Keyword Specific Cloud Computing

It is now a known fact that Internet of things (Iot) and Cloud computing will be the way ahead. Store and transmit of massive amounts of data is on the cards in the coming years which will profoundly affect other areas of everyday life in the next generation. Cloud and Iot are merged together is seen as an enabler of a large number of application scenarios. As an example at the start of 2016 automakers are building a driverless taxi service. Keeping this in mind a novel idea of keyword based Cloud Computing is brought about in this paper which gives out entire data to the user if the user types the keyword of the required entity

iWRAP: An Interface Threading Approach with Application to Prediction of Cancer-Related Protein–Protein Interactions

Current homology modeling methods for predicting protein–protein interactions (PPIs) have difficulty in the “twilight zone” (< 40%) of sequence identities. Threading methods extend coverage further into the twilight zone by aligning primary sequences for a pair of proteins to a best-fit template complex to predict an entire three-dimensional structure. We introduce a threading approach, iWRAP, which focuses only on the protein interface. Our approach combines a novel linear programming formulation for interface alignment with a boosting classifier for interaction prediction. We demonstrate its efficacy on SCOPPI, a classification of PPIs in the Protein Databank, and on the entire yeast genome. iWRAP provides significantly improved prediction of PPIs and their interfaces in stringent cross-validation on SCOPPI. Furthermore, by combining our predictions with a full-complex threader, we achieve a coverage of 13% for the yeast PPIs, which is close to a 50% increase over previous methods at a higher sensitivity. As an application, we effectively combine iWRAP with genomic data to identify novel cancer-related genes involved in chromatin remodeling, nucleosome organization, and ribonuclear complex assembly. iWRAP is available at http://iwrap.csail.mit.edu.National Institutes of Health (U.S.) (Grant 1R01GM081871

Redundancy in Face Image Recognition

Many researchers paid attention to formulate different algorithms to faces and its classes for accurate classification but, did not paid attention to the fact that redundancy may exists even though faces with different classes are effectively classified. Researchers working on SVD and its extended algorithm versions which were based on face matrix decomposition for face recognition concluded that they are the best algorithms for classification of occluded faces. The problem with these designed algorithms is that there is every likely hood of having more than one value of amplification factor along with classified faces. It is pointed out by researchers that every face will be having one and only one amplification factor and its classified face. This factor will definitely add to the already existing facial recognition problems and challenges. Here is a paper which shows the redundancy in recognition which will be treated as an added problem and challenge for facial recognition