Which of the following application architecture do you suggest as the best option for email for an organization? Consider small, medium and large organizations to provide your suggestions.
1. Two-tier client-server
2. web-based
Answer:
2. Web based
Explanation:
Web based applications provides cloud based email solutions provider that benefits small organisation as much as big organisations because the investment is minimal and affordable and gives no problem in deployment of resources to manage services with no risk.
It provides multiple point access by providing email solutions that enables multiple point of access gives rise to flexibility of access by members of the organisation.
It provides powerful admin control which makes management of email services easy and provides high security for services.
Select the part you want to move. Highlight it, and click ctrl+x. Then, go to the place you want to put it. Click ctrl+v. It should be cut and pasted
B. Graphic Organizers
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Answer:
The answer to the given question the option "a".
Explanation:
In computer science, Excel is a spreadsheet program. It provides us a table in this table we create a grid of text, numbers, and formulas specifying calculations, graphs, and charts and insert picture in the table. In the table aligned cells are by default is left. because in the spreadsheet all the data will insert from the left to right. So the correct answer to this question is the option "a".
Answer:
Check explaination
Explanation:
You are to expect the majority classifier to score about 50% on leave-once-out cross-validation but scores zero (0%) every time.
This is so due to the fact that each data set is divided into 'x' subsets of equal size in a leave-one-out cross-validation.
From given data, a data set consisting of 100 positive and 100 negative examples. Here, using the majority classifier with the leave-one-out cross-validation.
The majority classifier is specified a set of training data and the majority in the training set, regardless of input that is always outputs the class.
If we continue making use of the majority classifier with the leave-one-out cross-validation is unbalanced for small permutations.
When an instance is deleted from the data set which is the majority in the training set, the majority inducer predicts one of the other two classes and always errors in classifying the test instances.
The leave-one-out estimated accuracy for a majority classifier. It will always predict the wrong answer. So, scores occurred 0% every time.