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Material Identification Using a Microwave Sensor Array and Machine Learning

Luke Harrsion, Maryam Ravan, Dhara Tandel, Kunyi Zhang, Tanvi Patel, Reza Khalaj Amineh

Electronics 2020 cited by 31

Engineering & Materials Microwave and Dielectric Measurement Techniques partially solved importance 3/10 scale stated

Identifying materials via microwave sensing, where single-resonator systems limit characterization to narrow frequency ranges and reduce identification accuracy.

Details Source Open-access PDF model: not stated in source

Prediction of Poststroke Depression Based on the Outcomes of Machine Learning Algorithms

Yeong Hwan Ryu, Seo‐Young Kim, Tae Uk Kim, Seong Jae Lee, Soo Jun Park, Ho-Youl Jung, Jung Keun Hyun

Journal of Clinical Medicine 2022 cited by 24

Medicine & Health Stroke Rehabilitation and Recovery ai assisted importance 3/10 scale stated

Poststroke depression is a major psychiatric complication after stroke, and predicting which patients will develop it is difficult.

Details Source Open-access PDF model: support vector machine (best performer); a...

Rate coefficients of the H + H2O2 → H2 + HO2 reaction on an accurate fundamental invariant-neural network potential energy surface

Xiaoxiao Lu, Qingyong Meng, Xingan Wang, Bina Fu, Dong Hui Zhang

The Journal of Chemical Physics 2018 cited by 24

Science & Research Advanced Chemical Physics Studies solved importance 2/10 scale stated

Experimental rate coefficients for the H + H2O2 → H2 + HO2 reaction varied widely, especially at low temperatures, so accurate values were needed.

Details Source model: Fundamental invariant-neural network (FI-N...

Vibrational energies for HFCO using a neural network sum of exponentials potential energy surface

Ekadashi Pradhan, Alex Brown

The Journal of Chemical Physics 2016 cited by 24

Physics & Astronomy Advanced Chemical Physics Studies solved importance 2/10 scale stated

Accurately computing the vibrational energies of formyl fluoride (HFCO) requires an accurate six-dimensional potential energy surface fitted to expensive ab initio data.

Details Source model: Neural network sum-of-exponentials fitting...

Advancing Crack Detection Using Deep Learning Solutions for Automated Inspection of Metallic Surfaces

Snehal Rathi, Omkar Mirajkar, Shubhangi Shukla, Laukik Deshmukh, Lokesh Dangare

Indian Journal of Information Sources and Services 2024 cited by 33

Engineering & Materials Industrial Vision Systems and Defect Detection ai assisted importance 2/10 scale stated

Conventional image processing struggles to detect cracks on metallic surfaces (hex nuts) under real-world lighting and shadow variation.

Details Source Open-access PDF model: Deep Convolutional Neural Network (CNN)