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Computational models are foundational concepts in computer science; many of these models such as P systems are based on natural biological processes. P systems represent a wide framework for a variety of concepts of data mining, as models of data clustering approaches. Data clustering is a technique for analyzing data based on its structure that is widely utilized for many applications. In this paper, the proposed model (PSO-MFM) has combined the Particle Swarm Optimization algorithm (PSO) with Mitochondrial Fusion Model to overcome some constraints of clustering techniques. The solving of clustering problem based on particle swarm is investigated in the proposed model when mutual dynamic rules are used. It can find the best cluster centers for a data set and improve clustering performance by utilizing the distributed parallel computing concept of mutual dynamic rules of mitochondrial fusion model. The comparative results demonstrate that the proposed strategy outperforms competition models when it comes to clustering accuracy, stability and the most efficient in time complexity. [ABSTRACT FROM AUTHOR]

Text classification is a classical task in natural language processing. Prior traditional text classification methods rely on manually extracted features to a great extent, which are easily influenced by human subjectivity. Some existing text classification methods based on the artificial neural networks sometimes neglect the contextual semantic relationships of discontinuous word sequences, resulting in poor learning results. To alleviate these problems, we propose an attention-based gated graph neural network in the framework of coupled P systems (CPGANN) to automatically extract feature representations of nodes. The gating unit with attention is introduced to aggregate neighbor information to capture context semantic relations, and effectively alleviate the long-term dependence on discontinuous words. In order to obtain more discriminative nodes for classification, the attention mechanism is employed in CPGANN to extract keyword nodes before readout to aggregate subgraph representations. Extensive experiments on four real-world datasets demonstrate that CPGANN outperforms all other state-of-the-art baseline algorithms.

P systems with active membranes, as a sort of basic P system, include in communication rules and out communication rules, where communication rules are controlled by polarizations. However, the communication of objects among living cells may be controlled by several factors, such as proteins, polarizations, etc. Based on this biological fact, in this article, a new class of P systems, named P systems with proteins on active membranes (known as PAM P systems) is considered, where the movement of objects is controlled by both proteins and polarizations. The computational theory of PAM P systems is discussed. More specifically, we show that PAM P systems achieve Turing universality when the systems use two membranes, one protein and one polarization. Moreover, the PAM P systems, with the help of membrane division rules, make the SAT problem solvable. These results indicate that PAM P systems are also a sort of powerful system. [ABSTRACT FROM AUTHOR]

Currently, as an effect of the COVID-19 pandemic, bioinformatics, genomics, and biological computations are gaining increased attention. Genomes of viruses can be represented by character strings based on their nucleobases. Document similarity metrics can be applied to these strings to measure their similarities. Clustering algorithms can be applied to the results of their document similarities to cluster them. P systems or membrane systems are computation models inspired by the flow of information in the membrane cells. These can be used for various purposes, one of them being data clustering. This paper studies a novel and versatile clustering method for genomes and the utilization of such membrane clustering models using document similarity metrics, which is not yet a well-studied use of membrane clustering models. [ABSTRACT FROM AUTHOR]

The tissue P system (TPS) possesses intrinsic attributes of parallel execution in comprehensive data and instruction space, which provides fast convergence during the transition from local to global optima. Method-In this study, we have proposed and built a TPSysIR framework using the TPS for image registration that optimizes upon the mutual information (MI) similarity metric to find a global solution. Result-The model was tested on single-and multimodal brain MRI scans and other prominent optimization-based image registration techniques. Conclusion-Results show that, among all methods, TPSysIR provides better MI values with minimum deviation in a range of experiment setups conducted iteratively. [ABSTRACT FROM AUTHOR]

The tissue P system (TPS) possesses intrinsic attributes of parallel execution in comprehensive data and instruction space, which provides fast convergence during the transition from local to global optima. Method- In this study, we have proposed and built a TPSysIR framework using the TPS for image registration that optimizes upon the mutual information (MI) similarity metric to find a global solution. Result- The model was tested on single- and multimodal brain MRI scans and other prominent optimization-based image registration techniques. Conclusion- Results show that, among all methods, TPSysIR provides better MI values with minimum deviation in a range of experiment setups conducted iteratively.

We consider tissue P systems working on vesicles of multisets with the very simple operations of insertion, deletion, and substitution of single objects. With the whole multiset being enclosed in a vesicle, sending it to a target cell can be indicated in those simple rules working on the multiset. As derivation modes we consider the sequential derivation mode, where, if possible, one rule is applied in a derivation step, and the set maximally parallel derivation mode, where in each derivation step a non-extendable set of rules indicating the same target cell is applied. With the set maximally parallel derivation mode, computational completeness can already be obtained with tissue P systems having a tree structure, whereas tissue P systems even with an arbitrary communication structure are not computationally complete when working in the sequential mode. Adding polarizations — only the three polarizations − 1 , 0 , 1 are sufficient — allows for obtaining computational completeness even for tissue P systems working in the sequential mode. [ABSTRACT FROM AUTHOR]

The evolution of simulation and implementation of P systems has been intense since the theoretical model of computation was created. In the field of software simulation of P systems, the proposals made so far have taken advantage mainly of the parallelism of GPUs, but not of the parallelism of existing multi-core processors. This paper proposes a new model for simulating P systems using a multi-threaded approach in a multi-core processor. This simulation approach establishes a new paradigm that is entirely in line with the philosophy of P-systems: since objects must react in parallel, asynchronously and autonomously with other objects, simulation using multiple synchronized threads completely mimics the behavior of objects within a membrane. This proposal has been implemented and tested using a simulator programmed in C#, and its correct operation has been tested for confluent and non-confluent systems. The experimental results confirm that the simulator scales well with the number of hardware threads of a multiprocessor. The obtained results show that the new model is correct and that it can be extended to other more complex types of P systems, in order to discover which are the limit of this multi-threaded approach when running it in multi-core processors. • A pioneering approach for membrane simulation based on CPU multi-threading. • The approach can be straightforwardly used to simulate generic transitive P systems. • A multi-threaded implementation of the P system simulator has been tested. • Performance speedup results directly proportional to the number of hardware threads. [ABSTRACT FROM AUTHOR]

As one of the important artificial intelligence fields, brain-like computing attempts to give machines a higher intelligence level by studying and simulating the cognitive principles of the human brain. Compared with the traditional neural network, the spiking neural network (SNN) has better biogenesis and stronger computing power. In this paper, an SNN learning model based on an evolutionary membrane algorithm is proposed to solve the problem of supervised classification. The proposed algorithm uses the P system's object, reaction rules, and membrane structure to solve these problems. Specifically, the proposed algorithm can automatically adjust the learning parameters of the network by adjusting the synaptic weight in the learning stage of the spiking neural model according to different application data, providing a better solution model for balance exploration and exploitation. In the simulation experiment, effectiveness verification research is carried out. The simulation results show that compared with other experimental algorithms, the proposed algorithm has a competitive advantage in solving twelve supervised classification benchmark problems through learning curves and quantified classification results.

• We consider the computing power of a model of polarizationless P systems with active membranes. • This model uses evolution, communication, dissolution, and (weak) membrane division rules. • We prove that these P systems compute at least all problems in the complexity class ▪. • We do so by simulating nondeterministic Turing machines, used as oracles for a deterministic Turing machine. • The simulation is given for semi-uniform families of P systems, but it can be easily extended to uniform families. Among the computational features that determine the computing power of polarizationless P systems with active membranes, the depth of the membrane hierarchy is one of the least explored. It is known that this model of P systems can solve ▪-complete problems when no constraints are given on the depth of the membrane hierarchy, whereas the complexity class P ∥ # P is characterized by monodirectional shallow P systems with minimal cooperation, whose depth is 1. No similar result is currently known for polarizationless systems without cooperation or other additional features. In this paper we show that these P systems, using a membrane hierarchy of depth 2, are able to solve at least all decision problems that are in the complexity class ▪, the class of problems solved in polynomial time by deterministic Turing machines that are given the possibility to make a polynomial number of parallel queries to oracles for ▪ problems. [ABSTRACT FROM AUTHOR]

Bio-inspired self-repairing hardware is a distributed self-adaptive system, characterized by powerful fault-tolerant ability and environment adaptivity. However, it suffers from some difficulties such as large resource consumption and degraded circuit performances. From the viewpoint of cybernetics and computer science, the cellular differentiation and substitution process of bio-inspired self-repairing hardware can be converted into dynamic placement problems on a reconfigurable system. Current systems can only generate some predefined fault-free placements from a finite number of initial placements. The aim of this paper was to achieve high-quality placements from arbitrary initial placements. Based on P systems, an analysis has been made on the limitations of current systems and an improved computing system has been developed to achieve the ergodic property. Its computational power has been verified by a constructive proof. Moreover, centering on the problem how to improve the placement quality on a distributed platform, the optimization model, task allocation, optimization strategy, and membrane optimization algorithm have been designed and developed. The optimization performances were verified and the calculation amount was exhibited by experiments. Finally, it indicated by comparison that the proposed approach would reduce the resource consumption and maintain good circuit performances.

P systems with active membranes, as a sort of basic P system, include in communication rules and out communication rules, where communication rules are controlled by polarizations. However, the communication of objects among living cells may be controlled by several factors, such as proteins, polarizations, etc. Based on this biological fact, in this article, a new class of P systems, named P systems with proteins on active membranes (known as PAM P systems) is considered, where the movement of objects is controlled by both proteins and polarizations. The computational theory of PAM P systems is discussed. More specifically, we show that PAM P systems achieve Turing universality when the systems use two membranes, one protein and one polarization. Moreover, the PAM P systems, with the help of membrane division rules, make the SAT problem solvable. These results indicate that PAM P systems are also a sort of powerful system.

The model of membrane computing, also known under the name of P systems, is a bio-inspired large-scale parallel computing paradigm having a good potential for the design of massively parallel algorithms. For its implementation it is very natural to choose hardware platforms that have important inherent parallelism, such as field-programmable gate arrays (FPGAs) or compute unified device architecture (CUDA)-enabled graphic processing units (GPUs). This article performs an overview of all existing approaches of hardware implementation in the area of P systems. The quantitative and qualitative attributes of FPGA-based implementations and CUDA-enabled GPU-based simulations are compared to evaluate the two methodologies. [ABSTRACT FROM AUTHOR]

Syntactic models constitute one of the main areas of mathematical studies on picture array generation. A number of 2D grammar models have been proposed with motivation from a variety of applications. Parallel Contextual Array Insertion Deletion Grammar (PCAIDG) and Pure 2D Context-Free Grammar (P2DCFG) are two such 2D grammars. In the case of P2DCFG, two new variants are studied with interesting properties. In this paper we show that PCAIDG has higher generative power than the generative powers of two variants of P2DCFG. Further, P systems associated with the grammars PCAIDG, P2DCFG and their variants are considered in the literature. In this paper, we show that the generative power of Parallel Contextual Array Insertion Deletion P System (PCAIDPS) is greater than the generative power of the P system associated with a variant of P2DCFG. [ABSTRACT FROM AUTHOR]

A spiking neural P system (SN P system) is a model of computation inspired by the mechanism of a network of spiking neurons. A homogeneous SN P system is an SN P system whose neurons have identical rule set. In this work, we introduced an algorithm that, under certain condition, transforms a non-homogeneous SN P system Π to a homogeneous SN P system Π ′ that computes the same set as the system Π. Two transformation operations called neuron translation and neuron scaling are introduced which are then used by the homogenization algorithm. [ABSTRACT FROM AUTHOR]

As one of the important artificial intelligence fields, brain-like computing attempts to give machines a higher intelligence level by studying and simulating the cognitive principles of the human brain. A spiking neural network (SNN) is one of the research directions of brain-like computing, characterized by better biogenesis and stronger computing power than the traditional neural network. A liquid state machine (LSM) is a neural computing model with a recurrent network structure based on SNN. In this paper, a learning algorithm based on an evolutionary membrane algorithm is proposed to optimize the neural structure and hyperparameters of an LSM. First, the object of the proposed algorithm is designed according to the neural structure and hyperparameters of the LSM. Second, the reaction rules of the proposed algorithm are employed to discover the best neural structure and hyperparameters of the LSM. Third, the membrane structure is that the skin membrane contains several elementary membranes to speed up the search of the proposed algorithm. In the simulation experiment, effectiveness verification is carried out on the MNIST and KTH datasets. In terms of the MNIST datasets, the best test results of the proposed algorithm with 500, 1000 and 2000 spiking neurons are 86.8%, 90.6% and 90.8%, respectively. The best test results of the proposed algorithm on KTH with 500, 1000 and 2000 spiking neurons are 82.9%, 85.3% and 86.3%, respectively. The simulation results show that the proposed algorithm has a more competitive advantage than other experimental algorithms.

Sudoku is known as a NP-complete combinatorial number-placement puzzle. In this study, we propose the first cP system solution to generalised Sudoku puzzles with m×m cells grouped in m blocks. By using a fixed constant number of rules, our cP system can solve all Sudoku puzzles in sublinear steps. We evaluate the cP system and discuss its formal verification. [ABSTRACT FROM AUTHOR]

P systems have been known to provide efficient polynomial (often linear) deterministic solutions to hard problems. In particular, cP systems have been shown to provide very crisp and efficient solutions to such problems, which are typically linear with small coefficients. Building on a recent result by Henderson et al., which solves SAT in square-root-sublinear time, this paper proposes an orders-of-magnitude-faster solution, running in logarithmic time, and using a small fixed-sized alphabet and ruleset (25 rules). To the best of our knowledge, this is the fastest deterministic solution across all extant P system variants. Like all other cP solutions, it is a complete solution that is not a member of a uniform family (and thus does not require any preprocessing). Consequently, according to another reduction result by Henderson et al., cP systems can also solve k-colouring and several other NP-complete problems in logarithmic time.

Nowadays, the success of neural networks as reasoning systems is doubtless. Nonetheless, one of the drawbacks of such reasoning systems is that they work as black-boxes and the acquired knowledge is not human readable. In this paper, we present a new step in order to close the gap between connectionist and logic based reasoning systems. We show that two of the most used inference rules for obtaining negative information in rule based reasoning systems, the so-called Closed World Assumption and Negation as Finite Failure can be characterized by means of spiking neural P systems, a formal model of the third generation of neural networks born in the framework of membrane computing. [ABSTRACT FROM AUTHOR]

It was recently found that dendrites are not just a passive channel. They can perform mixed computation of analog and digital signals, and therefore can be abstracted as information processors. Moreover, dendrites possess a feedback mechanism. Motivated by these computational and feedback characteristics, this article proposes a new variant of neural-like P systems, dendrite P (DeP) systems, where neurons simulate the computational function of dendrites and perform a firing–storing process instead of the storing–firing process in spiking neural P (SNP) systems. Moreover, the behavior of the neurons is characterized by dendrite rules that are abstracted by two characteristics of dendrites. Different from the usual firing rules in SNP systems, the firing of a dendrite rule is controlled by the states of the corresponding source neurons. Therefore, DeP systems can provide a collaborative control capability for neurons. We discuss the computational power of DeP systems. In particular, it is proven that DeP systems are Turing-universal number generating/accepting devices. Moreover, we construct a small universal DeP system consisting of 115 neurons for computing functions. • We consider dendrites as information processors. • We propose a new model, dendrite P systems (DeP systems, in short). • We prove that DeP systems are Turing-universal number generating/accepting devices. • We construct a small universal DeP system of 115 neurons for computing functions. [ABSTRACT FROM AUTHOR]

In this paper we propose a k -nearest neighbors (kNN) classifier optimized by P systems, called kNN-P, which can improve the performance of the original kNN classifier. A P system consisting of multiple cells is considered as its computational framework. Under the control of both evolution rules and communication rules, each cell determines the optimal set of k -nearest neighbors for a test sample. The proposed algorithm is evaluated on eighteen benchmark datasets and compared with classical kNN algorithm and eight recently developed improved algorithms. Experimental results demonstrate the availability and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Membrane computing is a new computability model, called P system introduced by Păun [5] inspired by the functions of living cells. Tile Pasting System is a parallel generating model proposed to generate colorful two dimensional geometrical patterns [6]. Ceterachi et al. [1] linked the two areas of membrane computing and picture grammar and proposed array rewriting grammars. In this paper we propose Tetrahedral Tile Pasting system and Tetrahedral Tile pasting P system for generating three dimensional geometrical patterns by gluing edges along the base of the tetrahedral tiles and study some of its properties. [ABSTRACT FROM AUTHOR]

P systems with mitochondria enzymes layer computing is a recent version of P systems; it integrates a new feature inspired from the enzymes gate of a natural cell to the cell-like P systems. The model of a computational layer is well known as a problem of Non-Deterministic (NP-complete) in polynomial time. In this paper, we propose a P systems with enzymes to solve one of the most NP-hard problems, which is the determination of the maximal clique in a given undirected graph. In this context, the evolution strategy proposed is based on using objects under the control of enzymes placed on layers. [ABSTRACT FROM AUTHOR]

Image thresholding is an important research direction of image segmentation that aims to divide image into meaningful sub-regions. This paper introduces an optimal thresholding by a cell-like membrane algorithm with enhanced particle swarm optimization (PSO) with hyperparameter, namely MAPSOH. Under the membrane evolution-communication mechanism, the designed hyperparameter method for PSO parameters can obtain better convergence in less time. According to the special membrane structure, a modification of PSO is employed to find the best multi-level thresholding for image segmentation problem effectively. The experiments demonstrate that the proposed method has better quantitative statistical comparisons and qualitative performance in comparison with several existing methods. [ABSTRACT FROM AUTHOR]