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Combinatorial optimization problems are problems that have a large number of discrete solutions and a cost function for evaluating those solutions in comparison to one another. With the vital need of solving the combinatorial problem, several research efforts have been concentrated on the biological entities behaviors to utilize such behaviors in population-based metaheuristic. This paper presents bee colony algorithms which is one of the sophisticated biological nature life. A brief detail of the nature of bee life has been presented with further classification of its behaviors. Furthermore, an illustration of the algorithms that have been derived from bee colony which are bee colony optimization, and artificial bee colony. Finally, a comparative analysis has been conducted between these algorithms according to the results of the traveling salesman problem solution. Where the bee colony optimization (BCO) rendered the best performance in terms of computing time and results.

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The increasing volume of generated crime information readily available on the web makes the process of retrieving and analyzing and use of the valuable information in such texts manually a very difficult task. This work is focus on designing models for extracting crime-specific information from the Web. Thus, this paper proposes an ensemble framework for crime named entity recognition task. The main aim is to efficiently integrating feature sets and classification algorithms to synthesize a more accurate classification procedure. First, three well-known text classification algorithms, namely Naïve Bayes, Support Vector Machine and K-Nearest Neighbor classifiers, are employed as base-classifiers for each of the feature sets. Second, weighted voting ensemble method is used to combine theses three classifiers. To evaluate these models, a manually annotated data set that is obtained from BERNAMA is used. Experimental results demonstrate that using ensemble model is an effective way to combine different feature sets and classification algorithms for better classification performance. The ensemble model achieves an overall F-measure of 89.48% for identifying crime type and 93.36% for extracting crime-related entities. The results of the ensemble model trained with suitable features outperform baseline models.

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Thresholding is a type of image segmentation, where the pixels change to make the image easier to analyze. In bi-level thresholding, the image in grayscale format is transformed into a binary format. The traditional methods for image thresholding may be inefficient in finding the best threshold and take longer computation time. Recently, metaheuristic swarm-based algorithms were applied for optimization in different applications to find optimal solutions with minimum computational time. The proposed work aims to optimize the fitness function obtained by the Otsu algorithm using a metaheuristic swarm-based algorithm called the bat algorithm. As a result, the optimal threshold value for bi-level images in cloud detection was obtained. Also, one of the trajectory-based algorithms called hill climbing was applied to optimize the fitness function taken from the Otsu algorithm. The HYTA dataset was used to evaluate the work, which was later confirmed through testing. The findings of experiments indicated that the developed algorithm is promising and the performance of the metaheuristic population-based algorithm is better than the trajectory-based algorithm in terms of efficiency and computational time for image thresholding.

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The primary objective of studies related to medical diagnoses, predictions, and classifications is to help health care clinicians with support in the context of more precise, economical and trouble-free systems. This investigation focuses on the review of newly developed computational intelligence algorithms in the context of computer-generated neural networks, fuzzy logic and neuro-fuzzy systems. Relevant literature in this area offers concise explanations on the concept of computational intelligence algorithms and how they are employed to analyse, forecast and solve predicaments in the medical domain. This work includes a portrayal of the benefits that come with the various methods employed for arriving at solutions to medical problems.

ملخص البحث :

The Bat algorithm, inspired by microbat foraging behavior, has been utilized to solve optimization issues in continuous and discrete spaces. This algorithm can stagnate after some initial stage. Many methods and tactics have been tried to improve performance by increasing the diversity of the solution and therefore increasing the performance. A variant of the bat algorithm is the binary bat algorithm, in which the new bat position is limited to binary numbers only. The binary bat technique is now widely used to address issues in practically every area of optimization, feature selection, classification, and engineering procedures. Because there have been few reviews of the binary bat algorithm, the proposed study conducted a review of recent applications and modifications.