Search results for: impact models.

Authors: M. S. El-Asfoury, M. A. El-Hadek

Abstract:

The complex shape of the human pelvic bone was successfully imaged and modeled using finite element FE processing. The bone was subjected to quasi-static and dynamic loading conditions simulating the effect of both weight gain and impact. Loads varying between 500 – 2500 N (~50 – 250 Kg of weight) was used to simulate 3D quasi-static weight gain. Two different 3D dynamic analyses, body free fall at two different heights (1 and 2 m) and forced side impact at two different velocities (20 and 40 Km/hr) were also studied. The computed resulted stresses were compared for the four loading cases, where Von Misses stresses increases linearly with the weight gain increase under quasi-static loading. For the dynamic models, the Von Misses stress history behaviors were studied for the affected area and effected load with respect to time. The normalization Von Misses stresses with respect to the applied load were used for comparing the free fall and the forced impact load results. It was found that under the forced impact loading condition an over lapping behavior was noticed, where as for the free fall the normalized Von Misses stresses behavior was found to nonlinearly different. This phenomenon was explained through the energy dissipation concept. This study will help designers in different specialization in defining the weakest spots for designing different supporting systems.

Keywords: Pelvic Bone, Static and Dynamic Analysis, Three- Dimensional Finite Element Analysis.

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Authors: Neerja Bhatnagar, Ben A. Juliano, Renee S. Renner

Abstract:

This paper presents data annotation models at five levels of granularity (database, relation, column, tuple, and cell) of relational data to address the problem of unsuitability of most relational databases to express annotations. These models do not require any structural and schematic changes to the underlying database. These models are also flexible, extensible, customizable, database-neutral, and platform-independent. This paper also presents an SQL-like query language, named Annotation Query Language (AnQL), to query annotation documents. AnQL is simple to understand and exploits the already-existent wide knowledge and skill set of SQL.

Keywords: Annotation query language, data annotations, data annotation models, semantic data annotations.

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Authors: F. Pagliara, F. Mauriello

Abstract:

The objective of this paper is to investigate the relationship between the increase of accessibility brought by high speed rail (HSR) systems and the tourism market in Italy. The impacts of HSR projects on tourism can be quantified in different ways. In this manuscript, an empirical analysis has been carried out with the aid of a dataset containing information both on tourism and transport for 99 Italian provinces during the 2006-2016 period. Panel data regression models have been considered, since they allow modelling a wide variety of correlation patterns. Results show that HSR has an impact on the choice of a given destination for Italian tourists while the presence of a second level hub mainly affects foreign tourists. Attraction variables are also significant for both categories and the variables concerning security, such as number of crimes registered in a given destination, have a negative impact on the choice of a destination.

Keywords: Tourists, overnights, high speed rail, attractions, security.

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Authors: Josip Visković, Josip Arnerić, Ante Rozga

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Based on the fact that volatility is time varying in high frequency data and that periods of high volatility tend to cluster, the most successful and popular models in modeling time varying volatility are GARCH type models. When financial returns exhibit sudden jumps that are due to structural breaks, standard GARCH models show high volatility persistence, i.e. integrated behavior of the conditional variance. In such situations models in which the parameters are allowed to change over time are more appropriate. This paper compares different GARCH models in terms of their ability to describe structural changes in returns caused by financial crisis at stock markets of six selected central and east European countries. The empirical analysis demonstrates that Markov regime switching GARCH model resolves the problem of excessive persistence and outperforms uni-regime GARCH models in forecasting volatility when sudden switching occurs in response to financial crisis.

Keywords: Central and east European countries, financial crisis, Markov switching GARCH model, transition probabilities.

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Authors: Dr.K.Sri Rama Krishna, J.Lakshmi Narayana, Dr.L.Pratap Reddy

Abstract:

Microstrip lines, widely used for good reason, are broadband in frequency and provide circuits that are compact and light in weight. They are generally economical to produce since they are readily adaptable to hybrid and monolithic integrated circuit (IC) fabrication technologies at RF and microwave frequencies. Although, the existing EM simulation models used for the synthesis and analysis of microstrip lines are reasonably accurate, they are computationally intensive and time consuming. Neural networks recently gained attention as fast and flexible vehicles to microwave modeling, simulation and optimization. After learning and abstracting from microwave data, through a process called training, neural network models are used during microwave design to provide instant answers to the task learned.This paper presents simple and accurate ANN models for the synthesis and analysis of Microstrip lines to more accurately compute the characteristic parameters and the physical dimensions respectively for the required design specifications.

Keywords: Neural Models, Algorithms, Microstrip Lines, Analysis, Synthesis

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Authors: B. A. Gobin, R. K. Subramanian

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This paper gives an overview of how an OWL ontology has been created to represent template knowledge models defined in CML that are provided by CommonKADS. CommonKADS is a mature knowledge engineering methodology which proposes the use of template knowledge model for knowledge modelling. The aim of developing this ontology is to present the template knowledge model in a knowledge representation language that can be easily understood and shared in the knowledge engineering community. Hence OWL is used as it has become a standard for ontology and also it already has user friendly tools for viewing and editing.

Keywords: Ontology, OWL, Template Knowledge Models, CommonKADS

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Authors: A. Camões, M. Ferreira Dias, M. Amorim

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In recent years, the social economy is increasingly seen as a viable means to address social problems. Social enterprises, as well as public projects and initiatives targeted to meet social purposes, offer organizational models that assume heterogeneity, flexibility and adaptability to the ‘real world and real problems’. Despite the growing popularity of social initiatives, decision makers still face a paucity in what concerns the available models and tools to adequately assess its sustainability, and its impacts, notably the nature of its contribution to economic growth. This study was carried out at the local level, by analyzing the social impact initiatives and projects promoted by the Municipality of Albergaria-a-Velha (Câmara Municipal de Albergaria-a-Velha -CMA), a municipality of 25,000 inhabitants in the central region of Portugal. This work focuses on the challenges related to the qualifications and employability of citizens, which stands out as one of the key concerns in the Portuguese economy, particularly expressive in the context of small-scale cities and inland territories. The study offers a characterization of the Municipality, its socio-economic structure and challenges, followed by an exploratory analysis of multiple sourced data, collected from the CMA's documental sources as well as from privileged informants. The purpose is to conduct detailed analysis of the CMA's social projects, aimed at characterizing its potential impact for the model of qualifications and employability of the citizens of the Municipality. The study encompasses a discussion of the socio-economic profile of the municipality, notably its asymmetries, the analysis of the social projects and initiatives, as well as of data derived from inquiry actors involved in the implementation of the social projects and its beneficiaries. Finally, the results obtained with the Better Life Index will be included. This study makes it possible to ascertain if what is implicit in the literature goes to the encounter of what one experiences in reality.

Keywords: Measurement, municipalities, social economy, social impact.

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Authors: K. Kamalanand, S. Srinivasan

Abstract:

Analysis of blood vessel mechanics in normal and diseased conditions is essential for disease research, medical device design and treatment planning. In this work, 3D finite element models of normal vessel and atherosclerotic vessel with 50% plaque deposition were developed. The developed models were meshed using finite number of tetrahedral elements. The developed models were simulated using actual blood pressure signals. Based on the transient analysis performed on the developed models, the parameters such as total displacement, strain energy density and entropy per unit volume were obtained. Further, the obtained parameters were used to develop artificial neural network models for analyzing normal and atherosclerotic blood vessels. In this paper, the objectives of the study, methodology and significant observations are presented.

Keywords: Blood vessel, atherosclerosis, finite element model, artificial neural networks

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Authors: M. Madiajagan, B. Vijayakumar

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The ability of information systems to operate in conjunction with each other encompassing communication protocols, hardware, software, application, and data compatibility layers. There has been considerable work in industry on the development of component interoperability models, such as CORBA, (D)COM and JavaBeans. These models are intended to reduce the complexity of software development and to facilitate reuse of off-the-shelf components. The focus of these models is syntactic interface specification, component packaging, inter-component communications, and bindings to a runtime environment. What these models lack is a consideration of architectural concerns – specifying systems of communicating components, explicitly representing loci of component interaction, and exploiting architectural styles that provide well-understood global design solutions. The development of complex business applications is now focused on an assembly of components available on a local area network or on the net. These components must be localized and identified in terms of available services and communication protocol before any request. The first part of the article introduces the base concepts of components and middleware while the following sections describe the different up-todate models of communication and interaction and the last section shows how different models can communicate among themselves.

Keywords: Interoperability, component packaging, communication technology, heterogeneous platform, component interface, middleware.

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Authors: H. Rong, X. C. Yin, J. Yang, Y. N. Shen

Abstract:

Based on Rayleigh beam theory, the sub-impacts of a free-free beam struck horizontally by a round-nosed rigid mass is simulated by the finite difference method and the impact-separation conditions. In order to obtain the sub-impact force, a uniaxial compression elastic-plastic contact model is employed to analyze the local deformation field on contact zone. It is found that the horizontal impact is a complicated process including the elastic plastic sub-impacts in sequence. There are two sub-zones of sub-impact. In addition, it found that the elastic energy of the free-free beam is more suitable for the Poisson collision hypothesis to explain compression and recovery processes.

Keywords: beam, sub-impact, elastic-plastic deformation, finite difference method.

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Authors: Fazıl Gökgöz, Fahrettin Filiz

Abstract:

Electricity prices have sophisticated features such as high volatility, nonlinearity and high frequency that make forecasting quite difficult. Electricity price has a volatile and non-random character so that, it is possible to identify the patterns based on the historical data. Intelligent decision-making requires accurate price forecasting for market traders, retailers, and generation companies. So far, many shallow-ANN (artificial neural networks) models have been published in the literature and showed adequate forecasting results. During the last years, neural networks with many hidden layers, which are referred to as DNN (deep neural networks) have been using in the machine learning community. The goal of this study is to investigate electricity price forecasting performance of the shallow-ANN and DNN models for the Turkish day-ahead electricity market. The forecasting accuracy of the models has been evaluated with publicly available data from the Turkish day-ahead electricity market. Both shallow-ANN and DNN approach would give successful result in forecasting problems. Historical load, price and weather temperature data are used as the input variables for the models. The data set includes power consumption measurements gathered between January 2016 and December 2017 with one-hour resolution. In this regard, forecasting studies have been carried out comparatively with shallow-ANN and DNN models for Turkish electricity markets in the related time period. The main contribution of this study is the investigation of different shallow-ANN and DNN models in the field of electricity price forecast. All models are compared regarding their MAE (Mean Absolute Error) and MSE (Mean Square) results. DNN models give better forecasting performance compare to shallow-ANN. Best five MAE results for DNN models are 0.346, 0.372, 0.392, 0,402 and 0.409.

Keywords: Deep learning, artificial neural networks, energy price forecasting, Turkey.

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Authors: Changhyuk Jang, Keechul Jung

Abstract:

Human pose estimation can be executed using Active Shape Models. The existing techniques for applying to human-body research using Active Shape Models, such as human detection, primarily take the form of silhouette of human body. This technique is not able to estimate accurately for human pose to concern two arms and legs, as the silhouette of human body represents the shape as out of round. To solve this problem, we applied the human body model as stick-figure, “skeleton". The skeleton model of human body can give consideration to various shapes of human pose. To obtain effective estimation result, we applied background subtraction and deformed matching algorithm of primary Active Shape Models in the fitting process. The images which were used to make the model were 600 human bodies, and the model has 17 landmark points which indicate body junction and key features of human pose. The maximum iteration for the fitting process was 30 times and the execution time was less than .03 sec.

Keywords: Active shape models, skeleton, pose estimation.

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Authors: Ya-Fen Lee, Yun-Yao Chi

Abstract:

This paper aims to quantify the impact of natural disaster on tourism by the change of annual visitors to scenic spots. The data of visitors to Alishan, Sun Moon Lake, Sitou and Palace Museum in Taiwan during 1986 to 2012 year is collected, and the trend analysis is used to predict the annual visitors to these scenic spots. The findings show that 1999 Taiwan earthquake had significant effect on the visitors to Alishan, Sun Moon Lake and Sitou with an average impact of 55.75% during 1999 to 2000 year except for Palace Museum. The impact was greater as closer epicenter of 1999 earthquake. And the discovery period of visitors is about 2 to 9 years. Further, the impact of heavy rainfall on Alishan, Taiwan is estimated. As the accumulative rainfall reaches to 500 mm, the impact on visitors can be predicted. 

Keywords: Impact, Natural disaster, tourism, visitors.

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Authors: H. Chaker, T. Slama, N. Elyétim

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This paper analyzes the effects of gender, affiliation, prior work experience, social work, and vicarious learning through family role models on entrepreneurial skills development by students when they followed the teaching through the entrepreneurship method in Tunisia. We suggest that these variables enhance the development of students’ entrepreneurial skills when combined with teaching through entrepreneurship. The article assesses the impact of these combinations by comparing their effects on the development of thirteen students’ entrepreneurial competencies, namely entrepreneurial mindset, core self-evaluation, entrepreneurial attitude, entrepreneurial knowledge, creativity, financial literacy, managing ambiguity, marshaling of resources, planning, teaching methods, entrepreneurial teachers, innovative employee, and entrepreneurial intention. We use a two-sample independent t-test to make the comparison, and the results indicate that, when combined with teaching through the entrepreneurship method, students with prior work experience developed better six entrepreneurial skills; students with social work developed better three entrepreneurial skills, men developed better four entrepreneurial skills than women. However, all students developed their entrepreneurial skills through this practical method regardless of their affiliation and their vicarious learning through family role models.

Keywords: Affiliation, entrepreneurial skills, gender, role models, social work, teaching through entrepreneurship, vicarious learning, work experience.

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Authors: S. Boria

Abstract:

In the last years, the crashworthiness of an automotive body structure can be improved, since the beginning of the design stage, thanks to the development of specific optimization tools. It is well known how the finite element codes can help the designer to investigate the crashing performance of structures under dynamic impact. Therefore, by coupling nonlinear mathematical programming procedure and statistical techniques with FE simulations, it is possible to optimize the design with reduced number of analytical evaluations. In engineering applications, many optimization methods which are based on statistical techniques and utilize estimated models, called meta-models, are quickly spreading. A meta-model is an approximation of a detailed simulation model based on a dataset of input, identified by the design of experiments (DOE); the number of simulations needed to build it depends on the number of variables. Among the various types of meta-modeling techniques, Kriging method seems to be excellent in accuracy, robustness and efficiency compared to other ones when applied to crashworthiness optimization. Therefore the application of such meta-model was used in this work, in order to improve the structural optimization of a bumper for a racing car in composite material subjected to frontal impact. The specific energy absorption represents the objective function to maximize and the geometrical parameters subjected to some design constraints are the design variables. LS-DYNA codes were interfaced with LS-OPT tool in order to find the optimized solution, through the use of a domain reduction strategy. With the use of the Kriging meta-model the crashworthiness characteristic of the composite bumper was improved.

Keywords: Composite material, crashworthiness, finite element analysis, optimization.

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Authors: Xuan Sun, Mingbo Tong

Abstract:

To understand the factors which affect impact damage on composite structures, particularly the effects of impact position and ribs. In this paper, a finite element model (FEM) of low-velocity impact damage on the composite structure was established via the nonlinear finite element method, combined with the user-defined materials subroutine (VUMAT) of the ABAQUS software. The structural elements chosen for the investigation comprised a series of stiffened composite panels, representative of real aircraft structure. By impacting the panels at different positions relative to the ribs, the effect of relative position of ribs was found out. Then the simulation results and the experiments data were compared. Finally, the factors which affect impact damage on the structures were discussed. The paper was helpful for the design of stiffened composite structures.

Keywords: Stiffened, Low-velocity, Impact, Abaqus, Impact Energy.

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Authors: P.Cordero, R.Poler, R.Sanchis

Abstract:

The environmental impacts caused by the current production and consumption models, together with the impact that the current economic crisis, bring necessary changes in the European industry toward new business models based on sustainability issues that could allow them to innovate and improve their competitiveness. This paper analyzes the key environmental issues and the current and future market trends in one of the most important industrial sectors in Spain, the furniture sector. It also proposes new decision support tools -diagnostic kit, roadmap and guidelines- to guide companies to implement sustainability criteria into their organizations, including eco-design strategies and other economical and social strategies in accordance with the sustainability definition, and other available tools such as eco-labels, environmental management systems, etc., and to use and combine them to obtain the results the company expects to help improve its competitiveness.

Keywords: Furniture sector, eco-design, sustainability, economical crisis, market trends, roadmap

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Authors: Asfaw Gezae Daful

Abstract:

In the present study, two distinctly different approaches are followed for modeling of reactive distillation column, the equilibrium stage model and the nonequilibrium stage model. These models are simulated with a computer code developed in the present study using MATLAB programming. In the equilibrium stage models, the vapor and liquid phases are assumed to be in equilibrium and allowance is made for finite reaction rates, where as in the nonequilibrium stage models simultaneous mass transfer and reaction rates are considered. These simulated model results are validated from the experimental data reported in the literature. The simulated results of equilibrium and nonequilibrium models are compared for concentration, temperature and reaction rate profiles in a reactive distillation column for Methyl Tert Butyle Ether (MTBE) production. Both the models show similar trend for the concentration, temperature and reaction rate profiles but the nonequilibrium model predictions are higher and closer to the experimental values reported in the literature.

Keywords: Reactive Distillation, Equilibrium model, Nonequilibrium model, Methyl Tert-Butyl Ether

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Authors: Andrea Bellucci, Martina Tofi

Abstract:

The aim of paper is to analyze business models of bancassurance in Italy for life business. The life insurance business is very developed in the Italian market and banks branches have 80% of the market share. Given its maturity, the life insurance market needs to consolidate its organizational form to allow for the development of non-life business, which nowadays collects few premiums but represents a great opportunity to enlarge the market share of bancassurance using its strength in the distribution channel while the market share of independent agents is decreasing. Starting with the main business model of bancassurance for life business, this paper will analyze the performances of life companies in the Italian market by balance sheet indicators and by main discriminant variables of business models. The study will observe trends from 2013 to 2015 for the Italian market by exploiting a database managed by Associazione Nazionale delle Imprese di Assicurazione (ANIA). The applied approach is based on a bottom-up analysis starting with variables and indicators to define business models’ classification. The statistical classification algorithm proposed by Ward is employed to design business models’ profiles. Results from the analysis will be a representation of the main business models built by their profile related to indicators. In that way, an unsupervised analysis is developed that has the limit of its judgmental dimension based on research opinion, but it is possible to obtain a design of effective business models.

Keywords: Balance sheet indicators, Bancassurance, business models, ward algorithm.

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Authors: E. Ardil, Erdem Uçar, Parvinder S. Sandhu

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As the majority of faults are found in a few of its modules so there is a need to investigate the modules that are affected severely as compared to other modules and proper maintenance need to be done on time especially for the critical applications. In this paper, we have explored the different predictor models to NASA-s public domain defect dataset coded in Perl programming language. Different machine learning algorithms belonging to the different learner categories of the WEKA project including Mamdani Based Fuzzy Inference System and Neuro-fuzzy based system have been evaluated for the modeling of maintenance severity or impact of fault severity. The results are recorded in terms of Accuracy, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results show that Neuro-fuzzy based model provides relatively better prediction accuracy as compared to other models and hence, can be used for the maintenance severity prediction of the software.

Keywords: Software Metrics, Fuzzy, Neuro-Fuzzy, SoftwareFaults, Accuracy, MAE, RMSE.

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Authors: Jihye Jeon

Abstract:

This paper analyzes the conceptual framework of three statistical methods, multiple regression, path analysis, and structural equation models. When establishing research model of the statistical modeling of complex social phenomenon, it is important to know the strengths and limitations of three statistical models. This study explored the character, strength, and limitation of each modeling and suggested some strategies for accurate explaining or predicting the causal relationships among variables. Especially, on the studying of depression or mental health, the common mistakes of research modeling were discussed.

Keywords: Multiple regression, path analysis, structural equation models, statistical modeling, social and psychological phenomenon.

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Authors: Zuraida Alwadood, Adibah Shuib, Norlida Abd Hamid

Abstract:

This paper presents the review of past studies concerning mathematical models for rescheduling passenger railway services, as part of delay management in the occurrence of railway disruption. Many past mathematical models highlighted were aimed at minimizing the service delays experienced by passengers during service disruptions. Integer programming (IP) and mixed-integer programming (MIP) models are critically discussed, focusing on the model approach, decision variables, sets and parameters. Some of them have been tested on real-life data of railway companies worldwide, while a few have been validated on fictive data. Based on selected literatures on train rescheduling, this paper is able to assist researchers in the model formulation by providing comprehensive analyses towards the model building. These analyses would be able to help in the development of new approaches in rescheduling strategies or perhaps to enhance the existing rescheduling models and make them more powerful or more applicable with shorter computing time.

Keywords: Mathematical modelling, Mixed-integer programming, Railway rescheduling, Service delays.

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Authors: S. Pattanapairoj, D. Chetchotsak

Abstract:

This paper proposed classification models that would be used as a proxy for hard disk drive (HDD) functional test equitant which required approximately more than two weeks to perform the HDD status classification in either “Pass" or “Fail". These models were constructed by using committee network which consisted of a number of single neural networks. This paper also included the method to solve the problem of sparseness data in failed part, which was called “enforce learning method". Our results reveal that the constructed classification models with the proposed method could perform well in the sparse data conditions and thus the models, which used a few seconds for HDD classification, could be used to substitute the HDD functional tests.

Keywords: Sparse data, Classifications, Committee network

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Authors: Li Yu, Chenlu Jiao, Hongrun Xin, Yan Wang, Kaiyang Wang

Abstract:

In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry.

Keywords: Deep learning, convolutional neural network, LSTM, housing prediction.

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Authors: Alireza Vafaei Sadr, Vida Abedi, Jiang Li, Ramin Zand

Abstract:

Missing data is a common challenge in medical research and can lead to biased or incomplete results. When the data bias leaks into models, it further exacerbates health disparities; biased algorithms can lead to misclassification and reduced resource allocation and monitoring as part of prevention strategies for certain minorities and vulnerable segments of patient populations, which in turn further reduce data footprint from the same population – thus, a vicious cycle. This study compares the performance of six imputation techniques grouped into Linear and Non-Linear models, on two different real-world electronic health records (EHRs) datasets, representing 17864 patient records. The mean absolute percentage error (MAPE) and root mean squared error (RMSE) are used as performance metrics, and the results show that the Linear models outperformed the Non-Linear models in terms of both metrics. These results suggest that sometimes Linear models might be an optimal choice for imputation in laboratory variables in terms of imputation efficiency and uncertainty of predicted values.

Keywords: EHR, Machine Learning, imputation, laboratory variables, algorithmic bias.

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Authors: Darren Zou

Abstract:

Forecasting the stock market is a very challenging task. Various economic indicators such as GDP, exchange rates, interest rates, and unemployment have a substantial impact on the stock market. Time series models are the traditional methods used to predict stock market changes. In this paper, a machine learning method, Bayesian Additive Regression Tree (BART) is used in predicting stock market indexes based on multiple economic indicators. BART can be used to model heterogeneous treatment effects, and thereby works well when models are misspecified. It also has the capability to handle non-linear main effects and multi-way interactions without much input from financial analysts. In this research, BART is proposed to provide a reliable prediction on day-to-day stock market activities. By comparing the analysis results from BART and with time series method, BART can perform well and has better prediction capability than the traditional methods.

Keywords: Bayesian, Forecast, Stock, BART.

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Authors: F. Djeffal, N. Lakhdar, T. Bendib

Abstract:

The study of the transport coefficients in electronic devices is currently carried out by analytical and empirical models. This study requires several simplifying assumptions, generally necessary to lead to analytical expressions in order to study the different characteristics of the electronic silicon-based devices. Further progress in the development, design and optimization of Silicon-based devices necessarily requires new theory and modeling tools. In our study, we use the PSO (Particle Swarm Optimization) technique as a computational tool to develop analytical approaches in order to study the transport phenomenon of the electron in crystalline silicon as function of temperature and doping concentration. Good agreement between our results and measured data has been found. The optimized analytical models can also be incorporated into the circuits simulators to study Si-based devices without impact on the computational time and data storage.

Keywords: Particle Swarm, electron mobility, Si-based devices, Optimization.

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Authors: Seyoung Kim, Jeongmin Kim, Kwang Ryel Ryu

Abstract:

A database that records average traffic speeds measured at five-minute intervals for all the links in the traffic network of a metropolitan city. While learning from this data the models that can predict future traffic speed would be beneficial for the applications such as the car navigation system, building predictive models for every link becomes a nontrivial job if the number of links in a given network is huge. An advantage of adopting k-nearest neighbor (k-NN) as predictive models is that it does not require any explicit model building. Instead, k-NN takes a long time to make a prediction because it needs to search for the k-nearest neighbors in the database at prediction time. In this paper, we investigate how much we can speed up k-NN in making traffic speed predictions by reducing the amount of data to be searched for without a significant sacrifice of prediction accuracy. The rationale behind this is that we had a better look at only the recent data because the traffic patterns not only repeat daily or weekly but also change over time. In our experiments, we build several different k-NN models employing different sets of features which are the current and past traffic speeds of the target link and the neighbor links in its up/down-stream. The performances of these models are compared by measuring the average prediction accuracy and the average time taken to make a prediction using various amounts of data.

Keywords: Big data, k-NN, machine learning, traffic speed prediction.

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Authors: Mohammadreza Razzazi, Maryam Bayat

Abstract:

Selection of the best possible set of suppliers has a significant impact on the overall profitability and success of any business. For this reason, it is usually necessary to optimize all business processes and to make use of cost-effective alternatives for additional savings. This paper proposes a new efficient context-aware supplier selection model that takes into account possible changes of the environment while significantly reducing selection costs. The proposed model is based on data clustering techniques while inspiring certain principles of online algorithms for an optimally selection of suppliers. Unlike common selection models which re-run the selection algorithm from the scratch-line for any decision-making sub-period on the whole environment, our model considers the changes only and superimposes it to the previously defined best set of suppliers to obtain a new best set of suppliers. Therefore, any recomputation of unchanged elements of the environment is avoided and selection costs are consequently reduced significantly. A numerical evaluation confirms applicability of this model and proves that it is a more optimal solution compared with common static selection models in this field.

Keywords: Supplier Selection, Context-Awareness, OnlineAlgorithms, Data Clustering.

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Authors: Pavan K. Rallabandi, Kailash C. Patidar

Abstract:

In this paper, we present an optimization technique or a learning algorithm using the hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov models (HMMs). In order to improve the sequence/pattern recognition/classification performance by applying a hybrid/neural symbolic approach, a gradient descent learning algorithm is developed using the Real Time Recurrent Learning of Recurrent Neural Network for processing the knowledge represented in trained Hidden Markov Models. The developed hybrid algorithm is implemented on automata theory as a sample test beds and the performance of the designed algorithm is demonstrated and evaluated on learning the deterministic finite state automata.

Keywords: Hybrid systems, Hidden Markov Models, Recurrent neural networks, Deterministic finite state automata.

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