The Experts below are selected from a list of 237078 Experts worldwide ranked by ideXlab platform
Chongqing Kang - One of the best experts on this subject based on the ideXlab platform.
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Socio-Demographic Information Identification
Smart Meter Data Analytics, 2020Co-Authors: Yi Wang, Qixin Chen, Chongqing KangAbstract:This chapter investigates how such characteristics can be inferred from fine-grained smart meter data. A deep convolutional neural network (CNN) first automatically extracts features from massive load profiles. A support vector machine (SVM) then identifies the characteristics of the consumers. Comprehensive comparisons with state-of-the-art and advanced machine learning techniques are conducted. Case studies on an Irish dataset demonstrate the effectiveness of the proposed deep CNN-based method, which achieves higher accuracy in identifying the socio-Demographic Information about the consumers.
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Deep Learning-Based Socio-Demographic Information Identification From Smart Meter Data
IEEE Transactions on Smart Grid, 2019Co-Authors: Yi Wang, Qixin Chen, Dahua Gan, Jingwei Yang, Daniel S. Kirschen, Chongqing KangAbstract:Smart meters provide large amounts of data and the value of this data is getting increased attention because a better understanding of the characteristics of consumers helps utilities and retailers implement more effective demand response programs and more personalized services. This paper investigates how such characteristics can be inferred from fine-grained smart meter data. A deep convolutional neural network (CNN) first automatically extracts features from massive load profiles. A support vector machine then identifies the characteristics of the consumers. Comprehensive comparisons with state-of-the-art and advanced machine learning techniques are conducted. Case studies on an Irish dataset demonstrate the effectiveness of the proposed deep CNN-based method, which achieves higher accuracy in identifying the socio-Demographic Information about the consumers.
Claire Wollen - One of the best experts on this subject based on the ideXlab platform.
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Guides: Demographic Information for Business: Contact
2013Co-Authors: Claire WollenAbstract:This guide will point you in the direction of sources for Demographic Information. Sources come from the Governments of Canada and Ontario, local municipalities, the U.S. Census Bureau, the United Nations, and subscription databases.
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Guides: Demographic Information for Business: Library Databases
2013Co-Authors: Claire WollenAbstract:This guide will point you in the direction of sources for Demographic Information. Sources come from the Governments of Canada and Ontario, local municipalities, the U.S. Census Bureau, the United Nations, and subscription databases.
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Guides: Demographic Information: American and International Sources
2013Co-Authors: Claire WollenAbstract:This guide will point you in the direction of sources for Demographic Information. Sources come from the Governments of Canada and Ontario, local municipalities, the U.S. Census Bureau, the United Nations, and subscription databases.
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Guides: Demographic Information for Business: Ontario Municipalities
2013Co-Authors: Claire WollenAbstract:This guide will point you in the direction of sources for Demographic Information. Sources come from the Governments of Canada and Ontario, local municipalities, the U.S. Census Bureau, the United Nations, and subscription databases.
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Guides: Demographic Information for Business: City of Brampton
2013Co-Authors: Claire WollenAbstract:This guide will point you in the direction of sources for Demographic Information. Sources come from the Governments of Canada and Ontario, local municipalities, the U.S. Census Bureau, the United Nations, and subscription databases.
Yi Wang - One of the best experts on this subject based on the ideXlab platform.
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Socio-Demographic Information Identification
Smart Meter Data Analytics, 2020Co-Authors: Yi Wang, Qixin Chen, Chongqing KangAbstract:This chapter investigates how such characteristics can be inferred from fine-grained smart meter data. A deep convolutional neural network (CNN) first automatically extracts features from massive load profiles. A support vector machine (SVM) then identifies the characteristics of the consumers. Comprehensive comparisons with state-of-the-art and advanced machine learning techniques are conducted. Case studies on an Irish dataset demonstrate the effectiveness of the proposed deep CNN-based method, which achieves higher accuracy in identifying the socio-Demographic Information about the consumers.
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Deep Learning-Based Socio-Demographic Information Identification From Smart Meter Data
IEEE Transactions on Smart Grid, 2019Co-Authors: Yi Wang, Qixin Chen, Dahua Gan, Jingwei Yang, Daniel S. Kirschen, Chongqing KangAbstract:Smart meters provide large amounts of data and the value of this data is getting increased attention because a better understanding of the characteristics of consumers helps utilities and retailers implement more effective demand response programs and more personalized services. This paper investigates how such characteristics can be inferred from fine-grained smart meter data. A deep convolutional neural network (CNN) first automatically extracts features from massive load profiles. A support vector machine then identifies the characteristics of the consumers. Comprehensive comparisons with state-of-the-art and advanced machine learning techniques are conducted. Case studies on an Irish dataset demonstrate the effectiveness of the proposed deep CNN-based method, which achieves higher accuracy in identifying the socio-Demographic Information about the consumers.
Hung-hsuan Chen - One of the best experts on this subject based on the ideXlab platform.
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WI - Visited Websites May Reveal Users’ Demographic Information and Personality
IEEE WIC ACM International Conference on Web Intelligence, 2019Co-Authors: Cheng-you Lien, Guo-jhen Bai, Hung-hsuan ChenAbstract:This study shows that simple supervised learning algorithms can easily predict a user’s personality and Demographic Information based on the features derived from the users’ browsing logs, even when the logs are not recorded with the finest granularity (i.e., each visited URL of a user). This is different from the analytical formula of Cambridge Analytica (CA), which reported that it needs to know each user’s detailed liked objects (e.g., articles, pages, etc.) on Facebook with a fine granularity (i.e., CA needs to know the liked articles, not only the types of the articles) to predict user Information. However, we employed only the visited website categories to predict a user’s gender, age, relationship status, and big six personality scores, which is an authoritative index to represent an individual’s personality in six dimensions. We also show that applying simple clustering as a preprocessing step enhances the predictive power. As a result, the data collectors, even when storing only a coarse granularity of the visited URLs of the users, may leverage such Information to identify a user’s preferences/tastes and her/his private Information without notifying users.
Steven M. Valles - One of the best experts on this subject based on the ideXlab platform.
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Exploitation of a high genomic mutation rate in Solenopsis invicta virus 1 to infer Demographic Information about its host, Solenopsis invicta
Journal of invertebrate pathology, 2010Co-Authors: Clare Allen, Juan A. Briano, Laura Varone, Steven M. VallesAbstract:Abstract The RNA-dependent RNA polymerase (RdRp) region of Solenopsis invicta virus 1 (SINV-1) was sequenced from 47 infected colonies of S. invicta , S. richteri , S. geminata , and S. invicta / richteri hybrids collected from across the USA, northern Argentina, and northern Taiwan in an attempt to infer Demographic Information about the recent S. invicta introduction into Taiwan by phylogenetic analysis. Nucleotide sequences were calculated to exhibit an overall identity of >90% between geographically-separated samples. A total of 171 nucleotide variable sites (representing 22.4% of the region amplified) were mapped across the SINV-1 RdRp alignment and no insertions or deletions were detected. Phylogenetic analysis at the nucleotide level revealed clustering of Argentinean sequences, distinct from the USA sequences. Moreover, the SINV-1 RdRp sequences derived from recently introduced populations of S. invicta from northern Taiwan resided within the multiple USA groupings implicating the USA as the source for the recent introduction of S. invicta into Taiwan. Examination of the amino acid alignment for the RdRp revealed sequence identity >98% with only nine amino acid changes observed. Seven of these changes occurred in less than 4.3% of samples, while 2 (at positions 1266 and 1285) were featured prominently. Changes at positions 1266 and 1285 accounted for 36.2% and 34.0% of the samples, respectively. Two distinct groups were observed based on the amino acid residue at position 1266, Threonine or Serine. In cases where this amino acid was a Threonine, 90% of these sequences possessed a corresponding Valine at position 1285; only 10% of the Threonine 1266 -containing sequences possessed an Isoleucine at the 1285 position. Among the Serine 1266 group, 76% possessed an Isoleucine at position 1285, while only 24% possessed a Valine. Thus, it appears that the Threonine 1266 /Valine 1285 and Serine 1266 /Isoleucine 1285 combinations are predominant phenotypes.
