2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI)

23-24 Nov. 2017

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  • [Front cover]

    Publication Year: 2017, Page(s):c1 - c4
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  • [Title page]

    Publication Year: 2017, Page(s): 1
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  • [Copyright notice]

    Publication Year: 2017, Page(s): 1
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  • Table of contents

    Publication Year: 2017, Page(s):iii - v
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  • Integrating data science and R programming at an early stage

    Publication Year: 2017, Page(s):1 - 5
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (273 KB) | HTML iconHTML

    The use of data has become an integral part of everyday life. Hence, introducing data science with games would generate an interest and prepare students for the rapidly changing world of technology. This study is especially intended to teach data science (DS) and R programming using games for cognitive learning. Tangible learning with real-life examples were used to teach Data Science. A correlati... View full abstract»

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  • Quality assessment of large scale dimensionality reduction methods

    Publication Year: 2017, Page(s):6 - 10
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (397 KB) | HTML iconHTML

    The application of spectral dimension reduction algorithms has been limited to small-to-medium datasets due to the high computational costs associated with solving the generalized eigenvector decomposition problem. This study uses the Nystrom method to approximate the large similarity matrices used in the algorithms, thus making it possible to extend their application to large scale datasets. The ... View full abstract»

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  • Improvement algorithms of perceptually important point identification for time series data mining

    Publication Year: 2017, Page(s):11 - 15
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (386 KB) | HTML iconHTML

    In the field of time series data mining, the concept of the Perceptually Important Point (PIP) identification process is proposed for financial time series pattern matching and it is then found suitable for time series dimensionality reduction and representation. Its strength is on preserving the overall shape of the time series by identifying the salient points in it. With the rise of Big Data, t... View full abstract»

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  • A large-scale filter method for feature selection based on spark

    Publication Year: 2017, Page(s):16 - 20
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (409 KB) | HTML iconHTML

    Recently, enormous volumes of data are generated in information systems. That's why data mining area is facing new challenges of transforming this “big data” into useful knowledge. In fact, “big data” relies low density of information (low data quality) and data redundancy, which negatively affect the data mining process. Therefore, when the number of variables describing the data is high, feature... View full abstract»

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  • Accuracy based weighted aging ensemble (AB-WAE) — Algorithm for data stream classification

    Publication Year: 2017, Page(s):21 - 24
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (139 KB) | HTML iconHTML

    Nowadays, most of the data comes continuously and its distribution may change over the time. Unfortunately, most of classifiers assume that statistical characteristics of used predicting model are not being changed. This work presents modification of the previously proposed Weighted Aging Classifier Ensemble (WAE), called Accuracy Based WAE (AB-WAE), which can easily adapt to the probability chara... View full abstract»

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  • Applying machine learning to big data streams : An overview of challenges

    Publication Year: 2017, Page(s):25 - 29
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (251 KB) | HTML iconHTML

    The importance of processing stream data increases with new technologies and new use cases. Applying machine learning to stream data and process them in real time leads to challenges in different ways. Model changes, concept drift or insufficient time to train models are a few examples. We illustrate big data characteristics and machine learning techniques derived from literature and conclude with... View full abstract»

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  • Use of reliability engineering concepts in machine learning for classification

    Publication Year: 2017, Page(s):30 - 34
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (268 KB) | HTML iconHTML

    In reliability engineering, the reliability of a system is estimated by considering the dependencies between the system's components. The probability of a system failure is then expressed in terms of the states of its components. Meanwhile, in some machine learning approaches, the probability of class membership is expressed in terms of the values (which can be seen as `states') of various feature... View full abstract»

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  • Interactive concepts for shaping generative models of spatial behavior

    Publication Year: 2017, Page(s):35 - 41
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (554 KB) | HTML iconHTML

    A technique widely used in video based situation assessment, and especially in anomaly detection, is the analysis of spatial behavior in terms of motion profiles recorded along trajectories. An intuitive assessment metric is the deviation from normal behavior, where generative models are a natural choice for capturing the underlying statistics. Applying such outlier methods in open world scenarios... View full abstract»

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  • An enhanced clustering analysis based on glowworm swarm optimization

    Publication Year: 2017, Page(s):42 - 49
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (451 KB) | HTML iconHTML

    Data clustering has always been an important aspect of data mining. Extracting clusters from data could be very difficult especially when the features are large and the classes not clearly partitioned, hence the need for high-quality clustering techniques. The major shortcoming of various clustering techniques is that the number of clusters must be stated before the clustering starts. A recent suc... View full abstract»

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  • Change reaction strategies for DNSGA-II solving dynamic multi-objective optimization problems

    Publication Year: 2017, Page(s):50 - 54
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (474 KB) | HTML iconHTML

    Many real world optimization problems have multiple objectives that typically are in conflict with one another. Furthermore, at least one objective can even be dynamic. If all of these traits are present, the problem is called a dynamic multi-objective optimisation problems (DMOOPs). The non-dominated sorting genetic algorithm II (NSGA-II) is a standard or benchmark algorithm for static multi-obje... View full abstract»

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  • Cuckoo search: State-of-the-art and opportunities

    Publication Year: 2017, Page(s):55 - 59
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (139 KB) | HTML iconHTML

    Since the development of cuckoo search (CS) by Yang and Deb in 2009, CS has been applied in a diverse range of applications. This paper first outlines the key features of the algorithm and its variants, and then briefly summarizes the state-of-the-art developments in many applications. The opportunities for further research are also identified. View full abstract»

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  • Variation of ant colony optimization parameters for solving the travelling salesman problem

    Publication Year: 2017, Page(s):60 - 65
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (448 KB) | HTML iconHTML

    This paper describes the Ant Colony Optimization (ACO) algorithm for solving the Travelling Salesman Problem. ACO is a swarm intelligence approach where the agents (ants) communicate using a chemical substance called pheromone, which evaporates over time. This principle is used for finding the shortest possible route between cities based on previously investigated connections. The algorithm is eva... View full abstract»

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  • New kinetostatic criterion for robot parametric optimization

    Publication Year: 2017, Page(s):66 - 70
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (454 KB) | HTML iconHTML

    The paper deals with a practical approach of parametric optimization of robots. The main idea is to introduce a new criterion which makes possible to evaluate maximum demanded 2-norm of forces/torques of the robot actuators in the case that the end-effector of the robot is to move inside given workspace (specified by the desired positions) with required maximum acceleration in any direction. There... View full abstract»

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  • A segmentation technology for multivariate contextual time series

    Publication Year: 2017, Page(s):71 - 74
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (355 KB) | HTML iconHTML

    A time series is a series of data points indexed in time order, mining multivariate contextual time series (MCTS) should pay more attention to time order. This paper proposes a new method for splitting the MCTS into a number of segments, uses the concept of scenarios and themes to represent MCTS instead of data points and extracts important contextual features to carry out the multidimensional fit... View full abstract»

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  • Five-elements cycle optimization algorithm for solving continuous optimization problems

    Publication Year: 2017, Page(s):75 - 79
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (308 KB) | HTML iconHTML

    The Five-elements Cycle Optimization Algorithm (FECO) is proposed in this paper inspired by the theory of Five-elements in Chinese traditional culture. It is built for finding the optimal solution of continuous functions based on the Five-elements Cycle Model which characterizes the mechanism of generation and restriction among five elements. The comparison with 11 optimization algorithms based on... View full abstract»

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  • Playing the game of snake with limited knowledge: Unsupervised neuro-controllers trained using particle swarm optimization

    Publication Year: 2017, Page(s):80 - 84
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (247 KB) | HTML iconHTML

    Methods in the domain of artificial intelligence (AI) have been applied to develop agents capable of playing a variety of games. The single-player variant of Snake is a well-known and popular video game that requires a player to navigate a line-based representation of a snake through a two-dimensional playing area, while avoiding collisions with the walls of the playing area and the body of the sn... View full abstract»

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  • Feature selection for an SVM based webpage classifier

    Publication Year: 2017, Page(s):85 - 88
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (241 KB) | HTML iconHTML

    Machine-learning techniques are a handy tool for deriving insights from data extracted from the web. Because of the structure of web data extracted by web crawlers there is need for preprocessing the data to extract features that can be used to train a machine learning classifier. The number of available features that can be linked to a website is huge. Narrowing down to a minimum number of featur... View full abstract»

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  • Community detection in networks using atom stabilization algorithm

    Publication Year: 2017, Page(s):89 - 93
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (287 KB) | HTML iconHTML

    Community detection problem has great importance for better understanding of the relationships among the nodes as well as the overall network. In this paper, Atom Stabilization Algorithm (ASA) is considered for identifying communities. Modified Isolability is used as an objective function. Isolability measures the ability of group of nodes to isolate them from rest of the network. The results are ... View full abstract»

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  • Accessibility and search engine optimization on scalable vector graphics

    Publication Year: 2017, Page(s):94 - 98
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (328 KB) | HTML iconHTML

    The purpose of this paper was to carry out a study about techniques for description of images in Scalable Vector Graphics (SVG), and also assess the way in which search engines index this content. The study addressed the importance of the description of images, how this description has an impact on the web accessibility for screen reader users, and the way in which this content is indexed by searc... View full abstract»

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  • Eye-motion detection system for mnd patients

    Publication Year: 2017, Page(s):99 - 103
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (619 KB) | HTML iconHTML

    This paper aims to develop an eye-motion based communication system for motor neuron disease (MND) patients to contact with care providers any time they want when they lie on the bed. This eye-motion detection system involves technical modules of eye-blink detection, gaze estimation and head pose estimation on MND patients. The system comprises a rotating arm with a camera, an infrared light sourc... View full abstract»

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  • Emotional journey for an emotion provoking cycling exergame

    Publication Year: 2017, Page(s):104 - 108
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (347 KB) | HTML iconHTML

    In this work we present a novel concept for affective entertainment, which we call Emotional Journey. It provides a dynamic and adaptive story path based on a player's emotional responses and yields improved accurate recognition of the player's emotions. We conducted a case study with 25 players to evaluate our concept using our cycling exercise machine. We evaluated three different journey types ... View full abstract»

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