<abstract xmlns="http://www.w3.org/1999/xhtml"> This article presents short-term predictions using neural networks tuned by energy associated to series based-predictor filter for complete and incomplete datasets. A benchmark of high roughness time series from Mackay Glass (MG), Logistic (LOG), Henon (HEN) and some univariate series chosen from NN3 Forecasting Competition are used. An average smoothing technique is assumed to complete the data missing in the dataset. The Hurst parameter estimated through wavelets is used to estimate the roughness of the real and forecasted series. The validation and horizon of the time series is presented by the 15 values ahead. The performance of the proposed filter shows that even a short dataset is incomplete, besides a linear smoothing technique employed; the prediction is almost fair by means of SMAPE index. Although the major result shows that the predictor system based on energy associated to series has an optimal performance from several chaotic time series, in particular, this method among other provides a good estimation when the short-term series are taken from one point observations.</abstract>

Department of Electrical and Electronic Engineering, Universidad Nacional de Cordoba Velez Sarsfield Ave. 1611, Cordoba,

Advanced Intelligent Systems Laboratory, Institute of Automatic Universidad Nacional de San JuanSan Juan,

Energy Associated Tuning Method for Short-Term Series Forecasting by Complete and Incomplete Datasets

<abstract xmlns="http://www.w3.org/1999/xhtml"> Estimating the connectivity of a network from events observed at each node has many applications. One prominent example is found in neuroscience, where spike trains (sequences of action potentials) are observed at each neuron, but the way in which these neurons are connected is unknown. This paper introduces a novel method for estimating connections between nodes using a similarity measure between sequences of event times. Specifically, a normalized positive definite kernel defined on spike trains was used. The proposed method was evaluated using synthetic and real data, by comparing with methods using transfer entropy and the Victor-Purpura distance. Synthetic data was generated using CERM (Coupled Escape-Rate Model), a model that generates various spike trains. Real data recorded from the visual cortex of an anaesthetized cat was analyzed as well. The results showed that the proposed method provides an effective way of estimating the connectivity of a network when the time sequences of events are the only available information.</abstract>

Kernel Analysis for Estimating the Connectivity of a Network with Event Sequences

<abstract xmlns="http://www.w3.org/1999/xhtml"> A novel data knowledge representation with the combination of structure learning ability of preprocessed collaborative fuzzy clustering and fuzzy expert knowledge of Takagi- Sugeno-Kang type model is presented in this paper. The proposed method divides a huge dataset into two or more subsets of dataset. The subsets of dataset interact with each other through a collaborative mechanism in order to find some similar properties within each-other. The proposed method is useful in dealing with big data issues since it divides a huge dataset into subsets of dataset and finds common features among the subsets. The salient feature of the proposed method is that it uses a small subset of dataset and some common features instead of using the entire dataset and all the features. Before interactions among subsets of the dataset, the proposed method applies a mapping technique for granules of data and centroid of clusters. The proposed method uses information of only half or less/more than the half of the data patterns for the training process, and it provides an accurate and robust model, whereas the other existing methods use the entire information of the data patterns. Simulation results show the proposed method performs better than existing methods on some benchmark problems.</abstract>

Department of Computer Science, National Chiao Tung University, Hsinchu,

Department of Electrical Engineering, National Chiao Tung University, Hsinchu, Taiwan

Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney,

Department of Electrical Engineering, National Chiao Tung University, Hsinchu,

School of Computing, National University of Singapore,

Faculty of Computing, Universiti Teknologi Malaysia, Skudai Johor,

A New Mechanism for Data Visualization with Tsk-Type Preprocessed Collaborative Fuzzy Rule Based System

<abstract xmlns="http://www.w3.org/1999/xhtml">The adaptive educational systems within e-learning platforms are built in response to the fact that the learning process is different for each and every learner. In order to provide adaptive e-learning services and study materials that are tailor-made for adaptive learning, this type of educational approach seeks to combine the ability to comprehend and detect a person’s specific needs in the context of learning with the expertise required to use appropriate learning pedagogy and enhance the learning process. Thus, it is critical to create accurate student profiles and models based upon analysis of their affective states, knowledge level, and their individual personality traits and skills. The acquired data can then be efficiently used and exploited to develop an adaptive learning environment. Once acquired, these learner models can be used in two ways. The first is to inform the pedagogy proposed by the experts and designers of the adaptive educational system. The second is to give the system dynamic self-learning capabilities from the behaviors exhibited by the teachers and students to create the appropriate pedagogy and automatically adjust the e-learning environments to suit the pedagogies. In this respect, artificial intelligence techniques may be useful for several reasons, including their ability to develop and imitate human reasoning and decision-making processes (learning-teaching model) and minimize the sources of uncertainty to achieve an effective learning-teaching context. These learning capabilities ensure both learner and system improvement over the lifelong learning mechanism. In this paper, we present a survey of raised and related topics to the field of artificial intelligence techniques employed for adaptive educational systems within e-learning, their advantages and disadvantages, and a discussion of the importance of using those techniques to achieve more intelligent and adaptive e-learning environments.</abstract>

The Computational Intelligence Centre, School of Computer Science and Electronic Engineering University of Essex, Colchester,

United Kingdom of Great Britain and Northern Ireland

Faculty of Computing and Information Technology, King Abdulaziz University Jeddah,

A Survey of Artificial Intelligence Techniques Employed for Adaptive Educational Systems within E-Learning Platforms

<abstract xmlns="http://www.w3.org/1999/xhtml"> Learning vector quantization (LVQ) is one of the most powerful approaches for prototype based classification of vector data, intuitively introduced by Kohonen. The prototype adaptation scheme relies on its attraction and repulsion during the learning providing an easy geometric interpretability of the learning as well as of the classification decision scheme. Although deep learning architectures and support vector classifiers frequently achieve comparable or even better results, LVQ models are smart alternatives with low complexity and computational costs making them attractive for many industrial applications like intelligent sensor systems or advanced driver assistance systems.Nowadays, the mathematical theory developed for LVQ delivers sufficient justification of the algorithm making it an appealing alternative to other approaches like support vector machines and deep learning techniques.This review article reports current developments and extensions of LVQ starting from the generalized LVQ (GLVQ), which is known as the most powerful cost function based realization of the original LVQ. The cost function minimized in GLVQ is an soft-approximation of the standard classification error allowing gradient descent learning techniques. The GLVQ variants considered in this contribution, cover many aspects like bordersensitive learning, application of non-Euclidean metrics like kernel distances or divergences, relevance learning as well as optimization of advanced statistical classification quality measures beyond the accuracy including sensitivity and specificity or area under the ROC-curve.According to these topics, the paper highlights the basic motivation for these variants and extensions together with the mathematical prerequisites and treatments for integration into the standard GLVQ scheme and compares them to other machine learning approaches. For detailed description and mathematical theory behind all, the reader is referred to the respective original articles.Thus, the intention of the paper is to provide a comprehensive overview of the stateof- the-art serving as a starting point to search for an appropriate LVQ variant in case of a given specific classification problem as well as a reference to recently developed variants and improvements of the basic GLVQ scheme.</abstract>

Computational Intelligence Group, University of Applied Sciences Mittweida,

Computational Intelligence Group, University of Applied Sciences Mittweida, Germany

Staatliche Berufliche Oberschule Kaufbeuren,

Can Learning Vector Quantization be an Alternative to SVM and Deep Learning? - Recent Trends and Advanced Variants of Learning Vector Quantization for Classification Learning

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Journal of Artificial Intelligence and Soft Computing Research

 Print version published by: The Journal of the Polish Neural Network Society and the University of Social Sciences in Lodz  Editor-in-Chief: Prof. Leszek Rutkowski, IEEE FellowFrequency of publishing: quarterlyMonths of publication: January, April, July, October  ISSN (print): 2083-2567ISSN (on-line): 2449-6499  Journal of Artificial Intelligence and Soft Computing Research is a dynamically developing international journal focused on the latest scientific results and methods constituting traditional artificial intelligence methods and soft computing techniques. Our goal is to bring together scientists representing both approaches and various research communities.  Why JAISCR?   Premier source of high quality research,  We have practice in publishing innovative articles dealing with various aspects of artificial intelligence methods and soft computing,  Extensive scope,  We are focused on permanent development of our journal.   JAISCR research areas  JAISCR publishes high quality, innovative research results in various areas of artificial intelligence and soft computing. These areas include, but are not limited to:   AI in Modelling and Simulation,  AI in Scheduling and Optimization,  Automated Reasoning and Inference,  Bioinformatics,  Cognitive Aspects of AI,  Computer Vision and Speech Understanding,  Data Mining,  Distributed Intelligent Processing,  Evolutionary Design,  Expert Systems,  Fuzzy Modelling and Control,  Hardware Implementations,  Heuristic Search,  Hybrid Models,  Information Retrieval,  Intelligent Database Systems,  Knowledge Engineering,  Mechatronics,  Multi-agent Systems,  Natural Language Processing,  Neural Network Theory and Architectures,  Neuro-informatics and Bio-inspired Models,  Pattern Recognition,  Robotics and Related Fields,  Rough Sets Theory: Foundations and Applications,  Supervised and Unsupervised Learning,  Swarm Intelligence and Systems,  Web Intelligence Applications &amp; Search,  Various Applications.   Submitting to JAISCR - a great advantage for the authorsWhy?   Fair and thorough peer review (at least three referees are required for the acceptance of each article),  Fast and efficient publication process,  Polishing the structure and technical format of each submission,  Open access (all articles published in JAISCR are open access - freely available on Sciendo platform),  Permanence (because of the open access form, permanent accessibility is assured),  High visibility within the field (the author's work is freely accessible to a global audience. In addition, articles are available through Sciendo),  Promotion of the published papers in the scientific community.   Archiving  Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections.  Plagiarism Policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process. 