Public health surveillance is crucial for early disease detection, outbreak prediction, and epidemic response. However, traditional surveillance systems primarily rely on structured clinical data, limiting their capacity to capture emerging health threats from diverse and unstructured sources. This study explores the integration of Natural Language Processing (NLP) and Artificial Intelligence (AI) to automate disease surveillance by analyzing unstructured data, including electronic health records (EHRs), social media posts, news reports, and online health forums. Leveraging state-of-the-art NLP techniques—such as transformer-based language models, named entity recognition (NER), sentiment... More >

This editorial explores the transformative role of generative artificial intelligence (AI) in augmenting human creativity and catalyzing cognitive evolution. Tracing its historical lineage from symbolic AI to transformer-based architectures, this editorial argues that generative AI is not merely a computational tool but a cognitive partner that reshapes our understanding of creativity, perception, and epistemology. The phenomenon of AI hallucination—often dismissed as error—is reframed as a window into the dynamics of both artificial and human cognition. Through technical and philosophical analysis, the paper discusses generative AI’s impact on fields ranging from art and architecture... More >

The scalability of modern AI is fundamentally limited by the availability of labeled data. While supervised learning achieves remarkable performance, it relies on large annotated datasets, which are expensive and time-consuming to acquire. This work explores self-supervised learning (SSL) as a promising solution to this challenge, enabling AI to scale effectively in data-scarce scenarios. This study demonstrates the effectiveness of the proposed SSL framework using the EuroSAT dataset, a benchmark for land cover classification where labeled data is limited and costly. The proposed approach integrates contrastive learning with multi-spectral augmentations, such as spectral jittering and band... More >

Interpreting NMR spectra to accurately predict molecular structures remains a significant challenge in chemistry due to the complexity of spectral data and the need for precise structural elucidation. This study introduces NMRGen, a generative modeling framework that predicts molecular structures from NMR spectra and molecular formulas. The framework combines a SMILES autoencoder (GRU-based encoder-decoder) and an NMR encoder (CNN and DNN layers) to map spectral data to molecular representations. The SMILES autoencoder compresses and reconstructs SMILES strings, while the NMR encoder processes NMR spectra to generate latent vectors aligned with those from the SMILES encoder. Experiments were... More >

With the increasing global focus on renewable energy and the growing proportion of renewable power in the energy mix, accurate forecasting of renewable power demand has become crucial. This study addresses this challenge by proposing a multimodal information fusion approach that integrates time series data and textual data to leverage complementary information from heterogeneous sources. We develop a hybrid predictive model combining CNN and Bi-GRU architectures. First, time series data (e.g., historical power generation) and textual data (e.g., policy documents) are preprocessed through normalization and tokenization. Next, CNNs extract spatial features from both data modalities, which are... More >