Journal of Intelligent & Fuzzy Systems - Volume 37, issue 6

Authors: Jodłowiec, Marcin | Krótkiewicz, Marek | Wojtkiewicz, Krystian

Article Type: Research Article

Abstract:  Knowledge representation is one of the most explored areas in nowadays computer science research. In this paper authors pursue definition and semantics of semantic networks that are defined as part of Semantic Knowledge Base being a hybrid knowledge oriented system. The approach presented in here aims at introducing advanced properties of networks such as cardinality, partitioning or certainty at the same time using simple structure based on two operands and operators. Following paper is an extension of a conference publication that introduced advanced aspects of semantic networks modelling with the use of Association-Oriented Metamodel. The extension includes a discussion related …to the formal description of the structure, as well as the description and use of association-oriented design patterns. Show more

Keywords: Semantic networks, Semantic Knowledge Base, partitioned semantic nets, association design patterns

DOI: 10.3233/JIFS-179353

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 6, pp. 7453-7464, 2019

Authors: Chen, Chun-Hao | Chiang, Bing-Yang | Hong, Tzung-Pei | Wang, Ding-Chau | Lin, Jerry Chun-Wei | Gankhuyag, Munkhjargal

Article Type: Research Article

Abstract: Investment is always an interesting and important issue for people since the international financial crises are hard to predict and the government’s policy may have an influence on economic activities. In the past, many researches have been proposed on portfolio issues. In some of these studies, the group stock portfolio (GSP) is utilized to provide various alternative stocks to an investor. The diverse group stock portfolio (DGSP) optimization approach has then been designed because the diversity of industries within a group can affect the performance of a final GSP. However, these approaches still have some problems to be solved. In …this paper, we propose an algorithm to improve the efficiency and effectiveness of the previous work. In the proposed approach, a new chromosome representation and an enhanced fitness function are applied to find a better DGSP with lower risk than before. Moreover, we design a fuzzy grouping genetic algorithm (FGGA) based on the concept of collective intelligence which utilizes the fuzzy logic to dynamically tune the parameters in the evolution process for finding an appropriate DGSP. A mechanism is also designed to repair non-feasible chromosomes in the population. Through the above improvements, the proposed approach can not only focus on finding the best solution but also speed up the evolution process. Finally, experiments made on real datasets show the merits of the proposed approach. Show more

Keywords: Collective intelligence, diverse group stock portfolio, fuzzy grouping genetic algorithm, grouping problem, individual repair mechanism, portfolio optimization

DOI: 10.3233/JIFS-179354

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 6, pp. 7465-7479, 2019

Authors: Grzonka, Daniel | Kołodziej, Joanna | Jakóbik, Agnieszka

Article Type: Research Article

Abstract: The monitoring of the computational processes in highly distributed environments remains challenging in today’s High Performance Computing. In this paper, we define the agent-based cloud monitoring system for supporting the computational tasks scheduling and resource allocation. The system consists of two types of agents, which may decide about the initialization of the schedule execution and monitor the work of the cloud computational nodes. The decision about running the new scheduling process is based on the expected number of available computational units in the specified time window. The efficiency of the proposed MAS-based model was justified through 40 empirical tests, where …clouds without and within the MAS support were compared. The multiagent system (MAS) effectiveness has been expressed in the average number of floating point operations completed at the cloud resources in one second. The obtained results show the importance of setting the optimal initial time for execution of the new schedule. Our experiments show that for running the new schedule, at least 25% of the computing units in the clouds should be in the idle mode. Also the batches of tasks should not be too large, cause the waiting time for new schedule for execution should be short and not greater than 10% of expected batch execution time. Show more

Keywords: multiagent systems, monitoring, computational cloud, autonomous agent, batch scheduling

DOI: 10.3233/JIFS-179355

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 6, pp. 7481-7492, 2019

Authors: Huk, Maciej

Article Type: Research Article

Abstract: Contextual neural networks are effective and very usable machine learning models being generalization of multilayer perceptron. They allow to solve classification problems with high accuracy while strongly limiting activity of connections between hidden neurons. Within this article we present novel study of properties of contextual neuronal networks with Hard and Exponential Rectifier activation functions and of their influence on behavior of the Generalized Error Backpropagation method. It is used to show how to optimize efficiency of the sorting phase of this algorithm when applied to train evaluated models. This considerably extends our previous related paper which was limited to analysis …of contextual neuronal networks with Leaky Rectifier and Sigmoidal activation functions. This article includes wide description of contextual neural networks and generalized error backpropagation algorithm as well as the discussion of their connection with self-consistency paradigm, which is frequently used in quantum physics. Also the relation of the latter with sorting methods and considered rectifier functions during training of contextual neural networks is studied in details. Conclusions are backed up by the results of performed experiments. Reported outcomes of simulations confirm the ability of contextual neural networks to limit activity of connections between their neurons and – what is more important – indicate the detailed rules of selection of the most efficient sorting algorithm for updating scan-paths of contextual neurons that are using Hard and Exponential Rectifier activation functions. Presented results have considerable value both for research and practical applications – especially where the efficiency of training of contextual neural networks is crucial. Show more

Keywords: Classifiers, self-consistency, aggregation functions, scan-paths

DOI: 10.3233/JIFS-179356

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 6, pp. 7493-7502, 2019

Authors: Nguyen, Binh Thanh

Article Type: Research Article

Abstract: The usefulness and ease of use of Big5 dashboard have been proposed to explore hierarchical structure of personality traits. First, Big5 system architecture and its components are described. Afterwards, we present how to calculate Big5 indicators from available big mobile data sets. Hereafter, Big5 traits can be predicted based on those just-specified indicators. To proof of our concepts, implementation results will be presented in the context of the Big5 dashboard which has been designed and developed to predict Big5 personalities in a representative and interactive manner.

Keywords: Big5 traits, personality, indicators, data warehouse, mobile logs, Naive Bayes classification, dashboard

DOI: 10.3233/JIFS-179357

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 6, pp. 7503-7509, 2019

Authors: Le Thi, Hoai An

Article Type: Research Article

Abstract: This paper deals with a new and efficient collective optimization approach, based on DC (Difference of Convex functions) programming and DCA (DC Algorithm), powerful tools of nonconvex programming. Exploiting the efficiency and the flexibility of DCA we develop the so-called collaborative DCA in which divers DCA based algorithms are cooperated in an effective way. Two versions of collaborative DCA are proposed and their applications on clustering, a fundamental problem in unsupervised learning, are studied. Numerical experiments are performed on several datasets. The comparative results with three DCA component algorithms show that the collaborative DCA outperforms them on quality and it …realizes a good trade-off between the quality of solutions and the running time. Show more

Keywords: Collective optimization, DC programming, DCA, Collaborative DCA, Clustering.

DOI: 10.3233/JIFS-179358

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 6, pp. 7511-7518, 2019

Authors: Li, Honggui | Trocan, Maria

Article Type: Research Article

Abstract: Isometric feature mapping (ISOMAP) is one of the classical methods of nonlinear dimensionality reduction (NLDR) and seeks for low dimensional (LD) structure of high dimensional (HD) data. However, the inverse problem of ISOMAP has never been investigated, which recovers the HD sample from the related LD sample, and its application prospect in data representation, generation, compression and visualization will be very brilliant. Because the inverse problem of ISOMAP is ill-posed and undetermined, the sparsity of HD data is employed to reconstruct the HD data from the corresponding LD data. The theoretical architecture of sparse reconstruction of ISOMAP representation comprises the …original ISOMAP algorithm, learning algorithm of sparse dictionary, general ISOAMAP embedding algorithm and sparse ISOMAP reconstruction algorithm. The sparse ISOMAP reconstruction algorithm is an optimization problem with sparse priors of the HD data, which is resolved by the alternating directions method of multipliers (ADMM). It is uncovered from the experimental results that, in the case of very LD ISOMAP representation, the proposed method outperforms the state-of-the-art methods, such as discrete cosine transformation (DCT) and sparse representation (SR), in the reconstruction performance of signal, image and video data. Show more

Keywords: Isometric feature mapping, inverse problem, sparse priors, sparse reconstruction, alternating directions method of multipliers

DOI: 10.3233/JIFS-179359

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 6, pp. 7519-7536, 2019

Authors: Kurowski, Adam | Mrozik, Katarzyna | Kostek, Bozena | Czyzewski, Andrzej

Article Type: Research Article

Abstract:  In this paper, a methodology for automatic brain activity class identification of EEG (electroencephalographic) signals is presented. EEG signals are gathered from seventeen subjects performing one of the three tasks: resting, watching a music video and playing a simple logic game. The methodology applied consists of several steps, namely: signal acquisition, signal processing utilizing z-score normalization, parametrization and activity classification. The EEG signal is acquired from a headset containing 14 electrodes. For the parametrization two methods are used, namely, Discrete Wavelet Transform (DWT) employed as a reference parametrization technique and autoencoder neural network. Parameters obtained with those methods are fed …to the input of classifiers which assigned them to one of three activity classes. Then, the effectiveness of the assignment of the frames of EEG data into appropriate classes is observed and compared. Results obtained using both methods show differences in accuracy with regard to the task detected depending on factors such as type of parametrization or complexity of the classifier employed for EEG activity classification. Show more

Keywords: EEG signal, discrete wavelet transform (DWT), autoencoder, EEG signal classification

DOI: 10.3233/JIFS-179360

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 6, pp. 7537-7543, 2019

Authors: Koczkodaj, Waldemar W. | Kakiashvili, T. | Li, Feng | Wolny-Dominiak, Alicja | Masiak, Jolanta

Article Type: Research Article

Abstract:  In this study, differential evolution (DE) optimization is proposed for rating scale predictability improvement. An arbitrary assignment of equal values for rating scale items is used as the classifier although domain experts are aware that the contribution of individual items may vary. Most academic examinations are conducted by the use of rating scales. Rating scales are also used in psychiatry (especially for screening). This study demonstrates that the differential evolution is effective for optimizing the predictability of rating scales.

Keywords: Rating scale, DE

DOI: 10.3233/JIFS-179361

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 6, pp. 7545-7553, 2019

Authors: Phuc, Do

Article Type: Research Article

Abstract: Real world data is often interconnected, forming large and complex heterogeneous information networks (HINs) with multiple types of objects and links such as bibliographic network (DBLP) and knowledge bases (YaGo). Querying meta-paths requires exploration of path instances which can be computational cost in large HINs. However, existing meta-path based studies mostly focus on analytical applications of meta-paths, rather than systems to query meta-paths efficiently in large HINs. To bridge this gap, in this work we present SparkHINlog, a system based on Apache Spark, to handle meta-paths queries efficiently on large scale HINs. In SparkHINlog we propose an algorithm to not …only translate meta-paths to Datalog rules, but also to manage the working memory area of Datalog efficiently to increase the scalability of SparkHINlog. To avoid the computing overhead of join operation to discover path instances when evaluating these rules, we leverage Motif Finding, a powerful tool of GraphFrames Library. With motif finding, SparkHINLog can speed up the time to evaluate the rules by path finding on graph instead on joining two relations. We conduct experimental comparisons with SparkDatalog, the state-of-the-art large-scale Datalog system, and verify the efficacy and effectiveness of our system in supporting meta-path queries. Show more

Keywords: Bibliographic network, datalog rules, heterogeneous information networks, meta-path, spark graphframes

DOI: 10.3233/JIFS-179362

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 6, pp. 7555-7566, 2019