IEEE Intelligent Systems

Issue 2 • Mar.-Apr. 2017

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Displaying Results 1 - 19 of 19
  • Front Cover 
  • Front Cover

    Publication Year: 2017, Page(s): c1
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  • House Advertisement 
  • New Membership Options for A Better Fit

    Publication Year: 2017, Page(s): c2
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  • Table of Contents 
  • Table of Contents

    Publication Year: 2017, Page(s):1 - 2
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  • From the Editors 
  • In Memoriam: Adele Howe

    Publication Year: 2017, Page(s):3 - 4
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  • Reviewer Thanks 
  • 2016 Reviewer Thanks

    Publication Year: 2017, Page(s):5 - 6
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  • Guest Editors' Introduction 
  • Big Data

    Publication Year: 2017, Page(s):7 - 8
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  • Big Data 
  • Challenges of Feature Selection for Big Data Analytics

    Publication Year: 2017, Page(s):9 - 15
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (713 KB) | HTML iconHTML

    We're surrounded by huge amounts of large-scale high-dimensional data, but learning tasks require reduced data dimensionality. Feature selection has shown its effectiveness in many applications by building simpler and more comprehensive models, improving learning performance, and preparing clean, understandable data. Some unique characteristics of big data such as data velocity and data variety ha... View full abstract»

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  • Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy

    Publication Year: 2017, Page(s):16 - 22
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (624 KB) | HTML iconHTML

    Astrophysics and cosmology are rich with data. The advent of wide-area digital cameras on large aperture telescopes has led to ever more ambitious surveys of the sky. Data volumes of entire surveys a decade ago can now be acquired in a single night, and real-time analysis is often desired. Thus, modern astronomy requires big data know-how, in particular, highly efficient machine learning and image... View full abstract»

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  • Structured Regression on Multiscale Networks

    Publication Year: 2017, Page(s):23 - 30
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (396 KB) | HTML iconHTML

    Structure-based regression algorithms generally suffer substantive speed losses and have exacting memory requirements compared to their structureless counterparts. Gaussian conditional random field (GCRF) models are one of the most time- and memory-efficient approaches to structured regression. The authors' previous speedups for the GCRF method allow for exact solutions on networks of up to 100,00... View full abstract»

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  • Online URL Classification for Large-Scale Streaming Environments

    Publication Year: 2017, Page(s):31 - 36
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (402 KB) | HTML iconHTML

    Large-scale streaming URLs are the norm in many commercial software products that aim to filter URLs based on their sensitivity or risk level. In such problem scenarios, filtering is typically done by classifying a URL using either its webpage content or certain additional contextual information. However, such approaches are slow and computationally expensive, as they require gathering and process... View full abstract»

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  • Local PurTree Spectral Clustering for Massive Customer Transaction Data

    Publication Year: 2017, Page(s):37 - 44
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (613 KB) | HTML iconHTML

    The clustering of customer transaction data is very important to retail and e-commerce companies. The authors propose a local PurTree spectral clustering algorithm for massive customer transaction data that uses a purchase tree to represent customer transaction data and a PurTree distance to compute the distance between two trees. The new method learns a data similarity matrix from the local dista... View full abstract»

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  • Recommendation 
  • Collaborative Recommendation with Multiclass Preference Context

    Publication Year: 2017, Page(s):45 - 51
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (526 KB) | HTML iconHTML

    Factorization- and neighborhood-based methods have been recognized as state-of-the-art approaches for collaborative recommendation tasks. In this article, the authors take user ratings as categorical multiclass preferences and propose a novel method called matrix factorization with multiclass preference context (MF-MPC), which integrates an enhanced neighborhood based on the assumption that users ... View full abstract»

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  • Heuristic Methods 
  • A Hybrid Approach for the Sudoku Problem: Using Constraint Programming in Iterated Local Search

    Publication Year: 2017, Page(s):52 - 62
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (564 KB) | HTML iconHTML

    Sudoku is not only a popular puzzle but also an interesting and challenging constraint satisfaction problem. Therefore, automatic solving methods have been the subject of several publications in the past two decades. Although current methods provide good solutions for small-sized puzzles, larger instances remain challenging. This article introduces a new local search technique based on the min-con... View full abstract»

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  • AI Innovation in Industry 
  • Emerging White-Collar Robotics: The Case of Watson Analytics

    Publication Year: 2017, Page(s):63 - 67
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (318 KB) | HTML iconHTML

    This article discusses easy and smart analytics through the example of Watson Analytics. The author reviews Watson Analytics capabilities, summarizes its strengths and limitations, and analyzes data in two case studies. View full abstract»

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  • Internet of Things 
  • IoT Quality Control for Data and Application Needs

    Publication Year: 2017, Page(s):68 - 73
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (591 KB) | HTML iconHTML

    The amount of Internet of Things (IoT) data is growing rapidly. Although there is a growing understanding of the quality of such data at the device and network level, important challenges in interpreting and evaluating the quality at informational and application levels remain to be explored. This article discusses some of these challenges and solutions of IoT systems at the different OSI layers t... View full abstract»

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  • Affective Computing and Sentiment Analysis 
  • Deep Learning-Based Document Modeling for Personality Detection from Text

    Publication Year: 2017, Page(s):74 - 79
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (328 KB) | HTML iconHTML

    This article presents a deep learning based method for determining the author's personality type from text: given a text, the presence or absence of the Big Five traits is detected in the author's psychological profile. For each of the five traits, the authors train a separate binary classifier, with identical architecture, based on a novel document modeling technique. Namely, the classifier is im... View full abstract»

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  • AI and Health 
  • Graph Structure Learning from Unlabeled Data for Early Outbreak Detection

    Publication Year: 2017, Page(s):80 - 84
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (390 KB) | HTML iconHTML

    Processes such as disease propagation and information diffusion often spread over some latent network structure that must be learned from observation. Given a set of unlabeled training examples representing occurrences of an event type of interest (such as a disease outbreak), the authors aim to learn a graph structure that can be used to accurately detect future events of that type. They propose ... View full abstract»

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  • Back Covers 
  • IEEE Computer Society

    Publication Year: 2017, Page(s): c3
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  • IEEE Computer Society 2017 Call for Major Award Nominations

    Publication Year: 2017, Page(s): c4
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Aims & Scope

IEEE Intelligent Systems serves users, managers, developers, researchers, and purchasers who are interested in intelligent systems and artificial intelligence, with particular emphasis on applications.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Daniel Zeng
University of Arizona