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Summary: In this paper, an enhanced Autoencoder model is proposed to identify elevated road traffic accident (RTA) risk based on traffic anomaly detection in an unsupervised manner. By introducing an attention mechanism and an enhanced loss, the model can effectively extract traffic condition features and optimize anomaly detection performance. Experimental results demonstrate the effectiveness of the model.
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(2023)
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Summary: This study focuses on automatic genre classification of Nigerian songs, building the ORIN dataset and training on four different classifiers, with results indicating XGBoost classifier performing the best. Further analysis reveals the similarity in timbral properties between some genres.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
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Summary: The paper discusses a new method to improve the accuracy of music genre classification, combining auditory image features, traditional acoustic features, and spectral features. Experimental results demonstrate that the proposed method outperforms many state-of-the-art classification methods in terms of classification accuracy and stability.
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(2022)
Review
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Summary: This study proposes a novel group consensus-based travel destination evaluation method with online reviews, which considers missing preference estimating and group consensus reaching process through sentiment matrix, weight vectors, and group consensus mechanism.
APPLIED INTELLIGENCE
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Article
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Summary: This study evaluates the robustness of commonly used musical and non-musical features on DL models for the music genre classification (MGC) task, finding that Mel-Scale-based features and Swaragram demonstrate high robustness across various datasets and DL models.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Theory & Methods
Guoli Cheng et al.
Summary: This paper proposes a new approach based on Adaboost to efficiently and accurately predict the performance of a given application with a given Spark configuration. The approach uses Adaboost to build performance models at the stage-level for Spark and minimizes modeling overhead by using projective sampling. Experimental results show that the approach outperforms previous methods in prediction error and cost.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2021)
Article
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Amir Karami et al.
Summary: Privacy needs and stigma present barriers for LGBT people in traditional settings, but social media offers an alternative platform for connection; LGBT users are more active on social media and account authenticity is not solely determined by LGBT-related words in profiles; a study categorizes LGBT Twitter users into three groups and develops a machine learning classifier based on profile features.
Proceedings Paper
Computer Science, Interdisciplinary Applications
Hui Yuan et al.
Summary: This paper discusses the concept of musical genres and the use of deep learning for automatic music genre classification. The case study demonstrates the effectiveness of parallelized deep neural networks in capturing the spatial and temporal characteristics of music data.
2021 IEEE 45TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE (COMPSAC 2021)
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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
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Article
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CANADIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING-REVUE CANADIENNE DE GENIE ELECTRIQUE ET INFORMATIQUE
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2018 IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM 2018)
(2018)
Proceedings Paper
Computer Science, Information Systems
Feng Rong
2016 INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION, BIG DATA & SMART CITY (ICITBS)
(2017)
Article
Computer Science, Artificial Intelligence
Amin Karami et al.
Article
Acoustics
G Tzanetakis et al.
IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING
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Article
Computer Science, Artificial Intelligence
L Breiman
