Search results for: model order selection

Authors: Samir K. Safi

Abstract:

The purpose of this study was to compare the performance of two types of models, namely MIDAS and MIDAS-GARCH, in predicting the volatility of crude oil returns based on gold price returns and the COVID-19 pandemic. The study aimed to identify which model would provide more accurate short-term and long-term predictions and which model would perform better in handling the increased volatility caused by the pandemic. The findings of the study revealed that the MIDAS model performed better in predicting short-term and long-term volatility before the pandemic, while the MIDAS-GARCH model performed significantly better in handling the increased volatility caused by the pandemic. The study highlights the importance of selecting appropriate models to handle the complexities of real-world data and shows that the choice of model can significantly impact the accuracy of predictions. The practical implications of model selection and exploring potential methodological adjustments for future research will be highlighted and discussed.

Keywords: GARCH-MIDAS, MIDAS, crude oil, gold, COVID-19, volatility

Procedia PDF Downloads 24

Authors: Sudha T., Naveen C.

Abstract:

Dengue is a serious public health issue that causes significant annual economic and welfare burdens on nations. However, enhanced optimization techniques and quantitative modeling approaches can predict the incidence of dengue. By advocating for a data-driven approach, public health officials can make informed decisions, thereby improving the overall effectiveness of sudden disease outbreak control efforts. The National Oceanic and Atmospheric Administration and the Centers for Disease Control and Prevention are two of the U.S. Federal Government agencies from which this study uses environmental data. Based on environmental data that describe changes in temperature, precipitation, vegetation, and other factors known to affect dengue incidence, many predictive models are constructed that use different machine learning methods to estimate weekly dengue cases. The first step involves preparing the data, which includes handling outliers and missing values to make sure the data is prepared for subsequent processing and the creation of an accurate forecasting model. In the second phase, multiple feature selection procedures are applied using various machine learning models and optimization techniques. During the third phase of the research, machine learning models like the Huber Regressor, Support Vector Machine, Gradient Boosting Regressor (GBR), and Support Vector Regressor (SVR) are compared with several optimization techniques for feature selection, such as Harmony Search and Genetic Algorithm. In the fourth stage, the model's performance is evaluated using Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) as assistance. Selecting an optimization strategy with the least number of errors, lowest price, biggest productivity, or maximum potential results is the goal. In a variety of industries, including engineering, science, management, mathematics, finance, and medicine, optimization is widely employed. An effective optimization method based on harmony search and an integrated genetic algorithm is introduced for input feature selection, and it shows an important improvement in the model's predictive accuracy. The predictive models with Huber Regressor as the foundation perform the best for optimization and also prediction.

Keywords: deep learning model, dengue fever, prediction, optimization

Procedia PDF Downloads 30

Authors: Francesco Marotti de Sciarra, Raffaele Barretta

Abstract:

Starting from nonlocal continuum mechanics, a thermodynamically new nonlocal model of Euler-Bernoulli nanobeams is provided. The nonlocal variational formulation is consistently provided and the governing differential equation for transverse displacement are presented. Higher-order boundary conditions are then consistently derived. An example is contributed in order to show the effectiveness of the proposed model.

Keywords: Bernoulli-Euler beams, nanobeams, nonlocal elasticity, closed-form solutions

Procedia PDF Downloads 346

Authors: Kaleb Shiferaw, Berhanu Geberemedhin, Dereje Legesse

Abstract:

Access to credit is often viewed as a key to transform semi-subsistence smallholders into market oriented producers. However, only a few studies have examined factors that affect farmers’ decision to allocate credit on farm activities in general and livestock production in particular. A trivariate probit model with double selection is employed to identify factors that affect farmers’ decision to allocate credit on livestock production using data collected from smallholder farmers in Ethiopia. After controlling for two sample selection bias – taking credit for the production season and decision to allocate credit on farm activities – land ownership and access to a livestock centered extension service are found to have a significant (p<0.001) effect on farmers decision to use credit for livestock production. The result showed farmers with large land holding, and access to a livestock centered extension services are more likely to utilize credit for livestock production. However since the effect of land ownership squared is negative the effect of land ownership for those who own a large plot of land lessens. The study highlights the fact that improving access to credit does not automatically translate into more productive households. Improving farmers’ access to credit should be followed by a focused extension services.

Keywords: livestock production, credit access, credit allocation, household decision, double sample selection

Procedia PDF Downloads 301

Authors: Joonas Pääkkönen

Abstract:

In sports, individuals and teams are typically interested in final rankings. Final results, such as times or distances, dictate these rankings, also known as places. Places can be further associated with ordered random variables, commonly referred to as order statistics. In this work, we introduce a simple, yet accurate order statistical ordinal regression function that predicts relay race places with changeover-times. We call this function the Fenton-Wilkinson Order Statistics model. This model is built on the following educated assumption: individual leg-times follow log-normal distributions. Moreover, our key idea is to utilize Fenton-Wilkinson approximations of changeover-times alongside an estimator for the total number of teams as in the notorious German tank problem. This original place regression function is sigmoidal and thus correctly predicts the existence of a small number of elite teams that significantly outperform the rest of the teams. Our model also describes how place increases linearly with changeover-time at the inflection point of the log-normal distribution function. With real-world data from Jukola 2019, a massive orienteering relay race, the model is shown to be highly accurate even when the size of the training set is only 5% of the whole data set. Numerical results also show that our model exhibits smaller place prediction root-mean-square-errors than linear regression, mord regression and Gaussian process regression.

Keywords: Fenton-Wilkinson approximation, German tank problem, log-normal distribution, order statistics, ordinal regression, orienteering, sports analytics, sports modeling

Procedia PDF Downloads 104

Authors: Y. Kourd, D. Lefebvre

Abstract:

The monitoring of industrial processes is required to ensure operating conditions of industrial systems through automatic detection and isolation of faults. This paper proposes a method of fault diagnosis based on a neuro-fuzzy hybrid structure. This hybrid structure combines the selection of threshold and decision tree. The validation of this method is obtained with the DAMADICS benchmark. In the first phase of the method, a model will be constructed that represents the normal state of the system to fault detection. Signatures of the faults are obtained with residuals analysis and selection of appropriate thresholds. These signatures provide groups of non-separable faults. In the second phase, we build faulty models to see the flaws in the system that cannot be isolated in the first phase. In the latest phase we construct the tree that isolates these faults.

Keywords: decision tree, residuals analysis, ANFIS, fault diagnosis

Procedia PDF Downloads 598

Authors: Mahmoudi Marwa, Bahim Nadhem, Aydi Abdelwaheb, Issaoui Wissal, S. Najet

Abstract:

This work represents a coupling between the geographic information system (GIS) and the multicriteria analysis aiming at the selection of an artificial recharge site by the treated wastewater for the Ariana governorate. On regional characteristics, bibliography and available data on artificial recharge, 13 constraints and 5 factors were hierarchically structured for the adequacy of an artificial recharge. The factors are subdivided into two main groups: environmental factors and economic factors. The adopted methodology allows a preliminary assessment of a recharge site, the weighted linear combination (WLC) and the analytical hierarchy process (AHP) in a GIS. The standardization of the criteria is carried out by the application of the different membership functions. The form and control points of the latter are defined by the consultation of the experts. The weighting of the selected criteria is allocated according to relative importance using the AHP methodology. The weighted linear combination (WLC) integrates the different criteria and factors to delineate the most suitable areas for artificial recharge site selection by treated wastewater. The results of this study showed three potential candidate sites that appear when environmental factors are more important than economic factors. These sites are ranked in descending order using the ELECTRE III method. Nevertheless, decision making for the selection of an artificial recharge site will depend on the decision makers in force.

Keywords: artificial recharge site, treated wastewater, analytical hierarchy process, ELECTRE III

Procedia PDF Downloads 142

Authors: Nader Parsazadeh

Abstract:

The variety of bed sediment load relationships, insufficient information and data, and the influence of river conditions make the selection of an optimum relationship for a given river extremely difficult. Hence, in order to select the best formulae, the bed load equations should be evaluated. The affecting factors need to be scrutinized, and equations should be verified. Also, re-evaluation may be needed. In this research, sediment bed load of Dez Dam at Tal-e Zang Station has been studied. After reviewing the available references, the most common formulae were selected that included Meir-Peter and Muller, using MS Excel to compute and evaluate data. Then, 52 series of already measured data at the station were re-measured, and the sediment bed load was determined. 1. The calculated bed load obtained by different equations showed a great difference with that of measured data. 2. r difference ratio from 0.5 to 2.00 was 0% for all equations except for Nilsson and Shields equations while it was 61.5 and 59.6% for Nilsson and Shields equations, respectively. 3. By reviewing results and discarding probably erroneous measured data measurements (by human or machine), one may use Nilsson Equation due to its r value higher than 1 as an effective equation for estimating bed load at Tal-e Zang Station in order to predict activities that depend upon bed sediment load estimate to be determined. Also, since only few studies have been conducted so far, these results may be of assistance to the operators and consulting companies.

Keywords: bed load, empirical relation ship, sediment, Tale Zang Station

Procedia PDF Downloads 344

Authors: Ali E. Abbas

Abstract:

Industrial Engineering is a broad multidisciplinary field with intersections and applications in numerous areas. When designing a product, it is important to determine the appropriate attributes of value and the preference function for which the product is optimized. This paper provides some guidelines on appropriate selection of attributes for preference and value functions for engineering design.

Keywords: decision analysis, industrial engineering, direct vs. indirect values, engineering management

Procedia PDF Downloads 281

Authors: Taha Anjamrooz, Sareh Rajabi, Salwa Bheiry

Abstract:

Selection between the available projects in bidding processes for the contractor is one of the essential areas to concentrate on. It is important for the contractor to choose the right projects within its portfolio during the tendering stage based on certain criteria. It should align the bidding process with its origination strategies and goals as a screening process to have the right portfolio pool to start with. Secondly, it should set the proper framework and use a suitable technique in order to optimize its selection process for concertation purpose and higher efforts during the tender stage with goals of success and winning. In this research paper, a two steps framework proposed to increase the efficiency of the contractor’s bidding process and the winning chance of getting the new projects awarded. In this framework, initially, all the projects pass through the first stage screening process, in which the portfolio basket will be evaluated and adjusted in accordance with the organization strategies to the reduced version of the portfolio pool, which is in line with organization activities. In the second stage, the contractor uses linear programming to optimize the portfolio pool based on available resources such as manpower, light equipment, heavy equipment, financial capability, return on investment, and success rate of winning the bid. Therefore, this optimization model will assist the contractor in utilizing its internal resource to its maximum and increase its winning chance for the new project considering past experience with clients, built-relation between two parties, and complexity in the exertion of the projects. The objective of this research will be to increase the contractor's winning chance in the bidding process based on the success rate and expected return on investment.

Keywords: bidding process, internal resources, optimization, contracting portfolio management

Procedia PDF Downloads 124

Authors: Vishweshwar V. Dixit

Abstract:

Kannudi is a reference editor introducing a method of input for Kannada, called OHOK!, that is, Ottu Hāku Ottu Koḍu!. This is especially suited for pressure-sensitive input devices, though the current online implementation uses the regular mechanical keyboard. OHOK! has three possible modes, namely, sva-ottu (self-conjunct), kandante (as you see), and andante (as you say). It may be noted that kandante mode does not follow the phonetic order. However, this model may work well for those who are inclined to visualize as they type rather than vocalize the sounds. Kannudi also demonstrates how domain knowledge can be effectively used to potentially increase speed, accuracy, and user-friendliness. For example, selection of a default vowel, automatic shunyification, and arkification. Also implemented are four types of Deletes that are necessary for phono-syllabic languages like Kannada.

Keywords: kannada, conjunct, reference editor, pressure input

Procedia PDF Downloads 75

Authors: M. A. Tayfun, M. A. Alkhalidi

Abstract:

The exact theoretical expression describing the probability distribution of nonlinear sea-surface elevations derived from the second-order narrowband model has a cumbersome form that requires numerical computations, not well-disposed to theoretical or practical applications. Here, the same narrowband model is re-examined to develop a simpler closed-form approximation suitable for theoretical and practical applications. The salient features of the approximate form are explored, and its relative validity is verified with comparisons to other readily available approximations, and oceanic data.

Keywords: ocean waves, probability distributions, second-order nonlinearities, skewness coefficient, wave steepness

Procedia PDF Downloads 411

Authors: Farnaz Farhangi

Abstract:

Many people in New Zealand believe that the access to water is inexhaustible, and it comes from a history of virtually unrestricted access to it. For the region like Hamilton which is one of New Zealand’s fastest growing cities, it is crucial for policy makers to know about the future water consumption and implementation of rules and regulation such as universal water metering. Hamilton residents use water freely and they do not have any idea about how much water they use. Hence, one of proposed objectives of this research is focusing on forecasting water consumption using different methods. Residential water consumption time series exhibits seasonal and trend variations. Seasonality is the pattern caused by repeating events such as weather conditions in summer and winter, public holidays, etc. The problem with this seasonal fluctuation is that, it dominates other time series components and makes difficulties in determining other variations (such as educational campaign’s effect, regulation, etc.) in time series. Apart from seasonality, a stochastic trend is also combined with seasonality and makes different effects on results of forecasting. According to the forecasting literature, preprocessing (de-trending and de-seasonalization) is essential to have more performed forecasting results, while some other researchers mention that seasonally non-adjusted data should be used. Hence, I answer the question that is pre-processing essential? A wide range of forecasting methods exists with different pros and cons. In this research, I apply double seasonal ARIMA and Artificial Neural Network (ANN), considering diverse elements such as seasonality and calendar effects (public and school holidays) and combine their results to find the best predicted values. My hypothesis is the examination the results of combined method (hybrid model) and individual methods and comparing the accuracy and robustness. In order to use ARIMA, the data should be stationary. Also, ANN has successful forecasting applications in terms of forecasting seasonal and trend time series. Using a hybrid model is a way to improve the accuracy of the methods. Due to the fact that water demand is dominated by different seasonality, in order to find their sensitivity to weather conditions or calendar effects or other seasonal patterns, I combine different methods. The advantage of this combination is reduction of errors by averaging of each individual model. It is also useful when we are not sure about the accuracy of each forecasting model and it can ease the problem of model selection. Using daily residential water consumption data from January 2000 to July 2015 in Hamilton, I indicate how prediction by different methods varies. ANN has more accurate forecasting results than other method and preprocessing is essential when we use seasonal time series. Using hybrid model reduces forecasting average errors and increases the performance.

Keywords: artificial neural network (ANN), double seasonal ARIMA, forecasting, hybrid model

Procedia PDF Downloads 304

Authors: Yi Huang, Clemens Guehmann

Abstract:

In order to monitor the thermal behavior of an asynchronous machine with squirrel cage rotor, a 9th-order extended Kalman filter (EKF) algorithm is implemented to estimate the temperatures of the stator windings, the rotor cage and the stator core. The state-space equations of EKF are established based on the electrical, mechanical and the simplified thermal models of an asynchronous machine. The asynchronous machine with simplified thermal model in Dymola is compiled as DymolaBlock, a physical model in MATLAB/Simulink. The coolant air temperature, three-phase voltages and currents are exported from the physical model and are processed by EKF estimator as inputs. Compared to the temperatures exported from the physical model of the machine, three parts of temperatures can be estimated quite accurately by the EKF estimator. The online EKF estimator is independent from the machine control algorithm and can work under any speed and load condition if the stator current is nonzero current system.

Keywords: asynchronous machine, extended Kalman filter, resistance, simulation, temperature estimation, thermal model

Procedia PDF Downloads 261

Authors: Tina Wu, Andrew Martin

Abstract:

The expansion of the Internet of Things (IoT) brings a new level of threat. Attacks on IoT are already being used by criminals to form botnets, launch Distributed Denial of Service (DDoS) and distribute malware. This opens a whole new digital forensic arena to develop forensic methodologies in order to have the capability to investigate IoT related crimes. However, existing proposed IoT forensic models are still premature requiring further improvement and validation, many lack details on the acquisition and analysis phase. This paper proposes an enhanced theoretical IoT digital forensic model focused on identifying and acquiring the main sources of evidence in a methodical way. In addition, this paper presents a theoretical acquisition framework of the different stages required in order to be capable of acquiring evidence from IoT devices.

Keywords: acquisition, Internet of Things, model, zoning

Procedia PDF Downloads 240

Authors: Hang Lo Lee, Ki Il Song, Hee Hwan Ryu

Abstract:

An accurate prediction of TBM(Tunnel Boring Machine) performance is very difficult for reliable estimation of the construction period and cost in preconstruction stage. For this purpose, the aim of this study is to analyze the evaluation process of various prediction models published since 2000 for TBM performance, and to select the optimal input parameters for the prediction model. A classification system of TBM performance prediction model and applied methodology are proposed in this research. Input and output parameters applied for prediction models are also represented. Based on these results, a statistical analysis is performed using the collected data from shield TBM tunnel in South Korea. By performing a simple regression and residual analysis utilizinFg statistical program, R, the optimal input parameters are selected. These results are expected to be used for development of prediction model of TBM performance.

Keywords: TBM performance prediction model, classification system, simple regression analysis, residual analysis, optimal input parameters

Procedia PDF Downloads 287

Authors: I. V. Pinto, M. R. Sooriyarachchi

Abstract:

It can be frequently observed that the data arising in our environment have a hierarchical or a nested structure attached with the data. Multilevel modelling is a modern approach to handle this kind of data. When multilevel modelling is combined with a binary response, the estimation methods get complex in nature and the usual techniques are derived from quasi-likelihood method. The estimation methods which are compared in this study are, marginal quasi-likelihood (order 1 & order 2) (MQL1, MQL2) and penalized quasi-likelihood (order 1 & order 2) (PQL1, PQL2). A statistical model is of no use if it does not reflect the given dataset. Therefore, checking the adequacy of the fitted model through a goodness-of-fit (GOF) test is an essential stage in any modelling procedure. However, prior to usage, it is also equally important to confirm that the GOF test performs well and is suitable for the given model. This study assesses the suitability of the GOF test developed for binary response multilevel models with respect to the method used in model estimation. An extensive set of simulations was conducted using MLwiN (v 2.19) with varying number of clusters, cluster sizes and intra cluster correlations. The test maintained the desirable Type-I error for models estimated using PQL2 and it failed for almost all the combinations of MQL. Power of the test was adequate for most of the combinations in all estimation methods except MQL1. Moreover, models were fitted using the four methods to a real-life dataset and performance of the test was compared for each model.

Keywords: goodness-of-fit test, marginal quasi-likelihood, multilevel modelling, penalized quasi-likelihood, power, quasi-likelihood, type-I error

Procedia PDF Downloads 120

Authors: H. Kiani, S. Moradi, B. Soltani Soulgani, S. Mousavian

Abstract:

Desalting/dehydration plants (DDP) are often installed in crude oil production units in order to remove water-soluble salts from an oil stream. In order to optimize this process, desalting unit should be modeled. In this research, artificial neural network is used to model efficiency of desalting unit as a function of input parameter. The result of this research shows that the mentioned model has good agreement with experimental data.

Keywords: desalting unit, crude oil, neural networks, simulation, recovery, separation

Procedia PDF Downloads 410

Authors: Nivedita Paul

Abstract:

The changing character of marriage be it arranged marriage, love marriage, polygamy, informal unions, all signify different gender relations in everyday lives. Marriages in India are part and parcel of the kinship and cultural practices. Arranged marriage is still the dominant form of marriage where spouse selection is the initiative and decision of the parents; but its form is changing, as women are now actively participating in spouse selection but with parental consent. Spouse selection related decision making is important because marriage as an institution brings social change and gender inequality; especially in a women’s life as marriages in India are mostly patrilocal. Moreover, the amount of say in spouse selection can affect a woman’s reproductive rights, domestic violence issues, household resource allocation, communication possibilities with the spouse/husband, marital life, etc. The present study uses data from Indian Human Development Survey II (2011-12) which is a nationally representative multitopic survey that covers 41,554 households. Currently, married women of age group 15-49 in their first marriage; whose year of marriage is from 1970s to 2000s have been taken for the study. Based on spouse selection experiences, the sample of women has been divided into three marriage categories-self, semi and family arranged. Women in self arranged or love marriage is the sole decision maker in choosing the partner, in semi arranged marriage or arranged marriage with consent both parents and women together take the decision, whereas in family arranged or arranged marriage without consent only parents take the decision. The main aim of the study is to find the relationship between spouse selection experiences and women’s autonomy in India. Decision making in economic matters, child and health related decision making, mobility and access to resources are taken to be proxies of autonomy. Method of ordinal regression has been used to find the relationship between spouse selection experiences and autonomy after marriage keeping other independent variables as control factors. Results show that women in semi arranged marriage have more decision making power regarding financial matters of the household, health related matters, mobility and accessibility to resources, when compared to women in family, arranged marriages. For freedom of movement and access to resources women in self arranged marriage have the highest say or exercise greatest power. Therefore, greater participation of women (even though not absolute control) in spouse selection may lead to greater autonomy after marriage.

Keywords: arranged marriage, autonomy, consent, spouse selection

Procedia PDF Downloads 128

Authors: Ismail Aslantas

Abstract:

Value-Added Models (VAMs), the statistical methods for evaluating the effectiveness of teachers and schools based on student achievement growth, has attracted decision-makers’ and researchers’ attention over the last decades. As a result of this attention, many studies have conducted in recent years to discuss these statistical models from different aspects. This research focused on the importance of conceptual variables in VAM estimations; therefor, this research was undertaken to examine the extent to which value-added effectiveness estimates for teachers can be affected by using context predictions. Using longitudinal data over three years from the international school context, value-added teacher effectiveness was estimated by ordinary least-square value-added models, and the effectiveness of the teachers was examined. The longitudinal dataset in this study consisted of three major sources: students’ attainment scores up to three years and their characteristics, teacher background information, and school characteristics. A total of 1,027 teachers and their 35,355 students who were in eighth grade were examined for understanding the impact of model specifications on the value-added teacher effectiveness evaluation. Models were created using selection methods that adding a predictor on each step, then removing it and adding another one on a subsequent step and evaluating changes in model fit was checked by reviewing changes in R² values. Cohen’s effect size statistics were also employed in order to find out the degree of the relationship between teacher characteristics and their effectiveness. Overall, the results indicated that prior attainment score is the most powerful predictor of the current attainment score. 47.1 percent of the variation in grade 8 math score can be explained by the prior attainment score in grade 7. The research findings raise issues to be considered in VAM implementations for teacher evaluations and make suggestions to researchers and practitioners.

Keywords: model specification, teacher effectiveness, teacher performance evaluation, value-added model

Procedia PDF Downloads 106

Authors: Lamia Bourdache, Amar Djema

Abstract:

The effect of non-Newtonian property (power law index n) on traveling waves of thin layer of power law fluid flowing over an inclined plane is investigated. For this, a simplified second-order two-equation model (SM) is used. The complete model is second-order four-equation (CM). It is derived by combining the weighted residual integral method and the lubrication theory. This is due to the fact that at the beginning of the instability waves, a very small number of waves is observed. Using a suitable set of test functions, second order terms are eliminated from the calculus so that the model is still accurate to the second order approximation. Linear, spatial, and temporal stabilities are studied. For travelling waves, a particular type of wave form that is steady in a moving frame, i.e., that travels at a constant celerity without changing its shape is studied. This type of solutions which are characterized by their celerity exists under suitable conditions, when the widening due to dispersion is balanced exactly by the narrowing effect due to the nonlinearity. Changing the parameter of celerity in some range allows exploring the entire spectrum of asymptotic behavior of these traveling waves. The (SM) model is converted into a three dimensional dynamical system. The result is that the model exhibits bifurcation scenarios such as heteroclinic, homoclinic, Hopf, and period-doubling bifurcations for different values of the power law index n. The influence of the non-Newtonian parameter on the nonlinear development of these travelling waves is discussed. It is found at the end that the qualitative characters of bifurcation scenarios are insensitive to the variation of the power law index.

Keywords: inclined plane, nonlinear stability, non-Newtonian, thin film

Procedia PDF Downloads 260

Authors: Ali Ulvi Uzer

Abstract:

Data reduction is an important topic in the field of pattern recognition applications. The basic concept is the reduction of multitudinous amounts of data down to the meaningful parts. The Principal Component Analysis (PCA) method is frequently used for data reduction. The Support Vector Machine (SVM) method is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data, the algorithm outputs an optimal hyperplane which categorizes new examples. This study offers a hybrid approach that uses the PCA for data reduction and Support Vector Machines (SVM) for classification. In order to detect the accuracy of the suggested system, two boreholes taken from the soil sample was used. The classification accuracies for this dataset were obtained through using ten-fold cross-validation method. As the results suggest, this system, which is performed through size reduction, is a feasible system for faster recognition of dataset so our study result appears to be very promising.

Keywords: feature selection, sequential forward selection, support vector machines, soil sample

Procedia PDF Downloads 427

Authors: Razieh Khalafi

Abstract:

The brain is the information processing centre of the human body. Stimuli in the form of information are transferred to the brain and then brain makes the decision on how to respond to them. In this research, we propose a new partial differential equation which analyses the EEG signals and make a relationship between the incoming stimuli and the brain response to them. In order to test the proposed model, a set of external stimuli applied to the model and the model’s outputs were checked versus the real EEG data. The results show that this model can model the EEG signal well. The proposed model is useful not only for modelling of EEG signal in case external stimuli but it can be used for modelling of brain response in case of internal stimuli.

Keywords: brain, stimuli, partial differential equation, response, EEG signal

Procedia PDF Downloads 531

Authors: Qiang Zhang, Xiaojian Hu

Abstract:

In multi-vehicle tracking, based on feature selection, the tracking system efficiently tracks vehicles in a video with minimal error in combination with color attribution, which focuses on presenting a simple and fast, yet accurate and robust solution to the problem such as inaccurately and untimely responses of statistics-based adaptive traffic control system in the intersection scenario. In this study, a real-time tracking system is proposed for multi-vehicle tracking in the intersection scene. Considering the complexity and application feasibility of the algorithm, in the object detection step, the detection result provided by virtual loops were post-processed and then used as the input for the tracker. For the tracker, lightweight methods were designed to extract and select features and incorporate them into the adaptive color tracking (ACT) framework. And the approbatory online feature selection algorithms are integrated on the mature ACT system with good compatibility. The proposed feature selection methods and multi-vehicle tracking method are evaluated on KITTI datasets and show efficient vehicle tracking performance when compared to the other state-of-the-art approaches in the same category. And the system performs excellently on the video sequences recorded at the intersection. Furthermore, the presented vehicle tracking system is suitable for surveillance applications.

Keywords: real-time, multi-vehicle tracking, feature selection, color attribution

Procedia PDF Downloads 132

Authors: Abdelouaheb Rouabhi

Abstract:

In order to study the plastic resource of structures, the elastic-plastic single degree of freedom model described by Prandtl diagram is widely used. The generalization of this model to tow degree of freedom beyond the scope of a simple rigid-plastic system allows investigating the plastic resource of structures under complex disproportionate by individual components of deformation (earthquake). This macro-model greatly increases the accuracy of the calculations carried out. At the same time, the implementation of the proposed macro-model calculations easier than the detailed dynamic elastic-plastic calculations existing software systems such as ANSYS.

Keywords: elastic-plastic, single degree of freedom model, rigid-plastic system, plastic resource, complex plastic deformation, macro-model

Procedia PDF Downloads 357

Authors: V. Benedico, C. Anacleto, A. Bearzi, L. Brice, V. Delahaye

Abstract:

The aim in present paper is to construct an affordable and reliable commodity prices based on a recalculation of its cost through time which allows visualize the potential risks and thus, take more appropriate decisions regarding forecasts. Here attention has been focused on Levy model, more reliable and realistic than classical random Gaussian one as it takes into consideration observed abrupt jumps in case of sudden price variation. In application to Energy Trading sector where it has never been used before, equations corresponding to Levy model have been written for electricity pricing in European market. Parameters have been set in order to predict and simulate the price and its evolution through time to remarkable accuracy. As predicted by Levy model, the results show significant spikes which reach unconventional levels contrary to currently used Brownian model.

Keywords: commodity pricing, Lévy Model, price spikes, electricity market

Procedia PDF Downloads 407

Authors: Joanna Kufel-Orlowska

Abstract:

Domestic violence is a social threat to public safety and order. It poses a threat not only to the family members of the perpetrator but also disturbs the functioning of society and even the state. In a situation of danger, an individual either defends himself or/and calls for help by contacting an appropriate institution whose aim is to ensure civil security. Most often, such contact takes place through a telephone conversation, which is aimed at diagnosing the problem and prompt intervention. People in different situations and in different ways, despite the general reporting standards, try to inform about the need for help. The article aims to present the results of research on non-standard forms of reporting domestic violence in the opinion of the Polish society and operators of the Polish emergency number 112 (911). The research was conducted in the form of a survey technique on a sample of 160 operators (purposeful selection) and 300 people living in Poland (random selection). The research was conducted in the form of online surveys. The study found that in Poland: 1. emergency number operators often receive reports of domestic violence although they are not always able to diagnose whether the case is strictly about violence; 2. non-standard reports of domestic violence are received by about 30% of emergency number operators. Non-standard should be understood as reports of violence that deviate from the norm, are unusual, or are reported by a non-victim. 3. The most common forms of reporting violence not directly are: pretending to talk to a friend, calling a cab, making an appointment with a dentist/doctor, calling a store and helping with the selection of goods, asking about the bank's hotline, not speaking (in order for the emergency number operator to hear what is going on). 4. Emergency number operators in Poland are properly trained and are able to recognize the threatening situation of the reporting party and conduct the conversation in a safe manner for the reporting party. On the other hand, Polish people support the ability to report violence in a non-standard way and would do so themselves in the event of a threat to their own life, health, or property, thus expecting the emergency number operator to recognize a report and help us.

Keywords: domestic violence, operator of the emergency number 112 (911), emergency call center, reporting domestic violence

Procedia PDF Downloads 77

Authors: Ridhish Kumar, Sudeep Nadukkandy, Anirban Roy

Abstract:

The development of reverse osmosis happened in 1960. Along the years this technique has been widely accepted all over the world for varied applications ranging from seawater desalination to municipal water treatment. Forward osmosis (FO) is one of the foremost technologies for low energy consuming solutions for water purification. In this study, we have carried out a detailed analysis on selection, design, and pricing for a prototype of potable water system for purifying water in emergency situations. The portable and light purification system is envisaged to be driven by FO. This pouch will help to serve as an emergency water filtration device. The current effort employs a model to understand the interplay of permeability and area on the rate of purification of water from any impure source/brackish water. The draw solution for the FO pouch is considered to be a combination of salt and sugar such that dilution of the same would result in an oral rehydration solution (ORS) which is a boon for dehydrated patients. However, the effort takes an extra step to actually estimate the cost and pricing of designing such a prototype. While the mathematical model yields the best membrane (compositions are taken from literature) combination in terms of permeability and area, the pricing takes into account the feasibility of such a solution to be made available as a retail item. The product is envisaged to be a market competitor for packaged drinking water and ORS combination (costing around $0.5 combined) and thus, to be feasible has to be priced around the same range with greater margins in order to have a better distribution. Thus a proper business plan and production of the same has been formulated in order to be a feasible solution for unprecedented calamities and emergency situations.

Keywords: forward osmosis, water treatment, oral rehydration solution, prototype

Procedia PDF Downloads 163

Authors: Yousef Abu Nahleh, Alhasan Hakami, Arun Kumar, Fugen Daver

Abstract:

This study considers the problem of calculating safety stocks in disaster situations inventory systems that face demand uncertainties. Safety stocks are essential to make the supply chain, which is controlled by forecasts of customer needs, in response to demand uncertainties and to reach predefined goal service levels. To solve the problem of uncertainties due to the disaster situations affecting the industry sector, the concept of Emergency Safety Stock (ESS) was proposed. While there exists a huge body of literature on determining safety stock levels, this literature does not address the problem arising due to the disaster and dealing with the situations. In this paper, the problem of improving the Order Quantity Model to deal with uncertainty of demand due to disasters is managed by incorporating a new idea called ESS which is based on the probability of disaster occurrence and uses probability matrix calculated from the historical data.

Keywords: Emergency Safety Stocks, safety stocks, Order Quantity Model, supply chain

Procedia PDF Downloads 324

Authors: Gong Zhilin, Jing Yang, Jian Yin

Abstract:

The purpose of this paper is to develop a novel system for credit card fraud detection based on sequential modeling of data using hybrid deep learning models. The projected model encapsulates five major phases are pre-processing, imbalance-data handling, feature extraction, optimal feature selection, and fraud detection with an ensemble classifier. The collected raw data (input) is pre-processed to enhance the quality of the data through alleviation of the missing data, noisy data as well as null values. The pre-processed data are class imbalanced in nature, and therefore they are handled effectively with the K-means clustering-based SMOTE model. From the balanced class data, the most relevant features like improved Principal Component Analysis (PCA), statistical features (mean, median, standard deviation) and higher-order statistical features (skewness and kurtosis). Among the extracted features, the most optimal features are selected with the Self-improved Arithmetic Optimization Algorithm (SI-AOA). This SI-AOA model is the conceptual improvement of the standard Arithmetic Optimization Algorithm. The deep learning models like Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and optimized Quantum Deep Neural Network (QDNN). The LSTM and CNN are trained with the extracted optimal features. The outcomes from LSTM and CNN will enter as input to optimized QDNN that provides the final detection outcome. Since the QDNN is the ultimate detector, its weight function is fine-tuned with the Self-improved Arithmetic Optimization Algorithm (SI-AOA).

Keywords: credit card, data mining, fraud detection, money transactions

Procedia PDF Downloads 104