Image captioning models can produce rapid Sentences, without visual relationships, or insert non-existing plausible objects. A common cause is to compress image evidence into visual symbols that carry a weak neighbourhood context. The multimodal context-enhanced visual representation learning framework (MCVRL) addresses this error mode by adding local neighbourhood descriptors, global scene tokens, prefix-conditioned visual doors and adaptive contextual corrections be-fore caption decoding. The encoder is trained with cross-entropy and contrast terms for image–text alignment. MSCOCO 2014’s Karpathy test classification results show that BLEU-4, METEOR, CIDEr and SPICE are more powerful captioning bases. The best configuration received a score of 1.352 of the CIDEr compared to 1.308 of BLIP-2 in the same evaluation protocol. The results of the ablation show that the most important contribution is the visual feature enriched by the context, followed by crossmodal gating and adaptive contextual attention. Qualitative examples show that objects with hallucinations are fewer and that spatial relationships are better recovered.
E. Divya, Johnson Kolluri, Kiran Siripuri· 2026 7th International Confe...· 0 citations
The rising rate of interconnected systems, cloud infrastructures, edge environments, and distributed network architectures have greatly exposed the vulnerability of the current digital infrastructures. This has exposed them to more advanced cybercrimes like denial-of-service attacks, malware injections, and data leaks. Also, there are sophisticated persistent threats that add more security burdens to such systems. The traditional Intrusion Detection Systems (IDS) are conventionally designed around central data collection and model training which result in the loss of privacy, a severely limited scale, a huge load on communications and a single point of failure. These constraints are even more deplorable in large and heterogeneous networks. To solve these issues, federated learning-based IDS models are suggested, but the existing practices fail to converge quickly, do not scale to non-IID data distributions and have an increased computation and communication cost which restricts its application. To overcome these issues, this paper proposes a Federated Enhanced Transformer-based Intrusion Detection System (FET-FIDS), a privacy-preserving and decentralized system of security, where federated learning is combined with Transformer-based self-attention. In the proposed architecture, a group of clients are introduced, each client is responsible for being trained on local network traffic data using FET-FIDS model. This method will help the system to learn intrusion patterns that are usually complicated to be learnt only in collaborative training. The locally trained model updates are then securely combined in a centralized server using adaptive federated averaging without having access to the raw data and, therefore, preserving their confidentiality of the data. The proposed architecture is effective in distributed and heterogeneous environments where under the experimental conditions taken into account in this study, its scalability, robustness and communication performance are improved. Using the provided means of wide-scale experimental analysis, the proposed FET-FIDS gives accuracy of 97.82%. It demonstrated that the proposed method is more effective, in terms of the detection, stability, and convergence behavior, than the existing centralized and federated IDS models. Further, it is shown that the framework can effectively deal with non-IID data distribution and the extensibility of the approach to different types of distributed network environment.
Jothi Prabha Appadurai, Revoori Swetha, V. Srinivas et al.· Scientific Reports· 1 citation
This paper examines quantum algorithms and their computational complexity through a unified framework combining mathematical modeling, system architecture, and empirical evaluation. Key complexity measures circuit depth, gate count, and query complexity are analyzed under NISQ constraints. A hybrid quantum classical optimization framework is introduced to improve efficiency and stability. Results show the full model achieves 94.2% accuracy with baseline runtime, while removing optimization lowers accuracy to 85.6% and increases runtime by 30%. Reducing qubits decreases cost but drops accuracy to 78.3%, and disabling error mitigation causes unstable performance at 70.1%. Comparative analysis indicates strong advantages of quantum algorithms for structured problems, especially in scalability and asymptotic complexity. However, performance remains sensitive to noise, limited qubits, and circuit depth, emphasizing the need for hardware–algorithm co-design.
