SolvedByAI

Queue

628 cases were harvested and are backed by a real paper, but they fail the test the index publishes on. Each one is listed here with the reason, so the exclusion can be checked rather than trusted.

All 628 assisted 358 not a result 183 unsupported 87
Paper Year Cited Why it is held
Evaluation of Machine Learning Algorithms for Air Quality Index (AQI) Predic... Journal of Reliability and Statistical Studies 2023 19 assisted The paper is a human-run comparative evaluation of six standard off-the-shelf ML algorithms reporting accuracy on the existing task of AQI prediction, so the AI is a component whose performance is benchmarked rather than a novel capability the paper contributes. “These machine-learning algorithms have been evaluated based on precision, recall, accuracy, etc. The result shows that Random Forest and Decision Tree...”
Using machine learning for transient classification in searches for gravitat... Monthly Notices of the Royal Astronomical Society 2020 18 assisted The ML classifier is one component in a human-run follow-up workflow, producing prioritization information for human telescope-time decisions rather than itself discovering counterparts, and the paper is a method demonstration on real and simulated data. “Towards this end, we use astrorapid, a multiband photometric light-curve classifier, to differentiate between kilonovae, supernovae, and other possibl...”
Quantifying the effects of environment and population diversity in multi-age... Autonomous Agents and Multi-Agent Systems 2022 18 assisted The paper's contribution is a human-designed empirical study quantifying how training factors (procedural generation, population size, intrinsic motivation) affect RL agents' generalization, with the agents serving as subjects of analysis rather than the AI itself producing the solved result. “we investigate and quantify the relationship between generalization and diversity in the multi-agent domain”
EssayGAN: Essay Data Augmentation Based on Generative Adversarial Networks f... Applied Sciences 2022 17 assisted EssayGAN is a data-augmentation component whose generated essays feed into a human-built and human-run AES training pipeline, rather than an AI that itself performs the end task of essay scoring. “the generated essays can be used in the supervised learning process for AES systems. Experimental results show that data augmentation using augmented...”
Forest Disturbance Monitoring Using Cloud-Based Sentinel-2 Satellite Imagery... Journal of Imaging 2024 17 assisted The random forest classifier is one component in a human-run remote-sensing workflow (field inventory data, NDVI index mapping, accuracy validation) whose overall contribution is the monitoring method, not an AI-produced end-to-end result. “a random forest machine learning classifier was applied to show the spatial distribution of dominant species”
Uncertainty Quantification for Machine Learning‐Based Ionosphere and Space W... Space Weather 2023 17 assisted The human authors implement and comparatively analyze uncertainty quantification methods around an ML forecasting model, making the AI a studied component in a human-run methodological evaluation rather than the agent producing the paper's result. “In this paper, we implement and analyze several uncertainty quantification approaches for an ML‐based model to forecast Vertical Total Electron Conten...”
Enriching automatic test case generation by extracting relevant test inputs... Empirical Software Engineering 2025 16 assisted The LLM is one component inside BRMiner, a human-designed pipeline whose extracted inputs merely feed traditional test generation tools (EvoSuite, Randoop) that actually produce the tests and detect the bugs. “leverages Large Language Models (LLMs) in combination with traditional techniques to extract relevant inputs from bug reports, thereby enhancing autom...”
Rain Discrimination with Machine Learning Classifiers for Opportunistic Rain... Sensors 2023 16 assisted The paper is a human-designed comparative evaluation of four off-the-shelf ML classifiers as one classification component inside a human-run rain-detection pipeline (data collection, preprocessing, feature extraction all done by the researchers), with the contribution being the classifier comparison rather than an AI capability that itself solved the problem end to end. “It reports a comparison among the results obtained by applying four different machine learning (ML) classifiers, namely the support vector machine (SV...”
Machine learning-driven discovery of novel therapeutic targets in diabetic f... Molecular Medicine 2024 15 assisted Machine learning feature selection was one step in a human-run multi-stage bioinformatics pipeline (differential expression, enrichment, ML, independent validation, single-cell analysis) that produced computational gene associations proposed as theoretical targets, not a demonstrated therapeutic result. “Through this integrated approach, SCUBE1 and RNF103-CHMP3 were identified as key genes significantly associated with DFU.”
Integrating machine learning and symbolic regression for predicting damage i... Scientific Reports 2025 15 assisted The ML and symbolic-regression components were guided tools inside a human-orchestrated framework of specimen fabrication, experimental testing, FEM validation, and design-of-experiments, with the resulting equation serving as a design aid rather than the AI itself delivering the end result. “Feature selection and ML models analyzed the dataset, providing insights that guided PySR in deriving interpretable equations.”
Stress-constrained optimization of multiscale structures with parameterized... Structural and Multidisciplinary Optimization 2024 15 assisted The neural networks serve as surrogate models for property and stress prediction inside a human-designed topology optimization framework, so AI is a component in a pipeline humans ran rather than the agent producing the designs. “their material properties are estimated using a neural network surrogate model for homogenized constitutive relations”
Machine Learning-Based Cost-Effective Smart Home Data Analysis and Forecasti... Buildings 2023 14 assisted The AI model is one component in a human-run workflow (sensor correlation analysis, sensor removal, cost-reduction design); the paper reports a model's RMSE on a prediction task inside a larger design methodology, and its stated contribution is a design guide rather than the AI capability itself. “This process can reduce costs by 13%, thereby providing a design guide for a cost-effective IoT system.”
Non-Perturbative Identification and Subtyping of Amyloidosis in Human Kidney... Biosensors 2023 13 assisted The ML (t-SNE/DBSCAN) is a standard analysis component inside a human-run Raman spectroscopy feasibility study on already-diagnosed biopsy samples, and the abstract only claims 'potential' for identification rather than the AI itself delivering a novel solved result. “Our approach, using Raman spectroscopy with machine learning algorithms, shows the potential for the identification of amyloid in pathologic lesions.”
Controllable Music Playlist Generation Based on Knowledge Graph and Reinforc... Sensors 2022 12 assisted The paper proposes a playlist-generation method and validates it by reporting prediction performance on existing listening-history data, i.e., a model evaluated on an existing recommendation task rather than an AI-produced result that constitutes the contribution. “We confirm the effectiveness of the proposed method by verifying the prediction performance based on listening history and the guidance performance to...”
Prediction of Temperature of Liquid Steel in Ladle Using Machine Learning Te... Coatings 2023 11 assisted Machine learning is utilized as one component within a human-developed hybrid modeling workflow (FEM solution of the Fourier equation, inverse parameter identification, production-line verification), so the AI did not itself produce the primary result. “The models were based on the FEM solution of the Fourier equation, and machine learning approaches such as decision trees, linear regression, and arti...”
Financial Statement Fraud Detection Through an Integrated Machine Learning a... Journal of risk and financial management 2025 10 assisted The ML models produce fraud-risk predictions and interpretability outputs that inform human audit decisions (framed via decision curve analysis and audit cost simulation), making this a decision-support component evaluated retrospectively rather than an AI end-to-end solving fraud detection. “Decision curve analysis showed that calibrated thresholds improved decision efficiency by 7–9% and reduced over-audit costs by 3–4%, while an audit co...”
Machine learning-based damage sensing and self-healing of carbon fiber/nylon... Structural Health Monitoring 2023 10 assisted The machine learning predicted damage locations and recommended healing parameters, but the actual self-healing was a physical process (applying electrical current for local heating) carried out by the experimental setup, making the AI an informing component in a human-run materials workflow rather than the agent that produced the healing result. “The self-healing conditions such as the current input pairs of ACN and amount of the electrical current were determined through the artificial neural...”
RetroRanker: leveraging reaction changes to improve retrosynthesis predictio... Journal of Cheminformatics 2023 10 assisted RetroRanker is a re-ranking module layered on top of existing retrosynthesis models rather than a system that performs retrosynthesis end to end, and the reported contribution is improved benchmark accuracy for other models' candidate lists. “designed to mitigate the frequency bias in predictions of existing retrosynthesis models through re-ranking”
Automated Essay Scoring Using Transformer Models Psych 2021 9 assisted The paper's contribution is a methodological comparison of transformer vs. BOW models on a manually labeled classification task, with the AI positioned as a tool to help human raters rather than an agent that itself solved a problem end to end. “Further, we show how such models can help increase the accuracy of human raters, and we provide a detailed instruction on how to implement transformer...”
Reinforcement learning for mutation operator selection in automated program... Automated Software Engineering 2025 9 assisted The reinforcement learning agent is only a component (mutation operator selection) inside a heuristic program repair pipeline, and it did not improve the end task of patching bugs compared to random selection. “does not exhibit a noticeable improvement in the number of bugs patched in comparison with the baseline, uniform random selection”
Interpretable Machine Learning for High-Accuracy Reservoir Temperature Predi... Energies 2025 9 assisted The paper's contribution is a human-run comparative benchmark of ML models reporting accuracy metrics on an existing prediction task, not an AI-produced result that itself constitutes the solved problem. “This study conducts a comprehensive comparative analysis of advanced machine learning models... to evaluate their predictive performance for reservoir...”
Evaluating machine learning models for supernova gravitational wave signal c... Machine Learning Science and Technology 2024 7 assisted This is a human-run comparative evaluation of many ML models' classification accuracy on simulated GW data rather than an AI-produced scientific result; the models are the object of study, and no real EOS determination or discovery was made by an AI system. “Our study builds on previous work by examining how different machine learning models, parameters, and data preprocessing techniques impact classificat...”
Innovations in movement and behavioural ecology from camera traps: Day range... Methods in Ecology and Evolution 2021 43 assisted The machine learning behavior classifier is one component inside a human-designed statistical pipeline for estimating day range; the paper's contribution is the DR estimation methodology (DRRB), not an AI end-to-end solving of the task. “DRRB (i.e. estimating DR based on the classification of movement behaviours by a machine learning procedure and the ratio between speeds)”
Crop Yield Prediction Using Hybrid Machine Learning Approach: A Case Study o... Agriculture 2023 40 assisted The abstract reports comparative fit statistics of human-designed ML models on an existing yield-prediction task, making it a methodological model-comparison study rather than an AI-produced result that itself constitutes the paper's contribution. “The performances of the algorithms are com-pared on different fit statistics such as RMSE, MAD, MAPE, etc., using numeric agronomic traits of 518 lent...”
Application of Machine Learning to Bending Processes and Material Identifica... Metals 2021 35 assisted The shallow ANNs are human-designed surrogate predictors trained on human-run FEM simulations and embedded in human-led material-identification and bending-process workflows, so the AI functions as an assisting tool within a pipeline humans ran and completed rather than as the agent that end-to-end solved the problem. “The required data for designing the ANN solutions are collected from numerical simulation using finite element methodology (FEM), which in turn was va...”
Knowledge Discovery on Cryptocurrency Exchange Rate Prediction Using Machine... Sensors 2022 34 assisted The abstract reports XGBoost's prediction accuracy on the pre-existing task of cryptocurrency exchange-rate prediction, with the model acting as the predictive stage of a human-designed data-mining/blockchain pipeline rather than delivering an end-to-end capability that itself constitutes the paper's contribution. “The applied machine learning algorithm is XGBoost, which performs the highest prediction output, after accuracy measurement performance.”
Microbiome Preprocessing Machine Learning Pipeline Frontiers in Immunology 2021 31 assisted ML classifiers serve only as the evaluation instrument inside a human-designed study of preprocessing steps, whose actual contribution is a preprocessing pipeline/tool rather than an AI-produced result. “We computed the contribution of each step to the accuracy as measured by the Area Under Curve (AUC) of the classification.”
Bars formed in galaxy merging and their classification with deep learning Astronomy and Astrophysics 2020 29 assisted The CNN is an image-classification tool inside a human-run pipeline of N-body simulations, and the paper's contribution is the astrophysical finding about bar formation, not the AI's capability itself. “We look to investigate bar formation in galaxy merging using methods from deep learning to analyse our N-body simulations.”
Pre-service Language Teachers’ Task-specific Large Language Model Prompting... RELC Journal 2025 25 assisted The LLMs served as tools that assisted pre-service teachers in a human-run workflow of lesson plan improvement, and the paper's contribution is a qualitative description of those human prompting practices, not an AI-produced result. “The current study explored how pre-service language teachers interacted with LLMs to assist them in improving lesson plans”
Neural network potential energy surface for the low temperature ring polymer... The Journal of Chemical Physics 2021 25 assisted The neural network serves as a fitting tool to construct the potential energy surface, but the primary scientific results (rate constants, mechanism analysis) come from QCT and RPMD simulations that the researchers ran on that surface, making the AI one component in a human-driven workflow. “Quasi-classical trajectory (QCT) and ring polymer molecular dynamics (RPMD) studies have been carried on this PES to evaluate the rate constant temper...”
Prediction of Poststroke Depression Based on the Outcomes of Machine Learnin... Journal of Clinical Medicine 2022 24 assisted This is a human-designed and human-run clinical study in which standard machine learning algorithms served as the analytical method on researcher-collected assessment data, producing accuracy metrics that informed the authors' clinical conclusion about PSD predictability rather than an AI system autonomously solving the problem end to end. “PSD was successfully predicted using a support vector machine with a radial basis function kernel function (area under curve (AUC) = 0.711, accuracy =...”
Data imputation strategies for process optimization of laser powder bed fusi... Materials Science in Additive Manufacturing 2023 22 assisted The ML imputation methods (kNN, MICE, GINN) are components investigated within a human-run data-collation and evaluation workflow, with the paper's contribution being the human-performed method comparison and ensemble strategy rather than an AI end-to-end solving of a task. “Various imputation techniques of missing data of the SLM Ti6Al4V dataset, such as the k-nearest neighbor (kNN), multivariate imputation by chained equ...”
Study on growth rate of TiO <sub>2</sub>... Surface and Interface Analysis 2013 20 assisted The ANN is one modeling component within a human-run workflow (sol-gel experiments, MD simulation, AFM/SEM characterization) and merely fits experimental data to predict film thickness, with the paper's contribution being the film characterization rather than the AI capability itself. “the outputs from experimental data were analyzed by using the ANN in order to investigate the effects of deposition process parameters on the film thi...”
Predicting Online Shopping Behavior: Using Machine Learning and Google Analy... Applied Sciences 2024 18 assisted The ML models classify engagement levels as one component, but the paper's stated primary contribution is generating recommendations for human marketers and decision-makers, so the AI output informs humans rather than solving the problem itself. “Primarily, this paper goas is to generate a series of recommendations to help the decision-makers and marketers optimizing the marketing strategies.”
Prognosis Essay Scoring and Article Relevancy Using Multi-Text Features and... Symmetry 2017 15 assisted The paper's primary result is a proposed model's accuracy metrics (Cohen's kappa) on existing benchmark datasets relative to prior approaches, i.e., a prediction component intended to automate/assist human evaluation rather than an AI-produced standalone result. “The model achieves kappa values of 0.80 and 0.83 for the first and second data sets, respectively. Kappa values show that the proposed model has bette...”
Land Cover Mapping from Colorized CORONA Archived Greyscale Satellite Data a... Land 2021 14 assisted The deep learning component (DeOldify) only colorized the greyscale image as a preprocessing step, while the actual land cover classification result was produced by a human-run pipeline of geometric correction, segmentation, training-sample selection, and an SVM classifier. “The DeOldify Deep learning method embedded in the MyHeritage platform was initially applied to colourise the CORONA image.”
Radio Galaxy Zoo: giant radio galaxy classification using multidomain deep l... Monthly Notices of the Royal Astronomical Society 2021 assisted The abstract reports a human-run method evaluation, comparing multi-domain vs. single-domain CNN classification accuracy on an existing labeled task, rather than an AI-produced scientific result such as newly discovered giant radio galaxies, so the AI is the object of study inside a human-driven workflow. “the inclusion of multi-resolution survey data results in correction of 39% of the misclassifications seen from equivalent single domain networks for t...”
Rate coefficients of the H + H2O2 → H2 + HO2 reaction on an accurate fundame... The Journal of Chemical Physics 2018 24 assisted The neural network serves only as the potential energy surface input to human-run dynamics methods (RPMD, QCT, CVT/SCT) that actually computed the rate coefficients, making the AI one component in a larger simulation workflow. “calculated using the ring polymer molecular dynamics (RPMD), quasi-classical trajectory (QCT), and canonical variational transition state theory (CVT)...”