Answer:
false
Explanation:
The term 'computationally expensive' means that a given mathematical function, code or an algorithm has high computational complexity. In addition, the mathematical function or algorithm will require several steps and procedures to be completed. Based on this, the statement made in this question is not true.
Every application has access to specific opened port. If you only make a exception for the specific application only that application can bypass the firewall.
<span>a. uncheck ���hide protected operating system files��� in folder options</span>
Answer:
Following is the program in C++ Language
#include <iostream> // header file
using namespace std; // namespace std
int main() // main method
{
int n; // variable declaration
cout<<" Please enter the number :";
cin>>n; // Read the number
if(n>0) // check the condition when number is positive
{
cout<<n<<endl<<"The number is Positive"; // Display number
}
else if(n<0) // check the condition when number is Negative
{
cout<<n<<endl<<"The number is Negative";// Display number
}
else // check the condition when number is Zero
{
cout<<n<<endl<<"The number is Zero";// Display number
}
return 0;
}
Output:
Please enter the number:
64
The number is Positive
Explanation:
Following are the description of the program
- Declared a variable "n" of int type.
- Read the value of "n" by user.
- Check the condition of positive number by using if block statement .If n>0 it print the number is positive.
- Check the condition of negative number by using else if block statement If n<0 it print the number is negative.
- Finally if both the above condition is fail it print the message " The number is Zero"
Answer:
4. Supervised learning.
Explanation:
Supervised and Unsupervised learning are both learning approaches in machine learning. In other words, they are sub-branches in machine learning.
In supervised learning, an algorithm(a function) is used to map input(s) to output(s). The aim of supervised learning is to predict output variables for given input data using a mapping function. When an input is given, predictions can be made to get the output.
Unsupervised learning on the other hand is suitable when no output variables are needed. The only data needed are the inputs. In this type of learning, the system just keeps learning more about the inputs.
Special applications of supervised learning are in image recognition, speech recognition, financial analysis, neural networking, forecasting and a whole lot more.
Application of unsupervised learning is in pre-processing of data during exploratory analysis.
<em>Hope this helps!</em>
