Federated Learning (FL) has emerged as a principled paradigm for privacy-preserving decentralized machine learning, enabling model training across distributed embedded devices without centralizing sensitive data. This review examines FL as applied to resource-constrained embedded and edge AI systems, encompassing its architectural foundations, principal optimization algorithms, application domains, and security mechanisms. We analyze the interplay between FL's theoretical properties and the practical constraints imposed by heterogeneous embedded hardware, non-IID data distributions, bandwidth-limited IoT networks, and adversarial threat models. Application domains examined in depth include s... More >

This article [1] has been retracted by ICCK following an investigation conducted by the publisher. After publication, it was brought to the journal’s attention that some of the listed authors were unaware of the submission and had not provided their consent to be included as co-authors. In accordance with the COPE guidelines [2], the publisher initiated a formal investigation. It was confirmed that the author Shuyan Liu (School of Information Science and Technology, Yunnan University, Yunnan 650000, China) was unaware of the submission, did not contribute to the research or writing of the manuscript, and did not approve the final version for publication. As a result, the article is b... More >

The integration of Artificial Intelligence (AI) with cloud computing has emerged as a pivotal strategy for enterprises seeking scalable and intelligent modernization. This paper explores how cloud-based AI solutions are transforming enterprise ecosystems by offering highly scalable, flexible, and cost-effective platforms for deploying intelligent applications. We examine the convergence of AI-as-a-Service (AIaaS), cloud-native architectures, and data-driven decision-making, and how these capabilities collectively drive operational efficiency, customer engagement, and innovation—particularly within sectors such as healthcare, finance, and manufacturing. The study investigates key enablers i... More >

Abnormal fluctuations in financial markets may signal significant risks or market manipulation, so efficient time series anomaly detection methods are crucial for risk management. However, traditional statistical methods (e.g., ARIMA, GARCH) are difficult to adapt to the nonlinear and multi-scale characteristics of financial data, while single deep learning models (e.g., LSTM, Transformer) have limitations in capturing long-term trends and short-term fluctuations. In this paper, we propose WaveLST-Trans, a financial time series anomaly detection model based on the combination of wavelet transform (WT), LSTM and Transformer. The model first uses wavelet transform to perform multi-scale decomp... More >

Predicting personality traits automatically has emerged as a challenging problem in computer vision. This paper introduces an innovative multimodal feature learning framework for personality analysis in short video clips. For visual processing, we construct a facial graph and design a Geo-based two-stream network incorporating an attention mechanism, leveraging both Graph Convolutional Networks (GCN) and Convolutional Neural Networks (CNN) to capture static facial expressions. Additionally, ResNet18 and VGGFace networks are employed to extract global scene and facial appearance features at the frame level. To capture dynamic temporal information, we integrate a BiGRU with a temporal attentio... More >

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 perspective examines the transformative role of generative artificial intelligence (AI) in augmenting human creativity and catalyzing cognitive evolution. Tracing its historical development from symbolic AI to transformer-based architectures, it contends that generative AI is not simply a computational tool but a cognitive partner that reconfigures our understanding of creativity, perception, and epistemology. The phenomenon of AI hallucination—often dismissed as mere error—is reframed as a window into the dynamics of both artificial and human cognition. Through technical and philosophical analysis, the paper addresses generative AI's impact across domains ranging from art and archi... 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 >