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Data-driven predictions of a multiscale Lorenz 96 chaotic system using machine-learning methods: reservoir computing, artificial neural network, and long short-term memory network

Ashesh Chattopadhyay, Pedram Hassanzadeh, Devika Subramanian

Nonlinear processes in geophysics 2020 cited by 168

Climate & Environment Neural Networks and Reservoir Computing partially solved importance 4/10 scale stated

Predicting the short-term evolution and long-term statistics of a multiscale chaotic Lorenz 96 system when only the large-scale variables are observable and the smaller-scale processes are never known.

Details Source Open-access PDF model: RC-ESN (echo state network), ANN, RNN-LSTM

Classification of gravitational-wave glitches via dictionary learning

Miquel Llorens-Monteagudo, Alejandro Torres-Forné, José A. Font, Antonio Marquina

Classical and Quantum Gravity 2019 cited by 24

Physics & Astronomy Pulsars and Gravitational Waves Research partially solved importance 4/10 scale stated

Gravitational-wave interferometers like advanced LIGO produce transient noise signals (glitches) that must be classified and separated from real astrophysical signals.

Details Source Open-access PDF model: learned dictionaries (dictionary learning)

Insights and approaches using deep learning to classify wildlife

Zhongqi Miao, Kaitlyn M. Gaynor, Jiayun Wang, Ziwei Liu, Oliver Muellerklein, Mohammad Sadegh Norouzzadeh, Alex McInturff, Rauri C. K. Bowie et al.

Scientific Reports 2019 cited by 136

Biology & Life Sciences Advanced Neural Network Applications partially solved importance 4/10 scale stated

Conservationists need to automatically identify and classify wildlife species in large camera-trap image datasets, which is slow and error-prone manually.

Details Source Open-access PDF model: Convolutional neural network (CNN) with Gr...

Comprehensive evaluation of deep learning architectures for prediction of DNA/RNA sequence binding specificities

Ameni Trabelsi, Mohamed Amine Chaâbane, Asa Ben‐Hur

Bioinformatics 2019 cited by 153

Biology & Life Sciences Machine Learning in Bioinformatics ai assisted importance 4/10 scale stated

The relative merit of CNN, RNN, and hybrid deep learning architectures for predicting DNA- and RNA-binding specificity was unclear from existing studies.

Details Source Open-access PDF model: deepRAM (CNN, RNN, hybrid CNN/RNN)

Optimization of Molecules via Deep Reinforcement Learning

Zhenpeng Zhou, Steven Kearnes, Li Li, Richard N. Zare, Patrick F. Riley

Scientific Reports 2019 cited by 401

Biology & Life Sciences Computational Drug Discovery Methods partially solved importance 4/10 scale stated

Optimizing molecules for drug discovery, including maximizing drug-likeness while maintaining similarity to an original molecule during medicinal chemistry lead optimization.

Details Source Open-access PDF model: MolDQN (Molecule Deep Q-Networks)

DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences

Ingoo Lee, Jongsoo Keum, Hojung Nam

PLoS Computational Biology 2019 cited by 674

Biology & Life Sciences Computational Drug Discovery Methods ai assisted importance 4/10 scale stated

Identifying drug-target interactions experimentally is costly and labor-intensive, and conventional protein descriptors were insufficiently informative for accurate in silico prediction.

Details Source Open-access PDF model: DeepConv-DTI (convolutional neural network...

Prediction of 8-state protein secondary structures by a novel deep learning architecture

Buzhong Zhang, Jinyan Li, Qiang Lü

BMC Bioinformatics 2018 cited by 140

Biology & Life Sciences Protein Structure and Dynamics partially solved importance 4/10 scale stated

Accurately predicting 8-state protein secondary structure, which serves as an information bridge between primary sequence and tertiary structure for structure-based analysis.

Details Source Open-access PDF model: CNN + residual network + bidirectional RNN...

Toward Data‐Driven Weather and Climate Forecasting: Approximating a Simple General Circulation Model With Deep Learning

Sebastian Scher

Geophysical Research Letters 2018 cited by 303

Climate & Environment Meteorological Phenomena and Simulations partially solved importance 4/10 scale stated

Whether deep learning can emulate the dynamics of weather and climate general circulation models to enable data-driven forecasting.

Details Source Open-access PDF model: Deep neural network (not further specified...

Classification of Paediatric Inflammatory Bowel Disease using Machine Learning

Enrico Mossotto, James John Ashton, Tracy A F Coelho, R Mark Beattie, Ben D. MacArthur, Sarah A. Ennis

Scientific Reports 2017 cited by 157

Medicine & Health Inflammatory Bowel Disease partially solved importance 4/10 scale stated

Accurate diagnosis of paediatric inflammatory bowel disease subtypes (Crohn's, ulcerative colitis, IBDU) is needed for prompt, effective treatment.

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

Maize yield forecasting by linear regression and artificial neural networks in Jilin, China

Kanichiro Matsumura, Carlos F. Gaitán, K. SUGIMOTO, Alex J. Cannon, William W. Hsieh

The Journal of Agricultural Science 2014 cited by 100

Climate & Environment Smart Agriculture and AI partially solved importance 4/10 scale stated

Forecasting maize yields in Jilin, China from 1962-2004 using climate conditions and fertilizer data, which is hard to obtain.

Details Source Open-access PDF model: artificial neural networks

Using machine learning to partition streaming programs

Zheng Wang, Michael O’Boyle

ACM Transactions on Architecture and Code Optimization 2013 cited by 46

Software & Coding Parallel Computing and Optimization Techniques ai assisted importance 4/10 scale stated

Automatically partitioning streaming parallel programs for multicore systems is difficult because the ideal mapping depends on both the program and the underlying architecture.

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

Ensemble deep learning model for protein secondary structure prediction using NLP metrics and explainable AI

Uvarani Vignesh, R. Parvathi, K. Gokul Ram

Results in Engineering 2024 cited by 24

Biology & Life Sciences Protein Structure and Dynamics partially solved importance 4/10 scale stated

Predicting protein secondary structure accurately is difficult, and better predictions are needed to understand protein function.

Details Source Open-access PDF model: Ensemble deep learning model (components n...

Prediction of Three-Directional Ground Reaction Forces during Walking Using a Shoe Sole Sensor System and Machine Learning

Takeshi Yamaguchi, Yuya Takahashi, Yoshihiro Sasaki

Sensors 2023 cited by 20

Medicine & Health Lower Extremity Biomechanics and Pathologies partially solved importance 4/10 scale stated

Measuring three-directional ground reaction forces during walking normally requires lab force plates; a wearable shoe sole sensor system was developed to predict them.

Details Source Open-access PDF model: Multiple linear regression and Gaussian pr...

Deep learning acceleration of multiscale superresolution localization photoacoustic imaging

Jongbeom Kim, Gyuwon Kim, Lei S. Li, Pengfei Zhang, Jin Young Kim, Yeonggeun Kim, Hyung Ham Kim, Lihong V. Wang et al.

Light Science & Applications 2022 cited by 126

Medicine & Health Photoacoustic and Ultrasonic Imaging solved importance 4/10 scale stated

Superresolution photoacoustic imaging achieves very high spatial resolution but requires superimposing many raw image frames, sacrificing temporal resolution and making imaging slow.

Details Source Open-access PDF model: deep neural networks (DNNs), not further s...

Automated claustrum segmentation in human brain MRI using deep learning

Hongwei Li, Aurore Menegaux, Benita Schmitz‐Koep, Antonia Neubauer, Felix J.B. Bäuerlein, Suprosanna Shit, Christian Sorg, Bjoern H. Menze et al.

Human Brain Mapping 2021 cited by 28

Medicine & Health Medical Image Segmentation Techniques partially solved importance 4/10 scale stated

The claustrum's delicate, sheet-like structure made it infeasible to segment with conventional methods in human brain MRI, limiting in vivo research.

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

Automated robot‐assisted surgical skill evaluation: Predictive analytics approach

Mahtab Jahanbani Fard, Sattar Ameri, R. Darin Ellis, Ratna Babu Chinnam, Abhilash K. Pandya, Michael D. Klein

International Journal of Medical Robotics and Computer Assisted Surgery 2017 cited by 188

Robotics & Autonomy Surgical Simulation and Training partially solved importance 4/10 scale stated

Surgical skill assessment has predominantly been subjective, lacking objective automated evaluation of surgeon performance.

Details Source Open-access PDF model: k-nearest neighbours, logistic regression,...

Deep reinforcement learning for multi-agent coordination

Kehinde O. Aina, Sehoon Ha

Artificial Life and Robotics 2025 cited by 8

Robotics & Autonomy Reinforcement Learning in Robotics partially solved importance 4/10 scale stated

Multiple robots working in narrow, confined environments suffer congestion and interference that hinder collective task performance, and coordinating them without explicit communication is hard.

Details Source Open-access PDF model: S-MADRL (stigmergic multi-agent deep RL wi...

Financial Statement Fraud Detection Through an Integrated Machine Learning and Explainable AI Framework

Gunjargal Lkhagvadorj, Tsolmon Sodnomdavaa

Journal of risk and financial management 2025 cited by 10

Business & Industry Financial Distress and Bankruptcy Prediction partially solved importance 4/10 scale stated

Detecting financial statement fraud is hard under weak regulatory oversight, information asymmetry and severe class imbalance.

Details Source Open-access PDF model: Stacking Ensemble (Random Forest, XGBoost,...

Enriching automatic test case generation by extracting relevant test inputs from bug reports

Wendkûuni C. Ouédraogo, Laura Plein, Kader Kaboré, Andrew Habib, Jacques Klein, David Lo, Tegawendé François Bissyandé

Empirical Software Engineering 2025 cited by 16

Software & Coding Software Testing and Debugging Techniques partially solved importance 4/10 scale stated

Automated test generation tools struggle to produce relevant test inputs, limiting the bug-detection effectiveness of generated tests.

Details Source Open-access PDF model: LLMs combined with traditional techniques...

<scp>GeoAnalystBench</scp> : A <scp>GeoAI</scp> Benchmark for Assessing Large Language Models for Spatial Analysis Workflow and Code Generation

Qianheng Zhang, Song Gao, Wei Chen, Yibo Zhao, Ying Nie, Ziru Chen, Shijie Chen, Yu Su et al.

Transactions in GIS 2025 cited by 16

Software & Coding Geographic Information Systems Studies partially solved importance 4/10 scale stated

It was unclear whether large language models can actually automate real-world geospatial analysis and GIS workflows, and rigorous evaluation of this capability was lacking.

Details Source Open-access PDF model: ChatGPT-4o-mini and DeepSeek-R1-7B (among...