EBK EXPERIENCING MIS,
8th Edition
ISBN: 9780134792729
Author: BOYLE
Publisher: PEARSON CUSTOM PUB.(CONSIGNMENT)
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Expert Solution & Answer
Chapter 3, Problem 2EGDQ
Explanation of Solution
Automated decision making:
Computers are trying to reveal professionals and managers from taking certain types of decisions. Computer program will analyze the data and make judgments to configure stuffs such as diagnose and treat patient’s illness, configure complex computer bugs. Automated decision making system are very slow to emerge.
The need for automated decision system has been recognized. It is nowadays used in business. It has been established to support the managers and other higher official where taking a decision plays a serious role. Some decision support tools proposals the potential for sophisticated statistical alertness into business problems.
Automated decision making systems are based on the business rules...
Expert Solution & Answer
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Students have asked these similar questions
Design a dynamic programming algorithm for the Longest Alternating Subsequence problem
described below:
Input: A sequence of n integers
Output: The length of the longest subsequence where the numbers alternate between being larger and
smaller than their predecessor
The algorithm must take O(n²) time. You must also write and explain the recurrence.
Example 1:
Input: [3, 5, 4, 1, 3, 6, 5, 7, 3, 4]
Output: 8 ([3, 5, 4, 6, 5, 7, 3, 4])
Example 2:
Input: [4,7,2,5,8, 3, 8, 0, 4, 7, 8]
Output: 8 ([4, 7, 2, 5, 3, 8, 0,4])
(Take your time with this for the subproblem for this one)
Design a dynamic programming algorithm for the Coin-change problem described below:
Input: An amount of money C and a set of n possible coin values with an unlimited supply of each
kind of coin.
Output: The smallest number of coins that add up to C exactly, or output that no such set exists.
The algorithm must take O(n C) time. You must also write and explain the recurrence.
Example 1:
Input: C24, Coin values = = [1, 5, 10, 25, 50]
Output: 6 (since 24 = 10+ 10+1+1 +1 + 1)
Example 2:
Input: C = 86, Coin values = [1, 5, 6, 23, 35, 46, 50]
Output: 2 (since 86 = 46+35+5)
Design a dynamic programming algorithm for the Longest Common Subsequence problem de-
scribed below
Input: Two strings x = x1x2 xm and y = Y1Y2... Yn
Output: The length of the longest subsequence that is common to both x and y.
.
The algorithm must take O(m n) time. You must also write and explain the recurrence.
(I want the largest k such that there are 1 ≤ i₁ < ... < ik ≤ m and 1 ≤ j₁ < ... < jk ≤ n such that
Xi₁ Xi2 Xik = Yj1Yj2 ··· Yjk)
Example 1:
Input: x = 'abcdefghijklmnopqrst' and y = 'ygrhnodsh ftw'
Output: 6 ('ghnost' is the longest common subsequence to both strings)
Example 2:
Input: x = 'ahshku' and y = ‘asu'
Output: 3 ('asu' is the longest common subsequence to both strings)
Chapter 3 Solutions
EBK EXPERIENCING MIS,
Ch. 3.6 - Prob. 1SWCh. 3.6 - Prob. 2SWCh. 3.6 - Prob. 3SWCh. 3.6 - Prob. 4SWCh. 3 - Prob. 1EGDQCh. 3 - Prob. 2EGDQCh. 3 - Prob. 3EGDQCh. 3 - Prob. 4EGDQCh. 3 - Prob. 1ARQCh. 3 - Prob. 2ARQ
Ch. 3 - Prob. 3ARQCh. 3 - Prob. 4ARQCh. 3 - Prob. 5ARQCh. 3 - Prob. 6ARQCh. 3 - Prob. 1UYKCh. 3 - Prob. 2UYKCh. 3 - Prob. 3UYKCh. 3 - Prob. 4CECh. 3 - Prob. 5CECh. 3 - Prob. 6CECh. 3 - Prob. 7CECh. 3 - Prob. 8CSCh. 3 - Prob. 9CSCh. 3 - Prob. 10CSCh. 3 - Prob. 11CSCh. 3 - Prob. 12CSCh. 3 - Prob. 13CSCh. 3 - Prob. 14CSCh. 3 - Prob. 15CSCh. 3 - Prob. 16CSCh. 3 - Prob. 17MLMCh. 3 - Prob. 18MLM
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