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Muhammad Asim Saleem

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Editorial Jul 2026

Special Issue on “Agentic AI for Explainable Big Data Exploration and Visual Analytics”

The ongoing data revolution is being reshaped by the convergence of Agentic Arti ﬁ cial Intelligence (Agentic AI), explainable data analytics, and visual reasoning frameworks. As Big Data continues to expand across scienti ﬁ c, industrial, and social domains, the ability to autonomously explore massive datasets while maintaining explainability, interpretability, and transparency has become a critical challenge. Traditional data analytics systems, although powerful in computation, often operate as “ black boxes, ” limiting human understanding, trust, and accountability in high-stakes decision-making processes. This Special Issue explores new theoretical foundations, models, and architectures that empower explain-able Big Data analytics through Agentic AI, focusing on interpretable reasoning, knowledge graphs, causal inference, human-in-the-loop learning, and visual data storytelling. It seeks to highlight interdisciplinary research spanning AI, data science, cognitive computing, human – computer interaction, and visualization, with applications across health care, ﬁ nance, smart cities, Industry 5.0, and climate informatics. The ﬁ rst article suggests a Big Data – Driven Person-alized Instruction framework powered by Explainable Agentic AI (X-AI). This work advances a scalable and culturally responsive paradigm for personalized education by combining big data analytics, agentic autonomy, and explainable AI. The authors of the second article have developed an Agentic AI pipeline integrating genome-wide association study (GWAS) data from FinnGen Release 12 (6854 cases; 384,461 controls). GWAS identi ﬁ ed a strongly associated locus on chromosome 19q13.32, with lead variant rs429358 (P = 2.79

Sunil Prajapat, Mohammad Shabaz, Muhammad Asim Saleem et al. · Big Data · 0 citations

#federated learning Open access Aug 2026

AI ‐Enabled 6G Space‐Air‐Ground–Integrated Networks for Ultra‐Reliable Low Latency Internet of Medical Things Healthcare

This paper explores an AI‐assisted resource scheduling and cooperative learning model in a space–air–ground combined network (SAGIN) to possess ultra‐reliable low‐latency Internet of Medical Things (IoMT) applications. The generated healthcare data by the IoMT devices are processed by three levels in the considered scenario including the LEO satellites, the UAV swarms, and the ground MEC servers and adhere to strict latency, reliability, and privacy requirements. We aim at designing a multi‐tier resource allocation policy and federated learning policy that coordinates end‐to‐end latency and energy consumption and at the same time is highly accurate in terms of the model given privacy constraints. In this direction, we come up with a multi agent—deep deterministic policy gradient (MA‐DDPG) agent that allocates resources in a distributed manner and a hierarchical federated learning (HFL) system with delay‐sensitive aggregation to train models privately. Extensive simulation findings indicate that the presented framework can achieve 4.2 ms latency, 99.92% reliability, and 96.2% federated (global) model accuracy and 67% minimization of communication overhead, all of which are superior to baseline and the state‐of‐the‐art approaches in a variety of measures.

T. Sardar, Gousia Thahniyath, Ahlam I. Almusharraf et al. · Transactions on Emerging Tel... · 0 citations