Prediction of software vulnerability based deep symbiotic genetic algorithms: Phenotyping of dominant-features

The detection of software vulnerabilities is considered a vital problem in the software security area for a long time. Nowadays, it is challenging to manage software security due to its increased complexity and diversity. So, vulnerability detection applications play a significant part in software development and maintenance. The ability of the forecasting techniques in vulnerability detection is still weak. Thus, one of the efficient defining features methods that have been used to determine the software vulnerabilities is the metaheuristic optimization methods. This paper proposes a novel software vulnerability prediction model based on using a deep learning method and SYMbiotic Genetic algorithm. We are first to apply Diploid Genetic algorithms with deep learning networks on software vulnerability prediction to the best of our knowledge. In this proposed method, a deep SYMbiotic-based genetic algorithm model (DNN-SYMbiotic GAs) is used by learning the phenotyping of dominant-features for software vulnerability prediction problems. The proposed method aimed at increasing the detection abilities of vulnerability patterns with vulnerable components in the software. Comprehensive experiments are conducted on several benchmark datasets; these datasets are taken from Drupal, Moodle, and PHPMyAdmin projects. The obtained results revealed that the proposed method (DNN-SYMbiotic GAs) enhanced vulnerability prediction, which reflects improving software quality prediction.

     

Augmented grasshopper optimization algorithm by differential evolution: a power scheduling application in smart homes

With the increasing number of electricity consumers, production, distribution, and consumption problems of produced energy have appeared. This paper proposed an optimization method to reduce the peak demand using smart grid capabilities. In the proposed method, a hybrid Grasshopper Optimization Algorithm (GOA) with the self-adaptive Differential Evolution (DE) is used, called HGOA. The proposed method takes advantage of the global and local search strategies from Differential Evolution and Grasshopper Optimization Algorithm. Experimental results are applied in two scenarios; the first scenario has universal inputs and several appliances. The second scenario has an expanded number of appliances. The results showed that the proposed method (HGOA) got better power scheduling arrangements and better performance than other comparative algorithms using the classical benchmark functions. Moreover, according to the computational time, it runs in constant execution time as the population is increased. The proposed method got 0.26?% enhancement compared to the other methods. Finally, we found that the proposed HGOA always got better results than the original method in the worst cases and the best cases.

     

Amended hybrid multi-verse optimizer with genetic algorithm for solving task scheduling problem in cloud computing

The Journal of Supercomputing - The central cloud facilities based on virtual machines offer many benefits to reduce the scheduling costs and improve service availability and accessibility. The...     

Viscoplastic modeling of asphalt mixes with the effects of anisotropy, damage and aggregate characteristics

This paper presents an approach for constitutive modeling of the viscoplastic behavior of asphalt mixes. This approach utilizes an anisotropic non-associated flow rule based on the Drucker–Prager yield surface. The selection of this yield surface is motivated by the field stress paths and material properties associated with permanent deformation at high temperatures. The efficacy of the model is demonstrated by analyzing data from compressive triaxial tests conducted at different confining pressures and strain rates for three different mixes. The model parameters are related to the experimental measurements of aggregate shape characteristics, aggregate surface energy, inherent anisotropic distribution of aggregates, and microstructure damage measured using X-ray computed tomography and image analysis techniques. Establishing the relationship between the model parameters and material properties is important in order to optimize the mix properties, and achieve desirable mix performance.     

Hybrid clustering analysis using improved krill herd algorithm

In this paper, a novel text clustering method, improved krill herd algorithm with a hybrid function, called MMKHA, is proposed as an efficient clustering way to obtain promising and precise results in this domain. Krill herd is a new swarm-based optimization algorithm that imitates the behavior of a group of live krill. The potential of this algorithm is high because it performs better than other optimization methods; it balances the process of exploration and exploitation by complementing the strength of local nearby searching and global wide-range searching. Text clustering is the process of grouping significant amounts of text documents into coherent clusters in which documents in the same cluster are relevant. For the purpose of the experiments, six versions are thoroughly investigated to determine the best version for solving the text clustering. Eight benchmark text datasets are used for the evaluation process available at the Laboratory of Computational Intelligence (LABIC). Seven evaluation measures are utilized to validate the proposed algorithms, namely, ASDC, accuracy, precision, recall, F-measure, purity, and entropy. The proposed algorithms are compared with the other successful algorithms published in the literature. The results proved that the proposed improved krill herd algorithm with hybrid function achieved almost all the best results for all datasets in comparison with the other comparative algorithms.     

Natural Vibrations of Open-Variable Thickness Circular Cylindrical Shells in High Temperature Field

The feasibility of using the transfer matrix method (TMM) to analyze open-variable thickness circular cylindrical shells exposed to a high-temperature field is explored theoretically. In the approach to the problem, the thermal degradation (TG) of thermoelastic characteristics of the material is considered. Natural frequencies and mode shapes for the cylindrical shells are investigated in detail by combining the vibration theory with the TMM. The governing equations of vibration for this system are expressed by the matrix differential equations, and the coefficient matrices are derived. After the relationship between the transfer matrix and the coefficient matrix is established, the fourth-order Runge-Kutta method is used numerically to solve the matrix equation. Once the transfer matrix of single component has been obtained, the product of each component matrix can compose the matrix of the entire structure. The frequency equations and mode shape are formulated in terms of the elements of the structural matrices. Finite-element numerical simulation has validated the present formulas of natural frequencies. Numerical illustrations, supplying pertinent information on the implications of the TG, are presented for various curvatures, aspect ratios, boundary conditions, and thickness ratios, and the pertinent conclusions are outlined.     

Indoor residual spray and insecticide-treated bednets for malaria control: theoretical synergisms and antagonisms

Indoor residual spray (IRS) of insecticides and insecticide-treated bednets (ITNs) are the two most important malaria vector control tools in the tropical world. Application of both tools in the same locations is being implemented for malaria control in endemic and epidemic Africa. The two tools are assumed to have synergistic benefits in reducing malaria transmission because they both act at multiple stages of the transmission cycle. However, this assumption has not been rigorously examined, empirically or theoretically. Using mathematical modelling, we obtained the conditions for which a combination strategy can be expected to improve upon single control tools. Specifically, spraying of dichlorodiphenyltrichloroethane (DDT) in all houses where residents are not using ITNs can reduce transmission of malaria (R₀) by up to 10 times more than the reduction achieved through ITNs alone. Importantly, however, we also show how antagonism between control tools can arise via interference of their modes of action. Repellent IRS reduces the likelihood that ITNs are contacted within sprayed houses and ITNs reduce the rate at which blood-fed mosquitoes rest on sprayed walls. For example, 80 per cent coverage of ITNs and DDT used together at the household level resulted in an R₀ of 11.1 when compared with an R₀ of 0.1 achieved with 80 per cent ITN coverage without DDT. While this undesired effect can be avoided using low-repellence pyrethroid chemicals for IRS, the extent of the potential benefits is also attenuated. We discuss the impact that this result will likely have on future efforts in malaria control combination strategy.     

A parametric study on supersonic/hypersonic flutter behavior of aero-thermo-elastic geometrically imperfect curved skin panel

In this paper, the effect of the system parameters on the flutter of a curved skin panel forced by a supersonic/hypersonic unsteady flow is numerically investigated. The aeroelastic model investigated includes the third-order piston theory aerodynamics for modeling the flow-induced forces and the Von Kármán non-linear strain-displacement relation in conjunction with the Kirchhoff plate hypothesis for the panel structural modeling. Structural non-linearities are considered and are due to the non-linear coupling between out-of-plane bending and in-plane stretching. The effects of thermal degradation and Kelvin??s model of structural damping independent on time and temperature are also considered. The aero-thermo-elastic governing equations are developed from the geometrically imperfect non-linear theory of infinitely long two-dimensional curved panels. Computational analysis and discussion of the finding along with pertinent conclusions are presented.     

Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering

The text clustering technique is an appropriate method used to partition a huge amount of text documents into groups. The documents size affects the text clustering by decreasing its performance. Subsequently, text documents contain sparse and uninformative features, which reduce the performance of the underlying text clustering algorithm and increase the computational time. Feature selection is a fundamental unsupervised learning technique used to select a new subset of informative text features to improve the performance of the text clustering and reduce the computational time. This paper proposes a hybrid of particle swarm optimization algorithm with genetic operators for the feature selection problem. The k-means clustering is used to evaluate the effectiveness of the obtained features subsets. The experiments were conducted using eight common text datasets with variant characteristics. The results show that the proposed algorithm hybrid algorithm (H-FSPSOTC) improved the performance of the clustering algorithm by generating a new subset of more informative features. The proposed algorithm is compared with the other comparative algorithms published in the literature. Finally, the feature selection technique encourages the clustering algorithm to obtain accurate clusters.