<u>Implication of technological literacy to a teacher training today
:</u>
- First of training has to be under technology literacy before training starts. Trainer also should have enough knowledge on technology literacy.
- Trainer can take printed materials to share the document on the topic which to be covered and circulated to trainers
- Since it given as general topic, training teacher has to cover the following topic, general on computer and purpose of computer today's life, life cycle of computer grown from old PC to laptop and tablet.
- Next topic such be covered operating system and explain about the operating system and different technology is used.
- Revolution on technology should be also covered and explained in details.
Answer:
#include <iostream>
using namespace std;
class Str{ ///baseclass
public :
string super_str;
string getStr()
{
return super_str;
}
void setStr(string String)
{
super_str=String;
}
};
class str : public Str{ //inheriting Str publicly
public :
string sub_str;
string getstr()
{
return sub_str;
}
void setstr(string String)
{
sub_str=String;
}
bool notstartswith()
{
int n=sub_str.length(); //to find length of substr
bool flag=false;
for(int i=0;i<n;i++) //Loop to check beginning of Str
{
if(super_str[i]!=sub_str[i])
{
flag=true;
break;
}
}
return flag;
}
};
int main()
{
str s; //object of subclass
s.setStr("Helloworld");
s.setstr("Hey");
if(s.notstartswith()==1) //checking if str is substring of Str
cout<<"Str does not start with str";
else
cout<<"Str starts with str";
return 0;
}
OUTPUT :
Str does not start with str
Explanation:
Above program is implemented the way as mentioned. for loop is being used to check the beginning of the str starts with substring or not.
Answer:
If it on g mail you go to the spam box and then press the spammers to account and then press block and then you will block them.
Answer:
The answer is nearest-neighbor learning.
because nearest neighbor learning is classification algorithm.
It is used to identify the sample points that are separated into different classes and to predict that the new sample point belongs to which class.
it classify the new sample point based on the distance.
for example if there are two sample points say square and circle and we assume some center point initially for square and circle and all the other points are added to the either square or circle cluster based on the distance between sample point and center point.
while the goal of decision tree is to predict the value of the target variable by learning some rules that are inferred from the features.
In decision tree training data set is given and we need to predict output of the target variable.
Explanation:
