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Subject Areas :seyed mohammad reza milani hoseini 1
1 - elmosanat
Keywords: -,
Abstract :
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References:
1. Goodfellow I., Bengio Y., and Courville A., Machine Learning Basics, Deep learning 1 ,98-164, 2016.
2. Zhang Xian-Da., Machine learning., A Matrix Alg Ebra Approach to Artificial Intelligence. Springer, Singapore, 223-440, 2020.
3. Bishop., Christopher M., Pattern Recognition and Machine Learning. Springer, 2006.
4. Szepesvári Csaba., Algorithms for Reinforcement Learning, Synthesis Lectures on Artificial Intelligence and Machine Learning 4.1,1-103, 2010.
5. Verbeeck, N., Richard M., Caprioli, and Raf Van de Plas., Unsu.
Pervised Machine Learning for Exploratory data Analysis in Imaging Mass Spectrometry, Mass Spectrometry Reviews 39.3, 245-291, 2020.
6. Leclerc P., et al., Machine Learning-based Prediction of Glioma Margin from 5-ALA Induced PpIX Fluorescence Spectroscopy, Scientific Reports 10.1, 1-9, 2020.
7. Ye Jia-Jia., Chu-Hong L., and Xing-Jiu H., Analyzing the Anodic Stripping Square Wave Voltammetry of Heavy Metal Ions Via Machine Learning: Information Beyond a Single Voltammetric Peak, Journal of Electroanalytical Chemistry, 113934, 2020.
8. Davari, S A., and Anthony S., Wexler, Quantification of Toxic Metals Using Machine Learning Techniques and Spark Emission Spectroscopy, Atmospheric Measurement Techniques 13.10, 5369-5377, 2020.
9. Chen Ch., Joeska H., and Swen R., Predicting Fishiness off-flavour and Identifying Compounds of Lipid Oxidation in Dairy Powders by SPME-GC/MS and Machine Learning, International Dairy journal 77, 19-28, 2018.
10. Tian Haojie., et al, Electromagnetic Interference Diagnosis Based on HPLC Timing Sequence Topology and Machine Learning, IOP Conference Series: Earth and Environmental Science. Vol. 371. No. 5. IOP Publishing, 2019.
11. Lebanov Leo., et al, Random Forests Machine Learning Applied to Gas Chromatography–Mass Spectrometry Derived Average Mass Spectrum data Sets for Classification and Characterisation of Essential Oils, Talanta 208, 120471, 2020.
12. Gastegger M., Jörg B., and Philipp M., Machine Learning Molecular Dynamics for the Simulation of Infrared Spectra, Chemical science, 8.10, 6924-6935, 2017.
13. Bertani F. R., et al, Optical Detection of Aflatoxins B in Grained Almonds Using Fluorescence Spectroscopy and Machine Learning Algorithms, Food Control, 112, 107073, 2020.
14 Xinhui Yu., et al., Rapid Discrimination of Coal Geographical Origin via Near-infrared Spectroscopy Combined with Machine Learning Algorithms, Infrared Physics & Technology, 105, 103180, 2020.
15. Cheng-Ming F., et al., Genome-wide Expression Analysis of Soybean MADS Genes Showing Potential Function in the Seed Development, PloS one, 8.4, e62288, 2013.
16. Ziqiang Yu., et al., Scalable Distributed Processing of K Nearest Neighbor Queries Over Moving Objects, IEEE Transactions on Knowledge and Data Engineering, 27.5, 1383-1396, 2014.
17. Goodacre Royston., et al., Rapid Identification of Streptococcus and Enterococcus Species using Diffuse Reflectance-absorbance Fourier Transform Infrared Spectroscopy and Artificial Neural Networks, FEMS Microbiology Letters, 140.2-3, 233-239, 1996.
18. Haddad El., Josette., et al., Artificial Neural Network for On-site Quantitative Analysis of Soils Using Laser Induced Breakdown Spectroscopy, Spectrochimica Acta Part B: Atomic Spectroscopy, 79, 51-57, 2013.
19.Tutu H., et al., Application of Artificial Neural Networks for Classification of Uranium Distribution in the Central Rand goldfield, South Africa, Environmental Modeling & Assessment, 10.2, 143-152, 2005.
20. Sarmanova Olga E., et al., A Method for Optical Imaging and Monitoring of the Excretion of Fluorescent Nanocomposites from the Body Using Artificial Neural Networks, Nanomedicine: Nanotechnology, Biology and Medicine, 14.4, 1371-1380, 2018.
21. Kanal I. Y., and Geoffrey R., Hutchison, Rapid Computational Optimization of Molecular Properties using Genetic Algorithms: Searching Across Millions of Compounds for Organic Photovoltaic Materials, arXiv preprint arXiv,1707.02949, 2017.
22. Kanal I. Y., et al., Efficient Computational Screening of Organic Polymer Photovoltaics, The Journal of Physical Chemistry letters, 4.10, 1613-1623, 2013.
23. Hong Ge., et al., Biomass Fuel Identification Using Flame Spectroscopy and Tree Model Algorithms, Combustion Science and Technology, 1-18, 2019.
24. Günay M., Erdem Lemi Türker., and Alper Tapan N., Decision Tree Analysis for Efficient CO2 Utilization in Electrochemical Systems, Journal of CO2 Utilization, 28, 83-95, 2018.
25. Abdullahi H. U., Usman A. G., and Abba S. I., Modelling the Absorbance of a Bioactive Compound in HPLC Method Using Artificial Neural Network and Multilinear Regression Methods, Dutse J Pure Appl Sci (DUJOPAS), 6.2, 362-371, 2020.
26. Mannodi-Kanakkithodi A., et al., Machine Learning Strategy for Accelerated Design of Polymer Dielectrics, Scientific reports 6, 20952, 2016.
27. Qisong Xu., and Jianwen J., Machine Learning for Polymer Swelling in Liquids, ACS Applied Polymer Materials, 2.8, 3576-3586, 2020.
28. Kumar J. N., et al., Machine Learning Enables Polymer Cloud-point Engineering Via Inverse Design, npj Computational Materials 5.1 ,1-6,2019.
29. Zhang Yun., and Xiaojie Xu., Machine Learning Glass Transition Temperature of Polymers, Heliyon, 6.10, e05055, 2020.
30. Xiaohong Wu., et al., Determination of Apple Varieties by Near Infrared Reflectance Spectroscopy Coupled with Improved Possibilistic Gath–Geva Clustering Algorithm. Journal of Food Processing and Preservation, 44.8, e14561, 2020.
1. Goodfellow I., Bengio Y., and Courville A., Machine Learning Basics, Deep learning 1 ,98-164, 2016.
2. Zhang Xian-Da., Machine learning., A Matrix Alg Ebra Approach to Artificial Intelligence. Springer, Singapore, 223-440, 2020.
3. Bishop., Christopher M., Pattern Recognition and Machine Learning. Springer, 2006.
4. Szepesvári Csaba., Algorithms for Reinforcement Learning, Synthesis Lectures on Artificial Intelligence and Machine Learning 4.1,1-103, 2010.
5. Verbeeck, N., Richard M., Caprioli, and Raf Van de Plas., Unsu.
Pervised Machine Learning for Exploratory data Analysis in Imaging Mass Spectrometry, Mass Spectrometry Reviews 39.3, 245-291, 2020.
6. Leclerc P., et al., Machine Learning-based Prediction of Glioma Margin from 5-ALA Induced PpIX Fluorescence Spectroscopy, Scientific Reports 10.1, 1-9, 2020.
7. Ye Jia-Jia., Chu-Hong L., and Xing-Jiu H., Analyzing the Anodic Stripping Square Wave Voltammetry of Heavy Metal Ions Via Machine Learning: Information Beyond a Single Voltammetric Peak, Journal of Electroanalytical Chemistry, 113934, 2020.
8. Davari, S A., and Anthony S., Wexler, Quantification of Toxic Metals Using Machine Learning Techniques and Spark Emission Spectroscopy, Atmospheric Measurement Techniques 13.10, 5369-5377, 2020.
9. Chen Ch., Joeska H., and Swen R., Predicting Fishiness off-flavour and Identifying Compounds of Lipid Oxidation in Dairy Powders by SPME-GC/MS and Machine Learning, International Dairy journal 77, 19-28, 2018.
10. Tian Haojie., et al, Electromagnetic Interference Diagnosis Based on HPLC Timing Sequence Topology and Machine Learning, IOP Conference Series: Earth and Environmental Science. Vol. 371. No. 5. IOP Publishing, 2019.
11. Lebanov Leo., et al, Random Forests Machine Learning Applied to Gas Chromatography–Mass Spectrometry Derived Average Mass Spectrum data Sets for Classification and Characterisation of Essential Oils, Talanta 208, 120471, 2020.
12. Gastegger M., Jörg B., and Philipp M., Machine Learning Molecular Dynamics for the Simulation of Infrared Spectra, Chemical science, 8.10, 6924-6935, 2017.
13. Bertani F. R., et al, Optical Detection of Aflatoxins B in Grained Almonds Using Fluorescence Spectroscopy and Machine Learning Algorithms, Food Control, 112, 107073, 2020.
14 Xinhui Yu., et al., Rapid Discrimination of Coal Geographical Origin via Near-infrared Spectroscopy Combined with Machine Learning Algorithms, Infrared Physics & Technology, 105, 103180, 2020.
15. Cheng-Ming F., et al., Genome-wide Expression Analysis of Soybean MADS Genes Showing Potential Function in the Seed Development, PloS one, 8.4, e62288, 2013.
16. Ziqiang Yu., et al., Scalable Distributed Processing of K Nearest Neighbor Queries Over Moving Objects, IEEE Transactions on Knowledge and Data Engineering, 27.5, 1383-1396, 2014.
17. Goodacre Royston., et al., Rapid Identification of Streptococcus and Enterococcus Species using Diffuse Reflectance-absorbance Fourier Transform Infrared Spectroscopy and Artificial Neural Networks, FEMS Microbiology Letters, 140.2-3, 233-239, 1996.
18. Haddad El., Josette., et al., Artificial Neural Network for On-site Quantitative Analysis of Soils Using Laser Induced Breakdown Spectroscopy, Spectrochimica Acta Part B: Atomic Spectroscopy, 79, 51-57, 2013.
19.Tutu H., et al., Application of Artificial Neural Networks for Classification of Uranium Distribution in the Central Rand goldfield, South Africa, Environmental Modeling & Assessment, 10.2, 143-152, 2005.
20. Sarmanova Olga E., et al., A Method for Optical Imaging and Monitoring of the Excretion of Fluorescent Nanocomposites from the Body Using Artificial Neural Networks, Nanomedicine: Nanotechnology, Biology and Medicine, 14.4, 1371-1380, 2018.
21. Kanal I. Y., and Geoffrey R., Hutchison, Rapid Computational Optimization of Molecular Properties using Genetic Algorithms: Searching Across Millions of Compounds for Organic Photovoltaic Materials, arXiv preprint arXiv,1707.02949, 2017.
22. Kanal I. Y., et al., Efficient Computational Screening of Organic Polymer Photovoltaics, The Journal of Physical Chemistry letters, 4.10, 1613-1623, 2013.
23. Hong Ge., et al., Biomass Fuel Identification Using Flame Spectroscopy and Tree Model Algorithms, Combustion Science and Technology, 1-18, 2019.
24. Günay M., Erdem Lemi Türker., and Alper Tapan N., Decision Tree Analysis for Efficient CO2 Utilization in Electrochemical Systems, Journal of CO2 Utilization, 28, 83-95, 2018.
25. Abdullahi H. U., Usman A. G., and Abba S. I., Modelling the Absorbance of a Bioactive Compound in HPLC Method Using Artificial Neural Network and Multilinear Regression Methods, Dutse J Pure Appl Sci (DUJOPAS), 6.2, 362-371, 2020.
26. Mannodi-Kanakkithodi A., et al., Machine Learning Strategy for Accelerated Design of Polymer Dielectrics, Scientific reports 6, 20952, 2016.
27. Qisong Xu., and Jianwen J., Machine Learning for Polymer Swelling in Liquids, ACS Applied Polymer Materials, 2.8, 3576-3586, 2020.
28. Kumar J. N., et al., Machine Learning Enables Polymer Cloud-point Engineering Via Inverse Design, npj Computational Materials 5.1 ,1-6,2019.
29. Zhang Yun., and Xiaojie Xu., Machine Learning Glass Transition Temperature of Polymers, Heliyon, 6.10, e05055, 2020.
30. Xiaohong Wu., et al., Determination of Apple Varieties by Near Infrared Reflectance Spectroscopy Coupled with Improved Possibilistic Gath–Geva Clustering Algorithm. Journal of Food Processing and Preservation, 44.8, e14561, 2020.