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Electronics
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Innovations in Artificial Intelligence, Natural Language Processing and Big Data
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Innovations in Artificial Intelligence, Natural Language Processing and Big Data

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 January 2025 | Viewed by 1658

Special Issue Editors

Dr. Agnieszka Konys

E-Mail Website
Guest Editor
Faculty of Computer Science and Information Technology, West Pomeranian University of Technology Szczecin, Zolnierska 49, 71-210 Szczecin, Poland
Interests: ontology; knowledge representation; semantic web technologies; OWL; RDF; knowledge engineering; knowledge bases; knowledge management; reasoning; information extraction; ontology learning; sustainability; sustainability assessment; ontology evaluation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Agnieszka Nowak-Brzezińska

E-Mail Website
Guest Editor
Institute of Computer Science, Faculty of Science and Technology, University of Silesia, ul. Będzińska 39, 41-200 Sosnowiec, Poland
Interests: knowledge representation and reasoning; rule-based knowledge bases; outliers mining; expert systems; decision support systems; information retrieval systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of Big Data, AI, and NLP represents a powerful convergence of technologies that enhance each other's capabilities and open new frontiers for innovation and application. These technologies are now essential in many domains. Given the fast advancements and multidisciplinary nature of Big Data, AI, and NLP, the complexity is challenging. Integrating Big Data, AI, and NLP is driving transformative changes across various industries, enhancing capabilities and efficiency and creating new opportunities for innovation. Utilizing AI, NLP, and Big Data has empowered us to achieve once unattainable tasks, such as recognizing intricate patterns and trends, forecasting outcomes, and automating decision-making processes. When combined, Big Data, AI, and NLP create a virtuous cycle of improvement and capability,  for example, enhanced learning and insights, real-time processing and decision-making, improved human-machine interaction, and scalable and adaptive systems. This Special Issue aims to provide a comprehensive overview of the innovations, current trends, emerging technologies, and persistent challenges in AI, Big Data, and NLP. This Special Issue will encompass a wide range of topics related to AI, Big Data and NLP, including but not limited to the following:

  • Emerging trends in AI, NLP and Big Data research;
  • Deep Learning Advancements and Reinforcement Learning;
  • Generative Pre-trained Transformers (GPT), Language Transformers and BERT;
  • Multilingual Language Models;
  • Real-time Data Processing;
  • Data Lakes and Lakehouses and Edge Computing;
  • Machine learning and deep learning for semantic analysis and language understanding;
  • Machine learning algorithms and techniques;
  • Text analytics, Classification and Extraction;
  • Social media analysis and sentiment analysis and summarization techniques;
  • Speech Processing and Recognition and Named Entity Recognition;
  • Semantic Search and Information retrieval;
  • Big Data Methods for Computational Linguistics;
  • Semantic technologies, language ontologies, and natural language understanding;
  • Emerging trends in computational linguistics and language models;
  • Applications of AI, NLP and Big Data in Industry 4.0;
  • Big Data analytics and visualization;
  • Human–AI interaction and collaboration.

Dr. Agnieszka Konys
Prof. Dr. Agnieszka Nowak-Brzezińska
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep learning advancements
  • reinforcement learning
  • generative pre-trained transformers (GPT)
  • language transformers and BERT
  • multilingual language models
  • real-time data processing
  • cloud computing and distributed systems for big data
  • data lakes and lakehouses
  • edge computing
  • semantic analysis
  • recommender systems
  • machine learning algorithms and techniques
  • text analytics, classification and extraction
  • social media analysis
  • sentiment analysis
  • speech processing and recognition
  • named entity recognition
  • semantic search and technologies
  • information retrieval
  • big data methods for computational linguistics
  • ontologies
  • big data analytics and visualization
  • human–AI interaction and collaboration

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Published Papers (2 papers)

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32 pages, 6218 KiB  
Article
Natural Language Processing and Machine Learning-Based Solution of Cold Start Problem Using Collaborative Filtering Approach
by Kamta Nath Mishra, Alok Mishra, Paras Nath Barwal and Rajesh Kumar Lal
Electronics 2024, 13(21), 4331; https://doi.org/10.3390/electronics13214331 - 4 Nov 2024
Viewed by 754
Abstract
In today’s digital era, the abundance of online services presents users with a daunting array of choices, spanning from streaming platforms to e-commerce websites, leading to decision fatigue. Recommendation algorithms play a pivotal role in aiding users in navigating this plethora of options, [...] Read more.
In today’s digital era, the abundance of online services presents users with a daunting array of choices, spanning from streaming platforms to e-commerce websites, leading to decision fatigue. Recommendation algorithms play a pivotal role in aiding users in navigating this plethora of options, among which collaborative filtering (CF) stands out as a prevalent technique. However, CF encounters several challenges, including scalability issues, privacy implications, and the well-known cold start problem. This study endeavors to mitigate the cold start problem by harnessing the capabilities of natural language processing (NLP) applied to user-generated reviews. A unique methodology is introduced, integrating both supervised and unsupervised NLP approaches facilitated by sci-kit learn, utilizing benchmark datasets across diverse domains. This study offers scientific contributions through its novel approach, ensuring rigor, precision, scalability, and real-world relevance. It tackles the cold start problem in recommendation systems by combining natural language processing (NLP) with machine learning and collaborative filtering techniques, addressing data sparsity effectively. This study emphasizes reproducibility and accuracy while proposing an advanced solution that improves personalization in recommendation models. The proposed NLP-based strategy enhances the quality of user-generated content, consequently refining the accuracy of Collaborative Filtering-Based Recommender Systems (CFBRSs). The authors conducted experiments to test the performance of the proposed approach on benchmark datasets like MovieLens, Jester, Book-Crossing, Last.fm, Amazon Product Reviews, Yelp, Netflix Prize, Goodreads, IMDb (Internet movie Database) Data, CiteULike, Epinions, and Etsy to measure global accuracy, global loss, F-1 Score, and AUC (area under curve) values. Assessment through various techniques such as random forest, Naïve Bayes, and Logistic Regression on heterogeneous benchmark datasets indicates that random forest is the most effective method, achieving an accuracy rate exceeding 90%. Further, the proposed approach received a global accuracy above 95%, a global loss of 1.50%, an F-1 Score of 0.78, and an AUC value of 92%. Furthermore, the experiments conducted on distributed and global differential privacy (GDP) further optimize the system’s efficacy. Full article
(This article belongs to the Special Issue Innovations in Artificial Intelligence, Natural Language Processing and Big Data)
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25 pages, 6051 KiB  
Article
Cross-Task Rumor Detection: Model Optimization Based on Model Transfer Learning and Graph Convolutional Neural Networks (GCNs)
by Wen Jiang, Facheng Yan, Kelan Ren, Xiong Zhang, Bin Wei and Mingshu Zhang
Electronics 2024, 13(18), 3757; https://doi.org/10.3390/electronics13183757 - 21 Sep 2024
Viewed by 546
Abstract
With the widespread adoption of social media, the rapid dissemination of rumors poses a severe threat to public perception and social stability, emerging as a major challenge confronting society. Hence, the development of efficient and accurate rumor detection models has become an urgent [...] Read more.
With the widespread adoption of social media, the rapid dissemination of rumors poses a severe threat to public perception and social stability, emerging as a major challenge confronting society. Hence, the development of efficient and accurate rumor detection models has become an urgent need. Given the challenges of rumor detection tasks, including data scarcity, feature complexity, and difficulties in cross-task knowledge transfer, this paper proposes a BERT–GCN–Transfer Learning model, an integrated rumor detection model that combines BERT (Bidirectional Encoder Representations from Transformers), Graph Convolutional Networks (GCNs), and transfer learning techniques. By harnessing BERT’s robust text representation capabilities, the GCN’s feature extraction prowess on graph-structured data, and the advantage of transfer learning in cross-task knowledge sharing, the model achieves effective rumor detection on social media platforms. Experimental results indicate that this model achieves accuracies of 0.878 and 0.892 on the Twitter15 and Twitter16 datasets, respectively, significantly enhancing the accuracy of rumor detection compared to baseline models. Moreover, it greatly improves the efficiency of model training. Full article
(This article belongs to the Special Issue Innovations in Artificial Intelligence, Natural Language Processing and Big Data)
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