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
Multimedia data have been continuously increasing in magnitude, and so has the sophistication of manipulation methods, thereby making the digital forensic investigation process more complicated. The easy access to sophisticated image editing software and AI-generated materials has brought up the issue of information integrity, the reliability of legal evidence, and public trust. Traditional image forensics methods are usually concerned with either the detection of visual artifacts based on convolutional neural networks (CNNs) or based on metadata analysis, frequently independently of each other. This paper presents a multi-modal fusion paradigm, comprising visual feature-based feature extraction and metadata inconsistency-based detectors, to improve the classification strength. A two-stream design is used, comprising a high-level visual artifact capturing the transfer learning-based MobileNetV2 network and an XGBoost classifier that analyses EXIF metadata discrepancies. The heterogeneous representations are merged in a feature-level fusion strategy to generate a final authenticity prediction. It was tested on individual datasets and a compiled dataset of 26,023 images from CoMoFoD, CG-1050 and CASIA v1 and v2. The suggested approach had an overall accuracy of 83.85%, which was higher than the visual-only (68.61%) and metadata-only (75.85%) baselines. These findings show that complementary visual and metadata cues are much more useful in detection, while the use of a lightweight backbone enables efficient, high-throughput forensic analysis suitable for real-world deployment.
Baysah Guwor, Mohammad Shabaz· Multimedia· 0 citations
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