A systematic literature review of blockchain cyber security

Since the publication of Satoshi Nakamoto's white paper on Bitcoin in 2008,blockchain has(slowly)become one of the most frequently discussed methods for securing data storage and transfer through decentralized,trustless,peer-to-peer systems.This research identifies peer-reviewed literature that seeks to utilize blockchain for cyber security purposes and presents a systematic analysis of the most frequently adopted blockchain security applications.Our findings show that the Internet of Things(IoT)lends itself well to novel blockchain applications,as do networks and machine visualization,public-key cryptography,web applications,certification schemes and the secure storage of Personally Identifiable Information(PII).This timely systematic review also sheds light on future directions of research,education and practices in the blockchain and cyber security space,such as security of blockchain in IoT,security of blockchain for AI data,and sidechain security.

PSO-DBNet for Peak-to-Average Power Ratio Reduction Using Deep Belief Network

Data transmission through a wireless network has faced various signal problems in the past decades.The orthogonal frequency division multiplexing(OFDM)technique is widely accepted in multiple data transfer patterns at various frequency bands.A recent wireless communication network uses OFDM in longterm evolution(LTE)and 5G,among others.The main problem faced by 5G wireless OFDM is distortion of transmission signals in the network.This transmission loss is called peak-to-average power ratio(PAPR).This wireless signal distortion can be reduced using various techniques.This study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless communication.Partial transmit sequence(PTS)helps in the fast transfer of data in wireless LTE.PTS is merged with deep belief neural network(DBNet)for the efficient processing of signals in wireless 5G networks.Result indicates that the proposed system outperforms other existing techniques.Therefore,PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm optimization.Hence,the specified design supports in improving the proposed PAPR reduction architecture.

An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms

Cardiovascular disease is among the top five fatal diseases that affect lives worldwide.Therefore,its early prediction and detection are crucial,allowing one to take proper and necessary measures at earlier stages.Machine learning(ML)techniques are used to assist healthcare providers in better diagnosing heart disease.This study employed three boosting algorithms,namely,gradient boost,XGBoost,and AdaBoost,to predict heart disease.The dataset contained heart disease-related clinical features and was sourced from the publicly available UCI ML repository.Exploratory data analysis is performed to find the characteristics of data samples about descriptive and inferential statistics.Specifically,it was carried out to identify and replace outliers using the interquartile range and detect and replace the missing values using the imputation method.Results were recorded before and after the data preprocessing techniques were applied.Out of all the algorithms,gradient boosting achieved the highest accuracy rate of 92.20%for the proposed model.The proposed model yielded better results with gradient boosting in terms of precision,recall,and f1-score.It attained better prediction performance than the existing works and can be used for other diseases that share common features using transfer learning.

Homotopy study of magnetohydrodynamic mixed convection nanofluid multiple slip flow and heat transfer from a vertical cylinder with entropy generation

Stimulated by thermal optimization in magnetic materials process engineering,the present investigation investigates theoretically the entropy generation in mixed convection magnetohydrodynamic(MHD)flow of an electrically-conducting nanofluid from a vertical cylinder.The mathematical model includes the effects of viscous dissipation,second order velocity slip and thermal slip,has been considered.The cylindrical partial differential form of the two-component non-homogenous nanofluid model has been transformed into a system of coupled ordinary differential equations by applying similarity transformations.The effects of governing parameters with no-flux nanoparticle concentration have been examined on important quantities of interest.Furthermore,the dimensionless form of the entropy generation number has also been evaluated using homotopy analysis method(HAM).The present analytical results achieve good correlation with numerical results(shooting method).Entropy is found to be an increasing function of second order velocity slip,magnetic field and curvature parameter.Temperature is elevated with increasing curvature parameter and magnetic parameter whereas it is reduced with mixed convection parameter.The flow is accelerated with curvature parameter but decelerated with magnetic parameter.Heat transfer rate(Nusselt number)is enhanced with greater mixed convection parameter,curvature parameter and first order velocity slip parameter but reduced with increasing second order velocity slip parameter.Entropy generation is also increased with magnetic parameter,second order slip velocity parameter,curvature parameter,thermophoresis parameter,buoyancy parameter and Reynolds number whereas it is suppressed with first order velocity slip parameter,Brownian motion parameter and thermal slip parameter.