Optimization of Fuzzy Support Vector Machine (FSVM) Performance by Distance-Based Similarity Measure Classification

This research aims to determine the maximum or minimum value of a Fuzzy Support Vector Machine (FSVM) Algorithm using the optimization function. As opposed to FSVM, which is less effective on large and complex data because of its sensitivity to outliers and noise, SVM is considered an effective method of data classification. One of the techniques used to overcome this inefficiency is fuzzy logic, with its ability to select the right membership function, which significantly affects the effectiveness of the FSVM algorithm performance. This research was carried out using the Gaussian membership function and the Distance-Based Similarity Measurement consisting of the Euclidean, Manhattan, Chebyshev, and Minkowsky distance methods. Subsequently, the optimization of the FSVM classification process was determined using four proposed FSVM models and normal SVM as comparison references. The results showed that the method tends to eliminate the impact of noise and enhance classification accuracy effectively. FSVM provides the best and highest accuracy value of 94% at a penalty parameter value of 1000 using the Chebyshev distance matrix. Furthermore, the model proposed will be compared to the performance evaluation model in preliminary studies. The result further showed that using FSVM with a Chebyshev distance matrix and a Gaussian membership function provides a better performance evaluation value. Doi: 10.28991/HIJ-2021-02-04-02 Full Text: PD

Proposal of a Fuzzy Model for Sensory Analysis of Cheeses

The consumer's preference for handcrafted raw milk cheeses has steadily grown because of its intensity and flavor variation compared to industrial cheese. “Minas frescal” cheese is one of the most popular cheeses in Brazil, its production has a high yield, besides simple and fast manufacturing. This work aimed to elaborate and perform physical-chemical and sensorial analyzes through the Fuzzy logic of homemade and industrialized fresh cheeses. The cheese samples were composed of homemade white cheese, homemade cheese of beet and industrial cheese and were submitted to tasters of the city of Petrópolis. This model has shown to be consistent and aims to improve cheese manufacturing processes capable of allowing greater consumer acceptability

An analysis of unconscious gender bias in academic texts by means of a decision algorithm

Inclusive language focuses on using the vocabulary to avoid exclusion or discrimination, specially referred to gender. The task of finding gender bias in written documents must be performed manually, and it is a time-consuming process. Consequently, studying the usage of non-inclusive language on a document, and the impact of different document properties (such as author gender, date of presentation, etc.) on how many non-inclusive instances are found, is quite difficult or even impossible for big datasets. This research analyzes the gender bias in academic texts by analyzing a study corpus of more than 12,000 million words obtained from more than one hundred thousand doctoral theses from Spanish universities. For this purpose, an automated algorithm was developed to evaluate the different characteristics of the document and look for interactions between age, year of publication, gender or the field of knowledge in which the doctoral thesis is framed. The algorithm identified information patterns using a CNN (convolutional neural network) by the creation of a vector representation of the sentences. The results showed evidence that there was a greater bias as the age of the authors increased, who were more likely to use non-inclusive terms; it was concluded that there is a greater awareness of inclusiveness in women than in men, and also that this awareness grows as the candidate is younger. The results showed evidence that the age of the authors increased discrimination, with men being more likely to use non-inclusive terms (up to an index of 23.12), showing that there is a greater awareness of inclusiveness in women than in men in all age ranges (with an average of 14.99), and also that this awareness grows as the candidate is younger (falling down to 13.07). In terms of field of knowledge, the humanities are the most biased (20.97), discarding the subgroup of Linguistics, which has the least bias at all levels (9.90), and the field of science and engineering, which also have the least influence (13.46). Those results support the assumption that the bias in academic texts (doctoral theses) is due to unconscious issues: otherwise, it would not depend on the field, age, gender, and would occur in any field in the same proportion. The innovation provided by this research lies mainly in the ability to detect, within a textual document in Spanish, whether the use of language can be considered non-inclusive, based on a CNN that has been trained in the context of the doctoral thesis. A significant number of documents have been used, using all accessible doctoral theses from Spanish universities of the last 40 years; this dataset is only manageable by data mining systems, so that the training allows identifying the terms within the context effectively and compiling them in a novel dictionary of non-inclusive terms

Uma Introdução às Support Vector Machines

This paper presents an introduction to the Support Vector Machines (SVMs), a Machine Learning technique that has received increasing attention in the last years. The SVMs have been applied to several pattern recognition tasks, obtaining results superior to those of other learning techniques in various applications.Neste artigo é apresentada uma introdução às Máquinas de Vetores de Suporte (SVMs, do Inglês Support Vector Machines), técnica de Aprendizado de máquina que vem recebendo crescente atenção nos últimos anos. As SVMs vêm sendo utilizadas em diversas tarefas de reconhecimento de padrões, obtendo resultados superiores aos alcançados por outras técnicas de aprendizado em várias aplicações

Smart Fall Detection by Enhanced SVM with Fuzzy Logic Membership Function

Falling is a critical issue for disabled people, and it leads to potentially serious injuries and death. Smart fall detection is a technology that depends on sensors and auxiliary devices that seek to improve the quality of life and enhance the lifestyle of disabled people. So far, the most widely used fall prediction methods collect data from inertial measurement unit (IMU) sensors. In addition, they use thresholds to identify falls based on artificial experiences or machine learning (ML) algorithms. Nonetheless, these approaches still require extensive classification and calibration. In this paper, we suggest a new technique to detect falls by combining Fuzzy Logic (FL) and Support Vector Machine (SVM). The FL model is built by using a fuzzy membership function along with the input dataset to obtain the intermediate output. Because combining these two algorithms is not an easy task, we leverage SVM with a kernel comprised of a fuzzy membership function and thus build a new model known as FSVM. Besides, the hyperplane of the SVM is used as the separating plane to replace the traditional threshold method for detecting falling Activities of Daily Living (ADLs) on a comprehensive dataset containing simulated falling ADLs, non-falling ADLs, and scripted ADLs, including falling ADLs and unscripted ADLs performed by volunteers with our designed device. The results show that no false-positive rate had been triggered, and 100% specificity was achieved for ADL. An overall accuracy of about 99.87% in detecting the fall function was obtained. Furthermore, the overall sensitivity of 100% with no false negative rate obtained was achieved by implementing the proposed method. The attained results validate that our introduced method can effectively learn from features extracted from a multiphase fall model.&nbsp

Contramedidas para evitar a falsificação do usuário no acesso a sistemas biométricos via imagens e vídeos

A autenticação de usuário é um passo importante para proteger as informações e neste campo a biometria da face é vantajosa. A biometria do rosto é natural, fácil de usar e menos invasiva. Esta tese complementa a biometria facial e visa desenvolver métodos de contramedidas capaz de detectar tentativas de acesso por usuário não autorizado fraudando a identidade de um usuário autorizado. Apesar do grande sucesso alcançado em biometria da face nas últimas décadas, pouca atenção tem sido dada ao problema crítico de ataque de apresentação, spoofing attack. Somente nos últimos anos que gradualmente os sistemas de reconhecimento facial estão cientes da vulnerabilidade dos ataques de apresentação. Fraudadores não autorizados podem tentar falsificar os sistemas de reconhecimento facial exibindo cópias falsificadas do rosto de um cliente autorizado, tais como fotos ou vídeos. Embora simples, estes ataques são geralmente muito bem sucedidos. Nesta tese foram desenvolvidas contramedidas capazes de detectar ataques de apresentação à sistemas biométricos faciais, sendo criadas três abordagens distintas para a detecção de ataques de apresentação. As metodologias utilizam informações de movimento, energia de deformação das faces, descritores de texturas e esteganoanálise adequadamente projetados para um espaço de dimensão reduzido através de projeções aleatórias, análise de componentes principais e análise discriminante linear. As abordagem são capazes de detectar ataques a cada quadro de um vídeo e, para o estudo de caso, duas bases de dados foram avaliadas. Uma delas é a base de dados do Centro de Biometria e Pesquisa em Segurança da Academia Chinesa de Ciências, CASIA. E a outra, é a base de dados de fotografia de impostor da Universidade Nanjing de Aeronáutica e Astronáutica, NUAA. Através de um classificador de redes neurais artificiais a melhor metodologia desenvolvida alcança uma taxa de erro HTER = 4:93% para NUAA e HTER = 6:51% para CASIA.User authentication is an important step in protecting the information and in this field the face biometry is advantageous. Face biometry is natural, easy to use and less invasive. The proposal of this work complements facial biometrics and aims to develop a method of countermeasures capable of detecting unauthorized access attempts by fraudulating the identity of an authorized user. Despite the great success achieved in face biometrics in recent decades, little attention has been paid to the critical problem of spoofing attack. It is only in recent years that facial recognition systems are gradually aware of the vulnerability of presentation attacks. Unauthorized fraudsters may attempt to falsify facial recognition systems by displaying fake copies of an authorized customer’s face, such as photos or videos. Though simple, these attacks are usually very successful. In this thesis were developed countermeasures capable of detecting presentation attacks to biometric facial systems. Three distinct approaches have been created for the detection of presentation attacks. The methodologies use motion information, face deformation energy, texture descriptors and steganoanalysis properly projected for a small dimension space through random projections, principal component analysis and linear discriminant analysis. The approaches are able to detect spoof attacks on each frame of a video and, for case study, two databases were evaluated. One of them is the database of the Center for Biometry and Security Research of the Chinese Academy of Sciences, CASIA. And the other, is the impostor photography database of Nanjing University of Aeronautics and Astronautics, NUAA. Through an artificial neural network classifier the best developed methodology achieves an error rate of HTER = 4:93% for NUAA and HTER = 6:51% for CASIA

Obtención de reglas de clasificación difusas utilizando técnicas de optimización : Caso de estudio Riesgo Crediticio

El aporte central de esta tesis es la definición de un nuevo método capaz de generar un conjunto de reglas de clasificación difusas de fácil interpretación, baja cardinalidad y una buena precisión. Estas características ayudan a identificar y comprender las relaciones presentes en los datos facilitando de esta forma la toma de decisiones. El nuevo método propuesto se denomina FRvarPSO (Fuzzy Rules variable Particle Swarm Oprmization) y combina una red neuronal competitiva con una técnica de optimización basada en cúmulo de partículas de población variable para la obtención de reglas de clasificación difusas, capaces de operar sobre atributos nominales y numéricos. Los antecedentes de las reglas están formados por atributos nominales y/o condiciones difusas. La conformación de estas últimas requiere conocer el grado de pertenencia a los conjuntos difusos que definen a cada variable lingüística. Esta tesis propone tres alternativas distintas para resolver este punto. Uno de los aportes de esta tesis radica en la definición de la función de aptitud o fitness de cada partícula basada en un ”Criterio de Votación” que pondera de manera difusa la participación de las condiciones difusas en la conformación del antecedente. Su valor se obtiene a partir de los grados de pertenencia de los ejemplos que cumplen con la regla y se utiliza para reforzar el movimiento de la partícula en la dirección donde se encuentra el valor más alto. Con la utilización de PSO las partículas compiten entre ellas para encontrar a la mejor regla de la clase seleccionada. La medición se realizó sobre doce bases de datos del repositorio UCI (Machine Learning Repository) y tres casos reales en el área de crédito del Sistema Financiero del Ecuador asociadas al riesgo crediticio considerando un conjunto de variables micro y macroeconómicas. Otro de los aportes de esta tesis fue haber realizado una consideración especial en la morosidad del cliente teniendo en cuenta los días de vencimiento de la cartera otorgada; esto fue posible debido a que se tenía información del cliente en un horizonte de tiempo, una vez que el crédito se había concedido Se verificó que con este análisis las reglas difusas obtenidas a través de FRvarPSO permiten que el oficial de crédito de respuesta al cliente en menor tiempo, y principalmente disminuya el riesgo que representa el otorgamiento de crédito para las instituciones financieras. Lo anterior fue posible, debido a que al aplicar una regla difusa se toma el menor grado de pertenencia promedio de las condiciones difusas que forman el antecedente de la regla, con lo que se tiene una métrica proporcional al riesgo de su aplicación.Tesis en cotutela con la Universitat Rovira i Virgili (URV) (España).Facultad de InformáticaUniversitat Rovira i Virgil