Uncertainty entropy-based exploratory evaluation method and its applications on missile effectiveness evaluation

Some attributes are uncertain for evaluation work because of incomplete or limited information and knowledge.It leads to uncertainty in evaluation results.To that end,an evaluation method,uncertainty entropy-based exploratory evaluation(UEEE),is proposed to guide the evaluation activities,which can iteratively and gradually reduce uncertainty in evaluation results.Uncertainty entropy(UE)is proposed to measure the extent of uncertainty.First,the belief degree distributions are assumed to characterize the uncertainty in attributes.Then the belief degree distribution of the evaluation result can be calculated by using uncertainty theory.The obtained result is then checked based on UE to see if it could meet the requirements of decision-making.If its uncertainty level is high,more information needs to be introduced to reduce uncertainty.An algorithm based on the UE is proposed to find which attribute can mostly affect the uncertainty in results.Thus,efforts can be invested in key attribute(s),and the evaluation results can be updated accordingly.This update should be repeated until the evaluation result meets the requirements.Finally,as a case study,the effectiveness of ballistic missiles with uncertain attributes is evaluated by UEEE.The evaluation results show that the target is believed to be destroyed.

Research of Entropy-Based Overall Performance Measurement Model of Enterprises Technology Alliance

The performance measurement of enterprise technology alliances is complex.In this article,evaluation mechanism of entropy has been applied to it.Above all,performance connotation of enterprise technology alliance is defined from the aspect of self-organizatlon theory.Then,on dynamic and systerna-tical view,an entropy-based overall performance measurement model for technology alliance is established,using its life-cycle as the principal line,which includes initial condition evaluation,process e- valuation as well as benefit evaluation.Finally,a case study is carried out to the demonstration of that model.The author believes that an improved performance measurement model based on alliance life-cycle would be practicability to alliance.

A Quasi-Newton Neural Network Based Efficient Intrusion Detection System for Wireless Sensor Network

In Wireless Sensor Networks(WSN),attacks mostly aim in limiting or eliminating the capability of the network to do its normal function.Detecting this misbehaviour is a demanding issue.And so far the prevailing research methods show poor performance.AQN3 centred efficient Intrusion Detection Systems(IDS)is proposed in WSN to ameliorate the performance.The proposed system encompasses Data Gathering(DG)in WSN as well as Intrusion Detection(ID)phases.In DG,the Sensor Nodes(SN)is formed as clusters in the WSN and the Distance-based Fruit Fly Fuzzy c-means(DFFF)algorithm chooses the Cluster Head(CH).Then,the data is amassed by the discovered path.Next,it is tested with the trained IDS.The IDS encompasses‘3’steps:pre-processing,matrix reduction,and classification.In pre-processing,the data is organized in a clear format.Then,attributes are presented on the matrix format and the ELDA(entropybased linear discriminant analysis)lessens the matrix values.Next,the output as of the matrix reduction is inputted to the QN3 classifier,which classifies the denial-of-services(DoS),Remotes to Local(R2L),Users to Root(U2R),and probes into attacked or Normal data.In an experimental estimation,the proposed algorithm’s performance is contrasted with the prevailing algorithms.The proposed work attains an enhanced outcome than the prevailing methods.

Realization Path for Inclusive Finance to Support Rural Revitalization in Poverty-stricken Areas

Based on the quarterly economic,social and financial development data of 39 poverty-stricken counties in Henan Province during 2016-2018,this paper utilized the entropy-based TOPSIS method to objectively measure the rural revitalization index,and then built the quantile regression model to study the effects of various elements of inclusive finance on different stages of rural revitalization.Research results show that industrial development,agricultural modernization,targeted poverty alleviation,endogenous demand,and rural governance are the main points of inclusive finance in poverty-stricken areas to support rural revitalization;the rural revitalization index indicates that compared with the Dabie Mountain area and the non-contiguous poverty-stricken areas,the rural revitalization of the Qinba Mountain area is slower;for inclusive finance supporting rural revitalization,it is necessary to bring into play the role of monetary policy tools in re-lending,functions of credit in supporting industrial development,and role of insurance in risk protection;furthermore,inclusive finance solves problems such as the diminishing marginal effect of physical machinery investment in rural revitalization support,financial support for the coordinated development of small farmers and new agricultural business entities,financial support for the development of the entire industry chain,and the"siphon effect"of capital.

Framework on low-carbon retrofit of rural residential buildings in arid areas of northwest China:A case study of Turpan residential buildings

At present,buildings in arid and hot regions are facing severe challenges of indoor comfort improvement and carbon emission reduction,especially in rural areas.Multi-objective optimization could be an effective tool for tackling the aforementioned challenges.Therefore,this paper proposes a life-cycle optimization framework considering thermal comfort,which is beneficial to promoting residents’motivation for low-carbon retrofit in arid climate regions.First,in response to the above problems,three objective functions are specified in the framework,which are global warming potential(GWP),life cycle cost(LCC),and thermal discomfort hours(TDH).To improve the optimization efficiency,this research uses Deep Neural Networks(DNN)combined with NSGA-II to construct a high-precision prediction model(meta-model for optimization)based on the energy consumption simulation database formed by the orthogonal multi-dimensional design parameters.The accuracy index of the modified model is R2>0.99,cv(RMSE)≤1%,and NMBE≤0.2%,which gets rid of the dilemma of low prediction accuracy of traditional machine learning models.In the scheme comparison and selection stage,the TOPSIS based on two empowerment methods is applied to meet different design tendencies,where the entropy-based method can avoid the interference of subjective preference and significantly improve the objectivity and scientific nature of decision analysis.Additionally,sensitivity analysis is conducted on the variables,which supports guidance for practitioners to carry out the low-carbon design.Finally,the multi-objective optimization analysis for a farmhouse in Turpan is taken as a case study to evaluate the performance of the framework.The results show that the framework could significantly improve the building performance,with 60.8%,52.5%,and 14.2%reduction in GWP,LCC,and TDH,respectively.

Deep restricted and additive homomorphic ElGamal privacy preservations over big healthcare data

Purpose-The purpose of this paper is to improve the privacy in healthcare datasets that hold sensitive information.Putting a stop to privacy divulgence and bestowing relevant information to legitimate users are at the same time said to be of differing goals.Also,the swift evolution of big data has put forward considerable ease to all chores of life.As far as the big data era is concerned,propagation and information sharing are said to be the two main facets.Despite several research works performed on these aspects,with the incremental nature of data,the likelihood of privacy leakage is also substantially expanded through various benefits availed of big data.Hence,safeguarding data privacy in a complicated environment has become a major setback.Design/methodology/approach-In this study,a method called deep restricted additive homomorphic ElGamal privacy preservation(DR-AHEPP)to preserve the privacy of data even in case of incremental data is proposed.An entropy-based differential privacy quasi identification and DR-AHEPP algorithms are designed,respectively,for obtaining privacy-preserved minimum falsified quasi-identifier set and computationally efficient privacy-preserved data.Findings-Analysis results using Diabetes 130-US hospitals illustrate that the proposed DR-AHEPP method is more significant in preserving privacy on incremental data than existing methods.Acomparative analysis of state-of-the-art works with the objective to minimize information loss,false positive rate and execution time with higher accuracy is calibrated.Originality/value-The paper provides better performance using Diabetes 130-US hospitals for achieving high accuracy,low information loss and false positive rate.The result illustrates that the proposed method increases the accuracy by 4%and reduces the false positive rate and information loss by 25 and 35%,respectively,as compared to state-of-the-art works.