Answer:
The Utility Software is system software that helps to maintain the proper and smooth functioning of a Computer System. Some examples are antivirus software, file management tools, compression tools, disk management tools.It is used to support the computer infrastructure - in contrast to application software, which is aimed at directly performing tasks that benefit ordinary users.
1. Using Listener Software:
Software like Adobe reader allows you to listen to the stored files rather than reading them, Akira can use this software while she need to read a file or an article, simply copy the article in pdf file and turn on listener for it.
2. Zoom in/ Zoom Out:
Most of the browsers these days allow you to zoom in and zoom out the web pages while reading them by pressing Alt and + sign for Zoom in and Alt and - for zoom out. This feature can be really helpful for reading purposes and also for images view as well.
3. Narrators
When Akira is completely fed up with reading the screen, she can use Narrators which are programs that convert every text on screen into audio file and she can listen to them.
4.Color Changers
These days software like color changer are very useful for people with low vision eyes. They change the color of screen such that there is no irritation in eyes while reading articles. They can also be used by her.
Depends on how you wanna do it, through iTunes or whatever other you are syncing from
Answer:
Explanation:
Allow you to view the document in different ways. Sorry if I am wrong. I am dumb
God bless, stay safe, and good luck! :)
Bayes’ Theorem provides a way that we can calculate the probability of a piece of data belonging to a given class, given our prior knowledge.
P(class|data) = (P(data|class) * P(class)) / P(data)
Where P(class|data) is the probability of class given the provided data.
Explanation:
- Naive Bayes is a classification algorithm for binary and multiclass classification problems.
- It is called Naive Bayes or idiot Bayes because the calculations of the probabilities for each class are simplified to make their calculations tractable.
This Naive Bayes tutorial is broken down into 5 parts:
Step 1: Separate By Class : Calculate the probability of data by the class they belong to, the so-called base rate. Separate our training data by class.
Step 2: Summarize Dataset : The two statistics we require from a given dataset are the mean and the standard deviation
The mean is the average value and can be calculated using :
mean = sum(x)/n * count(x)
Step 3: Summarize Data By Class : Statistics from our training dataset organized by class.
Step 4: Gaussian Probability Density Function : Probability or likelihood of observing a given real-value. One way we can do this is to assume that the values are drawn from a distribution, such as a bell curve or Gaussian distribution.
Step 5: Class Probabilities : The statistics calculated from our training data to calculate probabilities for new data. Probabilities are calculated separately for each class. This means that we first calculate the probability that a new piece of data belongs to the first class, then calculate the second class, on for all the classes.
