Page 227 - Emerging Trends and Innovations in Web-Based Applications and Technologies

International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
Component 4: Performance Evaluation Baselines and Comparison Methods
This component involves the evaluation of the performance The performance of Originality Guard is compared to the
of the plagiarism detection model, including metrics such as following baselines:
accuracy, precision, recall, and F1-score. 1. Turnitin: A commercial plagiarism detection tool widely
used in academia.
Relationships Between Variables and Components
The relationships between the variables and components of 2. Quetext: A plagiarism detection tool that uses advanced
the proposed research model are as follows: algorithms and natural language processing techniques.
 The quality of the preprocessed text data affects the
3. Random Forest Classifier: A machine learning-based
accuracy of the feature extraction component.
approach that uses a random forest classifier to detect
 The relevance of the extracted features affects the plagiarism.
performance of the plagiarism detection component.
VIII. CONCLUSION
 The performance of the plagiarism detection component This research paper presents the design, development, and
affects the accuracy of the performance evaluation evaluation of Originality Guard, an advanced plagiarism
component. detection tool. The main contributions of this research
include:
Hypotheses and Research Questions
1. A comprehensive review of existing plagiarism detection
The hypotheses and research questions guiding this study
tools and techniques, highlighting their strengths and
are as follows:
weaknesses.
 Hypothesis 1: The proposed plagiarism detection model
will outperform existing models in terms of accuracy 2. The development of a novel plagiarism detection
and efficiency. algorithm using machine learning and natural language
processing techniques, which demonstrates improved
 Hypothesis 2: The use of machine learning algorithms accuracy and efficiency.
will improve the accuracy of plagiarism detection.
3. A thorough evaluation of Originality Guard's
 Research Question 1: What are the most effective performance, using a diverse dataset and rigorous
features for plagiarism detection?
evaluation metrics, which demonstrates its effectiveness
 Research Question 2: How does the quality of the in detecting plagiarism.
preprocessed text data affect the performance of the
Originality Guard has the potential to significantly advance
plagiarism detection model?
plagiarism detection and accuracy, providing a valuable tool
VII. PERFORMANCE EVALUATION for academic institutions, professionals, and researchers. The
To assess the performance of Originality Guard, a tool's ability to detect plagiarism with high accuracy and
comprehensive evaluation framework is employed. The efficiency can help to:
evaluation metrics used include precision, recall, F1-score,
 Promote academic integrity and originality
and accuracy.
 Reduce the incidence of plagiarism and academic
Evaluation Metrics misconduct
1. Precision: The ratio of true positives (correctly detected
plagiarism instances) to the sum of true positives and  Improve the quality and reliability of academic research
false positives (incorrectly detected plagiarism
instances).  Enhance the credibility and reputation of academic
institutions
2. Recall: The ratio of true positives to the sum of true The development of Originality Guard also highlights the
positives and false negatives (undetected plagiarism importance of interdisciplinary research, combining insights
instances).
and techniques from computer science, linguistics, and
3. F1-score: The harmonic mean of precision and recall. education. The tool's design and development demonstrate
the potential for innovative solutions to complex problems,
4. Accuracy: The ratio of correctly classified instances through the application of advanced technologies and
(both plagiarism and non-plagiarism) to the total techniques.
number of instances.
Future work directions for Originality Guard include:
Experimental Setup and Procedures  Improving the tool's generalizability, to accommodate
The experimental setup consists of the following steps: diverse languages, formats, and styles
1. Data Preparation: The dataset is split into training
(80%) and testing sets (20%).  Exploring the application of deep learning techniques, to
further improve the tool's accuracy and efficiency
2. Model Training: Originality Guard is trained on the
training set.  Integrating Originality Guard with existing academic
management systems, to facilitate seamless adoption
3. Model Evaluation: The trained model is evaluated on the and implementation
testing set.
 Investigating the potential applications of Originality
4. Baseline Comparison: The performance of Originality Guard, in fields such as journalism, publishing, and
Guard is compared to existing plagiarism detection tools.
intellectual property law

IJTSRD | Special Issue on Emerging Trends and Innovations in Web-Based Applications and Technologies Page 217