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1.
PLoS One ; 15(12): e0242708, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33270654

RESUMO

In the process of software development, regression testing is one of the major activities that is done after making modifications in the current system or whenever a software system evolves. But, the test suite size increases with the addition of new test cases and it becomes in-efficient because of the occurrence of redundant, broken, and obsolete test cases. For that reason, it results in additional time and budget to run all these test cases. Many researchers have proposed computational intelligence and conventional approaches for dealing with this problem and they have achieved an optimized test suite by selecting, minimizing or reducing, and prioritizing test cases. Currently, most of these optimization approaches are single objective and static in nature. But, it is mandatory to use multi-objective dynamic approaches for optimization due to the advancements in information technology and associated market challenges. Therefore, we have proposed three variants of self-tunable Adaptive Neuro-fuzzy Inference System i.e. TLBO-ANFIS, FA-ANFIS, and HS-ANFIS, for multi-objective regression test suites optimization. Two benchmark test suites are used for evaluating the proposed ANFIS variants. The performance of proposed ANFIS variants is measured using Standard Deviation and Root Mean Square Error. A comparison of experimental results is also done with six existing methods i.e. GA-ANFIS, PSO-ANFIS, MOGA, NSGA-II, MOPSO, and TOPSIS and it is concluded that the proposed method effectively reduces the size of regression test suite without a reduction in the fault detection rate.


Assuntos
Algoritmos , Lógica Fuzzy , Heurística , Modelos Teóricos , Análise de Regressão , Reprodutibilidade dos Testes
2.
PLoS One ; 15(12): e0242449, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33259510

RESUMO

In this paper, two new aggregation operators based on Choquet integral, namely the induced generalized interval neutrosophic Choquet integral average operator(IGINCIA) and the induced generalized interval neutrosophic Choquet integral geometric operator(IG-INCIG), are proposed for multi-criteria decision making problems (MCDM). Firstly, the criteria are dependent to each other and the evaluation information of the criteria are expressed by interval neutrosophic numbers. Moreover, two indices which are inspired by the geometrical structure are established to compare the interval neutrosophic numbers. Then, a MCDM method is proposed based on the proposed aggregation operators and ranking indices to cope with MCDM with interactive criteria. Lastly, an investment decision making problem is provided to illustrate the practicality and effectiveness of the proposed approach. The validity and advantages of the proposed method are analyzed by comparing with some existing approaches. By a numerical example in company investment to expand business though five alternatives with considering four criteria, the optimal decision is made.


Assuntos
Tomada de Decisões , Técnicas de Apoio para a Decisão , Teoria da Decisão , Investimentos em Saúde/tendências , Algoritmos , Entropia , Lógica Fuzzy , Humanos
3.
Rev. enferm. UERJ ; 28: e35054, jan.-dez. 2020.
Artigo em Inglês, Português | LILACS, BDENF - Enfermagem | ID: biblio-1117622

RESUMO

Objetivo: avaliar a mobilidade do cliente com dermatose imunobolhosa antes e após aplicação do curativo com gaze vaselinada. Método: estudo quase experimental, interinstitucional, com clientes com dermatoses imunobolhosas hospitalizados em um hospital estadual e um hospital federal do Estado do Rio de Janeiro e uma instituição do Mato Grosso do Sul. Utilizou-se a lógica fuzzy para classificar a mobilidade dos sujeitos antes, 24 horas após e uma semana após aplicação do curativo. A pesquisa foi aprovada pelo Comitê de Ética em Pesquisa. Resultados: Incluídos 14 participantes, sendo nove com pênfigo vulgar, dois com pênfigo foliáceo e três com penfigóide bolhoso, entre 27 e 82 anos, predominando 11 mulheres. Após 24 horas, nenhum participante se considerou com baixa mobilidade, sete passaram a mobilidade média, e sete, alta, o que foi mantido uma semana após aplicação do curativo. Conclusão: constatou-se significativo aumento da mobilidade logo nas primeiras 24 horas após aplicação do curativo.


Objective: to assess the mobility of clients with immunobullous dermatoses, before and after applying vaseline gauze dressings. Method: in this quasi-experimental, interinstitutional study of inpatients with immunobullous dermatoses at a state hospital and a federal hospital in Rio de Janeiro State and an institution in Mato Grosso do Sul (Brazil), patient mobility before, 24 hours after, and one week after applying the dressing was classified using fuzzy logic. The study was approved by the research ethics committee. Results: 14 participants, nine with pemphigus vulgaris, two with pemphigus foliaceus, and three with bullous pemphigoid, aged between 27 and 82 years old, and predominantly (11) women. After 24 hours, none of the participants considered their mobility to be poor, seven began to be moderately mobile, and seven were highly mobile, and continued so one week after applying the dressing. Conclusion: mobility increased significant in the first 24 hours after applying the dressing.


Objetivo: evaluar la movilidad de clientes con dermatosis inmunobullosa, antes y después de la aplicación de apósitos de gasa con vaselina. Método: en este estudio cuasi-experimental, interinstitucional de pacientes hospitalizados con dermatosis inmunobullosa en un hospital estatal y un hospital federal en el estado de Río de Janeiro y una institución en Mato Grosso do Sul (Brazil), la movilidad del paciente antes, 24 horas después y una semana después la aplicación del apósito se clasificó mediante lógica difusa. El estudio fue aprobado por el comité de ética en investigación. Resultados: se incluyeron 14 participantes, nueve con pénfigo vulgar, dos con pénfigo foliáceo y tres con penfigoide ampolloso, con edades comprendidas entre 27 y 82 años, y predominantemente mujeres (n=11). Después de 24 horas, ninguno de los participantes consideró que su movilidad fuera pobre, siete comenzaron a ser moderadamente móviles y siete eran altamente móviles, y así continuaron una semana después de la aplicación del apósito. Conclusión: la movilidad aumentó significativamente en las primeras 24 horas después de la aplicación del apósitoconsideraba con baja movilidad, siete comenzaron a tener movilidad media y siete, alta, que se mantuvo una semana después de aplicar el apósito. Conclusión: hubo un aumento significativo en la movilidad en las primeras 24 horas después de aplicar el apósito.


Assuntos
Humanos , Masculino , Feminino , Adulto , Pessoa de Meia-Idade , Idoso , Idoso de 80 Anos ou mais , Vaselina/uso terapêutico , Bandagens , Dermatopatias Vesiculobolhosas/terapia , Penfigoide Bolhoso/terapia , Pênfigo/terapia , Limitação da Mobilidade , Brasil , Lógica Fuzzy , Lesão por Pressão/prevenção & controle , Prevenção Secundária , Ensaios Clínicos Controlados não Aleatórios como Assunto , Hospitais Públicos , Pacientes Internados , Cuidados de Enfermagem
4.
Rev. cuba. inform. méd ; 12(2): e382, tab, graf
Artigo em Espanhol | LILACS, CUMED | ID: biblio-1144460

RESUMO

La información y la comunicación son indispensables en momentos en que la humanidad está enfrentando un nuevo Coronavirus, SARS-Cov2, que ha ocasionado la pandemia denominada COVID-19. Este nuevo evento pone en tensión al sistema de salud de los países, así como las organizaciones de estos. El objetivo es modelar la madurez de la Información y comunicación en el enfrentamiento a la Covid 19. Se diseñó un modelo matemático difuso que tiene como base las normas del control interno relacionado con la Información y la comunicación, apoyado en la Lógica difusa compensatoria. Se tiene un modelo de madurez con seis estados para la Información y comunicación en el sistema de Salud como entidad presupuestada, basada en cuatro elementos: tecnología de la Información y comunicación, sistema de información, calidad de la información, así como responsabilidad y rendición de cuentas. Se resalta su necesidad actual en tiempos de enfrentamiento a la Covid 19(AU)


Information and communication are essential at a time when humanity is facing a new Coronavirus, SARS-Cov2, which has caused the pandemic called COVID-19. This new event puts tension in the health system of all countries, as well as their organizations. The objective is to model the maturity of Information and communication in the confrontation with Covid 19. A fuzzy mathematical model was designed based on the internal control standards related to Information and Communication, supported by the Fuzzy Compensatory Logic. There is a maturity model with six states for Information and Communication in the Health System as a budgeted entity, based on four elements: Information and Communication Technology, Information System, Quality of Information, and Responsibility and Accountability Bill. It is high lightened its current need in times of confrontation with the Covid 19(AU)


Assuntos
Software , Telemedicina , Lógica Fuzzy , Infecções por Coronavirus , Tecnologia da Informação
5.
PLoS One ; 15(11): e0241864, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33156877

RESUMO

HIV still constitutes a major public health problem in Africa, where the highest incidence and prevalence of the disease can be found in many rural areas, with multiple indigenous languages being used for communication by locals. In many rural areas of the KwaZulu-Natal (KZN) in South Africa, for instance, the most widely used languages include Zulu and Xhosa, with only limited comprehension in English and Afrikaans. Health care practitioners for HIV diagnosis and treatment, often, cannot communicate efficiently with their indigenous ethnic patients. An informatics tool is urgently needed to facilitate these health care professionals for better communication with their patients during HIV diagnosis. Here, we apply fuzzy logic and speech technology and develop a fuzzy logic HIV diagnostic system with indigenous multi-lingual interfaces, named Multi-linguAl HIV indigenouS fuzzy logiC-based diagnOstic sysTem (MAVSCOT). This HIV multilingual informatics software can facilitate the diagnosis in underprivileged rural African communities. We provide examples on how MAVSCOT can be applied towards HIV diagnosis by using existing data from the literature. Compared to other similar tools, MAVSCOT can perform better due to its implementation of the fuzzy logic. We hope MAVSCOT would help health care practitioners working in indigenous communities of many African countries, to efficiently diagnose HIV and ultimately control its transmission.


Assuntos
Infecções por HIV/diagnóstico , Saúde da População Rural/etnologia , Algoritmos , Feminino , Lógica Fuzzy , Infecções por HIV/etnologia , Humanos , Povos Indígenas , Masculino , Multilinguismo , Relações Médico-Paciente , Sensibilidade e Especificidade , África do Sul/etnologia
6.
PLoS One ; 15(11): e0241888, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33152020

RESUMO

Chicken egg products increased by 60% worldwide resulting in the farmers or traders egg industry. The double yolk (DY) eggs are priced higher than single yolk (SY) eggs around 35% at the same size. Although, separating DY from SY will increase more revenue but it has to be replaced at the higher cost from skilled labor for sorting. Normally, the separation of double yolk eggs required the expertise person by weigh and shape of egg but it is still high error. The purpose of this research is to detect double-yolked (DY) chicken eggs with weight and ratio of the egg's size using fuzzy logic and developing a low cost prototype to reduce the cost of separation. The K-means clustering is used for separating DY and SY, firstly. However, the error from this technique is still high as 15.05% because of its hard clustering. Therefore, the intersection zone scattering from using the weight and ratio of the egg's size to input of DY and SY is taken into consider with fuzzy logic algorithm, to improve the error. The results of errors from fuzzy logic are depended with input membership functions (MF). This research selects triangular MF of weight as low = 65 g, medium = 75 g and high = 85 g, while ratio of the egg is triangular MF as low = 1.30, medium = 1.40 and high = 1.50. This algorithm is not provide the minimum total error but it gives the low error to detect a double yolk while the real egg is SY as 1.43% of total eggs. This algorithm is applied to develop a double yolk egg detection prototype with Mbed platform by a load cell and OpenMV CAM, to measure the weight and ratio of the egg respectively.


Assuntos
Gema de Ovo/citologia , Algoritmos , Animais , Peso Corporal , Embrião de Galinha , Galinhas , Ovos , Feminino , Lógica Fuzzy , Modelos Teóricos , Óvulo
7.
PLoS One ; 15(11): e0242197, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33201892

RESUMO

The use of mobile communication devices in health care is spreading worldwide. A huge amount of health data collected by these devices (mobile health data) is nowadays available. Mobile health data may allow for real-time monitoring of patients and delivering ad-hoc treatment recommendations. This paper aims at showing how this may be done by exploiting the potentialities of fuzzy clustering techniques. In fact, such techniques can be fruitfully applied to mobile health data in order to identify clusters of patients for diagnostic classification and cluster-specific therapies. However, since mobile health data are full of noise, fuzzy clustering methods cannot be directly applied to mobile health data. Such data must be denoised prior to analyzing them. When longitudinal mobile health data are available, functional data analysis represents a powerful tool for filtering out the noise in the data. Fuzzy clustering methods for functional data can then be used to determine groups of patients. In this work we develop a fuzzy clustering method, based on the concept of medoid, for functional data and we apply it to longitudinal mHealth data on daily symptoms and consumptions of anti-symptomatic drugs collected by two sets of patients in Berlin (Germany) and Ascoli Piceno (Italy) suffering from allergic rhinoconjunctivitis. The studies showed that clusters of patients with similar changes in symptoms were identified opening the possibility of precision medicine.


Assuntos
Conjuntivite Alérgica/epidemiologia , Rinite Alérgica/epidemiologia , Adolescente , Criança , Análise por Conglomerados , Feminino , Lógica Fuzzy , Humanos , Masculino , Telemedicina/métodos
8.
Artigo em Inglês | MEDLINE | ID: mdl-33233826

RESUMO

Respiratory diseases are currently considered to be amongst the most frequent causes of death and disability worldwide, and even more so during the year 2020 because of the COVID-19 global pandemic. Aiming to reduce the impact of these diseases, in this work a methodology is developed that allows the early detection and prevention of potential hypoxemic clinical cases in patients vulnerable to respiratory diseases. Starting from the methodology proposed by the authors in a previous work and grounded in the definition of a set of expert systems, the methodology can generate alerts about the patient's hypoxemic status by means of the interpretation and combination of data coming both from physical measurements and from the considerations of health professionals. A concurrent set of Mamdani-type fuzzy-logic inference systems allows the collecting and processing of information, thus determining a final alert associated with the measurement of the global hypoxemic risk. This new methodology has been tested experimentally, producing positive results so far from the viewpoint of time reduction in the detection of a blood oxygen saturation deficit condition, thus implicitly improving the consequent treatment options and reducing the potential adverse effects on the patient's health.


Assuntos
/diagnóstico , Sistemas Especialistas , Hipóxia/diagnóstico , Hipóxia/prevenção & controle , Lógica Fuzzy , Humanos
9.
Artigo em Inglês | MEDLINE | ID: mdl-33136538

RESUMO

Neonatal seizures after birth may contribute to brain injury after an hypoxic-ischemic (HI) event, impaired brain development and a later life risk for epilepsy. Despite neural immaturity, seizures can also occur in preterm infants. However, surprisingly little is known about their evolution after an HI insult or patterns of expression. An improved understanding of preterm seizures will help facilitate diagnosis and prognosis and the implementation of treatments. This requires improved detection of seizures, including electrographic seizures. We have established a stable preterm fetal sheep model of HI that results in different types of post-HI seizures. These including the expression of epileptiform transients during the latent phase (0-6 h) of cerebral energy recovery, and bursts of high amplitude stereotypic evolving seizures (HAS) during the secondary phase of cerebral energy failure (∼6-72 h). We have previously developed successful automated machine-learning strategies for accurate identification and quantification of the evolving micro-scale EEG patterns (e.g. gamma spikes and sharp waves), during the latent phase. The current paper introduces, for the first time, a real-time approach that employs a 15-layer deep convolutional neural network (CNN) classifier, directly fed with the raw EEG time-series, to identify HAS in the 1024Hz and 256Hz down-sampled data in our preterm fetuses post-HI. The classifier was trained and tested using EEG segments during ∼6 to 48 hours post-HI recordings. The classifier accurately identified HAS with 98.52% accuracy in the 1024Hz and 97.78% in the 256Hz data. Clinical relevance-Results highlight the promising ability of the proposed CNN classifier for accurate identification of HI related seizures in the neonatal preterm brain, if further applied to the current 256Hz clinical recordings, in real-world.


Assuntos
Epilepsia , Análise de Ondaletas , Animais , Eletroencefalografia , Feminino , Feto , Lógica Fuzzy , Humanos , Recém-Nascido , Recém-Nascido Prematuro , Redes Neurais de Computação , Gravidez , Convulsões/diagnóstico , Ovinos
10.
Comput Methods Programs Biomed ; 197: 105762, 2020 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-33011666

RESUMO

BACKGROUND: The COVID-19 prevention and control constantly affects lives worldwide. In this paper, household medical products were analyzed using fuzzy logic. Considering the household anti-epidemic status, economic and environmental benefits, the adaptable design method of anti-epidemic products in the vestibule was proposed. The measure of adaptable design method still have shortcomings. Therefore, an improved method that is based on fuzzy logic programming is required. METHOD: Firstly, common medical product types used in vestibules and household anti-epidemic products were identified and summarized into product sets. Then matching degree matrix was obtained by functional configuration decomposition and matching calculations. Secondly, experts were invited to evaluate the paired comparative probability matrices and linguistic variables, and the evaluation data were converted by trapezoidal membership functions, fuzzy numbers and the defuzzification method to obtain the usage probability values (PR) for product functions. Finally, the matching degree value (P) and the product function (PF) were calculated by adaptability measure formula, and product function, the adaptability factor and the adaptability (A) were obtained. RESULTS AND DISCUSSION: Our results show that the degree of adaptability of each product function in the product set from PF1 to PF10can be evaluated. Based on the principles of sorting of values from high to low, the top five PF (n = 10) for P value is PF10, PF5, PF6, PF8 and PF1; The top five PF for P value is PF2, PF1, PF3, PF7 and PF8; The top five PF for A value is PF2(0.242), PF1(0.232), PF5(0.225), PF8(0.222) and PF3(0.221). These values allow us to summarize and draw visual charts according to the above data sorting mode. The higher the value of the product function, the more it can be prioritized for design development with functional cost savings, simplification or clustering. CONCLUSION: This study proposes an adaptable design method based on fuzzy logic programming. The data results in this study can guide the development and programming of the vestibule anti-epidemic products. The higher adaptability value of a product function indicates that it is more capable of being simplified, clustered, and adapting to changes in the product set.


Assuntos
/prevenção & controle , Desenho de Equipamento/métodos , Lógica Fuzzy , Pandemias/prevenção & controle , /epidemiologia , China/epidemiologia , Biologia Computacional , Desenho de Equipamento/estatística & dados numéricos , Utensílios Domésticos , Produtos Domésticos , Habitação , Humanos , Probabilidade , Inquéritos e Questionários
11.
Sci Rep ; 10(1): 16272, 2020 10 01.
Artigo em Inglês | MEDLINE | ID: mdl-33004993

RESUMO

The recent modification of species distribution ranges in response to a warmer climate has constituted a major and generalized biogeographic change. The main driver of the shift in distribution is the disequilibrium of the species ranges with their climatic favourability. Most species distribution modelling approaches assume equilibrium of the distribution with the environment, which hinders their applicability to the analysis of this change. Using fuzzy set theory we assessed the response to climate change of a historically African species, the Atlas Long-legged Buzzard. With this approach we were able to quantify that the Buzzard's distribution is in a latitudinal disequilibrium of the species distribution with the current climate of 4 km, which is driving the species range northwards at a speed of around 1.3 km/year, i.e., it takes 3 years for the species to occupy new climatically favourable areas. This speed is expected to decelerate to 0.5 km/year in 2060-2080.


Assuntos
Mudança Climática , Demografia , África , Animais , Aves , Mudança Climática/estatística & dados numéricos , Conservação dos Recursos Naturais , Demografia/estatística & dados numéricos , Lógica Fuzzy
12.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1011-1014, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018156

RESUMO

Early diagnosis and prognosis of babies with signs of hypoxic-ischemic encephalopathy (HIE) is currently limited and requires reliable prognostic biomarkers to identify at risk infants. Using our pre-clinical fetal sheep models, we have demonstrated that micro-scale patterns evolve over a profoundly suppressed EEG background within the first 6 hours of recovery, post HI insult. In particular, we have shown that high-frequency micro-scale spike transients (in the gamma frequency band, 80-120Hz) emerge immediately after an HI event, with much higher numbers around 2-2.5 h of the insult, with numbers gradually declining thereafter. We have also shown that the automatically quantified sharp waves in this phase are predictive of neural outcome. Initiation of some neuroprotective treatments within this limited window of opportunity, such as therapeutic hypothermia, optimally reduces neural injury. In clinical practice, it is hard to determine the exact timing of the injury, therefore, reliable automatic identification of EEG transients could be beneficial to help specify the phases of injury. Our team has previously developed successful machine- and deep-learning strategies for the identification of post-HI EEG patterns in an HI preterm fetal sheep model.This paper introduces, for the first time, a novel online fusion approach to train an 11-layers deep convolutional neural network (CNN) classifier using Wavelet-Fourier (WF) spectral features of EEG segments for accurate identification of high-frequency micro-scale spike transients in 1024Hz EEG recordings in our preterm fetal sheep. Sets of robust features were extracted using reverse biorthogonal wavelet (rbio2.8 at scale 7) and considering an 80-120Hz spectral frequency range. The WF-CNN classifier was able to accurately identify spike transients with a reliable high-performance of 99.03±0.86%.Clinical relevance-Results confirm the expertise of the method for the identification of similar patterns in the EEG of neonates in the early hours after birth.


Assuntos
Lógica Fuzzy , Análise de Ondaletas , Animais , Eletroencefalografia , Feminino , Humanos , Hipóxia , Recém-Nascido , Redes Neurais de Computação , Gravidez , Ovinos
13.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1015-1018, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018157

RESUMO

Diagnosis of hypoxic-ischemic encephalopathy (HIE) is currently limited and prognostic biological markers are required for early identification of at risk infants at birth. Using pre-clinical data from our fetal sheep models, we have shown that micro-scale EEG patterns, such as high-frequency spikes and sharp waves, evolve superimposed on a significantly suppressed background during the early hours of recovery (0-6 h), after an HI insult. In particular, we have demonstrated that the number of micro-scale gamma spike transients peaks within the first 2-2.5 hours of the insult and automatically quantified sharp waves in this period are predictive of neural outcome. This period of time is optimal for the initiation of neuroprotection treatments such as therapeutic hypothermia, which has a limited window of opportunity for implementation of 6 h or less after an HI insult. Clinically, it is hard to determine when an insult has started and thus the window of opportunity for treatment. Thus, reliable automatic algorithms that could accurately identify EEG patterns that denote the phase of injury is a valuable clinical tool. We have previously developed successful machine-learning strategies for the identification of HI micro-scale EEG patterns in a preterm fetal sheep model of HI. This paper employs, for the first time, reverse biorthogonal Wavelet-Scalograms (WS) as the inputs to a 17-layer deep-trained convolutional neural network (CNN) for the precise identification of high-frequency micro-scale spike transients that occur in the 80-120Hz gamma band during first 2 h period of an HI insult. The rbio-WS-CNN classifier robustly identified spike transients with an exceptionally high-performance of 99.82%.Clinical relevance-The suggested classifier would effectively identify and quantify EEG patterns of a similar morphology in preterm newborns during recovery from an HI-insult.


Assuntos
Lógica Fuzzy , Análise de Ondaletas , Animais , Eletroencefalografia , Feminino , Hipóxia , Redes Neurais de Computação , Gravidez , Ovinos
14.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1039-1042, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018163

RESUMO

Neonatal hypoxic-ischemic encephalopathy (HIE) evolves over different phases of time during recovery. Some neuroprotection treatments are only effective for specific, short windows of time during this evolution of injury. Clinically, we often do not know when an insult may have started, and thus which phase of injury the brain may be experiencing. To improve diagnosis, prognosis and treatment efficacy, we need to establish biomarkers which denote phases of injury. Our pre-clinical research, using preterm fetal sheep, show that micro-scale EEG patterns (e.g. spikes and sharp waves), superimposed on suppressed EEG background, primarily occur during the early recovery from an HI insult (0-6 h), and that numbers of events within the first 2 h are strongly predictive of neural survival. Thus, real-time automated algorithms that could reliably identify EEG patterns in this phase will help clinicians to determine the phases of injury, to help guide treatment options. We have previously developed successful automated machine learning approaches for accurate identification and quantification of HI micro-scale EEG patterns in preterm fetal sheep post-HI. This paper introduces, for the first time, a novel online fusion strategy that employs a high-level wavelet-Fourier (WF) spectral feature extraction method in conjunction with a deep convolutional neural network (CNN) classifier for accurate identification of micro-scale preterm fetal sheep post-HI sharp waves in 1024Hz EEG recordings, along with 256Hz down-sampled data. The classifier was trained and tested over 4120 EEG segments within the first 2 hours latent phase recordings. The WF-CNN classifier can robustly identify sharp waves with considerable high-performance of 99.86% in 1024Hz and 99.5% in 256Hz data. The method is an alternative deep-structure approach with competitive high-accuracy compared to our computationally-intensive WS-CNN sharp wave classifier.


Assuntos
Lógica Fuzzy , Análise de Ondaletas , Animais , Biomarcadores , Eletroencefalografia , Feminino , Humanos , Recém-Nascido , Redes Neurais de Computação , Gravidez , Ovinos
15.
Artigo em Inglês | MEDLINE | ID: mdl-33080931

RESUMO

The optimization of ecological water supplement scheme in Momoge National Nature Reserve (MNNR), using an interval-parameter two-stage stochastic programming model (IPTSP), still experiences problems with fuzzy uncertainties and the wide scope of the obtained optimization schemes. These two limitations pose a high risk of system failure causing high decision risk for decision-makers and render it difficult to further undertake optimization schemes respectively. Therefore, an interval-parameter fuzzy two-stage stochastic programming (IPFTSP) model derived from an IPTSP model was constructed to address the random variable, the interval uncertainties and the fuzzy uncertainties in the water management system in the present study, to reduce decision risk and narrow down the scope of the optimization schemes. The constructed IPFTSP model was subsequently applied to the optimization of the ecological water supplement scheme of MNNR under different scenarios, to maximize the recovered habitat area and the carrying capacity for rare migratory water birds. As per the results of the IPFTSP model, the recovered habitat areas for rare migratory birds under low, medium and high flood flow scenarios were (14.06, 17.88) × 103, (14.92, 18.96) × 103 and (15.83, 19.43) × 103 ha, respectively, and the target value was (14.60, 18.47) × 103 ha with a fuzzy membership of (0.01, 0.83). Fuzzy membership reflects the possibility level that the model solutions satisfy the target value and the corresponding decision risk. We further observed that the habitat area recovered by the optimization schemes of the IPFTSP model was significantly increased compared to the recommended scheme, and the increases observed were (5.22%, 33.78%), (11.62%, 41.88%) and (18.44%, 45.39%). In addition, the interval widths of the recovered habitat areas in the IPFTSP model were reduced by 17.15%, 17.98% and 23.86%, in comparison to those from the IPTSP model. It was revealed that the IPFTSP model, besides generating the optimal decision schemes under different scenarios for decision-makers to select and providing decision space to adjust the decision schemes, also shortened the decision range, thereby reducing the decision risk and the difficulty of undertaking decision schemes. In addition, the fuzzy membership obtained from the IPFTSP model, reflecting the relationship among the possibility level, the target value, and the decision risk, assists the decision-makers in planning the ecological water supplement scheme with a preference for target value and decision risk.


Assuntos
Aves , Lógica Fuzzy , Água , Animais , Ecossistema , Modelos Teóricos , Incerteza
16.
PLoS One ; 15(10): e0239960, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33017421

RESUMO

The outbreak of Corona Virus Disease 2019 (COVID-19) in Wuhan has significantly impacted the economy and society globally. Countries are in a strict state of prevention and control of this pandemic. In this study, the development trend analysis of the cumulative confirmed cases, cumulative deaths, and cumulative cured cases was conducted based on data from Wuhan, Hubei Province, China from January 23, 2020 to April 6, 2020 using an Elman neural network, long short-term memory (LSTM), and support vector machine (SVM). A SVM with fuzzy granulation was used to predict the growth range of confirmed new cases, new deaths, and new cured cases. The experimental results showed that the Elman neural network and SVM used in this study can predict the development trend of cumulative confirmed cases, deaths, and cured cases, whereas LSTM is more suitable for the prediction of the cumulative confirmed cases. The SVM with fuzzy granulation can successfully predict the growth range of confirmed new cases and new cured cases, although the average predicted values are slightly large. Currently, the United States is the epicenter of the COVID-19 pandemic. We also used data modeling from the United States to further verify the validity of the proposed models.


Assuntos
Infecções por Coronavirus/epidemiologia , Modelos Teóricos , Pneumonia Viral/epidemiologia , Probabilidade , Máquina de Vetores de Suporte , China/epidemiologia , Previsões , Lógica Fuzzy , Humanos , Redes Neurais de Computação , Pandemias , Estados Unidos/epidemiologia
17.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4620-4623, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019023

RESUMO

Wearable body area networks (BANs) have been widely used in activity measurements for kinematic information collection. This paper presents the design and implementation of a wearable device used as a training tool in freestyle swimming. The device supplies a close-loop control mechanism via a fuzzy logic controller. Swimming posture data is collected quantitatively and audibly fed back to swimmers in real time through bone conductors. Two recreational swimmers were invited to participate in a series of experiments including 7 days of baseline capability test (no feedback), 7 days of feedback training, and 2 days of retention test. It was found that both swimmers could well adapt to the feedback instructions. A maximum of 7.62% of lap time improvement and 29.64% of trunk roll improvement were observed in FB training, and such pattern was maintained after feedback was removed. We conclude that real-time fuzzy logic feedback can be used to improve recreational swimmers performance.


Assuntos
Lógica Fuzzy , Natação , Biorretroalimentação Psicológica , Retroalimentação , Humanos , Postura
18.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 6018-6023, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019343

RESUMO

In this project, a fully functional incubator with precise control with respect to temperature, humidity, and airflow was developed and assessed. In parallel with the development of the incubator, a heuristic simulation was created to test and tune the Mamdani fuzzy logic controller. The controller was then applied to the incubator prototype.


Assuntos
Lógica Fuzzy , Incubadoras para Lactentes , Umidade , Fenômenos Fisiológicos Respiratórios , Temperatura
19.
Artigo em Inglês | MEDLINE | ID: mdl-32915103

RESUMO

The present study represents an original approach to data interpretation of clinical data for patients with diagnosis diabetes mellitus type 2 (DMT2) using fuzzy clustering as a tool for intelligent data analysis. Fuzzy clustering is often used in classification and interpretation of medical data (including in medical diagnosis studies) but in this study it is applied with a different goal: to separate a group of 100 patients with DMT2 from a control group of healthy volunteers and, further, to reveal three different patterns of similarity between the patients. Each pattern is described by specific descriptors (variables), which ensure pattern interpretation by appearance of underling disease to DMT2.


Assuntos
Diabetes Mellitus Tipo 2/classificação , Diabetes Mellitus Tipo 2/diagnóstico , Lógica Fuzzy , Algoritmos , Análise por Conglomerados , Humanos , Masculino , Pessoa de Meia-Idade , Valor Preditivo dos Testes
20.
BMC Med Inform Decis Mak ; 20(1): 236, 2020 09 18.
Artigo em Inglês | MEDLINE | ID: mdl-32948169

RESUMO

BACKGROUND: Today's healthcare organizations want to implement secure and quality healthcare software as cyber-security is a significant risk factor for healthcare data. Considering security requirements during trustworthy healthcare software development process is an essential part of the quality software development. There are several Security Requirements Engineering (SRE) methodologies, framework, process, standards available today. Unfortunately, there is still a necessity to improve these security requirements engineering approaches. Determining the most suitable security requirements engineering method for trustworthy healthcare software development is a challenging process. This study is aimed to present security experts' perspective on the relative importance of the criteria for selecting effective SRE method by utilizing the multi-criteria decision making methods. METHODS: The study was planned and conducted to identify the most appropriate SRE approach for quality and trustworthy software development based on the security expert's knowledge and experience. The hierarchical model was evaluated by using fuzzy TOPSIS model. Effective SRE selection criteria were compared in pairs. 25 security experts were asked to response the pairwise criteria comparison form. RESULTS: The impact of the recognized selection criteria for effective security requirements engineering approaches has been evaluated quantitatively. For each of the 25 participants, comparison matrixes were formed based on the scores of their responses in the form. The consistency ratios (CR) were found to be smaller than 10% (CR = 9.1% < 10%). According to pairwise comparisons result; with a 0.842 closeness coefficient (Ci), STORE methodology is the most effective security requirements engineering approach for trustworthy healthcare software development. CONCLUSIONS: The findings of this research study demonstrate various factors in the decision-making process for the selection of a reliable method for security requirements engineering. This is a significant study that uses multi-criteria decision-making tools, specifically fuzzy TOPSIS, which used to evaluate different SRE methods for secure and trustworthy healthcare application development.


Assuntos
Assistência à Saúde , Lógica Fuzzy , Software , Instalações de Saúde , Humanos
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