Explanatory Data Science in Technology Applications

JUCS - Journal of Universal Computer Science 31(9): 873-876, doi: 10.3897/jucs.164654

Wolfram Luther^‡, A. J. Han Vinck^‡

‡ University of Duisburg‐Essen, Duisburg, Germany

Corresponding author: Wolfram Luther ( wolfram.luther@uni-due.de )

This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Citation: Luther W, Vinck AJH (2025) Explanatory Data Science in Technology Applications. JUCS - Journal of Universal Computer Science 31(9): 873-876. https://doi.org/10.3897/jucs.164654

Abstract

This volume presents a conference paper selection from the 4th Workshop on Collaborative Technologies and Data Science in Smart City Applications (CODASSCA 2024): Data Science and Reliable Machine Learning, held in Yerevan, Armenia, October 3-6, 2024, https://codassca2024.aua.am/. The special issues guest editors invited five groups of authors from Armenia, Chile, Germany, the UK, and the USA to submit enlarged versions of their CODASSCA 2024 papers There was also a J.UCS open call so that any author could submit papers on the highlighted subjects. The invitation to review the 16 contributions received was accepted by 16 experts, and, after three rounds, seven articles were finally accepted for publication in the special issue.

Keywords

AI-Driven Cyber Threats, Breast Cancer, Classification, Clustering, Deep Learning, Dempster-Shafer Theory, Dynamic AI Threat Intelligence, Dynamic Honeypots, Edge Training, Efficient Answers to Queries, Embedded Deep Learning, Expert Systems, Fixed-Point Quantization, Hybrid Indexes, Identification Code, Interpretability, K-Means, Machine Learning, Mass Assignment Functions, Memory Reduction, Metaverse Security, MITRE ATT&CK and PYTM Framework, Parallel Algorithms, Quantized Parameters, Pathologic Complete Response, Privacy, Record Linkage, Response System, Spatio-temporal Data, Stochastic Rounding, (Un)supervised Learning