Proteases are the most important group of enzymes utilized commercially in various arenas of industries, such as food, detergent, leather, dairy, pharmaceutical, diagnostics, and waste management, accounting for nearly 20% of the world enzyme market. Microorganisms of specially Bacillus genera serve as a vast repository of diverse set of industrially important enzymes and utilized for the large-scale enzyme production using a fermentation technology. Approximately 30%-40% of the cost of industrial enzymes originates from the cost of the growth medium. This study is attempted to produce protease from Bacillus subtilis (ATCC 6633) after optimization of various process parameters with the aid of solid-state fermentation using a cheap nutrient source such as wheat bran. B. subtilis (ATCC 6633) produces proteases of molecular weight 36 and 20 kDa, respectively, in the fermented medium as evident from SDS zymogram. Alkaline protease activity has been detected with optimum temperature at 50 °C and is insensitive to ethylenediaminetetraacetic acid. This thermostable alkaline protease exhibits dual pH optimum at 7 and 10 with moderate pH stability at alkaline pH range. It preserves its activity in the presence of detergent such as SDS, Tween 20, and Triton X-100 and may be considered as an effective additive to detergent formulation with some industrial importance.

Duplicate Detection is critical task of any database of any organization. Duplicates are nothing but the same real time entities or objects are presented in the form of different structure and in the different formats. We can find out the duplicates in relational data, in complex data and hierarchical data like XML. There are lots of works already presented in the past for finding the duplicates in the relational data. But nowadays there is more focus on finding duplicates in the XML data. Because of XML is very popular for data storing and extensively used for data exchange between the organizations. Here we have done an extensive literature survey on this topic and proposed a duplicate detection method that incorporates some of the existing paper's ideas and some of our original ideas. In addition to improving the efficiency and effectiveness, we also checks for its typographical errors when comparing the two XML elements. To test the correctness of Improved network pruning method, we are comparing it with existing duplicate detection system, and giving more focus on how we get higher precision and recall values in the various datasets we would used.

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