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The Modified ARIMA predicting algorithm Apply on Glucose Values Prediction 7th

7th International Conference on Electrical, Control, and Computer Engineering, InECCE 2023

作者： QingXiang, Bian As’array, Azizan XiangGuo, Cong bin Md Rezali, Khairil Anas bin Raja Ahmad, Raja Mohd Kamil Department of Mechanical and Manufacturing Engineering Faculty of Engineering Universiti Putra Malaysia Serdang Malaysia Department of Endocrinology Suzhou Municipal Hospital The Affiliated Suzhou Hospital of Nanjing Medical University Suzhou China Department of Electrical and Electronic Engineering Faculty of Engineering Universiti Putra Malaysia Serdang Malaysia

ISBN: (数字)9789819738519

ISBN: (纸本)9789819738502;9789819738533

In order to predict the blood glucose values of diabetic patients, this study uses two Autoregressive Integrated Moving Average Model (ARIMA) models—the self-optimized ARIMA model and the ARIMA model based on Bayesian optimization to analyze historical data from the continuous blood glucose monitoring system (CGM) and the equipment calibration value as the training data set to foretell a patient's blood sugar level in the future in order to prevent hypo- and hyperglycemic episodes. CGM data from 8 patients obtained by Suzhou Municipal Hospital in Jiangsu Province, China, was used to validate the data in this paper on the two models. Obtain and compare the minimum Mean Square Error (MSE) values of the prediction results of the two models at 15, 30, and 45 min. In order to improve the accuracy of the patient's blood glucose level prediction, the model using the smallest MSE value among the two ARIMA models was used as a method for predicting the patient's blood glucose level prediction. The ARIMA model based on Bayesian have a good performance with 0.330 MSE value in 15 min prediction than self-optimized ARIMA model which could be preciously predict the glucose value. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： ARIMA Continuous glucose monitoring predicting algorithm

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Clinical sign and biomarker-based algorithm to identify bacterial pneumonia among outpatients with lower respiratory tract infection in Tanzania

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BMC INFECTIOUS DISEASES 2022年第1期22卷 39-39页

作者： Hogendoorn, Sarika K. L. Lhopitallier, Loic Richard-Greenblatt, Melissa Tenisch, Estelle Mbarack, Zainab Samaka, Josephine Mlaganile, Tarsis Mamin, Aline Genton, Blaise Kaiser, Laurent D'Acremont, Valerie Kain, Kevin C. Boillat-Blanco, Noemie Univ Hosp Infect Dis Serv Lausanne Switzerland Univ Lausanne Lausanne Switzerland Univ Toronto Univ Hlth Network Toronto Gen Hosp Trop Dis UnitDept MedSandra Rotman Ctr Global H Toronto ON Canada Univ Hosp Dept Radiol Lausanne Switzerland Mwananyamala Hosp Dar Es Salaam Tanzania Ifakara Hlth Inst Dar Es Salaam Tanzania Univ Geneva Hosp Div Infect Dis Geneva Switzerland Univ Geneva Hosp Ctr Emerging Viral Dis Geneva Switzerland Fac Med Geneva Switzerland Univ Lausanne Ctr Primary Care & Publ Hlth Lausanne Switzerland Univ Basel Swiss Trop & Publ Hlth Inst Basel Switzerland

Background Inappropriate antibiotics use in lower respiratory tract infections (LRTI) is a major contributor to resistance. We aimed to design an algorithm based on clinical signs and host biomarkers to identify bacterial community-acquired pneumonia (CAP) among patients with LRTI. Methods Participants with LRTI were selected in a prospective cohort of febrile (>= 38 degrees C) adults presenting to outpatient clinics in Dar es Salaam. Participants underwent chest X-ray, multiplex PCR for respiratory pathogens, and measurements of 13 biomarkers. We evaluated the predictive accuracy of clinical signs and biomarkers using logistic regression and classification and regression tree analysis. Results Of 110 patients with LRTI, 17 had bacterial CAP. Procalcitonin (PCT), interleukin-6 (IL-6) and soluble triggering receptor expressed by myeloid cells-1 (sTREM-1) showed an excellent predictive accuracy to identify bacterial CAP (AUROC 0.88, 95%CI 0.78-0.98;0.84, 0.72-0.99;0.83, 0.74-0.92, respectively). Combining respiratory rate with PCT or IL-6 significantly improved the model compared to respiratory rate alone (p = 0.006, p = 0.033, respectively). An algorithm with respiratory rate (>= 32/min) and PCT (>= 0.25 mu g/L) had 94% sensitivity and 82% specificity. Conclusions PCT, IL-6 and sTREM-1 had an excellent predictive accuracy in differentiating bacterial CAP from other LRTIs. An algorithm combining respiratory rate and PCT displayed even better performance in this sub-Sahara African setting.

关键词： Bacterial community-acquired pneumonia predicting algorithm Biomarkers PCT

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Prediction of Multi-site Protein Subcellular Localization

Prediction of Multi-site Protein Subcellular Localization

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IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)

作者： Zhao, Qing Li, Na Fang, Li Qilu Inst Technol Sch Computat Sci & Informat Engn Jinan 250000 Peoples R China

ISBN: (纸本)9781728181172

Multi-site protein sub cellular localization prediction has close relationship with protein function, metabolic and transduction between signals. It can play an important role in speculating the biological function of protein, clearing the gene function and clarifying the internal mechanism of diseases. As some proteins located in two or more subcellulars at the same time, it is necessary for us to do the research on multi-site protein subcellular localization prediction. This paper reviewed the problem from the following four aspects, dataset construction, protein features extraction, predicting algorithm and algorithm evaluation.

关键词： multi-site subcellular localization protein features extraction predicting algorithm algorithm evaluation

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Application of grid partitioning based fuzzy inference system as a novel predictor to estimate dynamic viscosity of n-alkane

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PETROLEUM SCIENCE AND TECHNOLOGY 2019年第23期37卷 2309-2314页

作者： Esfandiarian, Ali Sedaghat, Milad Maniatpour, Ali Darvish, Houman Islamic Azad Univ Fars Sci & Res Branch Dept Petr Engn Marvdasht Iran Islamic Azad Univ Marvdasht Branch Dept Petr Engn Marvdasht Iran Islamic Azad Univ Gachsaran Branch Dept Chem Engn Gachsaran Iran

The oil recovery and rate of production are highly dependent on viscosity of reservoir fluid so this term becomes one of the attractive parameters in petroleum engineering. The viscosity of fluid is highly function of composition, temperature, and pressure so in this article, Grid partitioning based Fuzzy inference system approach is utilized as novel predictor to estimate dynamic viscosity of different normal alkanes in the wide range of operational conditions. In order to comparison of model output with actual data, an experimental dataset related to dynamic viscosity of n-alkanes is gathered. The graphical and statistical comparisons between model outputs and experimental data show the high quality performance of predicting algorithm. The coefficients of determination (R-2) of training and testing phases are 0.9985 and 0.9980, respectively. The mentioned statistical indexes represent the great accuracy of model in prediction of dynamic viscosity.

关键词： alkane dynamic viscosity grid partitioning based fuzzy inference system operational condition predicting algorithm

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Development of a novel ANFIS-GA method for investigation of effect of inhibitors on asphaltene precipitation

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PETROLEUM SCIENCE AND TECHNOLOGY 2019年第14期37卷 1626-1633页

作者： Esfandiarian, Ali Sedaghat, Milad Islamic Azad Univ Fars Sci & Res Branch Dept Petr Engn Marvdasht Iran Islamic Azad Univ Marvdasht Branch Dept Petr Engn Marvdasht Iran

Asphaltene precipitation and deposition in different parts of petroleum industry increase considerably cost and problems in oil production. Due to these facts controlling asphaltene precipitation becomes one of valuable topics for research in petroleum engineering. Utilization of Asphaltene inhibitors is known as one of the dominant methods for controlling asphaltene precipitation so in this paper Adaptive neuro-fuzzy inference system (ANFIS) is joint with Genetic algorithm (GA) to study effectiveness of asphaltene inhibitors on precipitation in terms of oil and inhibitors properties. In order to prepare and evaluate the ANFIS-GA algorithm, some reliable experimental data were gathered. The obtained results from the comparison shows the coefficient of determination (R-2) for training and testing phases are 0.98804 and 0.9916 respectively. The determined indexes and graphical comparisons expresses that ANFIS-GA has enough accuracy and potential to estimate effectiveness of inhibitors on asphaltene precipitation reductions.

关键词： ANFIS-GA asphaltene inhibitors precipitation predicting algorithm

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Proposing a novel and accurate model for forecasting dynamic viscosity of n-alkane in operational conditions

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PETROLEUM SCIENCE AND TECHNOLOGY 2018年第19期36卷 1531-1536页

作者： Taghavi, Hadi Taghavi, Hamed Amirkabir Univ Technol Dept Petr Engn Tehran Iran Petr Univ Technol Dept Petr Engn Ahvaz Iran

The viscosity of fluid is known as resistivity of fluid to flow and straightly affected by temperature and pressure. As it is obvious, the viscosity of reservoir fluid is known as one of the critical parameters which extensively effect on production. Therefore, in the present paper, multilayer perceptron artificial neural network (MLP-ANN) is used as a novel and accurate model to predict dynamic viscosity of normal alkanes in the operational conditions. To this end, 228 dynamic viscosity points as function of carbon number of n-alkane, temperature, and pressure were collected from a reliable paper. The comparison between MLP-ANN outputs and experimental dynamic viscosities is performed in graphical and statistical manners. The calculated coefficients of determination 0.99739 and 0.99051 for training and testing phases express the great ability of MLP-ANN algorithm in prediction of dynamic viscosity of n-alkane. According to the analysis, MLP-ANN has enough accuracy and potential to be used as software for which applicable in petroleum industry.

关键词： dynamic viscosity MLP-ANN normal alkane predicting algorithm reservoir condition

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Application of Grid partitioning based Fuzzy inference system method as a novel approach for prediction of interfacial tension of hydrocarbon and carbon dioxide

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PETROLEUM SCIENCE AND TECHNOLOGY 2018年第9-10期36卷 660-665页

作者： Darvish, Houman Asadabadi, Hamed Raji Sadeghi, Ashkan Maleki Rouhibakhsh, Karim Islamic Azad Univ Marvdasht Branch Dept Petr Engn Marvdasht Iran Islamic Azad Univ Sci & Res Branch Tehran Iran Shiraz Univ Sch Chem Petr & Gas Engn Dept Petr Engn Shiraz Iran

Nowadays the importance of enhanced oil recovery (EOR) processes increases because of increasing demand of energy and declination of oil reservoirs. Due to this fact the researchers attracted to study performance of EOR methods. one of the high efficient methods is carbon dioxide injection which is favorable because of low cost and environmental friendly viewpoints. One of important parameters which have straight effect on recovery of injection is interfacial tension between carbon dioxide and hydrocarbons. In the present investigation the main objective is proposing the Grid partitioning based Fuzzy inference system method as novel approach to predict interfacial tension of carbon dioxide and hydrocarbon in terms of temperature, pressure, liquid and gas densities and molecular weight of alkane. The coefficients of determination for different datasets of training and testing of estimating algorithm are determined as 0.9919 and 0.9899. This results express the algorithm has potential of estimating interfacial tension of hydrocarbons and carbon dioxide.

关键词： carbon dioxide injection EOR hydrocarbon interfacial tension predicting algorithm

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On prediction of asphaltene precipitation in different operational conditions utilization of LSSVM algorithm

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PETROLEUM SCIENCE AND TECHNOLOGY 2018年第16期36卷 1292-1297页

作者： Sedaghat, Milad Rouhibakhsh, Karim Islamic Azad Univ Dept Petr Engn Marvdasht branch Marvdasht Fars Iran Shiraz Univ Dept Petr Engn Sch Chem Petr & Gas Engn Shiraz Fars Iran

Asphaltene which is known as one of the fractions of oil, can cause the important problems during production of crude oil in reservoir, tubing and surface facilities so these problems can influence the production cost and time. In order to predicting and solving asphaltene problems, a powerful Least squares support vector machine (LSSVM) algorithm were developed for asphaltene precipitation estimation as function of dilution ratio, temperature, precipitant carbon number, asphaltene content and API of oil. A total number of 428 measured data were utilized to train and test of LSSVM algorithm. The average absolute relative deviation (AARD), the coefficient of determination (R-2) and root mean square error (RMSE) were determined as 7.7569, 0.98552 and 0.26312 respectively. Based on these statistical parameters and graphical analysis it can be concluded that the predicting algorithm has enough reliability and accuracy in prediction of asphaltene precipitation.

关键词： asphaltene precipitation LSSVM predicting algorithm operational conditions

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Application of MLP-ANN as novel tool for estimation of effect of inhibitors on asphaltene precipitation reduction

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PETROLEUM SCIENCE AND TECHNOLOGY 2018年第16期36卷 1272-1277页

作者： Baghban, Alireza Hekmati, Rasoul Hajiali, Mandi Lariche, Milad Janghorban Karnyab, Majid Amirkabir Univ Technol Dept Chem Engn Mahshahr Campus Mahshahr *** Iran Univ Houston Dept Math Houston TX 77204 USA Amirkabir Univ Technol Dept Comp Engn & Informat Technol Tehran Iran Abadan Sch Med Sci Abadan *** Iran Sch Chem Engn Comp Aided Proc Engn Lab CAPE Tehran Iran Univ Sci & Technol IUST Tehran Iran

Sedimentation of heavy fractions of oil such as asphaltene is the main concern in different parts of petroleum industries like production and transportation. Due to this fact, the inhibition of asphaltene precipitation becomes one of the great interests in the petroleum industry. In the present investigation, multi-layer perceptron artificial neural network (MLP-ANN) was developed to estimate asphaltene precipitation reduction as a function of concentration and kind of inhibitors and oil properties. To this end, a total number of 75 data points were extracted from reliable source for training and validation of predicting algorithm. The outputs of MLP-ANN were compared with experimental data graphically and statistically, the determined coefficients of determination (R-2) for training and testing are 0.996522 and 0.995239 respectively. These comparisons expressed the high quality of this algorithm in the prediction of asphaltene precipitation reduction. so the MLP-ANN can be used as a powerful machine for estimation of different processes in petroleum industries.

关键词： asphaltene inhibitor MLP-ANN precipitation predicting algorithm

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Application of LSSVM algorithm for estimating higher heating value of biomass based on ultimate analysis

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2018年第6期40卷 709-715页

作者： Duan, Min Liu, Zhenling Yan, Dijiao Peng, Wanxi Baghban, Alireza Xucang Univ Sch Traff & Transportat Xucang Peoples R China Henan Univ Technol Sch Management Zhengzhou Henan Peoples R China North China Elect Power Univ Sch Nucl Sci & Engn Beijing Peoples R China Henan Agr Univ Coll Forestry Zhengzhou Henan Peoples R China Amirkabir Univ Technol Dept Chem Engn Mahshahr Iran

The higher heating value (HHV) is known as one of the energy evaluation parameters for biomass which has wide application in economic aspects investigation of energy sources. In this investigation the LSSVM algorithm as novel predicting model in the purpose of estimation of higher heating value in terms of ultimate analysis. A total number of 78 experimental data for training and testing of the algorithm were gathered from *** the purpose of evaluation of estimating algorithm the results are reported graphically and statistically. The calculated statistical indexes for overall data such as Root mean square error (RMSE), average absolute relative deviation (AARD) and the coefficient of determination (R-2) are 9.2881, 0.038005 and 0.99996 respectively also the graphical results confirm the potential of LSSVM algorithm to be a predicting tool and a simple software for estimation of HHV as function of ultimate analysis.

关键词： Biomass energy source HHV LSSVM predicting algorithm ultimate analysis

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