Innovations in Artificial Intelligence, Natural Language Processing and Big Data

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

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• 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