Parameter Identification of Photovoltaic Models by Hybrid Adaptive JAYA Algorithm

As one of the most promising forms of renewable energy, solar energy is increasingly deployed. The simulation and control of photovoltaic (PV) systems requires identification of their parameters. A Hybrid Adaptive algorithm based on JAYA and Differential Evolution (HAJAYADE) is developed to identify these parameters accurately and reliably. The HAJAYADE algorithm consists of adaptive JAYA, adaptive DE, and the chaotic perturbation method. Two adaptive coefficients are introduced in adaptive JAYA to balance the local and global search. In adaptive DE, the Rank/Best/1 mutation operator is put forward to boost the exploration and maintain the exploitation. The chaotic perturbation method is applied to reinforce the local search further. The HAJAYADE algorithm is employed to address the parameter identification of PV systems through five test cases, and the eight latest meta-heuristic algorithms are its opponents. The mean RMSE values of the HAJAYADE algorithm from five test cases are 9.8602 × 10−4, 9.8294 × 10−4, 2.4251 × 10−3, 1.7298 × 10−3, and 1.6601 × 10−2. Consequently, HAJAYADE is proven to be an efficient and reliable algorithm and could be an alternative algorithm to identify the parameters of PV systems.

Perturbation based Fuzzified k-Mode Clustering Method for Privacy Preserving Recommender System

Recommender systems are extensively used today to ease out the problem of information overload and facilitate the product selection by users in e-commerce market. Both privacy and security are two major concerns of the user in these systems. For the protection of the user’s rating, there are several existing works on the basis of encryption or randomization methodologies. This paper proposes a methodology that not only protects the privacy of ratings but also provides better accuracy. After applying fuzzification on the user ratings, random rotation and perturbation methods are used before being fed to the collaborative filtering system. In this process, similar users are grouped into clusters by which recommendation is made. By considering different cluster size on four different datasets, the proposed fuzzified k-Mode clustering method provides less MAE and RMSE value as compared to other k-Means and k-Mode clustering approach and also achieves the better privacy than randomized perturbation method by obtaining IVDM value i.e. 0.67, 0.61, 0.55 and 0.7.

An Effective Solution of the Cube-Root Truly Nonlinear Oscillator: Extended Iteration Procedure

The cube-root truly nonlinear oscillator and the inverse cube-root truly nonlinear oscillator are the most meaningful and classical nonlinear ordinary differential equations on behalf of its various applications in science and engineering. Especially, the oscillators are used widely in the study of elastic force, structural dynamics, and elliptic curve cryptography. In this paper, we have applied modified Mickens extended iteration method to solve the cube-root truly nonlinear oscillator, the inverse cube-root truly nonlinear oscillator, and the equation of pendulum. Comparison is made among iteration method, harmonic balance method, He’s amplitude-frequency formulation, He’s homotopy perturbation method, improved harmonic balance method, and homotopy perturbation method. After comparison, we analyze that modified Mickens extended iteration method is more accurate, effective, easy, and straightforward. Also, the comparison of the obtained analytical solutions with the numerical results represented an extraordinary accuracy. The percentage error for the fourth approximate frequency of cube-root truly nonlinear oscillator is 0.006 and the percentage error for the fourth approximate frequency of inverse cube-root truly nonlinear oscillator is 0.12.

Link prediction of viral spike proteins and cell receptors using structural perturbation method

Many protein receptors for animal and human viruses have been discovered in decades of studies. The main determinant of virus entry is the binding of the viral spike protein to host cell receptors, which mediates membrane fusion.In this work, a bilayer network is constructed by integrating the similarity network of the viral spike proteins, the similarity network of host receptors, and the association network between viruses and receptors. The structural perturbation method (SPM) is used to predict possible emerging infection of a virus in potential new host organisms. The reliability of this method is based on the hypothesis that the major barrier to virus infection is the differences in the compatibility of spike proteins and cell receptors, which is determined by the amino acid sequences among species.