Artificial Intelligence (MDA522 Assignment 2) Assignment Help

Assessment Details and Submission Guidelines
Unit Code	MDA522
Unit Title	Artificial Intelligence
Assessment Type	Assignment 2: Group Assignment
Assessment Title	Development of AI Algorithms to Detect Explosives in Waste Receptables Using Vision Transformers
Purpose of the assessment (with ULO Mapping)	This assignment assesses the following Unit Learning Outcomes; students should be able to demonstrate their achievements in them. c. Manage and lead development of AI solutions for complex problems. d. Compare and analyse the performance of AI techniques in accordance with the industry standard.
Weight	25% of the total assessments
Total Marks	54 Marks
Word/Page limit	20 A4 pages (11-pt Calibri (Body) font and 2.54 cm margins on all four sides of each page with appropriate section headings, 1.5 line spacing.)
Due Date	Week 11 (29th September 2024, 11.59 PM)
Submission Guidelines	• All work must be submitted on Moodle by the due date along with a completed Assignment Cover Page. • Reference sources must be cited in the text of the report, and listed appropriately at the end in a reference list using IEEE bibliographic and referencing style. • Students must ensure before submission of final version of the assignment that the similarity percentage as computed by Turnitin has to be less than 10%. Assignments with more than 10% similarity may not be considered for marking.
Extension	If an extension of time to submit work is required, a Special Consideration Application must be submitted directly through AMS. You must submit this application within three working days of the assessment due date. Further information is available at:

Academic

Misconduct

Academic Misconduct is a serious offence. Depending on the seriousness of the case, penalties can vary from a written warning or zero marks to exclusion from the course or rescinding the degree. Students should make themselves familiar with the full policy and procedure available at:

For further information, please refer to the Academic Integrity Section in your Unit Description.

GenAI

In this assignment, the use of GENERATIVE AI (GenAI) IS PROHIBITED.

Purpose of the Assignment:

This assignment seeks to equip students with many desirable skills and knowledge in artificial intelligence (AI) and related disciplines, and to fulfill the intended learning outcomes of the unit. In particular, students are expected to use AI models, techniques and tools to solve complex real-life problems which are topical and relevant to modern industry. Students are grouped into 4 members each. All groups will work on the same general topic in a competitive manner, use similar tools to acquire similar set of skills. The task is done by the groups in a competitive manner in the sense that it will judge as the winner the group whose deep learning model detects the target with the highest predictive accuracy and lowest error. The project also has a research component besides practical programming in Python. By working in groups, students also acquire teamwork skills.

Project/Task Background:

This is a follow-up to Assignment 1 to enable students to further develop their skills in the applications of AI in computer vision. The project in this assignment is titled “Development of AI Algorithms to Detect Explosives in Waste Receptables Using Vision Transformers”. Some cities have witnessed the concealment of improvised explosive devices (IEDs), or explosives, in public waste receptacles by criminals to destroy people and property. This menace is causing mass removal of dustbins from public places and public transport in cities by city councils the world over as a strategy to solve the problem [1]. The consequence is littering to cause stench, pests, contamination of water bodies, spread of diseases and increased greenhouse gases.

The current solutions to the problem are blast-resistant and transparent dustbins. However, none of these approaches is optimal. First, none of them inherently catches the criminals to deter the act. Second, the awful appearance of rubbish is visible with transparent bins, while blast-resistant dustbins are bulky and costly.

AI algorithms used in computer vision have been historically dominated by convolutional neural networks (CNNs). Currently, the application of Vision Transformer (ViT) in computer vision is increasing. ViT is a model for image classification that employs a transformer-like architecture over patches of the image. A ViT converts an image into a sequence of non-overlapping patches, similar to how language models (e.g. transformers, LSTM and RNNs) handle text. Each patch is then transformed into a vector using a Large Language Model (LLM) and processed within a transformer architecture. ViTs are capable of grasping global information within images, transcending the limitations of local feature extraction performed by CNNs.

This assignment seeks to motivate students to develop a novel approach to protect dustbins from illicit use as we maintain them for their traditional role. This is a new research area so there may not be much available literature or datasets. The project is an intersection of AI, IoT and environmental security. However, the task will focus on the design of AI algorithms.

Methodology:

Students are expected to build AI/ML/DL models to be implemented in waste receptacles or rubbish bins to detect if rubbish thrown into a waste bin is a real waste or a potential explosive. They should search and review existing algorithm and techniques used. Then, attempt to design a new algorithm/model that is better in some sense than the existing ones, if any. Object detection is an image processing task under computer vision. Tasks involved include:

1) Search for an existing database of images of waste/garbage, and features of waste and detonations or IEDs to build a new database.

2) Through an extensive search select the appropriate AI algorithms and Python libraries used for image classification.

3) Use the tools and database constructed to train AI/DL/ML models based on ViTs to classify waste into either:

(a) 2 groups: real waste/garbage or IED. This will build a deep learning model for binary classification.

(b) More than 2 groups: real waste/garbage sorted into classes and IED being another class. This will build a deep learning model for multi-class classification.

Project Objectives and Deliverables:

The project falls under object detection in computer vision, and the following are the two deliverables or milestones expected in this project:

• Use the existing database constructed in [1], extend the database in [1] or construct a new database of IED images which is good enough, regarding veracity, volume and value, to solve the target problem.

• Construct of a deep learning model based on ViTs, using Keras in Jupyter Notebook, which can detect IEDs (explosives) thrown into a waste receptacle. This is the target problem.

Needed Tools:

• Laptop (students should come to class with their laptops)

• Python, Numpy, SciPy, Matplotlib, scikit-learn, Keras/Tensorflow, etc. running in Jupyter Notebook in Anaconda Python distribution.

Submission Instructions:

1) Each group shall submit only one report to the appropriate Moodle link for marking and grading.

2) Deliverables: the report must be in the following three (3) formats:

a. Groupk_Ass2.html: This is the programming/practical part which should be submitted in a single Jupyter Notebook under the name:

File → Download as → HTML (.html)

By executing from Jupyter Notebook. The Jupyter Notebook must use the Markdown markup language to explain the solution to the task concisely but clearly.

b. Groupk_Report_Ass2.doc: This is the file containing the project report. All figures and results in the Jupyter Notebook must be captured in this report.

c. Groupk.ppt: This is the file that summarizes the project report to be presented in class in Week 11.

where k is the Group number.

Note: Moodle cannot any file exceed 200 MB. Such files should be stored on a cloud platform and loaded into the Jupyter Notebook in a way that can be run without any further ado.

3) Format of group report (i.e. Groupk_Report_Ass2.doc). Each group report must be formatted as IEEE Journal paper with a cover page showing names and Student ID of students in group. The cover page should also contain a table showing the contributions of each student to the project and the reports. The sections of the main report should use the titles and numbering:

a. Title

b. Abstract

c. I. Introduction/background

d. II. Literature review (or related works)

(This section analyses works completed and published that relate the topic under review. Cited works should include at least 5 peer-reviewed journal papers)

e. III. Materials and Methods (aka Research Methods)

A. Data Used for the Model Training

(This subsection should include the source of the data and its

description/features)

B. Method/Algorithms/Models Used (the VIT model/s)

C. Experimental Setup

D. Model Training and Experiments

f. IV. Results and discussions

[This section documents a thorough comparison of the performance of any alternative methods used, or alternative values for model parameters, etc. How results could be improved, etc.]

g. V. Conclusions

h. Acknowledgment

i. References

(List of ONLY works cited in the body of the project report and presentation slides. IEEE referencing and bibliographic style must be perfectly adhered to.)

• Note that some of the sections in the report should not be numbered, namely Title, Abstract, Acknowledgement (if any) and References.

• Use illustrations in the form of figures and tables (copied from Jupyter Notebook) wherever they deem fit. Learn from the related works that you study as you do the project. If you read one paper per week, you would have studied at least 11 papers by the time the project is completed.

Final grade = 0.3*Presentation Grade + 0.7*(grade for project report and Jupyter Notebook grade)

Marking Guide for the Oral Presentation

Each group shall present a summary of its work in class in Week 11. The presentation shall be graded using the criteria in the table below.

Category	Scoring Criteria	Total Points	Score
Organization (15 points)	The type of presentation is appropriate for the topic and audience.	3
	Information is presented in a logical sequence.	3
	Presentation appropriately cites requisite number of references.	3

Content (45 points)	Introduction is attention-getting, lays out the problem well, and establishes a framework for the rest of the presentation.	3
	Technical terms are well-defined in language appropriate for the target audience.	3
	Presentation contains accurate information.	5
	Material included is relevant to the overall message/purpose.	5
	Appropriate amount of material is prepared, and points made reflect well their relative importance.	5
	There is an obvious conclusion summarizing the presentation.	3
Presentation (40 points)	Speaker maintains good eye contact with the audience and is appropriately animated (e.g., gestures, moving around, etc.).	3
	Speaker uses a clear, audible voice.	3
	Delivery is poised, controlled, and smooth.	2.5
	Good language skills and pronunciation are used.	2.5
	Visual aids are well prepared, informative, effective, and not distracting.	2.5
	Length of presentation is within the assigned time limits.	2.5
	Information was well communicated.	5
Score	Total Points	54

Marking Criteria: Rubric for the Main Report

Your submission will be marked using the criteria in this table.

Success Level Criteria	Strongly Agree [6 marks]	Agree [5 marks]	Somewhat Agree [4 marks]	Somewhat Disagree [3 marks]	Disagree [2 marks]	Strongly Disagree [1 mark]
[Abstract] The abstract (with length 200-250 words) includes background, literature review, research questions, research methodology, results, and conclusions.
[Background/ Introduction] The background section situates the study and explains its significance.
[Literature Review] The literature situates the reported research in the context of work by other researchers, identifies needed advances, and establishes justification for the present study.
[Research Questions] The research questions is framed in terms of needed work identified in the literature review and aligns that work with the research methodology.
[Research Methodology] The research methodology section consists of a comprehensive description of how the study was executed. The section written in such a manner others should be able to replicate the study. While the structure typically includes information on research design, sequence, collection, participants, and analysis, this section may vary depending on the nature of the study. Finally, the section must indicate that an Institutional Review Board or its equivalent has approved the study if it contains human subjects or that it was exempt.
[Results] The results section provides evidence that the research questions have been addressed. Results of quantitative studies should be reported according to standards identified by the American Psychological Association in terms of sample size, descriptive and inferential statistics, and effect size. The nature of the reporting may vary, depending on the nature of the study.
[Conclusions] The conclusion section describes how the results have answered the research questions and discusses implications of the findings within the larger context of professional communication. This section may also include study limitations and directions for further research.
[References & in-text citation] Adheres strictly to IEEE standards
[Spelling, grammar, cohesiveness and structure] No spelling and/or grammar mistakes; structure of report is perfectly that of an IEEE journal

Note: Research question should not stand a lone as a section, but integrated into the abstract, introduction, literature review, results and conclusions.

References

[1] A Gyasi-Agyei, “Detection of explosives in dustbins using deep transfer learning based multiclass classifiers,” Appl. Intell. , vol. 54, no. January 2024, p. 2314–2347, 2024.

[2] Gyasi-Agyei A (2023) I2Net: Database of IED Images for IED Detection in Public Waste Receptacles