The rise of the Internet accelerates the creation of various large-scale online social networks, which can be described the relationships and activities between human beings. The online social networks relationships in real world are too big to present with useful information to identify the criminal or cyber-attacks. This research proposed new information security analytic model for online social networks, which called Security Visualization Analytics (SVA) Model. SVA Model used the set of algorithms (1) Graph-based Structure algorithm to analyze the key factors of influencing nodes about density, centrality and the cohesive subgroup to identify the influencing nodes of anomaly and attack patterns (2) Supervised Learning with oneR classification algorithm was used to predict new links from such influencing nodes in online social networks on discovering surprising links in the existing ones of influencing nodes, which nodes in online social networks will be linked next from the attacked influencing nodes to monitor the risk. The results showed 42 influencing nodes of anomaly and attack patterns and can be predict 31 new links from such nodes were achieved by SVA Model with the accuracy of confidence level 95.0%. The new proposed model and results illustrated SVA Model was significance analysis. Such understanding can lead to efficient implementation of tools to links prediction in online social networks. They could be applied as a guide to further investigate of social networks behavior to improve the security model and notify the risk, computer viruses or cyber-attacks for online social networks in advance.

A new architecture and learning algorithms for the multidimensional hybrid cascade neural network with neuron pool optimization in each cascade are proposed in this paper. The proposed system differs from the well-known cascade systems in its capability to process multidimensional time series in an online mode, which makes it possible to process non-stationary stochastic and chaotic signals with the required accuracy. Compared to conventional analogs, the proposed system provides computational simplicity and possesses both tracking and filtering capabilities.

Natural and man-made disasters pose an ever-present threat to our society. Providing real time information is vital in emergency management. Current disaster information systems only use and manage relatively limited information, such as, within an organization or with a few organizations which have an agreement on information content and format. As a result, a large amount of potentially precious information and natural volunteer workers are ignored. On the other hand, social media have been used by people to propagate emergent situations. Therefore, in this project, we aim to maximally utilize all of the available emergency-related information from various sources of social media to better manage natural and man-made disasters. In particular, we propose a semantics-extended social search engine that can fetch highly relevant information from social media for emergency management purpose.

This paper presents an application of recently proposed robust integral of the sign of the error (RISE) feedback control scheme for a three degrees-of-freedom (DOF) robot manipulator tracking problem. This method compensates for nonlinear disturbances and uncertainties in the dynamic model, and results in asymptotic trajectory tracking. To avoid selecting parameters of the RISE controller by time-consuming trial and error method, particle swarm optimization (PSO) algorithm is employed. The objective of the PSO algorithm is to find a set of parameters that minimizes the mean of root squared error as the fitness function. The proposed method attains tracking goal, without any chattering in control input. Indeed, the existence of a unique integral sign term in the RISE controller avoids the occurrence of chattering phenomenon that usually happens in sliding mode controllers. Numerical simulations demonstrate the effectiveness of the proposed control scheme.

A Wireless Sensor Network is a combination of spatially distributed independent nodes deployed in dense environment, communicating wirelessly over limited bandwidth and frequency. Security and Qos is the major concern in wireless sensor network due to its wireless communication nature and constraints like low computation capability, less memory, bounded energy resources, susceptibility to physical capture or damages and the use of insecure wireless communication channels. These constraints make security along with the QoS, a challenge in wireless sensor network. The cryptographic schemes increases the level of security and make it secure against critical attacks but also has a significant impact on the QoS of wireless sensor network. In this paper, the different cryptographic schemes based on asymmetric key and symmetric key cryptography are evaluated. The symmetric key cryptography schemes require less time for processing, less power and also require less storage space as compared to asymmetric key cryptographic schemes, results in less impact on the QoS of wireless sensor network. In this paper, the QoS of wireless sensor network along with cryptographic schemes will be evaluated on the basis of metrics like throughput, jitter, end-to-end delay, total packet received and energy consumption.

In an advanced wireless network, trust is desirable for all routing protocols to secure data transmission. An enormous volume of important information communicates over the wireless network using trusted dynamic routing protocol, which is the enhancement of the DSR (Dynamic Source Routing) protocol to improve trust. Previously fuzzy logic, genetic algorithm, neural network has been used to modify DSR and good result has been obtained in few performance indicators and parameters. In this work an SVM based trusted DSR have been developed and better results have been presented. This new novel on demand trust based routing protocol for MANET is termed as Support vector machine based Trusted Dynamic Source Routing protocol, performance of STDSR has been improved in term of the detection ratio (%) at different mobility and no. of malicious node variation.

Human Genome Project has led to a huge inflow of genomic data. After the completion of human genome sequencing, more and more effort is being put into identification of splicing sites of exons and introns (donor and acceptor sites). These invite bioinformatics to analysis the genome sequences and identify the location of exon and intron boundaries or in other words prediction of splicing sites. Prediction of splice sites in genic regions of DNA sequence is one of the most challenging aspects of gene structure recognition. Over the last two decades, artificial neural networks gradually became one of the essential tools in bioinformatics. In this paper artificial neural networks with different numerical mapping techniques have been employed for building integrated model for splice site prediction in genes. An artificial neural network is trained and then used to find splice sites in human genes. A comparison between different mapping methods using trained neural network in terms of their precision in prediction of donor and acceptor sites will be presented in this paper. Training and measuring performance of neural network are carried out using sequences of the human genome (GRch37/hg19- chr21). Simulation results indicate that using Electron-Ion Interaction Potential numerical mapping method with neural network yields to the best performance in prediction.

In distributed computing system some nodes are very fast and some are slow and during the computation many fast nodes become idle or under loaded while the slow nodes become over loaded due to the uneven distribution of load in the system. In distributed system, the most common important factor is the information collection about loads on different nodes. The success of load balancing algorithm depends on how quickly the information about the load in the system is collected by a node willing to transfer or accept load. In this paper we have shown that the number of communication overheads depends on the number of overloaded nodes present in the domain of an under loaded nodes and vice-versa. We have also shown that communication overhead for load balancing is always fairly less than KN but in worst case our algorithm’s complexity becomes equal to KN.

This paper presents a new coupling metric (Coup), which is based on the formal definition of methods and variables of classes, and packages. The proposed metric has been validated theoretically against Briand properties as well as empirically using packages taken from two open source software systems and four experienced teams. We measure Coup value by our own CC tool. An attempt has also been made to present a strong correlation between Coup values and understandability of the packages and between Coup values and modified classes of the packages. The results indicate that Coup is used to predict understandability and modifiability of a package in Object-Oriented design. Finally this paper proves that Coup is a better predictor of understandability and modifiability of a package than other existing coupling metrics in the literature.