household surveys in these countries. Some of these insights relate to access to markets, returns to crops, education, land size, non-farm activities, ethnic affiliation, and rural infrastructure, with unavoidable variation due to differences in the coverage of the household surveys used. Another major theme studied for Cambodia is integration of farmers (especially smallholders) into markets, focusing on barriers between large and smallholders (e.g., transaction costs). An accelerated transition to a more market-oriented policy regime may promote not just a more efficient agriculture but also a more equitable outcome.

Lao PDR and Cambodia have been transitioning to a market-oriented policy regime. Both are agrarian economies with agriculture contributing about one-third of the gross domestic product (GDP). This study assessed their prospects of achieving the first Millennium Development Goal (MDG 1, extreme poverty eradication) and the centrality of agricultural growth in achieving this goal. As these are macro relationships, richer insights on determinants of poverty were obtained by detailed analyses of recent household surveys in these countries. Some of these insights relate to access to markets, returns to crops, education, land size, non-farm activities, ethnic affiliation, and rural infrastructure, with unavoidable variation due to differences in the coverage of the household surveys used. Another major theme studied for Cambodia is integration of farmers (especially smallholders) into markets, focusing on barriers between large and smallholders (e.g., transaction costs). An accelerated transition to a more market-oriented policy regime may promote not just a more efficient agriculture but also a more equitable outcome.