Mobile edge computing (MEC) becomes popular as it offers cloud services and functionalities to the edge devices, to enhance the quality of service (QoS) of end-users by offloading their computationally intensive tasks. At the same time, the rise in the number of internet of things (IoT) objectives poses considerable cybersecurity issues owing to the latest rise in the existence of attacks. Presently, the development of deep learning and hardware technologies offers a way to detect the present traffic condition, data offloading, and cyber-attacks in edge networks. The utilization of DL models finds helpful in several domains in which the MEC provides the decisive beneficiary of the approach for traffic prediction and attack detection since a large quantity of data generated by IoT devices enables deep models to learn better than shallow approaches. In this view, this paper presents a new DL based traffic prediction with a data offloading mechanism with cyber-attack detection (DLTPDO-CD) technique. The proposed model involves three major processes traffic prediction, data offloading, and attack detection. Initially, bidirectional long short term memory (BiLSTM) based traffic prediction to enable the proficient data offloading process. Then, the adaptive sampling cross entropy (ASCE) technique is executed to maximize the network throughput by making decisions related to offloading users to the WiFi system. Finally, a deep belief network (DBN) optimized by a barnacles mating optimizer (BMO) algorithm called BMO-DBN is applied as a detection tool for cyberattacks in MEC. Extensive simulation is carried out to ensure the proficient performance of the DLTPDO-CD model. The experimental outcome stated the superiority of the presented model over the compared methods under different dimensions.

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was first identified in December 2019 at Wuhan, China. At present, the outbreak of COVID-19 pandemic has resulted in severe consequences on both economic and social infrastructures of the developed and developing countries. Several studies have been conducted and ongoing still to design efficient models for diagnosis and treatment of COVID-19 patients. The traditional diagnostic models that use reverse transcription-polymerase chain reaction (rt-qPCR) is a costly and time-consuming process. So, automated COVID-19 diagnosis using Deep Learning (DL) models becomes essential. The primary intention of this study is to design an effective model for diagnosis and classification of COVID-19. This research work introduces an automated COVID-19 diagnosis process using Convolutional Neural Network (CNN) with a fusion-based feature extraction model, called FM-CNN. FM-CNN model has three major phases namely, pre-processing, feature extraction, and classification. Initially, Wiener Filtering (WF)-based preprocessing is employed to discard the noise that exists in input chest X-Ray (CXR) images. Then, the pre-processed images undergo fusion-based feature extraction model which is a combination of Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRM), and Local Binary Patterns (LBP). In order to determine the optimal subset of features, Particle Swarm Optimization (PSO) algorithm is employed. At last, CNN is deployed as a classifier to identify the existence of binary and multiple classes of CXR images. In order to validate the proficiency of the proposed FM-CNN model in terms of its diagnostic performance, extension experimentation was carried out upon CXR dataset. As per the results attained from simulation, FM-CNN model classified multiple classes with the maximum sensitivity of 97.22%, specificity of 98.29%, accuracy of 98.06%, and F-measure of 97.93%.

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Controlling the phase and crystal structure of nanomaterials is a challenging mission in a wet chemical method and has remarkable importance to the materials properties. Herein, we demonstrate a facile sol−gel method to synthesize Bi 2 O 3 , Fe 2 O 3 , BiFeO 3 , Bi 36 Fe 2 O 57 , secondary phase, and mixed phase of BiFeO 3 (Bi 25 FeO 40 and Bi 2 Fe 4 O 9 ) by tailoring the parameters such as molar concentration, calcination temperature, and duration. Further, all the electrode materials were demonstrated for supercapacitor (SC) application. The pure-phase BiFeO 3 nanoparticles show a highest specific capacitance of 253 F/g at a current density of 1 A/g compared to all other electrodes under a 3 M KOH electrolyte. The higher specific capacitance of BiFeO 3 nanoparticles is ascribed to their higher surface area, pure ABO 3 structure, and lower charge-transfer resistance. Moreover, the BiFeO 3 nanoparticles were also tested under a neutral electrolyte (1 M Na 2 SO 4 ) and found to have 3.7 times lower specific capacitance compared to the alkaline electrolyte (3 M KOH). The electrokinetic study of the as-synthesized active electrodes illustrates the maximum capacitive involvement to store the overall charge. The BiFeO 3 nanoparticles display outstanding stability with a retention rate of 99.02% after 1100 consecutive galvanostatic charge−discharge cycles at various current densities. Moreover, a solid-state symmetric SC device (SSD) was fabricated using BiFeO 3 nanoparticles. The device delivered a maximum energy density of 17.01 W h/kg at a current density of 1 A/g and a power density of 7.2 kW/kg at a current density of 10 A/g. The BiFeO 3 SSD showed an excellent capacitive retention rate of 88% after 5000 cycles, suggesting that it could be a promising electrode material for practical application in energy storage devices.

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