Network Analysis of co-authorship system of University of Sindh authors on Science Direct

Many complex systems have been modelled and analyzed as complex networks. These systems are huge and complex in terms of number of interconnecting components. In this research, we have analyzed the co-authorship network of Sindh university authors on science direct to understand the connectivity pattern of authors who published their articles over time. This research has found that the connectivity pattern of the authors is highly heterogonous due to the emergence of hubs in this system. Further, this network has shown highly clustered behavior with small world effect. These findings based on network analysis suggests that the co-authorship system is depending on few authors frequently publishing multiple papers in this network

Network Analysis of 500 Flights of USA Air Transportation System

People's interest in network analysis has grown considerably in last decade. The air transportation network is considered to be a complex network, full of vitality and ramification. The main purpose of this research is to model and analyze, from a network perspective, the US air transportation network, which is one of the unique in its nature in the world. We find several features of complex network of air transportation including local and global such as degree distribution, weighted degree, clustering coefficient and betweenness and closeness centralities. The USA air transportation network has shown small-world behavior and due to non-homogenous distribution of applied network analysis metrics the network is inclined to power-law distribution

Enhancing Cognitive Theory of Multimedia Leaning through 3D Animation

Cognitive theory of Multimedia learning has been a widely used principle in education. However, with current technological advancements and usage, the teaching and learning trend of children’s have also changed with more dependability towards technology. This research work explores and implement the use of 3D Animation as tool for multimedia learning based on cognitive theory. This new dimension in cognitive learning, will foster the latest multimedia tools and application driven through 3D Animation, Virtual Reality and Augmented Reality. The three principles, that facilitate cognitive theory of multimedia learning using animation, addressed in this research are temporal contiguity principle (screening matching narration with animation simultaneously rather than successively), personalization principle (screening text or dialogs in casual form rather than formal style) and finally the multimedia principle (screen animation and audio narration together instead of just narration). The result of this new model would yield a new technique of educating the young children through 3D animation and virtual reality. The adaptation of  cognitive theory through 3D animation as a source of multimedia learning with various key principles produces a reliable paradigm for educational enhancement

Node status detection and information diffusion in router network using scale-free network

In the field of computer networks various routing and inter networking algorithms and protocols have been introduced according to many performance metrics like network topology, scalability, speed, and congestion control requirements. In this paper we have used the concept of scale-free network theory to design a more robust data dissemination approach which can be used in one dynamical Autonomous Systems (AS) to know the appearance and disappearance of nodes, and speedily propagate the information to all nodes in the routers network. By taking advantage of the features of scale-free network behavior as found inhomogeneous structure, short path lengths, highly cluster and epidemiological spreading an enhanced algorithm has been introduced which effectively finds the node status in the network and speedily broadcasts the information of status to all nodes in the network

Two-mode complex network modeling of dengue epidemic in Selangor, Malaysia

There are many examples of complex systems in the world. These systems are very difficult to analyze due to huge number of interacting elements and complex interaction patterns between the elements. These systems can be properly analyzed by converting them into complex networks, where nodes can represent elements of the systems and links depicts the interaction patterns between these elements. Recently, many dengue fever cases are reported in Malaysia which shows the exponential increase of this fever in the country. In this paper we have formalized the dataset of dengue fever cases in Malaysia into two-mode complex network to analyze its impact on the localities. By applying network analysis metrics this research has revealed that few locations have high impact of dengue and they should be treated as critical to minimize the overall impact of spreadin

Analyzing the weighted dark networks using scale-free network approach

The task of identifying the main key nodes in the dark (covert) networks is very important for the researchers in the field of dark networks analysis. This analysis leads to locate the major nodes in the network as the functionality can be minimized by disrupting major key nodes in the network. In this paper, we have primarily focused on two basic network analysis metrics, degree and betweenness centrality. Traditionally, both these centrality measures have been applied on the bases of number of links connected with the nodes but without considering link weights. Like many other networks, dark networks also follow scale-free behavior and thus follow the power-law distribution where few nodes have maximum links. This, inhomogeneous structure of network causes the creation of key nodes. In this research, we analyze the behavior of nodes in dark networks based on degree and betweenness centrality measures by using 9/11 terrorist network dataset. We analyzed both these measures with weighted and un-weighted links to prove that weighted networks are much closer to scale-free phenomenon as compared to un-weighted networks

WSN based sensing model for smart crowed movement with identification: an extended study

With the advancement of IT and increase in world population rate, Crowed Management ( CM) has become a subject undergoing intense study among researchers. Technology providers fast and easily available means of transport and up-dates information access to the people that cause crowed at public places. This imposes a big challenge for crowed safety and security at public places such as airports, railway stations and check points. For example, crowed of pilgrims during Hajj and Ummrah while crossing the borders of Makkah, Kingdom of Saudi Arabia. To minimize the risk of such crowed safety and security, identification and verification of people is necessary which caused unwanted increment in processing time. It is observed that managing crowed during specific time period ( Hajj and Ummrah) with identification and verification became challenge. At present, many advanced technologies such as Internet of Things (IoT) are being used to solve the crowed management problem with minimal processing time. In this paper, we have presented a wireless sensor Network (WSN) based conceptual model for smart crowed movement with optimal verification of cluster members (CMs) and leads to minimal processing time for people identifications. This handles the crowed by forming groups and provides proactive support to handle them in organized manner. As a result, crowed can be managed to move safely from one place to another with group identification. By controlling the drop rate or unverified CMs rate, the performance of the smart movement can be increased. This decrease or control of the drop rate will also minimize the processing time and move the crowed in smart way

Two-mode network modeling and analysis of dengue epidemic behaviour in Gombak, Malaysia

Many complex systems in the form of network have been a great focus of research in the last decade. In fact, various domains have been modeled and analyzed as complex networks ranging from biological, technological, transportation, social and many others. The phenomenon of distribution of many dengue cases has been a great concern in Malaysia in recent years. Therefore, in this work we formalize and analyze the dengue spreading phenomenon from the perspective of complex network and model the dataset of dengue affected cases in Gombak, Selangor (Malaysia) into two-mode network. By using the real dataset of dengue cases in Malaysian states obtained from the Malaysian Health Ministry, we observe this network with global (Closeness, Betweenness and Short path-length)and local (Degree and Clustering coefficient) structure perspectives. We further formalize it by projecting from two-mode network using three methods of network projection. From the network analysis, we found that there are few localities that were affected again and again throughout the year. Further, few localities have high number of dengue cases as compared to others. From the global structures perspective, very few localities have shown closeness to all other localities and therefore easing the route for the propagation of dengue virus that has the highest weight in terms of number of dengue cases

Formal analysis of weighted longitudinal routers network via scale-free network: a case study

The process of identifying the central nodes in complex networks research has remained interesting and very important issue for network analysis. The identification of main nodes in the network can lead to many answers for the solution of security and other problems depending on the type of complex networks under analysis. Different topological metrics of the network can be used to locate the major nodes in the network but the degree and betweenness (Load) centralities perform very important role in evolution and communication of nodes in growing networks. Unfortunately, these metrics have been analyzed in different complex systems mainly either on the bases of the number of links to nodes in the network or with much focused from the perspective of weights of links. Therefore, locating the main nodes in the network not only depends on links but majorly on weight of links. Routers network of the internet is an example of scale-free nature which follow power-law distribution and causes inhomogeneous structure with some nodes with large number of links while many with a few. Further, in this type of distribution few nodes become very important. In this paper, we analyze the behavior of routers network by using two metrics of centralities with weighted and un-weighted links based on the dataset of PTCL routers network in Pakistan. Furthermore, by using centralities measures we try to show that weight of links is important as compare to number of links by following the concept from “rich get richer” to “fit get richer” in routers network. Moreover, we prove that weighted routers network is very close to scale-free networks as compared to unweighted, and due to this phenomenon these networks sustain their robustness

Calculus and its applications in scale-free networks

The purpose of this article is to highlight and emphasize the applications of calculus in scale-free networks, which have been found as property of many complex networks. There are many different types of real and manmade complex networks in various domain of life ranging from technological, social, biological, transportation and ecological networks among many others. All these networks have shown many similar structural properties in their formation and functions. This article focuses on the most prevalence type of networks known as Scale-Free Networks (SFN). These types of networks have two basic properties: growth and preferential node attachment. In this paper, we analyze and discuss the importance and usage of calculus in understanding the formation of these type of networks, as these networks continuously change their topology by inclusion of new nodes and links as they evolve. Further, as the calculus is the mathematical study of change therefore the applications of calculus in evolution process of complex networks with fitness model have been explained analytically based on fit-get-rich phenomenon in the complex networks as compared to simple BA1 model which is based, only on the consideration of number of nodes linkages in the networks