Accessing Cloud Services Using Token based Framework for IoT Devices

Nowadays cloud environments are used by many business service sectors like healthcare, retail marketing, banking, and many business fields. At the same time, the usage of Internet of Things (IoT) devices in different sectors also increasing tremendously. So, there is a general problem for securing any business service in enterprise cloud environments restricting by only authorized devices. We are proposing cryptographic techniques with the help of a token-based framework by enabling a secure handshake between consuming applications and the source business service which aims to authorize the target end consumers of the respective business service. The proposed work aims to achieve the desired secure handshake so that any consuming application or device requests the desired business service with a secret token and an input combination. The source business service creates a secure token using any latest robust cryptographic algorithm on the above input combination and returns the token to the consuming application. The consuming application requests to the source business service, it must pass the above token which if validated then only would receive the required data. Hence, in this paper, we propose the delegation of the authorization task to the end consumers, who are responsible to fetch the security tokens and use them in their application lifecycle.

A Novel LSSVM Model Integrated with GBO Algorithm to Assessment of Water Quality Parameters

In this study, a novel least square support vector machine (LSSVM) model integrated with gradient-based optimizer (GBO) algorithm is introduced for assessment of water quality parameters. For this purpose, three stations including Ahvaz, Armand, and Gotvand in the Karun river basin have been selected to model electrical conductivity (EC), and total dissolved solids (TDS). First, to prove the superiority of the LSSVM-GBO algorithm, the performance is evaluated with three benchmark datasets (Housing, LVST, Servo). Then, the results of the new hybrid algorithm were compared with those of artificial neural network (ANN), adaptive neuro-fuzzy interface system (ANFIS), and LSSVM algorithms. Input combination for assessment of water quality parameters EC and TDS consists of Ca+ 2, Cl-1, Mg+ 2, Na+ 1, SO4, HCO3, sodium absorption ratio (SAR), sum cation (Sum.C), sum anion (Sum.A), PH, and Q. The modelling results based on evaluation criteria showed the significant performance of LSSVM-GBO among all benchmark datasets and algorithms. Other results showed that in Ahvaz station, Sum.C, Sum.A, and Na+1 parameters, and in Gotvand and Armand stations, Sum.C, Sum.A, and Cl-1 parameters have the greatest impact on modelling EC and TDS parameters. In the next step, EC and TDS modelling was performed based on the best input combination and the best algorithm in different time delays. Based on the results, the highest accuracy of modelling EC and TDS parameters in Gotvand station was [0] month time delays.

Comparative study for deriving stagedischarge– sediment concentration relationships using soft computing techniques

Purpose: Knowledge of sediment load carried by any river is essential for designing and planning of hydro power and irrigation projects. So the aim of this study is to develop and evaluating the best soft-computing-based model with M5P and Random Forest regressionbased techniques for computation of sediment using datasets of daily discharge, daily gauge and sediment load at the Champua gauging site of the Upper Baitarani river basin of India. Design/methodology/approach: Last few decades, the soft computing techniques based models have been successfully used in water resources modelling and estimation. In this study, the potential of tree based models are examined by developing and comparing sediment load prediction models, based on M5P tree and Random forest regression (RF). Several M5P and RF based models have been applied to a gauging site of the Baitarani River at Odisha, India. To evaluate the performance of the selected M5P and RF-based models, three most popular statistical parameters are selected such as coefficient of correlation, root mean square error and mean absolute error. Findings: A comparison of the results suggested that RF-based model could be applied successfully for the prediction of sediment load concentration with a relatively higher magnitude of prediction accuracy. In RF-based models Qt, Q(t-1), Q(t-2), S(t-1), S(t-2), Ht and H(t-1) combination based M10 model work superior than other combination based models. Another major outcome of this investigation is Qt, Q(t-1) and S(t-1) based model M4 works better than other input combination based models using M5P technique. The optimum input combination is Qt, Q(t-1) and S(t-1) for the prediction of sediment load concentration of the Baitarani River at Odisha, India. Research limitations/implications: The developed models were tested for Baitarani River at Odisha, India.

Optimization by mixture design approach

Purpose The purpose of this study is to fit an appropriate mathematical model to express response variables as functions of the proportions of the mixture components. One purpose of statistical modeling in a mixture experiment is to model the blending surface such that predictions of the response for any mixture component, singly or in combination, can be made empirically. Testing of the model adequacy will also be an important part of the statistical procedure. Design/methodology/approach A series of mortar using air lime, marble and ceramic sanitary waste aggregates were prepared for statistically designed combinations. The combinations were designed based on the mixture-design concept of design of experiments; this mortar is often used as a filler material in restoration projects. The aim of this work is to find an optimal composition of a paste for the manufacture of air lime mortar with ceramic and marble waste. This investigation aims to recommend mix design for air lime-based mortar, by optimizing the input combination for different properties, and to predict properties such as mechanical strength, thermogravimetric and x-ray diffraction analysis with a high degree of accuracy, based on a statistical analysis of experimental data. Findings This paper discusses those mortar properties that architects, contractors and owners consider important. For each of these properties, the influence of ceramic and marble waste in the air lime mortar is explored. The flexibility of lime-based mortars with waste materials to meet a wide range of needs in both new construction and restoration of masonry projects is demonstrated. Originality/value The objective of the present investigation is to recommend mixture design for air lime mortar with waste, by optimizing the input combination for different properties, and to predict properties such as compressive strength, flexural strength with a high degree of accuracy, based on the statistical analysis of experimental data. The authors conducted a mixture design study that takes into account dependent parameters such as the constituents of our air lime-based mortar where we have determined an experiment matrix to which we have connected the two responses, namely, compressive and flexural strength. By introducing the desirability criteria of these two responses, using JMP software, we were able to obtain a mixture optimal for air lime mortar with ceramic and marble waste.

Shear force estimation in rough boundaries using SVR method

The accuracy of support vector regression (SVR) procedure in modeling the percentage of shear force carried by walls in a rectangular channel with rough boundaries was investigated. The SVR model is extended, and the more appropriate kernel function and input combination are studied. Finally, the SVR model with an exponential kernel function and three influence parameters was selected as the best SVR model with the lowest error. The output of this more appropriate SVR model is presented as a program. Then, this most appropriate SVR model is compared with three equations presented by other researchers for rough and smooth channels. The SVR model with the highest accuracy and lowest statistical values (RMSE of 0.565) performed the best compared with the other equations.

Can Decomposition Approaches Always Enhance Soft Computing Models? Predicting the Dissolved Oxygen Concentration in the St. Johns River, Florida

This study evaluates standalone and hybrid soft computing models for predicting dissolved oxygen (DO) concentration by utilizing different water quality parameters. In the first stage, two standalone soft computing models, including multilayer perceptron (MLP) neural network and cascade correlation neural network (CCNN), were proposed for estimating the DO concentration in the St. Johns River, Florida, USA. The DO concentration and water quality parameters (e.g., chloride (Cl), nitrogen oxides (NOx), total dissolved solid (TDS), potential of hydrogen (pH), and water temperature (WT)) were used for developing the standalone models by defining six combinations of input parameters. Results were evaluated using five performance criteria metrics. Overall results revealed that the CCNN model with input combination III (CCNN-III) provided the most accurate predictions of DO concentration values (root mean square error (RMSE) = 1.261 mg/L, Nash-Sutcliffe coefficient (NSE) = 0.736, Willmott’s index of agreement (WI) = 0.919, R2 = 0.801, and mean absolute error (MAE) = 0.989 mg/L) for the standalone model category. In the second stage, two decomposition approaches, including discrete wavelet transform (DWT) and variational mode decomposition (VMD), were employed to improve the accuracy of DO concentration using the MLP and CCNN models with input combination III (e.g., DWT-MLP-III, DWT-CCNN-III, VMD-MLP-III, and VMD-CCNN-III). From the results, the DWT-MLP-III and VMD-MLP-III models provided better accuracy than the standalone models (e.g., MLP-III and CCNN-III). Comparison of the best hybrid soft computing models showed that the VMD-MLP-III model with 4 intrinsic mode functions (IMFs) and 10 quadratic penalty factor (VMD-MLP-III (K = 4 and α = 10)) model yielded slightly better performance than the DWT-MLP-III with Daubechies-6 (D6) and Symmlet-6 (S6) (DWT-MLP-III (D6 and S6)) models. Unfortunately, the DWT-CCNN-III and VMD-CCNN-III models did not improve the performance of the CCNN-III model. It was found that the CCNN-III model cannot be used to apply the hybrid soft computing modeling for prediction of the DO concentration. Graphical comparisons (e.g., Taylor diagram and violin plot) were also utilized to examine the similarity between the observed and predicted DO concentration values. The DWT-MLP-III and VMD-MLP-III models can be an alternative tool for accurate prediction of the DO concentration values.

Maksimalisasi Keuntungan Dengan Pendekatan Metode Simpleks Kasus Pada Pabrik Sosis SM

The purpose of this study are to (1) determine the combination of inputs used in producing products such as beef sausages and veal sausage meatball; and (2) determine the optimal combination whether the product can provide the maximum profit. In order to determine the combination of inputs and maximum benefits can be used linear programming with graphical and simplex method. The valuation result shows that the optimal input combination would give a profit of Rp. 1.115 million per day.

French and English lexical blends in contrast

Two sets of 97 French and 374 English lexical units identified as lexical blends are examined from a contrastive perspective. It appears that English displays a wider variety of patterns than French does – a larger number of marginal types of lexical input combination, of lexical shortening and of phonological splitting. Striking dissimilarities between the two languages also include an inclination for the pattern of double inner shortening in English and the pattern of left-hand-side inner shortening in French, as well as a preference for semantic and phonological right-headedness in English and the absence of a preferred lateral head position in French.