TIM-7070 v1

Samaras, G., Mertiri, M., Xezonaki, M.-E., Theodorou, V., Chartsias, P. K., & Bozios, T. (2024). Democratizing predictive analytics with generative artificial intelligence towards ai-native networks. 2024 IEEE Symposium on Computers and Communications (ISCC), Computers and Communications (ISCC), 2024 IEEE Symposium On, 1–6. https://doi.org/10.1109/ISCC61673.2024.10733560
This article explores how foundation models and AutoML like Auto-TimeGPT enable hands-free, multimodal AI for anomaly detection and forecasting, advancing 6G network intelligence across Edge-Cloud infrastructures.

Li, B., Liu, T., Wang, W., Zhao, C., & Wang, S. (2025). Agent-as-a-service: An ai-native edge computing framework for 6g networks. IEEE Network, Network, IEEE, 39(2), 44–51. https://doi.org/10.1109/MNET.2024.3520987
This article details a recent trend in AI, Agent-as-a-Service (AaaS) which is an AI-native edge computing framework for 6G, enabling intelligent agents to autonomously manage tasks through planning, orchestration, and evolution.

Thomas, C. K., Chaccour, C., Saad, W., Debbah, M., & Hong, C. S. (2024). Causal reasoning: Charting a revolutionary course for next-generation ai-native wireless networks. IEEE Vehicular Technology Magazine, Vehicular Technology Magazine, IEEE, IEEE Veh. Technol. Mag, 19(1), 16–31. https://doi.org/10.1109/MVT.2024.3359357
To overcome AI’s limitations in wireless networks, this article proposes a causality-driven framework for building explainable, adaptable, and energy-efficient AI-native 6G systems.

Singh, R., Singh, S., & Singh, A. (2024). Landmark events, major trends, and the future of AI research: history, promise, hype. 2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA), Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA), 2024 2nd International Conference On, 1–5. https://doi.org/10.1109/AIMLA59606.2024.10531477
This study analyzes AI research trends from 1960–2023 using Google Scholar data, revealing growth patterns, key milestones, and recurring overestimations of near-term AI capabilities.

Yang, X., Xu, X., & Ying, J. (2024). Research trends in application of artificial intelligence in alzheimer’s disease: bibliometric and visualization analysis. 2024 IEEE 7th International Conference on Big Data and Artificial Intelligence (BDAI), Big Data and Artificial Intelligence (BDAI), 2024 IEEE 7th International Conference On, 247–253. https://doi.org/10.1109/BDAI62182.2024.10692826
This bibliometric study highlights exponential growth in AI-Alzheimer’s research since 2016, with a focus on ML/DL for diagnosis, and emerging interest in GANs, transfer learning, and Transformers.

Yutia, S. N., Tyas, S. H. Y., & Haryadi, D. (2024). Trends in research and artificial intelligence methods for steganalysis - a systematic literature review (SLR). 2024 International Conference on ICT for Smart Society (ICISS), ICT for Smart Society (ICISS), 2024 International Conference On, 1–6. https://doi.org/10.1109/ICISS62896.2024.10751004
This study reviews 2018–2023 AI-driven steganalysis research, highlighting CNN as the dominant method for detecting stegomalware, with stegoimage analysis comprising 77% of investigations.