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No more endless PDFs. Discover the core value of the latest top-tier research in one article.
Network structure, organizational learning culture, and employee creativity in system integration companies: The mediating effects of exploitation and exploration
Aggregated Markov Models of a Heterogeneous Population of Photovoltaic Panels
CrowdBuy: Privacy-friendly Image Dataset Purchasing via Crowdsourcing
Knowledge discovery in discretionary legal domains
Microlending on mobile social credit platforms: an exploratory study using Philippine loan contracts
An Upstream-Reciprocity-Based Strategy for Academic Social Networks Using Public Goods Game
Calling HCI professionals into health research: patient safety and health equity at stake
Privacy antecedents for SNS self-disclosure: The case of Facebook
Game theoretic analysis of a strategic model of competitive contagion and product adoption in social networks
Detecting Overlapping Communities in Social Networks Using A Modified Segmentation by Weighted Aggregation Approach
Finding a Secure Place: A Map-Based Crowdsourcing System for People With Autism
QuickWalk: Quick Trust Assessment for Vehicular Social Networks
Understanding History Through Networks: The Brazil Case Study
Between the Profiles: Another such Bias. Technology Acceptance Studies on Social Network Services
8090_2016 IEEE Educational Activities Board Awards.
Abstract-Falling is a serious problem in an aged society such that assessment of the risk of falls for individuals is imperative for the research and practice of falls prevention. This paper introduces an application of several machine learning methods for training a classifier which is capable of classifying individual older adults into a high risk group and a low risk group (distinguished by whether or not the members of the group have a recent history of falls). Using a 3D motion capture system, significant gait features related to falls risk are extracted. By training these features, classification hypotheses are obtained based on machine learning techniques (K Nearestneighbour, Naive Bayes, Logistic Regression, Neural Network, and Support Vector Machine). Training and test accuracies with sensitivity and specificity of each of these techniques are assessed. The feature adjustment and tuning of the machine learning algorithms are discussed. The outcome of the study will benefit the prediction and prevention of falls
Online Welfare Maximization of Sponsored Viral Marketing with Stochastically Arriving Spreaders
The Multiple Attribute Association Decision-Making Method to Make Online Advertisements Using Influential Users in Social Network
Mobility Dataset Generation for Vehicular Social Networks Based on Floating Car Data
Crowdsourcing aggregation with deep Bayesian learning
Does Optical Character Recognition and Caption Generation Improve Emotion Detection in Microblog Posts?
Improving cooperation in peer-to-peer systems using social networks
A Local-Global Influence Indicator Based Constrained Evolutionary Algorithm for Budgeted Influence Maximization in Social Networks
Maximizing Influence on Social Networks with Conjugate Learning Automata
International Journal of Human - Computer Studies
Frequent paern mining for kernel trace data
Discovering Stable Communities in Dynamic Multilayer Social Networks
Integrating Personality and Mood with Agent Emotions
Modeling Complex Social Systems: A New Network Point of View in Labour Markets
Exploiting Semantic and Social Technologies for Competency Management