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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 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

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Drug abuse pertains to the consumption of a substance that may induce adverse effects to a person. In international security studies, drug trafficking has become an important topic. In this regard, drug-related crimes are identified as an extremely significant challenge faced by any community. Several techniques for investigations in the crime domain have been implemented by many researchers. However, most of these researchers focus on extracting general crime entities. The number of studies that focus on the drug crime domain is relatively limited. This paper mainly aims to propose a rule-based named entity recognition model for drug-related crime news documents. In this work, a set of heuristic and grammatical rules is used to extract named entities, such as types of drugs, amount of drugs, price of drugs, drug hiding methods, and the nationality of the suspect. A set of grammatical and heuristic rules is established based on part-ofspeech information, developed gazetteers, and indicator word lists. The combined approach of heuristic and grammatical rules achieves a good performance with an overall precision of 86%, a recall of 87%, and an F1-measure of 87%. Results indicate that the ensemble of both heuristic and grammatical rules improves the extraction effectiveness in terms of macro-F1 for all entities

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Although valuable crime information is available in human-readable form in online newspapers and electronic archives, software systems that can extract and present relevant information are limited and are of significant interest to researchers in the field of information extraction. This work aims to extract available drug crime information from online newspaper articles. This work has the following subtasks: assess where and how drug traffickers hide drugs, identify the nationalities of drug dealers, identify the types (names) of drugs, and assess the quantity and prices of drugs in the local market. This paper presents a rule-based approach to extract information on the basis of a set of drug crime gazetteers and on a set of grammatical and heuristic rules. This work is validated through experiments. Results show that the technique developed here are promising.

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Most of the crimes committed today are reported on the Internet by news articles, blogs and social networking sites. With the increasing volume of crime information available on the web, a means to retrieve and exploit them and provide insight into the criminal behavior and networks must be determined to fight crime more efficiently and effectively. We believe that an electronic system must be designed for crime named entity recognition from the newspaper articles. Thus, this study designs and develops a crime named entity recognition based on machine learning approaches that extract nationalities, weapons, and crime locations in online crime documents. This study also collected a new corpus of crime and manually labeled them. A machine learning classification framework is proposed based on Naïve Bayes and SVM model in extracting nationalities, weapons, and crime location from online crime documents. To evaluate our model, a manually annotated data set was used, which was then validated by experiments. The results of the experiments showed that the developed techniques are promising.

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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 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.