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Usman J, Abba SI, Baig N, Abu-Zahra N, Hasan SW, Aljundi IH. Design and Machine Learning Prediction of In Situ Grown PDA-Stabilized MOF (UiO-66-NH 2) Membrane for Low-Pressure Separation of Emulsified Oily Wastewater. ACS APPLIED MATERIALS & INTERFACES 2024; 16:16271-16289. [PMID: 38514254 DOI: 10.1021/acsami.4c00752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/23/2024]
Abstract
Significant progress has been made in designing advanced membranes; however, persistent challenges remain due to their reduced permeation rates and a propensity for substantial fouling. These factors continue to pose significant barriers to the effective utilization of membranes in the separation of oil-in-water emulsions. Metal-organic frameworks (MOFs) are considered promising materials for such applications; however, they encounter three key challenges when applied to the separation of oil from water: (a) lack of water stability; (b) difficulty in producing defect-free membranes; and (c) unresolved issue of stabilizing the MOF separating layer on the ceramic membrane (CM) support. In this study, a defect-free hydrolytically stable zirconium-based MOF separating layer was formed through a two-step method: first, by in situ growth of UiO-66-NH2 MOF into the voids of polydopamine (PDA)-functionalized CM during the solvothermal process, and then by facilitating the self-assembly of UiO-66-NH2 with PDA using a pressurized dead-end assembly. A stable MOF separating layer was attained by enriching the ceramic support with amines and hydroxyl groups using PDA, which assisted in the assembly and stabilization of UiO-66-NH2. The PDA-s-UiO-66-NH2-CM membrane displayed air superhydrophilicity and underwater superoleophobicity, demonstrating its oil resistance and high antifouling behavior. The PDA-s-UiO-66-NH2-CM membrane has shown exceptionally high permeability and separation capacity for challenging oil-in-water emulsions. This is attributed to numerous nanochannels from the membrane and its high resistance to oil adhesion. The membranes showed excellent stability over 15 continuous test cycles, which indicates that the developed MOFs separating layers have a low tendency to be clogged by oil droplets during separation. Machine learning-based Gaussian process regression (GPR) models as nonparametric kernel-based probabilistic models were employed to predict the performance efficiency of the PDA-s-UiO-66-NH2-CM membrane in oil-in-water separation. The outcomes were compared with the support vector machine (SVM) and decision tree (DT) algorithm. This efficiency includes various metrics related to its separation accuracy, and the models were developed through feature engineering to identify and utilize the most significant factors affecting the membrane's performance. The results proved the reliability of GPR optimization with the highest prediction accuracy in the validation phase. The average percentage increase of the GPR model compared to the SVM and DT model was 6.11 and 42.94%, respectively.
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Affiliation(s)
- Jamilu Usman
- Interdisciplinary Research Centre for Membranes and Water Security (IRC-MWS), King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
| | - Sani I Abba
- Interdisciplinary Research Centre for Membranes and Water Security (IRC-MWS), King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
| | - Nadeem Baig
- Interdisciplinary Research Centre for Membranes and Water Security (IRC-MWS), King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
| | - Nidal Abu-Zahra
- Materials Science and Engineering Department, University of Wisconsin-Milwaukee, 3200 North Cramer Street, Milwaukee, Wisconsin 53201, United States
| | - Shadi W Hasan
- Center for Membranes and Advanced Water Technology (CMAT), Department of Chemical and Petroleum Engineering, Khalifa University of Science and Technology, P.O. Box 127788 Abu Dhabi, United Arab Emirates
| | - Isam H Aljundi
- Interdisciplinary Research Centre for Membranes and Water Security (IRC-MWS), King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
- Chemical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
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Uzun Ozsahin D, Duwa BB, Ozsahin I, Uzun B. Quantitative Forecasting of Malaria Parasite Using Machine Learning Models: MLR, ANN, ANFIS and Random Forest. Diagnostics (Basel) 2024; 14:385. [PMID: 38396424 PMCID: PMC10888406 DOI: 10.3390/diagnostics14040385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 01/23/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
Malaria continues to be a major barrier to socioeconomic development in Africa, where its death rate is over 90%. The predictive power of many machine learning models-such as multi-linear regression (MLR), artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFISs) and Random Forest classifier-is investigated in this study using data from 2207 patients. The dataset was reduced from the initial dataset of thirty-two criteria samples to fifteen. Assessment measures such as the root mean square error (RMSE), mean square error (MSE), coefficient of determination (R2), and adjusted correlation coefficient R were used. ANFIS, Random Forest, MLR, and ANN are among the models. After training, ANN outperforms ANFIS (97%), MLR (92%), and Random Forest (68%) with the greatest R (99%) and R2 (99%), respectively. The testing stage confirms the superiority of ANN. The paper also presents a statistical forecasting sheet with few errors and excellent accuracy for MLR models. When the models are assessed with Random Forest, the latter shows the least results, thus broadening the modeling techniques and offering significant insights into the prediction of malaria and healthcare decision making. The outcomes of using machine learning models for precise and efficient illness prediction add to an expanding body of knowledge, assisting healthcare systems in making better decisions and allocating resources more effectively.
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Affiliation(s)
- Dilber Uzun Ozsahin
- Department of Medical Diagnostic Imaging, College of Health Science, University of Sharjah, Sharjah 27272, United Arab Emirates
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey; (B.B.D.); (I.O.)
| | - Basil Barth Duwa
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey; (B.B.D.); (I.O.)
| | - Ilker Ozsahin
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey; (B.B.D.); (I.O.)
- Brain Health Imaging Institute, Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA
| | - Berna Uzun
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey; (B.B.D.); (I.O.)
- Department of Mathematics, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
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Chong JWR, Tang DYY, Leong HY, Khoo KS, Show PL, Chew KW. Bridging artificial intelligence and fucoxanthin for the recovery and quantification from microalgae. Bioengineered 2023; 14:2244232. [PMID: 37578162 PMCID: PMC10431731 DOI: 10.1080/21655979.2023.2244232] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 07/30/2023] [Accepted: 07/31/2023] [Indexed: 08/15/2023] Open
Abstract
Fucoxanthin is a carotenoid that possesses various beneficial medicinal properties for human well-being. However, the current extraction technologies and quantification techniques are still lacking in terms of cost validation, high energy consumption, long extraction time, and low yield production. To date, artificial intelligence (AI) models can assist and improvise the bottleneck of fucoxanthin extraction and quantification process by establishing new technologies and processes which involve big data, digitalization, and automation for efficiency fucoxanthin production. This review highlights the application of AI models such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS), capable of learning patterns and relationships from large datasets, capturing non-linearity, and predicting optimal conditions that significantly impact the fucoxanthin extraction yield. On top of that, combining metaheuristic algorithm such as genetic algorithm (GA) can further improve the parameter space and discovery of optimal conditions of ANN and ANFIS models, which results in high R2 accuracy ranging from 98.28% to 99.60% after optimization. Besides, AI models such as support vector machine (SVM), convolutional neural networks (CNNs), and ANN have been leveraged for the quantification of fucoxanthin, either computer vision based on color space of images or regression analysis based on statistical data. The findings are reliable when modeling for the concentration of pigments with high R2 accuracy ranging from 66.0% - 99.2%. This review paper has reviewed the feasibility and potential of AI for the extraction and quantification purposes, which can reduce the cost, accelerate the fucoxanthin yields, and development of fucoxanthin-based products.
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Affiliation(s)
- Jun Wei Roy Chong
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih, Selangor Darul Ehsan, Malaysia
| | - Doris Ying Ying Tang
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih, Selangor Darul Ehsan, Malaysia
| | - Hui Yi Leong
- ISCO (Nanjing) Biotech-Company, Nanjing, Jiangning, China
| | - Kuan Shiong Khoo
- Department of Chemical Engineering and Materials Science, Yuan Ze University, Taoyuan, Taiwan
- Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute, Chettinad Academy of Research and Education, Kelambakkam, Tamil Nadu, India
| | - Pau Loke Show
- Department of Chemical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Kit Wayne Chew
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore, Singapore
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Singh YR, Shah DB, Kulkarni M, Patel SR, Maheshwari DG, Shah JS, Shah S. Current trends in chromatographic prediction using artificial intelligence and machine learning. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:2785-2797. [PMID: 37264667 DOI: 10.1039/d3ay00362k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Artificial intelligence (AI) and machine learning (ML) gained tremendous growth and are rapidly becoming popular in various fields of prediction due to their potential abilities, accuracy, and speed. Machine learning algorithms employ historical data to analyze or predict information using patterns or trends. AI and ML were most employed in chromatographic predictions and particularly attractive options for liquid chromatography method development, as they can help achieve desired results faster, more accurately, and more efficiently. This review aims at exploring various AI and ML models employed in the determination of chromatographic characteristics. This review also aims to provide deep insight into reported artificial neural network (ANN) associated techniques which maintained better accuracy and significant possibilities for chromatographic characteristics prediction in liquid chromatography over classical linear models and also emphasizes the integration of a fuzzy system with an ANN, as this integrated study provides more efficient and accurate methods in chromatographic prediction than other linear models. This study also focuses on the retention prediction of a target molecule employing QSRR methodology combined with an ANN, highlighting a more effective technique than the QSRR alone. This approach showed the benefits of combining AI or ML algorithms with the QSRR to obtain more accurate retention predictions, emphasizing the potential of artificial intelligence and machine learning for overcoming adversities in analytical chemistry.
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Affiliation(s)
- Yash Raj Singh
- Department of Pharmaceutical Quality Assurance, LJ Institute of Pharmacy, LJ University, Ahmedabad, Gujarat, India
| | - Darshil B Shah
- Department of Pharmaceutical Quality Assurance, LJ Institute of Pharmacy, LJ University, Ahmedabad, Gujarat, India
| | - Mangesh Kulkarni
- Department of Pharmaceutical Technology, LJ Institute of Pharmacy, LJ University, Ahmedabad, Gujarat, India
| | - Shreyanshu R Patel
- Department of Pharmaceutical Technology, LJ Institute of Pharmacy, LJ University, Ahmedabad, Gujarat, India
| | - Dilip G Maheshwari
- Department of Pharmaceutical Quality Assurance, LJ Institute of Pharmacy, LJ University, Ahmedabad, Gujarat, India
| | - Jignesh S Shah
- Department of Pharmaceutical Regulatory Affairs, LJ Institute of Pharmacy, LJ University, Ahmedabad, Gujarat, India
| | - Shreeraj Shah
- Department of Pharmaceutical Technology, LJ Institute of Pharmacy, LJ University, Ahmedabad, Gujarat, India
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Madaki Z, Abacioglu N, Usman AG, Taner N, Sehirli AO, Abba SI. Novel Hybridized Computational Paradigms Integrated with Five Stand-Alone Algorithms for Clinical Prediction of HCV Status among Patients: A Data-Driven Technique. LIFE (BASEL, SWITZERLAND) 2022; 13:life13010079. [PMID: 36676028 PMCID: PMC9866913 DOI: 10.3390/life13010079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 12/19/2022] [Accepted: 12/21/2022] [Indexed: 12/29/2022]
Abstract
The emergence of health informatics opens new opportunities and doors for different disease diagnoses. The current work proposed the implementation of five different stand-alone techniques coupled with four different novel hybridized paradigms for the clinical prediction of hepatitis C status among patients, using both sociodemographic and clinical input variables. Both the visualized and quantitative performances of the stand-alone algorithms present the capability of the Gaussian process regression (GPR), Generalized neural network (GRNN), and Interactive linear regression (ILR) over the Support Vector Regression (SVR) and Adaptive neuro-fuzzy inference system (ANFIS) models. Hence, due to the lower performance of the stand-alone algorithms at a certain point, four different novel hybrid data intelligent algorithms were proposed, including: interactive linear regression-Gaussian process regression (ILR-GPR), interactive linear regression-generalized neural network (ILR-GRNN), interactive linear regression-Support Vector Regression (ILR-SVR), and interactive linear regression-adaptive neuro-fuzzy inference system (ILR-ANFIS), to boost the prediction accuracy of the stand-alone techniques in the clinical prediction of hepatitis C among patients. Based on the quantitative prediction skills presented by the novel hybridized paradigms, the proposed techniques were able to enhance the performance efficiency of the single paradigms up to 44% and 45% in the calibration and validation phases, respectively.
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Affiliation(s)
- Zachariah Madaki
- Department of Pharmacology, Faculty of Pharmacy, Near East University, North Cyprus, Mersin-10, 99138 Nicosia, Türkiye
| | - Nurettin Abacioglu
- Department of Pharmacology, Faculty of Pharmacy, Near East University, North Cyprus, Mersin-10, 99138 Nicosia, Türkiye
| | - A. G. Usman
- Operational Research Centre in Healthcare, Near East University, North Cyprus, Mersin-10, 99138 Nicosia, Türkiye
- Department of Analytical Chemistry, Faculty of Pharmacy, Near East University, North Cyprus, Mersin-10, 99138 Nicosia, Türkiye
- Correspondence: (A.G.U.); (S.I.A.)
| | - Neda Taner
- Department of Clinical Pharmacy, Faculty of Pharmacy, Istanbul Medipol University, 34810 Istanbul, Türkiye
| | - Ahmet. O. Sehirli
- Department of Pharmacology, Faculty of Dentistry, Nicosia, Near East University, North Cyprus, Mersin-10, 99138 Nicosia, Türkiye
| | - S. I. Abba
- Interdisciplinary Research Centre for Membrane and Water Security, Faculty of Petroleum and Minerals, King Fahd University, Dhahran 31261, Saudi Arabia
- Correspondence: (A.G.U.); (S.I.A.)
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Clinical Modelling of RVHF Using Pre-Operative Variables: A Direct and Inverse Feature Extraction Technique. Diagnostics (Basel) 2022; 12:diagnostics12123061. [PMID: 36553067 PMCID: PMC9777038 DOI: 10.3390/diagnostics12123061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 11/23/2022] [Accepted: 12/02/2022] [Indexed: 12/12/2022] Open
Abstract
Right ventricular heart failure (RVHF) mostly occurs due to the failure of the left-side of the heart. RVHF is a serious disease that leads to swelling of the abdomen, ankles, liver, kidneys, and gastrointestinal (GI) tract. A total of 506 heart-failure subjects from the Faculty of Medicine, Cardiovascular Surgery Department, Ege University, Turkey, who suffered from a severe heart failure and are currently receiving support from a ventricular assistance device, were involved in the current study. Therefore, the current study explored the application of both the direct and inverse modelling approaches, based on the correlation analysis feature extraction performance of various pre-operative variables of the subjects, for the prediction of RVHF. The study equally employs both single and hybrid paradigms for the prediction of RVHF using different pre-operative variables. The visualized and quantitative performance of the direct and inverse modelling approach indicates the robust prediction performance of the hybrid paradigms over the single techniques in both the calibration and validation steps. Whereby, the quantitative performance of the hybrid techniques, based on the Nash-Sutcliffe coefficient (NC) metric, depicts its superiority over the single paradigms by up to 58.7%/75.5% and 80.3%/51% for the calibration/validation phases in the direct and inverse modelling approaches, respectively. Moreover, to the best knowledge of the authors, this is the first study to report the implementation of direct and inverse modelling on clinical data. The findings of the current study indicates the possibility of applying these novel hybridised paradigms for the prediction of RVHF using pre-operative variables.
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Paritala J, Peraman R, Kondreddy VK, Subrahmanyam CVS, Ravichandiran V. Quantitative structure retention relationship (QSRR) approach for assessment of chromatographic behavior of antiviral drugs in the development of liquid chromatographic method. J LIQ CHROMATOGR R T 2022. [DOI: 10.1080/10826076.2022.2025827] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Jagadeesh Paritala
- Department of Pharmaceutical Analysis, Raghavendra Institute of Pharmaceutical Education and Research (RIPER)-Autonomous, Anantapur, India
| | - Ramalingam Peraman
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Hajipur, Bihar, India
| | - Vinod Kumar Kondreddy
- Department of Pharmaceutical Analysis, Raghavendra Institute of Pharmaceutical Education and Research (RIPER)-Autonomous, Anantapur, India
| | | | - V Ravichandiran
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Hajipur, Bihar, India
- National Institute of Pharmaceutical Education & Research (NIPER), Kolkata, India
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NENNİ M, DOĞAN A, ÇELEBİER M, SOYSEVEN M, KAYNAK MS, ABOUL-ENEIN HY, ARLİ G. ltrafiltration-based sample preparation and HPLC-UV determination of diclofenac in human plasma samples. Turk J Chem 2022; 46:777-785. [PMID: 37720620 PMCID: PMC10503973 DOI: 10.55730/1300-0527.3367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 06/16/2022] [Accepted: 01/18/2022] [Indexed: 11/13/2022] Open
Abstract
The sample preparation step is the initial step in pharmaceutical analysis. While ultrafiltration is a well-known technique used in the food and pharmaceutical industries, it has rarely been used to measure the plasma concentration of active pharmaceutical ingredients. This study aimed to analyze diclofenac sodium (DS) in human plasma samples using ultrafiltration-based sample preparation before high-performance liquid chromatography (HPLC) analysis. The advantages and limitations of ultrafiltration-based sample preparation in bioanalysis were evaluated by comparing the results with conventional methods. The precipitating agent was used before ultrafiltration. The analysis was carried on an HPLC-UV system with a C18 column (250 ×4.6 mm, 5 μm) and acetonitrile : phosphate buffer (pH 3.0, 10 mM) (70 : 30 v/v) was used as the mobile phase. The bioanalytical method was validated according to FDA guidelines and applied to spiked samples of DS in commercial human plasma samples. The LOD and LOQ values were 0.006 μg mL-1 and 0.020 μg mL-1, respectively. The method was linear in the range of 0.025-0.50 μgmL-1 with excellent determination coefficients (R2 > 0.9991). The findings of this analysis with low LOD and LOQ values and high recovery values with high trueness and precision proved the matrix minimizing the effect of the presented sample preparation technique.
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Affiliation(s)
- Merve NENNİ
- Department of Analytical Chemistry, Faculty of Pharmacy, Çukurova University, Adana,
Turkey
- Department of Analytical Chemistry, Faculty of Pharmacy, Hacettepe University, Ankara,
Turkey
| | - Ayşegül DOĞAN
- Department of Analytical Chemistry, Faculty of Pharmacy, Hacettepe University, Ankara,
Turkey
| | - Mustafa ÇELEBİER
- Department of Analytical Chemistry, Faculty of Pharmacy, Hacettepe University, Ankara,
Turkey
| | - Murat SOYSEVEN
- Department of Medical Services and Techniques, Yunus Emre Vocational School of Health Services, Eskişehir,
Turkey
| | - Mustafa Sinan KAYNAK
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Anadolu University, Eskişehir,
Turkey
| | - Hassan Y. ABOUL-ENEIN
- Department of Pharmaceutical and Medicinal Chemistry, National Research Center, Cairo,
Egypt
| | - Göksel ARLİ
- Department of Medical Services and Techniques, Yunus Emre Vocational School of Health Services, Eskişehir,
Turkey
- Department of Analytical Chemistry, Faculty of Pharmacy, Anadolu University, Eskişehir,
Turkey
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Abba S, Abdulkadir R, Sammen SS, Pham QB, Lawan A, Esmaili P, Malik A, Al-Ansari N. Integrating feature extraction approaches with hybrid emotional neural networks for water quality index modeling. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2021.108036] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
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10
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Hybrid data-intelligence algorithms for the simulation of thymoquinone in HPLC method development. JOURNAL OF THE IRANIAN CHEMICAL SOCIETY 2021. [DOI: 10.1007/s13738-020-02124-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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