Answer: WIDE AREA NETWORK (WAN)
Explanation: hopes this helps
The distinction between "computer architecture" and "computer organization" has become very fuzzy, if no completely confused or unusable. Computer architecture was essentially a contract with software stating unambiguously what the hardware does. The architecture was essentially a set of statements of the form "If you execute this instruction (or get an interrupt, etc.), then that is what happens. Computer organization, then, was a usually high-level description of the logic, memory, etc, used to implement that contract: These registers, those data paths, this connection to memory, etc.
Programs written to run on a particular computer architecture should always run correctly on that architecture no matter what computer organization (implementation) is used.
For example, both Intel and AMD processors have the same X86 architecture, but how the two companies implement that architecture (their computer organizations) is usually very different. The same programs run correctly on both, because the architecture is the same, but they may run at different speeds, because the organizations are different. Likewise, the many companies implementing MIPS, or ARM, or other processors are providing the same architecture - the same programs run correctly on all of them - but have very different high - level organizations inside them.
For Example,
If You Were Hosting Ark Survival And Suddenly The Server Was Off Then The Players Goes Off.The Server Is How We Connect To The Platform In Order To Do The Action Intended.
Answer:
the third one
Explanation:
the rest is below 28 apart from the third one which is 28 and is the most
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: