Posted: March 16th, 2022

This document is for Coventry University students for their own use in completing their

Artificial Neural Networks
This document is for Coventry University students for their own use in completing their
assessed work for this module and should not be passed to third parties or posted on any
website. Any infringements of this rule should be reported to
[email protected].
Faculty of Engineering, Environment and Computing
7088CEM Artificial Neural Networks
Assignment Brief 2021-2022

Module Title
Artificial Neural Networks Individual Cohort:
Resit Module Code
7088CEM/7014CEM
Coursework Title: Artificial Neural Networks Applications Hand out date:
28/02/2022
Lecturer
Dr. Marwan Fuad Due date and time:
Date: 04/04/2022
time: 18:00:00
Estimated Time (hrs): 25 h
Word Limit: about 4500 words Coursework type:
Individual written report 100 % of Module Mark
Submission arrangement online via Aula:
File types and method of recording: WORD using the “Assignments” link in 7088CEM/7014CEM
Mark and Feedback date (DD/MM/YY): 2 weeks after submission
Mark and Feedback method (e.g. in lecture, electronic via Aula): electronic via Aula
AssignmentTutorOnline

Module Learning Outcomes Assessed:
1. Acquire a deep knowledge of the constitutional concepts of artificial neural networks including
their biological inspiration.
2. Apply and compare the different architectures and learning approaches available in neural
network systems.
3. Design and develop different neural network models applying appropriate learning approaches
for real world applications.
4. Use the available neural network simulators, develop solutions to real-world problems and
appraise their limitations.
5. Critically evaluate the trends in neural network developments.
Task:
In this assignment, you have to select more than one task (classification, prediction, clustering,
regression, anomaly detection, motif discovery, etc), ideally for a problem inspired from the real world,
and explore how to best apply neural network learning algorithms to solve it. For a higher level of
difficulty, you can choose either more recent and advanced modelling approaches in neural networks
(such as deep neural networks) and/or more difficult applications, e.g., more complex problems from
image processing, signal processing, information retrieval, natural language processing, biology.
The main purpose of this assignment is to:
• Test the understanding of fundamental concepts of neural networks and their applications.
• Perform appropriate preparation of a data set and evaluate the performance of different neural
network algorithms on the chosen data set.
This document is for Coventry University students for their own use in completing their
assessed work for this module and should not be passed to third parties or posted on any
website. Any infringements of this rule should be reported to
[email protected].

• Gain practical experience in using neural network learning algorithms for solving a real-life problem.
• Demonstrate your ability to critically evaluate the results and compare different learning algorithms
and their results.
Procedure:
• You have to write a project proposal (maximum of 1 A4 page), giving the title of the project,
the description of the problem, the dataset you are using (its name and a direct link to it), and
the work plan. You have to submit the proposal by 13/03/2022 at 18.00 via 7088CEM project
proposal link. You do not need approval from your ML on the project, but the ML may ask you
to change parts, or the whole project.
• Your final submission will include a report (up to 4500 words – strict limit) where you present
your work.
Your report should typically have:
o A title.
o An introduction in which you briefly describe your project.
o Background /related work
o The problem you are solving/the tasks you are performing,/the method you are
applying
o Experimental section
o Discussion of your findings.
o Conclusion.
o References.
o Appendices (not included in the word count)
• In addition to submitting the project proposal separately by the aforementioned deadline, you
have to submit it, EXACTLY AS IT IS, in the appendix of the final CW.
Remarks:
• You can use any ANN algorithm/architecture that you like, whether it was covered in the
module or not.
• You can select a dataset of your choice from one of the open dataset repositories (e.g.,
Kaggle/UCI/others):
1. Machine Learning Repository: http://archive.ics.uci.edu/ml/;
2. Kaggle competitions: http://www.kaggle.com/competitions;
• Everything you do should be reproducible: a direct link to the dataset should be provided. The
code used, in its totality, should be included in the appendix. If you use a code that is not yours,
whether totally or partially, this should be very clearly indicated.
• You should provide in the appendix clear evidence, using screen captures, that you ran every part
of the experiments. The screen captures should also clearly show the device on which the
experiments were conducted/the software was installed
This document is for Coventry University students for their own use in completing their
assessed work for this module and should not be passed to third parties or posted on any
website. Any infringements of this rule should be reported to
[email protected].

• Except for the dataset, NO EXTERNAL LINKS ARE ALLOWED. Everything should be included in the
report
• Plagiarism and collusion are taken extremely seriously. Any part, from any source, of any type,
in any language, should be COMPLETELY AND CLEARLY citrated. If you use a figure/table/image
that is not yours, this should be indicated in the caption.
Mark distribution:
Technical quality (45 Marks):
• This aspect concerns the depth of the information presented in the report and the way this
information is presented, the rigor of the experiments, data preparation, selecting the right
algorithm for the application, any modification ( to the algorithm or the code) done by the
student, discussions of the findings/results.
Difficulty (15 Marks):
• This aspect concerns the difficulty of the problem, the difficulty/complexity of the
method/algorithm used, the complexity of the dataset used, performing several/complex tasks.
Originality (10 Marks):
• This aspect concerns the novelty of the application and/or the algorithm/method used
Reproducibility (10 Marks):
• This aspect concerns using screen shots of all the steps taken, providing the code (using the
right tool to include code in a WORD document), clear and straightforward explanation of the
steps taken to reproduce the results, including the figures
Style and format (10 Marks):
• This concerns the clarity of the report, proper size and resolution of the figures/images, correct
English, using a proper format.
The project proposal (10 Marks):
• Achievable goal, clear steps, suitability to a master degree
This document is for Coventry University students for their own use in completing their
assessed work for this module and should not be passed to third parties or posted on any
website. Any infringements of this rule should be reported to
[email protected].

Notes:
1. You are expected to use the Coventry University APA style for referencing. For support and
advice on this students can contact Centre for Academic Writing (CAW).
2. Please notify your registry course support team and module leader for disability support.
3. Any student requiring an extension or deferral should follow the university process as outlined
here.
4. The University cannot take responsibility for any coursework lost or corrupted on disks, laptops
or personal computer. Students should therefore regularly back-up any work and are advised to
save it on the University system.
5. If there are technical or performance issues that prevent students submitting coursework
through the online coursework submission system on the day of a coursework deadline, an
appropriate extension to the coursework submission deadline will be agreed. This extension will
normally be 24 hours or the next working day if the deadline falls on a Friday or over the
weekend period. This will be communicated via your Module Leader.
6. You are encouraged to check the originality of your work by using the draft Turnitin links on Aula.
7. Collusion between students (where sections of your work are similar to the work submitted by
other students in this or previous module cohorts) is taken extremely seriously and will be
reported to the academic conduct panel. This applies to both courseworks and exam answers.
8. A marked difference between your writing style, knowledge and skill level demonstrated in class
discussion, any test conditions and that demonstrated in a coursework assignment may result in
you having to undertake a Viva Voce in order to prove the coursework assignment is entirely your
own work.
9. If you make use of the services of a proof reader in your work you must keep your original version
and make it available as a demonstration of your written efforts.
10. You must not submit work for assessment that you have already submitted (partially or in full),
either for your current course or for another qualification of this university, with the exception of
resits, where for the coursework, you maybe asked to rework and improve a previous attempt.
This requirement will be specifically detailed in your assignment brief or specific course or module
information. Where earlier work by you is citable, i.e. it has already been published/submitted,
you must reference it clearly. Identical pieces of work submitted concurrently may also be
considered to be self-plagiarism.
Mark allocation guidelines to students

0-39 40-49 50-59 60-69 70+ 80+
Work mainly
incomplete
and /or
weaknesses in
most areas Most elements
completed;
weaknesses
outweigh
strengths Most elements
are strong,
minor
weaknesses Strengths in all
elements Most work
exceeds the
standard
expected All work
substantially
exceeds the
standard
expected
This document is for Coventry University students for their own use in completing their assessed work for this module and should not be passed to third
parties or posted on any website. Any infringements of this rule should be reported to [email protected].
Marking Rubric

GRADE ANSWER RELEVANCE ARGUMENT & COHERENCE EVIDENCE SUMMARY
First
≥70 Innovative response, answers the
question fully, addressing the learning
objectives of the assessment task.
Evidence of critical analysis, synthesis
and evaluation. A clear, consistent in-depth critical and
evaluative argument, displaying the ability
to develop original ideas from a range of
sources. Engagement with theoretical
and conceptual analysis. Wide range of appropriately supporting
evidence provided, going beyond the
recommended texts. Correctly
referenced. An outstanding, well-structured and
appropriately referenced answer,
demonstrating a high degree of
understanding and critical analytic skills.
Upper Second
60-69 A very good attempt to address the
objectives of the assessment task with an
emphasis on those elements requiring
critical review. A generally clear line of critical and
evaluative argument is presented.
Relationships between statements and
sections are easy to follow, and there is a
sound, coherent structure. A very good range of relevant sources is
used in a largely consistent way as
supporting evidence. There is use of
some sources beyond recommended
texts. Correctly referenced in the main. The answer demonstrates a very good
understanding of theories, concepts and
issues, with evidence of reading beyond
the recommended minimum. Well
organised and clearly written.
Lower Second
50-59 Competently addresses objectives, but
may contain errors or omissions and
critical discussion of issues may be
superficial or limited in places. Some critical discussion, but the argument
is not always convincing, and the work is
descriptive in places, with over-reliance on
the work of others. A range of relevant sources is used, but
the critical evaluation aspect is not fully
presented. There is limited use of sources
beyond the standard recommended
materials. Referencing is not always
correctly presented. The answer demonstrates a good
understanding of some relevant
theories, concepts and issues, but there
are some errors and irrelevant material
included. The structure lacks clarity.
Third
40-49 Addresses most objectives of the
assessment task, with some notable
omissions. The structure is unclear in
parts, and there is limited analysis. The work is descriptive with minimal
critical discussion and limited theoretical
engagement. A limited range of relevant sources used
without appropriate presentation as
supporting or conflicting evidence coupled
with very limited critical analysis.
Referencing has some errors. Some understanding is demonstrated but
is incomplete, and there is evidence of
limited research on the topic. Poor
structure and presentation, with few
and/or poorly presented references.
Fail
<40 Some deviation from the objectives of the
assessment task. May not consistently
address the assignment brief. At the
lower end fails to answer the question set
or address the learning outcomes. There
is minimal evidence of analysis or
evaluation. Descriptive with no evidence of theoretical
engagement, critical discussion or
theoretical engagement. At the lower end
displays a minimal level of understanding. Very limited use and application of
relevant sources as supporting evidence.
At the lower end demonstrates a lack of
real understanding. Poor presentation of
references. Whilst some relevant material is present,
the level of understanding is poor with
limited evidence of wider reading. Poor
structure and poor presentation, including
referencing. At the lower end there is
evidence of a lack of comprehension,
resulting in an assignment that is well
below the required standard.
Late submission 0 0 0 0

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Study Notes, Research Topics & Assignment Examples: Explain how to review performance of project team members throughout the lifecycle of a project. »ORIGINAL RESEARCH