Plagiarism Detection System Research Articles

Application of the KARP RABIN Algorithm for Plagiarism Detection System in Thesis Proposal Submission in the Department of Informatics Engineering STMIK Kaputama

Plagiarism is a serious threat, especially to academic honesty, so a detection system that can analyze various types of documents is needed. This research develops a plagiarism detection system using Optical Character Recognition (OCR) to convert image text into digital text. Rabin – Karp algorithm with rolling hash and Dice Coefficient Similarity is applied to measure similarities between documents. Testing is carried out on .doc, .txt, .jpg files. As a result, the system can detect plagiarism well in clear text and image documents, but accuracy can decrease in low-quality images. In conclusion, the similarity of content, sentence structure, and format affects the degree of similarity, while OCR techniques work effectively even though they are limited to low-quality images.

Impact of Generative AI in Academic Integrity and Learning Outcomes: A Case Study in the Upper East Region

With the increasing use of Generative Artificial Intelligence (AI) tools like ChatGPT and Bard, universities face challenges in maintaining academic integrity. This research investigates the impact of these tools on learning outcomes (factual knowledge, comprehension, critical thinking) in selected universities of Ghana's Upper East Region during the 2023-2024 academic year. The study specifically analyzes changes in student comprehension and academic integrity concerns when using Generative AI for content generation, research assistance, and summarizing complex topics. A mixed-methods approach was employed, combining qualitative data from interviews and open-ended questions with quantitative analysis of survey data and academic records. The research focuses on three institutions: C. K. Tedam University of Technology and Applied Sciences, Bolgatanga Technical University, and Regentropfen University College. A purposive sampling technique recruited 150 participants (50 from each university) who had used Generative AI tools. Key findings show that 72% of students reported improved understanding of course material through Generative AI use, yet 75% cited academic integrity as a primary concern. Quantitative analysis revealed a weak to moderate positive correlation (r = 0.45) between AI tool usage and improved grades, with variations depending on the specific AI tasks performed. Qualitative data highlighted concerns about overreliance on AI and its impact on critical thinking skills. This research contributes to the ongoing debate on AI's role in education by providing valuable insights for educators and policymakers worldwide. The findings suggest that while AI tools can enhance comprehension, ethical considerations and potential drawbacks related to critical thinking require careful attention. The study concludes with recommendations for integrating AI literacy programs, developing ethical guidelines, and implementing advanced plagiarism detection systems to harness the benefits of Generative AI while mitigating risks to academic integrity. Although specific to the Upper East Region of Ghana, these insights may be applicable to other educational systems with similar characteristics.

Enhancing Academic Integrity for Bangladesh's Educational Landscape

Integrating Artificial Intelligence (AI) tools in Bangladesh's academic landscape has ignited concerns about potentially eroding students' creative writing and critical thinking abilities. While AI offers efficient and personalized learning, there is a looming risk of students using it as a shortcut to success. Educators and policymakers must emphasize the cultivation of writing skills and critical thinking while guiding students to recognize the limitations of AI. While plagiarism checking is crucial for academic integrity, it often falls short in acknowledging students' originality. Natural Language Processing (NLP) emerges as a promising solution, augmenting plagiarism detection by comprehending context, nuances, and novel expressions. This paper proposed a model based on NLP that can incorporate a robust token identification mechanism into AI tools used by the students. A unique identification token is generated at the time of system login and stored in a publicly accessible resource, acting as a distinct marker for user activity. These identification tokens support a plagiarism detection system that continually reviews and compares activity records. It also enables a thorough examination of user involvement across various academic supplementary tools, such as academic writing, presentation creation, image generation, etc.

IMPLEMENTASI ALGORITMA RABIN KARP DAN OPTIMASI DENGAN ALGORITMA STEMMER SASTRAWI DALAM DETEKSI PLAGIAT PADA JURNAL SKRIPSI MAHASISWA

The problem of plagiarism is a serious problem in tertiary institutions, given the high number of plagiarism acts that do not uphold the principle of honesty. Problems related to plagiarism in the academic field have been widely discussed in previous studies. However, previous studies that were conducted only focused on checking thesis similarities, but had not implemented the stemming algorithm in processing the data used in the research. Therefore, in this study a plagiarism detection system will be developed in thesis journals by implementing the Rabin Karp algorithm and optimization with the Literary Stemmer algorithm so that it is expected to increase the accuracy of the results of plagiarism detection. Based on the results of tests carried out through a combination of the Rabin Karp algorithm and optimization with Stemmer Literature in detecting plagiarism of student thesis journals, an accuracy rate of 80% was obtained in detecting similarities in student thesis journals in this study and the average speed of execution time was 25.6934 seconds.

Detecting Source Code Plagiarism in Student Assignment Submissions Using Clustering Techniques

In pragmatic courses, graduate students are required to submit programming assignments, which have been susceptible to various forms of plagiarism. Detecting counterfeited code in an academic setting is of paramount importance, given the prevalence of publications and papers. Plagiarism, defined as the unauthorized replication of written work without proper acknowledgment, has become a critical concern with the advent of information and communication technology (ICT) and the widespread availability of scholarly publications online. However, the extensive use of freeware text editors has posed challenges in detecting source code plagiarism. Numerous studies have investigated algorithms for revealing different types of plagiarism and detecting source code plagiarism. In this research, we propose an innovative strategy that combines TF-IDF (Term Frequency-Inverse Document Frequency) modifications with K-means clustering, achieving a remarkable precision rate of 99.2%. Additionally, we explore the hierarchical clustering method, which estimates an even higher precision rate of 99.5% compared to previous techniques. To implement our approach, we utilize the Python programming language along with relevant libraries, providing a robust and efficient system for source code plagiarism detection in student assignment submissions.

The Game-Changing Impact of AI Chatbots on Education ChatGPT and Beyond

ChatGPT, an AI-driven chatbot, delivers coherent and valuable responses by analysing extensive data sets. This article explores the profound impact of ChatGPT on contemporary education, as discussed by prominent academics, scientists, distinguished researchers, and engineers. The study delves into ChatGPT's capabilities, its application in the education sector, and the identification of potential concerns and challenges. Initial assessments reveal variations in ChatGPT's performance across diverse subjects, including finance, coding, mathematics, and general public queries. While ChatGPT can assist educators by generating instructional content, providing suggestions, serving as an online educator for learners, answering questions and revolutionising education through smartphones and IoT devices, it has shortcomings. These include the risk of generating inaccurate or false information and evading plagiarism detection systems, a critical concern for maintaining originality. The commonly reported "hallucinations" in Generative AI, which applies to ChatGPT, also limit its utility in situations where precision is paramount. A notable deficiency in ChatGPT is the absence of a stochastic measure to facilitate authentic and empathetic communication with users. The article suggests that educational institutions' academic regulations and evaluation practices should be updated if ChatGPT is integrated into the educational toolkit. To effectively navigate the transformative effects of ChatGPT in the learning environment, it is imperative to educate teachers and students about its capabilities and limitations.

Ethical Dilemmas in Using AI for Academic Writing and an Example Framework for Peer Review in Nephrology Academia: A Narrative Review.

The emergence of artificial intelligence (AI) has greatly propelled progress across various sectors including the field of nephrology academia. However, this advancement has also given rise to ethical challenges, notably in scholarly writing. AI's capacity to automate labor-intensive tasks like literature reviews and data analysis has created opportunities for unethical practices, with scholars incorporating AI-generated text into their manuscripts, potentially undermining academic integrity. This situation gives rise to a range of ethical dilemmas that not only question the authenticity of contemporary academic endeavors but also challenge the credibility of the peer-review process and the integrity of editorial oversight. Instances of this misconduct are highlighted, spanning from lesser-known journals to reputable ones, and even infiltrating graduate theses and grant applications. This subtle AI intrusion hints at a systemic vulnerability within the academic publishing domain, exacerbated by the publish-or-perish mentality. The solutions aimed at mitigating the unethical employment of AI in academia include the adoption of sophisticated AI-driven plagiarism detection systems, a robust augmentation of the peer-review process with an "AI scrutiny" phase, comprehensive training for academics on ethical AI usage, and the promotion of a culture of transparency that acknowledges AI's role in research. This review underscores the pressing need for collaborative efforts among academic nephrology institutions to foster an environment of ethical AI application, thus preserving the esteemed academic integrity in the face of rapid technological advancements. It also makes a plea for rigorous research to assess the extent of AI's involvement in the academic literature, evaluate the effectiveness of AI-enhanced plagiarism detection tools, and understand the long-term consequences of AI utilization on academic integrity. An example framework has been proposed to outline a comprehensive approach to integrating AI into Nephrology academic writing and peer review. Using proactive initiatives and rigorous evaluations, a harmonious environment that harnesses AI's capabilities while upholding stringent academic standards can be envisioned.

The Use of Information Retrieval in Student Academic Document Plagiarism Detection System

The practice of plagiarism can threaten the credibility of academic documents such as final assignments, theses, theses, and dissertations. Universitas Muhammadiyah Ponorogo (UMPO) is one of the best Muhammadiyah Universities in Indonesia. All academic documents at UMPO are well stored. However, plagiarism is only found in the Turnitin database and in the title of academic documents. This research aims to pre-screen academic documents in the internal scope by creating a plagiarism application that is easy to use and cheaper in terms of cost than using Turnitin. The steps of making a plagiarism detection system in academic documents using information retrieval (IR) with waterfall development. The design process starts from system requirements analysis, design, implementation, and testing. This plagiarism detection system goes through several stages, namely tokenisation, stopword removal, stemming, and termweigthing. Tokenisation involves converting uppercase letters into lowercase letters and removing punctuation marks. Stopword removal removes noise, stemming converts words into basic forms, and termweigthing uses local and global weighting to calculate similarity with the Vector Space Model. Document similarity is calculated using cosine similarity, rejected documents are considered free of plagiarism. If there is no similarity, the document is accepted as a source document, while the rejected document is not stored in the database. Further research on the document plagiarism detection system that previously only used the txt file extension, in the future it can use the .doc or HTML extension so as to increase the effectiveness of the performance of academic document examination time.

Plagiarism in Secondary School: Minor Inaction with Major Consequences

Introduction. The ease of searching and copying someone elseʼs text on almost any topic is one of the most important reasons for the prevalence of plagiarism in academic papers. While plagiarism detectors have become a common tool for universities, such systems are practically not used in secondary schools. It is considered to be a serious problem, that high school graduates are unprepared for the academic papers’ originality requirements. Universities are making efforts to overcome this lack of readiness. The authors study the attitude of teachers and learners of the Russian secondary schools toward the problem of plagiarism. Materials and Methods. An anonymous online survey of teachers (n = 2,286) and learners (m = 4,555) of secondary schools in the Vologda, Kaliningrad and Lipetsk regions of the Russian Federation was conducted. The survey was carried out through the Google Forms service. The quantitative and qualitative analysis of the obtained data was carried out. Results. Learners acquire the habit of plagiarism already in secondary school. Secondary school learners are loyal to copying someone elseʼs text without reference to the source; although they partially admit that by doing so they do wrong. Teachers are less tolerant to this and ready to respond to plagiarism in learners’ papers by lowering marks/grades or by requiring learners to redo the work. Teachers do not always clearly understand which borrowings can be considered legitimate and correct, and which ones are not, they cannot explain this to their learners, cannot instill in them the necessary academic integrity, and cannot establish clear criteria for works evaluation.The use of plagiarism detection systems in schools is unsystematic and it is often the result of the initiative of individual teachers. There is also no methodology for using plagiarism detection systems in secondary schools. Discussion and Conclusion. The conducted studies for the first time provided actual confirmation of the hypotheses. It means that it is necessary to include plagiarism detection systems in the toolkit used in secondary school. We have obtained evidence, that it is necessary to implement such tools in the secondary education system step by step, as well as to create conditions, so that school teachers can acquire relevant skills. The materials of the article will be useful to scientists who study the problems of secondary and high education, managers of the secondary education system at all levels, teachers, lecturers and learners of pedagogical universities.

A Quantum Genetic Algorithm for Building a Semantic Textual Similarity Estimation Framework for Plagiarism Detection Applications.

The majority of the recent research on text similarity has been focused on machine learning strategies to combat the problem in the educational environment. When the originality of an idea is copied, it increases the difficulty of using a plagiarism detection system in practice, and the system fails. In cases like active-to-passive conversion, phrase structure changes, synonym substitution, and sentence reordering, the present approaches may not be adequate for plagiarism detection. In this article, semantic extraction and the quantum genetic algorithm (QGA) are integrated in a unified framework to identify idea plagiarism with the aim of enhancing the performance of existing methods in terms of detection accuracy and computational time. Semantic similarity measures, which use the WordNet database to extract semantic information, are used to capture a document's idea. In addition, the QGA is adapted to identify the interconnected, cohesive sentences that effectively convey the source document's main idea. QGAs are formulated using the quantum computing paradigm based on qubits and the superposition of states. By using the qubit chromosome as a representation rather than the more traditional binary, numeric, or symbolic representations, the QGA is able to express a linear superposition of solutions with the aim of increasing gene diversity. Due to its fast convergence and strong global search capacity, the QGA is well suited for a parallel structure. The proposed model has been assessed using a PAN 13-14 dataset, and the result indicates the model's ability to achieve significant detection improvement over some of the compared models. The recommended PD model achieves an approximately 20%, 15%, and 10% increase for TPR, PPV, and F-Score compared to GA and hierarchical GA (HGA)-based PD methods, respectively. Furthermore, the accuracy rate rises by approximately 10-15% for each increase in the number of samples in the dataset.

Discovery and recognition of formula concepts using machine learning

Citation-based Information Retrieval (IR) methods for scientific documents have proven effective for IR applications, such as Plagiarism Detection or Literature Recommender Systems in academic disciplines that use many references. In science, technology, engineering, and mathematics, researchers often employ mathematical concepts through formula notation to refer to prior knowledge. Our long-term goal is to generalize citation-based IR methods and apply this generalized method to both classical references and mathematical concepts. In this paper, we suggest how mathematical formulas could be cited and define a Formula Concept Retrieval task with two subtasks: Formula Concept Discovery (FCD) and Formula Concept Recognition (FCR). While FCD aims at the definition and exploration of a ‘Formula Concept’ that names bundled equivalent representations of a formula, FCR is designed to match a given formula to a prior assigned unique mathematical concept identifier. We present machine learning-based approaches to address the FCD and FCR tasks. We then evaluate these approaches on a standardized test collection (NTCIR arXiv dataset). Our FCD approach yields a precision of 68% for retrieving equivalent representations of frequent formulas and a recall of 72% for extracting the formula name from the surrounding text. FCD and FCR enable the citation of formulas within mathematical documents and facilitate semantic search and question answering, as well as document similarity assessments for plagiarism detection or recommender systems.

Using Retrieved Sources for Semantic and Lexical Plagiarism Detection

Plagiarism is described as using someone else's ideas or work without their permission. Using lexical and semantic text similarity notions, this paper presents a plagiarism detection system for examining suspicious texts against available sources on the Web. The user can upload suspicious files in pdf or docx formats. The system will search three popular search engines for the source text (Google, Bing, and Yahoo) and try to identify the top five results for each search engine on the first retrieved page. The corpus is made up of the downloaded files and scraped web page text of the search engines' results. The corpus text and suspicious documents will then be encoded as vectors. For lexical plagiarism detection, the system will leverage Jaccard similarity and Term Frequency-Inverse Document Frequency (TFIDF) techniques, while for semantic plagiarism detection, Doc2Vec and Sentence Bidirectional Encoder Representations from Transformers (SBERT) intelligent text representation models will be used. Following that, the system compares the suspicious text to the corpus text. Finally, a generated plagiarism report will show the total plagiarism ratio, the plagiarism ratio from each source, and other details.

An effective text plagiarism detection system based on feature selection and SVM techniques

Text plagiarism has greatly spread in the recent years, it becomes a common problem in several fields such as research manuscripts, textbooks, patents, academic circles, etc. There are many sentence similarity features were used to detect plagiarism, but each of them is not discriminative to differentiate the similarity cases. This causes the discovery of lexical, syntactic and semantic text plagiarism types to be a challenging problem. Therefore, a new plagiarism detection system is proposed to extract the most effective sentence similarity features and construct hyperplane equation of the selected features to distinguish the similarity cases with the highest accuracy. It consists of three phases; the first phase is used to preprocess the documents. The second phase is depended on two paths, the first path is based on traditional paragraph level comparison, and the second path is based on the computed hyperplane equation using Support Vector Machine (SVM) and Chi-square techniques. The third phase is used to extract the best plagiarized segment. The proposed system is evaluated on several benchmark datasets. The experimental results showed that the proposed system obtained a significant superiority in the performance compared to the systems with a higher ranking in the recent years. The proposed system achieved the best values 89.12% and 92.91% of the Plagdet scores, 89.34% and 92.95% of the F-measure scores on the complete test corpus of PAN 2013 and PAN 2014 datasets, respectively.

PlagCheck: An efficient way to identify Plagiarism using BERT

Plagiarism is stealing someone’s ideas and presenting them as yours. University students’ use of plagiarism is a serious problem that compromises their preparation and the university’s attempts to produce qualified graduates. Universities attempt to combat this issue with strong ethics regulations, but in order to put these policies into effect; they need the appropriate plagiarism detection tools at reasonable prices. In this article, we introduce PlagCheck, a high-volume, quick, and affordable plagiarism detection system created using the word embeddingsmodel and intended for use on text-based student assignments (essays, theses, homework). We go over the benefits of this approach in terms of cost, accuracy, and speed. This software will help educators and reduce their workload by helping them check plagiarism for each student’s assignment quickly and efficiently.

REWRITING IDENTIFICATION TECHNOLOGY FOR TEXT CONTENT BASED ON MACHINE LEARNING METHODS

Context. Paraphrased textual content or rewriting is one of the difficult problems of detecting academic plagiarism. Most plagiarism detection systems are designed to detect common words, sequences of linguistic units, and minor changes, but are unable to detect significant semantic and structural changes. Therefore, most cases of plagiarism using paraphrasing remain unnoticed.
 Objective of the study is to develop a technology for detecting paraphrasing in text based on a classification model and machine learning methods through the use of Siamese neural network based on recurrent and Transformer type – RoBERTa to analyze the level of similarity of sentences of text content.
 Method. For this study, the following semantic similarity metrics or indicators were chosen as features: Jacquard coefficient for shared N-grams, cosine distance between vector representations of sentences, Word Mover’s Distance, distances according to WordNet dictionaries, prediction of two ML models: Siamese neural network based on recurrent and Transformer type - RoBERTa.
 Results. An intelligent system for detecting paraphrasing in text based on a classification model and machine learning methods has been developed. The developed system uses the principle of model stacking and feature engineering. Additional features indicate the semantic affiliation of the sentences or the normalized number of common N-grams. An additional fine-tuned RoBERTa neural network (with additional fully connected layers) is less sensitive to pairs of sentences that are not paraphrases of each other. This specificity of the model may contribute to incorrect accusations of plagiarism or incorrect association of user-generated content. Additional features increase both the overall classification accuracy and the model’s sensitivity to pairs of sentences that are not paraphrases of each other.
 Conclusions. The created model shows excellent classification results on PAWS test data: precision – 93%, recall – 92%, F1score – 92%, accuracy – 92%. The results of the study showed that Transformer-type NNs can be successfully applied to detect paraphrasing in a pair of texts with fairly high accuracy without the need for additional feature generation.

Important Arguments Nomination Based on Fuzzy Labeling for Recognizing Plagiarized Semantic Text

Plagiarism is an act of intellectual high treason that damages the whole scholarly endeavor. Many attempts have been undertaken in recent years to identify text document plagiarism. The effectiveness of researchers’ suggested strategies to identify plagiarized sections needs to be enhanced, particularly when semantic analysis is involved. The Internet’s easy access to and copying of text content is one factor contributing to the growth of plagiarism. The present paper relates generally to text plagiarism detection. It relates more particularly to a method and system for semantic text plagiarism detection based on conceptual matching using semantic role labeling and a fuzzy inference system. We provide an important arguments nomination technique based on the fuzzy labeling method for identifying plagiarized semantic text. The suggested method matches text by assigning a value to each phrase within a sentence semantically. Semantic role labeling has several benefits for constructing semantic arguments for each phrase. The approach proposes nominating for each argument produced by the fuzzy logic to choose key arguments. It has been determined that not all textual arguments affect text plagiarism. The proposed fuzzy labeling method can only choose the most significant arguments, and the results were utilized to calculate similarity. According to the results, the suggested technique outperforms other current plagiarism detection algorithms in terms of recall, precision, and F-measure with the PAN-PC and CS11 human datasets.

Semantic textual similarity for modern standard and dialectal Arabic using transfer learning.

Semantic Textual Similarity (STS) is the task of identifying the semantic correlation between two sentences of the same or different languages. STS is an important task in natural language processing because it has many applications in different domains such as information retrieval, machine translation, plagiarism detection, document categorization, semantic search, and conversational systems. The availability of STS training and evaluation data resources for some languages such as English has led to good performance systems that achieve above 80% correlation with human judgment. Unfortunately, such required STS data resources are not available for many languages like Arabic. To overcome this challenge, this paper proposes three different approaches to generate effective STS Arabic models. The first one is based on evaluating the use of automatic machine translation for English STS data to Arabic to be used in fine-tuning. The second approach is based on the interleaving of Arabic models with English data resources. The third approach is based on fine-tuning the knowledge distillation-based models to boost their performance in Arabic using a proposed translated dataset. With very limited resources consisting of just a few hundred Arabic STS sentence pairs, we managed to achieve a score of 81% correlation, evaluated using the standard STS 2017 Arabic evaluation set. Also, we managed to extend the Arabic models to process two local dialects, Egyptian (EG) and Saudi Arabian (SA), with a correlation score of 77.5% for EG dialect and 76% for the SA dialect evaluated using dialectal conversion from the same standard STS 2017 Arabic set.

A Corpus for Evaluation of Cross Language Text Re-use Detection Systems

In recent years, the availability of documents through the Internet along with automatic translation systems have increased plagiarism, especially across languages. Cross-lingual plagiarism occurs when the source or original text is in one language and the plagiarized or re-used text is in another language. Various methods for automatic text re-use detection across languages have been developed whose objective is to assist human experts in analyzing documents for plagiarism cases. For evaluating the performance of these systems and algorithms, standard evaluation resources are needed. To construct cross lingual plagiarism detection corpora, the majority of earlier studies have paid attention to English and other European language pairs, and have less focused on low resource languages. In this paper, we investigate a method for constructing an English-Persian cross-language plagiarism detection corpus based on parallel bilingual sentences that artificially generate passages with various degrees of paraphrasing. The plagiarized passages are inserted into topically related English and Persian Wikipedia articles in order to have more realistic text documents. The proposed approach can be applied to other less-resourced languages. In order to evaluate the compiled corpus, both intrinsic and extrinsic evaluation methods were employed. So, the compiled corpus can be suitably included into an evaluation framework for assessing cross-language plagiarism detection systems. Our proposed corpus is free and publicly available for research purposes.

Penerapan Algoritma N-GRAM Dan WINNOWING Untuk Deteksi Plagiarisme Usulan Dokumen Perguruan Tinggi

Plagiarism which is a disgraceful act can occur in any series of documents, including one of them is a feasibility study book document as is often made by universities, foundations of organizing bodies, to consultants related to the interests of establishing universities as well as proposals for opening new study programs submitted to the Ministry of Education and Culture and Higher Education (Kemendikbud Dikti). Consultants, especially those with legal entities, will determine the smoothness of proposals to rejection of proposals due to flaws in the text, the most common of which is plagiarism, which requires consultants to remain skilled in ensuring the authentication of documents made to clients as well as to the institution or institution that is the evaluator. This plagiarism can be prevented by building a plagiarism detection system with a programming algorithm approach such as; The second N-Gram Algorithm and Winnowing Algorithm were successfully built by researchers using the Research and Development (R&D) method to detect plagiarism in the proposed college documents, in this case training documents and test documents in the form of systems and applications. The application is made web-based by utilizing an interface to make it easier for leaders to ensure the similarity and authentication of documents made by consultants. The n-gram algorithm is a document processing method that is usually used in spelling correction, word prediction and other processing and Winnowing Algorithm is one of the algorithms in the document fingerprinting method. Both are used to detect Plagiarism of Proposed College Documents with the Feasibility Study Book document object resulting in a level of accuracy and effectiveness with the parameters of similarity between documents as stated in the percentage of similarity (similarity percentage) which can be concluded the value of plagiarism between documents.

Reliable plagiarism detection system based on deep learning approaches

The phenomenon of scientific burglary has seen a significant increase recently due to the technological development in software. Therefore, many types of research have been developed to address this phenomenon. However, detecting lexical, syntactic, and semantic text plagiarism remains to be a challenge. Thus, in this study, we have computed and recorded all the features that reflect different types of text similarities in a new database. The created database is proposed for intelligent learning to solve text plagiarism detection problems. Using the created database, a reliable plagiarism detection system is also proposed, which depends on intelligent deep learning. Different approaches to deep learning, such as convolution and recurrent neural network architectures, were considered during the construction of this system. A comparative study was implemented to evaluate the proposed intelligent system on the two benchmark datasets: PAN 2013 and PAN 2014 of the PAN Workshop series. The experimental results showed that the proposed system based on long short-term memory (LSTM) achieved the first rank compared to up-to-date ranking systems.