MITS5509 – INTELLIGENT SYSTEMS FOR ANALYTICS

Learning Outcomes

researching a topic and writing a report. The assessment gives students the opportunity to experience in constructing a range of documents as deliverables of the Intelligent system for Analytics.

not only improve their presentation skill but also give them experience in researching a topic and writing a report. The assessment gives students the opportunity to experience in constructing a range of documents as deliverables of the Intelligent system for Analytics.

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For this component you will be required to do a 5-10 minutes video presentation on a recent academic paper on a topic related to Intelligent Systems for Analytics or Intelligent Systems.

    • 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 Artificial Intelligence 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 be related to Intelligent Systems for Analytics.

For this component you will write a report or critique on the paper you chose for Assignment1: the Case Study & Presentation 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 may use other sources to support your discussion or the chosen paper’s premises. Citation of sources is mandatory and must be in the IEEE style.

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 brief 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 discussed 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.

Question: The bankruptcy-prediction problem can be viewed as a problem of classification. The data set you will be using for this problem includes one ratio that has been computed from the financial statements of real-world firms. These ratios have been used in studies involving bankruptcy prediction. The first sample (training set) includes 68 data values on firms that went bankrupt and firms that did not. This will be your training sample.

The second sample (testing set) of 68 firms also consists of some bankrupt firms and some non-bankrupt firms. Your goal is to use different classifiers to build a training model, by randomly selecting the 40 data points (20 points from category 1 and 20 points from category 0), and then test its performance on the testing model by randomly selecting 40 data points from the testing set.

Students must use the following classifiers. The selection of the classifiers depends upon the members of the group, e.g. if the group has four members then they will use the four classifiers from the following five classifiers.

    1. Neural network
    2. Support vector machine
    3. Nearest neighbour algorithm
    4. Decision tree
    5. Naive Bayes

From the above data set, the group has to prepare a report which include the followings:

    1. Explain the process of building each classifier using the training set (add the screenshots).
    2. Explain how you evaluated the classifier.
    3. Create the confusion matrix based on 70% (training) / 30% (testing).
    4. Predict the category of the values (any random 40 values) in the table used for the Testing set.
    5. Compare the results between the different classifiers and discuss which one is the best and why.

Question: Create a DASHBOARD. For creating a dashboard, the group can use the above database or any other database. The group has to prepare a report which include the followings:

    1. Write an introduction about the dataset used and add the reference (link).
    2. Create at least four figures (different graphs) and add them to the dashboard.
    3. Add Screenshot of each of the steps.
    4. Describe the figures in the dashboard.

The student can use any software to create the dashboard such as Microsoft Excel, Power BI,Tableau, etc.