A vast amount of knowledge exists within biomedical literature, publications, clinical notes and online content. Identifying hidden, interesting or previously unknown biomedical knowledge from free text resources using an automated approach remains an important challenge. Towards this problem, we investigate the use of deep learning methods that have shown significant promise in identifying hidden patterns from large corpus of text in an unsupervised manner. For example, it can deduce that 'husband' - 'man' + 'woman' = 'wife'. We use the text corpus from MRDEF file in the Unified Medical Language System (UMLS) dataset as training set to discover potential relationships. To evaluate our approach, we cross-verify new relationships against the UMLS MRREL dataset and conduct a manual evaluation from a sample of the non-overlapping set. The algorithm found 32% of new relationships not originally represented in the UMLS. The deep learning methods provide a promising approach in discovering potential new biomedical knowledge from free text.

Quantum cryptography is marches towards secure communication by using quantum protocols. Number of quantum protocols has been evolved based on an entanglement in three decades; similarly during this meanwhile non-entanglement based protocols have been evolved within the same period also. Among number of different protocols a torch bearer was BB84 protocol. Even though different quantum communication protocols exist, the BB84 protocol proved its application by initial experiments whereas most of the other protocols are theoretical which marches towards the practical application yet. But in quantum mechanics principle, cryptography based on an entanglement and superposition of entangled particle. Furthermore, challenges ahead are development and design high sensitive equipments for measurement of an entangled particle position at output end. Particle entanglements open a new door for computation worlds such as speedup, security. In this article, we discuss quantum protocols, their challenges, and applications based on above discussion.

Many existing industrial and research data sets contain missing values. Data sets contain missing values due to various reasons, such as manual data entry procedures, equipment errors, and incorrect measurements. It is usual to find missing data in most of the information sources used. Missing values usually appear as "NULL" values in the database or as empty cells in the spreadsheet table. Multiple ways have been used to deal with the problem of missing data. The proposed model presents rough set theory as a technique to deal with missing data. This model can handle the missing values for condition and decision attributes, the web application was developed to predict these values.

In many number theoretic cryptographic algorithms, encryption and decryption is of the form xn mod p, where n and p are integers. Exponentiation normally takes more time than any arithmetic operations. It may be performed by repeated multiplication which will reduce the computational time. To reduce the time further fewer multiplications are performed in computing the same exponentiation operation using addition chain. The problem of determining correct sequence of multiplications requires in performing modular exponentiation can be elegantly formulated using the concept of addition chains. There are several methods available in literature in generating the optimal addition chain. But novel graph based methods have been proposed in this paper to generate the optimal addition chain where the vertices of the graph represent the numbers used in the addition chain and edges represent the move from one number to another number in the addition chain. Method 1 termed as GBAPAC which generates all possible optimum addition chains for the given integer n by considering the edge weight of all possible numbers generated from every number in addition chain. Method 2 termed as GBMAC which generates the minimum number of optimum addition chains by considering mutually exclusive edges starting from every number. Further, the optimal addition chain generated for an integer using the proposed methods are verified with the conjectures which already existed in the literature with respect to addition chains.

The subset sum problem is to decide whether for a given set of integers A and an integer S, a possible subset of A exists such that the sum of its elements is equal to S. The problem of determining whether such a subset exists is NP-complete; which is the basis for cryptosystems of knapsack type. In this paper a fast heuristic algorithm is proposed for solving subset sum problems in pseudo-polynomial time. Extensive computational evidence suggests that the algorithm almost always finds a solution to the problem when one exists. The runtime performance of the algorithm is also analyzed.