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Song J, Wang B, Hao X. Optimization Algorithms and Their Applications and Prospects in Manufacturing Engineering. MATERIALS (BASEL, SWITZERLAND) 2024; 17:4093. [PMID: 39203271 PMCID: PMC11356672 DOI: 10.3390/ma17164093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2024] [Revised: 07/28/2024] [Accepted: 08/12/2024] [Indexed: 09/03/2024]
Abstract
In modern manufacturing, optimization algorithms have become a key tool for improving the efficiency and quality of machining technology. As computing technology advances and artificial intelligence evolves, these algorithms are assuming an increasingly vital role in the parameter optimization of machining processes. Currently, the development of the response surface method, genetic algorithm, Taguchi method, and particle swarm optimization algorithm is relatively mature, and their applications in process parameter optimization are quite extensive. They are increasingly used as optimization objectives for surface roughness, subsurface damage, cutting forces, and mechanical properties, both for machining and special machining. This article provides a systematic review of the application and developmental trends of optimization algorithms within the realm of practical engineering production. It delves into the classification, definition, and current state of research concerning process parameter optimization algorithms in engineering manufacturing processes, both domestically and internationally. Furthermore, it offers a detailed exploration of the specific applications of these optimization algorithms in real-world scenarios. The evolution of optimization algorithms is geared towards bolstering the competitiveness of the future manufacturing industry and fostering the advancement of manufacturing technology towards greater efficiency, sustainability, and customization.
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Affiliation(s)
- Juan Song
- Department of Basic Courses, Suzhou City University, Suzhou 215104, China;
| | - Bangfu Wang
- College of Mechanical Engineering, Suzhou University of Science and Technology, Suzhou 215009, China;
| | - Xiaohong Hao
- Department of Basic Courses, Suzhou City University, Suzhou 215104, China;
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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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Usman AG, Tanimu A, Abba SI, Isik S, Aitani A, Alasiri H. Feasibility of the Optimal Design of AI-Based Models Integrated with Ensemble Machine Learning Paradigms for Modeling the Yields of Light Olefins in Crude-to-Chemical Conversions. ACS OMEGA 2023; 8:40517-40531. [PMID: 37929092 PMCID: PMC10620777 DOI: 10.1021/acsomega.3c05227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 10/09/2023] [Indexed: 11/07/2023]
Abstract
The prediction of the yields of light olefins in the direct conversion of crude oil to chemicals requires the development of a robust model that represents the crude-to-chemical conversion processes. This study utilizes artificial intelligence (AI) and machine learning algorithms to develop single and ensemble learning models that predict the yields of ethylene and propylene. Four single-model AI techniques and four ensemble paradigms were developed using experimental data derived from the catalytic cracking experiments of various crude oil fractions in the advanced catalyst evaluation reactor unit. The temperature, feed type, feed conversion, total gas, dry gas, and coke were used as independent variables. Correlation matrix analyses were conducted to filter the input combinations into three different classes (M1, M2, and M3) based on the relationship between dependent and independent variables, and three performance metrics comprising the coefficient of determination (R2), Pearson correlation coefficient (PCC), and mean square error (MSE) were used to evaluate the prediction performance of the developed models in both calibration and validations stages. All four single models have very low R2 and PCC values (as low as 0.07) and very high MSE values (up to 4.92 wt %) for M1 and M2 in both calibration and validation phases. However, the ensemble ML models show R2 and PCC values of 0.99-1 and an MSE value of 0.01 wt % for M1, M2, and M3 input combinations. Therefore, ensemble paradigms improve the performance accuracy of single models by up to 58 and 62% in the calibration and validation phases, respectively. The ensemble paradigms predict with high accuracy the yield of ethylene and propylene in the catalytic cracking of crude oil and its fractions.
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Affiliation(s)
- A. G. Usman
- Department
of Analytical Chemistry, Faculty of Pharmacy, Near East University, TRNC, Mersin 10, 99138 Nicosia, Turkey
- Operational
Research Centre in Healthcare, Near East
University, 99138 Nicosia, Turkish Republic of
Northern Cyprus
| | - Abdulkadir Tanimu
- Center
for Refining and Advanced Chemicals, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
| | - S. I. Abba
- Interdisciplinary
Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
| | - Selin Isik
- Department
of Analytical Chemistry, Faculty of Pharmacy, Near East University, TRNC, Mersin 10, 99138 Nicosia, Turkey
| | - Abdullah Aitani
- Center
for Refining and Advanced Chemicals, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
| | - Hassan Alasiri
- Center
for Refining and Advanced Chemicals, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
- Department
of Chemical Engineering, King Fahd University
of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
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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) 2022; 13:79. [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 12/19/2022] [Accepted: 12/21/2022] [Indexed: 12/29/2022] Open
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
| | - 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
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Uzun Ozsahin D, Balcioglu O, Usman AG, Ikechukwu Emegano D, Uzun B, Abba SI, Ozsahin I, Yagdi T, Engin C. Clinical Modelling of RVHF Using Pre-Operative Variables: A Direct and Inverse Feature Extraction Technique. Diagnostics (Basel) 2022; 12:3061. [PMID: 36553067 PMCID: PMC9777038 DOI: 10.3390/diagnostics12123061] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [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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Affiliation(s)
- Dilber Uzun Ozsahin
- Medical Diagnostic Imaging Department, College of Health Science, University of Sharjah, Sharjah 27272, United Arab Emirates
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
| | - Ozlem Balcioglu
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
- Department of Cardiovascular Surgery, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
| | - Abdullahi Garba Usman
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
| | - Declan Ikechukwu Emegano
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
| | - Berna Uzun
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
- Statistics Department, Carlos III University of Madrid, 28903 Madrid, Spain
- Department of Mathematics, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
| | - Sani Isah Abba
- Interdisciplinary Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
| | - Ilker Ozsahin
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
- Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY 10065, USA
| | - Tahir Yagdi
- Cardiovascular Surgery Department, Ege University, Izmir 35100, Turkey
| | - Cagatay Engin
- Cardiovascular Surgery Department, Ege University, Izmir 35100, Turkey
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Usman AG, IŞIK S, Abba SI. Qualitative prediction of Thymoquinone in the high‐performance liquid chromatography optimization method development using artificial intelligence models coupled with ensemble machine learning. SEPARATION SCIENCE PLUS 2022. [DOI: 10.1002/sscp.202200071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Abdullahi Garba Usman
- Department of Analytical Chemistry Faculty of Pharmacy Near East University Nicosia Turkish Republic of Northern Cyprus
- Operational research Centre in healthcare Near East University Nicosia Turkish Republic of Northern Cyprus
| | - Selin IŞIK
- Department of Analytical Chemistry Faculty of Pharmacy Near East University Nicosia Turkish Republic of Northern Cyprus
| | - Sani Isah Abba
- Interdisciplinary Research Center for Membrane and Water Security King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia
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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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