Vol 44, No 6 (2018)

Without any fixed infrastructure, the mobile ad hoc networks were distributed in the environment and this ad hoc network is nothing but a collection of the mobile nodes. In the network performance, transmitting plays a significant role, where it faces many complexities because of the dynamic changing behavior of mobile nodes. Environs Aware Neighbor-knowledge based Broadcasting (EANKBB) method and Optimal Cluster based Broadcasting using GA (OCBC-GA) were brought-in the earlier method, where the cluster head selection is performed optimally with the help of the genetic algorithm. The time sequence schedule is performed to permit each and every node to broadcast for the period of time, once after selecting the cluster head. But this work doesn’t concentrate much on the destination node’s location, where it has to transmit the broadcasted message to their neighbours, in order to attain the destination. This, in turn, leads to the memory overhead and utilization of the unwanted resource and then the node is permitted to broadcast the messages, if it has high priority and the rest of the nodes should wait until the prioritized node uncovered from the region. This will create issue in the performance of the rest of the nodes which occurs in the network. So, Node Failure Aware Broadcasting Mechanism (NFABM) is brought-in our work, to rectify these issues. With the help of the Hybrid Genetic cuckoo search optimization algorithm and location aware clustering, the cluster head selection is performed in the suggested work. The energy consumption, bandwidth, location and network coverage, were considered as the fitness value here and this, in turn, minimizes the number of broadcast message transmission by transmitting it only to the nodes that are located closer to the destination node. Prioritized Time Sequence Scheme helps our suggested work, to give the equal priority for entire nodes in the environment. Here, a time sequence is computed for every node and it is allocated to individual time period for every node to allow them to broadcast. This new algorithms works like allocating initial time sequence period for the prioritized node for the specific time and it would stop transmission when its time sequence completed. Hence, this concept helps to provide the priority to every node and also each node can be treated uniformly. In the NS2 simulation environment, the entire execution of the work is done, which proves that the suggested work lead to give the optimal result when compared with the current research method.

Machine learning is used as an effective support system in health diagnosis which contains large volume of data. More commonly, analyzing such a large volume of data consumes more resources and execution time. In addition, all the features present in the dataset do not support in achieving the solution of the given problem. Hence, there is a need to use an effective feature selection algorithm for finding the more important features that contribute more in diagnosing the diseases. The Particle Swarm Optimization (PSO) is one of the metaheuristic algorithms to find the best solution with less time. Nowadays, PSO algorithm is not only used to select the more significant features but also removes the irrelevant and redundant features present in the dataset. However, the traditional PSO algorithm has an issue in selecting the optimal weight to update the velocity and position of the particles. To overcome this issue, this paper presents a novel function for identifying optimal weights on the basis of population diversity function and tuning function. We have also proposed a novel fitness function for PSO with the help of Support Vector Machine (SVM). The objective of the fitness function is to minimize the number of attributes and increase the accuracy. The performance of the proposed PSO-SVM is compared with the various existing feature selection algorithms such as Info gain, Chi-squared, One attribute based, Consistency subset, Relief, CFS, Filtered subset, Filtered attribute, Gain ratio and PSO algorithm. The SVM classifier is also compared with several classifiers such as Naive Bayes, Random forest and MLP.

One of the secured communication methods in MANET is providing a Trust Management (TM). A TM calculates a trust value for all the participants in the communication for security. TM assigns a predefined threshold value for trust variable where it will be checked during communications. If the node has a trust value greater than the threshold value, then the nodes are selected as a most trusted node among the nodes else it is considered as a un-trusted node which cannot be permitted for communication. Various existing research works proposed various Trust Management algorithms and Trusted Routing Protocols. But the efficiency of the existing approaches is not satisfactory up to the market level. This problem takes into account and it is motivated to design and develop a novel QoS improved Trusted Routing Protocol (QoSTRP) for secured efficient data transmission in MANET. The efficiency of the proposed protocol is verified and the performance is evaluated by comparing the results with other existing methods.

The usage of mobile nodes is increasing very rapidly and so it is very essential to have an efficient channel allocation procedure for the next generation cellular networks. It is very expensive to increase the existing available spectrum. Hence, it is always better to utilize the existing spectrum in an effective way. In view of this, this paper proposes a channel allocation algorithm for next generation cellular networks which is based on deep learning. The system is made learned deeply to determine the number of channels that each base station can acquire and also dynamically varying based on the time. The originating and handoff calls are two different types of calls being considered in this paper. The number of channels that be exclusively used for originating calls and handoff calls is determined using deep learning. STWQ—Non-LA and STWQ—LAR are used to compare with the proposed work. The results show that the proposed algorithm, DLCA outperforms in terms of blocking and dropping probability.

Nowadays, software developers often face the following problem: there is a large amount of inputs that cause the program to crash. In practice, this amount of inputs is too large to be analyzed manually in a reasonable time. This paper contains an overview and analysis of existing methods for this problem. A new method for analyzing crashes to select unique defects is proposed. The method is based on comparison of control flow graphs (CFGs). For this purpose, a special metric is introduced: the graphs are considered similar if the metric does not exceed a certain threshold, which is a filtering parameter. Information about the graphs is collected dynamically at runtime through instrumentation of the program’s binary code. The method is applicable to binary executables and does not require any debugging information. The developers, having estimated their time and effort, can significantly reduce the number of crashes to be analyzed. In addition, an effective algorithm for fixing software bugs that cause crashes is proposed. The method is implemented as part of the fuzzer developed at the Institute for System Programming of the Russian Academy of Sciences (ISP RAS) and tested on a set of programs for x86-64/Linux. The test results show that the number of crashes to be analyzed can be reduced by several times.

Currently, one of the most efficient ways to detect software security flaws is taint analysis. It can be based on static code analysis, and it helps detect bugs that lead to vulnerabilities, such as code injection or leaks of private data. Two approaches to the implementation of tainted data propagation over the program intermediate representation are proposed and compared. One of them is based on dataflow analysis (IFDS), and the other is based on symbolic execution. In this paper, the implementation of both approaches in the framework of the existing static analyzer infrastructure for detecting bugs in C# programs are described. These approaches are compared from the viewpoint of the scope of application, quality of results, performance, and resource requirements. Since both approaches use a common infrastructure for accessing information about the program and are implemented by the same team of developers, the results of the comparison are more significant and accurate than usual, and they can be used to select the best option in the context of the specific program and task. Our experiments show that it is possible to achieve the same completeness regardless of the chosen approach. The IFDS-based implementation has higher performance comparing with the symbolic execution for detectors with a small amount of tainted data sources. In the case of multiple detectors and a large number of sources, the scalability of the IFDS approach is worse than the scalability of the symbolic execution.

High-quality annotated collections are a key element in constructing systems that use machine learning. In most cases, these collections are created through manual labeling, which is expensive and tedious for annotators. To optimize data labeling, a number of methods using active learning and crowdsourcing were proposed. This paper provides a survey of currently available approaches, discusses their combined use, and describes existing software systems designed to facilitate the data labeling process.

Multi-agent social systems (MASSes) are systems of autonomous interdependent agents, each pursuing its own goals and interacting with other agents and environment. The dynamics of the MASS cannot be adequately modeled by the methods borrowed from statistical physics because these methods do not reflect the main feature of social systems, viz., their ability to percept, process, and use external information. This important quality of distributed (swarm) intelligence has to be directly taken into account in a correct theoretical description of social systems. However, discussion of distributed intelligence (DI) in the literature is mostly restricted to distributed tasks, information exchange, and aggregated judgment, i.e., to the “sum” or “average” of independent intellectual activities. This approach ignores the empirically well-known phenomenon of “collective insight” in a group, which is a specific manifestation of MASS DI. In this paper, the state of art in modeling social systems and investigating intelligence per se is briefly characterized and a new modular model of intelligence is proposed. This model makes it possible to reproduce the most important result of intellectual activity, viz., the creation of new information, which is not reflected in the contemporary schemes (e.g., neural networks). In the framework of the modular approach, the correspondence between individual intelligence and MASS DI is discussed and prospective directions for future research are outlined. The efficiency of DI is estimated numerically by computer simulation of a simple system of agents with variable kinematic parameters (k_i) that move through a pathway with obstacles. Selection of fast agents with a positive mutation of the parameters provides ca. 20% reduction in the average passing time after 200–300 generations and creates a swarm movement whereby agents follow a leader and cooperatively avoid obstacles.

The paper is devoted to designing an automated algorithm for modeling the function domain of spline curves that is needed for developing tools for R-functional and voxel-functional construction of geometric models of complex shape. Mathematical approaches to solving this problem are discussed. The parametric dependence of a spline on the function domain is investigated. An automated algorithm for constructing the function domain of a spline curve based on a linear combination of the basis Bernstein polynomials is described.

For finding global approximate solutions to an algebraic equation in n unknowns, the Hadamard open polygon for the case n = 1 and Hadamard polyhedron for the case n = 2 are used. The solutions thus found are transformed to the coordinate space by a translation (for n = 1) and by a change of coordinates that uses the curve uniformization (for n = 2). Next, algorithms for the local solution of the algebraic equation in the vicinity of its singular (critical) point for obtaining asymptotic expansions of one-dimensional and two-dimensional branches are presented for n = 2 and n = 3. Using the Newton polygon (for n = 2), the Newton polyhedron (for n = 3), and power transformations, this problem is reduced to situations similar to those occurring in the implicit function theorem. In particular, the local analysis of solutions to the equation in three unknowns leads to the uniformization problem of a plane curve and its transformation to the coordinate axis. Then, an asymptotic expansion of a part of the surface under examination can be obtained in the vicinity of this axis. Examples of such calculations are presented.