Paper Released Cross Years Published using CNN RNN Classification in Python Projects

Paper Released Cross Years Published using CNN RNN Classification in Python Projects
Abstract
Tracking research papers published across multiple years is essential for analyzing trends, identifying influential works, and understanding the evolution of scientific knowledge. This project develops a Python-based system using CNN-RNN hybrid models to classify research papers based on their publication years and extract meaningful temporal patterns. The system processes textual features from paper titles, abstracts, and keywords, applies Convolutional Neural Networks (CNN) for feature extraction, and Recurrent Neural Networks (RNN) for sequential learning of temporal patterns. By automating the classification process, the system enables efficient analysis of cross-year publications, helps researchers identify emerging trends, and provides a scalable solution for academic data management.

Existing System
Traditional methods of analyzing paper publications over time rely on manual inspection, keyword searches, or metadata-based sorting. While effective for small datasets, these methods are inefficient and error-prone for large-scale bibliographic databases. Some automated systems use simple machine learning classifiers with hand-engineered features but fail to capture complex patterns in textual data or sequential trends across years. Existing approaches also struggle to handle unstructured textual data such as abstracts, which contain rich contextual information. Consequently, these systems are limited in accuracy, scalability, and their ability to provide deep insights into publication trends across multiple years.

Proposed System

The proposed system introduces a Python-based CNN-RNN hybrid framework for classifying research papers across multiple publication years. Text data from paper titles, abstracts, and keywords are preprocessed using tokenization, stop-word removal, and embedding techniques such as Word2Vec or GloVe. CNN layers extract local textual features and patterns, while RNN layers (e.g., LSTM or GRU) capture sequential dependencies and temporal relationships in the data. Python libraries such as Pandas, NumPy, TensorFlow, and Keras are utilized for data preprocessing, model building, and evaluation. The system can accurately classify papers based on publication years, detect trends, and identify cross-year research patterns. By combining CNN for feature extraction and RNN for temporal learning, the approach provides an efficient and scalable solution for bibliometric analysis and academic trend detection.

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