Deployment of social nets in multilayer model to identify key individuals using majority voting

Social web and social media are evidenced to be a rich source of user-generated social content. Social media includes multiple numbers of social dimensions represented by different social networks. The identification of important player in these real-world social networks has been in high emphasis due to its effectiveness in multiple disciplines, especially in law enforcement areas working on dark networks. Many algorithms have been proposed to identify key players according to the objective of interest using suitable network centrality measures. This paper proposes a new perspective of dealing with key player identification by redefining it as a problem of “Key Individual Identification,” across multiple social dimensions. Research deals with each social dimension as a layer in the multiple-layer social network model. The proposed technique extracts a number of features from each network based on social network analysis. The features are assembled to formulate a global feature set representing the behaviors of individuals in all networks individually. The technique then attempts to find key individuals using hybrid classifiers. The results from all classifiers are formulated, and the final decision of an individual to be part of the individual key set is based on majority voting. This novel technique gives good results on a number of known networks

Key individual identification using dimensional relevance in the stratum of networks

Different aspects of social networks have increasingly been under investigation from last decades. The social network studies range in various viewpoints from the structural and node measures to the information diffusion processes. The key node identification has been one of the limelight topics of social network analysis (SNA) specifically in a discipline like politics, criminology, marketing and etc. This research uses multiple networks constructed from the different social sites and real-life relationships to cover the multi-dimensional aspects of human relations. In the multi-relationship system, the different dimensions may differ in terms of relevance and weight. One of the most intriguing aspects of key node identification in the multi-dimensional system can be the consideration of dimensions relevance. This research covers the methodology to optimise the weights of dimensions using a number of centrality measures from each network layer covering multiple different objectives of interest. The study formulates the novel weighted feature set pertaining to layer relevance calculated based on layer relative importance through particle swarm optimization techniques. The framework applied ensemble-based approach on the weighted feature set along with node characteristics to predict key nodes in a network. The results are validated against ground truth data and accuracy achieved is promising

Detecting change from social networks using temporal analysis of email data

Social network analysis is one of the most recent areas of research which is being used to analyze behavior of a society, person and even to detect malicious activities. The information of time is very important while evaluating a social network and temporal information based analysis is being used in research to have better insight. Theories like similarity proximity, transitive closure and reciprocity are some well-known studies in this regard. Social networks are the representation of social relationships. It is quite natural to have a change in these relations with the passage of time. A longitudinal method is required to observe such changes. This research contributes to explore suitable parameters or features that can reflect the relationships between individual in network. Any foremost change in the values of these parameters can capture the change in network. In this paper we present a framework for extraction of parameters which can be used for temporal analysis of social networks. The proposed feature vector is based on the changes which are highlighted in a network on two consecutive time stamps using the differences in betweenness centrality, clustering coefficient and valued edges. This idea can further be used for detection of any specific change happening in a network. © Springer International Publishing AG 2018

Classification of Phonocardiogram using an Adaptive Fuzzy Inference System

Abstrac

Logic and algorithm partitioning

Ph.D.Vijay K. Madisett

Security Requirements Engineering Framework with BPMN 2.0.2 Extension Model for Development of Information Systems

With recent advancements of technologies such as Internet of Things and cloud computing, security of information systems has emerged as a critical issue. This has created a need for elicitation and analysis of the security requirements at an early stage of system development. These requirements should also be expressed using visual notations that can encapsulate the vision of different stakeholders related to security. While business process management notation (version 2.0.2) is a widely used graphical representation for business requirements and makes it easier to define and communicate business processes between different stakeholders of the system. Moreover, extension mechanisms are available to model the specific needs of an organization. Due to its flexible structure for defining new extensions, it can be adapted to model security requirements in the information system (IS). Towards this, we propose a threat profile security framework to define the security requirements of manufacturing systems for businesses, which are at a stage of infancy to adapt or evolve the IS with the changing needs of a business environment. In particular, the framework is modeled by extending Business Process Management Notation and is applied in a manufacturing industry process at the shop floor level. We show through a case study example that the threat goal-based framework is broader and, hence, covers a majority of security concerns of organizations

Relation mining using cross correlation of multi domain social networks

Social network analysis has emerged as an important research area in intelligence analysis to facilitate decision maker in more informed decisions. With the passage of time social networks services are increasingly being used in legal and criminal investigations including understanding and tracking of dark networks, i.e. illegal covert networks. Criminal organizations are well-suited to be studied using social network analysis as they consist of networks of individuals that span countries and continent using false identities over different networks to remain anonymous. There is great need to recognize the criminals carrying different fake identities to correctly track their immoral activities. This research article is focusing on devising a method which can be used to identify group of people i.e. criminals, terrorist or friends on different Social networks by analyzing patterns and performing correlations across different social network systems. Data integration using cross correlation is used to merge entities to form single focused networks. This single focused network also estimate the intensity of relationship between individuals in general networks based on the number of networks they are connected. To the best of our knowledge no such studies is done in the social network analysis, therefore this method is unique and offers innovations in making effective use of presence of people in different networks

Important attributes of customer satisfaction in telecom industry: a survey based study

Customer satisfaction has been acknowledged as critical success factors in any organizations. Recent developments in telecom sector shows that communication services providers (CSPs) are engaged in various marketing and survey activities to discover the satisfaction level of their customers. In general, some subscribers complain about the poor network coverage, voice call quality, internet service etc. while other are satisfied with the quality of services. The aim of this paper is to conduct a servery based study to identify and highlight major attributes that effect customer's satisfaction which will eventually help telecom services providers to improve their customer experience management index. Consequently, this paper examines the factors that have resilient or fragile influence on customer satisfaction. Based on the contemporary research six attributes i.e. network coverage, voice call quality, drop call rate, SMS delivery, internet service and call setup duration have been considered and tested in this research to find the customer satisfaction. For this purpose of study, 200 respondents of a CSP are selected from Islamabad, Pakistan. A telephonic survey is conducted to rate each of the factor and their overall satisfaction. The results reveal that network coverage, voice call quality and internet service have the highest impact on the level of customer satisfaction