Synergic Deep Learning For Smart Health Diagnosis Of Covid-19 For Connected Living And Smart Cities

COVID-19 pandemic has led to a significant loss of global deaths, economical status, and so on. To prevent and control COVID-19, a range of smart, complex, spatially heterogeneous, control solutions, and strategies have been conducted. Earlier classification of 2019 novel coronavirus disease (COVID-19) is needed to cure and control the disease. It results in a requirement of secondary diagnosis models, since no precise automated toolkits exist. The latest finding attained using radiological imaging techniques highlighted that the images hold noticeable details regarding the COVID-19 virus. The application of recent artificial intelligence (AI) and deep learning (DL) approaches integrated to radiological images finds useful to accurately detect the disease. This article introduces a new synergic deep learning (SDL)-based smart health diagnosis of COVID-19 using Chest X-Ray Images. The SDL makes use of dual deep convolutional neural networks (DCNNs) and involves a mutual learning process from one another. Particularly, the representation of images learned by both DCNNs is provided as the input of a synergic network, which has a fully connected structure and predicts whether the pair of input images come under the identical class. Besides, the proposed SDL model involves a fuzzy bilateral filtering (FBF) model to pre-process the input image. The integration of FBL and SDL resulted in the effective classification of COVID-19. To investigate the classifier outcome of the SDL model, a detailed set of simulations takes place and ensures the effective performance of the FBF-SDL model over the compared methods

Photophysics of ruthenium(II) complexes carrying amino acids in the ligand 2,2'-bipyridine and intramolecular electron transfer from methionine to photogenerated Ru(III)

New ruthenium(II) complexes carrying methionine and phenylalanine in the bipyridine ligand, [Ru(bpy)2(4-Me-4'-(CONH-l-methionine methyl ester)-2,2'-bipyridine)](PF6)2 (IV) and [Ru(bpy)2(4-Me-4'-(CONH-l-phenylalanine ethyl ester)-2,2'-bpy)](PF6)2 (V) have been synthesized and characterized and their photophysical properties studied. Flash photolysis measurements of complex IV, in the presence of an electron acceptor, methyl viologen (MV2+) show that an intermolecular electron transfer from the excited state of Ru(II) in complex IV, to MV2+ takes place, forming Ru(III) and the methyl viologen cation radical, MV+. The formation of MV+ in this system is confirmed using time-resolved transient absorption spectroscopy. This intermolecular electron transfer is followed by intramolecular electron transfer from the thioether moiety (methionine) to the photogenerated Ru(III), regenerating Ru(II). Graphical abstract New ruthenium(II) complexes carrying methionine and phenylalanine in the bipyridine ligand, [Ru(bpy)2(4-Me-4'-(CONH-l-methionine methyl ester)-2,2'-bipyridine)](PF6)2 (IV) and [Ru(bpy)2(4-Me-4'-(CONH-l-phenylalanine ethyl ester)-2,2'-bpy)](PF6)2 (V) have been synthesized and characterized and their photophysical properties studied. Flash photolysis measurements of complex IV, in the presence of an electron acceptor, methylviologen (MV2+) show that an intermolecular electron transfer from the excited state of Ru(II) in complex IV, to MV2+ takes place, forming Ru(III) and the methylviologen cation radical, MV+. The formation of MV+ in this system is confirmed using time-resolved transient absorption spectroscopy. This intermolecular electron transfer is followed by intramolecular electron transfer from the thioether moiety (methionine) to the photogenerated Ru(III), regenerating Ru(II)

Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images

Funding Information: Mohammad Shorfuzzaman sincerely acknowledge the financial support of Taif University Researchers Supporting Project Number (TURSP-2020/79), Taif University, Taif, Saudi Arabia. Publisher Copyright: © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.In recent times, COVID-19 infection gets increased exponentially with the existence of a restricted number of rapid testing kits. Several studies have reported the COVID-19 diagnosis model from chest X-ray images. But the diagnosis of COVID-19 patients from chest X-ray images is a tedious process as the bilateral modifications are considered an ill-posed problem. This paper presents a new metaheuristic-based fusion model for COVID-19 diagnosis using chest X-ray images. The proposed model comprises different preprocessing, feature extraction, and classification processes. Initially, the Weiner filtering (WF) technique is used for the preprocessing of images. Then, the fusion-based feature extraction process takes place by the incorporation of gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRM), and local binary patterns (LBP). Afterward, the salp swarm algorithm (SSA) selected the optimal feature subset. Finally, an artificial neural network (ANN) is applied as a classification process to classify infected and healthy patients. The proposed model's performance has been assessed using the Chest X-ray image dataset, and the results are examined under diverse aspects. The obtained results confirmed the presented model's superior performance over the state of art methods.Peer reviewe

Toward Blockchain-Enabled Privacy-Preserving Data Transmission in Cluster-Based Vehicular Networks

In recent times, vehicular ad hoc networks (VANET) have become a core part of intelligent transportation systems (ITSs), which aim to achieve continual Internet connectivity among vehicles on the road. The VANET has been used to improve driving safety and construct an ITS in modern cities. However, owing to the wireless characteristics, the message transmitted through the network can be observed, altered, or forged. Since driving safety is a major part of VANET, the security and privacy of these messages must be preserved. Therefore, this paper introduces an efficient privacy-preserving data transmission architecture that makes use of blockchain technology in cluster-based VANET. The cluster-based VANET architecture is used to achieve load balancing and minimize overhead in the network, where the clustering process is performed using the rainfall optimization algorithm (ROA). The ROA-based clustering with blockchain-based data transmission, called a ROAC-B technique, initially clusters the vehicles, and communication takes place via blockchain technology. A sequence of experiments was conducted to ensure the superiority of the ROAC-B technique, and several aspects of the results were considered. The simulation outcome showed that the ROAC-B technique is superior to other techniques in terms of packet delivery ratio (PDR), end to end (ETE) delay, throughput, and cluster size

An optimal cascaded recurrent neural network for intelligent COVID-19 detection using Chest X-ray images

Funding Information: The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number 959. Publisher Copyright: © 2021 Elsevier B.V.In recent times, COVID-19, has a great impact on the healthcare sector and results in a wide range of respiratory illnesses. It is a type of Ribonucleic acid (RNA) virus, which affects humans as well as animals. Though several artificial intelligence-based COVID-19 diagnosis models have been presented in the literature, most of the works have not focused on the hyperparameter tuning process. Therefore, this paper proposes an intelligent COVID-19 diagnosis model using a barnacle mating optimization (BMO) algorithm with a cascaded recurrent neural network (CRNN) model, named BMO-CRNN. The proposed BMO-CRNN model aims to detect and classify the existence of COVID-19 from Chest X-ray images. Initially, pre-processing is applied to enhance the quality of the image. Next, the CRNN model is used for feature extraction, followed by hyperparameter tuning of CRNN via the BMO algorithm to improve the classification performance. The BMO algorithm determines the optimal values of the CRNN hyperparameters namely learning rate, batch size, activation function, and epoch count. The application of CRNN and hyperparameter tuning using the BMO algorithm shows the novelty of this work. A comprehensive simulation analysis is carried out to ensure the better performance of the BMO-CRNN model, and the experimental outcome is investigated using several performance metrics. The simulation results portrayed that the BMO-CRNN model has showcased optimal performance with an average sensitivity of 97.01%, specificity of 98.15%, accuracy of 97.31%, and F-measure of 97.73% compared to state-of-the-art methods.Peer reviewe