Understanding how localized changes in one variable affect others in multivariate time series is essential for diagnostics and decision-making in complex systems. Existing models often fail to capture realistic inter-feature dynamics when simulating "what-if" scenarios, leading to inaccurate or uncorrelated reconstructions. We propose CFORVAE, a variational autoencoder framework that explicitly addresses this limitation by combining temporal decomposition with frequency-domain feature correlation modeling. Our architecture uses a dual-path encoding of trend and seasonal components, each projected into attention-pooled latent spaces, and applies Fourier Neural Operators (FNO) to capture cross-feature dependencies in the spectral domain. This decomposition-correlation design enables component-specific latent manipulation and ensures that local modifications propagate realistically across correlated variables. Through extensive experiments, we show that CFORVAE outperforms state-of-the-art baselines in preserving temporal and feature-level dependencies, especially under adjustment-based reconstructions, making it a powerful tool for interpretable "what-if" analysis and diagnostics.

Generative artificial intelligence is increasingly transforming education by enabling adaptive learning environments, personalized instructional support, and collaborative knowledge creation, while also assisting research, assessment, and academic administration. In the context of fifth-generation education systems, the integration of generative artificial intelligence offers new opportunities to enhance student engagement, support innovative pedagogical practices, and improve scholarly productivity. This study presents a systematic review of recent research on the adoption of generative artificial intelligence in primary, secondary, and higher education institutions, using established guidelines for systematic literature reviews and bibliometric analysis. The review examines applications of generative artificial intelligence for learners, educators, and researchers, and identifies key challenges including academic integrity risks, algorithmic bias, ethical concerns, data privacy issues, and unequal access to advanced educational technologies. By analyzing research trends, thematic relationships, and existing gaps, this study provides insights for the design of adaptive learning systems, the promotion of responsible use of artificial intelligence in education, and the direction of future research efforts. Overall, the findings highlight the potential of generative artificial intelligence to support human–machine collaboration, lifelong learning, and inclusive educational environments, offering practical guidance for policymakers, educators, and engineers seeking to apply artificial intelligence for meaningful educational impact.

In this work, we introduce a time series forecasting architecture that incorporates autocorrelation while leveraging dual encoders operating at multiple scales. Unlike traditional models that rely on predefined trend and seasonal components, our architecture applies learnable decomposition in the frequency domain and employs two separate encoders per scale: one focusing on low-pass filtering to capture trends and the other utilizing high-pass filtering to model seasonal variations. This learnable filtering allows the model to dynamically adapt and isolate trend and seasonal components directly in the frequency domain. A central and distinguishing innovation in our approach is the introduction of an integrated confidence scoring mechanism based on the reconstruction error of the same encoder backbone used for forecasting. This confidence score provides a direct, learned metric of prediction certainty, which we use to dynamically adjust the final forecasting output, effectively mitigating risks from uncertain predictions. We achieve the integration of autocorrelation by computing lagged differences in temporal processing, enabling the model to capture dependencies across time more effectively. In our architecture, each encoder processes the input through fully connected layers to handle temporal and channel interactions, and the same encoder backbone is used for both reconstruction and forecasting outputs. By combining frequency-domain filtering, autocorrelation-based temporal modeling, channel-wise transformations, and proposed confidence scoring mechanism, our model not only accurately captures long-term dependencies and fine-grained patterns, but also operates more efficiently compared to other state-of-the-art methods. Its lightweight design ensures faster processing while maintaining high precision in forecasting across diverse datasets and time horizons.

This paper introduces Fuzzy Attention Network-Based Transformer (FANTF), an approach for time series forecasting, classification, and anomaly detection. FANTF combines fuzzy logic concepts with transformer architectures through a novel fuzzy attention mechanism. The proposed mechanism is inspired by Type-2 fuzzy set theory. It uses a learnable Gaussian uncertainty perturbation to model uncertainty in attention relevance scores. This makes it possible for the model to accurately capture uncertainty in temporal relationships than normal deterministic attention. Transformers’ capacity to record complex temporal patterns and multivariate connections is enhanced by FANTF. The approach is both architecture-agnostic and task-agnostic. The proposed fuzzy attention module can be used as a direct replacement for the standard attention layer. To evaluate its effectiveness, FAN is integrated into five transformer backbones: Transformer, Informer, PatchTST, Crossformer, and iTransformer. The fuzzy attention mechanism produces softer and more diverse attention distributions. It provides uncertainty-aware interpretations through attention heatmap visualizations. Experimental evaluations on some real-life datasets reveal that FANTF enhances the forecasting performance, classification, and anomaly detection tasks over traditional transformer-based models. FANTF achieves statistically significant and competitive performance improvements over standard Transformer-based baselines on most benchmark datasets ( , Wilcoxon signed-rank test), observed under noisy and non-stationary time series conditions.