Shamir's (t, n)-SS scheme is very simple to generate and distribute the shares for a secret among n participants by using such polynomial. We assume the dealer a mutually trust parity when he distributes the shares to participants securely. In addition when the participants pooling their shares in the secret reconstruction phase a honest participants can always reconstruct the real secret by Pooling areal shares. The property of verifiability enables participants to verify that their shares are consistent. Tompa and Woll suggested an important cheating scenario in Shamir's secret reconstruction. They found a solution to remove a single cheater with small probability, unfortunately, their scheme is based on computational assumptions. In addition each participants will receive a huge number of shares. In this paper we will construct scheme to be information-theoretically secure verifiable secret sharing which does not contain a single cheater. On the other hand we will eliminate these problems in Tompa and Woll scheme. Our proposed scheme is not only to detect and identify a cheater, but to prevent him from recovering the secret when the honest participants cannot.

Research Domain Selection plays an important role for researchers to identify a particular document based on their discipline or research areas. This paper presents a framework which consists of two phases. In the first phase, a word list is constructed for each area of the research paper. In the second phase, the word list is continuously updated based on the new domains of research documents. Primary area and Sub area of the documents are identified by applying pre-processing and text classification techniques. Naive Bayes classifier is used to find the probability of various areas. An area having the highest probability is considered as primary area of the document. In this paper text classification procedures is condensed as that are utilized to arrange the content archives into predefined classes. Based on the performance analysis, it has been observed that the obtained results are efficient when compared to manual judgement.

Root-mean-square-deviation (RMSD) is an indicator in protein-structure-prediction-algorithms (PSPAs). Goal of PSP algorithms is to obtain 0 Å RMSD from native protein structures. Protein structure and RMSD prediction is very essential. In 2013, the estimated RMSD proteins based on nine features were obtained best results using D2N (Distance to the native). We presented in This paper proposed approach to reduce predicted RMSD Error Than the actual amount for RMSD and calculate mean absolute error (MAE), through feed forward neural network, adaptive neuro fuzzy method. ANFIS is achieved better and more accurate results.