American Research Journal of Computer Science and Information Technology

Ammar Suhail Balouch

The goal of a distributed computation algorithm is to determine the result of a function of numerical elements, which are distributed in multi sets.It is known that computation of holistic aggregation functions on distributed multi sets indeed requires more work than non holistic aggregation functions. But with this article we will prove that the computation of a holistic function, which named exact median, can be computed efficiently by providing both a candidate finding and a deterministic location algorithms which computes the position of exact median, dispelling the misconception that solving distributed median computation through parallel aggregation is infeasible. Some of most important part in Big Data field is to evaluate massive data values. A special case in this field is the calculation of smallest values (specially the median) of distributed multi sets containing enormous data. Many approximation algorithms and algorithms with iterative or recursive steps of determination of median give solutions for the computation of median. But firstly sometime approximate value is dangerous for some data evaluation projects or researchs and secondly with other algorithms, the data blocking time is too long through the iteration or the recursionbetweenglobal node and local nodes. This article focuses on a solution that gives a best effectively computation for this problem named PCM-oMaRS algorithm. The PCM- oMaRS algorithm guarantees the maximal reduction steps of the computation of the exact median in distributed multi sets and proves that we can compute the exact median effectively without needing the usage of recursive or iterative methods at the global communication level, which reducesthe blocking timemaximally. This algorithm provides more efficient execution not only in distributed multi sets even in local multi set with enormous data.

N.Raveendran1, Dr. Antony Selvadoss Thanamani2

Cloud computing is an emerging technology at present. Lots of small and medium businesses industries are increasingly realizing the benefits of cloud computing and are moving towards cloud computing environment. By using cloud computing, the users, quickly and dynamically share their resources at minimal costs. Due to this increased efficiency and the decreased cost, this service has got a huge popularity in digital libraries of many institutions. The institutions which use cloud for their digital libraries are little discomfort due to many uncovered potential and critical threats which remain most security issues to consider on cloud computing. Security is a major issue in digital library system. The security weakness in digital libraries leads to unauthorized access of confidential information or loss of integrity of the stored data. Also, it may leads to loss of trust for publishers and for other content providers on library.The abundance of these systems and emergence of new and different threats require efficient security mechanisms. To overcome these issues, this paperintroduced autonomic based cloud computing approach for self-protecting and self-optimizing digital library database system environment that captures dynamic and fine-grained trade-offs between security and quality of service.

Ammar Suhail Balouch

The recognition of motion is widely used in games development field but it is active too in care systems. Recognition of motion according measurements needs data (values) from many sensors, like Position, Velocity, Acceleration, Orintation, etc. We have two major ways to determine which placements of sensors on the body are required to recognize the motion. The first way connects its work with the results of other science branches like sports science and game development. The other one depends on the following strategy. Many sensors were placed on the body, without the knowledge, which sensors are required. Then according an analysis of the stored data for each sensors, the behavioral similarity of these sensors will be extracted.The target of both ways is to reduce the cost of building a suit of sensors, and simultaneously to keep the results of the recognition of motion correct.In this paper we follow the second way and define a new regression analysis“ReSeVA” depending on vector definition(on its angles and longs) and on the principle of Newton’s law of metion.

Abdullah Alsulami, Darren Teo, Weiyi He

Combining iris biometric system and cryptography will provide a strong security authentication and high performance. Iris-based cryptosystem appears to be the strongest way to generate a key and authenticate users among other biometric recognition such as voice and fingerprint that because the randomness, uniqueness, and stability of Iris. Iris biometric cryptography is a technique used to secure communication and encrypt data between parties using biometric features to provide a False Accept Ratio (FAR) and a False Reject Ratio (FRR). This report also described the effective biometric security techniques for iris recognition system with high confidence and high performance. The Iris system is based on an experimental analysis of chosen iris images for same and different persons using several steps. The system steps based on the segmentation, normalization, image enhancement, feature extraction, and encoding.

Abdullah Alsulami 

Nowadays managing big data is one of the most challenges in data management. Big data mining has become a major focus of data management in the world helping to provide detailed data that informs major decisions, to adequately understand the concept of big data mining. This research paper is analysis four research articles and conferences papers from various aspects of big data mining regarding their approaches, issues, findings and improvements that articles have had in the field of data mining, and addresses the gap in data mining literature. The large volumes of data and the various types, and sources of data that act as barriers to big data mining will be discussed in details. The application of big data mining in clinical medicine will be discussed as well to identify how it improves the clinical decision making and practices. The methods that have been used to analyse and increase the efficiency and accuracy of the data mining process will be identified to provide a critical information to guide industries to make the proper decision.