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Modeling and Simulation in Engineering, Economics, and Management
This book contains the refereed proceedings of the International Conference on Modeling and Simulation in Engineering, Economics, and Management, MS 2013, held in Castellón de la Plana, Spain, in June 2013. The event was co-organized by the AMSE Association and the SoGReS Research Group of the Jaume I University. This edition of the conference paid special attention to modeling and simulation in diverse fields of business management. The 28 full papers in this book were carefully reviewed and selected from 65 submissions. They are organized in topical sections on: modeling and simulation in CSR and sustainable development; modeling and simulation in finance and accounting; modeling and simulation in management and marketing; modeling and simulation in economics and politics; knowledge-based expert and decision support systems; and modeling and simulation in engineering.
Calculation of the Variance in Surveys of the Economic Climate
Public opinion surveys have become progressively incorporated into systems of official statistics. Surveys of the economic climate are usually qualitative because they collect opinions of businesspeople and/or experts about the long-term indicators described by a number of variables. In such cases, the responses are expressed in ordinal numbers, that is, the respondents verbally report, for example, whether during a given trimester the sales or the new orders have increased, decreased or remained the same as in the previous trimester. This data allows for the ability to calculate the percent of respondents in the total population (results are extrapolated), who select every one of the three ...
Machine Learning in Insurance
Machine learning is a relatively new field, without a unanimous definition. In many ways, actuaries have been machine learners. In both pricing and reserving, but also more recently in capital modelling, actuaries have combined statistical methodology with a deep understanding of the problem at hand and how any solution may affect the company and its customers. One aspect that has, perhaps, not been so well developed among actuaries is validation. Discussions among actuaries’ “preferred methods” were often without solid scientific arguments, including validation of the case at hand. Through this collection, we aim to promote a good practice of machine learning in insurance, considering the following three key issues: a) who is the client, or sponsor, or otherwise interested real-life target of the study? b) The reason for working with a particular data set and a clarification of the available extra knowledge, that we also call prior knowledge, besides the data set alone. c) A mathematical statistical argument for the validation procedure.
Machine Learning in Healthcare and Security
This book brings together a blend of different areas of machine learning and recent advances in the area. From the use of ML in healthcare to security, this book encompasses several areas related to ML while keeping a check on traditional ML algorithms. Machine Learning in Healthcare and Security: Advances, Obstacles, and Solutions describes the predictive analysis and forecasting techniques in different emerging and classical areas using the approaches of ML and AI. It discusses the application of ML and AI in medical diagnostic systems and deals with the security prevention aspects of ML and how it can be used to tackle various emerging security issues. This book also focuses on NLP and understanding the techniques, obstacles, and possible solutions. This is a valuable reference resource for researchers and postgraduate students in healthcare systems engineering, computer science, cyber-security, information technology, and applied mathematics.
Congress on Intelligent Systems
This book is a collection of selected papers presented at the First Congress on Intelligent Systems (CIS 2020), held in New Delhi, India during September 5 – 6, 2020. It includes novel and innovative work from experts, practitioners, scientists and decision-makers from academia and industry. It covers topics such as Internet of Things, information security, embedded systems, real-time systems, cloud computing, big data analysis, quantum computing, automation systems, bio-inspired intelligence, cognitive systems, cyber physical systems, data analytics, data/web mining, data science, intelligence for security, intelligent decision making systems, intelligent information processing, intellige...
Advances in Parallel Computing Algorithms, Tools and Paradigms
Recent developments in parallel computing for various fields of application are providing improved solutions for handling data. These newer, innovative ideas offer the technical support necessary to enhance intellectual decisions, while also dealing more efficiently with the huge volumes of data currently involved. This book presents the proceedings of ICAPTA 2022, the International Conference on Advances in Parallel Computing Technologies and Applications, hosted as a virtual conference from Bangalore, India, on 27 and 28 January 2022. The aim of the conference was to provide a forum for the sharing of knowledge about various aspects of parallel computing in communications systems and netwo...
Soft Computing in Management and Business Economics
This book is a collection of selected papers presented at the Annual Meeting of the European Academy of Management and Business Economics (AEDEM), held at the Faculty of Economics and Business of the University of Barcelona, 05 – 07 June, 2012. This edition of the conference has been presented with the slogan “Creating new opportunities in an uncertain environment”. There are different ways for assessing uncertainty in management but this book mainly focused on soft computing theories and their role in assessing uncertainty in a complex world. The present book gives a comprehensive overview of general management topics and discusses some of the most recent developments in all the areas of business and management including management, marketing, business statistics, innovation and technology, finance, sports and tourism. This book might be of great interest for anyone working in the area of management and business economics and might be especially useful for scientists and graduate students doing research in these fields.
Using Tree-based Models to Predict Drunk Driving at Police Preventive Checkpoints
This Master's Degree Thesis has the following objectives: Understand tree based models and their main advantages and disadvantages Be aware how unbalanced data affects tree based models Deepen drunk driving knowledge in Catalonia Develop a predictive model to detect drunk drivers In this Master Degree Thesis it is described how road safety is a world wide problem and drunk driving an issue to tackle. Original data from Police preventive checkpoints is described and different tree based models are introduced as Classification and Regression Trees, Bagging and Random Forest. It is also detailed the Class Imbalance Problem with several approaches to deal with it. This models are used to develop a predictive model to detect drunk drivers.
