Department of Electronics, Computing & Maths
http://hdl.handle.net/10545/338134
2021-05-06T12:34:56ZGraph and Network Theory for the Analysis of Criminal Networks
http://hdl.handle.net/10545/625734
Graph and Network Theory for the Analysis of Criminal Networks
Cavallaro, Lucia; Bagdasar, Ovidiu; De Meo, Pasquale; Fumara, Giacomo; Liotta, Antonio
Social Network Analysis is the use of Network and Graph Theory to study social phenomena, which was found to be highly relevant in areas like Criminology. This chapter provides an overview of key methods and tools that may be used for the analysis of criminal networks, which are presented in a real-world case study. Starting from available juridical acts, we have extracted data on the interactions among suspects within two Sicilian Mafia clans, obtaining two weighted undirected graphs. Then, we have investigated the roles of these weights on the criminal networks properties, focusing on two key features: weight distribution and shortest path length. We also present an experiment that aims to construct an artificial network which mirrors criminal behaviours. To this end, we have conducted a comparative degree distribution analysis between the real criminal networks, using some of the most popular artificial network models: Watts-Strogats, Erdős-Rényi, and Barabási-Albert, with some topology variations. This chapter will be a valuable tool for researchers who wish to employ social network analysis within their own area of interest.
2021-02-19T00:00:00ZAn LMI Approach-Based Mathematical Model to Control Aedes aegypti Mosquitoes Population via Biological Control
http://hdl.handle.net/10545/625730
An LMI Approach-Based Mathematical Model to Control Aedes aegypti Mosquitoes Population via Biological Control
Dianavinnarasi, J.; Raja, R.; Alzabut, J.; Niezabitowski, M.; Selvam, G.; Bagdasar, O.
In this paper, a novel age-structured delayed mathematical model to control Aedes aegypti mosquitoes via Wolbachia-infected mosquitoes is introduced. To eliminate the deadly mosquito-borne diseases such as dengue, chikungunya, yellow fever, and Zika virus, the Wolbachia infection is introduced into the wild mosquito population at every stage. This method is one of the promising biological control strategies. To predict the optimal amount of Wolbachia release, the time varying delay is considered. Firstly, the positiveness of the solution and existence of both Wolbachia present and Wolbachia free equilibrium were discussed. Through linearization, construction of suitable Lyapunov–Krasovskii functional, and linear matrix inequality theory (LMI), the exponential stability is also analyzed. Finally, the simulation results are presented for the real-world data collected from the existing literature to show the effectiveness of the proposed model.
2021-03-09T00:00:00ZBotnet detection used fast-flux technique, based on adaptive dynamic evolving spiking neural network algorithm
http://hdl.handle.net/10545/625719
Botnet detection used fast-flux technique, based on adaptive dynamic evolving spiking neural network algorithm
Almomani, Ammar; Nawasrah, Ahmad Al; Alauthman, Mohammad; Betar, Mohammed Azmi Al; Meziane, Farid
A botnet refers to a group of machines. These machines are controlled distantly by a specific attacker. It represents a threat facing the web and data security. Fast-flux service network (FFSN) has been engaged by bot herders for cover malicious botnet activities. It has been engaged by bot herders for increasing the lifetime of malicious servers through changing the IP addresses of the domain name quickly. In the present research, we aimed to propose a new system. This system is named fast flux botnet catcher system (FFBCS). This system can detect FF-domains in an online mode using an adaptive dynamic evolving spiking neural network algorithm. Comparing with two other related approaches the proposed system shows a high level of detection accuracy, low false positive and negative rates, respectively. It shows a high performance. The algorithm's proposed adaptation increased the accuracy of the detection. For instance, this accuracy reached (98.76%) approximately.
2021-01-28T00:00:00ZSeverity Estimation of Plant Leaf Diseases Using Segmentation Method
http://hdl.handle.net/10545/625712
Severity Estimation of Plant Leaf Diseases Using Segmentation Method
Entuni, Chyntia Jaby; Afendi Zulcaffle, Tengku Mohd; Kipli, Kuryati; Kurugollu, Fatih
Plants have assumed a significant role in the history of humankind, for the most part as a source of nourishment
for human and animals. However, plants typically powerless to different sort of diseases such as leaf blight, gray
spot and rust. It will cause a great loss to farmers and ranchers. Therefore, an appropriate method to estimate
the severity of diseases in plant leaf is needed to overcome the problem. This paper presents the fusions of the
Fuzzy C-Means segmentation method with four different colour spaces namely RGB, HSV, L*a*b and YCbCr
to estimate plant leaf disease severity. The percentage of performance of proposed algorithms are recorded and
compared with the previous method which are K-Means and Otsu’s thresholding. The best severity estimation
algorithm and colour space used to estimate the diseases severity of plant leaf is the combination of Fuzzy
C-Means and YCbCr color space. The average performance of Fuzzy C-Means is 91.08% while the average
performance of YCbCr is 83.74%. Combination of Fuzzy C-Means and YCbCr produce 96.81% accuracy. This
algorithm is more effective than other algorithms in terms of not only better segmentation performance but also
low time complexity that is 34.75s in average with 0.2697s standard deviation.
2020-11-09T00:00:00Z