Most of the existing face photo de-meshing methods have accomplished promising results; there are certain quality problems with these methods like the inpainted regions would appear blurry and unpleasant boundaries becoming visible. Such artifacts cause generated face photos unreal. Therefore, we propose an effective image-to-image translation framework called Face De-meshing Using Generative Adversarial Networks (De-mesh GANs). The De-mesh GANs is a two-stage model: (i) binary mask generating module, is a three convolution layers-based encoder–decoder network architecture that automatically generates a binary mask for the meshed region, and (ii) face photo de-meshing module, is a GANs-based network that eliminates the mesh mask and synthesizes the meshed area. An arrangement of careful losses (reconstruction loss, adversarial loss, and perceptual loss) is used to reassure the better quality of the de-mesh face photos. To facilitate the training of the proposed model, we have designed a dataset of clean/corrupted photo pairs using the CelebA dataset. Qualitative and quantitative evaluations of the De-mesh GANs on real-world corrupted face photo images show better performance than the previously proposed face photo de-meshing models. Furthermore, we also offer the ablation study for performance assessment of the additional network i.e., perceptual network.

The cloud-fog computing paradigm is a novel hybrid computing model that delivers computational services to Fog nodes situated near data sources. This paradigm features a volatile and dynamic network topology, comprising heterogeneous IoT devices with varying computational capabilities, alongside a large number of diverse end-user requests. These complexities present significant challenges for researchers in establishing a robust, energy-efficient, and reliable communication environment. Efficient and optimal task scheduling is among these challenges, as it involves finding appropriate computing resources for processing tasks. Assigning tasks to fog nodes reduces delay but increases energy consumption, while routing tasks to cloud servers conserves energy but prolongs transmission delay. Therefore, it is essential to develop an optimal task scheduling algorithm for a reliable, delay-efficient, and energy-efficient communication environment. To address this, we propose a Multi-objective Harris Hawks Optimization (HHO)-based task scheduling algorithm (MoHHOTS) for cloud-fog computing networks, aiming to optimize task scheduling with the objectives of minimizing delay and energy consumption. MoHHOTS is implemented in MATLAB and evaluated against state-of-the-art benchmark algorithms, including MOGWO and the cloud-fog cooperation algorithm. Leveraging the high convergence and stochastic operators of the HHO algorithm, alongside a balanced approach to iteration between diversification and intensification, the proposed algorithm provides a set of trade-off solutions via the Pareto-optimal Front. Simulation results demonstrate the efficacy of the proposed solution, achieving improvements of up to 25% over a similar scheduling algorithm in terms of optimizing transmission delay and energy consumption.

The technology of visual servoing, with the digital twin as its driving force, holds great promise and advantages for enhancing the flexibility and efficiency of smart manufacturing assembly and dispensing applications. The effective deployment of visual servoing is contingent upon the robust and accurate estimation of the vision‐motion correlation. Network‐based methodologies are frequently employed in visual servoing to approximate the mapping between 2D image feature errors and 3D velocities, offering promising avenues for improving the accuracy and reliability of visual servoing systems. These developments have the potential to fully leverage the capabilities of digital twin technology in the realm of smart manufacturing. However, obtaining sufficient training data for these methods is challenging, and thus improving model generalization to reduce data requirements is imperative. To address this issue, we offer a learning‐based approach for estimating Jacobian matrices of visual servoing that organically combines an extreme learning machine (ELM) and a differential evolutionary algorithm (DE). In the first stage, the pseudoinverse of the image Jacobian matrix is approximated using the ELM, which solves the problems associated with traditional visual servoing and is resistant to outside influences such as image noise and mistakes in camera calibration. In the second stage, differential evolution is utilized to select input weights and hidden layer bias and to determine ELM’s output weights. Experimental results conducted on a digital twin operating platform for 4‐DOF robot with an eye‐in‐hand configuration demonstrate better performance than classical visual servoing and traditional ELM‐based visual servoing in various cases.

In recent years, ozone (O3) has gradually become the primary pollutant plaguing urban air quality. Accurate and efficient ozone prediction is of great significance to the prevention and control of ozone pollution. The air quality monitoring network provides multisource pollutant concentration monitoring data for ozone prediction, but ozone prediction based on multisource monitoring data still faces the challenges of each station’s series of data. Aiming at the problems of low prediction accuracy and low computational efficiency in traditional atmospheric ozone concentration prediction, ozone concentration prediction using dual series decomposition was proposed by variational mode decomposition (VMD), ensemble empirical mode decomposition (EEMD), and long short‐term memory (LSTM). First, the historical data series of Nanjing air quality monitoring stations is decomposed by VMD, and then the EEMD algorithm is applied to the residual of VMD to obtain several characteristic intrinsic mode function (IMF) components; each characteristic IMF component is trained by LSTM to obtain the prediction result of each component, and then the final result can be obtained by linear superposition. The proposed method achieved the best results with R2 = 99%, MSE = 5.38, MAE = 4.54, and MAPE = 3.12. Because LSTM has strong adaptive learning ability and good memory function, it has the learning advantage of long‐term memory for long‐term data, and the prediction results are more accurate. According to the data, the proposed method is superior to the baseline models in terms of statistical metrics. As a result, the proposed hybrid method can serve as a reliable model for ozone forecasting.