Abstract The vehicle routing problem with time windows (VRPTW) has gained much attention recently due to its wide application in operations research and logistics. VRPTW has been proven to be an NP-hard problem whose optimal solution is computationally costly. Scholars have proposed many methods, such as exact algorithms, heuristics, and metaheuristics, to find near-optimal solutions for the VRPTW. Exact algorithms are limited to small-scale problems, while heuristic algorithms and metaheuristics often converge to locally optimal solutions, despite their applicability to larger-scale problems. This paper proposes a novel membrane-inspired evolutionary algorithm framework (MEAF) consisting of isolated evolutionary rules, communication output rules, communication input rules, fusion-exchange information operation, and membrane dissolution rules. By leveraging the advantages of multiple metaheuristics algorithms and avoiding the pitfalls of local optima, MEAF offers a promising solution to address complex problems. The effectiveness of the proposed MEAF is verified by applying three classical metaheuristics, namely Genetic Algorithm (GA), Ant Colony System (ACS), and Particle Swarm Algorithm (PSO), to solve the VRPTW problem. The experiments are run on 56 instances of Solomon with 100 client benchmarks. The evaluation of the experimental results combined with the mean and standard deviation values show that the algorithm performs better in 54 out of 56 instances, demonstrating the effectiveness and stability of the proposed algorithm.

Feature selection, as one of the essential dimensionality reduction techniques, hasbecome one popular yet challenging area in the field, such as data mining andmachine learning. Unlike feature extraction (such as principle component analysis andnon-negative matrix factorization), preserving the entire information but losing thefeature relevance. Data processing in feature selection will lose information, leading tothe need to develop lightweight, efficient, and practical methods that preserve the datainformation as much as possible while performing dimensional reduction. In this paper,we propose a rankability-based feature selection method. The rankability concept wasproposed in 2019, similar to the entropy concept, and has not been studied widely yet.The proposed method is lightweight in terms of complexity, which requires no iterativeoptimization or auxiliary estimator tools.We experimented with sixteen datasets and compared our results with four otheralgorithms. The results show that our rankability-based feature selection methodoutperforms the fuzzy entropy-based method on five datasets in eight, and the averageaccuracy increased by 0.1482, 0.1078, and 0.1157, respectively. Then, in the varieddimension-reducing experiments, the proposed method shows superiority on fourdatasets out of eight and is competitive with others on two datasets out of eight.

Despite notable progress in domain adaptation for classification, regression-based domain adaptation remains challenging, particularly in terms of handling complex data structures, ensuring cross-domain generalization, and maintaining the precision and mathematical rigor required to validate model effectiveness. Unlike classification tasks, which are more resilient to variations in feature scaling, regression tasks are notably more sensitive, making their performance more vulnerable in domain adaptation scenarios. In this paper, we propose a generalized regularization technique grounded in the Grassmannian manifold to address the feature alignment problem. This approach leverages the underlying manifold structure of the data while preserving mathematical bounds, thereby enhancing the precision and efficiency of problem-solving. To demonstrate the effectiveness of the proposed algorithm, we apply it to estimate multi-output parameters in two distinct domains: 1) the Arabidopsis thaliana plant dataset, collected from a high-throughput phenotyping platform at Palacký University, and 2) the publicly available dSprites shape recognition with six adaptation tasks. These tasks are critical to advance agricultural research and address generalization challenges in multi-output regression. Accurate predictions provide deeper insights into plant growth and health, thereby supporting more effective crop management strategies. We evaluate the effectiveness of our framework by comparing it with state-of-the-art regression alignment techniques that are independent of the underlying backbone and adaptable to transfer learning tasks. Experimental results show that our framework consistently outperforms existing methods,results description. The source code will be made publicly available upon acceptance at https://github.com/lingping-fuzzy.

Hierarchical Federated Learning (HFL) extends traditional Federated Learning (FL) by introducing multi-level aggregation in which model updates pass through clients, edge servers, and a global server. While this hierarchical structure enhances scalability, it also increases vulnerability to adversarial attacks — such as data poisoning and model poisoning — that disrupt learning by introducing discrepancies at the edge server level. These discrepancies propagate through aggregation, affecting model consistency and overall integrity. Existing studies on adversarial behaviour in FL primarily rely on single-metric approaches — such as cosine similarity or Euclidean distance — to assess model discrepancies and filter out anomalous updates. However, these methods fail to capture the diverse ways adversarial attacks influence model updates, particularly in highly heterogeneous data environments and hierarchical structures. Attackers can exploit the limitations of single-metric defences by crafting updates that seem benign under one metric while remaining anomalous under another. Moreover, prior studies have not systematically analysed how model discrepancies evolve over time, vary across regions, or affect clustering structures in HFL architectures. To address these limitations, we propose the Model Discrepancy Score (MDS), a multi-metric framework that integrates Dissimilarity, Distance, Uncorrelation, and Divergence to provide a comprehensive analysis of how adversarial activity affects model discrepancies. Through temporal, spatial, and clustering analyses, we examine how attacks affect model discrepancies at the edge server level in 3LHFL and 4LHFL architectures and evaluate MDS’s ability to distinguish between benign and malicious servers. Our results show that while 4LHFL effectively mitigates discrepancies in regional attack scenarios, it struggles with distributed attacks due to additional aggregation layers that obscure distinguishable discrepancy patterns over time, across regions, and within clustering structures. Factors influencing detection include data heterogeneity, attack sophistication, and hierarchical aggregation depth. These findings highlight the limitations of single-metric approaches and emphasize the need for multi-metric strategies such as MDS to enhance HFL security.

ABSTRACT In this paper, the improved chameleon swarm algorithm (ICSA) enhances the exploration–exploitation balance while optimizing feature subset selection. The integration of Lévy flight‐based exploration refines ICSA's search strategy, complemented by rotation‐type refinement and adaptive parameter‐setting mechanisms. These modifications ensure that exploration aligns effectively with the feature selection process, leading to a more adaptive and efficient approach. To evaluate ICSA's effectiveness, it is tested on the NSL‐KDD benchmark, a well‐established dataset in intrusion detection systems. Performance is assessed based on key metrics, including accuracy, detection rate, false alarm rate, execution time, and the number of selected features. Comparative analysis against six advanced classifiers demonstrates that ICSA achieves superior results with minimal computational overhead. The algorithm attains the highest accuracy (97.91%) and detection rate (98.75%), the fastest execution time, and the lowest false alarm rate (0.0021), eliminating the need for excessive feature selection. These results confirm that modifying feature selection mechanisms within ICSA significantly enhances computational efficiency and detection performance, as validated through rigorous experimental testing at the classifier level.

Fetal examinations are a significant and challenging field of healthcare. Cardiotocography is the most commonly used method for monitoring fetal heart rate and uterine contractions. As a promising alternative to cardiotocography, fetal phonocardiography is beginning to emerge. It is an entirely non-invasive, passive, and low-cost method. However, it is tough to estimate the ideal form of the fetal sound signal in most cases due to the presence of disturbances. The disturbances originate from movements or rotations of the fetal body, making fetal heart sound processing difficult. This study presents an automatic method for segmenting the fetal heart sounds in a phonocardiographic signal that is loaded with different types of disturbances and analyzes which of these disturbances most affect segmentation accuracy. To provide a comprehensive investigation, we propose a hybrid classifier based on Transformer and eXtreme Gradient Boosting, short for XGBoost, to improve segmentation performance by decision-making integration. 2000 segments of data from the Research Resource for Complex Physiologic Signals, PhysioNet repository, and created synthetic data (873 recordings) were used for the experiment. In the S1 label, our proposed method ranks first among all compared algorithms in precision, recall, F1, and accuracy score, tying with Transformer in recall score. It achieves an accuracy increase of 5% and 1.3% compared to XGBoost and Transformer, respectively. Similarly, in the S2 label, there is a precision score increase of 5.8% and 3.7% compared to XGBoost and Transformer, respectively. In general, our proposed method shows effective and promising performance..

An important feature of real networks is their hierarchy and the existence of overlapping communities. Hierarchical agglomerative clustering is one way to determine the hierarchy of a network. To ensure the existence of overlapping communities, it is appropriate to choose the base elements for clustering – edges, cliques, etc. These base elements can then have common vertices and naturally provide the possibility of overlap. The proposed community detection method uses hierarchical agglomerative clustering on the 2-edge-connected component of the graph. Communities are constructed from maximal cliques as base elements. Novel dissimilarities for hierarchical agglomerative clustering were introduced for the merging of cliques. The dissimilarities use the size of the overlapped cliques and closed trail distance to express dissimilarity between communities in networks. The single linkage approach contains and extends the results of k-CPM. The proposed algorithm utilizing deterministic dissimilarity achieves comparable or superior outcomes compared to standard algorithms used for hierarchical or overlapping community detection.

This study proposes a novel artificial protozoa optimizer (APO) that is inspired by protozoa in nature. The APO mimics the survival mechanisms of protozoa by simulating their foraging, dormancy, and reproductive behaviors. The APO was mathematically modeled and implemented to perform the optimization processes of metaheuristic algorithms. The performance of the APO was verified via experimental simulations and compared with 32 state-of-the-art algorithms. Wilcoxon signed-rank test was performed for pairwise comparisons of the proposed APO with the state-of-the-art algorithms, and Friedman test was used for multiple comparisons. First, the APO was tested using 12 functions of the 2022 IEEE Congress on Evolutionary Computation benchmark. Considering practicality, the proposed APO was used to solve five popular engineering design problems in a continuous space with constraints. Moreover, the APO was applied to solve a multilevel image segmentation task in a discrete space with constraints. The experiments confirmed that the APO could provide highly competitive results for optimization problems. The source codes of Artificial Protozoa Optimizer are publicly available at https://seyedalimirjalili.com/projects and https://ww2.mathworks.cn/matlabcentral/fileexchange/162656-artificial-protozoa-optimizer.

Transformer architectures have been applied to graph-specific data such as protein structure and shopper lists, and they perform accurately on graph/node classification and prediction tasks. Researchers have proved that the attention matrix in Transformers has low-rank properties, and the self-attention plays a scoring role in the aggregation function of the Transformers. However, it can not solve the issues such as heterophily and over-smoothing. The low-rank properties and the limitations of Transformers inspire this work to propose a Global Representation (GR) based attention mechanism to alleviate the two heterophily and over-smoothing issues. First, this GR-based model integrates geometric information of the nodes of interest that conveys the structural properties of the graph. Unlike a typical Transformer where a node feature forms a Key, we propose to use GR to construct the Key, which discovers the relation between the nodes and the structural representation of the graph. Next, we present various compositions of GR emanating from nodes of interest and α-hop neighbors. Then, we explore this attention property with an extensive experimental test to assess the performance and the possible direction of improvements for future works. Additionally, we provide mathematical proof showing the efficient feature update in our proposed method. Finally, we verify and validate the performance of the model on eight benchmark datasets that show the effectiveness of the proposed method.

Since the last three decades, numerous search strategies have been introduced within the framework of different evolutionary algorithms (EAs). Most of the popular search strategies operate on the hypercube (HC) search model, and search models based on other hypershapes, such as hyper-spherical (HS), are not investigated well yet. The recently developed spherical search (SS) algorithm utilizing the HS search model has been shown to perform very well for the bound-constrained and constrained optimization problems compared to several state-of-the-art algorithms. Nevertheless, the computational burdens for generating an HS locus are higher than that for an HC locus. We propose an efficient technique to construct an HS locus by approximating the orthogonal projection matrix to resolve this issue. As per our empirical experiments, this technique significantly improves the performance of the original SS with less computational effort. Moreover, to enhance SS's search capability, we put forth a self-adaptation technique for choosing the effective values of the control parameters dynamically during the optimization process. We validate the proposed algorithm's performance on a plethora of real-world and benchmark optimization problems with and without constraints. Experimental results suggest that the proposed algorithm remains better than or at least comparable to the best-known state-of-the-art algorithms on a wide spectrum of problems.

Random mechanisms including mutations are an internal part of evolutionary algorithms, which are based on the fundamental ideas of Darwin’s theory of evolution as well as Mendel’s theory of genetic heritage. In this paper, we debate whether pseudo-random processes are needed for evolutionary algorithms or whether deterministic chaos, which is not a random process, can be suitably used instead. Specifically, we compare the performance of 10 evolutionary algorithms driven by chaotic dynamics and pseudo-random number generators using chaotic processes as a comparative study. In this study, the logistic equation is employed for generating periodical sequences of different lengths, which are used in evolutionary algorithms instead of randomness. We suggest that, instead of pseudo-random number generators, a specific class of deterministic processes (based on deterministic chaos) can be used to improve the performance of evolutionary algorithms. Finally, based on our findings, we propose new research questions.

Differential evolution is a powerful nature-inspired real-parameter optimization algorithm that has been successfully used to solve a number of hard optimization problems. It has been used to tackle both continuous and discrete optimization problems. The application of a continuous method to discrete problems involves several challenges, including solution representation and search space–solution space mapping. In this work, we study random key encoding, a popular encoding scheme that is used to represent permutations in high-dimensional continuous spaces. We analyze the search space it constitutes, study its structure and properties, and introduce two novel modifications of the encoding. We investigate the proposed encoding strategies in the context of four variants of the differential evolution algorithm and demonstrate their usefulness for two widespread permutation problems: 1) the linear ordering problem and 2) the traveling salesman problem.

Summary Chatbots are artificial communication systems becoming increasingly popular and not all their security questions are clearly solved. People use chatbots for assistance in shopping, bank communication, meal delivery, healthcare, cars, and many other actions. However, it brings an additional security risk and creates serious security challenges which have to be handled. Understanding the underlying problems requires defining the crucial steps in the techniques used to design chatbots related to security. There are many factors increasing security threats and vulnerabilities. All of them are comprehensively studied, and security practices to decrease security weaknesses are presented. Modern chatbots are no longer rule‐based models, but they employ modern natural language and machine learning techniques. Such techniques learn from a conversation, which can contain personal information. The paper discusses circumstances under which such data can be used and how chatbots treat them. Many chatbots operate on a social/messaging platform, which has their terms and conditions about data. The paper aims to present a comprehensive study of security aspects in communication with chatbots. The article could open a discussion and highlight the problems of data storage and usage obtained from the communication user—chatbot and propose some standards to protect the user.

Summary This article presents new methods of securing data with particular emphasis on their applications in Cloud Computing. Methods of hiding data dedicated to processes of its security will be illustrated by cryptographic techniques, particularly data splitting protocols. Cryptographic information sharing algorithms are a particularly important type of techniques allowing confidential or secret data to be secured. These algorithms have been enhanced with new classes of solutions, including semantic protocols of secret sharing. The innovation of this solution consists in the presentation of smart data securing algorithms that use techniques of the semantic sharing of secret information. The information subjected to the process of splitting and hiding can take various forms, including that of services. Because of this aspect, namely, the various forms of the secret, and the use of universal information hiding techniques, a particularly important aspect of the solutions presented here is the ability to use them in various areas of data security. One of such areas comprises Cloud Computing, which forms the leading subject of this publication. In this article, the authors describe intelligent cryptography techniques dedicated to security services data. The most important achievement is the introduction of a new class of data encryption protocols, such as semantic threshold schemes.

Summary This paper presents a new data analysis technology based on human‐oriented analysis. This analysis covers semantic methods of data description and interpretation referring to marketing preferences of system users. The proposed methods of cognitive marketing – in order to interpret fully all possible preferences which can occur – are a subject to an analysis focusing on their meaning and usefulness at the stage of product evaluation, promotion, but also regarding product distribution and price. All these processes will constitute marketing analysis due to the possibilities to assess some preferences of the analyzed data. The essence of this paper is a possibility to present the methodology of cognitive marketing based on the application of the meaning analysis which reaches the human brain and the attention attractors registered by the brain, which give rise to some interest or, quite the contrary, which remain unnoticed. A new solution is dedicated to deep semantic analysis based on the registration, processing, and analysis of attention attractors and their perception by individual person. Such attractors can be detected on the basis of observation of how attention is focused on some specific features of the examined information groups. The variety of the examined information and data can enable a wide‐ranging analysis of the issue discussed here; as a result, can assess the degree to which human attention focuses on the process of meaning interpretation of various cognitive features and observations.

This article describes the methodology and the possibilities of collecting operation data in a mobile network provider. First, the architecture and the principles used in the system are described. The precision analysis of the population commuting in the region and during the pandemic and nonpandemic times. Moreover, several ideas about further utilization of the data will be formulated and described. Finally, a graph-based approach that describes the creation of the community structure between the people and the means of its analysis.

In this article, quantum inspired incarnations of two swarm based meta-heuristic algorithms, namely, Crow Search Optimization Algorithm and Intelligent Crow Search Optimization Algorithm have been proposed for automatic clustering of colour images. The performance and effectiveness of the proposed algorithms have been judged by experimenting on 15 Berkeley images and five publicly available real life images of different sizes. The validity of the proposed algorithms has been justified with the help of four different cluster validity indices, namely, Pakhira Bandyopadhyay Maulik, I-index, Silhouette and CS-measure. Moreover, Sobol's sensitivity analysis has been performed to tune the parameters of the proposed algorithms. The experimental results prove the superiority of proposed algorithms with respect to optimal fitness, computational time, convergence rate, accuracy, robustness, t -test and Friedman test. Finally, the efficacy of the proposed algorithms has been proved with the help of quantitative evaluation of segmentation evaluation metrics.

Topological data analysis is a new theoretical trend using topological techniques to mine data. This approach helps determine topological data structures. It focuses on investigating the global shape of data rather than on local information of high-dimensional data. The Mapper algorithm is considered as a sound representative approach in this area. It is used to cluster and identify concise and meaningful global topological data structures that are out of reach for many other clustering methods. In this article, we propose a new method called the Shape Fuzzy C-Means (SFCM) algorithm, which is constructed based on the Fuzzy C-Means algorithm with particular features of the Mapper algorithm. The SFCM algorithm can not only exhibit the same clustering ability as the Fuzzy C-Means but also reveal some relationships through visualizing the global shape of data supplied by the Mapper. We present a formal proof and include experiments to confirm our claims. The performance of the enhanced algorithm is demonstrated through a comparative analysis involving the original algorithm, Mapper, and the other fuzzy set based improved algorithm, F-Mapper, for synthetic and real-world data. The comparison is conducted with respect to output visualization in the topological sense and clustering stability.

Single row facility layout problem is an important problem encountered in facility design, factory construction, production optimization, and other areas. At the same time, it is a challenging NP-hard combinatorial optimization problem that has been addressed by many advanced algorithms. In practical scenarios, real-world problems can be cast as single row facility location problem instances with different high-level properties and efficient algorithms that can solve them are sought. This work uses a variant of the self-organizing migration algorithm developed recently for permutation problems to tackle the single row facility layout problem and evaluates its accuracy and performance.

Differential evolution is an efficient evolutionary optimization paradigm that has shown a good ability to solve a variety of practical problems, including combinatorial optimization ones. Single row facility layout problem is an NP-hard permutation problem often found in facility design, factory construction, production optimization, and other areas. Real-world problems can be cast as large single row facility location problem instances with different high-level properties and efficient algorithms that can solve them efficiently are needed. In this work, the differential evolution is used to solve the single row facility location problem and the ability of three different variants of the algorithm to evolve solutions to various problem instances is studied.

Summary Digital steganography aims at hiding secret messages in digital data transmitted over insecure channels. The JPEG format is prevalent in digital communication, and images are often used as cover objects in digital steganography. Optimization methods can improve the properties of images with embedded secret but introduce additional computational complexity to their processing. AVX instructions available in modern CPUs are, in this work, used to accelerate data parallel operations that are part of image steganography with advanced optimizations.

In this paper, we address the hesitant information in enhancement task often caused by differences in image contrast. Enhancement approaches generally use certain filters which generate artifacts or are unable to recover all the objects details in images. Typically, the contrast of an image quantifies a unique ratio between the amounts of black and white through a single pixel. However, contrast is better represented by a group of pixels. We have proposed a novel image enhancement scheme based on intuitionistic hesitant fuzzy sets (IHFSs) for drone images (dronogram) to facilitate better interpretations of target objects. First, a given dronogram is divided into foreground and background areas based on an estimated threshold from which the proposed model measures the amount of black/white intensity levels. Next, we fuzzify both of them and determine the hesitant score indicated by the distance between the two areas for each point in the fuzzy plane. Finally, a hyperbolic operator is adopted for each membership grade to improve the photographic quality leading to enhanced results via defuzzification. The proposed method is tested on a large drone image database. Results demonstrate better contrast enhancement, improved visual quality, and better recognition compared to the state-of-the-art methods.

Summary Quasigroup operations are essential for a wide range of cryptographic procedures that includes cryptographic hash functions, electronic signatures, pseudorandom number generators, and stream and block ciphers. Quasigroup cryptography achieves high levels of security at low memory and computational costs by an iterative application of quasigroup operations to streams and blocks of data. The use of large quasigroups can further improve the strength of cryptographic operations. However, the order of used quasigroups is the main factor affecting the memory requirements of quasigroup cryptographic schemes. Alternative quasigroup representations that do not store their multiplication tables in computer memory yield increased computational costs. In any case, an efficient implementation of quasigroup operations is critical for practical applications of quasigroup cryptography. Residue number systems allow a fast, concurrent realization of addition and multiplication. In this work, residue arithmetic is used to accelerate quasigroup operations, and an efficient computational approach to their implementation, designed with respect to the extended instruction sets of modern processors, is proposed.

This paper proposes a design of hierarchical fuzzy inference tree (HFIT). An HFIT produces an optimum tree-like structure, i.e., a natural hierarchical structure that accommodates simplicity by combining several low-dimensional fuzzy inference systems (FISs). Such a natural hierarchical structure provides a high degree of approximation accuracy. The construction of the HFIT takes place in two phases. First, a nondominated sorting-based multiobjective genetic programming (MOGP) is applied to obtain a simple tree structure (a low complexity model) with a high accuracy. Second, the differential evolution algorithm is applied to optimize the obtained tree's parameters. In the derived tree, each node acquires a different input's combination, where the evolutionary process governs the input's combination. Hence, HFIT nodes are heterogeneous in nature, which leads to a high diversity among the rules generated by the HFIT. Additionally, the HFIT provides an automatic feature selection because it uses MOGP for the tree's structural optimization that accepts inputs only relevant to the knowledge contained in data. The HFIT was studied in the context of both type-1 and type-2 FISs, and its performance was evaluated through six application problems. Moreover, the proposed multiobjective HFIT was compared both theoretically and empirically with recently proposed FISs methods from the literature, such as McIT2FIS, TSCIT2FNN, SIT2FNN, RIT2FNS-WB, eT2FIS, MRIT2NFS, IT2FNN-SVR, etc. From the obtained results, it was found that the HFIT provided less complex and highly accurate models compared to the models produced by the most of other methods. Hence, the proposed HFIT is an efficient and competitive alternative to the other FISs for function approximation and feature selection.

Fuzzy Similarity Measure (FSM) is one of the most used techniques for classification, pattern recognition or knowledge reduction. While many type-1 FSMs exist, few ones exist for type-2 fuzzy sets. In this paper, we introduce three similarity measures between Interval Type-2 Fuzzy Sets (IT-2 FSs) as an extension of some distance measures between Intuitionistic Fuzzy Sets (IFSs). Many definitions and properties are exposed in order to prove that the formulas presented are indeed similarity measures. Experimental results are presented, comparison with other existing type-2 FSMs is done and interpretation is given in order to satisfy FSM properties.

Modern data science uses topological methods to find the structural features of data sets before further supervised or unsupervised analysis. Geometry and topology are very natural tools for analysing massive amounts of data since geometry can be regarded as the study of distance functions. Mathematical formalism, which has been developed for incorporating geometric and topological techniques, deals with point cloud data sets, i.e. finite sets of points. It then adapts tools from the various branches of geometry and topology for the study of point cloud data sets. The point clouds are finite samples taken from a geometric object, perhaps with noise. Topology provides a formal language for qualitative mathematics, whereas geometry is mainly quantitative. Thus, in topology, we study the relationships of proximity or nearness, without using distances. A map between topological spaces is called continuous if it preserves the nearness structures. Geometrical and topological methods are tools allowing us to analyse highly complex data. These methods create a summary or compressed representation of all of the data features to help to rapidly uncover particular patterns and relationships in data. The idea of constructing summaries of entire domains of attributes involves understanding the relationship between topological and geometric objects constructed from data using various features. A common thread in various approaches for noise removal, model reduction, feasibility reconstruction, and blind source separation, is to replace the original data with a lower dimensional approximate representation obtained via a matrix or multi-directional array factorization or decomposition. Besides those transformations, a significant challenge of feature summarization or subset selection methods for Big Data will be considered by focusing on scalable feature selection. Lower dimensional approximate representation is used for Big Data visualization. The cross-field between topology and Big Data will bring huge opportunities, as well as challenges, to Big Data communities. This survey aims at bringing together state-of-the-art research results on geometrical and topological methods for Big Data.

A wireless sensor network is a sensing system composed of a few or thousands of sensor nodes. These nodes, however, are powered by internal batteries, which cannot be recharged or replaced, and have a limited lifespan. Traditional two-tier networks with one sink node are thus vulnerable to communication gaps caused by nodes dying when their battery power is depleted. In such cases, some nodes are disconnected with the sink node because intermediary nodes on the transmission path are dead. Energy load balancing is a technique for extending the lifespan of node batteries, thus preventing communication gaps and extending the network lifespan. However, while energy conservation is important, strategies that make the best use of available energy are also important. To decrease transmission energy cost and prolong network lifespan, a three-tier wireless sensor network is proposed, in which the first level is the sink node and the third-level nodes communicate with the sink node via the service sites on the second level. Moreover, this study aims to minimize the number of service sites to decrease the construction cost. Statistical evaluation criteria are used as benchmarks to compare traditional methods and the proposed method in the simulations.

The Differential Evolution (DE) is a powerful bio-inspired algorithm searching optimal solutions. The actual DE modifications can handle the real, integer and discrete valued problems. The values of the discrete-valued variables represent the integer indices addressing the discrete samples in the ordered array. The optimization in unordered samples leads to a random search. This paper proposes a novel modification dealing with d-dimensional discrete vertices. A vertex hashing is used to strengthen the local properties of a dataset and to improve the spatial convergence of the evolution.

Effort overruns is common problem in software development. Our main intention is to support estimation by method for classification of use cases. The goal of this paper is to evaluate usage of the feed-forward neural network for the Use Case classification purposes. Experimental results show that the feed-forward neural network classifier, using softmax activation function in the output layer and hyperbolic tangent activation function in the hidden layer, offers the best classification performance.

Introduction of the fuzzy-set enabled the modeling of uncertain and noisy information. Type-2 fuzzy set took this further ahead by allowing fuzzy membership function to be fuzzy itself. In this work, we discussed an interval type-2 fuzzy inference system (IT2FIS). The training of the IT2FIS was provided in supervised manner by using metaheuristic algorithms. We comprehensively illustrated the formulation of the IT2FIS into an optimization problem. A precise genotype (a real vector) mapping of IT2FIS and a population-based strategy for optimum rule-base selection is described in this work. Since the IT2FIS learning is computationally difficult and costly, which we described in detail in this work, a comprehensive comparison between the performances of the metaheuristic algorithms were examined. The obtained results suggest that the IT2FIS learning was faster at the initial iterations of the metaheuristic learning, but tend to slow and get stuck in local minima. However, the metaheuristic algorithms, differential evaluation and bacteria foraging optimization offered significantly better results when compared to artificial bee colony, gray wolf optimization, particle swarm optimization and the other fuzzy inference models chosen for comparisons from literature.

When dealing with real world data; noise, complexity, dimensionality, uncertainty and irrelevance can lead to low performance and insignificant judgment. Fuzzy logic is a powerful tool for controlling conflicting attributes which can have similar effects and close meanings. In this paper, an interval type-2 fuzzy feature selection is presented as a new approach for removing irrelevant features and reducing complexity. We demonstrate how can Feature Selection be joined with Interval Type-2 Fuzzy Logic for keeping significant features and hence reducing time complexity. The proposed method is compared with some other approaches. The results show that the number of attributes is proportionally small.

General partial differential equations, which can describe any complex functions, may be solved by an adapted method of the similarity analysis that models polynomial data relations of discrete observations. The proposed new differential polynomial networks define and substitute for a selective form of the general partial differential equation using fraction derivative units to model an unknown system or pattern. Convergent series of relative derivative substitution terms, produced in all network layers, describe the partial derivative changes of some combinations of input variables to generalize elementary polynomial data relations. The general differential equation is decomposed into polynomial network backward structure, which defines simple and composite sum derivative terms in respect of previous layers variables. The proposed method enables to form more complex and varied derivative selective series models than standard soft-computing techniques. The sigmoidal function, commonly employed as an activation function in artificial neurons, may improve the abilities of the polynomial networks and substituting derivative terms to approximate complicated periodic multi-variable or time-series functions in a system model.

Space-filling curves (SFCs) are straightforward and efficient methods for a sparse space clustering. They are utilized in research areas like classification, computer vision, computer graphics and/or machine learning. Most of the SFCs are based on a regular orthogonal grid. Generally, a hierarchical properties of Quad-trees (2D) or Octrees (n-dimensional) are utilized for a vertex hashing. However, the regular hexagonal grid is applicable for 2D tiling as well. The hexagonal shape is principally not a reptile, so the construction of hexagonal SFCs or a query structure is still a complex task. The Gosper curve (Flowsnake) is a self similar fractal that groups the hexagons to a composite called the Gosper island. This paper proposes a novel method constructing a Gosper-like space-filling curve of 2D vertices. The final algorithm is tested on several datasets and the results are discussed.

Up to now, the problem of ensuring collaborative activities in e-Learning against dishonest students' behaviour has been mainly tackled with technological security solutions. Over the last years, technological security solutions have evolved from isolated security approaches based on specific properties, such as privacy, to holistic models based on technological security comprehensive solutions, such as public key infrastructures, biometric models and multidisciplinary approaches from different research areas. Current technological security solutions are feasible in many e-Learning scenarios but on-line assessment involves certain requirements that usually bear specific security challenges related to e-Learning design. In this context, even the most advanced and comprehensive technological security solutions cannot cope with the whole scope of e-Learning vulnerabilities. To overcome these deficiencies, our previous research aimed at incorporating information security properties and services into on-line collaborative e-Learning by a functional approach based on trustworthiness assessment and prediction. In this paper, we present a peer-to-peer on-line assessment approach carried out in a real on-line course developed in our real e-Learning context of the Open University of Catalonia. The design presented in this paper is conducted by our trustworthiness security methodology with the aim of building peer-to-peer collaborative activities, which enhances security e-Learning requirements. Eventually, peer-to-peer visualizations methods are proposed to manage security e-Learning events, as well as on-line visualization through peer-to-peer tools, intended to analyse collaborative relationship.

Breast cancer is one from various diseases that has got great attention in the last decades.This due to the number of women who died because of this disease.Segmentation is always an important step in developing a CAD system.This paper proposed an automatic segmentation method for the Region of Interest (ROI) from breast thermograms.This method is based on the data acquisition protocol parameter (the distance from the patient to the camera) and the image statistics of DMR-IR database.To evaluated the results of this method, an approach for the detection of breast abnormalities of thermograms was also proposed.Statistical and texture features from the segmented ROI were extracted and the SVM with its kernel function was used to detect the normal and abnormal breasts based on these features.The experimental results, using the benchmark database, DMR-IR, shown that the classification accuracy reached (100%).Also, using the measurements of the recall and the precision, the classification results reached 100%.This means that the proposed segmentation method is a promising technique for extracting the ROI of breast thermograms.

Random Forest (RF) classification algorithm is widely used in the area of information retrieval and became a basis for some extended branches of classification and/or regression algorithms. Cluster Forest (CF) represents a particular branch, and brings usually better results than individual clustering algorithms. This article describes a new ensemble clustering algorithm based on CF that internally uses a probabilistic model called Gaussian Mixture Model (GMM). Finally, Expectation-maximization algorithm is used for estimation of GMM parameters. The proposed ensemble clustering algorithm will be compared with several different approaches and tested on eight datasets.

We present an approach for emotion recognition using information of the pupil. In last years, the pupil variables have been used as an assessment of emotional arousal. In this article, we generate signals of pupil size and gaze position monitored during image viewing. The emotions are provoked by visual stimuli of colored images. Those images were taken from the International Affective Picture System which has been the reference for objective emotional assessment based on visual stimuli. For recognising the emotions we use the evolution of the eye tracking data during a window of time. The learning dataset is composed by the evolution of the pupil size and the gaze position, and labels associated to the emotional states. We study two kinds of learning tools based on Neural Networks. We obtain promising empirical results that show the potential of using temporal learning tools for emotion recognition.

The main goal of this paper is to detect faces from noisy images using three different classification methods and compare the results obtained from the classification methods. The faces are described by a set of images. Many other unsupervised statistical algorithms such as Principal Component Analysis (PCA) or Singular Value Decomposition (SVD) use only one image per person to extract features from the face. These approaches can lose important information, for example a relationship between images of the same person taken under different conditions. It shows that data structure like tensor and it decomposition increase the quality of recognition in this task because it better captures important features of one face taken from several images. The accuracy of the tensor approach is compared with other well-known techniques such as Support Vector Machine (SVM) and Neural Network (NN).

Dealing with distributed data is one of the challenges for clustering, as most clustering techniques require the data to be centralized. One of them, k-means, has been elected as one of the most influential data mining algorithms for being simple, scalable, and easily modifiable to a variety of contexts and application domains. However, exact distributed versions of k-means are still sensitive to the selection of the initial cluster prototypes and require the number of clusters to be specified in advance. Additionally, preserving data privacy among repositories may be a complicating factor. In order to overcome k-means limitations, two different approaches were adopted in this paper: the first obtains a final model identical to the centralized version of the clustering algorithm and the second generates and selects clusters for each distributed data subset and combines them afterwards. It is also described how to apply the algorithms compared while preserving data privacy. The algorithms are compared experimentally from two perspectives: the theoretical one, through asymptotic complexity analyses, and the experimental one, through a comparative evaluation of results obtained from a collection of experiments and statistical tests. The results obtained indicate which algorithm is more suitable for each application scenario.

Visualization of complex real-world data is an essential part of network processing. Complex high-dimensional or networked data ought to be presented in a form suitable for machine and human analysis. Therefore, advanced methods of dimension reduction or projection to low-dimensional spaces are investigated. In this work we use Differential Evolution as a real-parameter optimization metaheuristic algorithm to minimize the error function used in Sammon's projection and compare its results with the results obtained by a traditional heuristic algorithm for Sammon's projection. The metaheuristic algorithm achieves lower projection error and its results are demonstrated on a 2D visualization of real-world data from the domain of social networks.

Linear ordering problem is a popular NP-hard combinatorial optimization problem attractive for its complexity, rich library of test data, and variety of real world applications. It has been solved by a number of heuristic as well as metaheuristic methods in the past. The implementation of nature-inspired metaheuristic optimization and search methods usually depends on streams of integer and floating point numbers generated in course of their execution. The pseudo-random numbers are utilized for an in-silico emulation of probability-driven natural processes such as arbitrary modification of genetic information (mutation, crossover), partner selection, and survival of the fittest (selection, migration) and environmental effects (small random changes in particle motion direction and velocity). Deterministic chaos is a well known mathematical concept that can be used to generate sequences of seemingly random real numbers within selected interval in a predictable and well controllable way. In the past, it has been used as a basis for various pseudo-random number generators with interesting properties. Recently, it has been shown that it can be successfully used as a source of stochasticity for nature-inspired algorithms solving a continuous optimization problem. In this work we compare effectiveness of the differential evolution with different pseudo-random number generators and chaotic systems as sources of stochasticity when solving the linear ordering problem.

The column subset selection problem is a well-known hard optimization problem of selecting an optimal subset of k columns from the matrix A (m x n), k

Optimization of neural network (NN) is significantly influenced by the transfer function used in its active nodes. It has been observed that the homogeneity in the activation nodes does not provide the best solution. Therefore, the customizable transfer functions whose underlying parameters are subjected to optimization were used to provide heterogeneity to NN. For experimental purposes, a meta-heuristic framework using a combined genotype representation of connection weights and transfer function parameter was used. The performance of adaptive Logistic, Tangent-hyperbolic, Gaussian and Beta functions were analyzed. Concise comparisons between different transfer function and between the NN optimization algorithms are presented. The comprehensive analysis of the results obtained over the benchmark dataset suggests that the Artificial Bee Colony with adaptive transfer function provides the best results in terms of classification accuracy over the particle swarm optimization and differential evolution algorithms.

The performance of the meta-heuristic algorithms often depends on their parameter settings. Appropriate tuning of the underlying parameters can drastically improve the performance of a meta-heuristic. The Ant Colony Optimization (ACO), a population based meta-heuristic algorithm inspired by the foraging behavior of the ants, is no different. Fundamentally, the ACO depends on the construction of new solutions, variable by variable basis using Gaussian sampling of the selected variables from an archive of solutions. A comprehensive performance analysis of the underlying parameters such as: selection strategy, distance measure metric and pheromone evaporation rate of the ACO suggests that the Roulette Wheel Selection strategy enhances the performance of the ACO due to its ability to provide non-uniformity and adequate diversity in the selection of a solution. On the other hand, the Squared Euclidean distance-measure metric offers better performance than other distance-measure metrics. It is observed from the analysis that the ACO is sensitive towards the evaporation rate. Experimental analysis between classical ACO and other meta-heuristic suggested that the performance of the well-tuned ACO surpasses its counterparts.

Graph is a standard data structures mainly used to impose relations in a connected network of objects. Graphs are drawn mostly in ℝ2space with vertices as points and edges as straight lines connecting vertices. Methods for reduction of dimension are intended for projecting high-dimensional data to a new representation in low-dimensional space. Objective of this work is to use selected dimension reduction methods as a main step in construction of a graph drawing. Graph drawings by selected methods are compared to each other and also to the classical approach presented by Kamada and Kawai.

Artificial bee colony is a meta-heuristic optimization algorithm based on the behavior of honey bee swarm. These bees work largely independently of other bees, making the algorithm suitable for parallel implementation. Within this paper, we introduce the algorithm itself and its subsequent parallelization utilizing the CUDA platform. The runtime speedup is demonstrated on several commonly used test functions for optimization. The algorithm is subsequently applied to the problem of clustering real data.

Due to the annual increase in energy prices, photovoltaic power stations (PVPS) are often used as a primary source of power for smart off-grid houses. Integration of this kind of energy source is challenging because it is a source of variably generated power due to meteorological uncertainty, but the cost of this energy source rapidly decreases. In this paper, we present results of the short term prediction method of generated power for small PVPS based on self-organizing maps, previously introduced power profiles, their sequences and computing F-Score as an alternative to commonly used algorithms.

SUMMARY Evolutionary clustering algorithms have been proven as a good ability to find clusters in data. Among their advantages belong the abilities to adapt to data and to determine the number of clusters automatically, thus requiring lessa prioriassumptions about analyzed objects than traditional clustering methods. Unfortunately, such a clustering by genetic algorithms and evolutionary algorithms in general suffers from high computational costs when it comes to recurrent fitness function evaluation. Computing on graphic processing units (GPUs) is a recent programming and development paradigm bringing high performance parallel computing closer to general audience. Modern general purpose GPUs are composed of tens to thousands of computational cores that can execute programs in parallel using the single instruction multiple data parallel processing approach. General purpose GPU programs need to be designed and implemented in a data parallel way and with respect to the architecture of target devices to fully utilize their high performance. This study presents a design, implementation, and evaluation of a data parallel genetic algorithm for density‐based clustering. The algorithm was implemented and evaluated on the nVidia Compute Unified Device Architecture (CUDA) platform. Copyright © 2013 John Wiley & Sons, Ltd.

Distributed environments and emerging highly-parallel platforms provide a suitable hardware infrastructure for parallel Evolutionary Computation. Partitioned Global Address Space model is a well-known parallel computing model used to implement scalable algorithms for many-core systems and clusters. This study investigates the Unified Parallel C programming language as a tool for implementation of scalable evolutionary algorithms for high-dimensional problems. The design concepts and initial implementation are demonstrated on the Differential Evolution algorithm. The mapping of Differential Evolution concepts to Unified Parallel C features is presented and three variants of parallel Differential Evolution for many-core shared memory systems and clusters of computers with distributed memory are implemented and evaluated in the environment of a small real-world cluster.

The electrical activity of brain or EEG signal is very complex data system that may be used to many different applications such as device control using mind. It is not easy to understand and detect useful signals in continuous EEG data stream. In this paper, we are describing an application of data compression which is able to recognize important patterns in this data. The proposed algorithm uses Lampel-Ziv complexity for complexity measurement and it is able to successfully detect patterns in EEG signal.

Formal Concept Analysis is a well known method for the analysis of object-attribute data. However it is difficult to deploy when it comes to large datasets. Several complexity issues of this method can be addressed by reduction using matrix factorization methods. However their behavior within this environment is not easily explainable, but can be partly illustrated using several both general and Formal Concept Analysis-specific measures. We have also introduced a new notion of Primary Concepts which may help in understanding the reduction process.

Stochastic nature-inspired optimization and search methods depend on streams of integer and floating point numbers generated in course of their execution. The pseudo-random numbers are utilized for in-silico emulation of probability-driven natural processes such as modification of genetic information (mutation, crossover), partner selection, and survival of the fittest (selection, migration) and environmental effects (small random changes in motion direction and velocity). Deterministic chaos is a well known mathematical concept that can be used to generate sequences of seemingly random real numbers within selected interval in a predictable and controllable way. In the past, it has been used as a basis for various pseudo-random number generators with interesting properties. This work provides an empirical comparison of the performance of genetic algorithms, differential evolution, and particle swarm optimization using different pseudo-random number generators and chaotic systems as sources of stochasticity.

Nature inspired algorithms implement successful optimization and adaptation strategies observed in the nature. Various bio-inspired algorithms mimic the behavioural patterns of plants, animals, their communities and their evolution. Surprisingly, the behavioural patterns and survival strategies of protozoa, one of the most prevalent and successful species on Earth, did not receive significant attention from the bio-inspired computing community until present time. This study proposes a new variant of Particle Swarm Optimization incorporating behaviour inspired by protozoa and evaluates the performance of such an algorithm on a set of well known test functions.

In this article, we introduce a novel method for spam detection based on a combination of Bayesian filtering, signature trees, and data compression–based similarity. Bayesian filtering is one of the most popular and most efficient algorithms for dealing with spam detection. The problem with Bayesian filtering is that it is unable to classify any e-mail without doubt and sometimes spam e-mails are classified as regular e-mails. This novel method sorts out this problem by using signature trees and data compression–based similarity. The main result of this article is an up to 99% improvement in spam detection precision using this novel method.

There may be several reasons why people publish together. Above all, the fact that the authors share common professional interests is the main reason. In our research we work with the DBLP dataset which contains the basic bibliographic information of publications from the computer science field. These data are freely available and contain highly relevant information about publication activity from the period of nearly fifty years, even though they are not complete. One of the goals of our research is to analyze and visualize the evolution of authors and co-authorship from the point of view of research topics. We present the results of our research in this paper. One of the results is also visualization in our online FORCOA.NET system.

Spectral partitioning is a well known method in the area of graph and matrix analysis. Several approaches based on spectral partitioning and spectral clustering were used to detect structures in real world networks and databases. In this paper, we use the spectral partitioning to detect communities in a co-authorship network. The partitioning depends heavily on the weighting of the underlying network. We use an intuitive weighting scheme based on the ant colony optimization and show the communities found by spectral partitioning when using the ACO inspired weighting and when using trivial weighting based on the number of interactions between the authors.

Many decisions during making a project estimation and planning are based on previous experience and competency of a manager. Evaluated completed projects provide rich knowledge about similar decisions and reality. This paper focuses on the approach how to estimate value of specific parameters of the projects utilizing parameterized use case model. The proven methods are used -- SOM for grouping similar use-cases and fuzzy rules for calculating the search parameter value. An important part of the approach there is to build up and maintain comparative database of valued use cases.

Unsupervised clustering of large data sets is a complicated NP-hard task. Due to its complexity, various metaheuristic machine learning algorithms have been used to automate or aid the clustering process. Genetic and evolutionary algorithms have been deployed to find clusters in data sets with success. However, also evolutionary clustering suffers from the high computational demands when it comes to fitness function evaluation. The GPU computing is a recent programming and development paradigm introducing high performance parallel computing to general audience. This work presents an initial design and implementation of a genetic algorithm for density based clustering on the GPU using the nVidia CUDA platform.

This study introduces a new soft computing method for expert identification in social networks based on formal concept analysis and fuzzy rules. Expert identification is an important task in social network analysis and there are several methods to identify people who have experience in given area. In this paper, we propose a hybrid approach where the formal concept analysis is used for finding author's profiles based on keywords and fuzzy rules to learn the properties of the authors and to enhance the set of experts.

Document classification is a well-known problem that is focused on assigning predefined labels or categories to the documents found in the searched collection. Many classical algorithms were developed for solving of this problem. They usually have large time complexity and with increasing number of documents it is necessary to find algorithm which are able to find solution in reasonable time. Such algorithms are usually inspired by biological processes. Even such meta-heuristics algorithms become too slow when the number of documents is really large and it is necessary to optimize them for faster processing. This paper describes a document classification algorithm based on Particle Swarm Optimization with implementation of one and two GPUs.

Renewable energy sources are becoming a significant part of todays energy mix. The unstable production of many renewable energy sources including photovoltaic and wind power plants puts increased demands on power transmission systems and on the power grid as a whole. Soft computing methods can contribute to the prediction of electric energy production of renewable resources and therefore to the reliability of the power transmission networks. This work compares two soft computing methods that utilize genetic programming to evolve predictors of a selected renewable energy resource that meets the real world criterion of high output variance and relatively large installed power (in context of the power distribution system of the Czech Republic).

Linear Ordering Problem (LOP) is a well know NP-hard problem combinatorial optimization problem attractive for its complexity, rich library of test data and variety of real world applications. In this paper, we use differential evolution accelerated by the GPU using the nVidia CUDA platform to find good LOP solutions. The well known LOLIB library was used to evaluate the efficiency and precision of the approach in solving LOP instances.

Differential evolution is an efficient populational meta -- heuristic optimization algorithm successful in solving difficult real world problems. Due to the simplicity of its operations and data structures, it is suitable for a parallel implementation on multicore systems and on the GPU. In this paper, we design a simple yet highly parallel implementation of the differential evolution using the CUDA architecture. We demonstrate the speedup obtained by the proposed parallelization of the differential evolution on an NP hard combinatorial optimization problem and on a benchmark function of many variables.

We use genetic programming to evolve accurate predictors (fuzzy rules) that are deployed to estimate the tension in a power plant generator. The meta-heuristic is compared to the finite element method that was used to compute estimated tension. In contrast to the finite element method, the fuzzy predictor (once found) approximates the tension in the facility quickly and with sufficient precision.

Spectral partitioning is a well known method in the area of graph and matrix analysis. Several approaches based on spectral partitioning and spectral clustering were used to detect structures and mine data from real world networks. In this paper, we use a simple spectral decomposition to analyze a co-authorship network. We use a straightforward approach based on algebraic connectivity and characteristic valuation and show that even this simple form of spectral partitioning is useful for the analysis of relations and communities in a co-authorship networks.

Strategy is a kernel subsystem of the robot soccer game. According to the strategy description in our work, there are loop strategies which are likely to get robots in a trap of executing repeated actions. In this paper, we propose method using eigenvalues to judge the existence of loop strategies in our rules set. We present the concept of condition-decision relation matrix by which the loop strategies can be found, too. The experiment illustrates our method.

In our paper we use neural networks for the tuning of image feature extraction algorithms and for the analysis of orthophotomaps. In our approach we split an aerial photo into a regular grid of segments and for each segment we detect a set of features. These features describe the segment from the viewpoint of general image analysis (color, tint, texture, etc.) as well as from the viewpoint of the shapes in the segment. We also present our approach to the validation of extracted features using a neural network. In the paper we present an experiment based on orthophotomap segment clustering using the formal concept analysis with a selected group of features.

Fuzzy sets and fuzzy logic can be used for efficient data classification by fuzzy rules and fuzzy classifiers. This paper presents an application of genetic programming to the evolution of fuzzy classifiers based on extended Boolean queries. Extended Boolean queries are well known concept in the area of fuzzy information retrieval. An extended Boolean query represents a complex soft search expression that defines a fuzzy set on the collection of searched documents. We interpret the data mining task as a fuzzy information retrieval problem and we apply a proven method for query induction from data to find useful fuzzy classifiers. The ability of the genetic programming to evolve useful fuzzy classifiers is demonstrated on two use cases in which we detect faulty products in a product processing plant and discover intrusions in a computer network.

In this paper we explore the possibility of an efficient organization of fuzzy signatures using the so-called S-Tree. We illustrate the usefullnes of the presented approach on a real-world example of a content-based image retrieval system. Images from the dataset are described using a fuzzy set of features. This description can be translated into a fuzzy signature and these signatures can be stored in a tree structure - similar to the B+tree - that allows efficient retrieval. Several variants are considered and evaluated.

In this paper, we discuss the need for efficient approximate string matching. We present the well-known Aho-Corasick automaton for locating multiple patterns and discuss an approach for fuzzification of this automaton. Along with some motivational examples, we propose and illustrate a novel algorithm for automaton construction.

Fuzzy classifiers and fuzzy rules are powerful tools in data mining and knowledge discovery. In this work, intrusion detection is approached as a data mining task and genetic programming is deployed to evolve fuzzy classifiers for detection of intrusion and security problems. We train the fuzzy classifier on a data set modeled as a fuzzy information retrieval collection and investigate its ability to detect illegitimate actions. Proposed approach is experimentally evaluated on the popular KDD Cup intrusion detection data set.

Computer security is very important in these days. Computers are used probably in any industry and their protection against attacks is very important task. The protection usually consist in several levels. The first level is preventions. Intrusion detection system (IDS) may be used as next level. IDS is useful in detection of intrusions, but also in monitoring of security issues and the traffic. This paper present IDS based on Flexible Neural Trees. Flexible neural tree is hierarchical neural network, which is automatically created using evolutionary algorithms to solving of defined problem. This is very important, because it is not necessary to set the structure and the weights of neural networks prior the problem is solved. The accuracy of proposed technique is always above 98% and the speed of decision making process enable its using in real-time applications.

Attacks on the computer infrastructures are becoming an increasingly serious problem. Whether it is banking, e-commerce businesses, health care, law enforcement, air transportation, or education, we are all becoming increasingly reliant upon the networked computers. The possibilities and opportunities are limitless; unfortunately, so too are the risks and chances of malicious intrusions. Intrusion detection is required as an additional wall for protecting systems despite of prevention techniques and is useful not only in detecting successful intrusions, but also in monitoring attempts to security, which provides important information for timely countermeasures. This paper presents some improvements to some of our previous approaches using a Non-negative Matrix factorization approach. To improve the performance (detection accuracy) and computational speed (scaling) a GPU implementation is detailed. Empirical results indicate that the speedup was up to 500x for the training phase and up to 190x for the testing phase.

This paper highlights a framework for cyber surveillance of unlawful activities for critical infrastructure protection. The framework uses a computational grid based environment, which is capable of distributed data mining and real time surveillance.

Recently several terrorist acts have created disruptions in the airline industries, tourism as well as the financial markets. It is believed just like the World War II had accelerated the development of nuclear energy, the longer-term impact of the current war on terrorism could lead to the allocation of major resources (manpower, funding etc.) towards dealing with this. In this paper, we present a social network concept to visualize a terrorist network. Visualization is very important part for analyzing a network since it can quickly provide good insight into the network structure, major members, and their properties. We used a matrix factorization methods called Semi Discrete Decomposition, which is highly suitable for dealing with huge networks. Empirical results using the 9-11 network data illustrate the efficiency of the proposed approach.

Spectral clustering is a data mining method used for finding patterns in high dimensional datasets. It has been applied effectively to solve many problems in signal processing, bioinformatics, etc. In this paper spectral clustering was implemented to find students’ patterns of behavior in an elearning system, to explore the relationship between the similarity of students’behavior and their academic performance.

Time modulated antenna arrays attracted the attention of researchers for the synthesis of low/ultra-low side lobes in recent past. In this article we propose an improved variant of a recently developed ecologically inspired metaheuristic, well-known as Invasive Weed Optimization (IWO), to solve the real parameter optimization problem related to the design of time-modulated linear antenna arrays with ultra low Side Lobe Level (SLL), Side Band Level (SBL) and Main Lobe Beam Width (BWFN). We improvise the classical IWO by introducing two parallel populations and a more explorative routine of changing the mutation step-size with iterations. Experimental results indicate that the proposed algorithm achieves better performance over the design problem as compared to the conventional Taylor Series based method and the only known metaheuristic approach based on the Differential Evolution (DE) algorithm.

Several variants of the Particle Swarm Optimization (PSO) algorithm have been proposed in recent past to tackle the multi-objective optimization problems based on the concept of Pareto optimality. Although a plethora of significant research articles have so far been published on analysis of the stability and convergence properties of PSO as a single-objective optimizer, till date, to the best of our knowledge, no such analysis exists for the multi-objective PSO (MOPSO) algorithms. This paper presents a first, simple analysis of the general Pareto-based MOPSO and finds conditions on its most important control parameters (the inertia factor and acceleration coefficients) that control the convergence behavior of the algorithm to the Pareto front in the objective function space. Limited simulation supports have also been provided to substantiate the theoretical derivations.

Design of non-uniform linear antenna arrays is one of the most important electromagnetic optimization problems of current interest. In this article, an adaptive Differential Evolution (DE) algorithm has been used to optimize the spacing between the elements of the linear array to produce a radiation pattern with minimum side lobe level and null placement control. DE is arguably one of the best real parameter optimizers of current interest takes very few control parameters and is easy to implement in any programming language. In this study two very simple adaptation schemes are used to regulate the control parameters F and Cr, upon which the performance of DE is critically dependent. The adaptation schemes are based on the objective function values of the target vectors and donor vectors. The adaptive DE-variant has been used to solve three difficult instances of the design problem and the optimization goal in each example is easily achieved. The results of the proposed algorithm have been shown to meet or beat the recently published results obtained using other state-of-the-art metaheuristics like the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Memetic Algorithms (MA), and Tabu Search (TS) in a statistically meaningful way.

The robot soccer game, as a part of standard applications of distributed system control in real time, provides numerous opportunities for the application of AI. Real-time dynamic strategy description and strategy learning possibility based on game observation are important to discover opponent’s strategies, search tactical group movements and synthesize proper counter-strategies. In this paper, the game is separated into physical part and logical part including strategy level and abstract level. Correspondingly, the game strategy description and prediction of ball motion are built up. The way to use this description, such as learning rules and adapting team strategies to every single opponent, is also discussed. Cluster analysis is used to validate the strategy extraction.

The formulation of user queries is an important part of the information retrieval process. In the complex environment of the World Wide Web and other large data collections, it is often not easy for the users to express their information needs in an optimal way. In this paper, we investigate evolutionary algorithms (in particular genetic programming) as a tool for the optimization of user queries and seek for its good settings.

Personalization is a promising way of improvement of the search services in large document collections and on the Web. User modeling is in the core of many personalization efforts because accurate user model can provide essential information for user specific search adjustments and result set processing. In this paper, we propose and study user modeling technique based on click-through data, relevance propagation and ant colony optimization.

Non-negative matrix factorization is an important method helpful in the analysis of high dimensional datasets. It has a number of applications including pattern recognition, data clustering, information retrieval or computer security. One its significant drawback lies in its computational complexity. In this paper, we introduce a new method allowing fast approximate transformation from input space to feature space defined by non-negative matrix factorization and discuss some examples of its application.

This paper presents a semi-automatic system for home video annotation that searches into the video contents and retrieves video shots for a specific person. The proposed system is composed of four phases; 1) shot detection phase that detects shots boundaries and divides the original video into shots, 2) face detection and recognition phase that detects faces in video shots based on Haar-like features and uses the Principal Component Analysis (PCA) algorithm for features extraction in order to select the eigenvectors with the largest eigenvalues, 3) face clustering and annotation phase that groups faces with similar facial features into the same cluster and apply face annotation on persons' shots (person's faces) using User Interface, and 4) retrieving phase that enables the user to enter a query to search by person's name then retrieves the video shots for this query from the database of the video shots. The proposed system is simple and provides a friendly user interface. It greatly reduces workload and enhances the accuracy of annotating person's faces in home videos.

The parameter estimation or identification problem, which frequently arises, while developing the mathematical models, may be formulated as a nonlinear global optimization problem. Here the objective is to find the set of parameters to minimize the function quantifying the goodness of the fit subject to the system dynamics. The mathematical model of the problem is often multimodal in nature and requires a suitable global optimization method for its solution. In the present study we show the application of a Modified Differential Evolution (MDE) for solving parameter estimation problem. We have considered two test cases. A comparison of numerical results with other algorithms shows the competence of MDE over basic DE and other methods.

Invasive Weed Optimization Algorithm IWO) is an ecologically inspired metaheuristic that mimics the process of weeds colonization and distribution and is capable of solving multi-dimensional, linear and nonlinear optimization problems with appreciable efficiency. In this article a modified version of IWO has been used for training the feed-forward Artificial Neural Networks (ANNs) by adjusting the weights and biases of the neural network. It has been found that modified IWO performs better than another very competitive real parameter optimizer called Differential Evolution (DE) and a few classical gradient-based optimization algorithms in context to the weight training of feed-forward ANNs in terms of learning rate and solution quality. Moreover, IWO can also be used in validation of reached optima and in the development of regularization terms and non-conventional transfer functions that do not necessarily provide gradient information

This paper proposes a simple, metaheuristic clustering technique, inspired by the mountain clustering method of Yager and Filev, for detecting general quadric shell type clusters. The algorithm employs an ecologically inspired metaheurisitc algorithm, called Invasive Weed Optimization (IWO) to evolve a set of cluster prototypes in the shape of curves/hyper-surfaces. The objective function is modeled using the concept of the mountain function from Yager and Filev's work. The metaheuristic approach can be extended to solid clusters and various shell clusters like circular, elliptical, rectangular etc. The proposed method is tested on several synthetic datasets as well as real images to detect circular and elliptical shell clusters and the results obtained are found to be very promising.

In the present work, we propose a new variant of basic DE algorithm called CMDE-G which uses Cauchy mutation (CM) operator. In this algorithm, at the end of every generation, CM is applied as a local search mechanism to explore the neighborhood of the best individual in the population. The performance of CMDE-G algorithm is analyzed on a set of 10 standard benchmark problems and four nontraditional composite functions. Simulation results show that the proposed algorithm helps in improving the solution quality besides maintaining a good convergence rate.

Based on the study of developments in many fields of computer vision, a novel computer vision navigation system for mobile tracking robot is presented. Three irrelevant technologies, pattern recognition, binocular vision and motion estimation, make up of the basic technologies of our robot. The non-negative matrix factorization (NMF) algorithm is applied to detect the target. The application method of NMF in our robot is demonstrated. Interesting observations on distance measurement and motion capture are discussed in detail. The reasons resulting in error of distance measurement are analyzed. According to the models and formulas of distance measurement error, the error type could be found, which is helpful to decrease the distance error. Based on the diamond search (DS) technology applied in MPEG-4, an improved DS algorithm is developed to meet the special requirement of mobile tracking robot.

In this paper, we present a novel algorithm for measuring protein similarity based on their 3-D structure (protein tertiary structure). The algorithm used a suffix tree for discovering common parts of main chains of all proteins appearing in the current research collaboratory for structural bioinformatics protein data bank (PDB). By identifying these common parts, we build a vector model and use some classical information retrieval (IR) algorithms based on the vector model to measure the similarity between proteins--all to all protein similarity. For the calculation of protein similarity, we use term frequency × inverse document frequency ( tf × idf ) term weighing schema and cosine similarity measure. The goal of this paper is to introduce new protein similarity metric based on suffix trees and IR methods. Whole current PDB database was used to demonstrate very good time complexity of the algorithm as well as high precision. We have chosen the structural classification of proteins (SCOP) database for verification of the precision of our algorithm because it is maintained primarily by humans. The next success of this paper would be the ability to determine SCOP categories of proteins not included in the latest version of the SCOP database (v. 1.75) with nearly 100% precision.

It is a fact that vast majority of attention is given to protecting against external threats, which are considered more dangerous. However, some industrial surveys have indicated they have had attacks reported internally. Insider Attacks are an unusual type of threat which are also serious and very common. Unlike an external intruder, in the case of internal attacks, the intruder is someone who has been entrusted with authorized access to the network. This paper presents a Non-negative Matrix factorization approach to detect inside attacks. Comparisons with other established pattern recognition techniques reveal that the Non-negative Matrix Factorization approach could be also an ideal candidate to detect internal threats.

There are many search engines in the Web and when asked, they return a long list of search results, ranked by their relevancies to the given query. Web users have to go through the list and examine the titles and (short) snippets sequentially to identify their required results. In this paper we present how usage of Nonnegative Matrix Factorization (NMF) can be good solution for the search results clustering.

Since the availability of social networks data and the range of these data have significantly grown in recent years, new aspects have to be considered. In this paper we address computational complexity of social networks analysis and clarity of their visualization. Our approach uses combination of Formal Concept Analysis and well-known matrix factorization methods. The goal is to reduce the dimension of social network data and to measure the amount of information which is lost during the reduction.

Current Intrusion Detection Systems (IDS) examine all data features to detect intrusion or misuse patterns. Some of the features may be redundant or contribute little (if anything) to the detection process. The purpose of this research is to identify important input features in building an IDS that is computationally efficient and effective. This paper propose a novel matrix factorization approach for feature deduction and design of intrusion detection systems. Experiment results indicate that the proposed method is efficient.

Since the appearance in 1993, first approaching the Shannon limit, the turbo codes gave a new direction for the channel encoding field, especially since they were adopted for multiple norms of telecommunications, such as deeper communication. To obtain an excellent performance, it is necessary to design robust turbo code interleaver. In this research, we investigated genetic algorithms as a promising optimization method to find good performing interleavers for large frame sizes. In this paper, we present our work, compare with several previous approaches and present experimental results.

The requirements imposed on information retrieval systems are increasing steadily. The vast number of documents in today's large databases and espe-cially on World Wide Web causes notable problems when searching for concrete information. It is difficult to find satisfactory information that accurately matches user information needs even if it is present in the database. One of the key elements when searching the web is proper formulation of user queries. Search effectiveness can be seen as the accuracy of matching user information needs against the retrieved information. Personalized search applications can notably contribute to the improvement of web search effectiveness. In this paper, we investigate two user modelling and search optimization techniques based on genetic algorithms and ant colony optimization.

XML (Extensible Mark-up Language) has been embraced as a new approach to data modeling. Nowadays, more and more information is formatted as semi-structured data, e.g., articles in a digital library, documents on the web, and so on. Implementation of an efficient system enabling storage and querying of XML documents requires development of new techniques. Many different techniques of XML indexing have been proposed in recent years.

In this paper, we compare three methods for grouping of binary variables: neural network Boolean factor analysis, hierarchical clustering, and a linear factor analysis on the mushroom dataset. In contrast to the latter two traditional methods, the advantage of neural network Boolean factor analysis is its ability to reveal overlapping classes in the dataset. It is shown that the mushroom dataset provides a good demonstration of this advantage because it contains both disjunctive and overlapping classes.

Since the appearance in 1993, first approaching the Shannon limit, the Turbo Codes give a new direction for the channel encoding field, especially since they were adopted for multiple norms of telecommunications, such as deeper communication. To obtain an excellent performance it is necessary to design robust turbo code interleaver. We are investigating genetic algorithms as a promising optimization method to find good performing interleaver for the large frame sizes. In this paper, we present our work, compare with several previous approaches and present experimental results.

The requirements imposed on information retrieval systems are increasing steadily. The vast number of documents in today's large databases and especially on World Wide Web causes notable problems when searching for concrete information. It is difficult to find satisfactory information that accurately matches user information needs even if it is present in the database. One of the key elements when searching the web is proper formulation of user queries. Search effectiveness can be seen as the accuracy of matching user information needs against the retrieved information. Personalized search applications can notably contribute to the improvement of web search effectiveness. It has been shown, that genetic programming can evolve search queries towards users interests captured by the means of relevance. In this paper, we propose user modelling technique based on relevance estimation and provide experimental results in web search framework with evolutionary query optimization.

Since the appearance in 1993, first approaching the Shannon limit, the Turbo Codes give a new direction for the channel encoding field, especially since they were adopted for multiple norms of telecommunications, such as deeper communication. To obtain an excellent performance it is necessary to design robust turbo code interleaver. We are investigating genetic algorithms as a promising optimization method to find good performing interleaver for the large frame sizes. In this paper, we present our work, compare with several previous approaches and present experimental results.

Rapidly growing amount of the data available on World Wide Web (WWW) as the ultimate collection of public accessible text documents complicates the task of acquiring usable and relevant information from raw data. Several methods of improvement and optimization of information retrieval (IR) systems providing user interaction with WWW documents have been introduced and discussed. In this paper, theory and application of genetic programming (GP) as an optimization method of modern user oriented IR and fuzzy IR (FIR) systems optimization of search queries is presented.

Multi-dimensional data structures are applied in many real index applications, i.e. data mining, indexing multimedia data, indexing of text documents and so on. Many index structures and algorithms have been proposed. There are two major approaches to multi-dimensional indexing: data structures to indexing metric and vector spaces. R-trees, R*-trees and (B)UB-trees are representatives of the vector data structures. These data structures provide efficient processing of many types of queries, i.e. point queries, range queries and so on. As far as the vector data structures are concerned, the range query retrieves all points in defined hyper box in an n-dimensional space. The narrow range query is an important type of the range query. Its processing is inefficient in vector data structures. Moreover, the efficiency decreases as the dimension of the indexed space increases. We depict an application of the signature for more efficient processing of narrow range queries. The approach puts the signature into the multi-dimensional data structures like R-tree or UB-tree but original functionalities are preserved, i.e. the range query algorithm for general range query. The novel data structure is called the signature data structure, e.g., Signature R-tree or Signature UB-tree.

This paper introduces a novel method for semantic analysis of Web pages. Analysis is performed with regard to unwritten and empirically proven agreement between users and Web designers using Web patterns. This method is based on extraction of patterns which are characteristics for concrete domain. Patterns provide formalization of the agreement and allow assignment of semantics to parts of Web pages. Experimental results verify the effectives of the proposed method

We describe an application of information extraction from company Web sites focusing on product offers. A statistical approach to text analysis is used in conjunction with different ways of image classification. Ontological knowledge is used to group the extracted items into structured objects. The results are stored in an RDF repository and made available for structured search.

Summary form only given. We present a new compression method, called WLZW, which is a word-based modification of classic LZW. The algorithm is two-phase, it uses only one table for words and non-words (so called tokens), and a single data structure for the lexicon is usable as a text index. The length of words and non-words is restricted. This feature improves the compress ratio achieved. Tokens of unlimited length alternate, when they are read from the input stream. Because of restricted length of tokens alternating of tokens is corrupted, because some tokens are divided into several parts of same type. To save alternating of tokens two special tokens are created. They are empty word and empty non-word. They contain no character. Empty word is inserted between two non-words and empty non-word between two words. Alternating of tokens is saved for all sequences of tokens. The alternating of tokens is an important piece of information. With this knowledge the kind of the next token can be predicted. One selected (so-called victim) non-word can be deleted from input stream. An algorithm to search the victim is also presented. In the decompression phase, a deleted victim is recognized as an error in alternating of words and non-words in sequence. The algorithm was tested on many texts in different formats (ASCII, RTF). The Canterbury corpus, a large set, was used as a standard for publication results. The compression ratio achieved is fairly good, on average 25%-22%. Decompression is very fast. Moreover, the algorithm enables evaluation of database queries in given text. This supports the idea of leaving data in the compressed state as long as possible, and to decompress it when it is necessary.