Special Issue: Cognitive-inspired NLP for Big Data-driven Multimedia Information Processing

Cognitive-inspired NLP for Big Data-driven Multimedia Information Processing

NLP makes it possible for cognitive systems to decipher textual meaning. You can grasp the meaning of a word or term by referring to the context provided by phrases, sentences, even intricate entire publications. In order to determine the actual meaning of text-based data, this context is essential. The study of the relationship between computers and languages is known as natural language processing, or NLP. It is a subfield of data science and artificial intelligence. Comprehensive and effective analysis of textual and audio data requires the use of natural language processing, or NLP. It can overcome the variations in slang, dialects, and grammatical errors common in everyday speech. Artificial Intelligence (AI) has a specialization called Natural Language Processing (NLP). It facilitates the processing and comprehension of human language by machines, enabling them to carry out repetitive activities automatically. Any organization can benefit greatly from natural language processing in order to save time and money, automate and streamline procedures, and make data-driven choices in real-time.

 

Natural Language Processing (NLP) data can be found in text documents, news stories, chat logs, social media posts, and consumer evaluations. Sentiment analysis, language translation, text categorization, named entity recognition, and speech recognition are just a few of the applications for NLP data. The goal of natural language processing (NLP), a subfield of computer science, linguistics, and artificial intelligence, is to enable computers to understand human language including speech and text. Businesses are gaining insights from unstructured text data, such as emails, social media posts, online reviews, and more, thanks to the use of natural language processing techniques. Technologies for natural language processing (NLP) are essential for businesses that deal with a lot of unstructured material. Among the practical uses of NLP include speech recognition, chatbots, text extraction, text summarization, sentiment analysis, and speech analysis. Using natural language processing (NLP) has several benefits, one of which is improved human-computer communication. Instead of needing to learn a specialized programming language or enter commands in a specific format, users can communicate with computers using natural language thanks to natural language processing (NLP).

 

A collection of ideas and methods known as neuro-linguistic programming (NLP) aims to improve self-awareness, boost self-esteem, develop communication skills, and encourage constructive social behaviour. With applications involving tasks like translation, summarization, text production, and sentiment analysis, natural language processing (NLP) is one of the AI study fields that is expanding quickly. The field of natural language processing (NLP) is a combination of machine learning, linguistics, and computer science. The goal of natural language processing (NLP) is to enable computers to comprehend and produce human language. The field focuses on natural language communication between humans and computers. Rule-based and statistical models are the two primary categories into which NLP models fall. Rule-based models create and analyze natural language data using specified rules and dictionaries. Statistical models learn from language data and generate predictions using data-driven techniques and probabilistic methods. Morphological and Lexical Analysis: The study of vocabulary words and expressions is known as lexical analysis. Articles are invited that explore BITTHE240359_Cognitive-inspired NLP for Big Data-driven Multimedia Information Processing. Case studies and practitioner perspectives are also welcome.

 

Topics for this special issue include, the following:

  • Big data-driven intelligent traffic management using an online platform for incremental machine learning.
  • Approaching a hashtag recommendation for Twitter data analysis that is inspired by cognitive processes.
  • Workshop on Information Access Systems with a focus on psychology.
  • Natural language processing and computer vision combined in a multimedia robotics application.
  • Employing Cognitive Science Principles to Advance Perception in Artificial Intelligence.
  • Sentiment analysis using Big Data Analytics for Classification.
  • Domain-specific emotional models for online intelligence applications are automatically expanded.
  • Natural language processing in neural networks for online interaction analysis and control.
  • Leveraging geographical Twitter data to map customer sentiment regarding wireless services.
  • Employing GPT for sophisticated sentiment analysis and diverging from existing machine learning techniques.
  • A hybrid approach to sentiment analysis that combines lexicons and machine learning.
  • Combining information from several sources to do focused aspect-based financial sentiment research.

Timeline:

Submission deadline: November 30, 2024

Author notification: January 30, 2025

Revised papers due: March 25, 2025

Final notification: May 30, 2025

 

Guest Editor Details:

Dr. Uzair Aslam Bhatti
School of Information and Communication Engineering,
Hainan University, Haikou 570100, China.
Email: uzair@hainanu.edu.cn, uzairbhatti86@hotmail.com
Scopus ID; Google Scholar

Dr. Muhammad Asim Saleem
Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology,
Department of Electrical Engineering,
Faculty of Engineering, Chulalongkorn University,
Bangkok 10330, Thailand
Email: muhammadasim.s@chula.ac.th
Google Scholar

Dr. Maqbool Khan,
Assistant Professor,
Pak-Austria Fachhochschule-Institute of Applied Sciences and Technology,
Mang, Haripur, 22621, Pakistan
Email: maqbool.khan@scch.at
Google Scholar

Dr. Sibghat Ullah Bazai,
Department of Computer Engineering,
Balochistan University of Information Technology Engineering and Management Sciences,
Quetta, Pakistan,
Email: sibghat.ullah@buitms.edu.pk
Google Scholar

Dr. Yonis Gulzar,
Department of Management Information Systems,
King Faisal University,
PO Box 380, Al-Ahsa, 31982, Saudi Arabia Office: 2028
Email: ygulzar@kfu.edu.sa
Google Scholar

 

Note: No fees are charged for publication of this special issue