The development of more accurate and explainable health predictions is paramount in view of the increasing prevalence rates of chronic diseases and mental illnesses. Existing methods often under-utilize the rich, heterogeneous data streams coming from wearable devices, environmental sensors, and behavioral data, and hence fall short in making predictions that are both accurate and actionable. Most models lack transparency and cannot avoid privacy concerns due to the samples used from sensitive health data. This study specifically addresses the challenge of building a predictive healthcare framework capable of handling multimodal data, data streams that originate from different modalities (physiological, behavioral, and environmental), and exhibit intrinsic heterogeneity. Unlike general heterogeneous datasets, multimodal health data demands models that can effectively integrate structured, semistructured, and temporal information while ensuring privacy and interpretability. In this work, we propose an integrated comprehensive multimodal health prediction framework with five advanced methods, namely multimodal transformer networks (MTN), deep multitask neural networks (DMNN), Shapley additive explanations (SHAP) based explainability, Federated Learning, and Bayesian neural networks (BNN). MTN uses attention mechanisms to fuse different data modalities and captures cross-modal dependencies effectively, achieving an improvement of 8% to 12% in prediction accuracy. DMNN leverages multitask learning to share knowledge between related health prediction tasks, reducing error rates by 10%–15%. SHAP is used to provide localized, patient-specific explanations that improve clinical trust by up to 85%. This is done by privately training the models on decentralized datasets, which provides results without more than a 3% drop in precision compared with centralized models. Third, BNNs are used to quantify uncertainty in predictions, providing useful confidence intervals that improve clinical decision-making by 20%. The results obtained suggest significant improvements in predictive accuracy, transparency, and privacy preservation. This research not only improves health predictions through multimodal analysis but also tackles significant limitations in privacy, interpretability, and uncertainty quantification, thus promoting informed clinical decisions and customized patient care.

Traditional multimedia experiences are undergoing a transformation as generative AI integration fosters enhanced creative workflows, streamlines content creation processes, and unlocks the potential for entirely new forms of multimedia storytelling. It has potential to generate captivating visuals to accompany a documentary based solely on historical text descriptions, or creating personalized and interactive multimedia experiences tailored to individual user preferences. From the high-resolution cameras in our smartphones to the immersive experiences offered by the latest technologies, the impact of generative imaging undeniable. This study delves into the burgeoning field of generative AI, with a focus on its revolutionary impact on image generation. It explores the background of traditional imaging in consumer electronics and the motivations for integrating AI, leading to enhanced capabilities in various applications. The research critically examines current advancements in state-of-the-art technologies like DALL-E 2, Craiyon, Stable Diffusion, Imagen, Jasper, NightCafe, and Deep AI, assessing their performance on parameters such as image quality, diversity, and efficiency. It also addresses the limitations and ethical challenges posed by this integration, balancing creative autonomy with AI automation. The novelty of this work lies in its comprehensive analysis and comparison of these AI systems, providing insightful results that highlight both their strengths and areas for improvement. The conclusion underscores the transformative potential of generative AI in image generation, paving the way for future research and development to further enhance and refine these technologies. This article serves as a critical guide for understanding the current landscape and future prospects of AI-driven image creation, offering a glimpse into the evolving synergy between human creativity and artificial intelligence.

Abstract In today’s society, investment wealth management has become a mainstream of the contemporary era. Investment wealth management refers to the use of funds by investors to arrange funds reasonably, for example, savings, bank financial products, bonds, stocks, commodity spots, real estate, gold, art, and many others. Wealth management tools manage and assign families, individuals, enterprises, and institutions to achieve the purpose of increasing and maintaining value to accelerate asset growth. Among them, in investment and financial management, people’s favorite product of investment often stocks, because the stock market has great advantages and charm, especially compared with other investment methods. More and more scholars have developed methods of prediction from multiple angles for the stock market. According to the feature of financial time series and the task of price prediction, this article proposes a new framework structure to achieve a more accurate prediction of the stock price, which combines Convolution Neural Network (CNN) and Long–Short-Term Memory Neural Network (LSTM). This new method is aptly named stock sequence array convolutional LSTM (SACLSTM). It constructs a sequence array of historical data and its leading indicators (options and futures), and uses the array as the input image of the CNN framework, and extracts certain feature vectors through the convolutional layer and the layer of pooling, and as the input vector of LSTM, and takes ten stocks in U.S.A and Taiwan as the experimental data. Compared with previous methods, the prediction performance of the proposed algorithm in this article leads to better results when compared directly.

The paper presents the first-(integer) and fractional-order case studies of a novel pseudo-differential (P-D) voltage-mode all-pass filter (APF) employing a single differential voltage current conveyor (DVCC), one resistor, and a single grounded capacitor. The proposed filter brings significant reduction of complexity in comparison to available fully-differential or P-D filter topologies. Moreover, it was also shown that fractional-order capacitor can be used for gain response compensation of the proposed APF. The theoretical results of 0.8thand 1st-order APF were verified by Cadence IC6 Spectre simulations using new structure of DVCC via TSMC 0.18 μm CMOS process parameters supplied with ±0.9 V voltages.

In this paper, a new realization of a fully-differential (F-D) first-order all-pass filter (APF) operating in current mode (CM) is presented. In the proposed F-D CM APF a single adjustable current amplifier (ACA) and two current followers (CFs) with non-unity gain are used as active building blocks. Considering the input intrinsic resistance of CFs as useful active filter parameter, the proposed filter employs only a floating capacitor as external passive component. The pole frequency of the proposed resistorless circuit can be tuned via mutual change of input intrinsic resistance of CFs while the filter pass-band gain by means of gain of ACA. The theoretical results are verified by SPICE simulations, where TSMC 0.18 μm level-7 SCN018 CMOS process parameters were used.