M.Sc. Computer Science
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Recent Submissions
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Data mining using L-fuzzy concept analysis.
Association rules in data mining are implications between attributes of objects that hold in all instances of the given data. These rules are very useful to determine the properties of the data such as essential features ... -
Object Classification using L-Fuzzy Concept Analysis
Object classification and processing have become a coordinated piece of modern industrial manufacturing systems, generally utilized in a manual or computerized inspection process. Vagueness is a common issue related to ... -
A Functional Programming Language with Patterns and Copatterns
Since the emergence of coinductive data types in functional programming languages, various languages such as Haskell and Coq tried different ways in dealing with them. Yet, none of them dealt with coinductive data types ... -
A Hybrid Approach to Network Robustness Optimization using Edge Rewiring and Edge Addition
Networks are ubiquitous in the modern world. From computer and telecommunication networks to road networks and power grids, networks make up many crucial pieces of infrastructure that we interact with on a daily basis. ... -
Effect of the Side Effect Machines in Edit Metric Decoding
The development of general edit metric decoders is a challenging problem, especially with the inclusion of additional biological restrictions that can occur in DNA error correcting codes. Side effect machines (SEMs), an ... -
Swarm-based Algorithms for Neural Network Training
The main focus of this thesis is to compare the ability of various swarm intelligence algorithms when applied to the training of artificial neural networks. In order to compare the performance of the selected swarm ... -
A Relation-Algebraic Approach to L - Fuzzy Topology
Any science deals with the study of certain models of the real world. However, a model is always an abstraction resulting in some uncertainty, which must be considered. The theory of fuzzy sets is one way of formalizing ... -
Objective reduction in many-objective optimization problems
Many-objective optimization problems (MaOPs) are multi-objective optimization problems which have more than three objectives. MaOPs face significant challenges because of search efficiency, computational cost, decision ... -
Using Deep Learning for Predicting Stock Trends
Deep learning has shown great promise in solving complicated problems in recent years. One applicable area is finance. In this study, deep learning will be used to test the predictability of stock trends. Stock markets are ... -
Surface Areas of Some Interconnection Networks
An interesting property of an interconnected network (G) is the number of nodes at distance i from an arbitrary processor (u), namely the node centered surface area. This is an important property of a network due to its ... -
A Centrality Based Multi-Objective Disease-Gene Association Approach Using Genetic Algorithms
The Disease Gene Association Problem (DGAP) is a bioinformatics problem in which genes are ranked with respect to how involved they are in the presentation of a particular disease. Previous approaches have shown the strength ... -
Modelling and Proving Cryptographic Protocols in the Spi Calculus using Coq
The spi calculus is a process algebra used to model cryptographic protocols. A process calculus is a means of modelling a system of concurrently interacting agents, and provides tools for the description of communications ... -
Efficient Merging and Decomposition Variants of Cooperative Particle Swarm Optimization for Large Scale Problems
For large-scale optimization problems (LSOPs), an increased problem size reduces performance by both increasing the landscape complexity, as well as exponentially increasing the search space size. These contributing factors ... -
Deep Learning Concepts for Evolutionary Art
A deep convolutional neural network (CNN) trained on millions of images forms a very high-level abstract overview of any given target image. Our primary goal is to use this high-level content information of a given target ... -
Modal and Relevance Logics for Qualitative Spatial Reasoning
Qualitative Spatial Reasoning (QSR) is an alternative technique to represent spatial relations without using numbers. Regions and their relationships are used as qualitative terms. Mostly peer qualitative spatial reasonings ... -
Image Evolution Using 2D Power Spectra
Procedurally generated textures have seen use in many applications, are a high-interest topic when exploring evolutionary algorithms, and hold a central interest for digital art. However, there is an existing difficulty ... -
A Deep Learning Pipeline for Classifying Different Stages of Alzheimer's Disease from fMRI Data.
Abstract Alzheimer’s disease (AD) is an irreversible, progressive neurological disorder that causes memory and thinking skill loss. Many different methods and algorithms have been applied to extract patterns from ... -
Properties and Algorithms of the (n,k)-Arrangement Graphs and Augmented Cubes
The (n, k)-arrangement graph was first introduced in 1992 as a generalization of the star graph topology. Choosing an arrangement topology is more efficient in comparison with a star graph as we can have a closer number ... -
Complete computational sequence characterization of mobile element variations in the human genome using meta-personal genome data
While a large number of methods have been developed to detect such types of genome sequence variations as single nucleotide polymorphisms (SNPs) and small indels, comparatively fewer methods have been developed for finding ... -
Learning Strategies for Evolved Co-operating Multi-Agent Teams in Pursuit Domain
This study investigates how genetic programming (GP) can be effectively used in a multi-agent system to allow agents to learn to communicate. Using the predator-prey scenario and a co-operative learning strategy, ...
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