Page 226 - 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
1. Data Preprocessing Module: This module cleans and the plagiarism detection algorithms. The NLTK library's
normalizes the input text data, removing punctuation, stopwords corpus is used to identify and remove stopwords
special characters, and stop words. from the tokenized text.
2. Plagiarism Detection Engine: This engine utilizes a Stemming
combination of machine learning algorithms (e.g., Stemming involves reducing words to their base or root
supervised learning, deep learning) and natural form. This helps to reduce the dimensionality of the text data
language processing techniques (e.g., tokenization, and enables the application of various natural language
stemming) to detect plagiarism. processing techniques. The Porter Stemmer algorithm is
used to stem the tokenized text.
3. Accuracy Enhancement Module: This module employs
advanced techniques, such as semantic analysis and Lemmatization
contextual evaluation, to improve the accuracy of Lemmatization is a more advanced form of stemming that
plagiarism detection. uses a dictionary-based approach to reduce words to their
base or root form. The WordNet lemmatizer is used to
Originality Guard's innovative features include: lemmatize the tokenized text.
 Deep learning-based plagiarism detection: Utilizes
neural networks to identify patterns and anomalies in Noise Reduction and Data Cleaning
text data. Noise reduction and data cleaning are critical steps in
preprocessing the dataset. The following techniques are used
 Contextual evaluation: Considers the context in which
to reduce noise and clean the data:
the text is used to reduce false positives.
 Removing special characters and punctuation: Special
 Real-time feedback: Provides instant feedback to users,
characters and punctuation are removed from the text
enabling them to revise and improve their work.
data to reduce noise and improve the accuracy of the
IV. DATA COLLECTION plagiarism detection algorithms.
The dataset utilized for training and testing Originality Guard  Removing numbers and digits: Numbers and digits are
is a comprehensive and diverse collection of text samples, removed from the text data to reduce noise and improve
sourced from a wide range of academic and online sources.
the accuracy of the plagiarism detection algorithms.
The dataset, dubbed "Plagiarism Detection Corpus" (PDC),
 Removing whitespace and newline characters:
comprises approximately 50,000 text samples, including:
Whitespace and newline characters are removed from
 Academic papers from reputable journals and the text data to reduce noise and improve the accuracy
conferences
of the plagiarism detection algorithms.
 Online articles and blogs from various domains
 Removing duplicate texts: Duplicate texts are removed
 Websites and online repositories from the dataset to reduce noise and improve the
accuracy of the plagiarism detection algorithms.
 Student assignments and research papers
Vectorization
The PDC dataset covers multiple domains, including:
After preprocessing the text data, it is converted into
 Computer science and information technology
numerical vectors using the TF-IDF vectorizer. The TF-IDF
 Engineering and physical sciences
vectorizer calculates the term frequency and inverse
 Humanities and social sciences
document frequency of each word in the text data and
 Life sciences and medicine
represents it as a numerical vector.
The dataset's size and diversity ensure that Originality Guard The preprocessed dataset is then split into training and
can learn to detect plagiarism in various contexts, improving testing sets using the stratified shuffle split technique. The
its accuracy and reliability. The dataset is regularly updated training set is used to train the plagiarism detection model,
to include new sources and samples, ensuring that while the testing set is used to evaluate its performance.
Originality Guard remains effective in detecting plagiarism.
VI. PROPOSED RESEARCH MODEL
V. Data Preprocessing section:
Data preprocessing is a crucial step in preparing the dataset The proposed research model for this study is based on a
holistic approach to plagiarism detection, incorporating both
for training and testing Originality Guard. The goal of machine learning and natural language processing
preprocessing is to transform the raw text data into a format techniques. The model consists of four primary components:
that can be effectively processed by the plagiarism detection
algorithms. Component 1: Data Preprocessing
This component involves the preprocessing of the text data,
Tokenization
including tokenization, stopword removal, stemming, and
The first step in preprocessing is tokenization, which
lemmatization.
involves breaking down the text into individual words or
tokens. This is done using the NLTK library's word tokenizer. Component 2: Feature Extraction
Tokenization helps to reduce the dimensionality of the text This component involves the extraction of relevant features
data and enables the application of various natural language from the preprocessed text data, including TF-IDF
processing techniques. vectorization and sentiment analysis.
Stopword Removal Component 3: Plagiarism Detection
Stopwords are common words like "the", "and", "a", etc. that This component involves the use of machine learning
do not add much value to the meaning of the text. Removing algorithms, including supervised and unsupervised learning,
stopwords helps to reduce noise and improve the accuracy of to detect plagiarism in the text data.

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