write a code in java programming language and Demonstrate Artificial intelligence $/$machine language in in an Inventory management of car dealership system

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Project Objective

Modify your existing inventory program to incorporate AI$/$ML simulation that recommends the most efficient sorting or searching algorithm based on the nature of the dataset. The program should apply advanced OOP concepts

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Requirements

$1.$ AI$/$ML Simulation

$$ Implement logic that simulates AI$/$ML to analyze the inventory dataset and decide the best algorithm.

$$ Example factors to consider:

o Dataset size: Small datasets may favor simpler algorithms like Bubble Sort or Linear Search.

o Data structure: Sorted vs$.$ unsorted data can influence algorithm choice $($e$.$g$.,$ Binary Search requires sorted data$).$

o Operation frequency: Repeated sorting may benefit from an efficient algorithm like QuickSort or MergeSort.

o Memory constraints: Suggest algorithms that align with available memory $($e$.$g$.,$ Heap Sort for in$-$place sorting$).$

$$ AI$/$ML techniques:

o Rule$-$based system: Use conditional statements to recommend an algorithm based on predefined rules.

o Machine Learning $($optional$)$: Train a simple model $($e$.$g$.,$ using a decision tree$)$ to learn patterns in dataset characteristics and predict the best algorithm.

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$2.$ Program Features

$$ Inventory Management:

o Users can add, remove, or search for items.

o The program stores item information such as name, category, price, and quantity.

$$ Algorithm Recommendation:

o Suggest the best sorting algorithm $($e$.$g$.,$ QuickSort, MergeSort, BubbleSort$).$

o Suggest the best search algorithm $($e$.$g$.,$ Linear Search, Binary Search$).$

o Implement the recommended algorithm dynamically in the program when performing sorting or searching operations.

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$3.$ OOP Design

Use OOP principles to structure the program:

$$ Encapsulation: Organize inventory data and AI$/$ML logic into separate classes.

$$ Inheritance: Create a base class for algorithms and derived classes for specific types $($e$.$g$.,$ SortingAlgorithm, SearchingAlgorithm$).$

$$ Polymorphism: Implement a unified interface for different algorithms, enabling easy substitution.

$$ Abstraction: Hide the internal implementation details of algorithm selection.

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Implementation Steps

$1.$ Design Classes:

o InventoryManager: Manages item data and operations like adding, removing, and searching items.

o AlgorithmSelector: Implements AI$/$ML logic to recommend algorithms.

o Algorithm Base Class: Define a base class for all sorting and searching algorithms. Use inheritance to implement specific algorithms like QuickSort or BinarySearch.

$2.$ Develop the AI$/$ML Logic:

o Basic Approach: Use conditional statements to simulate AI behavior $($e$.$g$.,$ if dataset size $<10,$ recommend BubbleSort$).$

o Advanced Approach $($Optional$)$: Train a machine learning model $($e$.$g$.,$ decision tree$)$ on dataset characteristics and integrate it into the program.

$3.$ Integrate Algorithms:

o Implement sorting and searching algorithms.

o Allow the program to dynamically choose the recommended algorithm at runtime.

$4.$ Testing and Validation:

o Test the program with different datasets to ensure the AI$/$ML simulation recommends appropriate algorithms.

o Compare program performance using recommended algorithms versus other options.

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Example Workflow

$1.$ User Input:

o Inventory: Add items $($e$.$g$.,$ name, price, quantity$).$

o Action: User selects "Sort" or "Search."

$2.$ AI$/$ML Recommendation:

o The program evaluates the dataset $($size$,$ structure, etc.$).$

o It recommends an algorithm $($e$.$g$.,$ QuickSort for sorting or Binary Search for searching$).$

$3.$ Execution:

o The program applies the recommended algorithm to sort or search the inventory.

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Outcome

This project showcases advanced programming skills by simulating AI$/$ML within an OOP framework, providing practical algorithm recommendations based on inventory data.