Page 441 - 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
Recent research also focuses on combating evolving  Customizability: Allow educators to set thresholds and
challenges in plagiarism detection. For example, studies criteria for plagiarism detection.
explore the integration of blockchain technology to create 3.5. Evaluation
immutable records of intellectual property, ensuring the
 Performance Testing: Validate the system using
traceability and authenticity of academic work. Others standard datasets and metrics like precision, recall, and
investigate the impact of generative AI technologies, such as F1-score.
GPT-based models, which create sophisticated content that
may evade conventional detection systems.  Comparison with Existing Tools: Benchmark against
established systems to assess improvements in
Furthermore, academic literature emphasizes the
detection accuracy.
importance of user engagement and education alongside
technological solutions. Several researchers advocate for  User Feedback: Collect input from educators and
proactive measures, such as teaching students about the students to refine usability and effectiveness.
ethical use of resources and fostering a culture of integrity. IV.
Tools that provide educative features, such as citation PROPOSED RESEARCH MODEL
The proposed research model aims to provide a
guidance and originality improvement tips, have been shown
comprehensive framework for detecting and preventing
to positively influence academic practices.
plagiarism by leveraging advanced technologies. The model
Overall, the continuous evolution of plagiarism detection consists of the following key components:
technologies reflects the growing need to maintain academic
1. Input Data Layer
integrity in the face of complex challenges presented by the
 Academic Texts: Research papers, essays, and
digital age. By combining technological innovation with
assignments from diverse sources.
educational initiatives, institutions and researchers strive to
uphold ethical standards and promote originality in  Multilingual Content: Texts in different languages for
academic work. cross-language plagiarism detection.
III. PROPOSED WORK  AI-Generated Content: Samples created using AI tools
The proposed work focuses on enhancing plagiarism for detecting machine-generated plagiarism.
detection using Originality Guard, incorporating advanced
methodologies to address the challenges posed by the digital 2. Preprocessing Layer
age. The following steps outline the framework for this  Data Cleaning: Remove irrelevant data, formatting
solution: errors, and noise.
 Text Normalization: Perform tokenization,
3.1. Data Collection
 Dataset Development: Compile a diverse dataset of lemmatization, and language-specific processing.
academic texts, including essays, research papers, and  Feature Extraction: Identify syntactic, semantic, and
online articles. linguistic features critical for plagiarism detection.

 Multilingual Content: Incorporate texts from various 3. Detection Engine
languages to enable cross-language plagiarism detection.  Semantic Analysis Module: Use NLP techniques to
detect rephrased or paraphrased content.
 AI-Generated Content: Include samples created by AI
tools to train the system for detecting machine-  AI Content Detection Module: Identify text generated
generated text. by AI tools using deep learning models.
3.2. Preprocessing  Cross-Language Detection Module: Employ machine
 Data Cleaning: Eliminate noise such as formatting translation and multilingual analysis to detect translated
inconsistencies and metadata. plagiarism.

 Text Normalization: Perform tokenization and 4. Feedback and Reporting Layer
lemmatization to prepare text for analysis.  Real-Time Feedback: Provide instant suggestions for
improving originality.
 Feature Extraction: Identify unique language and
semantic features to improve detection accuracy.  Detailed Reports: Generate comprehensive plagiarism
reports highlighting matched sources and flagged
3.3. Model Development
 AI and ML Integration: Employ deep learning models to sections.
analyze textual similarities and semantics.  Educational Support: Offer citation guidance and
recommendations to foster ethical practices.
 NLP Techniques: Leverage natural language processing
to detect paraphrasing, summarization, and idea 5. Evaluation and Refinement Layer
rephrasing.  Performance Metrics: Evaluate the system using
precision, recall, and F1-score.
 Advanced Algorithms: Develop algorithms capable of
identifying AI-generated or translated content.  Benchmarking: Compare results with existing tools like
Turnitin and Plagscan.
3.4. System Integration
 Platform Development: Create a user-friendly platform  User Feedback: Incorporate feedback from educators
providing detailed plagiarism reports. and students to enhance usability and effectiveness.

 Real-Time Feedback: Incorporate live analysis to offer
immediate suggestions for improving originality.

IJTSRD | Special Issue on Emerging Trends and Innovations in Web-Based Applications and Technologies Page 431