In this paper the probabilistic method is presented for solving the minimum vertex cover problem using systems of non-linear equations that are formed on the basis of a neighborhood relationship of a particular vertex of a given graph. The minimum vertex cover problem is one of the classic mathematical optimization problems that have been shown to be NP-hard. It has a lot of real-world applications in different fields of science and technology. This study finds solutions to this problem by means of the two basic procedures. In the first procedure three probabilistic pairs of variables according to the maximum vertex degree are formed and processed accordingly. The second procedure checks a given graph for the presence of the leaf vertices. Special software package to check the validity of these procedures was written. The experiment results show that our method has significantly better time complexity and much smaller frequency of the approximation errors in comparison with one of the most currently efficient algorithms.

Currently, the service provider network capacity utilization is low due to the shortest multipath based routing protocols Opens Shortest Path First (OSPF) and Intermediate System-to-Intermediate System (ISIS). Due to inefficient routing approach, certain paths can be overloaded and link capacity is required while alternative paths are unused. The overall network has to be dimensioned with higher link bandwidth requirements introducing additional line, linecard, routing engine and overall solution cos. This paper provides improved Congestion Aware Multipath Routing (CAMRv2) algorithm overview. The new network routing algorithm allows higher throughput, network load-balancing and stability to ensure lower congestion and data drop on critical links. The algorithm discovers unused network resources and dynamically adapts to the actual traffic load and displacement. The focus in this paper is on new parameters for path computation performance improvement. Additionally, detailed IPv6 source routing CAMRv2 implementation for parallel coexistence with present networks is presented. Finally, the new routing algorithm is simulated in several scenarios over aggregation network. The result of simulations have proved better performance and resource utilization of the proposed algorithm in sparse aggregation network in terms of load-balancing between uplinks to the core network.

The focus of this work is on how the congestion experienced on the GSM network can be minimized. The voice calls is broken into sub-classes of services and a level of priority is established among the classes so that the most urgent and important service will have access to the channel by preempting the lower priority services during congestion. The voice communications over the GSM network using the different classes of subscribers were analyzed with Markov chain’s model. The steady state probabilities for voice services were derived. The blocking and dropping probabilities models for the different services were developed using the Multi-dimensional Erlang B. To give a new call a fair sharing of the channel, Time-Threshold scheme is employed. This scheme classifies handoff call as either prioritised call or new call according to its associated elapsed real time value. The models were implemented based on the blocking and dropping probabilities models to show how the congestion can be minimised for different subscribers based on their priority levels. The work shows that the models used gave significant reduction in congestion when compared to the traditional Erlang-B model used in GSM.

The cuckoo search algorithm (CS) is a simple and effective global optimization algorithm. It has been applied to solve a wide range of real-world optimization problem. In this paper, an improved Cuckoo Search Algorithm (ICS) is presented for determining the age and relative contribution of different stellar populations in galaxies. The results indicate that the proposed method performs better than, or at least comparable to state-of-the-art method from literature when considering the quality of the solutions obtained. The proposed algorithm will be applied to integrated color of galaxy NGC 3384. Simulation results further demonstrate the proposed method is very effective. The study revealed that cuckoo search can successfully be applied to a wide range of stellar population and space optimization problems.

Software defined networking (SDN) is rapidly evolving technology which provides a suitable environment for easily applying efficient monitoring policies on the networks. SDN provides a centralized control of the whole network from which monitoring of network traffic and resources can be done with ease. SDN promises to drastically simplify network monitoring and management and also enable rapid innovation of networks through network programmability. SDN architecture separates the control of the network from the forwarding devices. With the higher innovation provided by the SDN, security threats at open interfaces of SDN also increases significantly as an attacker can target the single centralized point i.e. controller, to attack the network. Hence, efficient adaptive monitoring and measurement is required to detect and prevent malicious activities inside the network. Various such techniques have already been proposed by many researchers. This paper describes a work of applying efficient adaptive monitoring on the network while maintaining the performance of the network considering monitoring overhead over the controller. This work represents effective bandwidth utilization for calculation of threshold range while applying anomaly detection rules for monitoring of the network. Accurate detection of anomalies is implemented and also allows valid users and applications to transfer the data without any restrictions inside the network which otherwise were considered as anomalies in previous technique due to fluctuation of data and narrow threshold window. The concept of fast switching also used to improve the processing speed and performance of the networks.

Detecting skew angle in a document image has been an area of research interest for a long time. This paper presents an experimental analysis of various existing skew detection techniques involving methods such as Radon transform, Hough transform, Principal Component Analysis (PCA), PCA with Wavelet transform and Moments with Wavelet transform. Detailed analysis of existing skew detection method against the parameters time complexity, space complexity, robustness, accuracy, flexibility, etc. has been carried out for seven different categories of digital documents. The categories of these documents spans from those containing handwritten text in different languages, to the ones with both text and pictures. Radon transform is observed to be the fastest method when the image size is small and works with virtually all types of documents. It is an accurate method as well as works faster, even with the document containing pictures. PCA method is also faster than Hough transform for machine printed documents but used less for real time skew distortion due to its limitations. If the document image size is large, then Moments with Wavelet transform has better time complexity than other methods, but do not work well with documents containing images. Hough transform is the most accurate method, though it is computationally expensive.

Big data technologies like Hadoop, NoSQL, Messaging Queues etc. helps in BigData analytics, drive business growth and to take right decisions in time. These Big Data environments are very dynamic and complex; they require performance validation, root cause analysis, and tuning to ensure success. In this paper we talk about how we can analyse and test the performance of these systems. We present the important factors in a big data that are primary candidates for performance testing like data ingestion capacity and throughput, data processing capacity, simulation of expected usage, map reduce jobs and so on and suggest measures to improve performance of bigdata.

In recent years, using mobile devices has a special place in human life and applicability of these devices leads to increased number of users. Business companies have integrated them with cloud computing technology and have provided mobile cloud in order to improve using mobile devices and overcome the energy consumption of mobile devices. In mobile cloud computing, computations and storages of mobile devices applications are transferred to cloud data centers and mobile devices are used merely as user interface to access services. Therefore, cloud computing will help to reduce energy consumption of mobile devices. In this paper, a new approach is given to reduce energy consumption of based on Learning Automata in mobile cloud computing. Simulation results show that our proposed approach dramatically saves energy consumption through determining the appropriate location for application.

Wireless Body Area Network (WBAN) is one of the most critical real time networks. In order to increase network lifetime, several protocols have been proposed in the Literature. Majority of these protocols are based on either coverage or residual energy of the network. However, none of the protocols is based on node criticality, communication count, coverage and residual energy. In this paper, we have taken into consideration all the above stated parameters to determine the optimal path for transmission of message. In particular, we are implementing this scenario for Athlete Activity Monitoring to identify the athlete with maximum physical stamina. As per best of our knowledge, only intra-BAN routing protocols are proposed till date. However, in this paper we have simulated Athlete Activity Monitoring scenario for intra-WBAN and inter-WBAN architecture. It is clear from the simulation results that our scheme performs much better in terms of energy consumption than earlier routing schemes.

Automation of manual work and systems is a vast growing trend as it brings in efficiency and quality in work. Online and offline aptitude exams are conducted by organizations and institutions for accessing the skills of students. An aptitude test is an effective method for testing the students however the creation of database for conducting aptitude tests is an uphill task, because the organization has to maintain to a very large database consisting of thousands of questions and the content has to be changed periodically to avoid repeating of questions. An Automated & Secure Content Generation System (ASCGS) is a framework that provides automated creation of aptitude questions by converting a single of type of question into multiple questions by altering its variables. Also the system automates the process of calculating the answer for every question by using its formula. Security in the system is provided by the means of encryption. The present system lags in many perspectives like every question in has to be created manually and also the answer for the question has to be computed manually. Since the entire work is being done manually so there is a high risk that some questions may contain error due to human fault and also the cost and effort required to create the content is large. The proposed system overcomes these shortcomings of the existing system as only one format is required to be created for one type of question thus saving time and human effort. Also it is no longer required to do mathematical calculation manually as it is done by the system, the user has to insert only the formula. The present system requires the organization very long time ranging from few weeks to few months for generating ten thousand questions. With the new system the same work can be done within few days time and with minimal cost. With the development of the system organizations will be relieved from the tedious work of content creation and also management of content will become easier and efficient. The software will enable the institutions to create aptitude questions of different levels thus enabling the institute to conduct aptitude tests for all the students of different classes based on their levels.