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Underwater spectral imaging combined with neural networks provides a practical means for intelligent detection and maintenance of offshore engineering. As an underwater detection technology, the data obtained from underwater hyperspectral imaging can be used for both qualitative analysis and quantitative detection of underwater targets, which is an efficient means of underwater target detection in offshore engineering. In this paper, An underwater hyperspectral imaging system based on PGP spectral structure line scanning imaging has been designed and developed for shallow water detection. It has a spectral range of 400–1000 nm, over 200 channels, a spectral resolution of 2.5 nm, a field of view angle of 35.2°, and a focal length of 25 mm. According to the design results, the system was assembled, calibrated and tested. Aiming at solving the shortcomings of the current underwater hyperspectral system, such as the inability of real-time observation of the scanned image, large data volume and complicated processing, the upper computer software was designed and developed to realize the functions of controlling and adjusting the parameter, real-time displaying the scanned image, reconstructing the image and outputting the data in ENVI standard format to the underwater hyperspectral system. The prototype of the system was used to conduct coral detection experiments in Weizhou Island, and the neural network model was used to classify the experimental data, and the overall classification accuracy was above 98%. The experimental results show that the whole system has good imaging quality and good underwater detection capability, and the developed host computer software greatly reduces the workload of raw data processing, improves the experimental detection efficiency, and provides a feasible technical solution for underwater intelligent detection and analysis. • A line-scanning underwater hyperspectral imaging system has been developed based on the PGP spectroscopic structure. • Development of software for the upper computer, which significantly improves the efficiency of underwater detection. • Combining neural network models to achieve accurate detection and classification of underwater targets. [ABSTRACT FROM AUTHOR]

• The DRL employs the Using Graph Convolutional Network (GCN) to extract the network architecture data, the node vector is created by fusing it with traffic-related features. • The deep Q-network uses this node vector to anticipate the best rewards and determine which FDNN task is carried out at the edge nodes. • In light of this choice, the FDNN classifier is deployed to categorize the medical information. • In the end, the simulation findings show that the DRL-LOA significantly enhances optimization, showcasing a 35.7% reduction in power usage and a of 27.9% decrease in latency on 20 edge nodes in cloud-edge systems. • These findings underscore DRL-LOA's efficacy in optimizing medical data categorization and addressing load-balancing challenges in cooperative cloud-edge networks. Addressing the limitations of traffic-centric approaches in cooperative cloud-edge networks, this paper introduces an adaptive deployment strategy for FDNN using LOA. However, the oversight of network structure hampers load-balancing efficiency in medical data categorization. To overcome this, a novel DRL-LOA approach integrates Deep Reinforcement Learning (DRL) with LOA, considering both network structure and traffic-related data. This DRL-LOA considers both network structure and traffic-related data for optimization tasks. The DRL employs the Using Graph Convolutional Network (GCN) to extract the network architecture data, the node vector is created by fusing it with traffic-related features. The deep Q-network uses this node vector to anticipate the best rewards and determine which FDNN task is carried out at the edge nodes. In light of this choice, the FDNN classifier is deployed to categorize the medical information. In the end, the simulation findings show that the DRL-LOA significantly enhances optimization, showcasing a 35.7% reduction in power usage and a of 27.9% % decrease in latency on 20 edge nodes in cloud-edge systems. These findings underscore DRL-LOA's efficacy in optimizing medical data categorization and addressing load-balancing challenges in cooperative cloud-edge networks. [ABSTRACT FROM AUTHOR]

• Low-cost measurement platform for particulate matter monitoring developed. • Artificial intelligence field calibration procedure of low-cost sensor proposed. • Combination of affine scaling and optimized neural network surrogate employed. • Correlation coefficient improved to 0.86 for particulate matter of size 1 μm. • Root-mean-squared error of 3 μm/m3 (1 μm particles) and 4.9 μm/m3 (10 μm particles) Due to detrimental effects of atmospheric particulate matter (PM), its accurate monitoring is of paramount importance, especially in densely populated urban areas. However, precise measurement of PM levels requires expensive and sophisticated equipment. Although low-cost alternatives are gaining popularity, their reliability is questionable, attributed to sensitivity to environmental conditions, inherent instability, and manufacturing imperfections. The objectives of this paper include (i) introduction of an innovative approach to field calibration for low-cost PM sensors using artificial intelligence methods, (ii) implementation of the calibration procedure involving optimized artificial neural network (ANN) and combined multiplicative and additive correction of the low-cost sensor readings, (iii) demonstrating the efficacy of the presented technique using a custom-designed portable PM monitoring platform and reference data acquired from public measurement stations. The results obtained through comprehensive experiments conducted using the aforementioned low-cost sensor and reference data demonstrate remarkable accuracy for the calibrated sensor, with correlation coefficients of 0.86 for PM 1 and PM 2.5 , and 0.76 PM 10 (particles categorized as having diameter equal to or less than 1 μm, 2.5 μm, and 10 μm, respectively), along with low RMSE values of only 3.1, 4.1, and 4.9 µg/m3. [ABSTRACT FROM AUTHOR]