FedRec:Trusted rank-based recommender scheme for service provisioning in federated cloud environment

The emergence of on-demand service provisioning by Federated Cloud Providers(FCPs)to Cloud Users(CU)has fuelled significant innovations in cloud provisioning models.Owing to the massive traffic,massive CU resource requests are sent to FCPs,and appropriate service recommendations are sent by FCPs.Currently,the FourthGeneration(4G)-Long Term Evolution(LTE)network faces bottlenecks that affect end-user throughput and latency.Moreover,the data is exchanged among heterogeneous stakeholders,and thus trust is a prime concern.To address these limitations,the paper proposes a Blockchain(BC)-leveraged rank-based recommender scheme,FedRec,to expedite secure and trusted Cloud Service Provisioning(CSP)to the CU through the FCP at the backdrop of base 5G communication service.The scheme operates in three phases.In the first phase,a BCintegrated request-response broker model is formulated between the CU,Cloud Brokers(BR),and the FCP,where a CU service request is forwarded through the BR to different FCPs.For service requests,Anything-as-aService(XaaS)is supported by 5G-enhanced Mobile Broadband(eMBB)service.In the next phase,a weighted matching recommender model is proposed at the FCP sites based on a novel Ranking-Based Recommender(RBR)model based on the CU requests.In the final phase,based on the matching recommendations between the CU and the FCP,Smart Contracts(SC)are executed,and resource provisioning data is stored in the Interplanetary File Systems(IPFS)that expedite the block validations.The proposed scheme FedRec is compared in terms of SC evaluation and formal verification.In simulation,FedRec achieves a reduction of 27.55%in chain storage and a transaction throughput of 43.5074 Mbps at 150 blocks.For the IPFS,we have achieved a bandwidth improvement of 17.91%.In the RBR models,the maximum obtained hit ratio is 0.9314 at 200 million CU requests,showing an improvement of 1.2%in average servicing latency over non-RBR models and a maximization trade-off of QoE index of 2.7688 at the flow request 1.088 and at granted service price of USD 1.559 million to FCP for provided services.The obtained results indicate the viability of the proposed scheme against traditional approaches.

Deep Learning-Based Robust Morphed Face Authentication Framework for Online Systems

The amalgamation of artificial intelligence(AI)with various areas has been in the picture for the past few years.AI has enhanced the functioning of several services,such as accomplishing better budgets,automating multiple tasks,and data-driven decision-making.Conducting hassle-free polling has been one of them.However,at the onset of the coronavirus in 2020,almost all worldly affairs occurred online,and many sectors switched to digital mode.This allows attackers to find security loopholes in digital systems and exploit them for their lucrative business.This paper proposes a three-layered deep learning(DL)-based authentication framework to develop a secure online polling system.It provides a novel way to overcome security breaches during the face identity(ID)recognition and verification process for online polling systems.This verification is done by training a pixel-2-pixel Pix2pix generative adversarial network(GAN)for face image reconstruction to remove facial objects present(if any).Furthermore,image-to-image matching is done by implementing the Siamese network and comparing the result of various metrics executed on feature embeddings to obtain the outcome,thus checking the electorate credentials.

Deep Learning-Based Model for Detection of Brinjal Weed in the Era of Precision Agriculture

The overgrowth of weeds growing along with the primary crop in the fields reduces crop production.Conventional solutions like hand weeding are labor-intensive,costly,and time-consuming;farmers have used herbicides.The application of herbicide is effective but causes environmental and health concerns.Hence,Precision Agriculture(PA)suggests the variable spraying of herbicides so that herbicide chemicals do not affect the primary plants.Motivated by the gap above,we proposed a Deep Learning(DL)based model for detecting Eggplant(Brinjal)weed in this paper.The key objective of this study is to detect plant and non-plant(weed)parts from crop images.With the help of object detection,the precise location of weeds from images can be achieved.The dataset is collected manually from a private farm in Gandhinagar,Gujarat,India.The combined approach of classification and object detection is applied in the proposed model.The Convolutional Neural Network(CNN)model is used to classify weed and non-weed images;further DL models are applied for object detection.We have compared DL models based on accuracy,memory usage,and Intersection over Union(IoU).ResNet-18,YOLOv3,CenterNet,and Faster RCNN are used in the proposed work.CenterNet outperforms all other models in terms of accuracy,i.e.,88%.Compared to other models,YOLOv3 is the least memory-intensive,utilizing 4.78 GB to evaluate the data.

GRADE: Deep learning and garlic routing-based secure data sharing framework for IIoT beyond 5G

The rise of automation with Machine-Type Communication(MTC)holds great potential in developing Industrial Internet of Things(IIoT)-based applications such as smart cities,Intelligent Transportation Systems(ITS),supply chains,and smart industries without any human intervention.However,MTC has to cope with significant security challenges due to heterogeneous data,public network connectivity,and inadequate security mechanism.To overcome the aforementioned issues,we have proposed a blockchain and garlic-routing-based secure data exchange framework,i.e.,GRADE,which alleviates the security constraints and maintains the stable connection in MTC.First,the Long-Short-Term Memory(LSTM)-based Nadam optimizer efficiently predicts the class label,i.e.,malicious and non-malicious,and forwards the non-malicious data requests of MTC to the Garlic Routing(GR)network.The GR network assigns a unique ElGamal encrypted session tag to each machine partaking in MTC.Then,an Advanced Encryption Standard(AES)is applied to encrypt the MTC data requests.Further,the InterPlanetary File System(IPFS)-based blockchain is employed to store the machine's session tags,which increases the scalability of the proposed GRADE framework.Additionally,the proposed framework has utilized the indispensable benefits of the 6G network to enhance the network performance of MTC.Lastly,the proposed GRADE framework is evaluated against different performance metrics such as scalability,packet loss,accuracy,and compromised rate of the MTC data request.The results show that the GRADE framework outperforms the baseline methods in terms of accuracy,i.e.,98.9%,compromised rate,i.e.,18.5%,scalability,i.e.,47.2%,and packet loss ratio,i.e.,24.3%.

Inflammation and fibrosis in chronic liver diseases including nonalcoholic fatty liver disease and hepatitis C

At present chronic liver disease(CLD),the third commonest cause of premature death in the United Kingdom is detected late,when interventions are ineffective,resulting in considerable morbidity and mortality.Injury to the liver,the largest solid organ in the body,leads to a cascade of inflammatory events.Chronic inflammation leads to the activation of hepatic stellate cells that undergo transdifferentiation to become myofibroblasts,the main extra-cellular matrix producing cells in the liver;over time increased extra-cellular matrix production results in the formation of liver fibrosis.Although fibrogenesis may be viewed as having evolved as a“wound healing”process that preserves tissue integrity,sustained chronic fibrosis can become pathogenic culminating in CLD,cirrhosis and its associated complications.As the reference standard for detecting liver fibrosis,liver biopsy,is invasive and has an associated morbidity,the diagnostic assessment of CLD by non-invasive testing is attractive.Accordingly,in this review the mechanisms by which liver inflammation and fibrosis develop in chronic liver diseases are explored to identify appropriate and meaningful diagnostic targets for clinical practice.Due to differing disease prevalence and treatment efficacy,disease specific diagnostic targets are required to optimally manage individual CLDs such as non-alcoholic fatty liver disease and chronic hepatitis C infection.To facilitate this,a review of the pathogenesis of both conditions is also conducted.Finally,the evidence for hepatic fibrosis regression and the mechanisms by which this occurs are discussed,including the current use of antifibrotic therapy.

Liver transplantation for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the commonest primary malignancy of the liver. It usually occurs in the setting of chronic liver disease and has a poor prognosis if untreated. Orthotopic liver transplantation (OLT) is a suitable therapeutic option for early,unresectable HCC particularly in the setting of chronic liver disease. Following on from disappointing initial results,the seminal study by Mazzaferro et al in 1996 established OLT as a viable treatment for HCC. In this study,the "Milan criteria" were applied achieving a 4-year survival rate similar to OLT for benign disease. Since then various groups have attempted to expand these criteria whilst maintaining long term survival rates. The technique of living donor liver transplantation has evolved over the past decade,particularly in Asia,and published outcome data is comparable to that of OLT. This article will review the evidence,indications,and the future direction of liver transplantation for liver cancer.

Granulocyte colony-stimulating factor as a novel adjunct to improve hepatitis B vaccination

Hepatitis B vaccination is successful in 95% of individuals. In the remainder, despite repeated attempts, immunization often remains unsuccessful. 'Non-response' leaves the individual susceptible to infection. Various strategies have been employed to overcome this. These include the use of adjuncts alongside conventional vaccines which activate immune responses. In this case report we demonstrate the successful use of the hematopoietic growth factor Granulocyte colonystimulating factor (G-CSF) as a vaccine adjunct in an individual who had previously failed conventional vaccination three times. The patient tolerated the regimen without any side effects and achieved a hepatitis B surface antibody titer greater than 100 IU/L. Use of G-CSF as a vaccine adjunct for hepatitis B has not previously been reported and the outcome in this case suggests that the use of G-CSF in this context warrants further exploration.

Selective Cluster-Based Temperature Monitoring System for Homogeneous Wireless Sensor Networks

Over the past few decades, there has been a revolution in ICT, and this has led to the evolution of wireless sensor networks （WSN）, in particular, wireless body area networks. Such networks comprise a specialized collection of sensor nodes （SNs） that may be deployed randomly in a body area network to collect data from the human body. In a health monitoring system, it may be es-sential to maintain constant environmental conditions within a specific area in the hospital. In this paper, we propose a tempera-ture-monitoring system and describe a case study of a health-monitoring system for patents critically ill with the same disease and in the same environment. We propose Enhanced LEACH Selective Cluster （E-LEACH-SC） routing protocol for monitoring the tem-perature of an area in a hospital. We modified existing Selective Cluster LEACH protocol by using a fixed-distance-based thresh-old to divide the coverage region in two subregions. Direct data transmission and selective cluster-based data transmission ap-proaches were used to provide short-range and long-distance coverage for the collection of data from the body of ill patients. Ex-tensive simulations were run by varying the ratio of node densities of the two subregions in the health-monitoring system. Last Node Alive （LNA）, which is a measure of network lifespan, was the parameter for evaluating the performance of the proposed scheme. The simulation results show that the proposed scheme significantly increases network lifespan compared with traditional LEACH and LEACH-SC protocols, which by themselves improve the overall performance of the health-monitoring system.