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Artificial Intelligence in Real World Problem Solving

AI870:Artificial Intelligence in Real World Problem Solving:

DescriptionDeliverable Length: 3-4 pages (not including cover page and resource page)

Assignment Details:

Evaluate the impact to society from the use of AI and its impact on business or other industries, such as government, agriculture, entertainment, aerospace, manufacturing, education, and healthcare.

  1. Find a case study or a scholarly article that investigates the issues related to the responsible and transparent use in explainable AI. Identify the industry affected by it, which can include academic use.
  2. Analyze the case study or article, noting at least three characteristics that illustrate the principles.
  3. Find additional references to support or provide counter arguments for your inquiry. These may include videos, conference proceedings, discussions by scholars, and podcasts.
  4. Assess the impact of explainable AI on the industry in your example. What are the problems associated with explainable AI?

Individual Project Rubric

The Individual Project (IP) Grading Rubric is a scoring tool that represents the performance expectations for the IP. This Individual Project Grading Rubric is divided into components that provide a clear description of what should be included within each component of the IP. It’s the roadmap that can help you in the development of your IP.

ExpectationPoints PossiblePoints EarnedComments
Investigate the scholarly literature or research resources to identify good sources of information.25
Analyze the research, noting the characteristics that illustrate the issues related to the topic25
Assess the impact of the topic in your analysis.25
Find additional references to support or provide counter arguments for your inquiry.25
Professional Language: Assignment contains accurate grammar, spelling, and/or punctuation with few or no errors. (APA formatting is required or style specified in assignment).25
Total Points125
Total Points Earned

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An approved IT Strategy Planning In Artificial Intelligence / Machine Learning

The research project will focus on research related to an approved IT Strategy Planning In Artificial Intelligence / Machine Learning, as it applies to an organization or industry. The paper must relate to a section of the course textbook or other materials from this course.

Requirements:

Must be written in APA format and the body should be 18 to 20 pages including introduction.

Contain at least 3-5 works cited (references).  At least 2 of the works cited should be peer-reviewed articles (not more than 5 years old) and 1 must be the textbook or other materials from this course.

No Wikipedia citations

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perceptron learning method and design an artificial neural network

The objective of this project is to use the perceptron learning method and design an artificial neural network (ANN) to train a simple system (single layer perceptron) for the recognition of handwritten digits (0, 1, …, 9).

Design a fully connected network structure of 784 input nodes and 10 output nodes.The input to your single layer network architecture will be a set of binary pixels representing a 28×28 image of handwritten digits. The output should indicate which of the digits (0,….,9) is in the input image.

Use the MNIST database of handwritten digits available on Blackboard-Homepage-Handwritten Digits Dataset.

Select a subset of the MNIST database consisting around 500 images of handwritten digits (0,…,9) for training the system, and use another 100 images for testing the system. Create binary or bipolar images of handwritten digits from gray scale images available in MNIST by simple thresholding (indicate the threshold value you used).

Plot a learning curve that illustrates the mean square error versus iterations.

(One iteration: apply all the training inputs once to the network and compute the mean square error).

Plot the percentage error in testing your handwritten digit recognition system as a bar chart.

(Mean error occurred while testing each digit with the test data).

Task #1: Repeat this experiment for different learning rate parameters (at least 3 experiments. Start

with a large value and gradually decrease to a small value).

Task #2: Repeat Task #1 with a large database. The first 10000 images for training (image indexes

from 0-9999) and test with another 1000 images (image indexes from 20001-21000).

Task #3: Repeat Task #2 with multilevel data (without thresholding the input data, normalize the

input data, use sigmoid function for output thresholding). What can you note comparing with part 2?

Task #4: Compare your results with the SVM results (what you have got from the last project).

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