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Yaseen ZM, Melini Wan Mohtar WH, Homod RZ, Alawi OA, Abba SI, Oudah AY, Togun H, Goliatt L, Ul Hassan Kazmi SS, Tao H. Heavy metals prediction in coastal marine sediments using hybridized machine learning models with metaheuristic optimization algorithm. CHEMOSPHERE 2024; 352:141329. [PMID: 38296204 DOI: 10.1016/j.chemosphere.2024.141329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/09/2024] [Accepted: 01/28/2024] [Indexed: 02/09/2024]
Abstract
This study proposes different standalone models viz: Elman neural network (ENN), Boosted Tree algorithm (BTA), and f relevance vector machine (RVM) for modeling arsenic (As (mg/kg)) and zinc (Zn (mg/kg)) in marine sediments owing to anthropogenic activities. A heuristic algorithm based on the potential of RVM and a flower pollination algorithm (RVM-FPA) was developed to improve the prediction performance. Several evaluation indicators and graphical methods coupled with visualized cumulative probability function (CDF) were used to evaluate the accuracy of the models. Akaike (AIC) and Schwarz (SCI) information criteria based on Dickey-Fuller (ADF) and Philip Perron (PP) tests were introduced to check the reliability and stationarity of the data. The prediction performance in the verification phase indicated that RVM-M2 (PBAIS = -o.0465, MAE = 0.0335) and ENN-M2 (PBAIS = 0.0043, MAE = 0.0322) emerged as the best model for As (mg/kg) and Zn (mg/kg), respectively. In contrast with the standalone approaches, the simulated hybrid RVM-FPA proved merit and the most reliable, with a 5 % and 18 % predictive increase for As (mg/kg) and Zn (mg/kg), respectively. The study's findings validated the potential for estimating complex HMs through intelligent data-driven models and heuristic optimization. The study also generated valuable insights that can inform the decision-makers and stockholders for environmental management strategies.
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Affiliation(s)
- Zaher Mundher Yaseen
- Civil and Environmental Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia; Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, Saudi Arabia.
| | - Wan Hanna Melini Wan Mohtar
- Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600, UKM, Bangi, Selangor, Malaysia; Environmental Management Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia.
| | - Raad Z Homod
- Department of Oil and Gas Engineering, Basrah University for Oil and Gas, Basra, Iraq.
| | - Omer A Alawi
- Department of Thermofluids, School of Mechanical Engineering, Universiti Teknologi Malaysia, 81310, UTM Skudai, Johor Bahru, Malaysia.
| | - Sani I Abba
- Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, Saudi Arabia.
| | - Atheer Y Oudah
- Department of Computer Sciences, College of Education for Pure Science, University of Thi-Qar, Nasiriyah, 64001, Iraq; Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Nasiriyah, 64001, Iraq.
| | - Hussein Togun
- Department of Mechanical Engineering, College of Engineering, University of Baghdad, Baghdad, Iraq.
| | - Leonardo Goliatt
- Computational and Applied Mechanics Department, Federal University of Juiz de Fora, 36036-900, Brazil.
| | - Syed Shabi Ul Hassan Kazmi
- Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention, and Guangdong Provincial Key Laboratory of Marine Biotechnology, Shantou University, Shantou, 515063, China.
| | - Hai Tao
- School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, 558000, Guizhou, China; Institute of Big Data Application and Artificial Intelligence, Qiannan Normal University for Nationalities, Duyun, 558000, Guizhou, China; Faculty of Data Science and Information Technology, INTI International University, 71800, Malaysia.
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Mati S, Radulescu M, Saqib N, Samour A, Ismael GY, Aliyu N. Incorporating Russo-Ukrainian war in Brent crude oil price forecasting: A comparative analysis of ARIMA, TARMA and ENNReg models. Heliyon 2023; 9:e21439. [PMID: 38027671 PMCID: PMC10660497 DOI: 10.1016/j.heliyon.2023.e21439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 10/19/2023] [Accepted: 10/20/2023] [Indexed: 12/01/2023] Open
Abstract
This article investigates the performance of three models - Autoregressive Integrated Moving Average (ARIMA), Threshold Autoregressive Moving Average (TARMA) and Evidential Neural Network for Regression (ENNReg) - in forecasting the Brent crude oil price, a crucial economic variable with a significant impact on the global economy. With the increasing complexity of the price dynamics due to geopolitical factors such as the Russo-Ukrainian war, we examine the impact of incorporating information on the war on the forecasting accuracy of these models. Our analysis shows that incorporating the impact of the war can significantly improve the forecasting accuracy of the models, and the ENNReg model with the inclusion of the dummy variable outperforms the other models during the war period. Including the war variable has enhanced the forecasting accuracy of the ENNReg model by 0.11%. These results carry significant implications regarding policymakers, investors, and researchers interested in developing accurate forecasting models in the presence of geopolitical events such as the Russo-Ukrainian war. The results can be used by the governments of oil-exporting countries for budget policies.
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Affiliation(s)
- Sagiru Mati
- Operational Research Center in Healthcare, Near East University, North Cyprus, 99138, Turkey
- Department of Economics, Yusuf Maitama Sule University, PMB 3099, Nigeria
| | - Magdalena Radulescu
- Department of Finance, Accounting and Economics, University of Pitesti, 110040 Pitesti, Romania
- Institute for Doctoral and Post-Doctoral Studies, Lucian Blaga University of Sibiu, 550024 Sibiu, Romania
| | - Najia Saqib
- Department of Finance, College of Business Administration, Prince Sultan University, Riyadh, Saudi Arabia
| | - Ahmed Samour
- Department of Accounting, Dhofar University, Salalah, Sultanate of Oman
| | - Goran Yousif Ismael
- Department of Business Administration, Noble Institute of Technology - Erbil, Kurdistan Region, Iraq
| | - Nazifi Aliyu
- NNPC RETAIL LTD, Nigerian National Petroleum Corporation, Nigeria
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Sarmiento Varón L, González-Puelma J, Medina-Ortiz D, Aldridge J, Alvarez-Saravia D, Uribe-Paredes R, Navarrete MA. The role of machine learning in health policies during the COVID-19 pandemic and in long COVID management. Front Public Health 2023; 11:1140353. [PMID: 37113165 PMCID: PMC10126380 DOI: 10.3389/fpubh.2023.1140353] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Accepted: 03/20/2023] [Indexed: 04/29/2023] Open
Abstract
The ongoing COVID-19 pandemic is arguably one of the most challenging health crises in modern times. The development of effective strategies to control the spread of SARS-CoV-2 were major goals for governments and policy makers. Mathematical modeling and machine learning emerged as potent tools to guide and optimize the different control measures. This review briefly summarizes the SARS-CoV-2 pandemic evolution during the first 3 years. It details the main public health challenges focusing on the contribution of mathematical modeling to design and guide government action plans and spread mitigation interventions of SARS-CoV-2. Next describes the application of machine learning methods in a series of study cases, including COVID-19 clinical diagnosis, the analysis of epidemiological variables, and drug discovery by protein engineering techniques. Lastly, it explores the use of machine learning tools for investigating long COVID, by identifying patterns and relationships of symptoms, predicting risk indicators, and enabling early evaluation of COVID-19 sequelae.
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Affiliation(s)
| | - Jorge González-Puelma
- Centro Asistencial Docente y de Investigación, Universidad de Magallanes, Punta Arenas, Chile
- Escuela de Medicina, Universidad de Magallanes, Punta Arenas, Chile
| | - David Medina-Ortiz
- Departamento de Ingeniería en Computación, Facultad de Ingeniería, Universidad de Magallanes, Punta Arenas, Chile
| | - Jacqueline Aldridge
- Departamento de Ingeniería en Computación, Facultad de Ingeniería, Universidad de Magallanes, Punta Arenas, Chile
| | - Diego Alvarez-Saravia
- Centro Asistencial Docente y de Investigación, Universidad de Magallanes, Punta Arenas, Chile
- Escuela de Medicina, Universidad de Magallanes, Punta Arenas, Chile
| | - Roberto Uribe-Paredes
- Departamento de Ingeniería en Computación, Facultad de Ingeniería, Universidad de Magallanes, Punta Arenas, Chile
| | - Marcelo A. Navarrete
- Centro Asistencial Docente y de Investigación, Universidad de Magallanes, Punta Arenas, Chile
- Escuela de Medicina, Universidad de Magallanes, Punta Arenas, Chile
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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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Manzar MS, Benaafi M, Costache R, Alagha O, Mu'azu ND, Zubair M, Abdullahi J, Abba S. New generation neurocomputing learning coupled with a hybrid neuro-fuzzy model for quantifying water quality index variable: A case study from Saudi Arabia. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101696] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Yassin MA, Tawabini B, Al-Shaibani A, Adetoro JA, Benaafi M, AL-Areeq AM, Usman AG, Abba SI. Geochemical and Spatial Distribution of Topsoil HMs Coupled with Modeling of Cr Using Chemometrics Intelligent Techniques: Case Study from Dammam Area, Saudi Arabia. Molecules 2022; 27:molecules27134220. [PMID: 35807465 PMCID: PMC9268374 DOI: 10.3390/molecules27134220] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 06/22/2022] [Accepted: 06/27/2022] [Indexed: 12/10/2022] Open
Abstract
Unconsolidated earthen surface materials can retain heavy metals originating from different sources. These metals are dangerous to humans as well as the immediate environment. This danger leads to the need to assess various geochemical conditions of the materials. In this study, the assessment of topsoil materials’ contamination with heavy metals (HMs) was conducted. The material’s representative spatial samples were taken from various sources: agricultural, industrial, and residential areas. The materials include topsoil, eolian deposits, and other unconsolidated earthen materials. The samples were analyzed using the ICP-OES. The obtained results based on the experimental procedure indicated that the average levels of the heavy metals were: As (1.21 ± 0.69 mg/kg), Ba (110.62 ± 262 mg/kg), Hg (0.08 ± 0.18 mg/kg), Pb (6.34 ± 14.55 mg/kg), Ni (8.95 ± 5.66 mg/kg), V (9.98 ± 6.08 mg/kg), Cd (1.18 ± 4.33 mg/kg), Cr (31.79 ± 37.9 mg/kg), Cu (6.76 ± 12.54 mg/kg), and Zn (23.44 ± 84.43 mg/kg). Subsequently, chemometrics modeling and a prediction of Cr concentration (mg/kg) were performed using three different modeling techniques, including two artificial intelligence (AI) techniques, namely, generalized neural network (GRNN) and Elman neural network (Elm NN) models, as well as a classical multivariate statistical technique (MST). The results indicated that the AI-based models have a superior ability in estimating the Cr concentration (mg/kg) than MST, whereby GRNN can enhance the performance of MST up to 94.6% in the validation step. The concentration levels of most metals were found to be within the acceptable range. The findings indicate that AI-based models are cost-effective and efficient tools for trace metal estimations from soil.
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Affiliation(s)
- Mohamed A. Yassin
- Interdisciplinary Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia; (M.A.Y.); (B.T.); (M.B.); (A.M.A.-A.)
| | - Bassam Tawabini
- Interdisciplinary Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia; (M.A.Y.); (B.T.); (M.B.); (A.M.A.-A.)
- College of Petroleum Engineering and Geosciences, King Fahad University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
| | | | - John Adedapo Adetoro
- Centre for Environmental Management and Control, Enugu Campus, University of Nigeria, Nsukka 410001, Nigeria;
| | - Mohammed Benaafi
- Interdisciplinary Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia; (M.A.Y.); (B.T.); (M.B.); (A.M.A.-A.)
| | - Ahmed M. AL-Areeq
- Interdisciplinary Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia; (M.A.Y.); (B.T.); (M.B.); (A.M.A.-A.)
| | - A. G. Usman
- Operational Research Centre in Healthcare, Near East University, TRNC, Mersin 10, Nicosia 99138, Cyprus;
- Department of Analytical Chemistry, Faculty of Pharmacy, Near East University, TRNC, Mersin 10, Nicosia 99138, Cyprus
| | - S. I. Abba
- Interdisciplinary Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia; (M.A.Y.); (B.T.); (M.B.); (A.M.A.-A.)
- Correspondence:
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Neurocomputing Modelling of Hydrochemical and Physical Properties of Groundwater Coupled with Spatial Clustering, GIS, and Statistical Techniques. SUSTAINABILITY 2022. [DOI: 10.3390/su14042250] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Groundwater (GW) is a critical freshwater resource for billions of individuals worldwide. Rapid anthropogenic exploitation has increasingly deteriorated GW quality and quantity. Reliable estimation of complex hydrochemical properties of GW is crucial for sustainable development. Real field and experimental studies in an agricultural area from the significant sandstone aquifers (Wajid Aquifer) were conducted. For the modelling purpose, three types of computational models, including the emerging Hammerstein–Wiener (HW), back propagation neural network (BPNN), and statistical multi-variate regression (MVR), were developed for the multi-station estimation of total dissolved solids (TDS) (mg/L) and total hardness (TH) (mg/L). A geographic information system (GIS) was used for the spatial variability assessment of 32 hydrochemical and physical properties of the GW aquifer. A comprehensive visualized literature review spanning several decades was conducted in order to gain an understanding of the existing research and debates relevant to a particular GW and artificial intelligence (AI) study. The experimental data, pre-processing, and feature selection were conducted to determine the most dominant variables for AI-based modelling. The estimation results were evaluated using determination coefficient (DC), mean bias error (MBE), mean square error (MSE), and root mean square error (RMSE). The outcomes proved that TDS (mg/L) and TH (mg/L) correlated more than 90% and 70–85% with Ca2+, Cl−, Br−, NO3−, and Fe, and Na+, SO42−, Mg2+, and F− combinations, respectively. HW-M1 justified promising among all the models with MBE = 1.41 × 10−11, 1.14 × 10−14, and MSE = 7.52 × 10−2, 3.88 × 10−11 for TDS (mg/L), TH (mg/L), respectively. The accuracy proved merit for the overall development of and practical estimation of hydrochemical variables (TDS, TH) (mg/L) and decision-making benchmarks.
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Spatiotemporal Variability Assessment of Trace Metals Based on Subsurface Water Quality Impact Integrated with Artificial Intelligence-Based Modeling. SUSTAINABILITY 2022. [DOI: 10.3390/su14042192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Increasing anthropogenic emissions due to rapid industrialization have triggered environmental pollution and pose a threat to the well-being of the ecosystem. In this study, the first scenario involved the spatio-temporal assessment of topsoil contamination with trace metals in the Dammam region, and samples were taken from 2 zones: the industrial (ID), and the agricultural (AG) area. For this purpose, more than 130 spatially distributed samples of topsoil were collected from residential, industrial, and agricultural areas. Inductively coupled plasma—optical emission spectroscopy (ICP-OES)—was used to analyze the samples for various trace metals. The second scenario involved the creation of different artificial intelligence (AI) models, namely an artificial neural network (ANN) and a support vector regression (SVR), for the estimation of zinc (Zn), copper (Cu), chromium (Cr), and lead (Pb) using feature-based input selection. The experimental outcomes depicted that the average concentration levels of HMs were as follows: Chromium (Cr) (31.79 ± 37.9 mg/kg), Copper (Cu) (6.76 ± 12.54 mg/kg), Lead (Pb) (6.34 ± 14.55 mg/kg), and Zinc (Zn) (23.44 ± 84.43 mg/kg). The modelling accuracy, based on different evaluation criteria, showed that agricultural and industrial stations showed performance merit with goodness-of-fit ranges of 51–91% and 80–99%, respectively. This study concludes that AI models could be successfully applied for the rapid estimation of soil trace metals and related decision-making.
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Palermo MB, Policarpo LM, Costa CAD, Righi RDR. Tracking machine learning models for pandemic scenarios: a systematic review of machine learning models that predict local and global evolution of pandemics. NETWORK MODELING ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS 2022; 11:40. [PMID: 36249862 PMCID: PMC9553296 DOI: 10.1007/s13721-022-00384-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/09/2022] [Accepted: 09/20/2022] [Indexed: 11/26/2022]
Abstract
This systematic review aims to study and classify machine learning models that predict pandemics' evolution within affected regions or countries. The advantage of this systematic review is that it allows the health authorities to decide what prediction model fits best depending upon the region's criticality and optimize hospitals' approaches to preparing and anticipating patient care. We searched ACM Digital Library, Biomed Central, BioRxiv+MedRxiv, BMJ, Computers and Applied Sciences, IEEEXplore, JMIR Medical Informatics, Medline Daily Updates, Nature, Oxford Academic, PubMed, Sage Online, ScienceDirect, Scopus, SpringerLink, Web of Science, and Wiley Online Library between 1 January 2020 and 31 July 2022. We divided the interventions into similarities between cumulative COVID-19 real cases and machine learning prediction models' ability to track pandemics trending. We included 45 studies that rated low to high risk of bias. The standardized mean differences (SMD) for the two groups were 0.18, 95% CI, with interval of [0.01, 0.35], I 2 =0, and p value=0.04. We built a taxonomic analysis of the included studies and determined two domains: pandemics trending prediction models and geolocation tracking models. We performed the meta-analysis and data synthesis and got low publication bias because of missing results. The level of certainty varied from very low to high. By submitting the 45 studies on the risk of bias, the levels of certainty, the summary of findings, and the statistical analysis via the forest and funnel plots assessments, we could determine the satisfactory statistical significance homogeneity across the included studies to simulate the progress of the pandemics and help the healthcare authorities to take preventive decisions.
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Affiliation(s)
- Marcelo Benedeti Palermo
- Software Innovation Laboratory-SOFTWARELAB, Programa de Pós-Graduação em Computação Aplicada, Universidade do Vale do Rio dos Sinos, Av. Unisinos 950, São Leopoldo, RS 93022-750 Brazil
| | - Lucas Micol Policarpo
- Software Innovation Laboratory-SOFTWARELAB, Programa de Pós-Graduação em Computação Aplicada, Universidade do Vale do Rio dos Sinos, Av. Unisinos 950, São Leopoldo, RS 93022-750 Brazil
| | - Cristiano André da Costa
- Software Innovation Laboratory-SOFTWARELAB, Programa de Pós-Graduação em Computação Aplicada, Universidade do Vale do Rio dos Sinos, Av. Unisinos 950, São Leopoldo, RS 93022-750 Brazil
| | - Rodrigo da Rosa Righi
- Software Innovation Laboratory-SOFTWARELAB, Programa de Pós-Graduação em Computação Aplicada, Universidade do Vale do Rio dos Sinos, Av. Unisinos 950, São Leopoldo, RS 93022-750 Brazil
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Iddrisu AK, A. Amikiya E, Otoo D. A predictive model for daily cumulative COVID-19 cases in Ghana. F1000Res 2021; 10:343. [PMID: 35464175 PMCID: PMC9005990 DOI: 10.12688/f1000research.52403.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/01/2022] [Indexed: 11/20/2022] Open
Abstract
Background: Coronavirus disease 2019 (COVID-19) is a pandemic that has affected the daily life, governments and economies of many countries all over the globe. Ghana is currently experiencing a surge in the number of cases with a corresponding increase in the cumulative confirmed cases and deaths. The surge in cases and deaths clearly shows that the preventive and management measures are ineffective and that policy makers lack a complete understanding of the dynamics of the disease. Most of the deaths in Ghana are due to lack of adequate health equipment and facilities for managing the disease. Knowledge of the number of cases in advance would aid policy makers in allocating sufficient resources for the effective management of the cases. Methods: A predictive tool is necessary for the effective management and prevention of cases. This study presents a predictive tool that has the ability to accurately forecast the number of cumulative cases. The study applied polynomial and spline models on the COVID-19 data for Ghana, to develop a generalized additive model (GAM) that accurately captures the growth pattern of the cumulative cases. Results: The spline model and the GAM provide accurate forecast values. Conclusion: Cumulative cases of COVID-19 in Ghana are expected to continue to increase if appropriate preventive measures are not enforced. Vaccination against the virus is ongoing in Ghana, thus, future research would consider evaluating the impact of the vaccine.
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Affiliation(s)
- Abdul-Karim Iddrisu
- Mathematics and Statistics, University of Energy and Natural Resources, Sunyani, Ghana
| | - Emmanuel A. Amikiya
- Department of Management Science, Ghana Institute of Management and Public Administration, Accra, Ghana
| | - Dominic Otoo
- Mathematics and Statistics, University of Energy and Natural Resources, Sunyani, Ghana
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