1,720,964 research outputs found
Enhanced convolutional neural network enabled optimized diagnostic model for COVID-19 detection
No full textComputed tomography (CT) films are used to construct cross-sectional pictures of a particular region of the body by using many x-ray readings that were obtained at various angles. There is a general agreement in the medical community at this time that chest CT is the most accurate approach for identifying COVID-19 disease. It was demonstrated that chest CT had a higher sensitivity than reverse transcription polymerase chain reaction (RT-PCR) for the detection of COVID-19 illness. This article presents gray-level co-occurrence matrix (GLCM) texture feature extraction and convolutional neural network (CNN)-enabled optimized diagnostic model for COVID-19 detection. In this diagnostic model, CT scan images of patients are given as input. Firstly, GLCM algorithm is used to extract texture features from the CT scan images. This feature extraction helps in achieving higher classification accuracy. Classification is performed using CNN. It achieves higher accuracy than the k-nearest neighbors (KNN) algorithm and multi-layer preceptor (MLP). The accuracy of GLCM based CNN is 99%, F1 score is 99% and the recall rate is also 98%. CNN has achieved better results than MLP and KNN algorithms for COVID-19 detection
Rough set theory-based feature selection and FGA-NN classifier for medical data classification
Get PDFThe prediction of heart disease is a difficult task, which needs much
experience and knowledge. In order to reduce the risk of heart disease
prediction, in this paper we proposed a rough set theory-based feature selection
and FGA-NN classifier. The overall process of the proposed system consists of
two main steps, such as: 1) feature reduction; 2) heart disease prediction. At
first, the kernel fuzzy c-means clustering with roughest theory (KFCMRS)
algorithm is applied to the high dimensional data to reduce the dimension of the
attribute. After that, the medical data classification is done through FGA-NN
classifier. To improve the prediction performance, hybridisation of firefly and
genetic algorithm (FGA) is utilised with NN for weight optimisation. At last,
the experimentation is performed by means of Cleveland, Hungarian, and
Switzerland datasets. The experimentation result proves that the FGA-NN
classifier outperformed the existing approach by attaining the accuracy of 83%
A hybrid modified artificial bee colony (ABC)-based artificial _neural network model for power management controller and hybrid energy system for energy source integration
Get PDFSmall MGS (microgrid systems) are capable of decreasing energy losses. Long-distance
power transmission lines are constructed by integrating distributed power sources with energy
storage subsystems, which is the current trend in the development of RES (renewable energy sources).
Although energies produced by RES do not cause pollution, they are stochastic and hence challenging
to manage. This disadvantage makes high penetration of RES risky for the stability, dependability,
and power quality of main electrical grids. The energies obtained from RES must thus be integrated
in the best possible way. To provide maximum energy sustainability and best energy usage, hybrid
energy systems must manage energy efficiently. In order to improve power management and
make better use of RES, this study offers a hybrid energy power management controller based
on hybrid MABC (modified artificial bee colony) and ANN (artificial neural network) for MGS,
PVS (photovoltaic system), and WT (wind turbine). Controlling power flows between grids and
energy sources is the suggested approach for power control. D/R (demands/responses), customer
reactions, offering priorities, D/R properties like COE (cost of energies), and sizes (lengths) are
considered in this work. Along with current techniques, a suggested model is implemented in the
MATLAB/Simulink platform
A hybrid modified artificial bee colony (ABC)-based artificial neural network model for power management controller and hybrid energy system for energy source integration
No full textSmall MGS (microgrid systems) are capable of decreasing energy losses. Long-distance
power transmission lines are constructed by integrating distributed power sources with energy
storage subsystems, which is the current trend in the development of RES (renewable energy sources).
Although energies produced by RES do not cause pollution, they are stochastic and hence challenging
to manage. This disadvantage makes high penetration of RES risky for the stability, dependability,
and power quality of main electrical grids. The energies obtained from RES must thus be integrated
in the best possible way. To provide maximum energy sustainability and best energy usage, hybrid
energy systems must manage energy efficiently. In order to improve power management and
make better use of RES, this study offers a hybrid energy power management controller based
on hybrid MABC (modified artificial bee colony) and ANN (artificial neural network) for MGS,
PVS (photovoltaic system), and WT (wind turbine). Controlling power flows between grids and
energy sources is the suggested approach for power control. D/R (demands/responses), customer
reactions, offering priorities, D/R properties like COE (cost of energies), and sizes (lengths) are
considered in this work. Along with current techniques, a suggested model is implemented in the
MATLAB/Simulink platform
Enhanced convolutional neural network enabled optimized diagnostic model for COVID-19 detection
Get PDFComputed tomography (CT) films are used to construct cross-sectional pictures of a particular region of the body by using many x-ray readings that were obtained at various angles. There is a general agreement in the medical community at this time that chest CT is the most accurate approach for identifying COVID-19 disease. It was demonstrated that chest CT had a higher sensitivity than reverse transcription polymerase chain reaction (RT-PCR) for the detection of COVID-19 illness. This article presents gray-level co-occurrence matrix (GLCM) texture feature extraction and convolutional neural network (CNN)-enabled optimized diagnostic model for COVID-19 detection. In this diagnostic model, CT scan images of patients are given as input. Firstly, GLCM algorithm is used to extract texture features from the CT scan images. This feature extraction helps in achieving higher classification accuracy. Classification is performed using CNN. It achieves higher accuracy than the k-nearest neighbors (KNN) algorithm and multi-layer preceptor (MLP). The accuracy of GLCM based CNN is 99%, F1 score is 99% and the recall rate is also 98%. CNN has achieved better results than MLP and KNN algorithms for COVID-19 detection
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A hybrid modified artificial bee colony (ABC)-based artificial neural network model for power management controller and hybrid energy system for energy source integration
Get PDFSmall MGS (microgrid systems) are capable of decreasing energy losses. Long-distance
power transmission lines are constructed by integrating distributed power sources with energy
storage subsystems, which is the current trend in the development of RES (renewable energy sources).
Although energies produced by RES do not cause pollution, they are stochastic and hence challenging
to manage. This disadvantage makes high penetration of RES risky for the stability, dependability,
and power quality of main electrical grids. The energies obtained from RES must thus be integrated
in the best possible way. To provide maximum energy sustainability and best energy usage, hybrid
energy systems must manage energy efficiently. In order to improve power management and
make better use of RES, this study offers a hybrid energy power management controller based
on hybrid MABC (modified artificial bee colony) and ANN (artificial neural network) for MGS,
PVS (photovoltaic system), and WT (wind turbine). Controlling power flows between grids and
energy sources is the suggested approach for power control. D/R (demands/responses), customer
reactions, offering priorities, D/R properties like COE (cost of energies), and sizes (lengths) are
considered in this work. Along with current techniques, a suggested model is implemented in the
MATLAB/Simulink platform
Rough set theory-based feature selection and FGA-NN classifier for medical data classification
No full textThe prediction of heart disease is a difficult task, which needs much
experience and knowledge. In order to reduce the risk of heart disease
prediction, in this paper we proposed a rough set theory-based feature selection
and FGA-NN classifier. The overall process of the proposed system consists of
two main steps, such as: 1) feature reduction; 2) heart disease prediction. At
first, the kernel fuzzy c-means clustering with roughest theory (KFCMRS)
algorithm is applied to the high dimensional data to reduce the dimension of the
attribute. After that, the medical data classification is done through FGA-NN
classifier. To improve the prediction performance, hybridisation of firefly and
genetic algorithm (FGA) is utilised with NN for weight optimisation. At last,
the experimentation is performed by means of Cleveland, Hungarian, and
Switzerland datasets. The experimentation result proves that the FGA-NN
classifier outperformed the existing approach by attaining the accuracy of 83%
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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