Detecting High Obfuscation Plagiarism: Exploring Multi-Features Fusion via Machine Learning

Leilei Kong; Zhimao Lu; Haoliang Qi; Zhongyuan Han

doi:10.14257/ijunesst.2014.7.4.35

Detecting High Obfuscation Plagiarism: Exploring Multi-Features Fusion via Machine Learning

International Journal of u- and e- Service Science and Technology ◽

10.14257/ijunesst.2014.7.4.35 ◽

2014 ◽

Vol 7 (4) ◽

pp. 385-396 ◽

Cited By ~ 3

Author(s):

Leilei Kong ◽

Zhimao Lu ◽

Haoliang Qi ◽

Zhongyuan Han

Keyword(s):

Machine Learning ◽

Features Fusion

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A tongue features fusion approach to predicting prediabetes and diabetes with machine learning

Journal of Biomedical Informatics ◽

10.1016/j.jbi.2021.103693 ◽

2021 ◽

Vol 115 ◽

pp. 103693

Author(s):

Jun Li ◽

Pei Yuan ◽

Xiaojuan Hu ◽

Jingbin Huang ◽

Longtao Cui ◽

...

Keyword(s):

Machine Learning ◽

Features Fusion ◽

Fusion Approach

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Detection of Brain Tumor based on Features Fusion and Machine Learning

Journal of Ambient Intelligence and Humanized Computing ◽

10.1007/s12652-018-1092-9 ◽

2018 ◽

Cited By ~ 22

Author(s):

Javeria Amin ◽

Muhammad Sharif ◽

Mudassar Raza ◽

Mussarat Yasmin

Keyword(s):

Machine Learning ◽

Brain Tumor ◽

Features Fusion

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Advanced Heterogeneous Feature Fusion Machine Learning Models and Algorithms for Improving Indoor Localization

Sensors ◽

10.3390/s19010125 ◽

2019 ◽

Vol 19 (1) ◽

pp. 125 ◽

Cited By ~ 3

Author(s):

Lingwen Zhang ◽

Ning Xiao ◽

Wenkao Yang ◽

Jun Li

Keyword(s):

Machine Learning ◽

Feature Fusion ◽

Indoor Positioning ◽

Time Of Arrival ◽

Learning Models ◽

Fusion Model ◽

Features Fusion ◽

Single Feature ◽

Heterogeneous Features ◽

Machine Learning Models

In the era of the Internet of Things and Artificial Intelligence, the Wi-Fi fingerprinting-based indoor positioning system (IPS) has been recognized as the most promising IPS for various applications. Fingerprinting-based algorithms critically rely on a fingerprint database built from machine learning methods. However, currently methods are based on single-feature Received Signal Strength (RSS), which is extremely unstable in performance in terms of precision and robustness. The reason for this is that single feature machines cannot capture the complete channel characteristics and are susceptible to interference. The objective of this paper is to exploit the Time of Arrival (TOA) feature and propose a heterogeneous features fusion model to enhance the precision and robustness of indoor positioning. Several challenges are addressed: (1) machine learning models based on heterogeneous features, (2) the optimization of algorithms for high precision and robustness, and (3) computational complexity. This paper provides several heterogeneous features fusion-based localization models. Their effectiveness and efficiency are thoroughly compared with state-of-the-art methods.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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