Software-defined infrastructure (SDI) is the definition of technical computing infrastructure entirely under the control of software with no operator or human intervention. It operates independent of any hardware-specific dependencies and are programmatically extensible.
Answer:
Time taken to travel from one track to the next = 0.08ms
Initial track= 15 0
4 (15-4)*(0.08)= 0.88
40 (40-4)*(0.08)= 2.88
35 (40-35)*(0.08)= 0.4
11 (35-11)*(0.08)= 1.92
14 (14-11)*(0.08)= 0.24
7 (14-7)*(0.08)= 0.56
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Total seek time=0.88+2.88+0.4+1.92+0.24+0.56=6.88ms
Explanation:
We caculate the seek time for each request, and then add them together to find the total seek time. The final track number for the current request becomes the current track of next request, and this process is repeated till the last request is processed.
Answer:
The correct answer to the following question will be option D. 300 nits.
Explanation:
LCD Monitors: LCD stands for Liquid Crystal Display, the display which uses two sheets of liquid crystal with polarizing material between the sheets and also known as Flat panel monitor.
- Each of the crystal in LCD's is like a shutter, it either allows to pass the light or it blocks the light. There is a fixed type of resolution in LCD
- LCD panels can be easily moved around all, lightweight, compact and small in size.
- An average 17-inch LCD monitor could be around 15 pounds, upwards 300 nits which gives the perfect brightness.
So, Option D is the correct answer.
There you go :) Written in C# you can add using tags yourself I believe
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:
