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Publisher: Springer-Verlag (Total: 2352 journals)

 Artificial Intelligence ReviewJournal Prestige (SJR): 0.833 Citation Impact (citeScore): 4Number of Followers: 18      Hybrid journal (It can contain Open Access articles) ISSN (Print) 1573-7462 - ISSN (Online) 0269-2821 Published by Springer-Verlag  [2352 journals]
• Machine Learning and Deep Learning frameworks and libraries for
large-scale data mining: a survey
• Abstract: The combined impact of new computing resources and techniques with an increasing avalanche of large datasets, is transforming many research areas and may lead to technological breakthroughs that can be used by billions of people. In the recent years, Machine Learning and especially its subfield Deep Learning have seen impressive advances. Techniques developed within these two fields are now able to analyze and learn from huge amounts of real world examples in a disparate formats. While the number of Machine Learning algorithms is extensive and growing, their implementations through frameworks and libraries is also extensive and growing too. The software development in this field is fast paced with a large number of open-source software coming from the academy, industry, start-ups or wider open-source communities. This survey presents a recent time-slide comprehensive overview with comparisons as well as trends in development and usage of cutting-edge Artificial Intelligence software. It also provides an overview of massive parallelism support that is capable of scaling computation effectively and efficiently in the era of Big Data.
PubDate: 2019-01-19

• Survey on supervised machine learning techniques for automatic text
classification
• Abstract: Supervised machine learning studies are gaining more significant recently because of the availability of the increasing number of the electronic documents from different resources. Text classification can be defined that the task was automatically categorized a group documents into one or more predefined classes according to their subjects. Thereby, the major objective of text classification is to enable users for extracting information from textual resource and deals with process such as retrieval, classification, and machine learning techniques together in order to classify different pattern. In text classification technique, term weighting methods design suitable weights to the specific terms to enhance the text classification performance. This paper surveys of text classification, process of different term weighing methods and comparison between different classification techniques.
PubDate: 2019-01-19

• From ants to whales: metaheuristics for all tastes
• Abstract: Nature-inspired metaheuristics comprise a compelling family of optimization techniques. These algorithms are designed with the idea of emulating some kind natural phenomena (such as the theory of evolution, the collective behavior of groups of animals, the laws of physics or the behavior and lifestyle of human beings) and applying them to solve complex problems. Nature-inspired methods have taken the area of mathematical optimization by storm. Only in the last few years, literature related to the development of this kind of techniques and their applications has experienced an unprecedented increase, with hundreds of new papers being published every single year. In this paper, we analyze some of the most popular nature-inspired optimization methods currently reported on the literature, while also discussing their applications for solving real-world problems and their impact on the current literature. Furthermore, we open discussion on several research gaps and areas of opportunity that are yet to be explored within this promising area of science.
PubDate: 2019-01-19

• Significance of processing chrominance information for scene
classification: a review
• Authors: V. Sowmya; D. Govind; K. P. Soman
Abstract: The primary objective of this paper is to provide a detailed review of various works showing the role of processing chrominance information for color-to-grayscale conversion. The usefulness of perceptually improved color-to-grayscale converted images for scene classification is then studied as a part of this presented work. Various issues identified for the color-to-grayscale conversion and improved scene classification are presented in this paper. The review provided in this paper includes, review on existing feature extraction techniques for scene classification, various existing scene classification systems, different methods available in the literature for color-to-grayscale image conversion, benchmark datasets for scene classification and color-to-gray-scale image conversion, subjective evaluation and objective quality assessments for image decolorization. In the present work, a scene classification system is proposed using the pre-trained convolutional neural network and Support Vector Machines developed utilizing the grayscale images converted by the image decolorization methods. The experimental analysis on Oliva Torralba scene dataset shows that the color-to-grayscale image conversion technique has a positive impact on the performance of scene classification systems.
PubDate: 2019-01-08
DOI: 10.1007/s10462-018-09678-0

• Marketing campaign targeting using bridge extraction in multiplex social
network
• Authors: Pantelis Vikatos; Prokopios Gryllos; Christos Makris
Abstract: In this paper, we introduce a methodology for improving the targeting of marketing campaigns using bridge prediction in communities based on the information of multilayer online social networks. The campaign strategy involves the identification of nodes with high brand loyalty and top-ranking nodes in terms of participation in bridges that will be involved in the evolution of the graph. Our approach is based on an efficient classification model combining topological characteristics of crawled social graphs with sentiment and linguistic traits of user-nodes, popularity in social media as well as meta path-based features of multilayer networks. To validate our approach we present a set of experimental results using a well-defined dataset from Twitter and Foursquare. Our methodology is useful to recommendation systems as well as to marketers who are interested to use social influence and run effective marketing campaigns.
PubDate: 2019-01-02
DOI: 10.1007/s10462-018-9675-6

• Review of liver segmentation and computer assisted detection/diagnosis
methods in computed tomography
• Authors: Mehrdad Moghbel; Syamsiah Mashohor; Rozi Mahmud; M. Iqbal Bin Saripan
Pages: 497 - 537
Abstract: Computed tomography (CT) imaging remains the most utilized modality for liver-related cancer screening and treatment monitoring purposes. Liver, liver tumor and liver vasculature segmentation from CT data is a prerequisite for treatment planning and computer assisted detection/diagnosis systems. In this paper, we present a survey on liver, liver tumor and liver vasculature segmentation methods that are using CT images, recent methods presented in the literature are viewed and discussed along with positives, negatives and statistical performance of these methods. Liver computer assisted detection/diagnosis systems will also be discussed along with their limitations and possible ways of improvement. In this paper, we concluded that although there is still room for improvement, automatic liver segmentation methods have become comparable to human segmentation. However, the performance of liver tumor segmentation methods can be considered lower than expected in both automatic and semi-automatic methods. Furthermore, it can be seen that most computer assisted detection/diagnosis systems require manual segmentation of liver and liver tumors, limiting clinical applicability of these systems. Liver, liver tumor and liver vasculature segmentation is still an open problem since various weaknesses and drawbacks of these methods can still be addressed and improved especially in tumor and vasculature segmentation along with computer assisted detection/diagnosis systems.
PubDate: 2018-12-01
DOI: 10.1007/s10462-017-9550-x
Issue No: Vol. 50, No. 4 (2018)

• A review on document image analysis techniques directly in the compressed
domain
• Authors: Mohammed Javed; P. Nagabhushan; Bidyut B. Chaudhuri
Pages: 539 - 568
Abstract: The rapid growth of digital libraries, e-governance, and internet based applications has caused an exponential escalation in the volume of ‘Big-data’ particularly due to texts, images, audios and videos that are being both archived and transmitted on a daily basis. In order to make their storage and transfer efficient, different data compression techniques are used in the literature. The ultimate motive behind data compression is to transform a big size data into small size data, which eventually implies less space while archiving, and less time in transferring. However, in order to operate/analyze compressed data, it is usually necessary to decompress it, so as to bring back the data to its original form, which unfortunately warrants an additional computing cost. In this backdrop, if operating upon the compressed data itself can be made possible without going through the stage of decompression, then the advantage that could be accomplished due to compression would escalate. Further due to compression, from the data structure and storage perspectives, the original visibility structure of the data also being lost, it turns into a potential challenge to trace the original information in the compressed representation. This challenge is the motivation behind exploring the idea of direct processing on the compressed data itself in the literature. The proposed survey paper specifically focuses on compressed document images and brings out two original contributions. The first contribution is that it presents a critical study on different image analysis and image compression techniques, and highlights the motivational reasons for pursuing document image analysis in the compressed domain. The second contribution is that it summarizes the different compressed domain techniques in the literature so far based on the type of compression and operations performed by them. Overall, the paper aims to provide a perspective for pursuing further research in the area of document image analysis and pattern recognition directly based on the compressed data.
PubDate: 2018-12-01
DOI: 10.1007/s10462-017-9551-9
Issue No: Vol. 50, No. 4 (2018)

• Multi-objective optimal design of fuzzy controller for structural
vibration control using Hedge-algebras approach
• Authors: Van-Binh Bui; Quy-Cao Tran; Hai-Le Bui
Pages: 569 - 595
Abstract: In this paper, the problem of multi-objective optimal design of hedge-algebras-based fuzzy controller (HAC) for structural vibration control with actuator saturation is presented. The main advantages of HAC are: (i) inherent order relationships among linguistic values of each linguistic variable are always ensured; (ii) instead of using any fuzzy sets, linguistic values of linguistic variables are determined by an isomorphism mapping called semantically quantifying mapping (SQM) based on a few fuzziness parameters of each linguistic variable and hence, the process of fuzzy inference is very simple due to SQM values occurring in the fuzzy rule base and (iii) when optimizing HAC, only a few design variables which are above fuzziness parameters are needed. As a case study, a HAC and optimal HACs (opHACs) based on multi-objective optimization view point have been designed to active control of a benchmark structure with active bracing system subjected to earthquake excitation. Control performance of controllers is also discussed in order to shown advantages of the proposed method.
PubDate: 2018-12-01
DOI: 10.1007/s10462-017-9549-3
Issue No: Vol. 50, No. 4 (2018)

• Machine learning based decision support systems (DSS) for heart disease
diagnosis: a review
Pages: 597 - 623
Abstract: The current review contributes with an extensive overview of decision support systems in diagnosing heart diseases in clinical settings. The investigators independently screened and abstracted studies related to heart diseases-based clinical decision support system (DSS) published until 8-June-2015 in PubMed, CINAHL and Cochrane Library. The data extracted from the twenty full-text articles that met the inclusion criteria was classified under the following fields; heart diseases, methods for data sets formation, machine learning algorithms, machine learning-based DSS, comparator types, outcome evaluation and clinical implications of the reported DSS. Out of total of 331 studies 20 met the inclusion criteria. Most of the studies relate to ischemic heart diseases with neural network being the most common machine learning (ML) technique. Among the ML techniques, ANN classifies myocardial infarction with 97% and myocardial perfusion scintigraphy with 87.5% accuracy, CART classifies heart failure with 87.6%, neural network ensembles classifies heart valve with 97.4%, support vector machine classifies arrhythmia screening with 95.6%, logistic regression classifies acute coronary syndrome with 72%, artificial immune recognition system classifies coronary artery disease with 92.5% and genetic algorithms and multi-criteria decision analysis classifies chest-pain patients with 91% accuracy respectively. There were 55% studies that validated the results in clinical settings while 25% validated the results through experimental setups. Rest of the studies (20%) did not report the applicability and feasibility of their methods in clinical settings. The study categorizes the ML techniques according to their performance in diagnosing various heart diseases. It categorizes, compares and evaluates the comparator based on physician’s performance, gold standards, other ML techniques, different models of same ML technique and studies with no comparison. It also investigates the current, future and no clinical implications. In addition, trends of machine learning techniques and algorithms used in the diagnosis of heart diseases along with the identification of research gaps are reported in this study. The reported results suggest reliable interpretations and detailed graphical self-explanatory representations by DSS. The study reveals the need for establishment of non-ambiguous real-time clinical data for proper training of DSS before it can be used in clinical settings. The future research directions of the ML-based DSS is mostly directed towards development of generalized systems that can decide on clinical measurements which are easily accessible and assessable in real-time.
PubDate: 2018-12-01
DOI: 10.1007/s10462-017-9552-8
Issue No: Vol. 50, No. 4 (2018)

• A sensitivity analysis method aimed at enhancing the metaheuristics for
continuous optimization
• Authors: Peio Loubière; Astrid Jourdan; Patrick Siarry; Rachid Chelouah
Pages: 625 - 647
Abstract: An efficient covering of the search space is an important issue when dealing with metaheuristics. Sensitivity analysis methods aim at evaluating the influence of each variable of a problem on a model (i.e. objective function) response. Such methods provide knowledge on the function behavior and would be suitable for guiding metaheuristics. To evaluate correctly the dimensions influences, usual sensitivity analysis methods need a lot of evaluations of the objective function or are constrained with an experimental design. In this paper, we propose a new method, with a low computational cost, which can be used into metaheuristics to improve their search process. This method is based on two global sensitivity analysis methods: the linear correlation coefficient technique and Morris’ method. We propose to transform the global study of a non linear model into a local study of quasi-linear sub-parts of the model, in order to evaluate the global influence of each input variable on the model. This sensitivity analysis method will use evaluations of the objective function done by the metaheuristic to compute a weight of each variable. Then, the metaheuristic will generate new solutions choosing dimensions to offset, according to these weights. The tests done on usual benchmark functions of sensitivity analysis and continuous optimization (CEC 2013) reveal two issues. Firstly, our sensitivity analysis method provides good results, it correctly ranks each dimension’s influence. Secondly, integrating a sensitivity analysis method into a metaheuristic (here, Differential Evolution and ABC with modification rate) improves its results.
PubDate: 2018-12-01
DOI: 10.1007/s10462-017-9553-7
Issue No: Vol. 50, No. 4 (2018)

• A survey of image data indexing techniques
• Authors: Saurabh Sharma; Vishal Gupta; Mamta Juneja
Abstract: The Index is a data structure which stores data in a suitably abstracted and compressed form to facilitate rapid processing by an application. Multidimensional databases may have a lot of redundant data also. The indexed data, therefore need to be aggregated to decrease the size of the index which further eliminates unnecessary comparisons. Feature-based indexing is found to be quite useful to speed up retrieval, and much has been proposed in this regard in the current era. Hence, there is growing research efforts for developing new indexing techniques for data analysis. In this article, we propose a comprehensive survey of indexing techniques with application and evaluation framework. First, we present a review of articles by categorizing into a hash and non-hash based indexing techniques. A total of 45 techniques has been examined. We discuss advantages and disadvantages of each method that are listed in a tabular form. Then we study evaluation results of hash based indexing techniques on different image datasets followed by evaluation campaigns in multimedia retrieval. In this paper, in all 36 datasets and three evaluation campaigns have been reviewed. The primary aim of this study is to apprise the reader of the significance of different techniques, the dataset used and their respective pros and cons.
PubDate: 2018-12-15
DOI: 10.1007/s10462-018-9673-8

• Covering-based intuitionistic fuzzy rough sets and applications in
multi-attribute decision-making
• Authors: Jianming Zhan; Bingzhen Sun
Abstract: Covering based intuitionistic fuzzy (IF) rough set is a generalization of granular computing and covering based rough sets. By combining covering based rough sets, IF sets and fuzzy rough sets, we introduce three classes of coverings based IF rough set models via IF $$\beta$$ -neighborhoods and IF complementary $$\beta$$ -neighborhood (IFC $$\beta$$ -neighborhood). The corresponding axiomatic systems are investigated, respectively. In particular, the rough and precision degrees of covering based IF rough set models are discussed. The relationships among these types of coverings based IF rough set models and covering based IF rough set models proposed by Huang et al. (Knowl Based Syst 107:155–178, 2016). Based on the theoretical analysis for coverings based IF rough set models, we put forward intuitionistic fuzzy TOPSIS (IF-TOPSIS) methodology to multi-attribute decision-making (MADM) problem with the evaluation of IF information problem. An effective example is to illustrate the proposed methodology. Finally, we deal with MADM problem with the evaluation of fuzzy information based on CFRS models. By comparative analysis, we find that it is more effective to deal with MADM problem with the evaluation of IF information based on CIFRS models than the one with the evaluation of fuzzy information based on CFRS models.
PubDate: 2018-12-13
DOI: 10.1007/s10462-018-9674-7

• A journey of Indian languages over sentiment analysis: a systematic review
• Authors: Sujata Rani; Parteek Kumar
Abstract: In recent years, due to the availability of voluminous data on web for Indian languages, it has become an important task to analyze this data to retrieve useful information. Because of the growth of Indian language content, it is beneficial to utilize this explosion of data for the purpose of sentiment analysis. This research depicts a systematic review in the field of sentiment analysis in general and Indian languages specifically. The current status of Indian languages in sentiment analysis is classified according to the Indian language families. The periodical evolution of Indian languages in the field of sentiment analysis, sources of selected publications on the basis of their relevance are also described. Further, taxonomy of Indian languages in sentiment analysis based on techniques, domains, sentiment levels and classes has been presented. This research work will assist researchers in finding the available resources such as annotated datasets, pre-processing linguistic and lexical resources in Indian languages for sentiment analysis and will also support in selecting the most suitable sentiment analysis technique in a specific domain along with relevant future research directions. In case of resource-poor Indian languages with morphological variations, one encounters problems of performing sentiment analysis due to unavailability of annotated resources, linguistic and lexical tools. Therefore, to provide efficient performance using existing sentiment analysis techniques, the aforementioned issues should be addressed effectively.
PubDate: 2018-12-12
DOI: 10.1007/s10462-018-9670-y

• Robust visual line-following navigation system for humanoid robots
• Authors: Li-Hong Juang; Jian-Sen Zhang
Abstract: This paper implements a novel line-following system for humanoid robots. Camera embedded on the robot’s head captures the image and then extracts the line using a high-speed and high-accuracy rectangular search method. This method divides the search location into three sides of rectangle and performs image convolution by edge detection matrix. The extracted line is used to calculate relative parameters, including forward velocity, lateral velocity and angular velocity that drive line-following walking. A proposed path curvature estimation method generates the forward velocity and guidance reference point of the robot. A classical PID controller and a PID controller with angle compensation are then used to set the lateral velocity and angular velocity of the robot, improving the performance in tracking a curved line. Line-following experiments for various shapes were conducted using humanoid robot NAO. Experimental results demonstrate the robot can follow different line shapes with the tracking error remaining at a low level. This is a significant improvement from existing biped robot visual navigation systems.
PubDate: 2018-12-08
DOI: 10.1007/s10462-018-9672-9

• Local structure learning of chain graphs with the false discovery rate
control
• Authors: Jingyun Wang; Sanyang Liu; Mingmin Zhu
Abstract: Chain graphs (CGs) containing both directed and undirected edges, offer an elegant generalisation of both Markov networks and Bayesian networks. In this paper, we propose an algorithm for local structure learning of CGs. It works by first learning adjacent nodes of each variable for skeleton identification and then orienting the edges of the complexes of the graph. To control the false discovery rate (FDR) of edges when learning a CG, FDR controlling procedure is embedded in the algorithm. Algorithms for skeleton identification and complexes recovery are presented. Experimental results demonstrate that the algorithm with the FDR controlling procedure can control the false discovery rate of the skeleton of the recovered graph under a user-specified level, and the proposed algorithm is also a viable alternative to learn the structure of chain graphs.
PubDate: 2018-12-04
DOI: 10.1007/s10462-018-9669-4

• A novel exponential distance and its based TOPSIS method for
interval-valued intuitionistic fuzzy sets using connection number of SPA
theory
• Authors: Harish Garg; Kamal Kumar
Abstract: The objective of this work is to present a novel multi-attribute decision making (MADM) method under interval-valued intuitionistic fuzzy (IVIF) set environment by integrating a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Set pair analysis (SPA) theory is the modern uncertainty theory which is composed by the three components, namely “identity”, “discrepancy” and “contrary” degrees of the connection number (CN) and overlap with the various existing theories for handling the uncertainties in the data. Thus, motivated by this, in the present work, an attempt is made to enrich the theory of information measure by presented some exponential based distance measures using CNs of the IVIF sets. The supremacy of the proposed measure is also discussed. Afterward, a TOPSIS method based on the proposed distance measures is developed to solve MADM problem under IVIF environment where each of the element is characteristics by IVIF numbers. The utility, as well as supremacy of the approach, is confirmed through a real-life numerical example and validate it by comparing their results with the several existing approaches results.
PubDate: 2018-11-21
DOI: 10.1007/s10462-018-9668-5

• A state of the art review of intelligent scheduling
• Authors: Mohammad Hossein Fazel Zarandi; Ali Akbar Sadat Asl; Shahabeddin Sotudian; Oscar Castillo
Abstract: Intelligent scheduling covers various tools and techniques for successfully and efficiently solving the scheduling problems. In this paper, we provide a survey of intelligent scheduling systems by categorizing them into five major techniques containing fuzzy logic, expert systems, machine learning, stochastic local search optimization algorithms and constraint programming. We also review the application case studies of these techniques.
PubDate: 2018-11-19
DOI: 10.1007/s10462-018-9667-6

• A survey on skin detection in colored images
• Authors: Sinan Naji; Hamid A. Jalab; Sameem A. Kareem
Abstract: Color is an efficient feature for object detection as it has the advantage of being invariant to changes in scaling, rotation, and partial occlusion. Skin color detection is an essential required step in various applications related to computer vision. The rapidly-growing research in human skin detection is based on the premise that information about individuals, intent, mode, and image contents can be extracted from colored images, and computers can then respond in an appropriate manner. Detecting human skin in complex images has proven to be a challenging problem because skin color can vary dramatically in its appearance due to many factors such as illumination, race, aging, imaging conditions, and complex background. However, many methods have been developed to deal with skin detection problem in color images. The purpose of this study is to provide an up-to-date survey on skin color modeling and detection methods. We also discuss relevant issues such as color spaces, cost and risks, databases, testing, and benchmarking. After investigating these methods and identifying their strengths and limitations, we conclude with several implications for future direction.
PubDate: 2018-11-17
DOI: 10.1007/s10462-018-9664-9

• Independence test and canonical correlation analysis based on the
alignment between kernel matrices for multivariate functional data
• Authors: Tomasz Górecki; Mirosław Krzyśko; Waldemar Wołyński
Abstract: In the case of vector data, Gretton et al. (Algorithmic learning theory. Springer, Berlin, pp 63–77, 2005) defined Hilbert–Schmidt independence criterion, and next Cortes et al. (J Mach Learn Res 13:795–828, 2012) introduced concept of the centered kernel target alignment (KTA). In this paper we generalize these measures of dependence to the case of multivariate functional data. In addition, based on these measures between two kernel matrices (we use the Gaussian kernel), we constructed independence test and nonlinear canonical variables for multivariate functional data. We show that it is enough to work only on the coefficients of a series expansion of the underlying processes. In order to provide a comprehensive comparison, we conducted a set of experiments, testing effectiveness on two real examples and artificial data. Our experiments show that using functional variants of the proposed measures, we obtain much better results in recognizing nonlinear dependence.
PubDate: 2018-11-10
DOI: 10.1007/s10462-018-9666-7

• Reflective agents for personalisation in collaborative games
• Authors: Damon Daylamani-Zad; Harry Agius; Marios C. Angelides
Abstract: The collaborative aspect of games has been shown to potentially increase player performance and engagement over time. However, collaborating players need to perform well for the team as a whole to benefit and thus teams often end up performing no better than a strong player would have performed individually. Personalisation offers a means for improving overall performance and engagement, but in collaborative games, personalisation is seldom implemented, and when it is, it is overwhelmingly passive such that the player is not guided to goal states and the effectiveness of the personalisation is not evaluated and adapted accordingly. In this paper, we propose and apply the use of reflective agents to personalisation (‘reflective personalisation’) in collaborative gaming for individual players within collaborative teams via a combination of individual player and team profiling in order to improve player and thus team performance and engagement. The reflective agents self-evaluate, dynamically adapting their personalisation techniques to most effectively guide players towards specific goal states, match players and form teams. We incorporate this agent-based approach within a microservices architecture, which itself is a set of collaborating services, to facilitate a scalable and portable approach that enables both player and team profiles to persist across multiple games. An experiment involving 90 players over a two-month period was used to comparatively assess three versions of a collaborative game that implemented reflective, guided, and passive personalisation for individual players within teams. Our results suggest that the proposed reflective personalisation approach improves team player performance and engagement within collaborative games over guided or passive personalisation approaches, but that it is especially effective for improving engagement.
PubDate: 2018-11-07
DOI: 10.1007/s10462-018-9665-8

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