The Internet of things (IoT) enables a diverse set of applications such as distribution automation, smart cities, wireless sensor networks, and advanced metering infrastructure (AMI). In smart grids (SGs), quality of service (QoS) and AMI traffic management need to be considered in the design of efficient AMI architectures. In this article, we propose a QoS-aware machine-learning-based framework for AMI applications in smart grids. Our proposed framework comprises a three-tier hierarchical architecture for AMI applications, a machine-learning-based hierarchical clustering approach, and a priority-based scheduling technique to ensure QoS in AMI applications in smart grids. We introduce a three-tier hierarchical architecture for AMI applications in smart grids to take advantage of IoT communication technologies and the cloud infrastructure. In this architecture, smart meters are deployed over a georeferenced area where the control center has remote access over the Internet to these network devices. More specifically, these devices can be digitally controlled and monitored using simple web interfaces such as REST APIs. We modify the existing K-means algorithm to construct a hierarchical clustering topology that employs Wi-SUN technology for bi-directional communication between smart meters and data concentrators. Further, we develop a queuing model in which different priorities are assigned to each item of the critical and normal AMI traffic based on its latency and packet size. The critical AMI traffic is scheduled first using priority-based scheduling while the normal traffic is scheduled with a first-in–first-out scheduling scheme to ensure the QoS requirements of both traffic classes in the smart grid network. The numerical results demonstrate that the target coverage and connectivity requirements of all smart meters are fulfilled with the least number of data concentrators in the design. Additionally, the numerical results show that the architectural cost is reduced, and the bottleneck problem of the data concentrator is eliminated. Furthermore, the performance of the proposed framework is evaluated and validated on the CloudSim simulator. The simulation results of our proposed framework show efficient performance in terms of CPU utilization compared to a traditional framework that uses single-hop communication from smart meters to data concentrators with a first-in–first-out scheduling scheme.

Pixel-level analysis of blood images plays a pivotal role in diagnosing blood-related diseases, especially Anaemia. These analyses mainly rely on an accurate diagnosis of morphological deformities like shape, size, and precise pixel counting. In traditional segmentation approaches, instance or object-based approaches have been adopted that are not feasible for pixel-level analysis. The convolutional neural network (CNN) model required a large dataset with detailed pixel-level information for the semantic segmentation of red blood cells in the deep learning domain. In current research work, we address these problems by proposing a multi-level deep convolutional encoder-decoder network along with two state-of-the-art healthy and Anaemic-RBC datasets. The proposed multi-level CNN model preserved pixel-level semantic information extracted in one layer and then passed to the next layer to choose relevant features. This phenomenon helps to precise pixel-level counting of healthy and anaemic-RBC elements along with morphological analysis. For experimental purposes, we proposed two state-of-the-art RBC datasets, i.e., Healthy-RBCs and Anaemic-RBCs dataset. Each dataset contains 1000 images, ground truth masks, relevant, complete blood count (CBC), and morphology reports for performance evaluation. The proposed model results were evaluated using crossmatch analysis with ground truth mask by finding IoU, individual training, validation, testing accuracies, and global accuracies using a 05-fold training procedure. This model got training, validation, and testing accuracies as 0.9856, 0.9760, and 0.9720 on the Healthy-RBC dataset and 0.9736, 0.9696, and 0.9591 on an Anaemic-RBC dataset. The IoU and BFScore of the proposed model were 0.9311, 0.9138, and 0.9032, 0.8978 on healthy and anaemic datasets, respectively.

Rapid development in sketch-to-image translation methods boosts the investigation procedure in law enforcement agencies. But, the large modality gap between manually generated sketches makes this task challenging. Generative adversarial network (GAN) and encoder-decoder approach are usually incorporated to accomplish sketch-to-image generation with promising results. This paper targets the sketch-to-image translation with heterogeneous face angles and lighting effects using a multi-level conditional generative adversarial network. The proposed multi-level cGAN work in four different phases. Three independent cGANs’ networks are incorporated separately into each stage, followed by a CNN classifier. The Adam stochastic gradient descent mechanism was used for training with a learning rate of 0.0002 and momentum estimates β1 and β2 as 0.5 and 0.999, respectively. The multi-level 3D-convolutional architecture help to preserve spatial facial attributes and pixel-level details. The 3D convolution and deconvolution guide the G1 , G2 and G3 to use additional features and attributes for encoding and decoding. This helps to preserve the direction, postures of targeted image attributes and special relationships among the whole image’s features. The proposed framework process the 3D-Convolution and 3D-Deconvolution using vectorization. This process takes the same time as 2D convolution but extracts more features and facial attributes. We used pre-trained ResNet-50, ResNet-101, and Mobile-Net to classify generated high-resolution images from sketches. We have also developed, and state-of-the-art Pakistani Politicians Face-sketch Dataset (PPFD) for experimental purposes. Result reveals that the proposed cGAN model’s framework outperforms with respect to Accuracy, Structural similarity index measure (SSIM), Signal to noise ratio (SNR), and Peak signal-to-noise ratio (PSNR).