Deep learning approaches for multi-modal sensor data analysis and abnormality detection

被引：0
|
作者：
Jadhav, Santosh Pandurang [1 ]
Srinivas, Angalkuditi [2 ]
Dipak Raghunath, Patil [3 ]
Ramkumar Prabhu, M. [4 ]
Suryawanshi, Jaya [1 ]
Haldorai, Anandakumar [5 ]
机构：
[1] Department of Information Technology, MVPS ‘KBT College of Engineering, Maharashtra, Nashik, India
[2] Department of Computer Science Applications, Koneru Lakshmaiah Education Foundation, Andhra Pradesh, Vaddeswaram, India
[3] Department of Computer Engineering, Amrutvahini College of Engineering, Maharashtra, Sangamner, India
[4] Department of ECE, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Tamil Nadu, Chennai, India
[5] Center for Future Networks and Digital Twin, Department of Computer Science and Engineering, Sri Eshwar College of Engineering, Tamil Nadu, Coimbatore,641202, India
来源：
Measurement: Sensors | / 33卷
关键词：
All Open Access; Gold;
D O I：
10.1016/j.measen.2024.101157
中图分类号：
学科分类号：
摘要：
42
引用
收藏
相关论文
共 50 条
  • [21] Multi-modal deep learning for landform recognition
    Du, Lin
    You, Xiong
    Li, Ke
    Meng, Liqiu
    Cheng, Gong
    Xiong, Liyang
    Wang, Guangxia
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2019, 158 : 63 - 75
  • [22] Deep Multi-modal Learning with Cascade Consensus
    Yang, Yang
    Wu, Yi-Feng
    Zhan, De-Chuan
    Jiang, Yuan
    PRICAI 2018: TRENDS IN ARTIFICIAL INTELLIGENCE, PT II, 2018, 11013 : 64 - 72
  • [23] Multi-modal deep distance metric learning
    Roostaiyan, Seyed Mahdi
    Imani, Ehsan
    Baghshah, Mahdieh Soleymani
    INTELLIGENT DATA ANALYSIS, 2017, 21 (06) : 1351 - 1369
  • [24] Heterogeneous Graph Learning for Multi-Modal Medical Data Analysis
    Kim, Sein
    Lee, Namkyeong
    Lee, Junseok
    Hyun, Dongmin
    Park, Chanyoung
    THIRTY-SEVENTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 37 NO 4, 2023, : 5141 - 5150
  • [25] Deep Object Tracking with Multi-modal Data
    Zhang, Xuezhi
    Yuan, Yuan
    Lu, Xiaoqiang
    2016 INTERNATIONAL CONFERENCE ON COMPUTER, INFORMATION AND TELECOMMUNICATION SYSTEMS (CITS), 2016, : 161 - 165
  • [26] Application and comparison of deep learning approaches for upper limb functionality evaluation based on multi-modal inertial data
    Shen, Chen
    Ning, Xinyu
    Zhu, Qixiu
    Miao, Sheng
    Lv, Haibin
    SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS, 2022, 33
  • [27] Co-Training for Deep Object Detection: Comparing Single-Modal and Multi-Modal Approaches
    Gomez, Jose L.
    Villalonga, Gabriel
    Lopez, Antonio M.
    SENSORS, 2021, 21 (09)
  • [28] Deep Multi-lnstance Learning Using Multi-Modal Data for Diagnosis for Lymphocytosis
    Sahasrabudhe, Mihir
    Sujobert, Pierre
    Zacharaki, Evangelia, I
    Maurin, Eugenie
    Grange, Beatrice
    Jallades, Laurent
    Paragios, Nikos
    Vakalopoulou, Maria
    IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2021, 25 (06) : 2125 - 2136
  • [29] Multi-modal Active Learning From Human Data: A Deep Reinforcement Learning Approach
    Rudovic, Ognjen
    Zhang, Meiru
    Schuller, Bjorn
    Picard, Rosalind W.
    ICMI'19: PROCEEDINGS OF THE 2019 INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION, 2019, : 6 - 15
  • [30] Review on the usage of deep learning models in multi-modal sentiment analysis
    Naga Durga Saile K.
    Venkatramaphanikumar S.
    Venkata Krishna Kishore K.
    Bhattacharyya D.
    Venkatramaphanikumar, S. (svrphanikumar@yahoo.com), 1600, Institute of Electronics Engineers of Korea (09): : 435 - 444
12345