Journal of Intelligent & Fuzzy Systems - Volume 32, issue 2

Authors: Vathi, Eleni | Siolas, Georgios | Stafylopatis, Andreas

Article Type: Research Article

Abstract: In recent years, the popularity of social networks has grown dramatically. Understanding and revealing the underlying community structure of these complex networks is an area of great interest, with a plethora of applications. In this paper, we present a methodology for identifying user communities on Twitter. Initially, Twitter features such as the shared content, the users’ interactions and the following relationships between the users are utilized to define a number of similarity metrics. These metrics are then used to compute the similarity between each pair in a set of Twitter users and by extension to group these users into communities. …Subsequently, we propose a novel method based on latent Dirichlet allocation to extract the topics discussed in each community and eliminate those which consist of everyday words. Additionaly, we introduce a method for automatically generating labels for the non-trivial topics. The methodology is evaluated with a real-world dataset created using the Twitter Searching API. Show more

Keywords: Community detection, topic modeling, Twitter

DOI: 10.3233/JIFS-169125

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1265-1275, 2017

Authors: Tran, Van Cuong | Hoang, Dinh Tuyen | Nguyen, Ngoc Thanh | Hwang, Dosam

Article Type: Research Article

Abstract: In recent years, information extraction from tweets has been challenging for researchers in the fields of knowledge discovery and data mining. Unlike formal text, such as news articles and pieces of longer content, tweets are of a specific nature: short, noisy, and with dynamic content. Thus, it is difficult to apply the traditional natural language processing algorithms to analyze them. Active learning is well-suited to many problems in natural language processing, especially when unlabeled data may be abundant, but labeled data is limited. The method proposed here aims to minimize annotation costs while maximizing the desired performance from the model. …The method recognizes named entities from tweet streams on Twitter by using an active learning method with different query strategies. The tweets are queried for labeling by a human annotator based on query-by-committee, uncertainty-based sampling, and diversity-based sampling. The experimental evaluations of the proposed method on tweet data achieved better results than random sampling. Show more

Keywords: Named entity recognition, active learning, tweet streams, query strategy

DOI: 10.3233/JIFS-169126

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1277-1287, 2017

Authors: Jędrzejowicz, Joanna | Jędrzejowicz, Piotr

Article Type: Research Article

Abstract: The paper proposes two variants of the ensemble distance-based and Naive-Bayes online classifiers with data reduction. In the first variant the reduced dataset is obtained through applying bias-correction fuzzy clustering. In the second we used the kernel-based fuzzy clustering as the data reduction tool. It is assumed that vectors of data with unknown class label arrive one by one, and that there is available an initial chunk of data with known class labels serving as the initial training set. Classification is carried-out in rounds. Each round involves a number of the classification decisions equal to the chunk size. For each …round a set of base classifiers is constructed using different distance metrics. Set of base classifiers is extended with the Naive-Bayes classifier. The unknown label of each incoming vector is determined through weighted majority voting. After each round has been completed the training set is replaced by the fresh one and the classification process is continued. The approach is validated through computational experiment involving a number of datasets often used for testing data streams mining algorithms. Show more

Keywords: Online classification, kernel-based fuzzy clustering, bias-correction fuzzy clustering

DOI: 10.3233/JIFS-169127

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1289-1296, 2017

Authors: Nguyen, Loan T.T. | Trinh, Truc | Nguyen, Ngoc-Thanh | Vo, Bay

Article Type: Research Article

Abstract: Mining frequent closed itemsets (FCIs) is important in mining non-redundant (minimal) association rules. Therefore, many algorithms have been developed for mining FCIs with reduced mining time and memory usage. For mining FCIs, algorithms use the minimum support threshold, minSup , to prune itemsets. However, using a fixed minSup is not suitable for mining top-rank-k FCIs. A large threshold will lead to a small number of generated FCIs, leading to insufficient FCIs to query when k is large. On the other hand, a small minSup will generate a huge number of generated FCIs, leading to large runtimes and high memory usage. In …this paper, we propose a method for mining top-rank-k FCIs without using a fixed minimum support threshold. A strategy is first used to eliminate 1-items that cannot generate FCIs belonging to top-rank-k FCIs. Next, based on the set of candidate 1-items, we propose TRK-FCI, a DCI-Plus-based algorithm, for mining top-rank-k FCIs. In the process of mining top-rank-k FCIs, TRK-FCI automatically increases minSup according to the mined FCIs, efficiently pruning itemsets that cannot belong to top-rank-k FCIs. We also modify the dynamic bit vector (DBV) structure and apply it to reduce memory usage and runtime in the TRK-FCI-DBV algorithm. Experimental results show that TRK-FCI-DBV is more efficient than TRK-FCI for various databases. Show more

Keywords: DCI-Plus, dynamic bit vectors, frequent closed itemsets, top-rank-k frequent closed itemsets

DOI: 10.3233/JIFS-169128

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1297-1305, 2017

Authors: Rodríguez-Fernández, Víctor | Menéndez, Héctor D. | Camacho, David

Article Type: Research Article

Abstract: Unmanned Aerial Vehicles (UAVs) are starting to provide new possibilities to human societies and their demand is growing according to the new industrial application fields for these revolutionary tools. The current systems are still evolving, specially from an Artificial Intelligence perspective, which is increasing the different tasks that UAVs can perform. However, the current state still requires a strong human supervision. As a consequence, a good preparation for UAV operators is mandatory due to some of their applications might affect human safety. During the training process, it is important to measure the performance of these operators according to different factors …that can help to decide what operators are more suitable for different kinds of missions creating operator profiles. Having this goal in mind, this work aims to present an extensive and robust methodology to automatically extract different performance profiles from the training process of operators in an UAV simulation environment. Our method combines the definition of a set of performance metrics with clustering techniques to define operators profiles, ensuring that the behavior discrimination is suitable and consistent. Show more

Keywords: UAVs, Human-Robot Interaction, computer-based simulation, clustering, performance metrics, behavioral analysis

DOI: 10.3233/JIFS-169129

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1307-1319, 2017

Authors: Leon, Florin | Curteanu, Silvia

Article Type: Research Article

Abstract: The concept of a large margin is central to support vector machines and it has recently been adapted and applied for nearest neighbour classification. In this paper, a modification of this method is proposed in order to be used for regression problems. This model also allows the use of a set of prototypes with different distance metrics, which can increase the flexibility of the method especially for problems with large number of instances. The learning of the distance metrics is performed by two optimization methods, namely an evolutionary algorithm and an approximate differential approach. A real world problem, i.e. the …prediction of the corrosion resistance of some alloys containing titanium and molybdenum is considered as a case study. It is shown that the suggested method provides very good results compared to other well-known regression algorithms. Show more

Keywords: Large margin, nearest neighbour regression, prototypes, distance metrics, evolutionary algorithm, approximate differential optimization

DOI: 10.3233/JIFS-169130

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1321-1332, 2017

Authors: Plata, Diego Rueda | Ramos-Pollán, Raúl | González, Fabio A.

Article Type: Research Article

Abstract: This work proposes a supervised layer-wise strategy to train deep convolutional neural networks (DCNs) particularly suited for small, specialized image datasets. DCNs are increasingly being used with considerable success in image classification tasks and trained over large datasets (with more than 1M images and 10 K classes). Pre-trained successful DCNs can then be used for new smaller datasets (10 K to 100 K images) through a transfer learning process which cannot guarantee competitive a-priori performance if the new data is of different or specialized nature (medical imaging, plant recognition, etc.). We therefore seek out to find competitive techniques to train DCNs for such …small datasets, and hereby describe a supervised greedy layer-wise method analogous to that used in unsupervised deep networks. Our method consistently outperforms the traditional methods that train a full DCN architecture in a single stage, yielding an average of over 20% increase in classification performance across all DCN architectures and datasets used in this work. Furthermore, we obtain more interpretable and cleaner visual features. Our method is better suited for small, specialized datasets since we require a training cycle for each DCN layer and this increases its computing time almost linearly with the number of layers. Nevertheless, it still remains as a fraction of the computing time required to generate pre-trained models with large generic datasets, and poses no additional requirements on hardware. This constitutes a solid alternative for training DCNs when transfer learning is not possible and, furthermore, suggests that state of the art DCN performance with large datasets might yet be improved at the expense of a higher computing time. Show more

Keywords: Convolutional networks, deep learning, greedy layer-wise training

DOI: 10.3233/JIFS-169131

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1333-1342, 2017

Authors: Cañizares, Pablo C. | Merayo, Mercedes G. | Vara, Juan M.

Article Type: Research Article

Abstract: This paper introduces a new high-level domain-specific modelling language, LAnt , for the design of ant colony optimization algorithms. This language is used to represent the main elements required to define the structure of the algorithms and to capture the specific constraints associated to the problem to solve. It aims to support users with low experience in the development of solutions based on this paradigm. The proposal has been implemented as an Eclipse plug-in, including an editor and an integrated code generator.

Keywords: Artificial intelligence, ant colony optimization, model driven engineering, domain specific language

DOI: 10.3233/JIFS-169132

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1343-1354, 2017

Authors: Salehi, Saber | Selamat, Ali | Kuca, Kamil | Krejcar, Ondrej | Sabbah, Thabit

Article Type: Research Article

Abstract: Spam email problem is a major shortcoming of email technology for computer security. In this research, a granular classifier model is proposed to discover hyper-boxes in the geometry of information granules for spam detection in three steps. In the first step, the k-means clustering algorithm is applied to find the seed_points to build the granular structure of the spam and non-spam patterns. Moreover, the key part of the spam and non-spam classifiers’ structure is captured by applying the interval analysis through the high homogeneity of the patterns. In the second step, PSO algorithm is hybridized with the k-means to optimize …the formalized information granules’ performance. The size of the hyperboxes is expanded away from the center of the granules by PSO. There are some patterns that do not placed in any of the created clusters and known as noise points. In the third step, the membership function in fuzzy sets is applied to solve the noise points’ problem by allocating the noise points through the membership grades. The proposed model is evaluated based on the accuracy, misclassification and coverage criteria. Experimental results reveal that the performance of our proposed model is increased through applying Particle Swarm Optimization and fuzzy set. Show more

Keywords: Spam detection, hyperbox geometry of classifiers, granular classifier, membership functions, particle swarm optimisation, k-means clustering algorithm

DOI: 10.3233/JIFS-169133

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1355-1363, 2017

Authors: Huk, M.

Article Type: Research Article

Abstract: In this paper, we show that a contextual neural network with artificial neurons performing a conditional aggregation of signals can be trained by the generalized backpropagation algorithm. To allow this algorithm to be used for training contextual neural networks, we derive appropriate generalized delta rules. Our approach is constructed on the basis of introduced generalized representation of the aggregation function in an ordered groups space and division of its attention function into binary scan-path and contribution functions. The advantage of the proposed representation is that it clarifies the description of the aggregation process by using Stark’s scan-path theory and allows …us to achieve results independent from the actual form of the attention functions used during aggregation. As such, the proposed solution is valid for the whole presented family of conditional aggregation functions and is a considerable extension of the previously reported results. In particular, the obtained results are valid for the introduced exemplary attention functions which illustrate performed calculations. Moreover, the presented solution can be further extended by considering real valued, non-binary contribution functions inside ordered aggregation functions. Especially promising are its possible applications in large deep neural networks and energy-limited systems. Show more

Keywords: Contextual neural networks, conditional aggregation, classification, dynamic inputs selection, selective attention

DOI: 10.3233/JIFS-169134

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1365-1376, 2017

Authors: Siemiński, Andrzej | Kopel, Marek

Article Type: Research Article

Abstract: The paper presents a study on the efficiency of Ant Colony Communities (ACC) used to solve the Travelling Salesman Problem. The ACC is an approach to parallelize the Ant Colony Optimization algorithm (ACO). An ACC is made up of a Community Server that coordinates the work of a set ant colony clients. Each client implements a classical ACO algorithm. The individual colonies process cargos of data obtained from the server and send them back the as partial results. The paper presents a general description of the ACC concept and describes in details two ways of implementing it. The first one …uses an inhomogeneous environment of traditional computers working in an asynchronous mode. The second one uses the homogenous Hadoop environment and the processing is done in a synchronized mode. The performance of the Communities is estimated by low level measures: their power and scalability. The high level measure deals with the length of obtained routes. The paper presents also the taxonomy of parallel implementations of the Ant Colony Optimization. Show more

Keywords: Ant Colony Optimization, Travelling Salesman Problem, ACO parallel implementations, sockets, Hadoop, MapReduce, scalability, computational power

DOI: 10.3233/JIFS-169135

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1377-1388, 2017

Authors: Tian, Feng | Zhang, Rong | Lewandowski, Jacek | Chao, Kuo-Ming | Li, Longzhuang | Dong, Bo

Article Type: Research Article

Abstract: Consolidation of services is one of the key problems in cloud data centers. It consists of two separate but related issues: Virtual machine (VM) placement and VM migration problems. In this paper, a VM consolidation scheme is proposed that turns the virtual machine consolidation (VMC) problem into a vector packing optimization problem based on deadlock-free migration (DFM) to minimize the energy consumptions. To solve this NP-hard and computationally infeasible for large data centers problem, a novel algorithm named Chicken Swarm Optimization based on deadlock-free migration (DFM-CSO) algorithm is proposed. The DFM-CSO algorithm is characterized by the ‘one-step look-ahead with n-VMs …migration in parallel (OSLA-NVMIP)’ method, which carries out the VM migration validation and the rearrangement of target physical host, as well as records the migration order for each solution placement, so that VM transfer can be completed according to the migration sequence. The experimental results, for both real and synthetic datasets, show that the proposed algorithm with higher convergence rate is favourable in comparison with the other deadlock-free migration algorithms. Show more

Keywords: VM consolidation, VM placement, deadlock-free migration, Chicken Swarm Optimization

DOI: 10.3233/JIFS-169136

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1389-1400, 2017

Authors: Czarnowski, Ireneusz | Jędrzejowicz, Piotr

Article Type: Research Article

Abstract: Data reduction can increase generalization abilities of the learning model and shorten learning time. It can be particularly helpful in analyzing big data sets. This paper focuses on the machine learning from examples with data reduction. In the paper data reduction is carried out by selection of relevant instances, called prototypes. The discussed approach bases on the assumption that the selection of prototypes is carried-out by a team of agents and that the prototype instances are selected from clusters of instances under the constraint that from each cluster a single prototype is obtained. For cluster initialization the kernel-based fuzzy clustering …algorithm is used. Main feature of the proposed approach is integrating data reduction with the stacking technique. Stacked generalization assures diversification among prototypes, and hence, base classifiers. To validate the proposed approach we have carried-out computational experiment. We have also evaluated experimentally the influence of the clustering method and the number of stacking folds used, on the classification accuracy. Show more

Keywords: Learning from big data, data reduction, stacked generalization, kernel-based clustering

DOI: 10.3233/JIFS-169137

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1401-1411, 2017

Authors: Lin, Szu-Yin | Chiu, Yao-Ching | Lewandowski, Jacek | Chao, Kuo-Ming

Article Type: Research Article

Abstract: The traditional data analysis and prediction method assumes that data distribution is normal and will not change. Therefore, it can predict unlabeled data by analyzing the static and historical data. However, in today’s big-data environment, which is changing frequently, the traditional approaches can no longer be effective, as they cannot handle concept drift problems in a Dynamic Data Driven Application System (DDDAS). This study proposes a parallel detection and prediction method for concept drift problems in DDDAS. The proposed method can detect dynamic and changing data, and then feedback to the prediction model to revise for better subsequent predictions. Furthermore, …this method computes a global prediction result by aggregating local predictions in the resource bounded environment. Therefore, the prediction accuracy increases, and the computation time decreases. In the simulation, the Map-Reduce technology is used for parallel processing. The simulation results show that the prediction accuracy is raised by 14%, and the execution time is improved by almost 45%. Show more

Keywords: Dynamic data-driven application system, concept drift, Map-Reduce

DOI: 10.3233/JIFS-169138

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1413-1426, 2017

Authors: Ksieniewicz, Paweł | Graña, Manuel | Woźniak, Michał

Article Type: Research Article

Abstract: Recently, the representation learning is the fucus of intense research of machine learning community. The underlying idea is that the key for successful discrimination of difficult datasets is a good feature extraction. A transformation of the data space into another space where classification is easy. This work proposes a novel transformation into feature space that follows a photographic intuition: that we can build from pairs of features in original space some kind of photographic plate where the sample data are projected to create a picture of the data distribution in the feature subspace defined by the feature pair. These …photographic plates may be used as individuals of a classifier ensemble. The approach allows a natural definition of a confidence weight affecting each individual classifier out for the construction of a combination rule used by the ensemble. Hence the name Paired Feature Multilayer Ensemble (PFME ). The approach is naturally naive parallel, insensitive to sample size, robust to dimension increase, and allows a regularization in feature space which is independent from original input space. The proposed approach was evaluated on the basis of the computer experiments carried out on the benchmark datasets. Show more

Keywords: Machine learning, representation learning, classifier ensemble, hyperspectral image

DOI: 10.3233/JIFS-169139

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1427-1436, 2017

Authors: Nguyen, Tuong Tri | Hwang, Dosam | Jung, Jason J.

Article Type: Research Article

Abstract: The imbalanced data problem occurs when the number of representative instances for classes of interest is much lower than for other classes. The influence of imbalanced data on classification performance has been discussed in some previous research as a challenge to be studied. In this paper, we propose a method to solve the imbalanced data problem by focusing on preprocessing, including: i) sampling techniques (i.e., under-sampling, over-sampling, and hybrid-sampling) and ii) the instance weighting method to increase the number of features in minority classes and to reduce comprehensive coverage in majority classes. The experimental results show that the noisy data …is reduced, making a smaller sized dataset, and training time decreases significantly. Moreover, distinct properties of each class are examined effectively. Refined data is used as input for Naive Bayes and support vector machine classifiers for the targets of the training process. The proposed methods are evaluated based on the number of non-geotagged resources that are labeled correctly with their geo-locations. In comparison with previous research, the proposed method achieves accuracy of 84%, whereas previous results were 75%. Show more

Keywords: Imbalanced datasets, geotags resources, sampling method, instance weighting, location prediction

DOI: 10.3233/JIFS-169140

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1437-1448, 2017

Authors: García-Saiz, Diego | Zorrilla, Marta

Article Type: Research Article

Abstract: The task of selecting the most suitable classification algorithm for each data set under analysis is still today a unsolved research problem. This paper therefore proposes a meta-learning based framework that helps both, practitioners and non-experts data mining users to make informed decisions about the goodness and suitability of each available technique for their data set at hand. In short, the framework is supported by an experimental database that is fed with the meta-features extracted from training data sets and the performance obtained by a set of classifiers applied over them, with the aim of building an algorithm recommender using …regressors. This will allow the end-user to know, for a new unseen data set, the predicted accuracy of this set of algorithms ranked by this value. The experimentation performed and discussed in this paper is addressed to evaluate which meta-features are more significant and useful for characterising data sets with the end goal of building algorithm recommenders and to test the feasibility of these recommenders. The study is carried out on data sets from the educational arena, in particular, targeted to predict students’ performance in e-learning courses. Show more

Keywords: Meta-learning, regression, student performance, educational data mining

DOI: 10.3233/JIFS-169141

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1449-1459, 2017

Authors: Król, Dariusz | Nowakowski, Filip

Article Type: Research Article

Abstract: This paper assesses the possibility of using a popular middleware platform based on a multiple agent paradigm in full compliance with Real Time Specifications for Java. Two reference scenarios are discussed: one testing thread-to-thread activation (creating and releasing threads), the other featuring agent-to-agent execution for road traffic simulation (creating and releasing agents). These preemptive tasks must be scheduled with minimum delay, therefore, timing correctness as thread-to-thread activation latency and agent-to-agent execution latency is a critical performance index. Given this requirement, the study presented describes an empirical investigation of timing and capacity impact on two platforms: Solaris and Windows …in both RT and non-RT versions. The experiments showed the impact on performance of platform type and of capacity with the non-RT approach in particular yielding less accuracy but better stability in agent execution. Suggestions as to how this real-time multi-agent approach might be made more effective are included in the paper. Show more

Keywords: Multi-agent simulation, real-time system, middleware platform, JADE, FIPA-compliant system, vehicle traffic system, performance evaluation, timing correctness

DOI: 10.3233/JIFS-169142

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1461-1473, 2017

Authors: Sobeslav, Vladimir | Balik, Ladislav | Hornig, Ondrej | Horalek, Josef | Krejcar, Ondrej

Article Type: Research Article

Abstract: This article presents a security system proposal, providing a low-level endpoint security and network activity monitoring. Its focus is to provide a necessary information for local administrators, who does not necessarily have the knowledge of networking infrastructure or access to it, according to the security policies of a parent organization. The proposed system is designed for academic research environments, where it serves as a tool for an extended security in protection of sensitive data used in research and development against the local and remote threads. The developed system was extended to contain central security point which acts as a server …with IDS/IPS capability and enrich the whole functionality of distributed firewalling system. Show more

Keywords: Firewall, endpoint, local security, packet inspection, iptables, advfirewall, java firewall, research data security, open source

DOI: 10.3233/JIFS-169143

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1475-1484, 2017

Authors: Camacho, Azahara | Merayo, Mercedes G. | Núñez, Manuel

Article Type: Research Article

Abstract: Healthcare is one of the most important concerns of society, being extremely relevant the accuracy and quality of their services. Basically, eHealth can be considered as the area where electronic processes and communications are used to improve the quality of medical assistance. Despite the relative maturity of the field, recalls and problems related to medical devices, applications and services are still very frequent. Therefore, it is necessary to improve and expand current research to provide advances that can be transmitted to the society. As an initial step, it is essential to have a good understanding of the current state-of-the-art of …eHealth. The main goal of this paper is to identify the most relevant and recent work on eHealth but, due to the immensity of the field and the scope of the journal, we will concentrate on the use of two specific technologies: databases and collective intelligence. In addition to review the main concepts related to eHealth and the most influent academic papers, we will describe projects which are essential for the correct performance of many healthcare services. As a result of our study, we reached some interesting conclusions that might be useful for future projects and we encourage to apply them in order to avoid some of the problems that we have found in existing projects. Show more

Keywords: eHealth, databases, collective intelligence, security

DOI: 10.3233/JIFS-169144

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1485-1496, 2017

Authors: Blazek, Pavel | Kuca, Kamil | Jun, Daniel | Krejcar, Ondrej

Article Type: Research Article

Abstract: The value of data is hidden in their organization, accessibility and usability. During a research the data are collected from external sources and created too. If they are not stored in a central database, it is difficult to reuse them and it could lead to inefficiency in a form of duplicated information and time wasting for finding the same information in public sources. An output of research can also be a long time expected medication as a biohazard substance. Both ideas have a common problem of sensitivity and security. It is necessary to protect these data either for secreting against …competitors or hiding information from terrorists. For the research team, the Biomedical Database System offers besides security also improvement of data flow in an organization, clear arrangement and efficiency. Show more

Keywords: Biomedical research, information system, data collecting, data processing, security

DOI: 10.3233/JIFS-169145

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1497-1508, 2017

Authors: Madeyski, Lech | Kitchenham, Barbara

Article Type: Research Article

Abstract: Researchers have identified problems with the validity of software engineering research findings. In particular, it is often impossible to reproduce data analyses, due to lack of raw data, or sufficient summary statistics, or undefined analysis procedures. The aim of this paper is to raise awareness of the problems caused by unreproducible research in software engineering and to discuss the concept of reproducible research (RR) as a mechanism to address these problems. RR is the idea that the outcome of research is both a paper and its computational environment. We report some recent studies that have cast doubts on the reliability …of research outcomes in software engineering. Then we discuss the use of RR as a means of addressing these problems. We discuss the use of RR in software engineering research and present the methodology we have used to adopt RR principles. We report a small working example of how to create reproducible research. We summarise advantages of and problems with adopting RR methods. We conclude that RR supports good scientific practice and would help to address some of the problems found in empirical software engineering research. Show more

Keywords: Reproducible research, empirical software engineering, scientific practice

DOI: 10.3233/JIFS-169146

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1509-1521, 2017

Authors: Horálek, Josef | Holík, Filip | Horák, Oldřich | Petr, Lukáš | Sobeslav, Vladimir

Article Type: Research Article

Abstract: This paper acquaints with a created application for generating Rainbow Tables and the results of testing Rainbow Tables, according to the length of the chosen chain. The paper presents a specialized application containing its own algorithms for reduction functions, changing the length of chain, generating Rainbow Tables and measuring the effectivity of the password search in detail. Within the executed tests, the dependence of Rainbow Tables size on the password length, the affection of the hash search by the size of the chosen chain and their links to collisions, which arise from the principle of using the reduction function, were …observed. The results objectively describe the pros and cons of using Rainbow Tables and show the possibilities and restrictions for their effective usage. Show more

Keywords: Rainbow Tables, hash, MD5, efficiency testing, breaking hash

DOI: 10.3233/JIFS-169147

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1523-1534, 2017

Authors: Błaszczyk, Piotr | Turek, Wojciech | Cetnarowicz, Krzysztof | Byrski, Aleksander

Article Type: Research Article

Abstract: In the paper we describe the process of development and verification of the novel traffic simulation method based on a credible driver model. The model is based on the actions of real drivers recorded in series of experiments. The developed simulation method reflects the unpredictable and totally individual characteristics of individual drivers. We show that the characteristics is a very significant factor in overall traffic efficiency. We believe that our credible driver model may become a basis for novel traffic management systems and active-safety mechanisms installed in the vehicles.

Keywords: Driver model, simulation, traffic management

DOI: 10.3233/JIFS-169148

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1535-1546, 2017

Authors: Nalepa, Jakub | Blocho, Miroslaw

Article Type: Research Article

Abstract: The pickup and delivery problem with time windows is an NP-hard discrete optimization problem with two objectives—to minimize the fleet serving transportation requests, and to minimize the distance traveled during this service. Although there exist exact algorithms for tackling this problem, they are still difficult to apply in massively large practical scheduling scenarios due to their time complexities. Hence, the approximate methods became the main stream of research in this field. In this paper, we propose an adaptive guided ejection search algorithm for solving the pickup and delivery with time windows. The pivotal part of this technique is the pre-processing …step, in which the instance characteristics concerning its underlying structure are extracted in the clustering and histogram-based analyses. Then, the k -nearest neighbor algorithm is applied to classify the instance to an appropriate class. Finally, the most suitable variant of our enhanced guided ejection search algorithm is adaptively chosen for solving this instance based on the classification outcome. An extensive experimental study performed on the full Li and Lim’s benchmark (encompassing 354 problem instances belonging to 6 classes) revealed that our pre-processing allows for achieving very high classification accuracy, thus for selecting the best variant of the enhanced guided ejection search. Show more

Keywords: Adaptation, k-NN algorithm, clustering, guided ejection search, PDPTW

DOI: 10.3233/JIFS-169149

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1547-1559, 2017

Authors: Hudziak, Mariusz | Pozniak-Koszalka, Iwona | Koszalka, Leszek | Kasprzak, Andrzej

Article Type: Research Article

Abstract: The objective of this paper is to give a tool for the practical users, looking for the efficient way for solving pathfinding problem, concerning planning the best paths for the simultaneously moving agents in the crowded environment with obstacles. The proposed approach is based on the two-stage approach. In the first stage, a navigation mesh for passable regions in rectangular 2D environment is created using Quad-trees algorithm. In the second stage, a path is found for each agent present in environment using Dijkstra or A* algorithm. To find efficient paths in crowded environment, density information for each passable region is …stored. Density information is further mapped on graph edges along with the distance values. The key point is that the moving agents reevaluate their paths accordingly to the re-planning strategy. Three strategies are considered: (i) periodical re-planning, (ii) periodical with initial re-planning, and (iii) the proposed way called event-driven re-planning. The created and implemented experimentation system can be adopted by the practical user for testing the two-stage combinations of algorithms. The results of investigations, based on simulation experiments made with this system, presented in the paper, showed that the proposed approach is promising. Show more

Keywords: Pathfinding, crowded environment, multi-agent, algorithm, experimentation system

DOI: 10.3233/JIFS-169150

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1561-1573, 2017

Authors: Zhang, Haoxi | Sanin, Cesar | Szczerbicki, Edward | Zhu, Ming

Article Type: Research Article

Abstract: In this paper, we propose the Neural Knowledge DNA, a framework that tailors the ideas underlying the success of neural networks to the scope of knowledge representation. Knowledge representation is a fundamental field that dedicates to representing information about the world in a form that computer systems can utilize to solve complex tasks. The proposed Neural Knowledge DNA is designed to support discovering, storing, reusing, improving, and sharing knowledge among machines and computing devices. It is constructed in a similar fashion of how DNA formed: built up by four essential elements. As the DNA produces phenotypes, the Neural Knowledge DNA …carries information and knowledge via its four essential interrelated elements, namely, Networks, Experiences, States, and Actions; which store the detail of the artificial neural networks for training and reusing such knowledge. The novelty of this approach is that it uses previous decisional experience to collect and expand intelligence for future decision making formalized support. The experience based collective computational techniques of Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA) are used to develop aforesaid decisional sustenance. Together with artificial neural networks and reinforcement learning, the proposed Neural Knowledge DNA is used to catch knowledge of a very simple maze problem, and the results show that our Neural Knowledge DNA is a very promising knowledge representation approach for artificial neural network-based intelligent systems. Show more

Keywords: Neural knowledge DNA, neural networks, deep learning, reinforcement learning, knowledge representation

DOI: 10.3233/JIFS-169151

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1575-1584, 2017

Authors: Sanin, Cesar | Shafiq, Imran | Waris, Mohammad Maqbool | Toro, Carlos | Szczerbicki, Edward

Article Type: Research Article

Abstract: Engineering collective intelligence is paramount in current industrial times. This research proposes and presents case studies for collective knowledge structures required in the industry field. Knowledge structures such as Set of Experience and Decisional DNA are extended into more advanced knowledge structures for manufacturing processes. These structures are called Virtual Engineering Object, Virtual Engineering Process and Virtual Engineering Factory. All knowledge structures are implemented and tested in two industrial manufacturing cases of collective knowledge, plus one more case of manufacturing innovation where the case study results proved them as practical standards for engineering collective intelligence.

Keywords: Decisional DNA, set of experience knowledge structure, virtual engineering object, virtual engineering process, virtual engineering factory

DOI: 10.3233/JIFS-169152

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1585-1599, 2017

Authors: Wahyono, | Jo, Kang-Hyun

Article Type: Research Article

Abstract: Detecting human carrying baggage from video sequences is one of the important modules in identifying unattended baggage for video surveillance system. Hence, this paper addresses a framework for implementing such module. As the video was recorded using a static camera, the background modeling is firstly constructed for extracting foreground regions. These regions are considered as candidate of human by further verifying them using a general human detector. To identify whether the human is carrying baggage or not, the human region is divided into several components such as head, body, leg and baggage components according to the spatial information of baggage …relative to a human body proportion. The scalable histogram of oriented gradient features of each component are extracted and the feature dimension is reduced by applying genetic algorithm. The features are trained using a support vector machine (SVM) over each component regarded as a weak classifier. The boosting machine is employed to combine these weak classifiers into a strong classifier for final decision. In experiment, standard public dataset are used to evaluate the effectiveness of our proposed approach. The results verified that the proposed framework outperforms the state-of-the-art methods and can be considered as one of the solutions for aforementioned task. Show more

Keywords: Human carrying baggage detection, spatial component model, video surveillance, background modeling, boosting machine, genetic algorithm, support vector machine, scalable histogram of oriented gradient, unattended baggage

DOI: 10.3233/JIFS-169153

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1601-1613, 2017

Authors: Choroś, Kazimierz

Article Type: Research Article

Abstract: Content-based indexing methods need to analyze all frames of a video. Because such a procedure is extremely time consuming the indexing may be limited to the key-frames, i.e. to only one or to a few frames for every shot or for every video scene. The detection of the key-frame of a shot or of a scene requires very effective temporal segmentation methods. Content-based indexing of videos is based on the automatic detection of a video structure. A video shot is the main structural video unit. The temporal aggregation results in grouping of shots into scenes of a given category. Moreover, …the determination of the most likely category on the basis of time relations also reduces the analysis time enabling us to apply the adequate method of content-based indexing. The main problem is to select report shots and non-report shots because usually different indexing strategies should be applied. The paper examines the usefulness of the temporal aggregation method and pre-categorization of shots in news videos to reduce processing time taken by a very time-consuming content-based video indexing process. Show more

Keywords: Content-based video indexing, news videos, video structures, temporal aggregation, news shot categorization, digital video segmentation, category probability, video key-frames, temporal relations

DOI: 10.3233/JIFS-169154

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1615-1626, 2017

Authors: Duong, Trong Hai | Nguyen, Duc Anh | Nguyen, Van Du | Van Huan, Nguyen

Article Type: Research Article

Abstract: User-based collaborative filtering often considers a set of users who rated on a target item and computes similarities between other users and the target user to select his/her neighbors, then extrapolates the target user’s rating from the neighbors’ ratings. This traditional approach uses only the neighbors’ ratings for recommendation measurement. However, according to our study, dissimilar users whose ratings still significantly influence to the target user’s rating prediction. In addition, to choose a video to watch, a user often takes in to consideration multi criteria. We analyze users’ behavior to choose a video. They often explore genres or tags, then …read abstraction before choosing a video to watch. Therefore, their ratings and the information of a video have a strong correlation. Therefore, based on the fuzzy neural network, a new collaborative filtering method for video recommendation is proposed. Here, the fuzzy neural network is used to learn users’ ratings with respect to their behaviors. The proposal here is to adjust a model of the neural network with input is users’ behavior and output is their ratings for each target video. Concretely, the behavior of a user (or user  profile ) is learned by the users’ ratings and the information of the corresponding videos. In addition, for each target video, all users’ profile who made ratings on it will be collected. Then each profile is treated as an input of the fuzzy neural network and the corresponding rating value is treated as output of the fuzzy neural network. The rating of a user on the target video will be predicted based on the trained neural network. The experiments with netflix dataset reveals that the proposed method is a significantly effective approach. Show more

Keywords: Recommender system, collaborative filtering, user profile, ANFIS, neural network

DOI: 10.3233/JIFS-169155

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1627-1638, 2017