Friday, May 27th, 2022

List of Common Computing Methodologies to Use in Graduate-Level Computer Science Dissertation

Introduction

Are you about to start working on your computer science dissertation? Oh, it is great to hear that you are actually going to start your dissertation writing. In such a case, do you know the computing methodologies that you will need to use in your dissertation? Probably, you do not know about those methodologies. No problem, there is no need to worry because it happens with many computer science students. They start working on their dissertation prior to knowing about different computing methodologies. It is why today’s article is all about discussing those methodologies. So, let’s start our discussion formally by defining the term computing methodologies first.

What are computing methodologies?

In a computer science dissertation, students analyse a particular problem. To do this analysis, they employ different computed techniques. Therefore, computing methodologies are the computer-assisted analysis and the processing of problems. The researcher uses different computer programs and neural networks to solve the problem at hand.

List of common computing methodologies

Science is based on methods, which is the study of how things work. In scientific research, the methodology describes the procedure of the research. The same is the case with a dissertation in the computer science field. It explains different computing methodologies that researchers use to solve a problem. However, the list of common methodologies, along with a brief explanation used in this field, is as follows.

Soft computing

The first methodology on the list is soft computing. In this methodology, the researcher uses approximate calculations to provide solutions. Remember that this methodology is not that precise. It means that the solution to the problem might be imprecise, but it will be usable. This computing methodology enables solutions for problems that are either unsolvable or too time-consuming to be solved by current hardware. This computing is also sometimes referred to as computational intelligence. The constituents of this computing are as follows;

  • Fuzzy logic (FL)
  • Evolutionary computation (EC)
  • Machine learning (ML)
  • Probabilistic reasoning (PR)

The researchers employ soft computing where the problem is ill-defined. It has many applications in industrial, commercial and domestic situations. One prominent application of this computing is in medical science, where it is used to analyse pictures. It can analyse different patterns in pictures of X-rays and microscopes. The Genetic Algorithm and Genetic Programming help in analysing the genetic issues.

Fog computing

It is the second methodology of computing. This computing involves the deployment of services and resources from data sources to the cloud. It does not link directly to the cloud, thus, reducing the data latency and response time. This computing offers multiple endpoints rather than a single endpoint. This is the thing which reduces the latency of the data. There are many features of this computing methodology. Some of those are mentioned below:

  • It offers more control over privacy. It allows storing locally rather than sending it to the cloud. This reduces the chances of vulnerability and enhances data security.
  • The response latency gets reduced by using fog computing. It is because all the data is stored on devices rather than in the cloud. It simply means that data is collected from where it was deployed. So, there will be no latency.
  • Increased business agility is another plus of fog computing. Due to improved data security and less response time, it provides better and more effective business functioning.

Swarm intelligence (SI)

Out of all other computing methodologies, this methodology is deduced from nature. It is a branch of computational intelligence that deals with natural and artificial systems. Those systems are composed of many individuals connected with each other through decentralised control. The self-organisation is also very important in this type of computing. Mostly, this concept is employed in artificial intelligence. One possible example of this intelligence is the colonies of ants and other natural bird colonies.

In this computing methodology, the individuals do not know about self-organisation. But all the individuals actually exhibit that in their movement. For example, a group of ants walking on the earth. The group moves together in a state of self-organisation. But the individual ants do not know that they are organised. The same analogy works in the robotics industry. The computer programs make the robots self-organised.

Artificial Neural Network (ANN)

An artificial neural network or simply neural network is a computational model that mimics the working of brain cells. ANN uses learning algorithms that make automatic adjustments as the human brain does. They can also make adjustments as they receive new input or commands. Due to the ability of this neural network, they are the best suited for non-linear statistical data modelling.

The structure of ANN consists of three or more layers. All the layers are connected with each other in some ways. However, the first layer consists of input neurons. This layer sends data to the next deeper layer. Finally, the last layer, called as output layer, gives the output. 

Machine learning algorithms

The last computing methodology is using machine learning algorithms to answer the dissertation questions. Those algorithms solve complex problems that ordinary computers and the human brain cannot. Such algorithms do not depend on the equations to get the desired information. They just take the data and draw the information required. The machine learning algorithms adaptively increase the performance of computing methods. They do so by analysing an increased number of samples.

You must have heard about deep learning. It is also a type of machine learning. It imitates the ways how humans learn things. However, the main functions of machine learning are as follows;

  • It automates the analytical model building by excluding the use of equations
  • It learns directly from the data, identifies patterns and makes decisions
  • The human intervention in the whole process is very low

Conclusion

Acing the graduate-level computer science dissertation is a difficult task. But if you have the right set of techniques and know the computing methodologies, you can produce a good piece of writing. The methodologies mentioned above are the most common. So, you must pay proper attention and read them carefully.

Author Bio:

Robert Fawl is a professional Content writer & Content Marketer. Based in London, Robert is an author and blogger with experience in encounter composing on various topics including but not limited to Essay Writing, Dissertation Writing, Coursework Writing Services, Thesis Writing Services and Assignment Writing etc.

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