Automatic 2d image segmentation using tissue-like p system

This paper uses P-Lingua, a standard programming language that is designed specifically for P systems, to automatically simulate the region-based segmentation of 2D images. P-Lingua, which is based on membrane computing, links to Java Netbeans using the PLinguaCore4 Java library to automatically codify the pixels of the input image as long as automatically draw the output segmented image. Many methods have been suggested previously and used for artificial image segmentation, but to the best of our knowledge, none of those techniques were automatic, where the image was codified manually and the visualization of the output image was done manually in the tissue simulator which takes time and effort, especially when dealing with large images in the system. Two types of pixel adjacency have been utilized in this paper, namely; 4-adjacency and 8-adjacency. The jacquard index method has been used to measure the accuracy of the segmentation. The results of the proposed method demonstrated that beside its ability to automatically segmenting 2D images with arbitrary sizes, it is more efficient and faster than the tissue simulator tool, since the latter needs the input image to be codified manually pixel by pixel which makes it impractical for real-world applications

Lightning search algorithm: a comprehensive survey

The lightning search algorithm (LSA) is a novel meta-heuristic optimization method, which is proposed in 2015 to solve constraint optimization problems. This paper presents a comprehensive survey of the applications, variants, and results of the so-called LSA. In LSA, the best-obtained solution is defined to improve the effectiveness of the fitness function through the optimization process by finding the minimum or maximum costs to solve a specific problem. Meta-heuristics have grown the focus of researches in the optimization domain, because of the foundation of decision-making and assessment in addressing various optimization problems. A review of LSA variants is displayed in this paper, such as the basic, binary, modification, hybridization, improved, and others. Moreover, the classes of the LSA’s applications include the benchmark functions, machine learning applications, network applications, engineering applications, and others. Finally, the results of the LSA is compared with other optimization algorithms published in the literature. Presenting a survey and reviewing the LSA applications is the chief aim of this survey paper

A Kernel-Based Membrane Clustering Algorithm

The existing membrane clustering algorithms may fail to handle the data sets with non-spherical cluster boundaries. To overcome the shortcoming, this paper introduces kernel methods into membrane clustering algorithms and proposes a kernel-based membrane clustering algorithm, KMCA. By using non-linear kernel function, samples in original data space are mapped to data points in a high-dimension feature space, and the data points are clustered by membrane clustering algorithms. Therefore, a data clustering problem is formalized as a kernel clustering problem. In KMCA algorithm, a tissue-like P system is designed to determine the optimal cluster centers for the kernel clustering problem. Due to the use of non-linear kernel function, the proposed KMCA algorithm can well deal with the data sets with non-spherical cluster boundaries. The proposed KMCA algorithm is evaluated on nine benchmark data sets and is compared with four existing clustering algorithms

A Comprehensive Survey on the Recent Variants and Applications of Membrane-Inspired Evolutionary Algorithms

In the last decade, the application of membrane-inspired evolutionary algorithms in real-life problems has attracted much attention due to their flexibility and parallelizability. Almost seven years have passed since the first membrane algorithms survey paper was published in 2014. Considering the importance and ongoing research on such algorithms and their applications in various disciplines, this paper presents a comprehensive review of the published literature and suggests future directions. This review aims to summarize and analyze membrane algorithms based on the used nature-inspired algorithm, membrane structure, membrane rules, and their merits and demerits. Furthermore, an extensive bibliography about their real-world applications is presented

Correction to: A Comprehensive Survey on the Recent Variants and Applications of Membrane-Inspired Evolutionary Algorithms (Archives of Computational Methods in Engineering, (2022), 10.1007/s11831-021-09693-5)

This correction is published as second author forgot to include secondary affiliations and should be read as: Yonsei Frontier Lab, Yonsei University, Seoul, South Korea. Email: [email protected] Original article has been updated

Authentication for ID cards based on colour visual cryptography and facial recognition

Modern identification cards can be used for a myriad of applications such as electronic passports, ATM cards or payment cards for public transportation. Despite their ease of use, user authentication is an important factor that must be taken into consideration. In addition to the use of passwords, biometric data such as fingerprints or iris images can also be included as part of a multi-factor authentication system. However, these methods require secure storage of the biometric template and active participation from the user. In this paper, we propose a new method of authentication for identification cards based on colour visual cryptography and facial recognition. A colour image of the user will be encrypted using visual cryptography and split into two share images, one of which will be stored on the database and the other will be stored in the card. When the card is placed onto a sensor, the two shares will overlap to uncover the original image. The recovered image is then fed into a trained facial recognition algorithm to verify the user’s identity. The proposed method is evaluated based on recognition rate and runtime. Experimental results indicate the feasibility of the proposed method for practical application and can be used as a starting point for future work in the area

From existing trends to future trends in privacy‐preserving collaborative filtering

WOS: 000363679700002The information overload problem, also known as infobesity, forces online vendors to utilize collaborative filtering algorithms. Although various recommendation methods are widely used by many electronic commerce sites, they still have substantial problems, including but not limited to privacy, accuracy, online performance, scalability, cold start, coverage, grey sheep, robustness, being subject to shilling attacks, diversity, data sparsity, and synonymy. Privacy-preserving collaborative filtering methods have been proposed to handle the privacy problem. Due to the increasing popularity of privacy protection and recommendation estimation over the Internet, prediction schemes with privacy are still receiving increasing attention. Because research trends might change over time, it is critical for researchers to observe future trends. In this study, we determine the existing trends in the privacy-preserving collaborative filtering field by examining the related papers published mainly in the last few years. Comprehensive examinations of the most up-to-date related studies are described. By scrutinizing the contemporary inclinations, we present the most promising possible research trends in the near future. Our proposals can help interested researchers direct their research toward better outcomes and might open new ways to enrich privacy-preserving collaborative filtering studies. WIREs Data Mining Knowl Discov 2015, 5:276-291. doi: 10.1002/widm.1163 For further resources related to this article, please visit the .TUBITAK [114E571]This work is supported by TUBITAK under grant no. 114E571