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Seyedeh Bentolhoda Hosseinian, Milad Ghani, Jahan Bakhsh Raoof
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Experimental design and optimization
10.1016/s0169-7439(98)00065-3 · 1998
Response surface methodology (RSM) as a tool for optimization in analytical chemistry
10.1016/j.talanta.2008.05.019 · 2008
Application of factorial and response surface methodology in modern experimental design and optimization
10.1080/10408340600969478 · 2006
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2024
Status and future trends in wastewater management strategies using artificial intelligence and machine learning techniques
10.1016/j.chemosphere.2024.142477 · 2024
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Artificial intelligence and machine learning techniques for suicide prediction: integrating dietary patterns and environmental contaminants
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AI vision and machine learning for enhanced automation in food industry: a systematic review
10.1016/j.foohum.2025.100587 · 2025
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10.1039/d3ay00362k · 2023
Smart Analytical chemistry: advances of artificial intelligence (AI) and machine learning (ML) in analytical and bioanalytical research-A review
2026
Simulation for response surface in the HPLC optimization method development using artificial intelligence models: a data-driven approach
10.1016/j.chemolab.2020.104007 · 2020
Accelerated chemical science with AI
10.1039/d3dd00213f · 2024
Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost
10.1038/s41467-020-16201-z · 2020
Understanding of machine learning with deep learning: architectures, workflow, applications and future directions
10.3390/computers12050091 · 2023
Unresolved referenced work
2020
Does machine learning really work?
1997
Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence
10.1007/s10928-020-09685-1 · 2020
Supervised machine learning algorithms: classification and comparison
10.14445/22312803/ijctt-v48p126 · 2017
Unsupervised learning
10.1162/neco.1989.1.3.295 · 1989
Artificial neural networks: fundamentals, computing, design, and application
10.1016/s0167-7012(00)00201-3 · 2000
What is machine learning, artificial neural networks and deep learning?—examples of practical applications in medicine
10.3390/diagnostics13152582 · 2023
Siri, Siri, in my hand: who’s the fairest in the land? on the interpretations, illustrations, and implications of artificial intelligence
10.1016/j.bushor.2018.08.004 · 2019
An ontol. Adapt. Pers. E-learning syst. assist. By Softw. agents cloud storage
2015
Emerging trends in artificial intelligence
2019
A brief history of artificial intelligence: on the past, present, and future of artificial intelligence
10.1177/0008125619864925 · 2019
Unresolved referenced work
Kept as external metadata until matched
Applications of computational chemistry, artificial intelligence, and machine learning in aquatic chemistry research
10.1016/j.cej.2021.131810 · 2021
Integrated omics: tools, advances and future approaches
10.1530/jme-18-0055 · 2019
Using machine learning approaches for multi-omics data analysis: a review
10.1016/j.biotechadv.2021.107739 · 2021
Current advances in solid-phase microextraction technique as a green analytical sample preparation approach
10.1080/17518253.2025.2486135 · doi-reference
Analytical methods for determination of opioids in biological samples (2014-2025): a green chemistry perspective
10.1016/j.trac.2026.118892 · doi-reference
Artificial intelligence, machine learning, and drug repurposing in cancer
10.1080/17460441.2021.1883585 · doi-reference
Explainable AI to facilitate understanding of neural network-based metabolite profiling using NMR spectroscopy
10.3390/metabo14060332 · doi-reference
DeepSpectra: an end-to-end deep learning approach for quantitative spectral analysis
10.1016/j.aca.2019.01.002 · doi-reference
Deep convolutional autoencoder for the simultaneous removal of baseline noise and baseline drift in chromatograms
10.1016/j.chroma.2021.462093 · doi-reference
Unsupervised feature-learning for hyperspectral data with autoencoders
10.3390/rs11070864 · doi-reference
Small data, big challenges: machine-and deep-learning strategies for data-limited drug discovery
10.1016/j.addr.2025.115762 · doi-reference
Deep learning modelling techniques: current progress, applications, advantages, and challenges
10.1007/s10462-023-10466-8 · doi-reference
Recent advances and applications of deep learning methods in materials science
10.1038/s41524-022-00734-6 · doi-reference
Machine learning in gas separation membrane developing: ready for prime time
10.1016/j.seppur.2023.123493 · doi-reference
Application of deep learning and machine learning models with enhanced feature extraction for the prediction of plant extraction yields using supercritical CO2: an optimization and comparative analysis
10.1016/j.supflu.2025.106755 · doi-reference
Evaluation of VOCs from fungal strains, building insulation materials and indoor air by solid phase microextraction arrow, thermal desorption–gas chromatography-mass spectrometry and machine learning approaches
10.1016/j.envres.2023.115494 · doi-reference
Development of nontargetd volatilomics with solid-phase microextraction for the authentication of plant-based milk alternatives
10.1016/j.talanta.2025.128239 · doi-reference
Response surface methodology for process optimization in livestock wastewater treatment: a review
10.1016/j.heliyon.2024.e30326 · doi-reference
Dissolvable layered double hydroxide coated magnetic nanoparticles for extraction followed by high performance liquid chromatography for the determination of phenolic acids in fruit juices
10.1016/j.chroma.2014.09.024 · doi-reference
Metal-organic framework mixed-matrix disks: versatile supports for automated solid-phase extraction prior to chromatographic separation
10.1016/j.chroma.2017.01.069 · doi-reference
Solid supports and New advanced materials used in microextraction processes metal-organic framework-based solid-phase microextraction for air samples analysis: a mini-review
10.1016/j.trac.2024.118021 · doi-reference
Magnetic sorbents: synthetic pathways and application in dispersive (micro) extraction techniques for bioanalysis
10.1016/j.trac.2023.117486 · doi-reference
Enhanced machine learning prediction of biochar adsorption for dyes: parameter optimization and experimental validation
10.1007/s44246-025-00213-9 · doi-reference
Recent trends in synthesis and application of 2D mxene-based nanomaterials for microextraction purposes
10.1016/j.microc.2025.113788 · doi-reference
Extraction of phenolic acids and tetramethylpyrazine in Shanxi aged vinegar base on vortex-assisted liquid-liquid microextraction-hydrophobic deep eutectic solvent: COSMO-RS calculations and ANN-GA optimization
10.1016/j.foodchem.2024.141353 · doi-reference
Enhancing food quality analysis: the transformative role of artificial neural networks in modern analytical techniques
10.1080/10408347.2025.2554239 · doi-reference
Rapid extraction of copper ions in water, tea, milk and apple juice by solvent-terminated dispersive liquid–liquid microextraction using p-sulfonatocalix (4) arene: optimization by artificial neural networks coupled bat inspired algorithm and response su
10.1007/s13197-019-03892-6 · doi-reference
Optimization of solvent terminated dispersive liquid–liquid microextraction of copper ions in water and food samples using artificial neural networks coupled bees algorithm
10.1007/s00128-017-2263-7 · doi-reference
SUSSOL—Using artificial intelligence for greener solvent selection and substitution
10.3390/molecules25133037 · doi-reference
Solvent screening for separation processes using machine learning and high-throughput technologies
10.1021/cbe.4c00170 · doi-reference
Recent trends in microextraction techniques employed in analytical and bioanalytical sample preparation
10.3390/separations4040036 · doi-reference
Simultaneous extraction of Cu2+ and Cd2+ ions in water, wastewater, and food samples using solvent-terminated dispersive liquid–liquid microextraction: optimization by multiobjective evolutionary algorithm based on decomposition
10.1007/s10661-019-7383-6 · doi-reference
Particle swarm optimization–artificial neural network modeling and optimization of leachable zinc from flour samples by miniaturized homogenous liquid–liquid microextraction
10.1016/j.jfca.2013.11.002 · doi-reference
Support vector models-based quantitative structure–retention relationship (QSRR) in the development and validation of RP-HPLC method for multi-component analysis of anti-diabetic drugs
10.1007/s10337-023-04292-x · doi-reference
Deep learning in spectral analysis: modeling and imaging
10.1016/j.trac.2024.117612 · doi-reference
ANN-assisted forecasting of adsorption efficiency to remove heavy metals
10.3906/kim-1902-28 · doi-reference
Application of complex systems topologies in artificial neural networks optimization: an overview
10.1016/j.eswa.2021.115073 · doi-reference
Magnetic hydrophobic deep eutectic Solvent-based microextraction for rapid and simultaneous detection of sulfonamide and estrogen residues in milk using high-performance liquid chromatography-diode array detection
10.1016/j.microc.2025.114107 · doi-reference
Sensitive quantification of acetochlor and metolachlor in water using Taguchi-optimized DLLME coupled with high-performance liquid chromatography
10.1016/j.microc.2024.110499 · doi-reference
Application of hollow fiber-protected liquid-phase microextraction combined with GCMS– in determining endrin, chlordane, and dieldrin in rice samples
10.1007/s10653-023-01570-3 · doi-reference
Optimization of a macro-element extraction system from cocoa honey samples using ultrasound and Doehlert design with multiple responses
10.1016/j.foodchem.2025.143058 · doi-reference
Factorial design optimization of dispersive liquid–liquid microextraction for analysis of metals in natural and drinking waters
10.1016/j.microc.2022.108029 · doi-reference
MOF-199@ tryptophan coated on magnetic bar for stir-bar sorptive extraction of warfarin and aspirin from biological samples followed by quantification through HPLC-UV
10.1016/j.jchromb.2025.124877 · doi-reference