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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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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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Deep learning for water quality
Understanding and predicting the quality of inland waters are challenging, particularly in the context of intensifying climate extremes expected in...
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A deep ensemble learning method for cherry classification
In many agricultural products, information technologies are utilized in classification processes at the desired quality. It is undesirable to mix...
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Deep Kernel learning for reaction outcome prediction and optimization
Recent years have seen a rapid growth in the application of various machine learning methods for reaction outcome prediction. Deep learning models...
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Machine learning in preclinical drug discovery
Drug-discovery and drug-development endeavors are laborious, costly and time consuming. These programs can take upward of 12 years and cost US $2.5...
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Structure-based, deep-learning models for protein-ligand binding affinity prediction
The launch of AlphaFold series has brought deep-learning techniques into the molecular structural science. As another crucial problem,...
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Deep Learning-Enabled Image Classification for the Determination of Aluminum Ions
AbstractIn this work, an image classification based on deep learning for quantitative field determination of aluminum ions (Al 3+ ) was developed....
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Evaluation of polymer electrolyte membrane electrolysis by explainable machine learning, optimum classification model, and active learning
In this work, a database of 789 experimental points extracted from 30 academic publications was used. The primary objective was to use novel...
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Classification of hazelnut varieties based on bigtransfer deep learning model
Hazelnut is an agricultural product that contributes greatly to the economy of the countries where it is grown. The human factor plays a major role...
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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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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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Meta-learning-based Inductive logistic matrix completion for prediction of kinase inhibitors
AbstractProtein kinases become an important source of potential drug targets. Developing new, efficient, and safe small-molecule kinase inhibitors...
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Unsupervised manifold embedding to encode molecular quantum information for supervised learning of chemical data
Molecular representation is critical in chemical machine learning. It governs the complexity of model development and the fulfillment of training...
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Deep Learning-Based Automated Analysis of NK Cell Cytotoxicity in Single Cancer Cell Arrays
The cytotoxicity assay of immune cells based on live cell imaging offers comprehensive information at the single cell-level information, but the data...
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Discovery of potent inhibitors of α-synuclein aggregation using structure-based iterative learning
Machine learning methods hold the promise to reduce the costs and the failure rates of conventional drug discovery pipelines. This issue is...
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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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Comparison of reinforcement learning techniques for controlling a CSTR process
One of the main promises of Industry 4.0 is the incorporation of computational intelligence techniques in industrial process control. For the...
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Hierarchical Molecular Graph Self-Supervised Learning for property prediction
Molecular graph representation learning has shown considerable strength in molecular analysis and drug discovery. Due to the difficulty of obtaining...
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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...
