866 research outputs found
Prediction of pathological stage in patients with prostate cancer: a neuro-fuzzy model
The prediction of cancer staging in prostate cancer is a process for estimating the likelihood that the cancer has spread before treatment is given to the patient. Although important for determining the most suitable treatment and optimal management strategy for patients, staging continues to present significant challenges to clinicians. Clinical test results such as the pre-treatment Prostate-Specific Antigen (PSA) level, the biopsy most common tumor pattern (Primary Gleason pattern) and the second most common tumor pattern (Secondary Gleason pattern) in tissue biopsies, and the clinical T stage can be used by clinicians to predict the pathological stage of cancer. However, not every patient will return abnormal results in all tests. This significantly influences the capacity to effectively predict the stage of prostate cancer. Herein we have developed a neuro-fuzzy computational intelligence model for classifying and predicting the likelihood of a patient having Organ-Confined Disease (OCD) or Extra-Prostatic Disease (ED) using a prostate cancer patient dataset obtained from The Cancer Genome Atlas (TCGA) Research Network. The system input consisted of the following variables: Primary and Secondary Gleason biopsy patterns, PSA levels, age at diagnosis, and clinical T stage. The performance of the neuro-fuzzy system was compared to other computational intelligence based approaches, namely the Artificial Neural Network, Fuzzy C-Means, Support Vector Machine, the Naive Bayes classifiers, and also the AJCC pTNM Staging Nomogram which is commonly used by clinicians. A comparison of the optimal Receiver Operating Characteristic (ROC) points that were identified using these approaches, revealed that the neuro-fuzzy system, at its optimal point, returns the largest Area Under the ROC Curve (AUC), with a low number of false positives (FPR = 0.274, TPR = 0.789, AUC = 0.812). The proposed approach is also an improvement over the AJCC pTNM Staging Nomogram (FPR = 0.032, TPR = 0.197, AUC = 0.582)
Automatic synthesis of fuzzy systems: An evolutionary overview with a genetic programming perspective
Studies in Evolutionary Fuzzy Systems (EFSs) began in the 90s and have experienced a fast development since then, with applications to areas such as pattern recognition, curve‐fitting and regression, forecasting and control. An EFS results from the combination of a Fuzzy Inference System (FIS) with an Evolutionary Algorithm (EA). This relationship can be established for multiple purposes: fine‐tuning of FIS's parameters, selection of fuzzy rules, learning a rule base or membership functions from scratch, and so forth. Each facet of this relationship creates a strand in the literature, as membership function fine‐tuning, fuzzy rule‐based learning, and so forth and the purpose here is to outline some of what has been done in each aspect. Special focus is given to Genetic Programming‐based EFSs by providing a taxonomy of the main architectures available, as well as by pointing out the gaps that still prevail in the literature. The concluding remarks address some further topics of current research and trends, such as interpretability analysis, multiobjective optimization, and synthesis of a FIS through Evolving methods
Підсистема прийняття рішень на базі нечітких нейронних мереж
Робота публікується згідно наказу ректора від 29.12.2020 р. №580/од "Про розміщення кваліфікаційних робіт здобувачів вищої освіти в репозиторії НАУ".Керівник дипломної роботи: д.т.н., проф., завідувач кафедри авіаційних комп’ютерно-інтегрованих комплексів, Синєглазов Віктор МихайловичThe purpose of scientific work: development of a subsystem for decision-making on the basis of fuzzy neural networks, improvement of existing algorithms.
The thesis considers theoretical and software part of the development of the decision-making subsystem for solving the classification problem. The author substantiates the relevance of using fuzzy neural networks to solve the problem of classification, analyzes the existing topologies of fuzzy neural networks and fuzzy classifiers, basic algorithms to improve results and combine them into a single structure, identified their shortcomings and proposed a solution to eliminate them An optimization and improvement algorithm for solving the classification problem based on the creation of an ensemble of fuzzy neural networks, namely, a fuzzy TSK classifier, is proposed. This software architecture allows you to create a neural classifier that improves the results of an existing solution. And expands the range of calculations performed to classify the input data.Мета наукової роботи: розробка підсистеми для прийняття рішень на базі нечітких нейронних мереж, покращення існуючих алгоритмів.
В дипломній роботі розглядається теоретична та програмна частина розробки підсистеми прийняття рішень для розв’язання задачі класифікації. Автором обґрунтовано актуальність використання нечітких нейронних мереж для вирішення задачі класифікації, проведено аналіз існуючих топологій нечітких нейронних мереж та нечітких класифікаторів, основних алгоритмів для покращення результатів та поєднання їх в єдину структуру, виявлено їх недоліки та запропоноване рішення, що дозволяє їх усунути Запропоновано алгоритм оптимізації та покращення для вирішення задачі класифікації на основі створення ансамблю з нечітких нейронних мереж а саме, нечіткого класифікатора TSK. Дана програмна архітектура дозволяє створити нейронний класифікатор який покращує результати уже існуючого рішення. Та розширює спектр виконуваних обчислювань для класифікації вхідних даних
Fuzzy Logic
The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems
A literature review on the application of evolutionary computing to credit scoring
The last years have seen the development of many credit scoring models for assessing the creditworthiness of loan applicants. Traditional credit scoring methodology has involved the use of statistical and mathematical programming techniques such as discriminant analysis, linear and logistic regression, linear and quadratic programming, or decision trees. However, the importance of credit grant decisions for financial institutions has caused growing interest in using a variety of computational intelligence techniques. This paper concentrates on evolutionary computing, which is viewed as one of the most promising paradigms of computational intelligence. Taking into account the synergistic relationship between the communities of Economics and Computer Science, the aim of this paper is to summarize the most recent developments in the application of evolutionary algorithms to credit scoring by means of a thorough review of scientific articles published during the period 2000–2012.This work has partially been supported by the Spanish Ministry of Education and Science under grant TIN2009-14205 and the Generalitat Valenciana under grant PROMETEO/2010/028
Dynamics under Uncertainty: Modeling Simulation and Complexity
The dynamics of systems have proven to be very powerful tools in understanding the behavior of different natural phenomena throughout the last two centuries. However, the attributes of natural systems are observed to deviate from their classical states due to the effect of different types of uncertainties. Actually, randomness and impreciseness are the two major sources of uncertainties in natural systems. Randomness is modeled by different stochastic processes and impreciseness could be modeled by fuzzy sets, rough sets, Dempster–Shafer theory, etc
A sensitivity comparison of Neuro-fuzzy feature extraction methods from bearing failure signals
This thesis presents an account of investigations made into building bearing fault classifiers for outer race faults (ORF), inner race faults (IRF), ball faults (BF) and no fault (NF) cases using wavelet transforms, statistical parameter features and Artificial Neuro-Fuzzy Inference Systems (ANFIS). The test results showed that the ball fault (BF) classifier successfully achieved 100% accuracy without mis-classification, while the outer race fault (ORF), inner race fault (IRF) and no fault (NF) classifiers achieved mixed results
Decision Support Systems for Risk Assessment in Credit Operations Against Collateral
With the global economic crisis, which reached its peak in the second half of 2008, and
before a market shaken by economic instability, financial institutions have taken steps to protect
the banks’ default risks, which had an impact directly in the form of analysis in credit institutions
to individuals and to corporate entities. To mitigate the risk of banks in credit operations, most
banks use a graded scale of customer risk, which determines the provision that banks must
do according to the default risk levels in each credit transaction. The credit analysis involves
the ability to make a credit decision inside a scenario of uncertainty and constant changes and
incomplete transformations. This ability depends on the capacity to logically analyze situations,
often complex and reach a clear conclusion, practical and practicable to implement.
Credit Scoring models are used to predict the probability of a customer proposing to
credit to become in default at any given time, based on his personal and financial information
that may influence the ability of the client to pay the debt. This estimated probability, called the
score, is an estimate of the risk of default of a customer in a given period. This increased concern
has been in no small part caused by the weaknesses of existing risk management techniques
that have been revealed by the recent financial crisis and the growing demand for consumer
credit.The constant change affects several banking sections because it prevents the ability to
investigate the data that is produced and stored in computers that are too often dependent on
manual techniques.
Among the many alternatives used in the world to balance this risk, the provision of
guarantees stands out of guarantees in the formalization of credit agreements. In theory, the
collateral does not ensure the credit return, as it is not computed as payment of the obligation
within the project. There is also the fact that it will only be successful if triggered, which involves
the legal area of the banking institution. The truth is, collateral is a mitigating element
of credit risk. Collaterals are divided into two types, an individual guarantee (sponsor) and the
asset guarantee (fiduciary). Both aim to increase security in credit operations, as an payment
alternative to the holder of credit provided to the lender, if possible, unable to meet its obligations
on time. For the creditor, it generates liquidity security from the receiving operation. The
measurement of credit recoverability is a system that evaluates the efficiency of the collateral
invested return mechanism.
In an attempt to identify the sufficiency of collateral in credit operations, this thesis
presents an assessment of smart classifiers that uses contextual information to assess whether
collaterals provide for the recovery of credit granted in the decision-making process before
the credit transaction become insolvent. The results observed when compared with other approaches
in the literature and the comparative analysis of the most relevant artificial intelligence
solutions, considering the classifiers that use guarantees as a parameter to calculate the
risk contribute to the advance of the state of the art advance, increasing the commitment to
the financial institutions.Com a crise econômica global, que atingiu seu auge no segundo semestre de 2008, e diante
de um mercado abalado pela instabilidade econômica, as instituições financeiras tomaram
medidas para proteger os riscos de inadimplência dos bancos, medidas que impactavam diretamente
na forma de análise nas instituições de crédito para pessoas físicas e jurídicas. Para
mitigar o risco dos bancos nas operações de crédito, a maioria destas instituições utiliza uma
escala graduada de risco do cliente, que determina a provisão que os bancos devem fazer de
acordo com os níveis de risco padrão em cada transação de crédito. A análise de crédito envolve
a capacidade de tomar uma decisão de crédito dentro de um cenário de incerteza e mudanças
constantes e transformações incompletas. Essa aptidão depende da capacidade de analisar situações
lógicas, geralmente complexas e de chegar a uma conclusão clara, prática e praticável
de implementar.
Os modelos de Credit Score são usados para prever a probabilidade de um cliente
propor crédito e tornar-se inadimplente a qualquer momento, com base em suas informações
pessoais e financeiras que podem influenciar a capacidade do cliente de pagar a dívida. Essa
probabilidade estimada, denominada pontuação, é uma estimativa do risco de inadimplência de
um cliente em um determinado período. A mudança constante afeta várias seções bancárias,
pois impede a capacidade de investigar os dados que são produzidos e armazenados em computadores
que frequentemente dependem de técnicas manuais.
Entre as inúmeras alternativas utilizadas no mundo para equilibrar esse risco, destacase
o aporte de garantias na formalização dos contratos de crédito. Em tese, a garantia não
“garante” o retorno do crédito, já que não é computada como pagamento da obrigação dentro do
projeto. Tem-se ainda, o fato de que esta só terá algum êxito se acionada, o que envolve a área
jurídica da instituição bancária. A verdade é que, a garantia é um elemento mitigador do risco
de crédito. As garantias são divididas em dois tipos, uma garantia individual (patrocinadora) e
a garantia do ativo (fiduciário). Ambos visam aumentar a segurança nas operações de crédito,
como uma alternativa de pagamento ao titular do crédito fornecido ao credor, se possível, não
puder cumprir suas obrigações no prazo. Para o credor, gera segurança de liquidez a partir da
operação de recebimento. A mensuração da recuperabilidade do crédito é uma sistemática que
avalia a eficiência do mecanismo de retorno do capital investido em garantias.
Para tentar identificar a suficiência das garantias nas operações de crédito, esta tese
apresenta uma avaliação dos classificadores inteligentes que utiliza informações contextuais
para avaliar se as garantias permitem prever a recuperação de crédito concedido no processo de
tomada de decisão antes que a operação de crédito entre em default. Os resultados observados
quando comparados com outras abordagens existentes na literatura e a análise comparativa das
soluções de inteligência artificial mais relevantes, mostram que os classificadores que usam
garantias como parâmetro para calcular o risco contribuem para o avanço do estado da arte,
aumentando o comprometimento com as instituições financeiras
Performance Evaluation of Smart Decision Support Systems on Healthcare
Medical activity requires responsibility not only from clinical knowledge and skill but
also on the management of an enormous amount of information related to patient care. It is
through proper treatment of information that experts can consistently build a healthy wellness
policy. The primary objective for the development of decision support systems (DSSs) is
to provide information to specialists when and where they are needed. These systems provide
information, models, and data manipulation tools to help experts make better decisions in a
variety of situations.
Most of the challenges that smart DSSs face come from the great difficulty of dealing
with large volumes of information, which is continuously generated by the most diverse types
of devices and equipment, requiring high computational resources. This situation makes this
type of system susceptible to not recovering information quickly for the decision making. As a
result of this adversity, the information quality and the provision of an infrastructure capable
of promoting the integration and articulation among different health information systems (HIS)
become promising research topics in the field of electronic health (e-health) and that, for this
same reason, are addressed in this research. The work described in this thesis is motivated
by the need to propose novel approaches to deal with problems inherent to the acquisition,
cleaning, integration, and aggregation of data obtained from different sources in e-health environments,
as well as their analysis.
To ensure the success of data integration and analysis in e-health environments, it
is essential that machine-learning (ML) algorithms ensure system reliability. However, in this
type of environment, it is not possible to guarantee a reliable scenario. This scenario makes
intelligent SAD susceptible to predictive failures, which severely compromise overall system
performance. On the other hand, systems can have their performance compromised due to the
overload of information they can support.
To solve some of these problems, this thesis presents several proposals and studies
on the impact of ML algorithms in the monitoring and management of hypertensive disorders
related to pregnancy of risk. The primary goals of the proposals presented in this thesis are
to improve the overall performance of health information systems. In particular, ML-based
methods are exploited to improve the prediction accuracy and optimize the use of monitoring
device resources. It was demonstrated that the use of this type of strategy and methodology
contributes to a significant increase in the performance of smart DSSs, not only concerning precision
but also in the computational cost reduction used in the classification process.
The observed results seek to contribute to the advance of state of the art in methods
and strategies based on AI that aim to surpass some challenges that emerge from the integration
and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to
quickly and automatically analyze a larger volume of complex data and focus on more accurate
results, providing high-value predictions for a better decision making in real time and without
human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento
e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações
relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações
que os especialistas podem consistentemente construir uma política saudável de bem-estar. O
principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações
aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações,
modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores
decisões em diversas situações.
A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade
de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos
tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação
torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a
tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão
de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas
de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde
eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho
descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar
com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de
diferentes fontes em ambientes de e-saúde, bem como sua análise.
Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é
importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade
do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário
totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas
de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os
sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que
podem suportar.
Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e
estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos
relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta
tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os
métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o
uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo
de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD
inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional
utilizado no processo de classificação.
Os resultados observados buscam contribuir para o avanço do estado da arte em métodos
e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que
advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados
em inteligência artificial é possível analisar de forma rápida e automática um volume maior de
dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana
Fuzzy Logic
Fuzzy Logic is becoming an essential method of solving problems in all domains. It gives tremendous impact on the design of autonomous intelligent systems. The purpose of this book is to introduce Hybrid Algorithms, Techniques, and Implementations of Fuzzy Logic. The book consists of thirteen chapters highlighting models and principles of fuzzy logic and issues on its techniques and implementations. The intended readers of this book are engineers, researchers, and graduate students interested in fuzzy logic systems
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