A Neural Network based Attendance Monitoring and Database Management System using Fingerprint Recognition and Matching

Singh, Seema

doi:10.1109/conecct47791.2019.9012893

2019 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) 2019

DOI: 10.1109/conecct47791.2019.9012893

|View full text |Cite

A Neural Network based Attendance Monitoring and Database Management System using Fingerprint Recognition and Matching

Seema Singh¹

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Workload-Based Performance Tuning in Database Management Systems through Integration of Artificial Intelligence

Kalyan Jupudi,

Kishore Mysuru,

Mekala

2024

International Journal of Innovative Science and Research Techno

View full text Add to dashboard Cite

Traditional methods of performance tuning in Database Management Systems (DBMS) are facing significant challenges in adapting to the dynamic nature of modern workloads. Reactive approaches and static configurations often lead to performance bottlenecks and inefficient resource utilization. In response, this paper proposes a novel approach for workload-based performance tuning through the integration of Artificial Intelligence (AI). By leveraging AI techniques such as machine learning and predictive modeling, the proposed methodology aims to automate the analysis of workload patterns, predict future trends, and dynamically adjust DBMS configurations for optimal performance. The paper discusses the key components of the proposed methodology, including workload characterization, predictive modeling, and adaptive configuration management. A hypothetical case study in an e-commerce database environment illustrates the implementation and potential performance improvements achieved through AI-powered tuning. Furthermore, the paper explores real-world applications, future research directions, challenges, and best practices for implementing workload-based tuning with AI integration. Overall, this paper presents a comprehensive framework for leveraging AI to enhance DBMS performance, scalability, and efficiency in dynamic environments.

show abstract

Workload-Based Performance Tuning in Database Management Systems through Integration of Artificial Intelligence

Kalyan Jupudi,

Kishore Mysuru,

Mekala

2024

International Journal of Innovative Science and Research Techno

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A Neural Network based Attendance Monitoring and Database Management System using Fingerprint Recognition and Matching

Cited by 1 publication

References 4 publications

Workload-Based Performance Tuning in Database Management Systems through Integration of Artificial Intelligence

Workload-Based Performance Tuning in Database Management Systems through Integration of Artificial Intelligence

Contact Info

Product

Resources

About