International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 01 | Jan 2026
p-ISSN: 2395-0072
www.irjet.net
Case-Based Reasoning for Mobile Helpdesk System Dnyaneshwari Nitin Patil¹, Achal Ekanath Hujare², Pranita Shrikant Dalavi³, Archana. V. Jadhav4 1,2,3,4 Student, Computer Engineering, Dr. D. Y. Patil Polytechnic, Kolhapur, India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - In the modern digital era, mobile users expect
diagnosis system with CBR. The methods here are good for retrieval & adaptation of past similar cases, which is exactly useful if your chat bot needs to pick a close matching previous case when no exact answer exists. [2] This one is directly about applying CBR in helpdesk systems. It studies processes by which helpdesk systems can retrieve past similar cases to resolve new tickets, reducing resolution time. Good match with your project idea. [3] This describes IHDF, a system used in a bank’s helpdesk environment for network/computer fault management. It uses hybrid knowledge representation, fuzzy/neighbor similarity matching, indexing, etc. Very practical case of how to build and maintain a case base and perform retrieval and reuse. [4] Introduces a reasoning system that starts with a simple/coarse set of features, then refines to more detailed features if necessary. So when matching a new query, system tries simpler comparisons first, and if not good enough, goes deeper. This is helpful when performance matters (mobile environment) and helps reduce computation. [5] Classic foundational paper on what CBR is, how it works (storing cases, retrieving, adapting, retaining), memory/organization/indexing, etc. Good for theory/background in yourdocumentation. [6] Aamodt and Plaza presented the classical Case-Based Reasoning cycle consisting of retrieve, reuse, revise, and retain phases. Their work emphasizes continuous learning and knowledge reuse, which forms the foundation of modern CBR-based intelligent systems, including helpdesk applications. [7] Pal and Shiu proposed an adaptive CaseBased Reasoning system that dynamically updates similarity measures based on new cases. This approach improves long-term system accuracy and learning efficiency, making it suitable for intelligent support systems. [8] Lu et al. introduced a coarse-to-fine CaseBased Reasoning approach where the system initially uses simple features and gradually applies detailed features for accurate matching. This method improves performance and reduces computational cost, especially in mobile environments. [9] Watson discussed real-world industrial applications of Case-Based Reasoning in customer support and troubleshooting systems. The study highlights how CBR reduces resolution time and improves decision consistency in helpdesk environments. [10]
fast and dependable technical support. Traditional helpdesk systems based on FAQs and manual assistance are often slow and inefficient. This paper presents an intelligent mobile helpdesk system using Case-Based Reasoning (CBR), where user problems are solved by reusing solutions from previously resolved cases. When a relevant case is found, the system delivers an immediate solution; otherwise, the issue is forwarded to an administrator and stored for future learning. The proposed system reduces response time, decreases manual effort, and improves overall user satisfaction, making it an effective and practical helpdesk solution. Key Words: Case-Based Reasoning (CBR), NLP, Mobile Helpdesk System, Intelligent Support System, Knowledge-Based System, Automated Problem Solving
1.INTRODUCTION The rapid growth of mobile technology has increased the demand for fast and reliable technical support. Mobile users frequently face issues related to applications, software updates, and network connectivity. Traditional helpdesk systems based on FAQs and manual assistance are often slow, inefficient, and unable to handle repeated or similar queries effectively. To overcome these limitations, intelligent helpdesk systems are required. Case-Based Reasoning (CBR) is an artificial intelligence technique that solves new problems by reusing solutions from previously solved cases. By learning from past experiences, CBR systems provide quick and consistent responses. This paper proposes an Intelligent Mobile Helpdesk System using Case-Based Reasoning. The system retrieves solutions from a case database and provides instant support to users. Unresolved queries are handled by an administrator and stored for future learning. The proposed approach reduces response time, minimizes manual effort, and improves overall helpdesk efficiency
2.LITERATURE SURVEY Introduces a system that combines large language models (LLMs) with retrieval-augmented generation to improve case-based reasoning. It can handle fuzzy or imprecise descriptions of cases, making it more flexible. Useful for medical/healthcare and legal domains; could be adapted to helpdesk + healthcare. [1] This paper shows how you can use fuzzy logic plus adaptive similarity weighting in a
© 2026, IRJET
|
Impact Factor value: 8.315
3. PROPOSED SYSTEM The proposed system is an intelligent mobile helpdesk designed using Case-Based Reasoning (CBR) to provide fast and accurate technical support. Users submit their
|
ISO 9001:2008 Certified Journal
|
Page 561