Research Publications

Contributing to the advancement of AI, machine learning, and natural language processing through peer-reviewed research

2
Total Publications
8
Total Citations
2
Journal Articles
0
Conference Papers

ASH CC Algo.: Coin Change Algorithm Optimization

Ashar Mehmood
2019
International Journal of Computer Applications
Medium Impact
3 citations

Abstract

The Coin Change problem is to represent a given amount V with fewest number of coins m. As a variation of knapsack problem, it is known to be NP-hard problem. Most of the time, Greedy alogrithm (time complexity O(m), space complexity O(1)), irrespective of real money system, doesn’t give optimal solution. Dynamic algorithm (time complexity O(mV), space complexity O(V)) gives optimal solution but is still expensive as amount V can be very large. In this paper, we have presented a suboptimal solution for the coin change problem which is much better than the greedy algorithm and has accuracy comparable to dynamic solution. Moreover, comparison of different algorithms has been stated in this paper. Proposed algorithm has a time complexity of O(m2f) and space complexity of O(1), where f is the maximum number of times a coin can be used to make amount V. It is, most of the time, more efficient as compared to dynamic algorithm and uses no memoization, this is a significant advantage over dynamic approach.

Keywords

Dynamic programmingCoin change problemoptimization methodsAlgorithm design and analysis
DOI: 10.5120/ijca2019918787
Type: Journal Article
PublishedView Paper

ASH Search: Binary Search Optimization

Ashar Mehmood
2019
High Impact
5 citations

Abstract

The binary search is a method of finding the position of an element in an ordered array. It continuously aims the middle element of array and check if it is the target element or not untills it finds its position. The best case complexity of binary search is O(1), whereas average and worst case time complexity is O(log n), where ‘n’ is the number of elements in the array. In this paper, I have proposed an algorithm which drastically improves the complexity of search algorithm in sorted array domain outperforming binary search, the paper also compares the proposed solution with other well-known search algorithms. The presented approach minimizes the space complexity, eliminates the need to analyze the scenario and look for the algorithm that best fits the given problem. The proposed algorithm has constant space complexity (O(1)) and time complexity of O(1) (constant time) in best case, O(log(log n)) in average case and O(log(n)) in worst case. Thus, most of the time proposed algorithm works very well as compared to other search algorithms in sorted array domain.

Keywords

Binary searchSorted searchingSearch optimizationInterpolation searchcomplexity analysis
DOI: 10.5120/ijca2019918788
Type: Journal Article
PublishedView Paper

Research Areas

Key domains where my research contributes to advancing the field

Natural Language Processing

Multilingual sentiment analysis, conversational AI, and language understanding systems

Financial Technology

Credit scoring models, alternative data analysis, and SME lending solutions

Computer Vision

Facial liveness detection, anti-spoofing techniques, and biometric security systems

Recommendation Systems

Large-scale clustering, personalization algorithms, and content discovery systems

Theoretical Computer Science

Algorithm designing and optimizations

Applied AI

Real-world AI applications, industry partnerships, and scalable ML solutions

Interested in Collaboration?

I'm always open to research collaborations, academic partnerships, and discussing innovative AI applications.