CMP7239 Coursework: Research Essay - Machine Learning Applications 2024-25 S2 | BCU

Assessment Summary

This is an individual assessment that requires students to submit a research essay on the analysis of machine learning applications in networks, cybersecurity, or smart systems.

Students need to demonstrate an understanding of the domain context of the application and relevant scenarios in which machine learning techniques are applied to build artificial intelligence in the system.

Assessment Details:

Title: Research Essay - Machine Learning Applications

Theme: Applications related to Computer Networks, or Cybersecurity (depending on the enrolled course)

Style: Individual report

Rationale:

Rationale:

In an era marked by a rapid surge in technological capabilities, machine learning stands as a cornerstone in shaping our digital future. Its paramount importance lies in its ability to revolutionize information processing, decision-making, and the fabric of intelligent systems. Beyond its roots in computer science, machine learning has become an indispensable tool across diverse domains, including networking, cybersecurity, and smart systems. Its usage extends from enhancing efficiency in data analysis to fortifying cybersecurity protocols and optimizing the functionality of smart technologies. The scope of machine learning is expansive, offering transformative possibilities in automating tasks, predicting patterns, and extracting insights from vast datasets. As we navigate an increasingly complex digital landscape, the importance of understanding and harnessing the capabilities of machine learning becomes ever more crucial for individuals and industries alike.

Recognizing the interdisciplinary nature of machine learning, the assessment aims to equip students with essential knowledge and investigation skills specific to their respective fields. By focusing on exploratory data analysis and domain-specific applications, the coursework seeks to foster a deep understanding of machine learning's practical implications, empowering students to harness its benefits while emphasizing the importance of originality and academic integrity in their research endeavors.

Description of the Tasks Required:

• This is an individual assessment that requires you to submit a research essay on the analysis of machine learning applications in your respective course (networks, cybersecurity, or smart systems).
• As a first step, you must select a topic after studying the related research papers and identify your title, problem statement and research questions.
• Then, for the purpose of literature review and study comparisons, you are required to select at least five research articles (journal articles are highly recommended than conference papers) related to your research questions for comparison. You must properly cite the papers you read in their entirety to compose a very compact and technical paper draft. Create a table in the following format for all cited papers in the literature review section,

• For the purpose of the analysis, you have to identify 1 research paper published in reliable journal publishers from 2010 onwards.
  o    Examples of publishers: IEEE, ACM, Elsevier, Springer, Wiley
  o    Scimago JR (https://www.scimagojr.com/) can be used to determine the reliability of the publishers.
• You will carry out the critical analysis of the machine learning applications in the selected research article. The analysis shall consist of contextual machine learning application, dataset description and exploratory analysis, working principles of a specific machine learning technique, justification of its use in the corresponding application, and effectiveness of the technique with recommendation for improvement.
• In the process of developing a machine learning model based on the selected research article, several key steps are involved. First, the relevant dataset is collected, ensuring alignment with the contextual machine learning application discussed in the article. Subsequently, data preprocessing activities address issues such as missing values, outliers, and feature scaling. Feature engineering is then undertaken to enhance the model's predictive capabilities through the creation or transformation of features. Following this, a specific machine learning technique is chosen, and its working principles are understood. The justification for the technique's selection is established based on its suitability for the given application. Implementation involves training at least two algorithms on the dataset, and results are evaluated using appropriate metrics. The comparative analysis of algorithm performance informs insights into their strengths and weaknesses. Finally, recommendations for improvement are provided, guiding potential adjustments to hyperparameters or data collection strategies to enhance the model's efficacy in addressing the specific machine learning application.
• You need to document the outcomes of your critical analysis into a research essay format of 3500 words (+/- 10%) with the structure given below.

Learning Outcomes to be Assessed:

1. Select and explain appropriate AI applications to deal with the complex data processing issues along with the justification of the appropriateness.
2. Synthesise data and information from a range of AI artefacts, determining the links between principles and applications
3. Identify and evaluate machine learning schemes to quantify a range of performance metrics related to emerging data processing challenges