Proteins are one of the most important molecules in living organisms so they play a vital structural role in the cells of living organism. They are constructed of several polypeptide chains of amino acids, which fold into complex tertiary Structure. The knowledge of the protein function is directly dependent on its three dimensional (tertiary) structure. The Physicochemical properties of proteins always guide to determine the quality of the protein tertiary structure. Therefore it has been rigorously used to distinguish native or native like structure from other predicted structure. The experiments were conducted on the CASP dataset to classify RMSD (target class) near to native protein tertiary structure or not. Kernel principal component analysis (KPCA) is used for feature extraction since it performs better than PCA on protein tertiary structure dataset due to their nonlinear structures. The proposed model compare with neural network classification method. The experiments conducted shows that support vector machine combined with KPCA feature extraction performs better than neural network classifier. More than our results show better performance in Gaussian KPCA feature extraction with respect to other kernels.

Wireless Sensor Networks (WSNs) are type of network which is mainly deployed to those areas where infrastructure network is unavailable and where people cannot reach easily. To those areas sensors are deployed to collect information about the environment. Since sensors are run by battery power, long time use of sensors to those remote areas, is one of the main challenges for WSN. To deal with long time use of battery power, low power consumption technique is used. IEEE 802.15.4 standardization is used for Low Rate Wireless Personal Area Network (LRWPAN). To get a lesser amount of consumption of power, few parameters of IEEE 802.15.4 are changed in our proposed strategy. At last comparison of proposed strategy and IEEE 802.15.4 is made where proposed strategy performs well for low power consumption.

In this paper a fully reused VLSI architecture of FM0/Manchester encoding technique for memory application has been proposed. In this paper we are encoding the 1 bit data into 16 bit data and storing it into a memory of certain address location given by the linear feedback shift register (LFSR),whose input is taken from the pseudo random sequence generator (PRSG). The encoded 16 bit data is stored into memory controller; the encoded data is decoded back into 1 bit data under the condition: when MSB bit is at logic state 1. By using FM0/Manchester encoding and decoding technique, the data will be secure , this process is easy and faster to carry out. This paper develops a fully reused VLSI architecture, and also exhibits an efficient performance.

Cloud computing is an important name in today’s age of computing. This is kind of computing platform or architecture which believes in virtualization. The virtualization is including Hardware, Software, Application Packages and others. Cloud computing uses commodity based hardware as its base. This is actually helps the organization to buy the run IT Infrastructure of their own. Thus it helps general organization to get service to other organization and need to pay depending upon requirement. Though cloud computing has some of the benefit but still treated as most advance result of computing research. Cloud computing deployment models may be classified in to three main category and some minor types. This paper is talks about cloud computing and its basic nature; including its various deployment model with special reference to SWOT Analysis.