Chao Lian 編集者 at IgMin Research
We aim to unite diverse scientific talents to foster dialogue and advance global research.
バイオグラフィー
Dr. Lian Chao is currently a research assistant at Northeastern University, and both his master's and doctoral degrees are studying at Northeastern University. His research interests include wearable devices, smart sensors, intelligent detection, human activity recognition (HAR), data mining, deep learning, etc. He has published more than 20 SCI papers in internationally renowned journals such as Advanced Intelligent Systems, IEEE Transactions on Instrumentation and Measurement, IEEE Sensors Journal, etc. He has served as a reviewer for several journals such as IEEE Sensors Journal, Sensors, International Journal of Sensors and Sensor Networks. He also has participated in the review of some important academic conferences, such as CVVR2023, MEAAC2023, etc.
研究の興味
Wearable devices, Smart sensors, Intelligent detection, Human activity recognition (HAR), Data mining, Deep learning
Editor
仕事内容
Research Assistant
Northeast University
School of Information Science and Engineering
China
トピック分野別の貢献
Why publish with us?
Global Visibility – Indexed in major databases
Fast Peer Review – Decision within 14–21 days
Open Access – Maximize readership and citation
Multidisciplinary Scope – Biology, Medicine and Engineering
Editorial Board Excellence – Global experts involved
University Library Indexing – Via OCLC
Permanent Archiving – CrossRef DOI
APC – Affordable APCs with discounts
Citation – High Citation Potential
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研究論文
- A Review inside Innovation: AI and Additive Manufacturing for Advanced Bone Scaffold Design
- Screening for Sexually Transmitted Infections in Adolescents with Genitourinary Complaints: Is There a Still Role for Endocervical Gram Stains?
- Lunar-Derived Propellants for Fueling Mars-Bound Spacecraft in Cis-Lunar Space
- A Comprehensive Methodology for Assessing the Business Reputation of Industrial and Production Personnel
- How Increased CO2 Warms the Earth
- Enhancing Missing Values Imputation through Transformer-Based Predictive Modeling
