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Identification of microstructures critically affecting material properties using machine learning framework based on metallurgists’ thinking process

Satoshi F. Noguchi, Hui Wang, Junya Inoue

Scientific Reports 2022 cited by 15

Engineering & Materials Machine Learning in Materials Science ai assisted importance 5/10 scale stated

Identifying which microstructures critically affect a material's physical properties normally requires human expert prior knowledge.

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

Deep reinforcement learning for map-less goal-driven robot navigation

Matej Dobrevski, Danijel Skočaj

International Journal of Advanced Robotic Systems 2021 cited by 45

Robotics & Autonomy Reinforcement Learning in Robotics partially solved importance 5/10 scale stated

Mobile robots normally need an explicit map of the environment to navigate, which is impractical or impossible in new or dynamic environments. The study developed a local navigation method that steers a robot to global goals without a map.

Details Source Open-access PDF model: Deep reinforcement learning, Advantage Act...

Deep Reinforcement Learning for End-to-End Local Motion Planning of Autonomous Aerial Robots in Unknown Outdoor Environments: Real-Time Flight Experiments

Oualid Doukhi, Deok Jin Lee

Sensors 2021 cited by 34

Robotics & Autonomy Reinforcement Learning in Robotics solved importance 5/10 scale stated

Micro aerial vehicles must autonomously navigate obstacles and reach goals in unknown, GPS-denied outdoor environments despite limited size and computational power.

Details Source Open-access PDF model: actor–critic deep reinforcement learning

A Rail-Temperature-Prediction Model Based on Machine Learning: Warning of Train-Speed Restrictions Using Weather Forecasting

Sunguk Hong, Cheoljeong Park, Seong Jin Cho

Sensors 2021 cited by 42

Engineering & Materials Railway Engineering and Dynamics ai assisted importance 5/10 scale stated

Rail tracks (especially continuous welded rails) can buckle in hot weather and cause derailments, so operators need early warning of dangerous rail temperatures.

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

Prior-Guided Image Reconstruction for Accelerated Multi-Contrast MRI via Generative Adversarial Networks

Salman Ul Hassan Dar, Mahmut Yurt, Mo Shahdloo, M. Emrullah Ildiz, Berk Tınaz, Tolga Çukur

IEEE Journal of Selected Topics in Signal Processing 2020 cited by 174

Medicine & Health Advanced MRI Techniques and Applications partially solved importance 5/10 scale stated

Multi-contrast MRI scans take excessive time because each added contrast lengthens acquisition, limiting clinical use.

Details Source Open-access PDF model: conditional generative adversarial network...

Learning Mobile Manipulation through Deep Reinforcement Learning

Cong Wang, Qifeng Zhang, Qiyan Tian, Shuo Li, Xiaohui Wang, David M. Lane, Yvan Pétillot, Sen Wang

Sensors 2020 cited by 99

Robotics & Autonomy Reinforcement Learning in Robotics ai assisted importance 5/10 scale stated

Whole-body mobile manipulation in unstructured environments using only on-board sensors is harder than fixed-base manipulation because the mobile base and manipulator must coordinate.

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

Wildlife surveillance using deep learning methods

Ruilong Chen, Ruth A. Little, Lyudmila S. Mihaylova, Richard John Delahay, Ruth Cox

Ecology and Evolution 2019 cited by 90

Biology & Life Sciences Advanced Neural Network Applications ai assisted importance 5/10 scale stated

Screening still and video camera surveillance of wild animals for species of interest is laborious and expensive, hindering wildlife conflict management such as bovine tuberculosis control.

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

Industrial Laser Welding Defect Detection and Image Defect Recognition Based on Deep Learning Model Developed

Honggui Deng, Yu Cheng, Yuxin Feng, Junjiang Xiang

Symmetry 2021 cited by 75

Engineering & Materials Industrial Vision Systems and Defect Detection ai assisted importance 5/10 scale stated

Existing methods poorly handle diverse industrial laser weld image data, so robust automatic defect detection and recognition is needed.

Details Source Open-access PDF model: deep convolutional neural network with tra...

Machine learning autoencoder‐based parameters prediction for solar power generation systems in smart grid

Ahsan Zafar, Yanbo Che, Muhammad Faheem, Muhammad Abubakar, Shujaat Ali, Muhammad Shoaib Bhutta

IET Smart Grid 2024 cited by 38

Engineering & Materials Solar Radiation and Photovoltaics partially solved importance 5/10 scale stated

Accurate prediction of solar power plant parameters is needed to minimise losses when traditional grids convert to smart grids.

Details Source Open-access PDF model: LSTM, CNN-LSTM, autoencoder-LSTM

Intelligent Identification and Prediction Mineral Resources Deposit Based on Deep Learning

Le Gao, Kun Wang, Xin Zhang, Chen Wang

Sustainability 2023 cited by 15

Science & Research Geochemistry and Geologic Mapping partially solved importance 5/10 scale stated

Predicting ore deposit locations is hindered by scarce labeled geological images, small irregular features with high image similarity, and hard-to-quantify prospecting factors.

Details Source Open-access PDF model: MFAF (MFCA-Net, SE-Net modules)

GAN-TL: Generative Adversarial Networks with Transfer Learning for MRI Reconstruction

Muhammad Yaqub, Jinchao Feng, Shahzad Ahmed, Kaleem Arshid, Muhammad Atif Bilal, Muhammad Pervez Akhter, Muhammad Sultan Zia

Applied Sciences 2022 cited by 28

Medicine & Health Advanced MRI Techniques and Applications partially solved importance 5/10 scale stated

GAN-based MRI reconstruction from under-sampled data normally requires large training sets of raw k-space patient data, which is difficult to gather in clinical practice.

Details Source Open-access PDF model: Generative Adversarial Network with transf...

DeepComplex: A Web Server of Predicting Protein Complex Structures by Deep Learning Inter-chain Contact Prediction and Distance-Based Modelling

Farhan Quadir, Raj S. Roy, Elham Soltanikazemi, Jianlin Jack Cheng

Frontiers in Molecular Biosciences 2021 cited by 21

Biology & Life Sciences Protein Structure and Dynamics ai assisted importance 5/10 scale stated

Predicting the quaternary structure of dimeric protein complexes, which is useful for function analysis, protein engineering and drug design.

Details Source Open-access PDF model: DeepComplex

Hybrid Forecasting Methodology for Wind Power-Photovoltaic-Concentrating Solar Power Generation Clustered Renewable Energy Systems

Simian Pang, Zixuan Zheng, Fan Luo, Xianyong Xiao, Lanlan Xu

Sustainability 2021 cited by 19

Climate & Environment Energy Load and Power Forecasting ai assisted importance 5/10 scale stated

Short-term forecasting of clustered hybrid renewable energy systems (wind, photovoltaic, concentrating solar power) is difficult due to spatial and temporal uncertainties.

Details Source Open-access PDF model: LSTM (long short-term memory network)

Digitaldlsorter: Deep-Learning on scRNA-Seq to Deconvolute Gene Expression Data

Carlos Torroja, Fátima Sánchez‐Cabo

Frontiers in Genetics 2019 cited by 51

Biology & Life Sciences Single-cell and spatial transcriptomics ai assisted importance 5/10 scale stated

Bulk RNA-Seq cannot distinguish whether observed expression changes come from shifting cell-type proportions or aberrant behavior of a specific cell type, while single-cell experiments remain complex and expensive.

Details Source Open-access PDF model: deep learning (specific architecture not s...

AI-accelerated Nazca survey nearly doubles the number of known figurative geoglyphs

Masato Sakai, Akihisa Sakurai, Siyuan Lu, J. Francisco Sibón Olano, Conrad M Albrecht, Hendrik F. Hamann, Marcus Freitag

Proceedings of the National Academy of Sciences 2024 cited by 18

Research & Humanities Archaeology and Cultural Heritage solved importance 4/10 scale stated

Finding figurative Nazca geoglyphs was extremely slow: only 430 were discovered over nearly a century, and small relief-type ones are hard to see.

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

Permutation invariant polynomial neural network approach to fitting potential energy surfaces. IV. Coupled diabatic potential energy matrices

Changjian Xie, Xiaolei Zhu, David R. Yarkony, Hua Guo

The Journal of Chemical Physics 2018 cited by 90

Physics & Astronomy Advanced Chemical Physics Studies solved importance 4/10 scale stated

Accurately representing coupled diabatic potential energy matrices of molecules, needed to model non-adiabatic photodissociation dynamics.

Details Source Open-access PDF model: PIP-NN (permutation invariant polynomial-n...

Hybrid Approach to Automated Essay Scoring: Integrating Deep Learning Embeddings with Handcrafted Linguistic Features for Improved Accuracy

Muhammad Faseeh, Abdul Jaleel, Naeem Iqbal, Anwar Ghani, Akmalbek Bobomirzaevich Abdusalomov, Asif Mehmood, Young Im Cho

Mathematics 2024 cited by 39

Education & Everyday Educational Technology and Assessment ai assisted importance 4/10 scale stated

Automated essay scoring systems face persistent challenges in delivering accurate and efficient evaluations.

Details Source Open-access PDF model: RoBERTa embeddings + Lightweight XGBoost (...