Scheduled System Maintenance on May 29th, 2015:
IEEE Xplore will be upgraded between 11:00 AM and 10:00 PM EDT. During this time there may be intermittent impact on performance. We apologize for any inconvenience.
By Topic

Intelligent Systems, IEEE

Issue 3 • Date May-June 2015

Filter Results

Displaying Results 1 - 15 of 15
  • Front Cover

    Publication Year: 2015 , Page(s): c1
    Save to Project icon | Request Permissions | PDF file iconPDF (1649 KB)  
    Freely Available from IEEE
  • Table of Contents

    Publication Year: 2015 , Page(s): c2 - 1
    Save to Project icon | Request Permissions | PDF file iconPDF (410 KB)  
    Freely Available from IEEE
  • AI Ethics: Science Fiction Meets Technological Reality

    Publication Year: 2015 , Page(s): 2 - 5
    Save to Project icon | Request Permissions | PDF file iconPDF (1217 KB)  
    Freely Available from IEEE
  • Predictive Analytics: Predictive Modeling at the Micro Level

    Publication Year: 2015 , Page(s): 6 - 8
    Save to Project icon | Request Permissions | PDF file iconPDF (1275 KB)  
    Freely Available from IEEE
  • Predicting Location-Based Sequential Purchasing Events by Using Spatial, Temporal, and Social Patterns

    Publication Year: 2015 , Page(s): 10 - 17
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1113 KB)  

    Location-based sequential event prediction is an interesting problem with many real-world applications. For example, knowing when and where people will use certain kinds of services could enable the development of robust anticipatory systems. A key to this problem is in understanding the nature of the process from which sequential data arises. Usually, human behavior exhibits distinct spatial, temporal, and social patterns. The authors examine three kinds of patterns extracted from sequential purchasing events and propose a novel model that captures contextual dependencies in spatial sequence, customers' temporal preferences, and social influence via an implicit network. Their model outperforms existing models based on evaluations using a real-world dataset of smartcard transaction records from a large educational institution with 13,753 students during a 10-month time period. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Twitter Hashtag Recommendation Model that Accommodates for Temporal Clustering Effects

    Publication Year: 2015 , Page(s): 18 - 25
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (883 KB)  

    Hashtags in social medial platforms such as Twitter are important for accessing related messages as well as for tracking and detecting events. Motivated by the observation that the average hashtag experiences a life cycle of increasing and decreasing popularity, the authors propose the Topic-over-Time Mixed Membership Model (TOT-MMM), a hashtag recommendation approach that captures the temporal clustering effect of latent topics in tweets. Their experiments on 1 million tweets suggest that TOT-MMM outperforms other hashtag recommendation approaches on tweet similarity and latent Dirichlet allocation. Combining TOT-MMM with the similarity-based approach yielded additional performance improvements. The authors' simulation studies on the British Petroleum oil disaster, which happened in April 2010, suggest that the combined approach successfully identifies a higher volume of additional event-related tweets and generates signals that lead to the lowest signal-detection delay at a reasonable false alarm rate of 1.34 percent. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Does Summarization Help Stock Prediction? A News Impact Analysis

    Publication Year: 2015 , Page(s): 26 - 34
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (986 KB)  

    The authors study the problem of how news summarization can help stock price prediction, proposing a generic stock price prediction framework to enable the use of different external signals to predict stock prices. Experiments were conducted on five years of Hong Kong Stock Exchange data, with news reported by Finet; evaluations were performed at individual stock, sector index, and market index levels. The authors' results show that prediction based on news article summarization can effectively outperform prediction based on full-length articles on both validation and independent testing sets. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Predicting Behavior

    Publication Year: 2015 , Page(s): 35 - 43
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (979 KB)  

    Behavior prediction has become an important area of emphasis, with applications ranging from e-commerce, marketing analytics, and financial forecasting to smart health, security informatics, and crime prevention. However, traditional behavior modeling approaches have shortcomings: heavy reliance on objective, observed data, and a failure to consider the granular, micro-level decisions and actions that collectively drive macro-level behavior. To address these shortcomings, the authors present a behavior prediction framework that advocates the integration of objective and perceptual information and decomposes behavior into a series of closely interrelated stages to facilitate enhanced behavior prediction performance. The utility of the framework is demonstrated through a series of experiments pertaining to prediction of auction fraud, e-commerce conversions, and customer churn. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Identifying Adverse Drug Events from Patient Social Media: A Case Study for Diabetes

    Publication Year: 2015 , Page(s): 44 - 51
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (566 KB)  

    Patient social media sites have emerged as major platforms for discussion of treatments and drug side effects, making them a promising source for listening to patients' voices in adverse drug event reporting. However, extracting patient reports from social media continues to be a challenge in health informatics research. In light of the need for more robust extraction methods, the authors developed a novel information extraction framework for identifying adverse drug events from patient social media. They also conducted a case study on a major diabetes patient social media platform to evaluate their framework's performance. Their approach achieves an f-measure of 86 percent in recognizing discussion of medical events and treatments, an f-measure of 69 percent for identifying adverse drug events, and an f-measure of 84 percent in patient report extraction. Their proposed methods significantly outperformed prior work in extracting patient reports of adverse drug events in health social media. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Multi-Agent-Based Power System Hybrid Dynamic State Estimator

    Publication Year: 2015 , Page(s): 52 - 59
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (453 KB)  

    This article proposes a multi-agent-based power system hybrid dynamic state estimator (PSHDSE) that uses field measurements from remote terminal units (RTUs) as well as phasor measurement units (PMUs). The standard cubature Kalman filter (CKF) process is modified to make it suitable for PSHDSE execution, and software agents are utilized to receive data and run PSHDSE for the RTU and PMU measurements separately. PSHDSE is solved by utilizing the CKF, the extended Kalman filter (EKF), and the unscented Kalman filter (UKF). The relative performances of the EKF, UKF, and CKF in executing PSHDSE are established through simulations on the IEEE 30 bus and practical 246-bus Indian test systems. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Engineering Ambient Intelligence Systems Using Agent Technology

    Publication Year: 2015 , Page(s): 60 - 67
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1936 KB)  

    This article shows how to model and implement an ambient intelligence (AmI) system using agent technology. The HERA project, undertaken by a consortium with members from academia as well as industry, applied the agent systems engineering methodology (ASEME), an agent-oriented software engineering approach, to develop a real-world system for the ambient assisted living application domain. This article focuses on the software architecture, along with the development method and validation results. The obtained results demonstrate the added value of agent technology use, along with how ASEME can be applied for modeling a real-world ambient intelligence system. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Principles for Human-Centered Interaction Design, Part 2: Can Humans and Machines Think Together?

    Publication Year: 2015 , Page(s): 68 - 75
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2353 KB)  

    This column introduces the Network Observatory, a human-centered cyber sensemaking system, and discusses the visual and sensemaking design principles employed therein. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Simple Is Beautiful: Toward Light Prediction Markets

    Publication Year: 2015 , Page(s): 76 - 80
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (915 KB)  

    This column examines the cognitive load of prediction markets from four steps of user's decision process (that is, timing, pricing, revisiting, and benefit) and discusses how a prediction market mechanism could have a low cognitive load. In the column, the authors propose that fixed-odds betting can be used as a prediction market mechanism with carefully designed event probability estimators. The development of low-cognitive load prediction markets needs the join efforts of computer scientists and economists. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • IEEE Cloud Computing Call for Papers House Advertisement

    Publication Year: 2015 , Page(s): c3
    Save to Project icon | Request Permissions | PDF file iconPDF (549 KB)  
    Freely Available from IEEE
  • New Title from IEEE CS Press House Advertisement

    Publication Year: 2015 , Page(s): c4
    Save to Project icon | Request Permissions | PDF file iconPDF (647 KB)  
    Freely Available from IEEE

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