Advances in generative and federated artificial intelligence enable privacy-aware diagnostic systems that integrate multimodal reasoning and explainability. This work introduces DermaGPT, a federated multimodal framework for dermatology decision support that emphasizes trustworthy use under heterogeneous, privacy-sensitive data. The system combines a PaLI-Gemma 2 vision–language backbone, fine-tuned with low-rank adaptation, with a retrieval-augmented large language model that generates clinically coherent and patient-friendly explanations. To improve robustness and calibration across sites, a meta-learned trust function (MLTF) dynamically re-weights client updates based on uncertainty, calibration, and domain-shift indicators. Evaluated on four institutional datasets and an external cohort of 4,452 biopsy-confirmed clinical and dermoscopic images, DermaGPT achieved 90.2% diagnostic accuracy across 11 lesion types and 93.3% accuracy in malignancy prediction, with well-calibrated outputs under federated training. Expert dermatologists rated its explanations as clear and clinically relevant; these ratings were obtained on class-level canonical exemplars rather than per-image reports. In our deployment threat model, images are processed locally by the vision module; when a third-party LLM is used, only text (a short diagnostic summary and the user question) is transmitted, which may still be considered sensitive health data. Taken together, these results indicate that a trust-aware, federated multimodal design can deliver interpretable, efficient, and privacy-aware dermatology decision support that is intended to augment rather than replace clinician judgment.

Sarcasm detection in text poses significant challenges for traditional sentiment analysis, as it often requires an understanding of context, word meanings, and emotional undertones. For example, in the sentence “I totally love working on Christmas holiday”, detecting sarcasm depends on capturing the contrast between affective words and their context. Existing methods often focus on single-embedding levels, such as word-level or affective-level, neglecting the importance of multi-level context. In this paper, we propose SAWE (Sentence, Affect, and Word Embeddings), a framework that combines sentence-level, affect-level, and context-dependent word embeddings to improve sarcasm detection. We use pre-trained transformer models SBERT and RoBERTa, enhanced with a bidirectional GRU and self-attention, alongside SenticNet to extract affective words. The combined embeddings are processed through a CNN and classified using a multilayer perceptron (MLP). SAWE is evaluated on two benchmark datasets, Sarcasm Corpus V2 (SV2) and Self-Annotated Reddit Corpus 2.0 (SARC 2.0), outperforming previous methods, particularly on long texts, with a 4.2% improvement on F1-Score for SV2. Our results emphasize the importance of multi-level embeddings and contextual information in detecting sarcasm, demonstrating a new direction for future research.

This paper proposes a new model that improves face recognition with challenges such as illumination variation and noise in facial images. The model combines state-of-the-art illumination normalization by Retinex theory with median filtering for improved noise reduction and accuracy of recognition. Using pre-trained VGG-16 weights, the model applies parts of the Boltzmann machine to extract intricate features from faces while minimizing overfitting. Further, this paper combines zero-shot facial expression recognition methodologies and illumination-robust face recognition within one facial analysis framework. The combination of models for pre-processing with large language models and more advanced feature extraction allows the model to perform well on various benchmarks from FERET to LFW, CelebA, and VGGFace2 datasets. These results have identified that the integration of superior normalization techniques along with zero-shot learning enhances face recognition and expression analysis in difficult, practical conditions. This solution is feasible for a practical situation and robust against real-world variations in illumination and noise. Future work will enhance computational efficiency and expand the applicability to various datasets.

Efficiently processing large-scale graphs for information retrieval tasks presents a formidable hurdle, demanding innovative solutions for enhancing user experiences. This paper introduces a framework that merges attention-based graph summarization with state-of-the-art graph sampling methods tailored explicitly for large-scale graph processing and information retrieval applications, all aimed at enriching user experiences. Our approach distinguishes itself through its adeptness in efficiently handling vast graph datasets, leveraging robust sampling techniques and attention mechanisms to enhance feature extraction. Central to our methodology is the utilization of graph summarization techniques, which focus on distilling pertinent information, thereby enhancing both the accuracy and computational efficiency of information retrieval and recommendation tasks. Through practical demonstrations, notably within academic databases, our framework showcases its effectiveness in real-world scenarios, offering a significant advancement in the realm of personal technology data management and information retrieval systems.

Biometric authentication has emerged as an important component in strengthening cyber security, providing a superior alternative to conventional password-based systems because of their distinctive and difficult-to-duplicate attributes. This study examines diverse biometric techniques—including fingerprint, facial recognition, palm vein, and behavioral biometrics—and the incorporation of Artificial Intelligence (AI) methodologies such as Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) to improve their dependability. Innovations including multi-modal biometrics, cancelable biometrics, and Genetic Encryption Algorithms (GEA) are examined, emphasizing their capacity to enhance security while mitigating privacy issues. The research additionally investigates continuous authentication inside IoT systems and future themes such as federated learning and quantum-resistant algorithms. Despite these developments, issues of computing complexity, scalability, consumer acceptance, and data privacy remain. The report concludes with recommendations for future research focused on optimizing biometric systems for broad implementation, improving data privacy, and creating adaptive, AI-driven methodologies to enhance biometric security.