Answer:
Demonstrate the register addressing mode for the following instructions. Also what addressing mode belongs to these instructions?
1. MOV CX, [BX+DI]
2. MOV AX, ARRAY[CX]
3. MOV BX, [CX+DI+6]
Answer:
Annotation symbol.
Explanation:
A flowchart can be defined as a graphical representation of an algorithm for a process or workflow.
Basically, a flowchart make use of standard symbols such as arrows, rectangle, diamond and an oval to graphically represent the steps associated with a system, process or workflow sequentially i.e from the beginning (start) to the end (finish).
A symbol shown as a three-sided box in a flowchart, that is connected to the step it references by a dashed line is known as an annotation symbol.
This ultimately implies that, it provides additional information in the form of remarks or comments with respect to the steps in a flowchart.
Answer:
Filtering procedure provides an essay and convenient way to visualize or work only with the data we desire without needing to delete those we do not need at that moment.
Filter allows you to view only the information you want to see. Only the required information is displayed at that moment once the filter criteria has been checked.
Filter temporarily hides rows of data that you do not need to view. Rows of data which aren't needed at the moment are temporarily hidden in other to allow user concentrate on the needed data.
Filter allows you to filter text and numeric data. Filtering allows for different filtering criteria, if the user requires working or viewing only on text or numeric data, it is possible with spreadsheet filter.
Explanation:
Filter allows you to view only the information you want to see.
Filter temporarily hides rows of data that you do not need to view
Filter allows you to filter text and numeric data.
Define variables
left is l
right is r
Ask input
left or right
Ask input value
Equate l or r to the input value
Show ladder with steps equal to input value and in the side of input variable
Answer:
40
Explanation:
Given that:
A neural network with 11 input variables possess;
one hidden layer with three hidden units; &
one output variable
For every input, a variable must go to every node.
Thus, we can calculate the weights of weight with respect to connections to input and hidden layer by using the formula:
= ( inputs + bias) × numbers of nodes
= (11 + 1 ) × 3
= 12 × 3
= 36 weights
Also, For one hidden layer (with 3 nodes) and one output
The entry result for every hidden node will go directly to the output
These results will have weights associated with them before computed in the output node.
Thus; using the formula
= (numbers of nodes + bais) output, we get;
= ( 3+ 1 ) × 1
= 4 weights
weights with respect to input and hidden layer total = 36
weights with respect to hidden and output layer total = 4
Finally, the sum of both weights is = 36 + 4
= 40
