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Department Lecturer Course Course No. COLLEGE OF ENGINEERING & TECHNOLOGY : .. Department of Computer Science ..……..........................………............ : .. Dr. Ibrahim Imam ....................................………............ : .. Introduction to Artificial Intelligence ..........……............ : .. CS 366 ……….. Sheet : ..……4.......... Q1. Suppose you have the following neural network, the weight between node xi and node xj is given by: (xi - xj) /(xi + xj) What decision shall be given to each of the cases in the following table? Can you drive a logical expression equivalent to this neural network? Explain. ============================================================= Q2. Give one difference between: • Supervised learning algorithm and supervised neural network algorithm • Expert system and expert system shell • Search and search space • Frames and decision rules ============================================================= Q3. Consider the following trained neural network. Use the sigmoid function to obtain the input and output value to each node (as I shown you in the lecture). • Use the test data in the given table to test the neural network. Calculate the decision provided by this neural network for each record/example. • Can you represent the decision column as a logical relationship using the three attributes? ============================================================= Q4. Suppose you are given the following trained neural network for the data to the right. Can you calculate the missing weight? If yes, show how. ============================================================= Q5. Given a database that contains four (4) attributes, W, X, Y, Z, and a decision attribute D. Design the input and output layers for a neural network in two (2) different ways given that the attribute domains are: W = {0, 2, 4}; X = {k, n}; Y = {10, 12, 14, 16, 18}; Z = {True, False} and the decisions are D = {a, b, c}. What is the input vector to the neural network for the example: (W=2)(X=n)(Y=10)(Z=True)(D=c) ============================================================= Q6. Given a database that contains four (4) attributes, W, X, Y, Z, and a decision attribute D. Design the input and output layers for a neural network in two (2) different ways given that the attribute domains are: W = {0, 2, 4}; X = {k, n}; Y = {10, 12, 14, 16, 18}; Z = {True, False} and the decisions are D = {a, b, c}.