E. Elakkiya

1. Automatically recognizing emotions from natural language text is one of the central challenges in affective computing and artificial intelligence [1, 2]. This technology can be used in various s...

Malware detection remains challenging because of privacy constraints, heterogeneous data distributions, and vulnerability to adversarial attacks in distributed environments. We proposed a federated adversarial learning framework for robust, privacy‐preserving malware classification. The framework introduces a two‐way federated strategy, in which two client groups train vision transformers using different patch sizes to capture fine‐ and coarse‐grained malware features. A hierarchical selective aggregation mechanism enhances the global model by weighting trustworthy client contributions and improving resilience to stragglers and heterogeneous data, while integrated adversarial training mitigates model poisoning and data leakage. Experiments conducted on the Malevis, BIG2015, and Malimg benchmarks demonstrated strong scalability, achieving high accuracy with four clients, and minimal performance degradation at eight and 12 clients. The proposed approach further outperformed standard and adversarial attention‐based vision transformers as well as state‐of‐the‐art federated approaches, delivering a scalable, secure, and interpretable solution for modern malware detection.

Partial shading poses a significant challenge in photovoltaic systems by creating multiple peaks in the power-voltage curve, complicating the task of accurately tracking the Maximum Power Point. Traditional maximum power point tracking methods often struggle to identify the true Global Maximum Power Point, leading to suboptimal energy harvesting. This paper proposes a novel hybrid tracking framework that integrates fuzzy logic, synthetic data generation using Generative Adversarial Networks (GANs), and time-series modeling with Transformer architectures. Fuzzy logic improves resilience to input uncertainties by translating raw data into interpretable fuzzy values. GANs augment the dataset by generating realistic synthetic samples, thereby improving generalization. The Transformer model leverages self-attention mechanisms to capture long-term temporal patterns in solar irradiance and power profiles. By combining these strengths, the proposed method delivers a robust and accurate global maximum power point tracking solution, particularly under dynamic and partially shaded environments. Experimental results demonstrate its superior performance and scalability compared to conventional maximum power point tracking approaches.