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Fisheye freshness detection using common deep learning algorithms and machine learning methods with a developed mobile application
AbstractFish is commonly ingested as a source of protein and essential nutrients for humans. To fully benefit from the proteins and substances in...
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Modeling of Oil Bitumen Quality Parameters Using Machine Learning Algorithms
The paper considers approaches, principles, and results of modeling the quality parameters of petroleum bitumen using machine learning algorithms...
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Prediction of soil thermal conductivity using individual and ensemble machine learning models
Soil thermal conductivity ( λ ) is an important parameter in thermal calculation and temperature-field analysis in geotechnical engineering. To...
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DFT and machine learning for predicting hydrogen adsorption energies on rocksalt complex oxides
The prediction of hydrogen adsorption energies on complex oxides by integrating DFT calculations and machine learning is considered. In particular,...
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Progresses and Challenges of Machine Learning Approaches in Thermochemical Processes for Bioenergy: A Review
Thermochemical conversions of nonedible biomass into energy are promising alternatives for ensuring a sustainable energy society. However,...
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Hyperparameter Optimized Rapid Prediction of Sea Bass Shelf Life with Machine Learning
The article focuses on the importance of sea bass, which is preferred by consumers in Turkey and worldwide. However, seafood can deteriorate rapidly...
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Identification of liquor adulteration based on machine learning and electrochemical sensor
This study introduces a novel approach to detecting liquor adulteration using machine learning algorithms in conjunction with electrochemical...
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The adaptive kernel-based extreme learning machine for state of charge estimation
The state of charge (SOC) is a key factor in the battery management, and the accuracy of its estimation plays an important role in battery-life...
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Machine learning for optical chemical multi-analyte imaging
Simultaneous sensing of metabolic analytes such as pH and O 2 is critical in complex and heterogeneous biological environments where analytes often...
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Supervised Machine Learning Mode for Predicting Gas-Liquid Flow Patterns in Upward Inclined Pipe
Accurate identification of gas-liquid two-phase flow patterns during oil and gas drilling is critical to analyzing bottom hole pressure, detecting...
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The Goldilocks paradigm: comparing classical machine learning, large language models, and few-shot learning for drug discovery applications
Recent advances in machine learning (ML) have led to newer model architectures including transformers (large language models, LLMs) showing state of...
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Comprehensive machine learning boosts structure-based virtual screening for PARP1 inhibitors
Poly ADP-ribose polymerase 1 (PARP1) is an attractive therapeutic target for cancer treatment. Machine-learning scoring functions constitute a...
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Predicting element concentrations by machine learning models in neutron activation analysis
Applications for machine learning (ML), deep learning, and other artificial intelligence models have shown great promise in nuclear physics,...
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Rapid detection of carbendazim residue in tea by machine learning assisted electrochemical sensor
The presence of pesticide residues in agricultural products, such as tea, poses significant health risks to consumers and has become a major concern...
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Tomato ripeness and shelf-life prediction system using machine learning
This study proposes an ensemble approach to develop a tomato ripeness and shelf life prediction system based on defects and color intensity. The...
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Data and Machine Learning in Polymer Science
Data-driven innovation has shown great power in solving problems in multifactor correlation, convergence and optimization, synergistic and...
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AutoTemplate: enhancing chemical reaction datasets for machine learning applications in organic chemistry
AbstractThis paper presents AutoTemplate, an innovative data preprocessing protocol, addressing the crucial need for high-quality chemical reaction...
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Consensus holistic virtual screening for drug discovery: a novel machine learning model approach
In drug discovery, virtual screening is crucial for identifying potential hit compounds. This study aims to present a novel pipeline that employs...
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Training of Machine Learning Potentials for the Modeling of Nucleation in Graphite
AbstractThe parameterization of machine learning potentials (MLP) for precise characterization of the interaction between carbon atoms in graphite...
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Machine Learning for Biological Design
We briefly present machine learning approaches for designing better biological experiments. These approaches build on machine learning predictors and...