Software Application for Smile Design Automation Using the Visagism Theory

Abstract: The visagism concept in dentistry tries to achieve the most appropriate smile design for every individual patient. The design is based on facial analysis, the patient’s personality and their preferences, and is calculated and visualized as a teeth configuration. The paper presents VisagiSMile — aesthetic dentistry software which automates the theory of dental visagism. The goal of VisagiSMile is to automate this process of creating aesthetic cases and to eliminate the human factor from the analysis. Based on innovative data mining approach, the software learns to classify patients’ faces correctly and then makes the necessary calculations to produce harmonious teeth configuration which can be further adjusted. Continue reading

Методология за предсказване на успеха на стартиращи компании

Резюме: Методиката и моделите за предсказване на успеха на стартиращи компании, представени в статията, са резултат от тригодишно проучване на предприемаческата екосистема в България. Предложени са модел на процеса на създаване на компания и модел за предсказване на успеха, които са базирани на проведено качествено изследване. Проведено е и количествено изследване върху набор от данни за 136 компании. Чрез прилагането на метода факторен анализ, са потвърдени предложените в модела категории и подкатегории фактори, определящи успеха на стартиращите компании. С помощта на софтуерните продукти за извличане на знания от данни – IBM SPSS Modeler и Weka са синтезирани класификационни модели, които с висока точност предсказват успеха. Получените резултати са приложени и внедрени в I3SP – информационна система за предсказване на успеха на стартиращи компании.

Abstract: The methodology and the success prediction models presented in the articles are results from three years of research of the entrepreneurial ecosystem in Bulgaria. A model of the process of creating a company and a success prediction model have been proposed, based on the conducted qualitative research. A qualitative research on a dataset for 136 companies has been conducted. The method factor analysis confirms the proposed categories and subcategories of factors in the model, which determine the success of start-ups. Classification models, which predict the success of start-ups with high accuracy, have been synthesized by applying the data mining software products IBM SPSS Modeler and Weka. The results are applied and implemented in I3SP – Information System for Start-ups Success Prediction. Continue reading

Методология за предсказване на успеха за технологични стартиращи компании в България – дисертация

Дисертация на тема: Методология за предсказване на успеха за технологични стартиращи компании в България – Модели и софтуер за прогнозиране на успеха на стартиращи компании

Структура на дисертацията

Текстът на дисертацията е организиран в 5 глави, списък на фигурите, списък на таблиците, основни използвани термини, заключение, библиография и 3 приложения.

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Сравнение на класификационни модели за стартиращи компании

Резюме: Проведено е количествено изследване на факторите за успех на стартиращи компании от България. Наборът от данни за 136 компании е анализиран с помощта на софтуерните продукти за извличане на знания от данни – IBM SPSS Modeler и Weka. Като резултат са синтезирани класификационни модели за предсказване на успеха на стартиращи компании от България. Получените модели са анализирани и сравнени, като са избрани най-точните и ефективни модели. Идентифицирани са факторите за успех на компаниите, включени в моделите, както и принципът на вземане на решение за тяхната класификация.

Автори: Янков, Б. Continue reading

Detailed Model of a Successful Startup

Have you ever wondered which factors make a successful startup stand out? I have prepared a model that lists the factors of importance for the success of young companies.

Entrepreneurial Team

  • Personality and Values
    • Authonomy
    • Confidence
    • Initiative
    • Locus of Control
    • Need for Achievement
    • Risk-taking Propensity
    • Tolerance of Ambiguity
  • Skills and Experience
    • Entrepreneurship Skills
    • Management Skills
    • Marketing Skills
    • Technical Skills
    • Human Resources Skills
    • Investment Skills
    • Start-up Experience
    • Experience in Similar Position
    • Industry Experience
    • General Management Experience
    • Formal Education
    • Field of Education
    • Age of Entrepreneur
    • Entrepreneurial Parents
  • Teamwork
    • Team Completeness
    • Team Knowledge
    • Team Skill
    • Team Attitude

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Figure 1. New venture success prediction model proposed by Yankov

Models and Tools for Technology Start-Up Companies Success Analysis

Abstract: The designs presented in the article are fastened in the authors’ years-long research on entrepreneurship and business model innovations. A quantitative research was performed to derive a model for predicting the success of Bulgarian startup companies. The authors started this research with in-depth inquiries of start-up companies in Bulgaria. Under our guidance, several research analysts investigated each start-up using approximately 100 questions. The preceding research stages included an overview and an analysis of existing success prediction models, a new abstract success prediction model, a venture creation process model and a qualitative research. The abstract success prediction model was extended with measurable variables with the help of a quantitative research of Bulgarian entrepreneurs. The current dataset of companies has been enriched with more cases and has been analyzed using data mining software: IBM SPSS Modeler, which automatically tests different models and suggests the best performing ones and also with the open source product Weka. The best derived model is a classification tree that correctly predicts the success of technology start-ups from the dataset in 83,76% of the test cases. The analysis revealed the answers to challenges and questions that start-up companies face and implemented a model that was deployed into an information system for start-ups success prediction. The developed information system will help to predict the success of start-ups. The software will evolve iteratively, and by involving more companies to use it, will grow its database. Continue reading

Information System for Forecasting the Success of Bulgarian Start-up Companies

Abstract: An Information System (IS) for predicting the success of Bulgarian start-up companies has been designed and developed. The IS is based on a model for success prediction derived from a quantitative research of 137 Bulgarian start-up companies. Entrepreneurs fill in a survey and based on a prediction model the IS estimates the chances of success of their start-up and shows a graphical decision tree with the factors that led to the result.

Authors: Yankov, B., Vitanov, N. Continue reading

Synthesis of Predictive Models for Start-up Companies

Abstract: A quantitative research is performed to improve the accuracy of a previously derived model for predicting the success of Bulgarian start-up companies. The preceding research stages include an overview and analysis of older success prediction models, creation of a new abstract success prediction model, a venture creation process model, a qualitative and a quantitative research. The dataset is extended with 31 more cases and is analyzed in higher detail using the IBM SPSS Modeler software. The previously derived models are compared to the new ones in terms of accuracy of the success prediction and the predicting variables.

Authors: Yankov, B. Continue reading

Start-up Companies Predictive Models Analysis

Abstract: A quantitative research is performed to derive a model for predicting the success of Bulgarian start-up companies. The preceding research stages included an overview and analysis of older success prediction models, a new abstract success prediction model, a venture creation process model and a qualitative research. The abstract success prediction model is extended with measurable variables which are included in a survey. The survey is currently in progress with 105 responses by owners and managers of Bulgarian companies. The current dataset was analyzed using the IBM SPSS Modeler software which automatically tests different models and suggests the best performing ones. The best derived model is a decision tree model that predicts the success of the start-up companies from the dataset with 91,86% probability using 11 variables.

Authors: Yankov, B., Haralampiev, K., Ruskov P. Continue reading