Breast Cancer Prediction and Detection Using Data Mining Classification Algorithms: A Comparative Study

Today, cancer has become a common disease that can afflict the life of one of every three people. Breast cancer is also one of the cancer types for which early diagnosis and detection is especially important. The earlier breast cancer is detected, the higher the chances of the patient being treated. Therefore, many early detection or prediction methods are being investigated and used in the fight against breast cancer. In this paper, the aim was to predict and detect breast cancer early with non-invasive and painless methods that use data mining algorithms. All the data mining classification algorithms in Weka were run and compared against a data set obtained from the measurements of an antenna consisting of frequency bandwidth, dielectric constant of the antenna’s substrate, electric field and tumor information for breast cancer detection and prediction. Results indicate that Bagging, IBk, Random Committee, Random Forest, and SimpleCART algorithms were the most successful algorithms, with over 90% accuracy in detection. This comparative study of several classification algorithms for breast cancer diagnosis using a data set from the measurements of an antenna with a 10-fold cross-validation method provided a perspective into the data mining methods’ ability of relative prediction. From data obtained in this study it can be said that if a patient has a breast cancer tumor, detection of the tumor is possible

Projeto de um filtro analógico gerador de pulsos prolato esferoidais para uso em sistemas ultra wideband

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, 2013.Este trabalho apresenta o projeto de um gerador de pulsos Prolato Esferoidais para uso em sistemas UWB (Ultra Wideband). Uma banda larga, maior do que 500 MHz, associada ao baixo consumo de potência e a transmissão de dados baseada em pulsos, fazem do UWB um sistema de comunicação atraente par uso em aplicações que necessitem de altas taxas de transferência de dados, baixo consumo e circuitos simples, como Rede de Sensores Sem Fio (RSSF) e aplicações na área biomédica, por exemplo. Dentre os vários tipos de pulsos que podem ser implementados para uso em UWB, este trabalho propõe a utilização do Pulso Prolato Esferoidal, ou da sua sigla em inglês, PSWF (Prolate Spheroidal Wave Funciton). Pulsos PSWF não possuem uma forma fechada, sendo então utilizados a partir de uma aproximação discreta. Partindo dessa aproximação, serão realizadas aproximações numéricas no domínio do tempo e de Laplace para obtenção de uma função de transferência a ser implementada através de uma representação ótima no Espaço de Estados. Esta representação será então implementada em circuito por meio de um filtro Gm-C. Utilizando essa aproximação, realizam-se outras aproximações no domínio do tempo que permite obter uma função no domínio do tempo que representa esse tipo de pulso. Essa função é então manipulada no domínio de Laplace e, aplicandose o método de Padé, usada para se obter uma função de transferência. Representa-se essa função de transferência por meio da representação ortonormal no Espaço de Estados, o qual possui um comportamento próximo do ótimo em termos de faixa dinâmica e esparsidade, além de possuir baixa sensibilidade a variação de valores, em relação às representações convencionais, como as formas canônicas. Utilizando-se células de transcondutância também desenvolvidas nesse trabalho, a representação ortonormal é implementada por meio de um filtro Gm-C. Este filtro é usado em uma proposta de comunicação m-ária, que combina PAM (Pulse Amplitude Modulation) com OPM (Orthogonal Pulse Modulation), para uso em sistemas UWB. Idealmente, deseja-se obter um gerador de pulsos que gere pulsos PSWF de primeira e segunda ordens para aplicações na faixa sub-giga , de 500 MHz a 1 GHz. Os pulsos utilizados terão duração de 10 ns. Porém, devido à limitações da tecnologia, o circuito final do filtro apresentou uma resposta em frquência inferior à especificada inicialmente (com duração de 5 μs e banda de 1 MHz - 2 MHz). No entanto, o filtro obtido foi capaz de gerar pulsos Prolato Esferoidais de primeira e segunda ordens, o que representa uma resposta funcional de todo o sistema,validando assim a metodologia proposta. _______________________________________________________________________________________ ABSTRACTThis paper presents the design of a pulse generator prolate spheroidal systems for use in UWB (Ultra Wideband). A large bandwidth, greater than 500 MHz, combined with low power consumption and pulse based data transmission, make UWB communication system attractive for use on applications requiring high data transfer rates, low power consumption and simple circuits as in Wireless Sensor Network (WSN) and biomedical applications, for example. Among the various types of pulses that can be implemented for use in UWB, this paper proposes the use Prolate Spheroidal Pulse (PSWF). PSWF pulses do not have a closed form, and are then used as a discrete approximation. Based on this approach, numerical approximations are performed in the time domain and Laplace to obtain a transfer function to be implemented through an optimal representation in State Space. This representation will then be implemented on the circuit by means of a Gm-C filter. Using this approximation, other approximations are realized in the time domain which achieves a function in the time domain representing this type of pulse. This function is then manipulated in the Laplace domain, and applying the method of Padé, used to obtain a transfer function. This transfer function is then represented through the orthonormal State Space representation, which has a near optimal behavior in terms of dynamic range and sparsity, besides having low sensitivity to changes in values, compared to conventional representations, as the canonical forms. Using transconductance cells also developed in this work, the orthonormal representation is implemented by means of a Gm-C filter. This filter is used in a proposed m-ary communication, combining PAM (Pulse Amplitude Modulation) with OPM (Orthogonal Pulse Modulation), for use in UWB systems. Ideally, it is desired to obtain a pulse generator that generates pulses PSWF first and second orders to applications in sub-giga, from 500 MHz to 1 GHz with pulses that have duration of 10 ns. However, due to limitations of the technology, the frequency response of the circuit of the filter is less than specified initially (lasting 5 mS and banda 1 MHz - 2 MHz). However, the obtained filter was able to generate PSWF pulses of first and second order, which represents a functional response of the whole system, thus validating the proposed method

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Advances in Bioengineering

The technological approach and the high level of innovation make bioengineering extremely dynamic and this forces researchers to continuous updating. It involves the publication of the results of the latest scientific research. This book covers a wide range of aspects and issues related to advances in bioengineering research with a particular focus on innovative technologies and applications. The book consists of 13 scientific contributions divided in four sections: Materials Science; Biosensors. Electronics and Telemetry; Light Therapy; Computing and Analysis Techniques

Machine Learning Methods for Structural Brain MRIs: Applications for Alzheimer’s Disease and Autism Spectrum Disorder

This thesis deals with the development of novel machine learning applications to automatically detect brain disorders based on magnetic resonance imaging (MRI) data, with a particular focus on Alzheimer’s disease and the autism spectrum disorder. Machine learning approaches are used extensively in neuroimaging studies of brain disorders to investigate abnormalities in various brain regions. However, there are many technical challenges in the analysis of neuroimaging data, for example, high dimensionality, the limited amount of data, and high variance in that data due to many confounding factors. These limitations make the development of appropriate computational approaches more challenging. To deal with these existing challenges, we target multiple machine learning approaches, including supervised and semi-supervised learning, domain adaptation, and dimensionality reduction methods.In the current study, we aim to construct effective biomarkers with sufficient sensitivity and specificity that can help physicians better understand the diseases and make improved diagnoses or treatment choices. The main contributions are 1) development of a novel biomarker for predicting Alzheimer’s disease in mild cognitive impairment patients by integrating structural MRI data and neuropsychological test results and 2) the development of a new computational approach for predicting disease severity in autistic patients in agglomerative data by automatically combining structural information obtained from different brain regions.In addition, we investigate various data-driven feature selection and classification methods for whole brain, voxel-based classification analysis of structural MRI and the use of semi-supervised learning approaches to predict Alzheimer’s disease. We also analyze the relationship between disease-related structural changes and cognitive states of patients with Alzheimer’s disease.The positive results of this effort provide insights into how to construct better biomarkers based on multisource data analysis of patient and healthy cohorts that may enable early diagnosis of brain disorders, detection of brain abnormalities and understanding effective processing in patient and healthy groups. Further, the methodologies and basic principles presented in this thesis are not only suited to the studied cases, but also are applicable to other similar problems

Smart Sensors for Healthcare and Medical Applications

This book focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. Specifically, the book briefly describes the potential of smart sensors in the aforementioned applications, collecting 24 articles selected and published in the Special Issue “Smart Sensors for Healthcare and Medical Applications”. We proposed this topic, being aware of the pivotal role that smart sensors can play in the improvement of healthcare services in both acute and chronic conditions as well as in prevention for a healthy life and active aging. The articles selected in this book cover a variety of topics related to the design, validation, and application of smart sensors to healthcare

Systematic Approaches for Telemedicine and Data Coordination for COVID-19 in Baja California, Mexico

Conference proceedings info: ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologies Raleigh, HI, United States, March 24-26, 2023 Pages 529-542We provide a model for systematic implementation of telemedicine within a large evaluation center for COVID-19 in the area of Baja California, Mexico. Our model is based on human-centric design factors and cross disciplinary collaborations for scalable data-driven enablement of smartphone, cellular, and video Teleconsul-tation technologies to link hospitals, clinics, and emergency medical services for point-of-care assessments of COVID testing, and for subsequent treatment and quar-antine decisions. A multidisciplinary team was rapidly created, in cooperation with different institutions, including: the Autonomous University of Baja California, the Ministry of Health, the Command, Communication and Computer Control Center of the Ministry of the State of Baja California (C4), Colleges of Medicine, and the College of Psychologists. Our objective is to provide information to the public and to evaluate COVID-19 in real time and to track, regional, municipal, and state-wide data in real time that informs supply chains and resource allocation with the anticipation of a surge in COVID-19 cases. RESUMEN Proporcionamos un modelo para la implementación sistemática de la telemedicina dentro de un gran centro de evaluación de COVID-19 en el área de Baja California, México. Nuestro modelo se basa en factores de diseño centrados en el ser humano y colaboraciones interdisciplinarias para la habilitación escalable basada en datos de tecnologías de teleconsulta de teléfonos inteligentes, celulares y video para vincular hospitales, clínicas y servicios médicos de emergencia para evaluaciones de COVID en el punto de atención. pruebas, y para el tratamiento posterior y decisiones de cuarentena. Rápidamente se creó un equipo multidisciplinario, en cooperación con diferentes instituciones, entre ellas: la Universidad Autónoma de Baja California, la Secretaría de Salud, el Centro de Comando, Comunicaciones y Control Informático. de la Secretaría del Estado de Baja California (C4), Facultades de Medicina y Colegio de Psicólogos. Nuestro objetivo es proporcionar información al público y evaluar COVID-19 en tiempo real y rastrear datos regionales, municipales y estatales en tiempo real que informan las cadenas de suministro y la asignación de recursos con la anticipación de un aumento de COVID-19. 19 casos.ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologieshttps://doi.org/10.1007/978-981-99-3236-

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov