Centralized and Labeled Academic Journal Library Using Machine Learning & Deep Learning Approaches

An academic journal library that is centralized and labeled by subject areas gives all learners, researchers, and scholars an opportunity to easily get the information they want in diverse fields. The study uses a high-level architecture design based on a data set from an open database which consists of components such as layout extraction, error detection, summarization, multi-label classification, and contextual search query optimization. These methodologies consist of a process for extracting the content, checking grammar or spelling errors, checking errors in structure, and a two-step summarization process. In multi-label classification, NLP and algorithms are used to tackle the problem of class imbalance. This study also presents a contextual analysis and search query refinement approach via NER, synonym expansion, and intent classification. Machine learning models along with relevant feature extraction enhance search result optimization. Some future works include extending error detection beyond IEEE and Springer format, studying summarization via ROUGE score and relevance measure, and enhancements in the evaluator metrics for user query analysis and search result ranking. This study intends to transform how academic papers are discovered to provide an enjoyable and informative knowledge-searching process.