Answer:Fraud detection through analytical method is used for detection of the fraud transactions,bribe activity etc in companies, business,etc. This techniques helps in the reduction of financial frauds in the organization, have the control over company to protect it,decrease in the fraud associated costs etc.
It has the capability of identifying the fraud which has happened or going to happen through the analytical ways and human interference. The organizations or companies require efficient processing and detection system for identification of such false happening.
Answer:
False.
Explanation:
"save" just preserves any edits made to file in its current state. "Save As" lets you rename it and change the file type.
Answer:
<u>Window.java</u>
- public class Window {
- int width;
- int height;
-
- public Window(int width, int height){
- this.width = width;
- this.height = height;
- }
- public int getWidth(){
- return width;
- }
- public int getHeight(){
- return height;
- }
-
- public int getClientAreaHeight(){
- return getHeight();
- }
- }
<u>Main.java</u>
- public class Main {
- public static void main (String [] args) {
- Window win1 = new Window(12, 15);
- System.out.println(win1.getClientAreaHeight());
- }
- }
Explanation:
<u>Window.java</u>
There is a Window class with two int type attributes, width and height (Line 1 - 3).
The constructor of this class will take two inputs, width and height and set these input to its attributes (Line 5 - 8). There are two methods getWidth and getHeight which will return the value of attributes width and height, respectively (Line 10 - 16).
The required new method getClientAreaHeight is defined in line 18 -20. This method will call the getHeight method to return the height value of the window (Line 19).
<u>Main.java</u>
We test the Window class by creating one Window instance and call the getClientAreaHeight method and print the return output (Line 1 -6).
We shall see 15 is printed.
Answer:
It we were asked to develop a new data compression tool, it is recommended to use Huffman coding since it is easy to implement and it is widely used.
Explanation:
The pros and the cons of Huffman coding
Huffman coding is one of the most simple compressing encoding schemes and can be implemented easily and efficiently. It also has the advantage of not being patented like other methods (e.g. arithmetic codingfor example) which however are superior to Huffman coding in terms of resulting code length.
One thing not mentioned so far shall not be kept secret however: to decode our 96 bit of “brief wit” the potential receiver of the bit sequence does need the codes for all letters! In fact he doesn’t even know which letters are encoded at all! Adding this information, which is also called the “Huffman table” might use up more space than the original uncompressed sentence!
However: for longer texts the savings outweigh the added Huffman table length. One can also agree on a Huffman table to use that isn’t optimized for the exact text to be transmitted but is good in general. In the English language for example the letters “e” and “t” occur most often while “q” and “z” make up the least part of an average text and one can agree on one Huffman table to use that on average produces a good (=short) result. Once agreed upon it doesn’t have to be transmitted with every encoded text again.
One last thing to remember is that Huffman coding is not restricted to letters and text: it can be used for just any symbols, numbers or “abstract things” that can be assigned a bit sequence to. As such Huffman coding plays an important role in other compression algorithms like JPG compression for photos and MP3 for audio files.
The pros and the cons of Lempel-Ziv-Welch
The size of files usually increases to a great extent when it includes lots of repetitive data or monochrome images. LZW compression is the best technique for reducing the size of files containing more repetitive data. LZW compression is fast and simple to apply. Since this is a lossless compression technique, none of the contents in the file are lost during or after compression. The decompression algorithm always follows the compression algorithm. LZW algorithm is efficient because it does not need to pass the string table to the decompression code. The table can be recreated as it was during compression, using the input stream as data. This avoids insertion of large string translation table with the compression data.
Answer:
Resource management is the system level transmission cellular networks and wireless communication.
Explanation:
Wireless communication is the process to continue to the address for faster response time,to the resource management.
Transmission is the provided by that more utilization and wireless resources available,and to discovered data.
Wireless communication system to demand the larger bandwidth and transmission using development to the system.
Wireless communication resources management the larger bandwidth and reliable transmission consumed all the system layer.
Resource management techniques tool are used in a preliminary concepts or mathematical tools,and average limited power battery.
Resource management are they necessary mathematical and fundamental tools are used in wireless communication.
Wireless communication in the provide that wireless industry in a wireless communication.
