The quality of a wine is important for the consumers as well as the wine industry. The traditional (expert) way of measuring wine quality is time-consuming. Nowadays, machine learning models are important tools to replace human tasks. In this case, there are several features to predict the wine quality but the entire features will not be relevant for better prediction. So, our thesis work is focusing on what wine features are important to get the promising result. For the purposeof classification model and evaluation of the relevant features, we used three algorithms namely support vector machine (SVM), naïve Bayes (NB), and artificial neural network (ANN). In this study, we used two wine quality datasets red wine and white wine. To evaluate the feature importance we used the Pearson coefficient correlation and performance measurement matrices such as accuracy, recall, precision, and f1 score for comparison of the machine learning algorithm. A grid search algorithm was applied to improve the model accuracy. Finally, we achieved the artificial neural network (ANN) algorithm has better prediction results than the Support Vector Machine (SVM) algorithm and the Naïve Bayes (NB) algorithm for both red wine and white wine datasets.

It is rarely possible to use an optimal classifier. Often the classifier used for a specific problem is an approximation of the optimal classifier. Methods are presented for evaluating the performance of an approximation in the model class of Bayesian Networks. Specifically for the approximation of class conditional independence a bound for the performance is sharpened. The class conditional independence approximation is connected to the minimum description length principle (MDL), which is connected to Jeffreys’ prior through commonly used assumptions. One algorithm for unsupervised classification is presented and compared against other unsupervised classifiers on three data sets. / <p>Report code: LiU-TEK-LIC 2006:11.</p>

This study investigates E-banking Operational Risk Assessment (ORA) to enable the development of a new ORA framework and methodology. The general view is that E-banking systems have modified some of the traditional banking risks, particularly Operational Risk (OR) as suggested by the Basel Committee on Banking Supervision in 2003. In addition, recent E-banking financial losses together with risk management principles and standards raise the need for an effective ORA methodology and framework in the context of E-banking. Moreover, evaluation tools and / or methods for ORA are highly subjective, are still in their infant stages, and have not yet reached a consensus. Therefore, it is essential to develop valid and reliable methods for effective ORA and evaluations. The main contribution of this thesis is to apply Fuzzy Inference System (FIS) and Tree Augmented Naïve Bayes (TAN) classifier as standard tools for identifying OR, and measuring OR exposure level. In addition, a new ORA methodology is proposed which consists of four major steps: a risk model, assessment approach, analysis approach and a risk assessment process. Further, a new ORA framework and measurement metrics are proposed with six factors: frequency of triggering event, effectiveness of avoidance barriers, frequency of undesirable operational state, effectiveness of recovery barriers before the risk outcome, approximate cost for Undesirable Operational State (UOS) occurrence, and severity of the risk outcome. The study results were reported based on surveys conducted with Nigerian senior banking officers and banking customers. The study revealed that the framework and assessment tools gave good predictions for risk learning and inference in such systems. Thus, results obtained can be considered promising and useful for both E-banking system adopters and future researchers in this area.

Natural Language Processing (NLP) is a branch in data science that marries artificial intelligence with linguistics. Essentially, it tries to program computers to understand human language, both spoken and written. Over the past decade, researchers have applied novel algorithms to gain a better understanding of human sentiment. While no easy feat, incredible improvements have allowed organizations, politicians, governments, and other institutions to capture the attitudes and opinions of the public. It has been particularly constructive for companies who want to check the pulse of a new product or see what the positive or negative sentiments are for their services. NLP has even become useful in boosting sales performance and improving training. Over the years, there have been countless studies on sales performance, both from a psychological perspective, where characteristics of salespersons are explored, and from a data science/AI (Artificial Intelligence) perspective, where text is analyzed to predict sales forecasting (Pai &amp; Liu, 2018) and coach sales agents using AI trainers (Luo et al., 2021). However, few studies have discussed how NLP models can help characterize sales performance using actual sales transcripts. Thus, there is a need to explore to what extent NLP models can inform B2B businesses of the characteristics embodied within their salesforce. This study aims to fill that literature gap. Through a partnership with a medium-sized tech company based out of California, USA, this study conducted an experiment to try and answer to what extent can we characterize sales performance based on real-life sales communication? And in what ways can conversational data inform the sales team at a California-based mid-sized tech company about how top performers communicate with customers? In total, over 5000 sentences containing over 110 000 words were collected and analyzed using two separate rule-based sentiment analysis techniques: TextBlob developed by Steven Loria (2013) and Valence Aware Dictionary and sEntiment Reasoner (VADER) developed by CJ Hutto and Eric Gilbert (2014). A Naïve Bayes classifier was then adopted to test and train each sentiment output from the two rule-based techniques. While both models obtained high accuracy, above 90%, it was concluded that an oversampled VADER approach yields the highest results. Additionally, VADER also tends to classify positive and negative sentences more correctly than TextBlob, when manually reviewing the output, hence making it a better model for the used dataset.

Genom litteratursökning och manuell datakompilering av sexualbrott mot barn, besvaras frågan “Vilka ord och fraser som förbrytare använder i konversationer är vanligt förekommande och kan användas som identifierande markörer av grooming?” och resulterade i en ordlista av könsord, sexuellt nedvärderande skällsord, och interjektioner som utrop, uppmaningar, och svordomar, som förekommer i högre utsträckning än i vardagliga konversationer. Denna lista användes för träning och test av en språkmodell som flaggar för skadlig data som kan indikera på grooming. Med en semistrukturerad intervju, kompletterat med litteratursökningen av sexualbrottmål besvarades frågan “Vilka sociala plattformar används av förbrytare för att kontakta barn med syfte att utsätta dem för sexualbrott, och varför är dessa plattformar mer använda än andra?”. Dessa metoder påvisade att Snapchat hade en överväldigande majoritet och var den mest använda plattformen, följt av Instagram på en andraplats, samt Tiktok och Kik på en gemensam tredjeplats. För att besvara den tredje frågeställningen “Kan identifiering av grooming underlättas genom Djupinlärning och Naturlig språkbehandling?" utfördes ett flertal experiment på den skapade detekteringsmodell med Naïve Bayes algoritmen som gav positiva utslag. Motiveringen till användandet av AI var att underlätta för IT-forensiker och utredare i deras arbete genom att snabbt identifiera förekomsten av grooming. Eftersom mängden data som extraheras är väldigt omfattande och innehållsklassificering har stor potential för automatisering, kan AI-modeller avsevärt minska arbetsbördan och öka effektiviteten. / By investigating and analyzing court cases, the question of which are the most commonly used words and phrases during grooming attempts that could be used as grooming indicators. A list was compiled and utilized as “harmful” and “harmless” for a training- and test dataset for an AI-model. The list contained snippets of conversations where genital, sexually derogatory terms, commands, and swear words averaged higher than in daily conversation. Through the methods of a semistructured interview and analyzing court cases, results of which social platforms perpetrators use to contact children could be compiled. This showed that Snapchat was by far the most prevalent platform used, followed by Instagram and in third place Tiktok and Kik Messaging. To answer this question, “harmless” data from the same platforms were used in the experiments. The third and final question, pertaining to the possibility of using an AI in grooming detection, was answered through multiple experiments. In an effort to determine if the conversations contained grooming or not, similar in fashion to e-mail spam classification problems, a script with Naïve Bayes as the classifier produced positive results. The goal of this study was to compile a list of words and phrases that, once used to train the model, could detect usage of these words and phrases. And notify the user if the current conversation has been flagged for suspected grooming attempts.

This study investigates E-banking Operational Risk Assessment (ORA) to enable the development of a new ORA framework and methodology. The general view is that E-banking systems have modified some of the traditional banking risks, particularly Operational Risk (OR) as suggested by the Basel Committee on Banking Supervision in 2003. In addition, recent E-banking financial losses together with risk management principles and standards raise the need for an effective ORA methodology and framework in the context of E-banking. Moreover, evaluation tools and / or methods for ORA are highly subjective, are still in their infant stages, and have not yet reached a consensus. Therefore, it is essential to develop valid and reliable methods for effective ORA and evaluations. The main contribution of this thesis is to apply Fuzzy Inference System (FIS) and Tree Augmented Naïve Bayes (TAN) classifier as standard tools for identifying OR, and measuring OR exposure level. In addition, a new ORA methodology is proposed which consists of four major steps: a risk model, assessment approach, analysis approach and a risk assessment process. Further, a new ORA framework and measurement metrics are proposed with six factors: frequency of triggering event, effectiveness of avoidance barriers, frequency of undesirable operational state, effectiveness of recovery barriers before the risk outcome, approximate cost for Undesirable Operational State (UOS) occurrence, and severity of the risk outcome. The study results were reported based on surveys conducted with Nigerian senior banking officers and banking customers. The study revealed that the framework and assessment tools gave good predictions for risk learning and inference in such systems. Thus, results obtained can be considered promising and useful for both E-banking system adopters and future researchers in this area.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal.

Cortical and subcortical microcircuits are continuously modified throughout life. Despite ongoing changes these networks stubbornly maintain their functions, which persist although destabilizing synaptic and nonsynaptic mechanisms should ostensibly propel them towards runaway excitation or quiescence. What dynamical phenomena exist to act together to balance such learning with information processing? What types of activity patterns do they underpin, and how do these patterns relate to our perceptual experiences? What enables learning and memory operations to occur despite such massive and constant neural reorganization? Progress towards answering many of these questions can be pursued through large-scale neuronal simulations.    In this thesis, a Hebbian learning rule for spiking neurons inspired by statistical inference is introduced. The spike-based version of the Bayesian Confidence Propagation Neural Network (BCPNN) learning rule involves changes in both synaptic strengths and intrinsic neuronal currents. The model is motivated by molecular cascades whose functional outcomes are mapped onto biological mechanisms such as Hebbian and homeostatic plasticity, neuromodulation, and intrinsic excitability. Temporally interacting memory traces enable spike-timing dependence, a stable learning regime that remains competitive, postsynaptic activity regulation, spike-based reinforcement learning and intrinsic graded persistent firing levels.    The thesis seeks to demonstrate how multiple interacting plasticity mechanisms can coordinate reinforcement, auto- and hetero-associative learning within large-scale, spiking, plastic neuronal networks. Spiking neural networks can represent information in the form of probability distributions, and a biophysical realization of Bayesian computation can help reconcile disparate experimental observations. / <p>QC 20170421</p>

Em anos recentes, muitos algoritmos bio-inspirados têm surgido para resolver problemas de classificação. Em confirmação a isso, a revista Nature, em 2002, publicou um artigo que já apontava para o ano de 2003 o uso comercial de Sistemas Imunológicos Artificiais para detecção de fraude em instituições financeiras por uma empresa britânica. Apesar disso, não observamos, a luz de nosso conhecimento, nenhuma publicação científica com resultados promissores desde então. Nosso trabalho tratou de aplicar Sistemas Imunológicos Artificiais (AIS) para detecção de fraude em cartões de crédito. Comparamos AIS com os métodos de Árvore de Decisão (DT), Redes Neurais (NN), Redes Bayesianas (BN) e Naive Bayes (NB). Para uma comparação mais justa entre os métodos, busca exaustiva e algoritmo genético (GA) foram utilizados para selecionar um conjunto paramétrico otimizado, no sentido de minimizar o custo de fraude na base de dados de cartões de crédito cedida por um emissor de cartões de crédito brasileiro. Em adição à essa otimização, fizemos também uma análise e busca por parâmetros mais robustos via multi-resolução, estes parâmetros são apresentados neste trabalho. Especificidades de bases de fraude como desbalanceamento de dados e o diferente custo entre falso positivo e negativo foram levadas em conta. Todas as execuções foram realizadas no Weka, um software público e Open Source, e sempre foram utilizadas bases de teste para validação dos classificadores. Os resultados obtidos são consistentes com Maes et al. que mostra que BN são melhores que NN e, embora NN seja um dos métodos mais utilizados hoje, para nossa base de dados e nossas implementações, encontra-se entre os piores métodos. Apesar do resultado pobre usando parâmetros default, AIS obteve o melhor resultado com os parâmetros otimizados pelo GA, o que levou DT e AIS a apresentarem os melhores e mais robustos resultados entre todos os métodos testados. / In 2002, January the 31st, the famous journal Nature, with a strong impact in the scientific environment, published some news about immune based systems. Among the different considered applications, we can find detection of fraudulent financial transactions. One can find there the possibility of a commercial use of such system as close as 2003, in a British company. In spite of that, we do not know of any scientific publication that uses Artificial Immune Systems in financial fraud detection. This work reports results very satisfactory on the application of Artificial Immune Systems (AIS) to credit card fraud detection. In fact, scientific financial fraud detection publications are quite rare, as point out Phua et al. [PLSG05], in particular for credit card transactions. Phua et al. points out the fact that no public database of financial fraud transactions is available for public tests as the main cause of such a small number of publications. Two of the most important publications in this subject that report results about their implementations are the prized Maes (2000), that compares Neural Networks and Bayesian Networks in credit card fraud detection, with a favored result for Bayesian Networks and Stolfo et al. (1997), that proposed the method AdaCost. This thesis joins both these works and publishes results in credit card fraud detection. Moreover, in spite the non availability of Maes data and implementations, we reproduce the results of their and amplify the set of comparisons in such a way to compare the methods Neural Networks, Bayesian Networks, and also Artificial Immune Systems, Decision Trees, and even the simple Naïve Bayes. We reproduce in certain way the results of Stolfo et al. (1997) when we verify that the usage of a cost sensitive meta-heuristics, in fact generalized from the generalization done from the AdaBoost to the AdaCost, applied to several tested methods substantially improves it performance for all methods, but Naive Bayes. Our analysis took into account the skewed nature of the dataset, as well as the need of a parametric adjustment, sometimes through the usage of genetic algorithms, in order to obtain the best results from each compared method.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal