Search results for: mixture regression model
19531 Impact of Infrastructural Development on Socio-Economic Growth: An Empirical Investigation in India
Authors: Jonardan Koner
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The study attempts to find out the impact of infrastructural investment on state economic growth in India. It further tries to determine the magnitude of the impact of infrastructural investment on economic indicator, i.e., per-capita income (PCI) in Indian States. The study uses panel regression technique to measure the impact of infrastructural investment on per-capita income (PCI) in Indian States. Panel regression technique helps incorporate both the cross-section and time-series aspects of the dataset. In order to analyze the difference in impact of the explanatory variables on the explained variables across states, the study uses Fixed Effect Panel Regression Model. The conclusions of the study are that infrastructural investment has a desirable impact on economic development and that the impact is different for different states in India. We analyze time series data (annual frequency) ranging from 1991 to 2010. The study reveals that the infrastructural investment significantly explains the variation of economic indicators.Keywords: infrastructural investment, multiple regression, panel regression techniques, economic development, fixed effect dummy variable model
Procedia PDF Downloads 36219530 Effect of Using Crumb Rubber with Warm-Mix-Asphalt Additive in Laboratory and Field Aging
Authors: Mustafa Akpolat, Baha Vural Kök
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Using a waste material such as crumb rubber (CR) obtained by waste tires has become an important issue in respect to sustainability. However, the CR modified mixture also requires high manufacture temperature as a polymer modified mixture. For this reason in this study, it is intended to produce a CR modified mixture with warm mix asphalt additives in the same mixture. Asphalt mixtures produced by pure, 10%CR, 10%CR+3% Sasobit and 10%CR+0.7% Evotherm were subjected to aging procedure in the laboratory and the field. The indirect tensile repeated tests were applied to aged and original specimens. It was concluded that the fatigue life of the mixtures increased significantly with the increase of aging time. CR+Sasobit modified mixture aged at the both field and laboratory gave the highest load cycle among the mixtures.Keywords: crumb rubber, warm mix asphalt, aging, fatigue
Procedia PDF Downloads 39519529 Prevention of Biocompounds and Amino Acid Losses in Vernonia amygdalina duringPost Harvest Treatment Using Hot Oil-Aqueous Mixture
Authors: Nneka Nkechi Uchegbu, Temitope Omolayo Fasuan
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This study investigated how to reduce bio-compounds and amino acids in V. amygdalina leaf during processing as a functional food ingredient. Fresh V. amygdalina leaf was processed using thermal oil-aqueous mixtures (soybean oil: aqueous and palm oil: aqueous) at 1:40 and 130 (v/v), respectively. Results indicated that the hot soybean oil-aqueous mixture was the most effective in preserving the bio-compounds and amino acids with retention potentials of 80.95% of the bio-compounds at the rate of 90-100%. Hot palm oil-aqueous mixture retained 61.90% of the bio-compounds at the rate of 90-100% and hot aqueous retained 9.52% of the bio-compounds at the same rate. During the debittering process, seven new bio-compounds were formed in the leaves treated with hot soybean oil-aqueous mixture, six in palm oil-aqueous mixture, and only four in hot aqueous leaves. The bio-compounds in the treated leaves have potential functions as antitumor, antioxidants, antihistaminic, anti-ovarian cancer, anti-inflammatory, antiarthritic, hepatoprotective, antihistaminic, haemolytic 5-α reductase inhibitor, nt, immune-stimulant, diuretic, antiandrogenic, and anaemiagenic. Alkaloids and polyphenols were retained at the rate of 81.34-98.50% using oil: aqueous mixture while aqueous recorded the rate of 33.47-41.46%. Most of the essential amino acids were retained at a rate above 90% through the aid of oil. The process is scalable and could be employed for domestic and industrial applications.Keywords: V. amygdalina leaf, bio-compounds, oil-aqueous mixture, amino acids
Procedia PDF Downloads 14119528 Survival Analysis Based Delivery Time Estimates for Display FAB
Authors: Paul Han, Jun-Geol Baek
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In the flat panel display industry, the scheduler and dispatching system to meet production target quantities and the deadline of production are the major production management system which controls each facility production order and distribution of WIP (Work in Process). In dispatching system, delivery time is a key factor for the time when a lot can be supplied to the facility. In this paper, we use survival analysis methods to identify main factors and a forecasting model of delivery time. Of survival analysis techniques to select important explanatory variables, the cox proportional hazard model is used to. To make a prediction model, the Accelerated Failure Time (AFT) model was used. Performance comparisons were conducted with two other models, which are the technical statistics model based on transfer history and the linear regression model using same explanatory variables with AFT model. As a result, the Mean Square Error (MSE) criteria, the AFT model decreased by 33.8% compared to the existing prediction model, decreased by 5.3% compared to the linear regression model. This survival analysis approach is applicable to implementing a delivery time estimator in display manufacturing. And it can contribute to improve the productivity and reliability of production management system.Keywords: delivery time, survival analysis, Cox PH model, accelerated failure time model
Procedia PDF Downloads 53419527 Supervised-Component-Based Generalised Linear Regression with Multiple Explanatory Blocks: THEME-SCGLR
Authors: Bry X., Trottier C., Mortier F., Cornu G., Verron T.
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We address component-based regularization of a Multivariate Generalized Linear Model (MGLM). A set of random responses Y is assumed to depend, through a GLM, on a set X of explanatory variables, as well as on a set T of additional covariates. X is partitioned into R conceptually homogeneous blocks X1, ... , XR , viewed as explanatory themes. Variables in each Xr are assumed many and redundant. Thus, Generalised Linear Regression (GLR) demands regularization with respect to each Xr. By contrast, variables in T are assumed selected so as to demand no regularization. Regularization is performed searching each Xr for an appropriate number of orthogonal components that both contribute to model Y and capture relevant structural information in Xr. We propose a very general criterion to measure structural relevance (SR) of a component in a block, and show how to take SR into account within a Fisher-scoring-type algorithm in order to estimate the model. We show how to deal with mixed-type explanatory variables. The method, named THEME-SCGLR, is tested on simulated data.Keywords: Component-Model, Fisher Scoring Algorithm, GLM, PLS Regression, SCGLR, SEER, THEME
Procedia PDF Downloads 39019526 Microstructure Analysis of Biopolymer Mixture (Chia-Gelatin) by Laser Confocal Microscopy
Authors: Emmanuel Flores Huicochea, Guadalupe Borja Mendiola, Jacqueline Flores Lopez, Rodolfo Rendon Villalobos
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The usual procedure to investigate the properties of biodegradable films has been to prepare the film, measure the mechanical or transport properties and then decide whether the mixture has better properties than the individual components, instead of investigating whether the mixture has biopolymer-biopolymer interaction, then prepare the film and finally measure the properties of the film. The work investigates the presence of interaction biopolymer-biopolymer in a mixture of chia biopolymer and gelatin using Laser Confocal Microscopy (LCM). Previously, the chia biopolymer was obtained from chia seed. CML analysis of mixtures of chia biopolymer-gelatin without Na⁺ ions exhibited aggregates of different size, in the range of 100-400 μm, of defined color, for the two colors, but no mixing of color was observed. The increased of gelatin in the mixture decreases the size and number of aggregates. The tridimensional microstructure reveled that there are two layers of biopolymers, chia and gelatin well defined. The mixture chia biopolymer-gelatin with 10 mM Na⁺ and with a ratio 75:25 (chia-gelatin) showed lower aggregated size than others mixture with and without ions. This result could be explained because the chia biopolymer is a polyelectrolyte and the added sodium ions reduce the molecular rigidity by neutralizing the negative charges that the chia biopolymer possesses and therefore a better biopolymer-biopolymer interaction is allowed between the biopolymer of chia and gelatin.Keywords: biopolymer-biopolymer interaction, confocal laser microscopy, CLM, microstructure, mixture chia-gelatin
Procedia PDF Downloads 20119525 The Suitability of Agile Practices in Healthcare Industry with Regard to Healthcare Regulations
Authors: Mahmood Alsaadi, Alexei Lisitsa
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Nowadays, medical devices rely completely on software whether as whole software or as embedded software, therefore, the organization that develops medical device software can benefit from adopting agile practices. Using agile practices in healthcare software development industries would bring benefits such as producing a product of a high-quality with low cost and in short period. However, medical device software development companies faced challenges in adopting agile practices. These due to the gaps that exist between agile practices and the requirements of healthcare regulations such as documentation, traceability, and formality. This research paper will conduct a study to investigate the adoption rate of agile practice in medical device software development, and they will extract and outline the requirements of healthcare regulations such as Food and Drug Administration (FDA), Health Insurance Portability and Accountability Act (HIPAA), and Medical Device Directive (MDD) that affect directly or indirectly on software development life cycle. Moreover, this research paper will evaluate the suitability of using agile practices in healthcare industries by analyzing the most popular agile practices such as eXtream Programming (XP), Scrum, and Feature-Driven Development (FDD) from healthcare industry point of view and in comparison with the requirements of healthcare regulations. Finally, the authors propose an agile mixture model that consists of different practices from different agile methods. As result, the adoption rate of agile practices in healthcare industries still low and agile practices should enhance with regard to requirements of the healthcare regulations in order to be used in healthcare software development organizations. Therefore, the proposed agile mixture model may assist in minimizing the gaps existing between healthcare regulations and agile practices and increase the adoption rate in the healthcare industry. As this research paper part of the ongoing project, an evaluation of agile mixture model will be conducted in the near future.Keywords: adoption of agile, agile gaps, agile mixture model, agile practices, healthcare regulations
Procedia PDF Downloads 23119524 Entropy Generation Analysis of Heat Recovery Vapor Generator for Ammonia-Water Mixture
Authors: Chul Ho Han, Kyoung Hoon Kim
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This paper carries out a performance analysis based on the first and second laws of thermodynamics for heat recovery vapor generator (HRVG) of ammonia-water mixture when the heat source is low-temperature energy in the form of sensible heat. In the analysis, effects of the ammonia mass concentration and mass flow ratio of the binary mixture are investigated on the system performance including the effectiveness of heat transfer, entropy generation, and exergy efficiency. The results show that the ammonia concentration and the mass flow ratio of the mixture have significant effects on the system performance of HRVG.Keywords: entropy, exergy, ammonia-water mixture, heat exchanger
Procedia PDF Downloads 39119523 Detecting Earnings Management via Statistical and Neural Networks Techniques
Authors: Mohammad Namazi, Mohammad Sadeghzadeh Maharluie
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Predicting earnings management is vital for the capital market participants, financial analysts and managers. The aim of this research is attempting to respond to this query: Is there a significant difference between the regression model and neural networks’ models in predicting earnings management, and which one leads to a superior prediction of it? In approaching this question, a Linear Regression (LR) model was compared with two neural networks including Multi-Layer Perceptron (MLP), and Generalized Regression Neural Network (GRNN). The population of this study includes 94 listed companies in Tehran Stock Exchange (TSE) market from 2003 to 2011. After the results of all models were acquired, ANOVA was exerted to test the hypotheses. In general, the summary of statistical results showed that the precision of GRNN did not exhibit a significant difference in comparison with MLP. In addition, the mean square error of the MLP and GRNN showed a significant difference with the multi variable LR model. These findings support the notion of nonlinear behavior of the earnings management. Therefore, it is more appropriate for capital market participants to analyze earnings management based upon neural networks techniques, and not to adopt linear regression models.Keywords: earnings management, generalized linear regression, neural networks multi-layer perceptron, Tehran stock exchange
Procedia PDF Downloads 41619522 Comparative Study od Three Artificial Intelligence Techniques for Rain Domain in Precipitation Forecast
Authors: Nabilah Filzah Mohd Radzuan, Andi Putra, Zalinda Othman, Azuraliza Abu Bakar, Abdul Razak Hamdan
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Precipitation forecast is important to avoid natural disaster incident which can cause losses in the involved area. This paper reviews three techniques logistic regression, decision tree, and random forest which are used in making precipitation forecast. These combination techniques through the vector auto-regression (VAR) model help in finding the advantages and strengths of each technique in the forecast process. The data-set contains variables of the rain’s domain. Adaptation of artificial intelligence techniques involved in rain domain enables the forecast process to be easier and systematic for precipitation forecast.Keywords: logistic regression, decisions tree, random forest, VAR model
Procedia PDF Downloads 44119521 6D Posture Estimation of Road Vehicles from Color Images
Authors: Yoshimoto Kurihara, Tad Gonsalves
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Currently, in the field of object posture estimation, there is research on estimating the position and angle of an object by storing a 3D model of the object to be estimated in advance in a computer and matching it with the model. However, in this research, we have succeeded in creating a module that is much simpler, smaller in scale, and faster in operation. Our 6D pose estimation model consists of two different networks – a classification network and a regression network. From a single RGB image, the trained model estimates the class of the object in the image, the coordinates of the object, and its rotation angle in 3D space. In addition, we compared the estimation accuracy of each camera position, i.e., the angle from which the object was captured. The highest accuracy was recorded when the camera position was 75°, the accuracy of the classification was about 87.3%, and that of regression was about 98.9%.Keywords: 6D posture estimation, image recognition, deep learning, AlexNet
Procedia PDF Downloads 14819520 Mixtures of Length-Biased Weibull Distributions for Loss Severity Modelling
Authors: Taehan Bae
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In this paper, a class of length-biased Weibull mixtures is presented to model loss severity data. The proposed model generalizes the Erlang mixtures with the common scale parameter, and it shares many important modelling features, such as flexibility to fit various data distribution shapes and weak-denseness in the class of positive continuous distributions, with the Erlang mixtures. We show that the asymptotic tail estimate of the length-biased Weibull mixture is Weibull-type, which makes the model effective to fit loss severity data with heavy-tailed observations. A method of statistical estimation is discussed with applications on real catastrophic loss data sets.Keywords: Erlang mixture, length-biased distribution, transformed gamma distribution, asymptotic tail estimate, EM algorithm, expectation-maximization algorithm
Procedia PDF Downloads 21719519 EarlyWarning for Financial Stress Events:A Credit-Regime Switching Approach
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We propose a new early warning model for predicting financial stress events for a given future time. In this model, we examine whether credit conditions play an important role as a nonlinear propagator of shocks when predicting the likelihood of occurrence of financial stress events for a given future time. This propagation takes the form of a threshold regression in which a regime change occurs if credit conditions cross a critical threshold. Given the new early warning model for financial stress events, we evaluate the performance of this model and currently available alternatives, such as the model from signal extraction approach, and linear regression model. In-sample forecasting results indicate that the three types of models are useful tools for predicting financial stress events while none of them outperforms others across all criteria considered. The out-of-sample forecasting results suggest that the credit-regime switching model performs better than the two others across all criteria and all forecasting horizons considered.Keywords: cut-off probability, early warning model, financial crisis, financial stress, regime-switching model, forecasting horizons
Procedia PDF Downloads 43019518 Modeling the Impacts of Road Construction on Lands Values
Authors: Maha Almumaiz, Harry Evdorides
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Change in land value typically occurs when a new interurban road construction causes an increase in accessibility; this change in the adjacent lands values differs according to land characteristics such as geographic location, land use type, land area and sale time (appraisal time). A multiple regression model is obtained to predict the percent change in land value (CLV) based on four independent variables namely land distance from the constructed road, area of land, nature of land use and time from the works completion of the road. The random values of percent change in land value were generated using Microsoft Excel with a range of up to 35%. The trend of change in land value with the four independent variables was determined from the literature references. The statistical analysis and model building process has been made by using the IBM SPSS V23 software. The Regression model suggests, for lands that are located within 3 miles as the straight distance from the road, the percent CLV is between (0-35%) which is depending on many factors including distance from the constructed road, land use, land area and time from works completion of the new road.Keywords: interurban road, land use types, new road construction, percent CLV, regression model
Procedia PDF Downloads 26319517 An Association Model to Correlate the Experimentally Determined Mixture Solubilities of Methyl 10-Undecenoate with Methyl Ricinoleate in Supercritical Carbon Dioxide
Authors: V. Mani Rathnam, Giridhar Madras
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Fossil fuels are depleting rapidly as the demand for energy, and its allied chemicals are continuously increasing in the modern world. Therefore, sustainable renewable energy sources based on non-edible oils are being explored as a viable option as they do not compete with the food commodities. Oils such as castor oil are rich in fatty acids and thus can be used for the synthesis of biodiesel, bio-lubricants, and many other fine industrial chemicals. There are several processes available for the synthesis of different chemicals obtained from the castor oil. One such process is the transesterification of castor oil, which results in a mixture of fatty acid methyl esters. The main products in the above reaction are methyl ricinoleate and methyl 10-undecenoate. To separate these compounds, supercritical carbon dioxide (SCCO₂) was used as a green solvent. SCCO₂ was chosen as a solvent due to its easy availability, non-toxic, non-flammable, and low cost. In order to design any separation process, the preliminary requirement is the solubility or phase equilibrium data. Therefore, the solubility of a mixture of methyl ricinoleate with methyl 10-undecenoate in SCCO₂ was determined in the present study. The temperature and pressure range selected for the investigation were T = 313 K to 333 K and P = 10 MPa to 18 MPa. It was observed that the solubility (mol·mol⁻¹) of methyl 10-undecenoate varied from 2.44 x 10⁻³ to 8.42 x 10⁻³ whereas it varied from 0.203 x 10⁻³ to 6.28 x 10⁻³ for methyl ricinoleate within the chosen operating conditions. These solubilities followed a retrograde behavior (characterized by the decrease in the solubility values with the increase in temperature) throughout the range of investigated operating conditions. An association theory model, coupled with regular solution theory for activity coefficients, was developed in the present study. The deviation from the experimental data using this model can be quantified using the average absolute relative deviation (AARD). The AARD% for the present compounds is 4.69 and 8.08 for methyl 10-undecenoate and methyl ricinoleate, respectively in a mixture of methyl ricinoleate and methyl 10-undecenoate. The maximum solubility enhancement of 32% was observed for the methyl ricinoleate in a mixture of methyl ricinoleate and methyl 10-undecenoate. The highest selectivity of SCCO₂ was observed to be 12 for methyl 10-undecenoate in a mixture of methyl ricinoleate and methyl 10-undecenoate.Keywords: association theory, liquid mixtures, solubilities, supercritical carbon dioxide
Procedia PDF Downloads 13019516 Comparison of Multivariate Adaptive Regression Splines and Random Forest Regression in Predicting Forced Expiratory Volume in One Second
Authors: P. V. Pramila , V. Mahesh
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Pulmonary Function Tests are important non-invasive diagnostic tests to assess respiratory impairments and provides quantifiable measures of lung function. Spirometry is the most frequently used measure of lung function and plays an essential role in the diagnosis and management of pulmonary diseases. However, the test requires considerable patient effort and cooperation, markedly related to the age of patients esulting in incomplete data sets. This paper presents, a nonlinear model built using Multivariate adaptive regression splines and Random forest regression model to predict the missing spirometric features. Random forest based feature selection is used to enhance both the generalization capability and the model interpretability. In the present study, flow-volume data are recorded for N= 198 subjects. The ranked order of feature importance index calculated by the random forests model shows that the spirometric features FVC, FEF 25, PEF,FEF 25-75, FEF50, and the demographic parameter height are the important descriptors. A comparison of performance assessment of both models prove that, the prediction ability of MARS with the `top two ranked features namely the FVC and FEF 25 is higher, yielding a model fit of R2= 0.96 and R2= 0.99 for normal and abnormal subjects. The Root Mean Square Error analysis of the RF model and the MARS model also shows that the latter is capable of predicting the missing values of FEV1 with a notably lower error value of 0.0191 (normal subjects) and 0.0106 (abnormal subjects). It is concluded that combining feature selection with a prediction model provides a minimum subset of predominant features to train the model, yielding better prediction performance. This analysis can assist clinicians with a intelligence support system in the medical diagnosis and improvement of clinical care.Keywords: FEV, multivariate adaptive regression splines pulmonary function test, random forest
Procedia PDF Downloads 30519515 Valorization of Local Materials in the Waterproofing Technique of Landfills Site "TLS"
Authors: M. Debieche, F. Kaoua
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This paper deals with the use two locals materials abundant in our country, with the view to use a mixture in the waterproofing the landfills. Our interest comes from the necessity to the environment protection, which has recently considerably grown. The site's waterproofing technique, in the landfills sites, is nowadays a very necessary condition to protect the environment, which requires the use of appropriate materials. To this end, an optimal mixture ensuring good performance in terms of hydraulic conductivity, durability and shear strength, mixtures based of sand at different concentrations of sodium bentonite, at compact state are prepared and studied. This study showed that a low permeability of mixture (sand / bentonite) can be achieved 6% of sodium bentonite. This mixture confers also good mechanical behavior, expressed by the recorded, reduction of friction (φ) and the increase of the cohesion (C). Thus, the selected formulation represents an optimal mixture for waterproofing systems. It guarantees an economical and ecological advantages.Keywords: hydraulic conductivity, sand, sodium bentonite, sustainability
Procedia PDF Downloads 26919514 Quantitative Structure-Activity Relationship Study of Some Quinoline Derivatives as Antimalarial Agents
Authors: M. Ouassaf, S. Belaid
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A series of quinoline derivatives with antimalarial activity were subjected to two-dimensional quantitative structure-activity relationship (2D-QSAR) studies. Three models were implemented using multiple regression linear MLR, a regression partial least squares (PLS), nonlinear regression (MNLR), to see which descriptors are closely related to the activity biologic. We relied on a principal component analysis (PCA). Based on our results, a comparison of the quality of, MLR, PLS, and MNLR models shows that the MNLR (R = 0.914 and R² = 0.835, RCV= 0.853) models have substantially better predictive capability because the MNLR approach gives better results than MLR (R = 0.835 and R² = 0,752, RCV=0.601)), PLS (R = 0.742 and R² = 0.552, RCV=0.550) The model of MNLR gave statistically significant results and showed good stability to data variation in leave-one-out cross-validation. The obtained results suggested that our proposed model MNLR may be useful to predict the biological activity of derivatives of quinoline.Keywords: antimalarial, quinoline, QSAR, PCA, MLR , MNLR, MLR
Procedia PDF Downloads 15019513 Orthogonal Regression for Nonparametric Estimation of Errors-In-Variables Models
Authors: Anastasiia Yu. Timofeeva
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Two new algorithms for nonparametric estimation of errors-in-variables models are proposed. The first algorithm is based on penalized regression spline. The spline is represented as a piecewise-linear function and for each linear portion orthogonal regression is estimated. This algorithm is iterative. The second algorithm involves locally weighted regression estimation. When the independent variable is measured with error such estimation is a complex nonlinear optimization problem. The simulation results have shown the advantage of the second algorithm under the assumption that true smoothing parameters values are known. Nevertheless the use of some indexes of fit to smoothing parameters selection gives the similar results and has an oversmoothing effect.Keywords: grade point average, orthogonal regression, penalized regression spline, locally weighted regression
Procedia PDF Downloads 40719512 Statistical Model of Water Quality in Estero El Macho, Machala-El Oro
Authors: Rafael Zhindon Almeida
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Surface water quality is an important concern for the evaluation and prediction of water quality conditions. The objective of this study is to develop a statistical model that can accurately predict the water quality of the El Macho estuary in the city of Machala, El Oro province. The methodology employed in this study is of a basic type that involves a thorough search for theoretical foundations to improve the understanding of statistical modeling for water quality analysis. The research design is correlational, using a multivariate statistical model involving multiple linear regression and principal component analysis. The results indicate that water quality parameters such as fecal coliforms, biochemical oxygen demand, chemical oxygen demand, iron and dissolved oxygen exceed the allowable limits. The water of the El Macho estuary is determined to be below the required water quality criteria. The multiple linear regression model, based on chemical oxygen demand and total dissolved solids, explains 99.9% of the variance of the dependent variable. In addition, principal component analysis shows that the model has an explanatory power of 86.242%. The study successfully developed a statistical model to evaluate the water quality of the El Macho estuary. The estuary did not meet the water quality criteria, with several parameters exceeding the allowable limits. The multiple linear regression model and principal component analysis provide valuable information on the relationship between the various water quality parameters. The findings of the study emphasize the need for immediate action to improve the water quality of the El Macho estuary to ensure the preservation and protection of this valuable natural resource.Keywords: statistical modeling, water quality, multiple linear regression, principal components, statistical models
Procedia PDF Downloads 8319511 Adsorption of Cd2+ from Aqueous Solutions Using Chitosan Obtained from a Mixture of Littorina littorea and Achatinoidea Shells
Authors: E. D. Paul, O. F. Paul, J. E. Toryila, A. J. Salifu, C. E. Gimba
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Adsorption of Cd2+ ions from aqueous solution by Chitosan, a natural polymer, obtained from a mixture of the exoskeletons of Littorina littorea (Periwinkle) and Achatinoidea (Snail) was studied at varying adsorbent dose, contact time, metal ion concentrations, temperature and pH using batch adsorption method. The equilibrium adsorption isotherms were determined between 298 K and 345 K. The adsorption data were adjusted to Langmuir, Freundlich and the pseudo second order kinetic models. It was found that the Langmuir isotherm model most fitted the experimental data, with a maximum monolayer adsorption of 35.1 mgkg⁻¹ at 308 K. The entropy and enthalpy of adsorption were -0.1121 kJmol⁻¹K⁻¹ and -11.43 kJmol⁻¹ respectively. The Freundlich adsorption model, gave Kf and n values consistent with good adsorption. The pseudo-second order reaction model gave a straight line plot with rate constant of 1.291x 10⁻³ kgmg⁻¹ min⁻¹. The qe value was 21.98 mgkg⁻¹, indicating that the adsorption of Cadmium ion by the chitosan composite followed the pseudo-second order kinetic model.Keywords: adsorption, chitosan, littorina littorea, achatinoidea, natural polymer
Procedia PDF Downloads 39519510 Support Vector Regression Combined with Different Optimization Algorithms to Predict Global Solar Radiation on Horizontal Surfaces in Algeria
Authors: Laidi Maamar, Achwak Madani, Abdellah El Ahdj Abdellah
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The aim of this work is to use Support Vector regression (SVR) combined with dragonfly, firefly, Bee Colony and particle swarm Optimization algorithm to predict global solar radiation on horizontal surfaces in some cities in Algeria. Combining these optimization algorithms with SVR aims principally to enhance accuracy by fine-tuning the parameters, speeding up the convergence of the SVR model, and exploring a larger search space efficiently; these parameters are the regularization parameter (C), kernel parameters, and epsilon parameter. By doing so, the aim is to improve the generalization and predictive accuracy of the SVR model. Overall, the aim is to leverage the strengths of both SVR and optimization algorithms to create a more powerful and effective regression model for various cities and under different climate conditions. Results demonstrate close agreement between predicted and measured data in terms of different metrics. In summary, SVM has proven to be a valuable tool in modeling global solar radiation, offering accurate predictions and demonstrating versatility when combined with other algorithms or used in hybrid forecasting models.Keywords: support vector regression (SVR), optimization algorithms, global solar radiation prediction, hybrid forecasting models
Procedia PDF Downloads 2719509 Spectral Mixture Model Applied to Cannabis Parcel Determination
Authors: Levent Basayigit, Sinan Demir, Yusuf Ucar, Burhan Kara
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Many research projects require accurate delineation of the different land cover type of the agricultural area. Especially it is critically important for the definition of specific plants like cannabis. However, the complexity of vegetation stands structure, abundant vegetation species, and the smooth transition between different seconder section stages make vegetation classification difficult when using traditional approaches such as the maximum likelihood classifier. Most of the time, classification distinguishes only between trees/annual or grain. It has been difficult to accurately determine the cannabis mixed with other plants. In this paper, a mixed distribution models approach is applied to classify pure and mix cannabis parcels using Worldview-2 imagery in the Lakes region of Turkey. Five different land use types (i.e. sunflower, maize, bare soil, and cannabis) were identified in the image. A constrained Gaussian mixture discriminant analysis (GMDA) was used to unmix the image. In the study, 255 reflectance ratios derived from spectral signatures of seven bands (Blue-Green-Yellow-Red-Rededge-NIR1-NIR2) were randomly arranged as 80% for training and 20% for test data. Gaussian mixed distribution model approach is proved to be an effective and convenient way to combine very high spatial resolution imagery for distinguishing cannabis vegetation. Based on the overall accuracies of the classification, the Gaussian mixed distribution model was found to be very successful to achieve image classification tasks. This approach is sensitive to capture the illegal cannabis planting areas in the large plain. This approach can also be used for monitoring and determination with spectral reflections in illegal cannabis planting areas.Keywords: Gaussian mixture discriminant analysis, spectral mixture model, Worldview-2, land parcels
Procedia PDF Downloads 19119508 Hydro-Mechanical Behavior of a Tuff and Calcareous Sand Mixture for Use in Pavement in Arid Region
Authors: I. Goual, M. S. Goual, M. K. Gueddouda, Taïbi Saïd, Abou-Bekr Nabil, A. Ferhat
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The aim of the paper is to study the hydro-mechanical behavior of a tuff and calcareous sand mixture. A first experimental phase was carried out in order to find the optimal mixture. This showed that the material composed of 80% tuff and 20% calcareous sand provides the maximum mechanical strength. The second experimental phase concerns the study of the drying-wetting behavior of the optimal mixture was carried out on slurry samples and compacted samples at the MPO. Experimental results let to deduce the parameters necessary for the prediction of the hydro-mechanical behavior of pavement formulated from tuff and calcareous sand mixtures, related to moisture. This optimal mixture satisfies the regulation rules and hence constitutes a good local eco-material, abundantly available, for the conception of pavements.Keywords: tuff, sandy calcareous, road engineering, hydro mechanical behaviour, suction
Procedia PDF Downloads 43419507 Numerical Modeling the Cavitating Flow in Injection Nozzle Holes
Authors: Ridha Zgolli, Hatem Kanfoudi
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Cavitating flows inside a diesel injection nozzle hole were simulated using a mixture model. A 2D numerical model is proposed in this paper to simulate steady cavitating flows. The Reynolds-averaged Navier-Stokes equations are solved for the liquid and vapor mixture, which is considered as a single fluid with variable density which is expressed as function of the vapor volume fraction. The closure of this variable is provided by the transport equation with a source term TEM. The processes of evaporation and condensation are governed by changes in pressure within the flow. The source term is implanted in the CFD code ANSYS CFX. The influence of numerical and physical parameters is presented in details. The numerical simulations are in good agreement with the experimental data for steady flow.Keywords: cavitation, injection nozzle, numerical simulation, k–ω
Procedia PDF Downloads 39119506 Research on the Spatio-Temporal Evolution Pattern of Traffic Dominance in Shaanxi Province
Authors: Leng Jian-Wei, Wang Lai-Jun, Li Ye
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In order to measure and analyze the transportation situation within the counties of Shaanxi province over a certain period of time and to promote the province's future transportation planning and development, this paper proposes a reasonable layout plan and compares model rationality. The study uses entropy weight method to measure the transportation advantages of 107 counties in Shaanxi province from three dimensions: road network density, trunk line influence and location advantage in 2013 and 2021, and applies spatial autocorrelation analysis method to analyze the spatial layout and development trend of county-level transportation, and conducts ordinary least square (OLS)regression on transportation impact factors and other influencing factors. The paper also compares the regression fitting degree of the Geographically weighted regression(GWR) model and the OLS model. The results show that spatially, the transportation advantages of Shaanxi province generally show a decreasing trend from the Weihe Plain to the surrounding areas and mainly exhibit high-high clustering phenomenon. Temporally, transportation advantages show an overall upward trend, and the phenomenon of spatial imbalance gradually decreases. People's travel demands have changed to some extent, and the demand for rapid transportation has increased overall. The GWR model regression fitting degree of transportation advantages is 0.74, which is higher than the OLS regression model's fitting degree of 0.64. Based on the evolution of transportation advantages, it is predicted that this trend will continue for a period of time in the future. To improve the transportation advantages of Shaanxi province increasing the layout of rapid transportation can effectively enhance the transportation advantages of Shaanxi province. When analyzing spatial heterogeneity, geographic factors should be considered to establish a more reliable modelKeywords: traffic dominance, GWR model, spatial autocorrelation analysis, temporal and spatial evolution
Procedia PDF Downloads 8119505 Instability Index Method and Logistic Regression to Assess Landslide Susceptibility in County Route 89, Taiwan
Authors: Y. H. Wu, Ji-Yuan Lin, Yu-Ming Liou
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This study aims to set up the landslide susceptibility map of County Route 89 at Ren-Ai Township in Nantou County using the Instability Index Method and Logistic regression. Seven susceptibility factors including Slope Angle, Aspect, Elevation, Distance to fold, Distance to River, Distance to Road and Accumulated Rainfall were obtained by GIS based on the Typhoon Toraji landslide area identified by Industrial Technology Research Institute in 2001. To calculate the landslide percentage of each factor and acquire the weight and grade the grid by means of Instability Index Method. In this study, landslide susceptibility can be classified into four grades: high, medium high, medium low and low, in order to determine the advantages and disadvantages of the two models. The precision of this model is verified by classification error matrix and SRC curve. These results suggest that the logistic regression model is a preferred method than instability index in the assessment of landslide susceptibility. It is suitable for the landslide prediction and precaution in this area in the future.Keywords: instability index method, logistic regression, landslide susceptibility, SRC curve
Procedia PDF Downloads 28119504 Breast Cancer Mortality and Comorbidities in Portugal: A Predictive Model Built with Real World Data
Authors: Cecília M. Antão, Paulo Jorge Nogueira
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Breast cancer (BC) is the first cause of cancer mortality among Portuguese women. This retrospective observational study aimed at identifying comorbidities associated with BC female patients admitted to Portuguese public hospitals (2010-2018), investigating the effect of comorbidities on BC mortality rate, and building a predictive model using logistic regression. Results showed that the BC mortality in Portugal decreased in this period and reached 4.37% in 2018. Adjusted odds ratio indicated that secondary malignant neoplasms of liver, of bone and bone marrow, congestive heart failure, and diabetes were associated with an increased chance of dying from breast cancer. Although the Lisbon district (the most populated area) accounted for the largest percentage of BC patients, the logistic regression model showed that, besides patient’s age, being resident in Bragança, Castelo Branco, or Porto districts was directly associated with an increase of the mortality rate.Keywords: breast cancer, comorbidities, logistic regression, adjusted odds ratio
Procedia PDF Downloads 8219503 Competition between Regression Technique and Statistical Learning Models for Predicting Credit Risk Management
Authors: Chokri Slim
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The objective of this research is attempting to respond to this question: Is there a significant difference between the regression model and statistical learning models in predicting credit risk management? A Multiple Linear Regression (MLR) model was compared with neural networks including Multi-Layer Perceptron (MLP), and a Support vector regression (SVR). The population of this study includes 50 listed Banks in Tunis Stock Exchange (TSE) market from 2000 to 2016. Firstly, we show the factors that have significant effect on the quality of loan portfolios of banks in Tunisia. Secondly, it attempts to establish that the systematic use of objective techniques and methods designed to apprehend and assess risk when considering applications for granting credit, has a positive effect on the quality of loan portfolios of banks and their future collectability. Finally, we will try to show that the bank governance has an impact on the choice of methods and techniques for analyzing and measuring the risks inherent in the banking business, including the risk of non-repayment. The results of empirical tests confirm our claims.Keywords: credit risk management, multiple linear regression, principal components analysis, artificial neural networks, support vector machines
Procedia PDF Downloads 14619502 Statistical Analysis of the Impact of Maritime Transport Gross Domestic Product (GDP) on Nigeria’s Economy
Authors: Kehinde Peter Oyeduntan, Kayode Oshinubi
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Nigeria is referred as the ‘Giant of Africa’ due to high population, land mass and large economy. However, it still trails far behind many smaller economies in the continent in terms of maritime operations. As we have seen that the maritime industry is the spark plug for national growth, because it houses the most crucial infrastructure that generates wealth for a nation, it is worrisome that a nation with six seaports lag in maritime activities. In this research, we have studied how the Gross Domestic Product (GDP) of the maritime transport influences the Nigerian economy. To do this, we applied Simple Linear Regression (SLR), Support Vector Machine (SVM), Polynomial Regression Model (PRM), Generalized Additive Model (GAM) and Generalized Linear Mixed Model (GLMM) to model the relationship between the nation’s Total GDP (TGDP) and the Maritime Transport GDP (MGDP) using a time series data of 20 years. The result showed that the MGDP is statistically significant to the Nigerian economy. Amongst the statistical tool applied, the PRM of order 4 describes the relationship better when compared to other methods. The recommendations presented in this study will guide policy makers and help improve the economy of Nigeria in terms of its GDP.Keywords: maritime transport, economy, GDP, regression, port
Procedia PDF Downloads 146