Concept explainers
Cohesion:
Cohesion shows the functional strength of any particular module. Module is a small part of any
Coupling:
Coupling shows the interaction between modules. It shows the interdependency and reliability of one module with the other. If any module significantly interacts with the other module, then it is said to be high coupling. This shows that the modules are highly interdependent.
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Chapter 7 Solutions
EBK COMPUTER SCIENCE: AN OVERVIEW
- Neurons compute? The activation function and the linear function, which is represented by the equation z = Wx + b, are both calculated by neurons. Both a linear function, represented by the equation z = Wx + b, and an activation function are computed by neurons. When g is present in a neuron, the input x is scaled linearly (Wx + b). Before using the result in an activation function, neurons perform a calculation to get the mean of all of their features.arrow_forwardCan you briefly describe Donald Norman's model of interaction's stages?arrow_forwardA learning curve is defined as: O a. A plot of the performance of the classifier on the y axis versus the complexity of the model on the x axis b. None of these reflect the definition of a learning curve O c. A plot of the performance of the classifier on the y axis on a growing test set on the x axis with a fixed size training set O d. A plot of the performance of the classifier on the y axis on a growing test set as the size of the training data grows on the x axis e. A plot of the performance of the classifier on the y axis on a fixed test set as the size of the training data grows on the x axis Clear my choicearrow_forward
- Does every State Diagram need a self-transition? Give examples to back up your assertions!arrow_forwardWith the aid of flocking, a path-finding problem for a group of entities can be solved so that the group's leader leads and the other members follow. Are there any groups for whom this strategy is inappropriate?arrow_forwardedit and add inter-class relations to the diagramarrow_forward
- For the confusion matrix shown, what is the precision for classes A. B, and C? What is the recall for classes A, B, and C? What is the overall accuracy of the system? Normalized Confustion Matrix Classifier Output (Computed Classification) Prediction A B C True Class of the Samples (Input) A 0.81 0.08 0.11 B 0.10 0.77 0.13 C 0.11 0.15 0.74arrow_forwardIs it necessary for each State Diagram to have a self-transition? Give arguments to back up your response!arrow_forwardIs it possible to entirely alter the interface of one of the Model-View-Controller framework's views without modifying the Model, taking the propagation mechanism into account? Explain why you hold your beliefs.Is it possible to entirely alter the interface of one of the Model-View-Controller framework's views without modifying the Model, taking the propagation mechanism into account? Explain why you hold your beliefs.arrow_forward
- Identify an Industrial application where intelligent control is necessary and implement Fuzzy based approach or neural network based approach in solving the issue. Develop the mathematical model and algorithm for the industrial application using MATLAB or any other open source software and run the simulation and get the output verified. Report the output with different training tolerance, different activation function and report the output of the simulation. Interpret the output of the designed algorithm with respect to change in training parameters.arrow_forwardWrite a note on the confusion matrix for a two class (binary) classification problem. Detail at least four measures, including false positive rate (FPR) and false negative rate (FNR), which can be calculated from the confusion matrix.arrow_forwardDo neurons engage in computation? Neurons perform computations involving both an activation function and a linear function (represented as z = Wx + b). Neurons perform a computation of a linear function (z = Wx + b) in conjunction with an activation function. The neuron linearly scales the input x through the function Wx + b, where g represents the scaling factor. Neurons perform a computation of the average value of all attributes prior to the application of the outcome to an activation function.3arrow_forward
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