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computer science
artificial intelligence a guide to intelligent
Artificial Intelligence A Guide To Intelligent Systems 3rd Edition Michael Negnevitsky - Solutions
What are the stages in the knowledge acquisition process? Why is knowledge acquisition often called a bottleneck of the process of knowledge engineering? How can the acquired data affect our choice of the system building tool?
What is a prototype? What is a test case? How do we test an intelligent system? What should we do if we have made a bad choice of system building tool?
Develop a genetic algorithm for the problem described in Question 19 assuming that there is a river that divides the city into two parts, West and East, at x ¼ 5 km. West and East are connected by a bridge located at x ¼ 5 km and y ¼ 5:5 km, as shown in Figure 9.48.Find the optimal location of
Why is adopting new intelligent technologies becoming problem driven, rather than curiosity driven, as it often was in the past?
What makes diagnosis and troubleshooting problems so attractive for expert system technology? What is a phone call rule?
Develop a genetic algorithm for optimising the location of an emergency response unit in order to minimise the response time to a medical emergency in a city. The city is mapped into a 7 km * 7 km grid, shown in Figure 9.47. A number in each sector of the grid represents an average number of
A set of rules shown below uses Bayesian accumulation of evidence for assessing chest pain complaints made by a patient. Evaluate the system and provide a full trace of the reasoning process. Determine the probability of heart disease if the patient suffers from chest pain and his or her
How do we choose a tool to develop an expert system? What are the advantages of expert system shells? How do we choose an expert system shell for building an intelligent system?
Develop a fuzzy system for direct marketing. Assume that the target group is defined as ‘fathers in their prime working years with income above average’. The ‘prime working years’ group is defined as between 40 and 55 years old, and the average income is $45,000. Make sure that your fuzzy
Develop a fuzzy expert system for detecting fraudulent claims for home insurance.Assume the following seven input variables: the number of claims filed by a claimant in the last 12 months, amount of the current claim, how long a claimant was with the current insurer, average balance on all accounts
Consider the set of five transactions shown in Table 10.7. Using the Apriori algorithm, find the frequent itemset and generate association rules. Assume that the minimum support is 60 per cent and the minimum confidence is 80 per cent. Table 10.7 An example of market basket
What is the basis for the popularity of neural networks? In what areas have neural networks been applied most successfully? Explain why and give examples.
Why do we need to massage data before using it in a neural network model? How do we massage the data? Give examples of massaging continuous and discrete data.What is 1 of N coding?
Develop a back-propagation neural network to classify rock crabs into males and females using five anatomic measurements (in mm): frontal lobe size, rear width, carapace length, carapace width and body depth. The data set contains 100 specimens, 50 males and 50 females. The data set is located on
Develop a back-propagation neural network for diagnosing breast cancer based on features computed from a digitised image of a fine needle aspirate (FNA) of a breast mass. The data set contains 150 cases. Each case is diagnosed as malignant or benign based on 10 real-valued features: radius,
Develop a neuro-fuzzy system with a heterogeneous structure for the problem described in Question 16. Note that first you need to redefine the problem so that the system output becomes the estimated risk of breast cancer.Question 16Develop a back-propagation neural network for diagnosing breast
Use a self-organising neural network for cluster analysis of waste-water treatment plant data. The data set contains 527 instances. Each instance has 38 attributes. The data set is located at the UCI Machine Learning Repository website:http://archive.ics.uci.edu/ml/datasets/Water+Treatment+Plant
What is data mining? Describe the overall process of data mining and knowledge discovery. How is data mining applied in practice? Give examples. What are data mining tools?
What is data exploration? Define data visualisation. Give examples of graphical data representation methods and their applications in everyday life.
What is a scatter plot? How do we define a linear regression model and determine regression coefficients? Explain how outliers can affect a linear regression model.What is robust regression?
What is principal component analysis? How does PCA work? Give an example. How do we decide how many principal components we should keep to capture most of the information contained in a given data set?
What is a database management system? What is a relational database? Define tables of the relational database Travel_agency.
What is a query? Give examples of basic SQL queries. What is the difference between database querying and data mining?
What is a data warehouse? What are its main characteristics? Why do we need to copy data from operational database management systems into a data warehouse?
What are a data cube and a starnet model? Give an example of a 3-D data cube and represent the cube in tabular form.
What is on-line analytical processing? How does OLAP facilitate data viewing from different perspectives? What operations can OLAP carry out?
What is a decision tree? What are dependent variables and predictors? What is the Gini coefficient? How does a decision tree select predictors?
What are the advantages and limitations of the decision tree approach to data mining?Why are decision trees particularly attractive to business professionals?
What is market basket analysis? What is an association rule? Give examples of association rules. Define support and confidence.
What is a frequent itemset? Construct an itemset lattice for the itemset {A, B, C, D, E} and explain the concept of the monotone and anti-monotone properties of support.
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