Novel Crow Swarm Optimization Algorithm and Selection Approach for Optimal Deep Learning COVID-19 Diagnostic Model.

Due to the COVID-19 pandemic, computerized COVID-19 diagnosis studies are proliferating. The diversity of COVID-19 models raises the questions of which COVID-19 diagnostic model should be selected and which decision-makers of healthcare organizations should consider performance criteria. Because of this, a selection scheme is necessary to address all the above issues.

Automated waste-sorting and recycling classification using artificial neural network and features fusion: a digital-enabled circular economy vision for smart cities.

Waste generation in smart cities is a critical issue, and the interim steps towards its management were not that effective. But at present, the challenge of meeting recycling requirements due to the practical difficulty involved in waste sorting decelerates smart city CE vision. In this paper, a digital model that automatically sorts the generated waste and classifies the type of waste as per the recycling requirements based on an artificial neural network (ANN) and features fusion techniques is proposed.

Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models.

Coronavirus disease 2019 (COVID-19) is a novel disease that affects healthcare on a global scale and cannot be ignored because of its high fatality rate. Computed tomography (CT) images are presently being employed to assist doctors in detecting COVID-19 in its early stages. In several scenarios, a combination of epidemiological criteria (contact during the incubation period), the existence of clinical symptoms, laboratory tests (nucleic acid amplification tests), and clinical imaging-based tests are used to diagnose COVID-19.

Characterization of Alginate-Gelatin-Cholesteryl Ester Liquid Crystals Bioinks for Extrusion Bioprinting of Tissue Engineering Scaffolds.

Tissue engineering (TE) is an innovative approach to tackling many diseases and body parts that need to be replaced by developing artificial tissues and organs. Bioinks play an important role in the success of various TE applications. A bioink refers to a combination of a living cell, biomaterials, and bioactive molecules deposited in a layer-by-layer form to fabricate tissue-like structures.

COVID-19 anomaly detection and classification method based on supervised machine learning of chest X-ray images.

The term COVID-19 is an abbreviation of Coronavirus 2019, which is considered a global pandemic that threatens the lives of millions of people. Early detection of the disease offers ample opportunity of recovery and prevention of spreading. This paper proposes a method for classification and early detection of COVID-19 through image processing using X-ray images.

COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images.

The outbreaks of Coronavirus (COVID-19) epidemic have increased the pressure on healthcare and medical systems worldwide. The timely diagnosis of infected patients is a critical step to limit the spread of the COVID-19 epidemic. The chest radiography imaging has shown to be an effective screening technique in diagnosing the COVID-19 epidemic.

MAFC: Multi-Agent Fog Computing Model for Healthcare Critical Tasks Management.

In healthcare applications, numerous sensors and devices produce massive amounts of data which are the focus of critical tasks. Their management at the edge of the network can be done by Fog computing implementation. However, Fog Nodes suffer from lake of resources That could limit the time needed for final outcome/analytics.

A fuzzy logic control in adjustable autonomy of a multi-agent system for an automated elderly movement monitoring application.

Autonomous agents are being widely used in many systems, such as ambient assisted-living systems, to perform tasks on behalf of humans. However, these systems usually operate in complex environments that entail uncertain, highly dynamic, or irregular workload. In such environments, autonomous agents tend to make decisions that lead to undesirable outcomes.

Latex agglutination using the periplasmic proteins antigen of Brucella melitensis is a successful, rapid, and specific serodiagnostic test for ovine brucellosis.

Brucellosis, especially caused by Brucella melitensis, is considered the most-widespread zoonosis in the world, particularly in developing countries. This study was planned to develop an accurate test for diagnosis of ovine brucellosis using a specific hot saline extracted soluble Brucella melitensis periplasmic proteins (SBPPs). The efficacy of the latex agglutination test (LAT) using SBPPs compared to the Rose Bengal test (RBT), buffered plate agglutination test (BPAT), serum agglutination test (SAT), and an indirect enzyme-linked immunosorbent assay (i-ELISA) was evaluated in the field diagnosis of ovine brucellosis.

Protective effects of camel milk against pathogenicity induced by Escherichia coli and Staphylococcus aureus in Wistar rats.

The aim of the present study was to investigate the protective effects of camel milk on hepatic pathogenicity induced by experimental infection with Escherichia (E. coli) and Staphylococcus aureus (S. aureus) in Wistar rats.