Efficient clustering and routing is a main challenge in a wireless sensor network (WSN). To achieve better quality-of-service (QoS) performance, this work introduces k-medoids with improved artificial-bee-colony (K-IABC)-based energy-efficient clustering and the cross-layer-based Harris-hawks-optimization-algorithm (CL-HHO) routing protocol for WSN. To overcome the power-asymmetry problem in wireless sensor networks, a cross-layer-based optimal-routing solution is proposed. The goal of cross-layer routing algorithms is to decrease network-transmission delay and power consumption. This algorithm which was used to evaluate and select the effective path route and data transfer was implemented using MATLAB, and the results were compared to some existing techniques. The proposed CL-HHO performs well in packet-loss ratio (PLR), throughput, end-to-end delay (E2E), jitter, network lifetime (NLT) and buffer occupancy. These results are then validated by comparing them to traditional routing strategies such as hierarchical energy-efficient data gathering (HEED), energy-efficient-clustering routing protocol (EECRP), Grey wolf optimization (GWO), and cross-layer-based Ant-Lion optimization (CL-ALO). Compared to the HEED, EECRP, GWO, and CL-ALO algorithms, the proposed CL-HHO outperforms them.

An extremely broadband circularly polarized (CP) antenna is presented. The proposed antenna consists of a crossed bowtie dipole loaded with four unequal parasitic cross-slots, four parasitic bowtie patches, and four parasitic rectangular strips. The cross-slots can generate a CP mode due to the sequential phases of the crossed bowtie dipole. The parasitic bowtie patches are gap-coupled with two arms of the crossed bowtie dipole, and they are sequentially rotated along diagonals to generate an additional CP mode. The parasitic strips also generate one CP mode due to the sequentially rotated configuration parallel to the crossed bowtie dipole. By properly combing with two CP modes of the crossed bowtie dipole, broadband CP operation is achieved. The experimental and simulated results are in agreement, and the measured results show that the proposed antenna exhibits a very wide impedance bandwidth of 93.1% (2.21-6.06 GHz) and an excellent axial ratio (AR) bandwidth of 90.9% (2.2-6.4 GHz). In addition, the proposed antenna produces a right-hand circular polarization (RHCP) with a peak gain of 8.6 dBi.

Recently, various methods have been developed to identify COVID-19 cases, such as PCR testing and non-contact procedures such as chest X-rays and computed tomography (CT) scans. Deep learning (DL) and artificial intelligence (AI) are critical tools for early and accurate detection of COVID-19. This research explores the different DL techniques for identifying COVID-19 and pneumonia on medical CT and radiography images using ResNet152, VGG16, ResNet50, and DenseNet121. The ResNet framework uses CT scan images with accuracy and precision. This research automates optimum model architecture and training parameters. Transfer learning approaches are also employed to solve content gaps and shorten training duration. An upgraded VGG16 deep transfer learning architecture is applied to perform multi-class classification for X-ray imaging tasks. Enhanced VGG16 has been proven to recognize three types of radiographic images with 99% accuracy, typical for COVID-19 and pneumonia. The validity and performance metrics of the proposed model were validated using publicly available X-ray and CT scan data sets. The suggested model outperforms competing approaches in diagnosing COVID-19 and pneumonia. The primary outcomes of this research result in an average F-score (95%, 97%). In the event of healthy viral infections, this research is more efficient than existing methodologies for coronavirus detection. The created model is appropriate for recognition and classification pre-training. The suggested model outperforms traditional strategies for multi-class categorization of various illnesses.

To support semantic inter‐operability between the biomedical information systems, it is necessary to determine the correspondences between the heterogeneous biomedical concepts, which is commonly known as biomedical ontology matching. Biomedical concepts are usually complex and ambiguous, which makes matching biomedical ontologies a challenge. Since none of the similarity measures can distinguish the heterogeneous biomedical concepts in any context independently, usually several similarity measures are applied together to determine the biomedical concepts mappings. However, the ignorance of the effects brought about by different biomedical concept mapping's preference on the similarity measures significantly reduces the alignment's quality. In this study, a non‐dominated sorting genetic algorithm (NSGA)‐III‐based biomedical ontology matching technique is proposed to effectively match the biomedical ontologies, which first utilises an ontology partitioning technique to transform the large‐scale biomedical ontology matching problem into several ontology segment‐matching problems, and then uses NSGA‐III to determine the optimal alignment without tuning the aggregating weights. The experiment is conducted on the anatomy track and large biomedic ontologies track which are provided by the Ontology Alignment Evaluation Initiative (OAEI), and the comparisons with OAEI's participants show the effectiveness of the authors' approach.