To improve the efficiency of Insertion Sort, one can implement Shell Sort to reduce unnecessary shifting. Shell Sort involves comparing elements that are far apart before gradually decreasing the gap for sorting.

Applying binary search to a dataset with both numerical and textual information presents challenges. Binary search relies on a consistent ordering, making it complex when dealing with mixed data types. Separate searches for numerical and textual parts may be required, impacting the overall efficiency of the algorithm. Consideration of data organization is crucial for its feasibility.

Insertion Sort exhibits constant complexity (O(1)) when the input array is already sorted. In such cases, there is no need for many comparisons or swaps as elements are already in their correct positions.

In a plagiarism detection system, the Edit Distance algorithm measures the similarity between documents by calculating the minimum number of operations (insertions, deletions, substitutions) required to transform one document into the other. This provides a quantitative measure of textual similarity for plagiarism analysis.

Prim's algorithm would be easier to adapt to dynamic changes in edge weights. This is because Prim's algorithm builds the minimum spanning tree incrementally, allowing for straightforward updates when edge weights change. Kruskal's algorithm, on the other hand, involves sorting edges, making dynamic updates less straightforward.

Circular queues help in reducing space wastage that occurs in linear queues. In linear queues, as elements are dequeued, the space at the front becomes unusable. Circular queues address this issue by wrapping around when reaching the end of the array, effectively utilizing the available space more efficiently.

For a music player playlist, a doubly linked list is a suitable choice. This is because a doubly linked list allows for easy traversal in both directions, enabling efficient operations like moving forward and backward through the playlist, which is a common requirement in music player applications.

In string compression, the process involves replacing repeated characters with a unique symbol. This helps to reduce the overall size of the string by representing repetitive sequences more efficiently.

DFS is typically preferred over BFS when memory usage is a critical factor, and the solution is deep in the search tree. This is because DFS explores as far as possible along each branch before backtracking, which might reduce memory requirements compared to BFS.

No, DFS is not an appropriate choice for finding the shortest path in a social network. DFS may find a path, but it may not be the shortest, as DFS explores one path deeply before backtracking. BFS, on the other hand, systematically explores all possible paths and ensures the shortest one is found.

When encountering cycles in a graph, BFS never revisits already visited nodes. BFS uses a queue to explore nodes, and once a node is visited, it is marked as such. Since BFS explores nodes level by level, it does not revisit nodes, ensuring that cycles do not lead to infinite loops.

To adapt Dijkstra's algorithm for toll costs, you should add toll costs to the edge weights. This modification ensures that the algorithm considers both distance and toll costs when finding the shortest path, providing a more accurate representation of the actual travel expenses.