7 research outputs found

    Big Data Analysis of Facebook Users Personality Recognition using Map Reduce Back Propagation Neural Networks

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    Abstract- Machine learning has been an effective tool to connect networks of enormous information for predicting personality.  Identification of personality-related indicators encrypted in Facebook profiles and activities are of special concern in most research efforts. This research modeled user personality based on set of features extracted from the Facebook data using Map-Reduce Back Propagation Neural Network (MRBPNN). The performance of the MRBPNN classification model was evaluated in terms of five basic personality dimensions: Extraversion (EXT), Agreeableness (AGR), Conscientiousness (CON), Neuroticism (NEU), and Openness to Experience (OPN) using True positive, False Positive, accuracy, precision and F-measure as metrics at the threshold value of 0.32. The experimental results reveal that MRBPNN model has accuracy of 91.40%, 93.89%, 91.33%, 90.43% and 89.13% CON, OPN, EXT, NEU and AGR respectively for personality recognition which is more computationally efficient than Back Propagation Neural Network (BPNN) and Support Vector Machine (SVM). Therefore, personality recognition based on MRBPNN would produce a reliable prediction system for various personality traits with data having a very large instance

    HYBRID FEATURE SELECTION AND SUPPORT VECTOR MACHINE FRAMEWORK FOR PREDICTING MAINTENANCE FAILURES

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    The main aim of predictive maintenance is to minimize downtime, failure risks and maintenance costs in manufacturing systems. Over the past few years, machine learning methods gained ground with diverse and successful applications in the area of predictive maintenance. This study shows that performing preprocessing techniques such as oversampling and features selection for failure prediction, is promising. For instance, to handle imbalanced data, the SMOTE-Tomek method is used. For features selection, three different methods can be applied: Recursive Feature Elimination, Random Forest and Variance Threshold. The data considered in this paper for simulation is used in literature; it is applied to aircraft engine sensors measurements to predict engines failure, while the predicting algorithm used is a Support Vector Machine. The results show that classification accuracy can be significantly boosted by using the preprocessing techniques

    Machine learning na previsão da conversão de clientes alvo

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    Mestrado Bolonha em Métodos Quantitativos para a Decisão Económica e EmpresarialNuma empresa, os clientes alvo representam uma parcela de clientes que são tidos como foco em ações de marketing para venda de determinado produto ou serviço. A conversão de clientes alvo a determinado produto ou serviço gera lucros para a empresa, sendo por isso importante direcionar as ações de marketing a clientes que são mais propensos à conversão. Neste sentido, este estudo tem como principal objetivo obter a probabilidade da conversão de clientes alvo à solução de pagamentos Digital Payment Gateway DPG da SIBS, através de técnicas e algoritmos de Machine Learning. O desenvolvimento deste estudo seguiu a metodologia Cross-Industry Standard Process for Data Mining (CRISP-DM). No balanceamento da classe target, foram utilizadas as técnicas SMOTE e SMOTETomek e os algoritmos de classificação implementados foram: XGBoost, Random Forest e a Regressão Logística. O modelo estimado que apresentou melhor desempenho foi obtido através do algoritmo Random Forest com recurso a dados balanceados através da técnica SMOTE. Este modelo reflete um acerto de 60% das observações pertencentes à classe minoritária.In a company, target customers represent a portion of customers who are the focus of marketing actions for the sale of a certain product or service. The conversion of target customers to a certain product or service generates profits for the company, so it is important to direct marketing actions to customers who are more likely to convert. In this sense, this study's main objective is to obtain the probability of conversion of target customers to the SIBS' Digital Payment Gateway DPG payment solution, through Machine Learning techniques and algorithms. The development of this study followed the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. In the balancing of the target class, the SMOTE and SMOTETomek techniques were used and the classification algorithms implemented were: XGBoost, Random Forest and Logistic Regression. The estimated model that presented the best performance was obtained through the Random Forest algorithm using balanced data through the SMOTE technique. This model reflects a hit of 60% of the observations belonging to the minority class.info:eu-repo/semantics/publishedVersio

    Crime Prediction Using Machine Learning

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    Predikce kriminality může v praxi významně zlepšit strategické rozmístění policejních hlídek ve městě, což pomáhá prevenci před vznikem kriminálních činů. Strojové učení je jedna z nejpoužívanějších metod pro predikci kriminality. Je však potřeba stále porovnávat různé typy algoritmů a postupy pro získání nejlepších výsledků. Tato práce porovnává několik druhů algoritmů. Pro učení modelů byla použita data poskytnutá Policí České republiky (PČR) za roky 2020 až 2021 na území města Ostravy. Do modelů vstupují vybrané kategorie trestných činů: krádeže, krádeže vloupáním, jiná majetková trestná činnost a přestupky proti majetku dle §50. V práci bylo porovnáno několik metod pro převzorkování nevybalancovaného datasetu. Jako nejlepší metoda byla zvolena SMOTETomek. Bylo zjištěno, že komplexnější algoritmy dosahují přesnějších výsledků predikce, například boostovací rozhodovací stromy nebo neuronová síť.In practice criminality prediction can significantly improve strategic positioning of police patrol in the city, which helps prevent crime from occurring. Machine learning is one of the most widely used method for this problem. However, there is still need to keep comparing various types of algorithms and approaches to get better results. This thesis compares several types of algorithms. Models was learned from data provided by Police of Czech Republic (PČR) for the years 2020 and 2021 on the territory of the city Ostrava. Only selected categories of crimes are entered into the models: theft, burglary, other property crimes and offences against property according to §50. Several methods for resampling the unbalanced dataset were compared in this paper. SMOTETomek was chosen as the best method. It was found that more complex algorithms, such as boosting decision trees or neural networks yield more effective results.548 - Katedra geoinformatikyvelmi dobř

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    SMOTETomek-Based Resampling for Personality Recognition

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