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Intelligent Systems For Analytics MITS5509

Subject Name – Intelligent Systems for Analytics
Subject Code – MITS5509
University Name – Victorian Institute of Technology, Australia

Intro to Intelligent Systems for Analytics, MITS5509.

Intelligent Systems for Analytics MITS5509 is a subject offered by the Victorian Institute of Technology to provide students with an in-depth understanding of the intelligent business system that can represent, reason about, and interpret data. Students will study modern-day business systems that have intelligent features such as algorithms for learning about the structure of data, examining the data to extract patterns and meaning, generating new information, and providing a strategy to act on the outcomes of their research.

This subject helps students who are pursuing a Master of Information and Technology degree, grasp the fundamentals of developing a Business Intelligence System that tackles challenges in the data analytics field. Content included Intelligent Systems for Analytics MITS5509.

Introduction to Business Intelligence

Decision Support System

Intelligent Systems for Data Warehouse systems

Evolving Intelligent Systems: Methods, Learning, and 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

Brief Understanding of Intelligent Systems For Analytics

Business Intelligence

To help business organizations make more data-driven decisions, Business intelligence (BI) incorporates business analytics, data mining, data visualization, data tools, infrastructure, and best practices.

In practice, you’ve got modern business intelligence when you have a holistic perspective of your company’s data and can utilize it to drive change, eliminate inefficiencies, and quickly adjust to market or supplier changes. BI applications include the activities of decision support systems, querying and reporting, online analytical processing (OLAP), statistical analysis, forecasting, and data mining.

Components of Business Intelligence Architecture

Data Warehouse

A data warehouse is a sort of data management system that is intended to facilitate and assist business intelligence (BI) and analytics activities. Data warehouses are designed mainly for querying and analysis, and they frequently store vast amounts of historical data.

Business Analytics

Business Analytics is a combination of skills. Technologies, and procedures to study an organization’s data and performance in order to obtain insights and make data-driven decisions in the future using statistical analysis. The purpose of Business Analytics is to determine which datasets are useful and which can boost revenue, productivity, and efficiency.

Business Performance Management

Business performance management (BPM) is a type of business intelligence that is used to track and manage the performance of an organization. This is accomplished through the use of key performance indicators (KPIs). Revenue, return on investment, overhead, and operational costs are among the KPIs. Corporate performance management is another name for business performance management (CPM).

User Interface

The user interface provides a graphical as well as a pictorial view of corporate performance measures, trends, and exceptions, e.g., Dashboards and Other Information Broadcasting Tools.

Benefits of Business Intelligence

Faster analysis and an intuitive dashboard.

Increased organizational efficiency.

Improved decision making

Improved customer experience

Improved employee satisfaction

Trusted and governed data

Increased competitive advantage

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Activities in Business Intelligence


Data Preparation

Before any data analysis, the data must be collected, cleaned, and arranged in a consistent manner. Business intelligence software may gather data from a variety of sources and format it using the same dimensions and measures.


Data Mining

Data mining identifies patterns in data that were previously undetectable. To extract information from data sets, a combination of statistics and machine learning is used.


Statistical Analysis

To acquire a more detailed view of the data, statistical components such as mean, median, range, variance, and so on can be employed. These categories are an important aspect of statistical analysis since they help companies better understand their metrics and develop a stronger Business Intelligence plan.


Descriptive Analysis

Using descriptive analysis to research historical data is also part of Business Intelligence. This means that companies can use historical data to better understand their prior business practices and habits, as well as whether or not they contributed to success or failure. They can then develop a future strategy based on that information.


Data Visualization

A variety of charts, such as area charts, bar charts, histograms, heat maps, treemaps, scatter plots, and Gantt charts, can be used to visualize data. All of these graphs enable decision-makers to have a better grasp of the data by visualizing it and identifying slowly developing trends or areas where crucial changes occur abruptly.


Visual Analysis

After the data visualizations have been developed, it is critical to visually evaluate the data. This visual analysis entails extracting business insights from data and presenting them in a visual format that may be used to alter company policy.


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Sample Paper for Intelligent Systems for Analytics MITS5509

Question 1: A 4-input neuron has weights of 0.3, 0.25, 0.5, and 0.7. The transfer function is the sigmoid function. The inputs are X1 = 2, X2 = 3, X3 = 5, and X4 = 6, respectively. What will be the output of the processing element (PE)? You need to show the equations used. (4marks)

Question 2: Discuss the difference between supervised and unsupervised learning methods with the help of examples. (4marks)

Question 3: Recently, you have been assigned as a consultant to a renowned restaurant where the top management is struggling to cope with the recent demand for takeaway orders. The existing analyst is not able to respond to this problem in the company. (10marks)
a) You want to use the four-step decision process to make a decision about a solution for this problem. You are required to list and discuss the four steps that you need to follow to make a decision.
b) Furthermore, to convert this solution into the company’s knowledge repository and document it in a reusable form, the consultant is going to apply the knowledge acquisition process to subject matter experts. Identify and list the knowledge acquisition difficulties and challenges the consultant may face during this process.

Question 4: The supermarket has a database of transactions where each transaction is a set of products that were bought together. The database of transactions consists of the following items: (10marks)
You are required to find the frequent items using the Apriori algorithm. The minimum support is 3. You need to show all the steps to find the frequent items. MITS5509

Question 5: Let’s assume that a prediction model is created for the insurance companies to classify the claims as “accepted” or “rejected”. A testing dataset is used to evaluate the prediction model. It contains 500 claim records. In reality, out of 500 cases, 300 are labeled “accepted” and 200 are labeled “rejected”.
Our model predicted (classified) out of 300, 230 claims correctly as “accepted” and the remaining as “rejected”. Similarly, out of 200 records of “rejected” data, 135 claims are correctly classified as “rejected” and the remaining are classified as “accepted”.
Assume the “accepted” group is a positive class and the “rejected” group is a negative class. Find the performance measures below using the confusion matrix: (7marks)
a) True Positive
b) True Negative
c) False Positive
d) False Negative
e) Sensitivity (True positive rate)
f) Specificity (True negative rate)
g) Accuracy

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