scholarly journals Passengers' Traffic Forecast of the Nigeria Airports using the Holt-Winters Additive Model

2020 ◽  
Vol 3 (2) ◽  
pp. 210-217
Author(s):  
AJ Tamber ◽  
OM Oladejo
Keyword(s):  
Research Work ◽  
Additive Model ◽  
Air Transport ◽  
Coefficient Of Determination ◽  
Traffic Forecast ◽  
Computer Package

This research work was carried out in response to the need as a result of increase in Nigeria population and the demand for air transport facilities, this research was carried out using the data of the Federal Airport Authority of Nigeria, which has a total number of 23 airports out of which four are international airports, seven are domestic airports and twelve are other domestic airports with the total number of 75,879,653 passengers between Jan. 2003 and Dec. 2011. The passengers' traffic of FAAN's data of 2003 to 2011 was collected and forecasted using the NCSS computer package to generate the Holt-Winters additive model with coefficient of determination, R2 of 90.99% and the models were used to forecast for the years 2012 to 2019 using the models.

Knee Muscle Force Estimating Model Using Machine Learning Approach

2020 ◽  
Author(s):  
Anurag Sohane ◽  
Ravinder Agarwal
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Muscle Force ◽  
Vastus Lateralis ◽  
Input Parameter ◽  
Research Work ◽  
Cost Effective ◽  
Coefficient Of Determination ◽  
Muscle Forces ◽  
Knee Muscle

Abstract Various simulation type tools and conventional algorithms are being used to determine knee muscle forces of human during dynamic movement. These all may be good for clinical uses, but have some drawbacks, such as higher computational times, muscle redundancy and less cost-effective solution. Recently, there has been an interest to develop supervised learning-based prediction model for the computationally demanding process. The present research work is used to develop a cost-effective and efficient machine learning (ML) based models to predict knee muscle force for clinical interventions for the given input parameter like height, mass and angle. A dataset of 500 human musculoskeletal, have been trained and tested using four different ML models to predict knee muscle force. This dataset has obtained from anybody modeling software using AnyPyTools, where human musculoskeletal has been utilized to perform squatting movement during inverse dynamic analysis. The result based on the datasets predicts that the random forest ML model outperforms than the other selected models: neural network, generalized linear model, decision tree in terms of mean square error (MSE), coefficient of determination (R2), and Correlation (r). The MSE of predicted vs actual muscle forces obtained from the random forest model for Biceps Femoris, Rectus Femoris, Vastus Medialis, Vastus Lateralis are 19.92, 9.06, 5.97, 5.46, Correlation are 0.94, 0.92, 0.92, 0.94 and R2 are 0.88, 0.84, 0.84 and 0.89 for the test dataset, respectively.

scholarly journals Reweigh Temperature as Estimator for Evaluation and Prediction

2020 ◽  
pp. 66-71
Author(s):  
M. K. Awasthi ◽  
Deepak Patle
Keyword(s):  
Minimum Temperature ◽  
Research Work ◽  
Coefficient Of Determination ◽  
Maximum Temperature ◽  
Subtropical Climate ◽  
Data Set ◽  
Hourly Data ◽  
Weather Underground ◽  
Region Temperature ◽  
Research Outcome

This study aimed to develop estimator for evaluation of reweigh temperature for prediction research extent. Research conducted in Jabalpur district of Madhya Pradesh, India, which comes under the humid subtropical climate region. Temperature recorded at one hour, two hour or three hour either side of maximum temperature may be averaged to get a plateaued value for that much time period. Hourly data on temperature recorded at Weather Underground site are regrouped into different temperature forms namely average of maximum and minimum temperature (Tav), weighted temperature (Twt), maximum temperature (Tmax), Temperature plateaued one hour, two hour and three hour either side of maximum temperature (Tp2, Tp4 and Tp6 respectively). These temperature forms are plotted for all twelve months. Integration of Tav and Tmax was done for estimation of weighted temperature. Values of coefficient of determination raised from fitting of linear regression between each of temperature form; Tmax, Tav, Twt, Tp2 Tp4 and Tp6 with actual pan evaporation. Data set comprises of daily records separately for all twelve months. Daily records are also regrouped into four more categories i.e. for whole year (365 days), hot months (April-May), cold months (December- January) and wet months (July-August). Though the r-squared values are found very low and explains that temperature alone cannot be taken as predictor of evaporation, which is a well comparative fact, but the purpose of presenting these values here to show the comparative effect of different temperature forms on evaporation. In hot months, the Twt with r-squared values of 0.49 seems to be more correlated than other temperature forms. But, in cold months Tmax, Tp2, Tp4 and Tp6 have more influence on evaporation than the Tav or Twt. The research outcome of the present study will be helpful to estimation of reweigh temperature rather average of maximum and minimum temperature for use in prediction research work.

scholarly journals Using data mining methods to improve discharge coefficient prediction in Piano Key and Labyrinth weirs

2021 ◽  
Author(s):  
Mahdi Majedi-Asl ◽  
Mehdi Foladipanah ◽  
Venkat Arun ◽  
Ravi Prakash Tripathi
Keyword(s):  
Discharge Coefficient ◽  
Gene Expression Programming ◽  
Research Work ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Effective Parameters ◽  
Data Set ◽  
Labyrinth Weir ◽  
Dimensional Parameters ◽  
Using Data

Abstract As a remarkable parameter, the discharge coefficient (Cd) plays an important role in determining weirs' passing capacity. In this research work, the support vector machine (SVM) and the gene expression programming (GEP) algorithms were assessed to predict Cd of piano key weir (PKW), rectangular labyrinth weir (RLW), and trapezoidal labyrinth weir (TLW) with gathered experimental data set. Using dimensional analysis, various combinations of hydraulic and geometric non-dimensional parameters were extracted to perform simulation. The superior model for the SVM and the GEP predictor for PKW, RLW, and TLW included , and respectively. The results showed that both algorithms are potential in predicting discharge coefficient, but the coefficient of determination (RMSE, R2, Cd(DDR)max) illustrated the superiority of the GEP performance over the SVM. The results of the sensitivity analysis determined the highest effective parameters for PKW, RLW, and TLW in predicting discharge coefficients are , , and Fr respectively.

scholarly journals TO DETERMINE CORRELATION BETWEEN MEAN PERIPHERAL LEUCOCYTE COUNTS AND MEAN LESION VOLUME IN ACUTE ISCHEMIC STROKE

2021 ◽  
Vol 71 (Suppl-1) ◽  
pp. S10-14
Author(s):  
Raees Iqbal Khan ◽  
Arshad Mehmood ◽  
Abdul Latif Khattak ◽  
Hina Syeda ◽  
Raja Jibran ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Research Work ◽  
Cross Sectional Study ◽  
Coefficient Of Determination ◽  
Military Hospital ◽  
Lesion Volume ◽  
Inclusion Criteria ◽  
Cross Sectional ◽  
Positive Correlation

Objective: To determine correlation between mean peripheral leucocyte counts and mean lesion volume in acute ischemic stroke. Study Design: Cross sectional study. Place and Duration of Study: We have performed all this research work Combined Military Hospital Quetta, from Jan 2018 to Dec 2018. Methodology: All admitted patients fulfilling the inclusion criteria were incorporated in the study. The diagnosis of acute ischemic stroke was based on symptoms of focal neurologic insufficiency and MRI brain evidence of infarct. Total peripheral leucocyte counts were calculated under the supervision of a pathologist who is the fellow of CPSP and has addition a l10 years of experience in pathology. All the above stated evidence including name, age, gender and address were documented in the study Performa. Results: Total no of patients in our study was 70. Of total patients 39 (55.71%) were male and 31 (4.29%) were females. Correlation between mean peripheral leucocytes counts and mean lesion volume in acute ischemic stroke was calculated as 15.97 ± 3.53 x 109 for TLC and 12.50 ± 3.24 for lesion volume, final results of R are 0.7936. Positive correlation revealed that both increased. X variable scores proportionately related with increased Y variable scores. Same goes for decreased values. The coefficient of determination R2 results turned out to be 0.6298. Conclusion: We concluded a positive correlation between mean peripheral leucocytes counts and mean lesion volume in acute ischemic stroke. Additional studies are mandatory to validate our findings to establish positive correlation.

scholarly journals River Sediment Transport Modeling of Ele River using Computational Fluid Dynamics Model of COMSOL Multiphysics Program

2021 ◽  
Vol 16 ◽  
pp. 1-7
Author(s):  
Joseph Ikenna Ubah ◽  
Louis Chukwuemeka Orakwe ◽  
Nelson Mbanefo Okoye ◽  
Kingsely Nnaemeka Ogbu
Keyword(s):  
Sediment Transport ◽  
Crop Production ◽  
Research Work ◽  
River System ◽  
Sediment Deposition ◽  
Low Flow ◽  
Coefficient Of Determination ◽  
Comsol Multiphysics ◽  
Particle Tracing ◽  
Study Results

Excessive sediment deposition results to hydro-ecological problems particularly for shallow streams that experience significant point-source pollution. In recent times, models have been employed to investigate sediment transport in river systems. The aim of this research work is to model sediment transport of Ele River using particle tracing methodology. The governing equations of fluid flow and particle movement were modelled using COMSOL Multiphysics 5.3a. The result was validated using experimental data and the model result showed good agreement with coefficient of determination of 0.99. Study results showed that sediment at the river banks posses lower velocities compared to sediments in midstream. This implies higher sediment deposition at the banks due to low flow velocity. These sediments deposition constitute problems to the river system through degradation of water quality and blocking irrigation nozzles, impacting irrigation efficiency and crop production.

scholarly journals Predicting Nanobinder-Improved Unsaturated Soil Consistency Limits Using Genetic Programming and Artificial Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Ahmed M. Ebid ◽  
Light I. Nwobia ◽  
Kennedy C. Onyelowe ◽  
Frank I. Aneke
Keyword(s):  
Genetic Programming ◽  
Unsaturated Soil ◽  
Unsaturated Soils ◽  
Research Work ◽  
Hydrated Lime ◽  
Soil Improvement ◽  
Coefficient Of Determination ◽  
Powder Materials ◽  
Efficient Design ◽  
Artificial Neural

Unsaturated soils used as compacted subgrade, backfill, or foundation materials react unfavorably under hydraulically bound environments due to swell and shrink cycles in response to seasonal changes. To overcome these undesirable conditions, additive stabilization processes are used to improve the volume change phenomenon in soils. However, the use of supplementary binders made from solid waste base powder materials has become necessary to deal with the hazards of greenhouse due to ordinary cement use. Meanwhile, several studies are being carried out to design infrastructures even with the limitations of insufficient or lack of equipment needed for efficient design performance. Intelligent prediction techniques have been used to overcome this shortcoming as the primary purpose of this research work. Therefore, in this work, genetic programming (GP) and artificial neural network (ANN) have been used to predict the consistency limits, i.e., liquid limits, plastic limit, and plasticity index of unsaturated soil treated with a composite binder known as hybrid cement (HC) made from blending nanostructured quarry fines (NQF) and hydrated-lime-activated nanostructured rice husk ash (HANRHA). The database needed for the prediction operation was generated from several experiments corresponding with treatment dosages of HANRHA between 0 and 12% at a rate of 0.1%. The results of the stabilization exercise showed substantial development on the soil properties examined, while the prediction exercise showed that ANN outclassed GP in terms of performance evaluation, which was conducted using sum of squared error (SSE) and coefficient of determination (R2) indices. Generally, nanostructuring of the component binder material has contributed to the success achieved in both soil improvement and efficiency of the models predicted.

scholarly journals Neural Network Models for Traffic Estimation in Mobile Networks in Lagos, Nigeria

2021 ◽  
pp. 292-309
Author(s):  
Sodiq Kazeem Adetunji ◽  
Adenowo Adetokunbo ◽  
Akinyemi Lateef
Keyword(s):  
Neural Network ◽  
Mobile Communication ◽  
Mobile Networks ◽  
Research Work ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Neural Network Models ◽  
Ann Modeling ◽  
Traffic Estimation

The network providers are now being challenged with their inability to accurate estimate and characterize traffic in a particular area, due to the increasing number of mobile communication services being rendered by the network providers Hence, this has been greatly undermining their design and planning processes and as such increasingly affected the Quality of Service(QoS).This research work addresses the traffic estimation in mobile communication network using Artificial Neural Network (ANN) approach using measured data collected in Lagos State,Nigeria.The Multilayer Perceptron (MLP) and Radial Basis Function (RBF) ANN techniques were used in the traffic modeling. The results of the ANN modeling showed that the Model 1 of MLP performed better than other models with Coefficient of Determination (R2) of 99%, Root Mean Square Error(RMSE) of 5.456 and Mean Bias Error(MBE) of 0.94.It is recommended that the dataset used in developing the ANN models be increased by collecting and using not more than 12months traffic data for ANN modeling .An appropriate design of the models should also be given a serious concern by choosing appropriate number of neurons at the hidden units of the neural networks .This will provide a good traffic estimation which the mobile network provider can be used in network design and planning.

scholarly journals Mapping of Chagas disease research: analysis of publications in the period between 1940 and 2009

2011 ◽  
Vol 44 (6) ◽  
pp. 708-716 ◽  
Author(s):  
José Manuel Ramos ◽  
Gregorio González-Alcaide ◽  
Joaquín Gascón ◽  
Félix Gutiérrez
Keyword(s):  
Chagas Disease ◽  
Latin American ◽  
Research Work ◽  
Developed Countries ◽  
The United States ◽  
Coefficient Of Determination ◽  
Disease Research ◽  
Bibliometric Review ◽  
Linear Fit ◽  
Number Of Publications

INTRODUCTION: Publications are often used as a measure of success in research work. Chagas disease occurs in Central and Southern America. However, during the past years, the disease has been occurring outside Latin America due to migration from endemic zones. This article describes a bibliometric review of the literature on Chagas disease research indexed in PubMed during a 70-year period. METHODS: Medline was used via the PubMed online service of the U.S. National Library of Medicine from 1940 to 2009. The search strategy was: Chagas disease [MeSH] OR Trypanosoma cruzi [MeSH]. RESULTS: A total of 13,989 references were retrieved. The number of publications increased steadily over time from 1,361 (1940-1969) to 5,430 (2000-2009) (coefficient of determination for linear fit, R²=0.910). Eight journals contained 25% of the Chagas disease literature. Of the publications, 64.2% came from endemic countries. Brazil was the predominant country (37%), followed by the United States (17.6%) and Argentina (14%). The ranking in production changed when the number of publications was normalized by estimated cases of Chagas disease (Panama and Uruguay), population (Argentina and Uruguay), and gross domestic product (Bolivia and Brazil). CONCLUSIONS: Several Latin American countries, where the prevalence of T. cruzi infection was not very high, were the main producers of the Chagas disease literature, after adjusting for economic and population indexes. The countries with more estimated cases of Chagas disease produced less research on Chagas disease than some developed countries.

scholarly journals Artificial Intelligence Based Modelling of Adsorption Water Desalination System

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1674
Author(s):  
Hesham Alhumade ◽  
Hegazy Rezk ◽  
Abdulrahim A. Al-Zahrani ◽  
Sharif F. Zaman ◽  
Ahmed Askalany
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Inference ◽  
Research Work ◽  
Water Desalination ◽  
Coefficient Of Performance ◽  
Coefficient Of Determination ◽  
Cooling Power ◽  
Anfis Model ◽  
Inference System ◽  
Output Performance

The main target of this research work is to model the output performance of adsorption water desalination system (AWDS) in terms of switching and cycle time using artificial intelligence. The output performance of the ADC system is expressed by the specific daily water production (SDWP), the coefficient of performance (COP), and specific cooling power (SCP). A robust Adaptive Network-based Fuzzy Inference System (ANFIS) model of SDWP, COP, and SCP was built using the measured data. To demonstrate the superiority of the suggested ANFIS model, the model results were compared with those achieved by Analysis of Variance (ANOVA) based on the maximum coefficient of determination and minimum error between measured and estimated data in addition to the mean square error (MSE). Applying ANOVA, the average coefficient-of-determination values were 0.8872 and 0.8223, respectively, for training and testing. These values are increased to 1.0 and 0.9673, respectively, for training and testing thanks to ANFIS based modeling. In addition, ANFIS modelling decreased the RMSE value of all datasets by 83% compared with ANOVA. In sum, the main findings confirmed the superiority of ANFIS modeling of the output performance of adsorption water desalination system compared with ANOVA.

scholarly journals Estimation of Global Solar Radiation using Modifed Angstrom Empirical formula on the basis of Meterological parameters in Himalaya Region Pokhara, Nepal

2016 ◽  
Vol 11 (1) ◽  
pp. 158-164
Author(s):  
Khem N. Poudyal
Keyword(s):  
Solar Radiation ◽  
Meteorological Parameters ◽  
Research Work ◽  
Global Solar Radiation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Mean Square ◽  
Performance Parameters ◽  
Sunshine Hour

This research work proposes the coefficient equation of modified Angstrom   model using sunshine hour and meteorological parameters for the estimation of global solar radiation in Himalaya Region Pokhara (28.22° N, 83.32° E),  Nepal . This site is about 800.0 m above from the sea level lying just 20.0 km south of the Machhaputre Himalayas.  The model coefficients a and b obtained in this research are 0.43 and 0.23 respectively. The performance parameters of the model are: Root Mean Square Error RMSE = 0.13 MJ/m2 /day, Mean Bias Error MBE= 0.02 MJ/m /day Mean Percentage MPE= 5 percent and coefficient of determination R2 = 0.70. Journal of the Institute of Engineering, 2015, 11(1): 158-164

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