fixed capacity Queue and Changing the Queue capacity using Artificial Intelligence
Fixed Capacity Queue
A fixed capacity queue is a queue that can store a huge amount of data (based on the memory capacity) but at a single point in time, it can store only a portion of that data (based on the array capacity). Generally, creating a fixed capacity queue is way more complex than creating a no-fixed capacity queue. But then why are we specifically studying fixed capacity queue? Because it has some key benefits over a no-fixed capacity queue like constant time complexity for enqueue operation and dequeue operation, constant space complexity, etc.
But despite all these benefits, we have one problem in the creation of this type of queue that can not be neglected. Like I said before, ‘a fixed capacity queue is a queue that can store a huge amount of data but at a single point in time, it can store only a portion of that data’. It is because when we create a fixed capacity queue, we have to tell the system, what is the size of the array that we are using for the creation of the queue. And we have to be very very careful in selecting the size of the array because if the array is full and we are still adding the data in the array then data can either be lost or we might get an error.
Artificial Intelligence and Fixed Capacity Queue
How can we use artificial intelligence and fixed capacity queue together to create a perfect fixed capacity queue? Well, we cannot. It is impossible to create a perfect fixed capacity queue. But why so? Because we can use artificial intelligence and create a model to find what size of the array should we select to create a fixed capacity queue, but we also know that no model can be 100% accurate.
But let’s say we still want to create a model that’ll help us in selecting the size of the array for the fixed capacity queue. In this case, we’ll require a dataset containing historical data. This historical data will be something like a table containing information like the maximum amount of data in a queue at any point in time and much more similar information. We’ll use an algorithm called linear regression algorithm. Now, why linear regression algorithm? Because linear regression algorithm is used to create a model that predicts the output that is a continuous number. The continuous number we’ll get will help us in selecting the size of the array. Once we get the size of the array, the work of artificial intelligence is done. Then we can create a queue like we usually do.
Real-Life Applications of Queue in Artificial Intelligence
AI is used in banking, which is so far higher value for banks, is in fraud detection. It can be hard for humans to understand patterns, but machines are good at it. Managing queues will resolve challenges in terms of operational efficiency as well as the cost involved in training fraud reviewers for particular fraud types. Also, will help to optimize the business process by responding to Fraud incidents in an efficient and effective manner.