International Journal of Modeling, Simulation, and Scientific Computing

Cloud computing is a technology that allows the end-users to access the network through a shared area of resources. As the demand for the cloud computing increases, vulnerabilities in the service provision also increase. EDoS is one of the attacks that take over the provider, financially affecting the various organizations which use the cloud data. This paper utilizes fuzzy entropy and lion neural learner (FLNL) for the classification of cloud users to mitigate EDoS attacks in the cloud. This technique includes a training phase, which creates a log file using various parameters and then transforms the features into database considering certain key features. There are two important stages in this classification approach: feature selection and classification. Here, the fuzzy entropy function is utilized for feature selection which effectively selects useful features without information loss. The classification is performed using lion neural learner (LNL) which incorporates Lion algorithm (LA) into the neural network and uses Levenberg–Marquardt (LM) algorithm. The experimental results finalize that the proposed FLNL is effective with 89% precision, 78% recall, and 83.13% of f-measure compared with the existing Naïve Bayes (NB), Neural $\mathrm{\text{Network}}+\mathrm{\text{Back}}$ Propagation $(\mathrm{\text{NN}}+\mathrm{\text{BP}})$, and Neural $\mathrm{\text{Network}}+\mathrm{\text{Levenberg}}$–Marquardt $(\mathrm{\text{NN}}+\mathrm{\text{LM}})$.

Nowadays, virtual machine migration (VMM) is a trending research since it helps in balancing the load of the Cloud effectively. Several VMM-based strategies defined in the literature have considered various metrics, such as load, energy, and migration cost for balancing the load of the model. This paper introduces a novel VMM strategy by considering the load of the Cloud network. Two important aspects of the proposed scheme are the load prediction through the support vector regression (SVR) and the optimal VM placement through the proposed dragonfly-based crow (D-Crow) optimization algorithm. The proposed D-Crow optimization algorithm is developed by incorporating crow search algorithm (CSA) into dragonfly algorithm (DA). Also, the proposed VMM strategy defines a load balancing model based on the energy consumption, load, and the migration cost to achieve the energy-aware VMM. The simulation of the proposed VMM strategy is done based on the metrics such as load, energy consumption, and the migration cost. From the results, it can be shown that the proposed VMM strategy surpassed other comparative models by achieving the minimum values of 7.3719%, 10.0368%, and 11.0639% for the load, energy consumption, and migration cost, respectively.

The unpredictable structure failures of carbon steel and low alloy steel leading to accidents may be caused by the propagation of a flaw or crack already present in the structure. Fracture toughness which describes the ability of a material containing a crack to resist fracture is one of the most important material properties for design applications of metallic structures. Since this material property is influenced by several parameters, namely material chemistry, heat treatment, morphology of structure, it requires millions of experiments to be conducted to understand and predict it. So, mathematical modeling is one of the solutions to find the effect of these parameters and design future alloys. Stress–intensity factor $(K_{\mathrm{\text{I}}C})$ is a quantitative parameter of fracture toughness determining a maximum value of stress which may be applied to a specimen containing a crack (notch) of a certain length. An artificial neural network (ANN) model was developed using over 100 sets of data to study the effect of alloying elements on fracture toughness, $K_{\mathrm{\text{I}}C}$ for the low alloy steel. 20% of data was used for training, 60% to develop predictive model and rest of the 20% for validation. The model can predict the fracture toughness of unknown new data close to 80% accuracy which is good enough for statistical modeling. The details of program code with ANN modeling steps have been explained. Prediction of fracture toughness by the model with variation of alloy composition as well as yield stress gives interesting and important information which may help in designing alloy which will resist crack propagation in a structure and hence enhance the life of structure to fail.

Under consideration is the method of probabilistic forecasting of macro parameters representing general patterns of an applied multi-agent system behavior. This system introduces the game interaction between a set of agents and a target. The behavior dynamics of such a system is described by a discrete-state discrete-time Markov random process and represented in the terms, which are considered convenient for interpretation and practical control of macro parameters. The corresponding calculations are based on common characteristics of the initial conditions. The probabilistic model of behavior of the system is generalized for the case of the mobile target and supplemented with formulas for the dynamical calculation of probabilistic distributions of the target’s defeat by the agents as well as the agent’s defeat by the target.

By using nonuniform (geometric) grid network, a new high-order finite-difference compact scheme has been obtained for the numerical solution of three-space dimensions partial differential equations of elliptic type. Single cell discretization to the elliptic equation makes it easier to compute and exhibit stability of the numerical solutions. The monotone and irreducible property of the Jacobian matrix to the system of difference equations analyses the converging behavior of the numerical solution values. As an experiment, applications of the compact scheme to Schrödinger equations, sine-Gordon equations, elliptic Allen–Cahn equation and Poisson’s equation have been presented with root mean squared errors of exact and approximate solution values. The results corroborate the reliability and efficiency of the scheme.

Online incremental learning is one of the emerging research interests among the researchers in the recent years. The sentiment classification through the online incremental learning faces many challenges due to the limitations in the memory and the computing resources available for processing the online reviews. This work has introduced an online incremental learning algorithm for classifying the train reviews. The sentiments available in the reviews provided for the public services are necessary for improving the quality of the service. This work proposes the online kernel optimization-based support vector machine (OKO-SVM) classifier for the sentiment classification of the train reviews. This paper is the extension of the previous work kernel optimization-based support vector machine (KO-SVM). The OKO-SVM classifier uses the proposed fuzzy bound for modifying the weight for each incoming review database for the particular time duration. The simulation uses the standard train review and the movie review database for the classification. From the simulation results, it is evident that the proposed model has achieved a better performance with the values of 84.42%, 93.86%, and 74.56% regarding the accuracy, sensitivity, and specificity while classifying the train review database.

Fractional-order differentiator is a principal component of the fractional-order controller. Discretization of fractional-order differentiator is essential to implement the fractional-order controller digitally. Discretization methods generally include indirect approach and direct approach to find the discrete-time approximation of fractional-order differentiator in the $Z$-domain as evident from the existing literature. In this paper, a direct approach is proposed for discretization of fractional-order differentiator in delta-domain instead of the conventional $Z$-domain as the delta operator unifies both analog system and digital system together at a high sampling frequency. The discretization of fractional-order differentiator is accomplished in two stages. In the first stage, the generating function is framed by reformulating delta operator using trapezoidal rule or Tustin approximation and in the next stage, the fractional-order differentiator has been approximated by expanding the generating function using continued fraction expansion method. The proposed method has been compared with two well-known direct discretization methods taken from the existing literature. Two examples are presented in this context to show the efficacy of the proposed discretization method using simulation results obtained from MATLAB.

Recent developments in 3D graphics technology have led to extensive processes on 3D meshes (e.g., compression, simplification, transmission and watermarking), these processes unavoidably cause the visual perceptual degradation of the 3D objects. The existing mesh visual quality evaluation metrics either require topology constrain or fail to reflect the perceived visual quality. Meanwhile, for the 3D objects that are observed on 2D screens by the users, it is reasonable to apply image metric to assess the distortion caused by mesh simplification. We attempt to explore the efficiency of image metric for assessing the visual fidelity of the simplified 3D model in this paper. For this purpose, several latest and most effective image metrics, 2D snapshots, number and pooling algorithms are involved in our study, and finally tested on the IEETA simplification database. The statistical data allow the researcher to select the optimal parameter for this image-based mesh visual quality assessment and provide a new perspective for the design and performance assessment of mesh simplification algorithms.

Engineering vehicles are widely used under various harsh working conditions. For many components in them, service loadings they suffered are usually random and nonstationary due to their remarkable characteristic called cyclic operation. To deal with that, section method can be applied. However, this method will neglect those transition cycles caused by switching load section, which can contribute a lot to fatigue. In order to consider those transition cycles, this paper applied the model called “Switching Markov Chain of Turning Points” (SMCTP). Then the expected rain-flow matrix is compared with the overall rain-flow matrix conducted by section method. The comparison result shows that SMCTP can perform well in processing nonstationary loadings. As a result, the Switching Markov Chain method (SMC) was proved to be effective in stochastically characterizing the nonstationary switching loadings of engineering vehicles.

Data publishing is an area of interest in present day technology that has gained huge attention of researchers and experts. The concept of data publishing faces a lot of security issues, indicating that when any trusted organization provides data to a third party, personal information need not be disclosed. Therefore, to maintain the privacy of the data, this paper proposes an algorithm for privacy preserved collaborative data publishing using the Genetic Grey Wolf Optimizer (Genetic GWO) algorithm for which a C-mixture parameter is used. The C-mixture parameter enhances the privacy of the data if the data does not satisfy the privacy constraints, such as the $k$-anonymity, $l$-diversity and the $m$-privacy. A minimum fitness value is maintained that depends on the minimum value of the generalized information loss and the minimum value of the average equivalence class size. The minimum value of the fitness ensures the maximum utility and the maximum privacy. Experimentation was carried out using the adult dataset, and the proposed Genetic GWO outperformed the existing methods in terms of the generalized information loss and the average equivalence class metric and achieved minimum values at a rate of 0.402 and 0.9, respectively.

Resonant converter (RC) was brought under research in the 80’s widely, which can attain very small switching loss, therefore, facilitating resonant topologies to function at the high switching frequency. It is well addressed in the review that the optimal parameterization of the resonant converter is a crucial task. While the literature has come out with different methodologies, they are highly conceptual and so the uncertainty due to high theoretical impact persists. This paper intends to develop a Parameter Optimization (PO) algorithm for designing and developing of LLC-RC. The proposed algorithm overwhelms the limitation by introducing a nonconceptual model based on the simulated outcome. Specifically, the resonant current under start-up conditions is acquired from the literary outcome, and the intelligent model is constructed. Based on the proposed model, a renowned search algorithm called as Whale Optimization Algorithm (WOA) is exploited to optimize the time constant of the resonant converter, which is a critical design parameter. The objective model is derived as a function of start-up time and so the start-up time can be minimized. Moreover, the response speed of the output voltage is also increased. The proposed Whale Optimization Algorithm based Parameter Optimization (WOAPO) is compared with the conventional techniques such as IAPO, Ant Bee Colony-PO (ABC-PO), Particle Swarm Optimization- PO (PSOPO), FireFly PO (FFPO) and Grey Wolf Optimization (GWOPO). The obtained result verifies the performance of the proposed method in modeling LLC-RC system.

As the effective capture region of optical motion capture system is limited by quantity, installation mode, resolution and focus of infrared cameras, the reflective markers on certain body parts (such as wrists, elbows, etc.) of multi-actual trainees may be obscured when they perform the collaborative interactive operation. To address this issue, motion data compensation method based on the additional feature information provided by the electromagnetic spatial position tracking equipment is proposed in this paper. The main working principle and detailed realization process of the proposed method are introduced step by step, and the practical implementation is presented to illustrate its validity and efficiency. The results show that the missing capture data and motion information of relevant obscured markers on arms can be retrieved with the proposed method, which can avoid the simulation motions of corresponding virtual operators being interrupted and deformed during the collaborative interactive operation process performed by multi-actual trainees with optical human motion capture system in a limited capture range.