**Example 1:Input: [1, 2, 4, 7, 7, 5]**

Output:
Second Smallest : 2
Second Largest : 5

Explanation:  The elements are sorted as 1, 2, 4, 5, 7, 7.
Hence, the second smallest element is 2, and the second largest element is 5.

**Example 2:Input: [1]**

Output:
Second Smallest : -1
Second Largest : -1

Explanation:  Since there is only one element in the array, it is both the largest and smallest element.
Therefore, there is no second smallest or second largest element present.

1. Brute Force

Algorithm

• Sort the array in ascending order.
• The element at the second index (index 1) is the second smallest element.
• The element at the second index from the end (index length-2) is the second largest element.

import java.util.Arrays;

class Solution {

    // Method to find the second smallest and second largest elements in the array
    public static void getElements(int[] arr, int n) {
        
        // Edge case: when the array has less than 2 elements
        if (n == 0 || n == 1) {
            System.out.println(-1 + " " + -1);  // Print -1 for both second smallest and second largest
            return;
        }

        // Sort the array to easily find the second smallest and second largest elements
        Arrays.sort(arr);

        // Second smallest element is at index 1 after sorting
        int small = arr[1];

        // Second largest element is at index n-2 after sorting
        int large = arr[n - 2];

        // Output the second smallest and second largest elements
        System.out.println("Second smallest is " + small);
        System.out.println("Second largest is " + large);
    }
}

public class Main {

    public static void main(String[] args) {
        
        // Initialize the array with elements
        int[] arr = {1, 2, 4, 6, 7, 5};
        
        // Calculate the size of the array
        int n = arr.length;
        
        // Call the method to find and print the second smallest and second largest elements
        Solution.getElements(arr, n);
    }
}

Complexity Analysis

Time Complexity: O(N log N) , for sorting the array.

Space Complexity: O(1) , as we are using a constant amount of space for variables.

2. Better Approach

Algorithm