Description:DETAILED DESCRIPTION
For the purpose of promoting an understanding of the principles of the invention, reference will
now be made to the embodiment illustrated in the following description. Specific language will be
used to describe the system and method, but it will be understood that no limitation of the scope
of the invention is thereby intended. Alterations, modifications, and variations in the illustrated
system, and further applications of the principles of the invention as illustrated, are contemplated
and would normally occur to one skilled in the art to which the invention relates.
It will be understood by those skilled in the art that the general description and the following
detailed description are exemplary and explanatory of the invention and are not intended to be
restrictive. Reference throughout this specification to “an embodiment,” “another embodiment,”
or similar language means that a particular feature, structure, or characteristic described in
connection with the embodiment is included in at least one embodiment of the present disclosure.
Thus, appearances of the phrases “in an embodiment,” “in another embodiment,” and similar
language throughout this specification may, but do not necessarily, all refer to the same
embodiment.
The terms "comprises," "comprising," or any other variations thereof are intended to cover a non
exclusive inclusion. This means that a process or method comprising a list of steps does not
exclude other steps that are not explicitly listed but may be inherent to such processes or methods.
Similarly, when one or more devices, sub-systems, structures, components, or elements are
described as "comprising... a," this does not, without more constraints, preclude the inclusion of
additional devices, sub-systems, structures, components, or elements that may be part of the system
or method. Unless otherwise defined, all technical and scientific terms used herein have the same
meaning as commonly understood by one of ordinary skill in the art to which this invention
belongs. The system, methods, and examples provided herein are illustrative and should not be
construed as limiting.
The present invention provides a computer-implemented system and method for predicting
research citation growth by integrating queueing theory and machine learning techniques. The
invention is designed to analyze citation trends, predict future research impact, and assist
researchers, universities, and funding agencies in making data-driven decisions. The system
comprises multiple components, each performing a critical function in citation prediction.
The data acquisition module is responsible for collecting bibliometric data from digital
repositories, research databases, and academic journals. It integrates with APIs and web crawling
techniques to ensure comprehensive data collection. The queueing-based modeling engine
classifies research articles based on citation growth patterns using M/M/8 queueing models or
other probabilistic techniques, enabling accurate categorization of research impact. The machine
learning processor refines citation predictions by applying supervised and unsupervised learning
algorithms, analyzing co-authorship networks, interdisciplinary influence, and time-series trends.
A database storage unit is employed to store structured bibliometric data, including citation
records, impact factors, h-index, and i10-index. The user interface module provides real-time
citation trajectory visualizations, predictive insights, and comparative analysis dashboards,
allowing researchers and institutions to monitor research impact effectively. , Claims:CLAIMS
I/We claim:
1. A computer-implemented system for predicting research citation growth, comprising:
o a data acquisition module configured to collect bibliometric data from digital
repositories, journals, and academic citation databases;
o a queueing-based modeling engine configured to classify research articles into
citation growth categories based on historical trends and real-time citation
accumulation;
o a machine learning processor configured to analyze historical citation patterns,
predict future citation trajectories, and update predictive models dynamically;
o a database storage unit for storing citation records, author metrics, and journal
impact factors; and
o a user interface module configured to display citation predictions, trend analysis,
and impact factor estimations for researchers, universities, and funding agencies.
2. The system of claim 1, wherein the queueing-based modeling engine applies an M/M/8
queueing model to predict the citation rate of a research publication based on arrival
patterns of citations over time.
3. The system of claim 1, wherein the machine learning processor employs supervised and
unsupervised learning algorithms to refine citation predictions based on evolving
citation trends, co-authorship networks, and interdisciplinary impact.
4. The system of claim 1, wherein the database storage unit integrates citation data with h
index, i10-index, and journal impact factor scores to provide comprehensive research
performance insights.
5. The system of claim 1, wherein the user interface module is configured to generate real
time citation trajectory graphs, heat maps, and comparative analysis reports for
researchers and institutions.
6. The system of claim 1, wherein the data acquisition module retrieves citation data from
open-access platforms, proprietary research databases, and government-funded
repositories using API integration and web scraping techniques.
7. A method for predicting research citation growth, comprising:
o collecting citation data from multiple academic sources;
o processing the data using a queueing-based citation prediction model;
o applying machine learning techniques to improve the accuracy of predictions;
o storing processed data in a structured bibliometric database; and
o displaying real-time citation growth trends through an interactive user interface.
8. The method of claim 7, wherein the citation prediction accuracy is enhanced using
reinforcement learning techniques, allowing the system to self-improve based on real
time citation feedback.