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MITS5509 Intelligent Systems for Analytics Assignment 1 and 2 Presentation and Research Report MITS5509 Assignment 1 and 2 Copyright © XXXXXXXXXXVIT, All Rights Reserved. 2 NOTE: This Document is used...

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MITS5509
Intelligent Systems for Analytics
Assignment 1 and 2
Presentation and Research Report
MITS5509 Assignment 1 and 2

Copyright © XXXXXXXXXXVIT, All Rights Reserved. 2
NOTE: This Document is used in conjunction with Assessment 1 [Presentation & participation]
and Assessment 2 [Research Report] in the Teaching and Assessment Plan document
[MITS5509_Teaching and Assessment Plan.doc]
Objective(s)
This assessment item relates to the unit learning outcomes as in the unit descriptor. This
assessment is designed to improve student presentation skills and to give students experience in
esearching a topic and writing a report relevant to the Unit of Study subject matter.
INSTRUCTIONS
These instructions apply to both Assignment 1 - Presentation and Assignment 2 Research
Report.
Assignment 1 - Presentation and Participation - 10% (Sessions 5-12) Individual Assignment
For this component you will be required to do a 5-10 minute presentation on a recent academic
paper on a topic related to Intelligent Systems for Analytics or Intelligent Systems. Some
possible topic areas include but are not limited to:
• Intelligent Systems for Data Warehouse systems
• Evolving Intelligent Systems: Methods, Learning, & Applications
• Distance Metric Learning in Intelligent Systems
• Intelligent Systems for Socially Aware Computing
• Data Mining techniques with IS
• frameworks for integrating AI and data mining
• Expert System
• Structure of knowledge Engineering
• IS and Support Vector Machines
• IS and Neural Network Architectures
• Heuristic Search Methods
• Genetic Algorithms and Developing GA Applications
The paper you select must be directly relevant to one of the above topics or another topic and
e related to Intelligent Systems for Analytics. The paper must be approved by your lecturer and
e related to what we are studying this semester in Intelligent Systems for Analytics. The pape
MITS5509 Assignment 1 and 2

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can be from any academic conference or other relevant Journal or online sources such as Google
Scholar, Academic department repositories, or a significant commercial company involved in
esearch such as IBM etc. All students must select a different paper. Thus, the paper must be
approved by your lecturer before proceeding. In case two students are wanting to present on the
same paper, the first who emails the lecturer with their choice will be allocated that paper. Please
note that popular magazine or web-site articles are not academic papers.
A grade of 10% of the Units mark will be awarded for your presentation and your participation in
other student presentations. You are to prepare a set of powerpoint slides for your presentation.
If you do not participate in at least 70% of other student’s presentations you will forfeit a
significant proportion of the marks for this component.
Note: if class numbers are large the presentations may be organized into groups, but students
will still all need to select their own individual paper for assignment 2. In the case where
presentations are a
anged in groups each group can decide which students paper will be used
for the presentation.
The presentations will occur in sessions 5-12 on the academic calendar for the semester and the
order of presentations will be by a
angement, but these will be evenly spread over those
sessions.
What to Submit.
There is no submission for this component, you are allocated marks based on your
presentation and participation
Assignment 2 - Research Report - 10% (Due Session 8) Individual Assignment
For this component you will write a report or critique on the paper you chose from Assignment
1, the Presentation and Participation component above.
Your report should be limited to approx. 1500 words (not including references). Use 1.5 spacing
with a 12 point Times New Roman font. Though your paper will largely be based on the chosen
article, you should use other sources to support your discussion or the chosen papers premises.
Citation of sources is mandatory and must be in the IEEE style.
Your report or critique must include:
MITS5509 Assignment 1 and 2

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Title Page: The title of the assessment, the name of the paper you are reporting on and its
authors, and your name and student ID.
Introduction: Identification of the paper you are critiquing/ reviewing, a statement of the
purpose for your report and a
ief outline of how you will discuss the selected article (one or
two paragraphs).
Body of Report: Describe the intention and content of the article. If it is a research report,
discuss the research method (survey, case study, observation, experiment, or other method)
and findings. Comment on problems or issues highlighted by the authors. Report on results
discussed and discuss the conclusions of the article and how they are relevant to the topics of
this Unit of Study.
Conclusion: A summary of the points you have made in the body of the paper. The conclusion
should not introduce any ‘new’ material that was not discussed in the body of the paper. (One
or two paragraphs)
References: A list of sources used in your text. They should be listed alphabetically by (first)
author’s family name. Follow the IEEE style.
The footer must include your name, student ID, and page number.
Note: reports submitted on papers which are not approved or not the approved paper registered
for the student will not be graded and attract a zero (0) grade.
What to Submit
Submit your report to the Moodle drop-box for Assignment 2. Note that this will be a
turn-it-in drop box and as such you will be provided with a similarity score. This will be
taken into account when grading the assignment. Note that incidents of plagiarism will
e penalized. If your similarity score is high you can re-submit your report, but re-
submissions are only allowed up to the due date. If you submit your assignment after the
due date and time re-submissions will not be allowed.
Please Note: All work is due by the due date and time. Late submissions will be penalized
MITS5509 Assignment 1 and 2

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at the rate of 10% per day including weekends.
Answered Same DayDec 18, 2021MITS5509

Solution

Dilpreet answered on Dec 19 2021
53 Votes
CRITICAL ANALYSIS OF DATA MINING TASKS
Table of contents
Introduction    3
Genetic programming framework    3
Description of genetic algorithms and genetic programming systems    4
Discussion of phenotype of genetic programming individual or data mining    5
Implementing genetic programming for the task of classification    5
Implementing genetic programming for the task of generalized rule induction    6
Findings    6
Conclusion    6
References    7
Introduction
Data mining can be defined as practices to determine patterns or gain information from a large set of pre existing data. It is analysis of large sets of raw data done through various methods in order to obtain meaningful information. This is used to extract useful knowledge from existing databases which contains voluminous data gathered from the real world. There are five major data mining tasks identified as deviation detection, summarization, classification, generalized rule induction and clustering. The article, which has been selected for critical analysis discusses about genetic programming framework for two major data mining tasks namely classification and generalized rule induction. This genetic programming framework encodes database queries co
esponding to high level rules, which are used to predict the values of some of the attributes Freitas [1].
The framework, which has been discussed in this article emphasizes particularly on the integration between relational database systems and the GP algorithm. This integration can prove to be extremely advantageous in case of data mining scalability, automatic parallelization and controls for data privacy. This report is also intended to highlight some genetic operators which have been designed and developed exclusively for data mining tasks such as classification and generalized rule induction. The intersection of machine learning and databases in data mining helps to extract comprehensive knowledge with high degree of autonomy. Data mining procedures significantly improve the efficiency of analyzing data sets to discover useful knowledge. Genetic programming framework is capable of addressing or incorporating all the characteristics of data mining procedures. This report is aimed to highlight the genetic programming framework for classification and generalized rule induction.
Genetic programming framework
Genetic programming framework encodes data base queries and works on algorithms, which are intended to co
espond to high level rules. These are then used to predict the values of some of the attributes. This framework is considered suitable for data mining tasks because of its robustness of algorithm and easily understandable structure of rule generation. This operates by submitting SQL queries to database servers helping to achieve integration between genetic programming and relational databases. Genetic programming algorithms or framework are capable of searching many parts of the database or the program simultaneously with high efficiency. These frameworks are more autonomous and thus help to minimize the need for specific domain knowledge. SQL servers automatically parallelize the queries generated through genetic programming frameworks.
Description of genetic algorithms and genetic programming systems
Several genetic algorithms have been developed to support the genetic programming framework. These genetic...
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