In today's interconnected world, digital communication has become ubiquitous, bringing with it a surge in cyberbullying, online extremism, and the spread of misinformation. These issues pose significant challenges to maintaining digital safety and integrity across the globe. The special issue of "Scalable Computing: Practice and Experience" addresses this critical concern by inviting research that applies neural network technology to combat these digital threats in various linguistic environments. Our goal is to showcase innovativea research that leverages neural network and machine learning techniques to analyze, identify, and neutralize these threats, paying particular attention to their scalable deployment across different languages and cultures.
As digital platforms evolve, they often become breeding grounds for harmful content, transcending not only linguistic but also geographical boundaries. This reality has made it imperative to develop solutions that are not only sophisticated but also scalable, ensuring they can be effectively applied in diverse environments. Neural networks, known for their ability to process and analyze vast amounts of complex data, emerge as powerful tools in this scenario. They offer the possibility to decipher the nuanced patterns of digital threats, making them detectable and manageable across multiple languages and cultures.
This special issue places a strong emphasis on scalable computing, recognizing the crucial role it plays in the deployment of neural networks and machine learning technologies. It aims to explore how these advanced computational methods can be adapted to function efficiently on a large scale, providing robust and flexible solutions for digital safety. We are interested in research that not only addresses the technical aspects of neural network implementation but also considers the broader implications of these technologies in diverse linguistic and cultural settings. The special issue will highlight how scalable computing can enhance the capability of neural networks to understand and counteract the complex landscape of digital threats, ensuring safer digital communication environments worldwide.

Recommended topics (but not limited to)

Topics of interest include, but are not limited to:

• Scalable neural network solutions for cyberbullying detection in multilingual environments.
• Machine learning strategies for addressing online extremism across various linguistic landscapes.
• Neural network techniques for identifying and mitigating misinformation in diverse languages.
• Development of scalable, language-neutral models for effective digital content monitoring.
• Evaluation of scalable neural network approaches in varied cultural and linguistic contexts.
• Exploration of ethical considerations and bias in scalable neural network applications across languages.
• Research on the scalability of neural networks in managing risks associated with digital communication.
• Analysis of language usage and dynamics in digital communication via scalable neural network models.
• Implementation of community-driven neural network projects for preventing digital threats, emphasizing scalability.
• Cross-linguistic studies on online threats using scalable neural network methodologies.
• Integration of visual and textual data analysis in scalable neural networks for comprehensive misinformation detection.
• Enhancements in scalable neural network architectures for robust multilingual digital security solutions.

Important dates

Submission deadline: 30 September 2024, 31 March 2025

Authors notification: 1 December 2024, 1 June 2025

Revision submission: 15 December 2024, 31 March 2025

Publication deadline: 1 September 2025

Submission guidelines

Original and unpublished works on any of the topics aforementioned or related are welcome. The SCPE journal has a rigorous peer-reviewing process and papers will be reviewed by at least two referees. All submitted papers must be formatted according to the journal's instructions, which can be found here.

During submission please select a Special Issue that you want to submit to and provide this information in the Comments for the Editor field.

Guest Editors

Lead Guest Editor: Dr. Akshi Kumar, Department of Computing, Goldsmiths, University of London, UK, email: Akshi.Kumar@gold.ac.uk

Dr. Akshi Kumar is a Senior Lecturer (Associate Professor) and Director of Post Graduate Research (PGR) in the Department of Computing at the Goldsmiths, University of London, London, United Kingdom. She is a Post-doc from Federal Institute of Education, Science and Technology of Ceará, Fortaleza, Brazil and a PhD from Faculty of Technology, University of Delhi, India. Her career has traversed diverse educational landscapes, including roles as a Senior Lecturer in AI & Data Science at Manchester Metropolitan University in Manchester, United Kingdom, an Associate Professor at Netaji Subhas University of Technology (NSUT) in New Delhi, India, and an Assistant Professor at Delhi Technological University (DTU) in New Delhi, India.
Dr. Kumar has been endorsed by the Royal Academy of Engineering, United Kingdom as an Exceptional promise in the field of Artificial Intelligence/Data Science in 2022. She has received 9 research awards for Excellence in Research from various National and International organizations. Her name has been included in the “Top 2% scientist of the world” list by Stanford University, USA in 2023, 2022 and 2021. Based on the list, her current world rank within the field of Artificial Intelligence and Image Processing is 2367. She has published more than 100 peer-reviewed journal papers including 60 SCIE publications, 70+ conference papers with 5 best paper awards and 2 patents with Indian Patent Office. Recently, she has published two written evidences on news integrity and cyber resilience in UK Parliament (House of Lords and House of Commons), impacting research in technology, AI, and cybersafety. She has successfully guided 6 doctorates, 33 Master thesis candidates. She has been serving as an Associate editor and guest editor in various high impact journals with reputed publishers. Her research interests are in affective computing, social network and media analytics, NLP, and AI for pervasive healthcare. She is a member of Turing NLP interest group, British computer society (BCS) and Senior member, IEEE.

Dr. Saurabh Raj Sangwan, chool of Computer Science & Engineering, Galgotias University, Greater Noida, India, email: saurabhraj.sangwan@galgotiasuniversity.edu.in

Dr. Saurabh Raj Sangwan is an Assistant Professor in the School of Computer Science and Engineering at Galgotias University, Greater Noida, India. He has received his doctorate from Netaji Subhas University of Technology, New Delhi in 2022. He did his bachelor’s degree in computer science and engineering from DCRUST, Murthal, India, and the M.Tech. degree in software engineering from the Department of Computer Science & Engineering, Delhi Technological University, Delhi, India, in 2018. He has a meritorious publication record with papers in high impact journals and reputed conferences. Dr. Sangwan is also a recipient of the commendable research award from NSUT, Delhi.  His research interests include cyber informatics, online behaviour, natural language processing and health informatics.

Prof. Victor Hugo C. de Albuquerque, Professor and Senior Researcher Department of Teleinformatics Engineering (DETI) / Federal University of Ceará (UFC), Brazil, email: victor.albuquerque@ieee.org

Prof. Victor Hugo C. de Albuquerque [M’17, SM’19] is a Professor and senior researcher at the Department of Teleinformatics Engineering (DETI)/Graduate Program in Teleinformatics Engineering (PPGETI) at the Federal University of Ceará (UFC), Brazil. He earned a Ph.D in Mechanical Engineering from the Federal University of Paraíba (UFPB, 2010), a MSc in Teleinformatics Engineering from the PPGETI/UFC (UFC, 2007). He completed a BSE in Mechatronics Engineering at the Federal Center of Technological Education of Ceará (CEFETCE, 2006). He specializes in Image Data Science, IoT, Machine/Deep Learning, Pattern Recognition, Automation and Control, and Robotics. Lead of the Biomedical Data Analytics Research Group: https://www.instagram.com/_biodata_/