A Steganography Based on Optimal Multi-Threshold Block Labeling

Hiding secret data in digital images is one of the major researchfields in information security.Recently,reversible data hiding in encrypted images has attracted extensive attention due to the emergence of cloud services.This paper proposes a novel reversible data hiding method in encrypted images based on an optimal multi-threshold block labeling technique(OMTBL-RDHEI).In our scheme,the content owner encrypts the cover image with block permutation,pixel permutation,and stream cipher,which preserve the in-block correlation of pixel values.After uploading to the cloud service,the data hider applies the prediction error rearrangement(PER),the optimal threshold selection(OTS),and the multi-threshold labeling(MTL)methods to obtain a compressed version of the encrypted image and embed secret data into the vacated room.The receiver can extract the secret,restore the cover image,or do both according to his/her granted authority.The proposed MTL labels blocks of the encrypted image with a list of threshold values which is optimized with OTS based on the features of the current image.Experimental results show that labeling image blocks with the optimized threshold list can efficiently enlarge the amount of vacated room and thus improve the embedding capacity of an encrypted cover image.Security level of the proposed scheme is analyzed and the embedding capacity is compared with state-of-the-art schemes.Both are concluded with satisfactory performance.

Cubic Meter Compressive Strength Prediction of Concrete

In order to improve the prediction accuracy of compressive strength of concrete,103 groups of concrete data were collected as the samples.We selected seven kinds of ingredients from the concrete samples, using Grid-SVM, PSO-SVM, and GA-SVM models to establish the prediction model of cubic meter compressive strength of concrete.The experimental results show that SVM model based on Grid optimization algorithm,SVM model based on Particle swarm optimization algorithm,SVM model based on Genetic optimization algorithm mean square error respectively are 0.001, 0.489 8, and 0.304 2, correlation coefficients are 0.994 8, 0.994 6, and 0.993 0. It is shown that cubic meter compressive strength prediction method based on Grid-SVM model is the best optimization algorithm.

Simulation of foamed concrete compressive strength prediction using adaptive neuro-fuzzy inference system optimized by nature-inspired algorithms

Concrete compressive strength prediction is an essential process for material design and sustainability.This study investigates several novel hybrid adaptive neuro-fuzzy inference system(ANFIS)evolutionary models,i.e.,ANFIS-particle swarm optimization(PSO),ANFIS-ant colony,ANFIS-differential evolution(DE),and ANFIS-genetic algorithm to predict the foamed concrete compressive strength.Several concrete properties,including cement content(C),oven dry density(O),water-to-binder ratio(W),and foamed volume(F)are used as input variables.A relevant data set is obtained from open-access published experimental investigations and used to build predictive models.The performance of the proposed predictive models is evaluated based on the mean performance(MP),which is the mean value of several statistical error indices.To optimize each predictive model and its input variables,univariate(C,O,W,and F),bivariate(C-O,C-W,C-F,O-W,O-F,and W-F),trivariate(C-O-W,C-W-F,O-W-F),and four-variate(C-O-W-F)combinations of input variables are constructed for each model.The results indicate that the best predictions obtained using the univariate,bivariate,trivariate,and four-variate models are ANFIS-DE-(O)(MP=0.96),ANFIS-PSO-(C-O)(MP=0.88),ANFIS-DE-(O-W-F)(MP=0.94),and ANFIS-PSO-(C-O-W-F)(MP=0.89),respectively.ANFIS-PSO-(C-O)yielded the best accurate prediction of compressive strength with an MP value of 0.96.

Influence of accelerated curing on the compressive strength of polymer-modified concrete

In recent building practice,rapid construction is one of the principal requisites.Furthermore,in designing concrete structures,compressive strength is the most significant of all parameters.While 3-d and 7-d compressive strength reflects the strengths at early phases,the ultimate strength is paramount.An effort has been made in this study to develop mathematical models for predicting compressive strength of concrete incorporating ethylene vinyl acetate(EVA)at the later phases.Kolmogorov-Smirnov(KS)goodness-of-fit test was used to examine distribution of the data.The compressive strength of EVA-modified concrete was studied by incorporating various concentrations of EVA as an admixture and by testing at ages of 28,56,90,120,210,and 365 d.An accelerated compressive strength at 3.5 hours was considered as a reference strength on the basis of which all the specified strengths were predicted by means of linear regression fit.Based on the results of KS goodness-of-fit test,it was concluded that KS test statistics value(D)in each case was lower than the critical value 0.521 for a significance level of 0.05,which demonstrated that the data was normally distributed.Based on the results of compressive strength test,it was concluded that the strength of EVA-modified specimens increased at all ages and the optimum dosage of EVA was achieved at 16%concentration.Furthermore,it was concluded that predicted compressive strength values lies within a 6%difference from the actual strength values for all the mixes,which indicates the practicability of the regression equations.This research work may help in understanding the role of EVA as a viable material in polymer-based cement composites.

Compressive strength prediction of sprayed concrete lining in tunnel engineering using hybrid machine learning techniques

Sprayed concrete lining is a commonly employed support measure in tunnel engineering,which plays an important role in construction safety.Compressive strength is a key performance indicator of sprayed concrete lining,and the traditional measuring method is time-consuming and laborious.This paper proposes various hybrid machine learning algorithms to accomplish the advanced prediction of compressive strength of sprayed concrete lining based on the mixture design.Two hundred and five sets of experimental data were collected from a water conveyance tunnel in northwestern China for model construction,and each set of data was made up of six basic input variables(i.e.,water,cement,mineral powder,superplasticizer,coarse aggregate,and fine aggregate)and one output variable(i.e.,compressive strength).In order to eliminate the correlation between input variables,a new composite indicator(i.e.,the water-binder ratio)was introduced to achieve dimensionality reduction.After that,four hybrid models in total were built,namely BPNN-QPSO,SVR-QPSO,ELM-QPSO,and RF-QPSO,where the hyper-parameters of BPNN,SVR,ELM,and RF were auto-tuned by QPSO.Engineering application results indicated that RF-QPSO achieved the lowest mean absolute percentage error(MAPE)of 3.47% and root mean square error(RMSE)of 1.30 and the highest determination coefficient(R^(2))of 0.93 in the four hybrid models.Moreover,RFQPSO had the shortest running time of 0.15 s,followed by SVR-QPSO(0.18 s),ELM-QPSO(1.19 s),and BPNN-QPSO(1.58 s).Compared with BPNN-QPSO,SVR-QPSO,and ELM-QPSO,RF-QPSO performed the most superior performance in terms of both prediction accuracy and running speed.Finally,the importance of input variables on the model performance was quantitatively evaluated,further enhancing the interpretability of RF-QPSO.

Machine learning based models for predicting compressive strength of geopolymer concrete

Recently,great attention has been paid to geopolymer concrete due to its advantageous mechanical and environmentally friendly properties.Much effort has been made in experimental studies to advance the understanding of geopolymer concrete,in which compressive strength is one of the most important properties.To facilitate engineering work on the material,an efficient predicting model is needed.In this study,three machine learning(ML)-based models,namely deep neural network(DNN),K-nearest neighbors(KNN),and support vector machines(SVM),are developed for forecasting the compressive strength of the geopolymer concrete.A total of 375 experimental samples are collected from the literature to build a database for the development of the predicting models.A careful procedure for data preprocessing is implemented,by which outliers are examined and removed from the database and input variables are standardized before feeding to the fitting process.The standard K-fold cross-validation approach is applied for evaluating the performance of the models so that overfitting status is well managed,thus the generalizability of the models is ensured.The effectiveness of the models is assessed via statistical metrics including root mean squared error(RMSE),mean absolute error(MAE),correlation coefficient(R),and the recently proposed performance index(PI).The basic mean square error(MSE)is used as the loss function to be minimized during the model fitting process.The three ML-based models are successfully developed for estimating the compressive strength,for which good correlations between the predicted and the true values are obtained for DNN,KNN,and SVM.The numerical results suggest that the DNN model generally outperforms the other two models.