Advances in AI for Data Analytics and Intelligent Systems

Dear Colleagues,

Artificial intelligence continues to advance the capabilities of data analytics and intelligent systems across many scientific and applied domains. Modern data environments are characterized by high volume, heterogeneity, rapid generation, and the need for adaptive, real-time analysis. This Special Issue focuses on methodological and system-level innovations in AI that enhance the modelling, interpretation, and operational use of diverse data sources. While selected areas of interest are outlined below, the scope of the Special Issue is not limited to these themes, and we welcome contributions from all fields where AI supports data-driven insight or intelligent system behaviour.

We invite work spanning the full spectrum of data types, including numerical time series, textual content, geospatial information, imagery, video, audio, sensor streams, remote sensing signals, and other multimodal or heterogeneous datasets. Relevant topics include supervised and unsupervised learning, deep learning architectures, representation learning, graph and spatial models, multimodal data fusion, generative modelling, anomaly detection, and uncertainty-aware methods. Studies addressing robustness, responsible and explainable AI, fairness considerations, or domain adaptation are highly encouraged.

Many real-world systems rely on distributed sensing and continuous data acquisition through IoT networks, edge devices, autonomous platforms, and remote monitoring technologies. Applications may span precision agriculture, livestock systems, environmental monitoring, health and biomedical analytics, industrial automation, transportation, civil infrastructure, and broader interdisciplinary domains. Contributions that address challenges such as incomplete data, asynchronous sampling, low-connectivity environments, or real-time decision support are highly relevant.

The Special Issue also encourages research on natural language processing and large language models, particularly when they enhance analytics, summarization, reasoning, or knowledge extraction in domain-specific settings. Work exploring edge AI, embedded intelligence, federated learning, and energy-efficient inference further aligns with the overarching theme of advancing data-driven intelligent systems.

By bringing together foundational advances and applied research, this Special Issue aims to provide a comprehensive view of how AI continues to drive innovation in data analytics, intelligent system design, and interdisciplinary decision support.

Dr. Mehmet Bakir
Prof. Dr. Aytuğ Onan
Prof. Dr. Huiping Cao
Guest Editors

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