Artificial intelligence has made substantial progress in many medical application scenarios. The quantity and complexity of pathology images are enormous, but conventional visual screening techniques are labor-intensive, time-consuming, and subject to some degree of subjectivity. Complex pathological data can be converted into mineable image features using artificial intelligence image analysis technology, enabling medical professionals to quickly and quantitatively identify regions of interest and extract information about cellular tissue. In this study, we designed a medical information assistance system for segmenting pathology images and quantifying statistical results, including data enhancement, cell nucleus segmentation, model tumor, and quantitative analysis. In cell nucleus segmentation, to address the problem of uneven healthcare resources, we designed a high-precision teacher model (HRMED_T) and a lightweight student model (HRMED_S). The HRMED_T model is based on visual Transformer and high-resolution representation learning. It achieves accurate segmentation by parallel low-resolution convolution and high-scaled image iterative fusion, while also maintaining the high-resolution representation. The HRMED_S model is based on the Channel-wise Knowledge Distillation approach to simplify the structure, achieve faster convergence, and refine the segmentation results by using conditional random fields instead of fully connected structures. The experimental results show that our system has better performance than other methods. The Intersection over the Union (IoU) of HRMED_T model reaches 0.756. The IoU of HRMED_S model also reaches 0.710 and params is only 3.99 M.

(1) Background: At present, physiological stress detection technology is a critical means for precisely evaluating the comprehensive health status of live fish. However, the commonly used biochemical tests are invasive and time-consuming and cannot simultaneously monitor and dynamically evaluate multiple stress levels in fish and accurately classify their health levels. The purpose of this study is to deploy wearable bioelectrical impedance analysis (WBIA) sensors on fish skin to construct a deep learning-based stress dynamic evaluation model for precisely estimating their accurate health status. (2) Methods: The correlation of fish (turbot) muscle nutrients and their stress indicators are calculated using grey relation analysis (GRA) for allocating the weight of the stress factors. Next, WBIA features are sieved using the maximum information coefficient (MIC) in stress trend evaluation modeling, which is closely related to the key stress factors. Afterward, a convolutional neural network (CNN) is utilized to obtain the features of the WBIA signals. Then, the long short-term memory (LSTM) method learns the stress trends with residual rectification using bidirectional gated recurrent units (BiGRUs). Furthermore, the Z-shaped fuzzy function can accurately classify the fish health status by the total evaluated stress values. (3) Results: The proposed CNN-LSTM-BiGRU-based stress evaluation model shows superior accuracy compared to the other machine learning models (CNN-LSTM, CNN-GRU, LSTM, GRU, SVR, and BP) based on the MAPE, MAE, and RMSE. Moreover, the fish health classification under waterless and low-temperature conditions is thoroughly verified. High accuracy is proven by the classification validation criterion (accuracy, F1 score, precision, and recall). (4) Conclusions: the proposed health evaluation technology can precisely monitor and track the health status of live fish and provides an effective technical reference for the field of live fish vital sign detection.

Continuous noninvasive blood glucose monitoring and estimation management by using photoplethysmography (PPG) technology always have a series of problems, such as substantial time variability, inaccuracy, and complex nonlinearity. This paper proposes a blood glucose (BG) prediction model for more precise prediction based on BG series decomposition by complete aggregation empirical mode decomposition based on adaptive white noise (CEEMDAN) and the gated recurrent unit (GRU) that is optimized by improved bacterial foraging optimization (IBFO). Hierarchical clustering technology recombines the decomposed BG series according to their sample entropy and the correlations with the original BG trends. Dynamic BG trends are regressed separately for each recombined BG series by the GRU model to realize the more precise estimations, which are optimized by IBFO for its structure and superparameters. Through experiments, the optimized and basic LSTM, RNN, and support vector regression (SVR) are compared to evaluate the performance of the proposed model. The experimental results indicate that the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the 15-min IBFO-GRU prediction is improved on average by about 13.1% and 18.4%, respectively, compared with those of the RNN and LSTM optimized by IBFO. Meanwhile, the proposed model improved the Clarke error grid results by about 2.6% and 5.0% compared with those of the IBFO-LSTM and IBFO-RNN in 30-min prediction and by 4.1% and 6.6% in 15-min ahead forecast, respectively. The evaluation outcomes of our proposed CEEMDAN-IBFO-GRU model have high accuracy and adaptability and can effectively provide early intervention control of the occurrence of hyperglycemic complications.

Printed circuit board (PCB) defect detection is essential for ensuring manufacturing quality and product reliability in electronics production. PCB designs often exhibit inherent symmetry in circuit layouts and periodic patterns, which defects disrupt by introducing asymmetries detectable in spatial and frequency domains. Contemporary methods predominantly emphasize either global context modeling or local detail preservation, yet fail to synergistically leverage their complementary characteristics. Existing approaches predominantly constrain operations to the spatial domain, neglecting complementary frequency-domain representations that encode periodic structures and spectral anomalies. We present PCB-FS, a unified frequency–spatial learning framework that augments YOLOv8 with three synergistic components: (i) Dual-Domain Convolution (DD-Conv) for adaptive spatial frequency feature extraction, (ii) Global–Local Axial Attention (GLA-Attention) for context-aware defect modeling, and (iii) Cross-Stage Partial Dynamic Shifted Large Kernel Convolution (C2f-DSLKConv) for efficient large-receptive-field fusion. Our DD-Conv module adaptively fuses spatial and frequency-domain representations, while the GLA-Attention mechanism enhances global context modeling without sacrificing local detail preservation. The C2f-DSLKConv module further enables efficient large-receptive-field spatial modeling and hierarchical feature aggregation. Our method exploits symmetry-breaking artifacts in PCB layouts to detect structural anomalies, achieving superior defect localization accuracy. A comprehensive evaluation on the enhanced PCB surface defect dataset (DsPCBSD+) demonstrates that PCB-FS achieves 86.2% mAP@50 and 52.4% mAP@50-95, establishing new state-of-the-art performance with significant margins over existing methods. Integrating frequency and spatial domain features significantly enhances PCB defect detection reliability and efficiency in practical applications.

With the advent of the mass tourism era, innovative tourism services have attracted increasing attention from numerous tourism consumers. The wide application of big data and cloud computing technology makes it possible to build an intelligent tourism service platform to improve the experience of tourists. This research proposes a series of building technical proposals for a smart tourism service platform based on big data technology. The platform mainly focuses on massive data-based tourism administration, such as emotion analysis, tourist route recommendation, tourist portrait, passenger flow trend prediction, using big data analysis, mining, and processing. It also produces a series of relevant services and constructs special applications with various functions to realize tourism data acquisitions, research, and manipulations. Finally, the intelligent tourism platform's massive data analysis and processing results provide feasible and optimized decision-making references for the government, enterprises, tourists, and other stakeholders. It puts forward countermeasures for the innovative tourism service platform's implementation and application to promote the sustainable development of creative tourism.