1,670 research outputs found
- Case of next-generation transportation market -
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μ.The present dissertation aims to provide insights into the application of different artificial neural network models in the analysis of consumer choice regarding next-generation transportation services (NGT). It categorizes consumers decisions regarding the adoption of new services according to Deweys buyer decision process and then analyzes these decisions using a variety of different methods. In particular, various artificial neural network (ANN) models are applied to predict consumers intentions. Also, the dissertation proposes an attention-based ANN model that identifies the key features that affect consumers choices. Consumers preferences for different types of NGT services are analyzed using a hierarchical Bayesian model. The analyzed consumer preferences are utilized to forecast demand for NGT services, evaluate government policies within the transportation market, and provide evidence regarding the social conflicts among traditional and new transportation services. The dissertation uses the Multiple Discrete-Continuous Extreme Value (MDCEV) model to analyze consumers decisions regarding the use of different transportation modes. It also utilizes this MDCEV model analysis to estimate the effect of NGT services on consumers travel mode selection behavior and the environmental effects of the transportation sector. Finally, the findings of the dissertations analyses are combined to generate marketing and policy insights that will promote NGT services in Korea.λ³Έ μ°κ΅¬λ κΈ°κ³νμ΅ κΈ°λ°μ μΈκ³΅μ§λ₯λ§κ³Ό κΈ°μ‘΄μ ν΅κ³μ λ§μΌν
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μ μλ λͺ¨νλ€μ μ€μ¦ μ°κ΅¬λ₯Ό μν΄ μ°¨μΈλ μλμ°¨ μμ‘ μλΉμ€μ λν μλΉμλ€μ μ¬μ© μν₯, μλΉμ€ λμμ λν μ νΈ, μμ‘ μλΉμ€λ³ μ¬μ©λμ λΆμνμλ€. μ€μ¦ μ°κ΅¬μμλ μ°¨μΈλ μλμ°¨ μμ‘ μλΉμ€λ₯Ό μμ©νκΈ°κΉμ§ μλΉμλ€μ΄ κ²½ννλ λ¨κ³λ³ μ ν μν©μ λ°μνμμΌλ©°, κ° λ¨κ³μμ λμΆλ κ²°κ³Όλ₯Ό ν΅ν΄ ν₯ν μ°¨μΈλ μλμ°¨ μμ‘ μλΉμ€μ μ±μ₯ κ°λ₯μ±κ³Ό μλΉμλ€μ μ΄λ νμ λ³νμ λν΄ μμΈ‘νμλ€. λ³Έ μ°κ΅¬λ₯Ό ν΅ν΄ μΈκ³΅μ§λ₯λ§μ΄ μλΉμ κ΄λ ¨ μ°κ΅¬μμ μ μ©νκ² νμ©λ μ μμμ 보μμΌλ©°, μΈκ³΅μ§λ₯λ§κ³Ό ν΅κ³μ λ§μΌν
μ νλͺ¨νμ΄ κ²°ν©λ κ²½μ° μλΉμλ€μ μ ν μ ν νμλΏλ§ μλλΌ, μ ν μ ν μμ¬κ²°μ κ³Όμ μ λ°μ κ±Έμ³ μλΉμ μ νΈλ₯Ό ν¬κ΄μ μΌλ‘ λΆμν μ μμμ νμΈνμλ€.Chapter 1. Introduction 1
1.1 Research Background 1
1.2 Research Objective 7
1.3 Research Outline 12
Chapter 2. Literature Review 14
2.1 Product and Technology Diffusion Theory 14
2.1.1. Extension of Adoption Models 19
2.2 Artificial Neural Network 22
2.2.1 General Component of the Artificial Neural Network 22
2.2.2 Activation Functions of Artificial Neural Network 26
2.3 Modeling Consumer Choice: Discrete Choice Model 32
2.3.1 Multinomial Logit Model 32
2.3.2 Mixed Logit Model 34
2.3.3 Latent Class Model 37
2.4 Modeling Consumer Heuristics in Discrete Choice Model 39
2.4.1 Consumer Decision Rule in Discrete Choice Model: Compensatory and Non-Compensatory Models 39
2.4.2 Choice Set Formation Behaviors: Semi-Compensatory Models 42
2.4.3 Modeling Consumer Usage: MDCEV Model 50
2.5 Difference between Artificial Neural Network and Choice Modeling 53
2.6 Limitations of Previous Studies and Research Motivation 58
Chapter 3. Methodology 63
3.1 Artificial Neural Network Models for Prediction 63
3.1.1 Multiple Perceptron Model 63
3.1.2 Convolutional Neural Network 69
3.1.3 Bayesian Neural Network 72
3.2 Feature Identification Model through Attention 77
3.3 Hierarchical Bayesian Model 83
3.4 Multiple Discrete-Continuous Extreme Value Model 86
Chapter 4. Empirical Analysis: Consumer Preference and Selection of Transportation Mode 98
4.1 Empirical Analysis Framework 98
4.2 Data 101
4.2.1 Overview of the Survey 101
4.3 Empirical Study I: Consumer Intention to New Type of Transportation 110
4.3.1 Research Motivation and Goal 110
4.3.2 Data and Model Setup 114
4.3.3 Result and Discussion 123
4.4 Empirical Study II: Consumer Choice and Preference for New Types of Transportation 142
4.4.1 Research Motivation and Goal 142
4.4.2 Data and Model Setup 144
4.4.3 Result and Discussion 149
4.5 Empirical Study III: Impact of New Transportation Mode on Consumers Travel Behavior 163
4.5.1 Research Motivation and Goal 163
4.5.2 Data and Model Setup 164
4.5.3 Result and Discussion 166
Chapter 5. Discussion 182
Bibliography 187
Appendix: Survey used in the analysis 209
Abstract (Korean) 241Docto
Customer Churn Prediction in Telecom Sector: A Survey and way a head
Β© 2021 International Journal of Scientific & Technology Research. This work is licensed under a Creative Commons Attribution 4.0 International License.The telecommunication (telecom)industry is a highly technological domain has rapidly developed over the previous decades as a result of the commercial success in mobile communication and the internet. Due to the strong competition in the telecom industry market, companies use a business strategy to better understand their customersβ needs and measure their satisfaction. This helps telecom companies to improve their retention power and reduces the probability to churn. Knowing the reasons behind customer churn and the use of Machine Learning (ML) approaches for analyzing customers' information can be of great value for churn management. This paper aims to study the importance of Customer Churn Prediction (CCP) and recent research in the field of CCP. Challenges and open issues that need further research and development to CCP in the telecom sector are exploredPeer reviewe
Safety Performance Prediction of Large-Truck Drivers in the Transportation Industry
The trucking industry and truck drivers play a key role in the United States commercial transportation sector. Accidents involving large trucks is one such big event that can cause huge problems to the driver, company, customer and other road users causing property damage and loss of life. The objective of this research is to concentrate on an individual transportation company and use their historical data to build models based on statistical and machine learning methods to predict accidents. The focus is to build models that has high accuracy and correctly predicts an accident. Logistic regression and penalized logistic regression models were tested initially to obtain some interpretation between the predictor variables and the response variable. Random forest, gradient boosting machine (GBM) and deep learning methods are explored to deal with high non-linear and complex data.
The cost of fatal and non-fatal accidents is also discussed to weight the difference between training a driver and encountering an accident. Since accidents are very rare events, the model accuracy should be balanced between predicting non-accidents (specificity) and predicting accidents (sensitivity). This framework can be a base line for transportation companies to emphasis the benefits of prediction to have safer and more productive drivers
Developing retail performance measurement and financial distress prediction systems by using credit scoring techniques
The current research develops a theoretical framework based on the ResourceAdvantage Theory of Competition (Hunt, 2000) for the selection of appropriate
variables. Using a review of the literature as well as to interviews and a survey, 170
potential retail performance variables were identified as possible for inclusion in the
model. To produce a relative simple model with the aim of avoiding over-fitting, a
limited number of key variables or principal components were selected to predict
default. Five credit-scoring techniques: Naive Bayes, Logistic Regression, Recursive
Partitioning, Artificial Neural Network, and Sequential Minimal Optimization (SMO)
were employed on a sample of 195 healthy and 51 distressed businesses from the
USA market over five time periods: 1994-1998, 1995-1999, 1996-2000, 1997-2001
and 1998-2002.Analyses provide sufficient evidence that the five credit scoring methodologies
have sound classification ability in the year before financial distress. Moreover, they
still remained sound even five years prior to financial distress. However, it is difficult
to conclude which modelling technique has the highest classification ability
uniformly, since model performance varied in terms of different time scales. The
analysis also showed that external environment influences do impact on default
assessment for all five credit-scoring techniques, but these influences are weak.
These findings indicate that the developed models are theoretically sound. There is
however a need to compare their performance to other approaches.To explore the issue of the model's performance two approaches are taken. First,
rankings from the study were compared with those from a standard rating systemβin
this case the well-established Moody's Credit Rating. It is assumed that the higher
the degree of similarity between the two sets of rankings, the greater the credibility
of the prediction model. The results indicated that the logistic regression model and
the SMO model were most comparable with Moody's. Secondly, the model's
performance was assessed by applying it to different geographical areas. The original
USA model was therefore applied to a new US data set as well as the European and
Japanese markets. Results indicated that all market models displayed similar
discriminating ability one year prior to financial distress. However, the USA model
performed relatively better than European and Japanese models five years before
financial distress. This implied that a financial distress model has potentially better
prediction ability when based on a single market.Following this result it was decided to explore the performance of a generic global
model, since model construction is time-consuming and costly. A composite model
was constructed by combining data from USA, European and Japanese markets. This
composite model had sound prediction performance, even up to five years before
financial distress, as the accuracy rate was above 85.15% and AUROC value was
above 0.7202. Comparing with the original USA model, the composite model has
similar prediction performance in terms of the accuracy rate. However, the composite
model presented a worse prediction utility based on the AUROC value. A future
research direction might be to include more world retailing markets in order to
ensure the model's prediction utility and practical applicability
A Comprehensive Survey of Data Mining-based Fraud Detection Research
This survey paper categorises, compares, and summarises from almost all
published technical and review articles in automated fraud detection within the
last 10 years. It defines the professional fraudster, formalises the main types
and subtypes of known fraud, and presents the nature of data evidence collected
within affected industries. Within the business context of mining the data to
achieve higher cost savings, this research presents methods and techniques
together with their problems. Compared to all related reviews on fraud
detection, this survey covers much more technical articles and is the only one,
to the best of our knowledge, which proposes alternative data and solutions
from related domains.Comment: 14 page
Review of Data Sources, QSARs and Integrated Testing Strategies for Skin Sensitisation
This review collects information on sources of skin sensitisation data and computational tools for the estimation of skin sensitisation potential, such as expert systems and (quantitative) structure-activity relationship (QSAR) models. The review also captures current thinking of what constitutes an integrated testing strategy (ITS) for this endpoint. The emphasis of the review is on the usefulness of the models for the regulatory assessment of chemicals, particularly for the purposes of the new European legislation for the Registration, Evaluation, Authorisation and Restriction of CHemicals (REACH), which entered into force on 1 June 2007. Since there are no specific databases for skin sensitisation currently available, a description of experimental data found in various literature sources is provided. General (global) models, models for specific chemical classes and mechanisms of action and expert systems are summarised. This review was prepared as a contribution to the EU funded Integrated Project, OSIRIS.JRC.I.3-Consumer products safety and qualit
Skin Sensitisation (Q)SARs/Expert Systems: from Past, Present to Future
This review describes the state of the art of available (Q)SARs/expert systems for skin sensitisation and evaluates their utility for potential regulatory use. There is a strong mechanistic understanding with respect to skin sensitisation which has facilitated the development of different models. Most existing models fall into one of two main categories either they are local in nature, usually specific to a chemical class or reaction chemical mechanism or else they are global in form, derived empirically using statistical methods. Some of the published global QSARs available have been recently characterised and evaluated elsewhere in accordance with the OECD principles. An overview of expert systems capable of predicting skin sensitisation is also provided. Recently, a new perspective regarding the development of mechanistic skin sensitisation QSARs so-called Quantitative Mechanistic Modelling (QMM) has been proposed, where reactivity and hydrophobicity, are used as the key parameters in mathematically modelling skin sensitisation. Whilst hydrophobicity can be conveniently modelled using log P, the octanol-water partition coefficient; reactivity is less readily determined from chemical structure. Initiatives are in progress to generate reactivity data for reactions relevant to skin sensitisation but more resources are required to realise a comprehensive set of reactivity data. This is a fundamental and necessary requirement for the future assessment of skin sensitisation.JRC.I.3-Toxicology and chemical substance
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