A Comparative Taxonomy of Parallel Algorithms for RNA Secondary Structure Prediction

RNA molecules have been discovered playing crucial roles in numerous biological and medical procedures and processes. RNA structures determination have become a major problem in the biology context. Recently, computer scientists have empowered the biologists with RNA secondary structures that ease an understanding of the RNA functions and roles. Detecting RNA secondary structure is an NP-hard problem, especially in pseudoknotted RNA structures. The detection process is also time-consuming; as a result, an alternative approach such as using parallel architectures is a desirable option. The main goal in this paper is to do an intensive investigation of parallel methods used in the literature to solve the demanding issues, related to the RNA secondary structure prediction methods. Then, we introduce a new taxonomy for the parallel RNA folding methods. Based on this proposed taxonomy, a systematic and scientific comparison is performed among these existing methods

MPF-LEACH: modified probability function for cluster head election in LEACH protocol

In this research, we enhance the LEACH protocol by updating Cluster Head (CH) election probability function (thresholds). More probability was given to an out-of-service CH to be elected again. The idea is to get benefit from CH residual energy in order to extend the network lifetime. A new threshold was introduced which guarantees a non-zero probability value of a CH. We proposed a newly developed research technique to enhance the original LEACH protocol. The enhancement focuses on extending a WSN’s lifetime, and increasing its throughput. It is achieved by giving more probability to re-elect the expired CH that has been removed from CHs list because of its insufficient residual energy. Several experiments were conducted to evaluate the efficiency of our proposed MPF-LEACH approach. From the experimental results, a remarkable enhancement in the network lifetime and throughput is achieved. We have improved the election probability threshold for the original LEACH protocol by benefiting from the CH residual energy. As a result, the whole network lifetime was increased due to the extra chance that is given to a CH to be elected again

A novel improved lemurs optimization algorithm for feature selection problems

The irrelevant and repeated features in high-dimensional datasets can negatively affect the final performance and accuracy of classification-based models. Therefore, feature selection (FS) techniques can be used to determine the most optimal relevant features. In this paper, we fuse a new enhanced model from Lemurs Optimization (LO) algorithm, called Enhanced Lemurs Optimization (ELO). We combine Opposition Based Learning (OBL) and Local Search Algorithm (LSA) to address exploration and exploitation challenges, respectively. Our proposed ELO algorithm incorporates U-shaped and Sigmoid transfer functions during the position update step, leading to improved accuracy and convergence. These new deployments based on the U-shaped and Sigmoid transfer functions are called ELO-U and ELO-S algorithms, respectively. The performance of all three new versions of our proposed optimization algorithms (ELO, ELO-U, and ELO-S) has been evaluated using 21 UCI datasets in different fields and sizes. Moreover, their results are also compared to other competitive algorithms. The evaluation process included several measurements such as fitness value, an average of selected features, and average accuracy. Experimental results demonstrate that our proposed ELO-U algorithm achieves the best average accuracy of 91.03%. Statistical analysis using Friedman and Wilcoxon tests confirms the superiority of ELO-U over other competitors