代做DTS306TC Security, Privacy and Ethics Coursework 2调试数据库编程

Module code and Title	DTS306TC Security, Privacy and Ethics
School Title	School of AI and Advanced Computing
Assignment Title	Coursework 2
Submission Deadline	5 pm China Time (UTC+8 Beijing) on Sat. 14th  Dec 2024
Final Word Count	1500 +/-5%
Video Length	5 minutes +/-5%

DTS306TC Security, Privacy and Ethics

Coursework 2

Submission deadline: 5 pm China Time (UTC+8 Beijing) on Sat. 14th  Dec 2024

Percentage in final mark: 70%

Maximum score: 100 marks

Learning outcomes assessed:

B: Evaluating the potential risks and benefits of AI technologies on privacy and personal data

C: Understanding the importance of fairness in AI systems and its implications

Late policy: 5% of the total marks available for the assessment shall be deducted from the assessment mark for each working day after the submission date, up to a maximum of five working days.

Risks:

•     Please  read  the   coursework  instructions  and  requirements  carefully.  Not  following  these instructions and requirements may result in loss of marks.

•     Plagiarism results in award of ZERO mark.

•    The formal procedure for submitting coursework at XJTLU is strictly followed. Submission link on Learning Mall will be provided in due course. The submission timestamp on Learning Mall will be used to check late submission.

Overview

Artificial intelligence has great effect on modern lives. In this coursework, the theme on framework design of new medical image-based bias-mitigated and fair computer-aided diagnosis system will be investigated and explored. The coursework consists of two parts. In Part 1, you need to complete a report based given theme. In Part 2, you need to explain your design via video presentation.

Part 1 (Individual Report: 70 marks)

Healthcare industry is rich of electronic (digital) medical data from different modalities. Deep learning has revolutionized the use of machine learning in healthcare industry by leveraging on the model’s automatic feature extraction and learning. To date, deep models have been applied for numerous computer-aided diagnosis tasks such as prediction, detection and classification. Despite its promising outlook, deep learning- based computer-aided diagnosis models still fail to earn the trust of medical doctors. In fact, there are reports of inaccurate missed diagnoses due to bias error, lack of understanding about the underlying mechanism of deep learning, and miscalibration in real practice. Therefore, there is an open call for fair and transparent computer-aided diagnosis model for trustworthy smart healthcare.

The aim of this task is to empirically assess the current status of computer-aided diagnosis, technologies, challenges and solutions in smart healthcare, and research questions in AI fairness to design a towards innovative  bias-mitigated  and  fair  deep   learning  medical  image-based  computer-aided diagnosis model framework design with predefined traits. Hence, you are required to equip yourselves with the understanding about the specific domain of medical imaging. Besides, you need to apply the knowledge acquired from the lectures and tutorials to complete this coursework. You also need to do literature review to identify further relevant information that is helpful to develop your report content.

Task Instructions:

(1) You are required to study the medical image-based artificial intelligence computer-aided diagnosis by using deep learning in smart healthcare domain. Therefore, literature review is needed. For beginner, you can  refer to the suggested  review  paper to  understand the domain of smart healthcare using AI:

Most Nilufa Yeasmin, Md Al Amin, Tasmim Jamal Jati, Zeyar Aung & Mohammad Abdul Azim. 2024. Advanced of AI in Image-Based Computer-Aided Diagnosis: A Review. Array. 23(2024) 100357.    Available Online:https://doi.org/10.1016/j.array.2024.10035

Moreover, you are required to study extra learning materials to familiarize yourselves with image- based computer-aided diagnosis by using deep learning. Please note that no mark will be given to the literature review nor content extract from the given review paper. However, this effort shall serve as your first steep for your proposed towards innovative bias-mitigated and fair deep learning medical image-based computer-aided diagnosis model framework design.

(2) Write a report on your proposed towards innovative bias-mitigated and fair deep learning medical image-based computer-aided diagnosis model framework design. The report should be written in a clear and concise manner with no more than 1,500 words+/-5%. in total length. Your final report should be detailed, relevant and rationale in addressing the following sections:

Important:  Do  not  repeat  existing  information that  is  in  the  research  papers. This will only contribute to low mark. Instead, you need to synthesize your own ideas/opinions based on your understanding and present them in your own words.

Part 2 (Individual Presentation: 30 marks) Task Instructions:

(1) Prepare and record a short individual presentation video of 5 minutes+/-5%. Your presentation should be clear, should be in no more than 10 Powerpoint slides and should not take beyond 5 minutes+/-5%. The presentation should address the followings:

i.         To introduce and explain the significance of your proposed towards innovative bias- mitigated   and   fair   deep   learning   medical   image-based   computer-aided diagnosis model framework design.

ii.         To  explain  how  your  proposed  model  design  can  effectively  become  General  Data

Protection Regulation (GDPR) and IEEE “Human Standards” with Implications for AI compliance  in order to promote your design to overseas  healthcare  market successfully.

Report Format:

Cover Page: This should include the Assessment Number, Assessment Title, Student Name, Student ID and Student Email

Body of the report: This should include all the relevant section headings to address each section as indicated above and marking rubrics.

References: Both your in-text and the references included in the “References” section at the end of the report should adhere strictly to the IEEE reference style.

Formatting requirement:

•     Use multiple spacing : 1.08 and spacing after: 8pt;

•     Use a standard 12-point font, font type: Tahoma

•     Use “Justify” body text

•     Put your page numbers at the top right (except the cover page)

•     Most importantly, always run a spelling and grammar check; however, remember, such checks may not pick up all errors. You should still edit your work manually and carefully.

Referencing:

It is compulsory to use IEEE reference style for citing and referencing research. Reference list is excluded from the imposed word limit.

Presentation Format:

Students are not requested to submit their presentation slides to the submission system. However, they must  present  their  Powerpoint  presentation  slides  clearly  throughout  the  video  presentation  period. Otherwise, the presentation will not be evaluated and ZERO marks will be given.

All video presentation must be uploaded to the Mediasite and attach the video presentation link at the last page of report. It is student responsibility to attach the link properly and apply the right accessibility setting in Mediasite to ensure examiners can access to the video presentation link in their computers during marking.

Please note that during marking, lecturers are not responsible for any inaccessible video presentation link at their computers due to any kind of reason or under any kind of circumstances, and has the right to give ZERO mark for the inaccessible video presentation link attached in the report.