INTEGRATING KANO MODEL WITH DATA MINING TECHNIQUES TO ENHANCE CUSTOMER SATISFACTION

The business world is becoming more competitive from time to time; therefore, businesses are forced to improve their strategies in every single aspect. So, determining the elements that contribute to the clients\u27 contentment is one of the critical needs of businesses to develop successful products in the market. The Kano model is one of the models that help determine which features must be included in a product or service to improve customer satisfaction. The model focuses on highlighting the most relevant attributes of a product or service along with customers’ estimation of how these attributes can be used to predict satisfaction with specific services or products. This research aims at developing a method to integrate the Kano model and data mining approaches to select relevant attributes that drive customer satisfaction, with a specific focus on higher education. The significant contribution of this research is to improve the quality of United Arab Emirates University academic support and development services provided to their students by solving the problem of selecting features that are not methodically correlated to customer satisfaction, which could reduce the risk of investing in features that could ultimately be irrelevant to enhancing customer satisfaction. Questionnaire data were collected from 646 students from United Arab Emirates University. The experiment suggests that Extreme Gradient Boosting Regression can produce the best results for this kind of problem. Based on the integration of the Kano model and the feature selection method, the number of features used to predict customer satisfaction is minimized to four features. It was found that either Chi-Square or Analysis of Variance (ANOVA) features selection model’s integration with the Kano model giving higher values of Pearson correlation coefficient and R2. Moreover, the prediction was made using union features between the Kano model\u27s most important features and the most frequent features among 8 clusters. It shows high-performance results

Implementation Particle Swarm Optimization to improve the performance of Naive Bayes on Diabetes Detection Data

Diabetes has certainly been widely known throughout the world as a serious disease. According to WHO data in 2014 there were about 422 million adults who had diabetes. This is very interesting because the increase is very visible, almost more than doubled when compared to in 1980 people with diabetes were only around 108 million people. The more sophisticated technological developments nowadays, various types of diseases can be detected computerized using the data mining method. In this study, the researcher proposes the particle swarm optimization method to improve the quality of the data to be used in the detection of diabetes using the nave Bayes method. The resulting model was tested to obtain the accuracy and AUC (Area Under Curve) of each algorithm so that it was found that testing using nave Bayes got an accuracy value of 96.15% with an AUC value of 0.991. Meanwhile, testing using the Naïve Bayes method based on attribute selection using the Particle Swarm Optimization (PSO) method, obtained an accuracy value of 97.13% with an AUC value of 0.995

Real Option Valuation of a Portfolio of Oil Projects

Various methodologies exist for valuing companies and their projects. We address the problem of valuing a portfolio of projects within companies that have infrequent, large and volatile cash flows. Examples of this type of company exist in oil exploration and development and we will use this example to illustrate our analysis throughout the thesis. The theoretical interest in this problem lies in modeling the sources of risk in the projects and their different interactions within each project. Initially we look at the advantages of real options analysis and compare this approach with more traditional valuation methods, highlighting strengths and weaknesses ofeach approach in the light ofthe thesis problem. We give the background to the stages in an oil exploration and development project and identify the main common sources of risk, for example commodity prices. We discuss the appropriate representation for oil prices; in short, do oil prices behave more like equities or more like interest rates? The appropriate representation is used to model oil price as a source ofrisk. A real option valuation model based on market uncertainty (in the form of oil price risk) and geological uncertainty (reserve volume uncertainty) is presented and tested for two different oil projects. Finally, a methodology to measure the inter-relationship between oil price and other sources of risk such as interest rates is proposed using copula methods.Imperial Users onl

Previsão Inteligente das alterações metabólicas no cancro retal com base em modelos de machine e deep learning

Machine learning, broadly speaking, applies statistical methods to training data to automatically adjust the parameters of a model, rather than a programmer needing to set them manually. Deep Learning is a sub-area of Machine Learning that studies how to solve complex and intuitive problems. The methodologies adopted, using computational means, such as the machines learned and those understood in the world in specific contexts from previous experiences and based on the hierarchy of concepts, use the most used concepts for the form and efficient solution of more varied complex problems. The main objective in this work is to study various classification algorithms in the area of machine learning, and validate until these points can use a solution for choosing more accurate methods in the selection of tests and in new statistics to improve the therapeutic response. The data involved in the training of classification algorithms refer to all patients with metabolic diseases shredding between the years 2003-2021 and the retrospective part. The best classification algorithms to develop are used in the decision support system in the most effective way in choosing the appropriate therapy for each of the future patients who predicted an approximate rate of 20 patients per year.Machine Learning, em termos gerais, aplica métodos estatísticos aos dados de treino para ajustar automaticamente os parâmetros de um modelo, em vez de um programador necessitar de defini-los manualmente. Deep Learning é uma subárea de Machine Learning que estuda como solucionar problemas complexos e intuitivos. As metodologias propostas permitem, com recurso a meios computacionais, que as máquinas aprendam e compreendam o mundo em determinados contextos a partir de experiências anteriores e com base na hierarquia de conceitos possam compreender conceitos mais complexos de forma a solucionarem eficientemente A mais variadíssima gama de problemas. O principal objetivo neste trabalho consiste no estudo de vários algoritmos de classificação na área de machine learning de forma a validar até que ponto estes podem representar uma solução para a escolha de métodos mais precisos na selecção dos doentes e em novas estratégias para melhorar a resposta terapêutica. Os dados envolvidos para treino dos algoritmos de classificação referem-se a todos os doentes tratados com doenças metabólicas entre os anos 2003-2021 na parte retrospectiva. Os melhores algoritmos de classificação a desenvolver serão usados num sistema de apoio à decisão que ajude de forma mais efetiva na escolha da terapia adequada para cada um dos futuros pacientes que se prevê surgirem a uma taxa aproximada de 20 pacientes por ano

Evaluation and validation of forest models: Insight from Mediterranean and scots pine models in Spain

Producción CientíficaForest models predict tree and stand evolution under different scenarios, thereby supporting decision-making in forest management. Models are complex structures composed of sub-models that estimate forest variables at tree and stand levels. Prediction accuracy has generally been evaluated independently of the model. Integrated sub-models make forest models easier to use and provide predictions for growth, survival, ingrowth and many other tree and stand variables with reduced effort. However, while individual submodel validation is widely practiced and normally done by each author individually, joint model validation remains less explored. This study deploys a useful methodology for evaluating and validating models. After comparing observed and predicted data, several case studies were then proposed to improve the accuracy of the joint model. We used the IBERO model, data from the Spanish National Forest Inventory and the SIMANFOR simulator platform. The accuracy of growth submodels was improved by calibrating their equations, though accuracy was not improved in survival and ingrowth submodels.FEDER - Junta de Castilla y León (CLU-2019-01 y CL-EI-2021-05)Project COMFOR-SUDOE: Integrated and intelligent management of complex forests and mixed-species plantations in Southwest Europe (SOE4/PA/E1012)Project SMART: Bosques mixtos : selvicultura, mitigación, adaptación, resiliencia y trade-offs (VA183P20)Project Integrated Forest Management along complexity gradients (IMFLEX) (PID2021-1262750B-C229

Location analysis of city sections: socio-demographic segmentation and restaurant potentiality estimation : a case study of Lisbon

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesOne of the objectives of this study is to perform classification of socio-demographic components for the level of city section in City of Lisbon. In order to accomplish suitable platform for the restaurant potentiality map, the socio-demographic components were selected to produce a map of spatial clusters in accordance to restaurant suitability. Consequently, the second objective is to obtain potentiality map in terms of underestimation and overestimation in number of restaurants. To the best of our knowledge there has not been found identical methodology for the estimation of restaurant potentiality. The results were achieved with combination of SOM (Self-Organized Map) which provides a segmentation map and GAM (Generalized Additive Model) with spatial component for restaurant potentiality. Final results indicate that the highest influence in restaurant potentiality is given to tourist sites, spatial autocorrelation in terms of neighboring restaurants (spatial component), and tax value, where lower importance is given to household with 1 or 2 members and employed population, respectively. In addition, an important conclusion is that the most attractive market sites have shown no change or moderate underestimation in terms of restaurants potentiality

Application of Predicted Models in Debt Management: Developing a Machine Learning Algorithm to Predict Customer Risk at EDP Comercial

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThis report is a result of a nine-month internship at EDP Comercial where the main project of research was the application of artificial intelligence tools in the field of debt management. Debt management involves a set of strategies and processes aimed at reducing or eliminating debt and the use of artificial intelligence has shown great potential to optimize these processes and minimize the risk of debt for individuals and organizations. In terms of monitoring and controlling the creditworthiness and quality of clients, debt management has mainly been responsive and reactive, attempting to recover losses after a client has become delinquent. There is a gap in the knowledge of how to proactively identify at-risk accounts before they fall behind on payments. To avoid the constant reactive response in the field, it was developed a machine-learning algorithm that predicts the risk of a client becoming in debt by analyzing their scorecard, which measures the quality of a client based on their infringement history. After preprocessing the data, XGBoost was implemented to a dataset of 3M customers with at least one active contract on EDP, on electricity or gas. Hyperparameter tuning was performed on the model to reach an F1 score of 0.7850 on the training set and 0.7835 on the test set. The results were discussed and based on those, recommendations and improvements were also identified

Protein Tertiary Model Assessment Using Granular Machine Learning Techniques

The automatic prediction of protein three dimensional structures from its amino acid sequence has become one of the most important and researched fields in bioinformatics. As models are not experimental structures determined with known accuracy but rather with prediction it’s vital to determine estimates of models quality. We attempt to solve this problem using machine learning techniques and information from both the sequence and structure of the protein. The goal is to generate a machine that understands structures from PDB and when given a new model, predicts whether it belongs to the same class as the PDB structures (correct or incorrect protein models). Different subsets of PDB (protein data bank) are considered for evaluating the prediction potential of the machine learning methods. Here we show two such machines, one using SVM (support vector machines) and another using fuzzy decision trees (FDT). First using a preliminary encoding style SVM could get around 70% in protein model quality assessment accuracy, and improved Fuzzy Decision Tree (IFDT) could reach above 80% accuracy. For the purpose of reducing computational overhead multiprocessor environment and basic feature selection method is used in machine learning algorithm using SVM. Next an enhanced scheme is introduced using new encoding style. In the new style, information like amino acid substitution matrix, polarity, secondary structure information and relative distance between alpha carbon atoms etc is collected through spatial traversing of the 3D structure to form training vectors. This guarantees that the properties of alpha carbon atoms that are close together in 3D space and thus interacting are used in vector formation. With the use of fuzzy decision tree, we obtained a training accuracy around 90%. There is significant improvement compared to previous encoding technique in prediction accuracy and execution time. This outcome motivates to continue to explore effective machine learning algorithms for accurate protein model quality assessment. Finally these machines are tested using CASP8 and CASP9 templates and compared with other CASP competitors, with promising results. We further discuss the importance of model quality assessment and other information from proteins that could be considered for the same