Solving problems via artificial intelligence techniques has widely prevailed in different aspects. Implementing artificial intelligence optimization algorithms for NP-hard problems is still challenging. In this manuscript, we work on implementing the Naked Mole-Rat Algorithm (NMRA) to solve the n-queens problems and overcome the challenge of applying NMRA to a discrete space set. An improvement of NMRA is applied using the aspect of local search in the Variable Neighborhood Search algorithm (VNS) with 2-opt and 3-opt. Introducing the Naked Mole Rat algorithm based on variable neighborhood search (NMRAVNS) to solve N-queens problems with different sizes. Finding the best solution or set of solutions within a plausible amount of time is the main goal of the NMRAVNS algorithm. The improvement of the proposed algorithm boosts the exploitation capability of the basic NMRA and gives a greater possibility, with the emerging search strategies, to find the global best solution. This algorithm proved successful and outperformed other algorithms and studies with a remarkable target. A detailed comparison is performed, and the data results are presented with the relevant numbers and values. NMRA and NMRAVNS comparisons are implemented and recorded. Later on, a comparison between the Meerkat Clan Algorithm, Genetic Algorithm, Particle Swarm Optimization, and NMRAVNS is tested and presented. Finally, NMRAVNS is evaluated against the examined genetic-based algorithm and listed to prove the success of the proposed algorithm. NMRAVNS outperformed previous findings and scored competitive results with a high number of queen sizes, where an average time reduction reached about 87% of other previous findings.
This research deals with compound sentences in the German language and how to transform them and transfer them into a main sentence, touching on their functions and characteristics. Actual to nominative, which is a unique feature of the German language, with some diverse examples taken from various sources.This case is distinguished, like other grammatical cases
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In this paper, we used four classification methods to classify objects and compareamong these methods, these are K Nearest Neighbor's (KNN), Stochastic Gradient Descentlearning (SGD), Logistic Regression Algorithm(LR), and Multi-Layer Perceptron (MLP). Weused MCOCO dataset for classification and detection the objects, these dataset image wererandomly divided into training and testing datasets at a ratio of 7:3, respectively. In randomlyselect training and testing dataset images, converted the color images to the gray level, thenenhancement these gray images using the histogram equalization method, resize (20 x 20) fordataset image. Principal component analysis (PCA) was used for feature extraction, andfinally apply four classification metho
... Show MoreLet R be a commutative ring with unity and an R-submodule N is called semimaximal if and only if
the sufficient conditions of F-submodules to be semimaximal .Also the concepts of (simple , semisimple) F- submodules and quotient F- modules are introduced and given some properties .