An Efficient Digital Confidentiality Scheme Based on Commutative Chaotic Polynomial

Multimedia Tools and Applications - One of the most critical aspects of this technologically progressive era is the propagation of information through an unsecured communication channel. The...     

Correction to: Automatic COVID-19 detection mechanisms and approaches from medical images: a systematic review

Multimedia Tools and Applications -     

Multi-objective optimization of a hatchback rear end utilizing fractional factorial design algorithm

Air flow has significant effects on fuel consumption, performance, and comfort. Decreasing drag coefficient enhances fuel consumption and vehicle performance. Moreover, omitting or reducing the power of aerodynamic noise sources provides passengers comfort. In this paper, optimization of a hatchback rear end is conducted considering drag and aerodynamic noise objectives. To this end, five geometrical parameters of the hatchback rear end are chosen as design variables in two levels. Numerical simulation is applied to survey air flow features around the models in the wind tunnel. To reduce the number of runs, fraction factorial design algorithm is applied to generate layout of the simulations which decreased the number of case studies to half. Main and interaction effects of these factors on drag coefficient and acoustic power of the rear end source are derived using analysis of variance. Optimum level for each parameter is chosen considering simultaneous drag and noise goals. Finally, characteristics of air flow and acoustic power around optimum model are discussed.

     

An analytical solution for static stability of multi-scale hybrid nanocomposite plates

An analytical answer to the buckling problem of a composite plate consisted of multi-scale hybrid nanocomposites is presented here for the first time. In other words, the constituent material of the structure is made of an epoxy matrix which is reinforced by both macro- and nanosize reinforcements, namely, carbon fiber (CF) and carbon nanotube (CNT). The effective material properties such as Young’s modulus or density are derived utilizing a micromechanical scheme incorporated with the Halpin–Tsai model. To present a more realistic problem, the plate is placed on a two-parameter elastic substrate. Then, on the basis of an energy-based Hamiltonian approach, the equations of motion are derived using the classical theory of plates. Finally, the governing equations are solved analytically to obtain the critical buckling load of the system. Afterward, the normalized form of the results is presented to emphasize the impact of each parameter on the dimensionless buckling load of composite plates. It is worth mentioning that the effects of various boundary conditions are covered, too. To show the efficiency of presented modeling, the results of this article are compared to those of former attempts.

     

Access point selection in the network of Internet of things (IoT) considering the strategic behavior of the things and users

The Journal of Supercomputing - With the expansion in the use of IoT, increasing the efficiency of these networks has become even more significant. Objects need reliable communications at suitable...     

Nonparametric frontier analysis models for efficiency evaluation in insurance industry: a case study of Iranian insurance market

Performance evaluation and efficiency analysis is considered to be one of the critical responsibilities of the management department. This paper investigates and assesses the efficiency and performance of Iranian insurance companies through nonparametric frontier analysis (FA) models. The two well-known nonparametric FA models, data envelopment analysis (DEA) and free disposal hull, are utilized to separate the efficient companies from the inefficient companies, and two well-known super-efficiency analysis models are utilized to rank the efficient units. For the further analysis, critical inputs are also identified for inefficient companies using DEA sensitivity analysis which is a powerful tool for planning performance improvement project, and the reference sets are also identified as the benchmark pattern for each inefficient unit. Managerial insights and implications of the study are discussed finally.     

Mobile-cloud assisted framework for selective encryption of medical images with steganography for resource-constrained devices

In this paper, the problem of outsourcing the selective encryption of a medical image to cloud by resource-constrained devices such as smart phone is addressed, without revealing the cover image to cloud using steganography. In the proposed framework, the region of interest of the medical image is first detected using a visual saliency model. The detected important data is then embedded in a host image, producing a stego image which is outsourced to cloud for encryption. The cloud which has powerful resources, encrypts the image and sent back the encrypted marked image to the client. The client can then extract the selectively encrypted region of interest and can combine it with the region of non-interest to form a selectively encrypted image, which can be sent to medical specialists and healthcare centers. Experimental results and analysis validate the effectiveness of the proposed framework in terms of security, image quality, and computational complexity and verify its applicability in remote patient monitoring centers.     

Alpha Fusion Adversarial Attack Analysis Using Deep Learning

The deep learning model encompasses a powerful learning ability that integrates the feature extraction, and classification method to improve accuracy. Convolutional Neural Networks (CNN) perform well in machine learning and image processing tasks like segmentation, classification, detection, identification, etc. The CNN models are still sensitive to noise and attack. The smallest change in training images as in an adversarial attack can greatly decrease the accuracy of the CNN model. This paper presents an alpha fusion attack analysis and generates defense against adversarial attacks. The proposed work is divided into three phases: firstly, an MLSTM-based CNN classification model is developed for classifying COVID-CT images. Secondly, an alpha fusion attack is generated to fool the classification model. The alpha fusion attack is tested in the last phase on a modified LSTM-based CNN (CNN-MLSTM) model and other pre-trained models. The results of CNN models show that the accuracy of these models dropped greatly after the alpha-fusion attack. The highest F1 score before the attack was achieved is 97.45 And after the attack lowest F1 score recorded is 22%. Results elucidate the performance in terms of accuracy, precision, F1 score and Recall.     

Adaptive simulation of two dimensional hyperbolic problems by Collocated Discrete Least Squares Meshless method

An adaptive refinement technique is presented in this paper and used in conjunction with the Collocated Discrete Least Squares Meshless (CDLSM) method for the effective simulation of two-dimensional shocked hyperbolic problems. The CDLSM method is based on minimizing the least squares functional calculated at collocation points chosen on the problem domain and its boundaries. The functional is defined as the weighted sum of the squared residuals of the differential equation and its boundary conditions. A Moving Least Squares (MLS) method is used here to construct the meshless shape functions. An error estimator based on the value of functional at nodal points used to discretize the problem domain and its boundaries is developed and used to predict the areas of poor solutions. A node moving strategy is then used to refine the predicted zones of poor solutions before the problem is resolved on the refined distribution of nodes. The proposed methodology is applied to some two dimensional hyperbolic benchmark problems and the results are presented and compared to the exact solutions. The results clearly show the capabilities of the proposed method for the effective and efficient solution of hyperbolic problems of shocked and high gradient solutions.     

An improved dynamic modeling of a multibody system with spherical joints

A dynamic modeling of multibody systems having spherical joints is reported in this work. In general, three intersecting orthogonal revolute joints are substituted for a spherical joint with vanishing lengths of intermediate links between the revolute joints. This procedure increases sizes of associated matrices in the equations of motion, thus increasing computational burden of an algorithm used for dynamic simulation and control. In the proposed methodology, Euler parameters, which are typically used for representation of a rigid-body orientation in three-dimensional Cartesian space, are employed to represent the orientation of a spherical joint that connects a link to its previous one providing three-degree-of-freedom motion capability. For the dynamic modeling, the concept of the Decoupled Natural Orthogonal Complement (DeNOC) matrices is utilized. It is shown in this work that the representation of spherical joints motion using Euler parameters avoids the unnecessary introduction of the intermediate links, thereby no increase in the sizes of the associated matrices with the dynamic equations of motion. To confirm the efficiency of the proposed representation, it is illustrated with the dynamic modeling of a spatial four-bar Revolute-Spherical–Spherical-Revolute (RSSR) mechanism, where the CPU time of the dynamic modeling based on proposed methodology is compared with that based on the revolute joints substitution. Finally, it is explained how a complex suspension and steering linkage can be modeled using the proposed concept of Euler parameters to represent a spherical joint.