Coronavirus desease 2019 (COVID-19) is a pandemic that has occurred in the world since 2019. Researchers have carried out various ways in dealing with this disease, starting from the screening stage to the stage of treatment and therapy for COVID-19 patients. As the gateway to the COVID-19 problem, screening has an essential role in a diagnosis that leads to appropriate treatment. In this paper, we will focus on the screening stage using digital image processing techniques, namely in calculating the area of white spots in the lungs of COVID-19 patients. The white patches are an early indication of how badly COVID-19 is attacking the patient. We use XRay Thorax image objects as research data in this paper. Although the current experimental results show that this method has a successful performance of 71.11%, it is pretty promising for further development due to its simplicity.
Coronavirus disease 2019 detection using deep features learningIJECEIAES
A Coronavirus disease 2019 (COVID-19) pandemic detection considers a critical and challenging task for the medical practitioner. The coronavirus disease spread so rapidly between people and infected more than one hundred and seventy million people worldwide. For this reason, it is necessary to detect infected people with coronavirus and take action to prevent virus spread. In this study, a COVID-19 classification methodology was adopted to detect infected people using computed tomography (CT) images. Deep learning was applied to recognize COVID-19 infected cases for different patients by employing deep features. This methodology can be beneficial for medical practitioners to diagnose infected patients. The results were based on a new data collection named BasrahDataset that includes different CT scan videos for Iraqi patients. The proposed system gave promised results with a 99% F1-score for detecting COVID-19.
A Predictive Factor For Short-Term Outcome In Patients With COVID-19: CT Scor...JohnJulie1
This study enrolled 253 patients (63 died in the hospital, 190 were discharged). Compared to survivors, non-survivors were older, mostly male, had a higher prevalence of preexisting comorbidity, higher incidences of hypoxemia, lymphopenia and bacterial coinfection (p<0.001 for each). Regarding CT evaluations, non-survivors had higher CT scores (14.3±3.4 vs. 8.1±2.9), higher incidences of bronchial dilation with mosaic (34.9% vs. 10.5%), emphysema (28.6% vs. 10.5%), and diffuse opacity distribution (76.1% vs. 36.8%; all p<0.001).
A Predictive Factor For Short-Term Outcome In Patients With COVID-19: CT Scor...suppubs1pubs1
This study enrolled 253 patients (63 died in the hospital, 190 were discharged). Compared to survivors, non-survivors were older, mostly male, had a higher prevalence of preexisting comorbidity, higher incidences of hypoxemia, lymphopenia and bacterial coinfection (p<0.001 for each). Regarding CT evaluations, non-survivors had higher CT scores (14.3±3.4 vs. 8.1±2.9), higher incidences of bronchial dilation with mosaic (34.9% vs. 10.5%), emphysema (28.6% vs. 10.5%), and diffuse opacity distribution (76.1% vs. 36.8%; all p<0.001).
A Predictive Factor For Short-Term Outcome In Patients With COVID-19: CT Scor...suppubs1pubs1
This study enrolled 253 patients (63 died in the hospital, 190 were discharged). Compared to survivors, non-survivors were older, mostly male, had a higher prevalence of preexisting comorbidity, higher incidences of hypoxemia, lymphopenia and bacterial coinfection (p<0.001 for each). Regarding CT evaluations, non-survivors had higher CT scores (14.3±3.4 vs. 8.1±2.9), higher incidences of bronchial dilation with mosaic (34.9% vs. 10.5%), emphysema (28.6% vs. 10.5%), and diffuse opacity distribution (76.1% vs. 36.8%; all p<0.001).
Predicting the status of COVID-19 active cases using a neural network time s...IJECEIAES
The design of intelligent systems for analyzing information and predicting the epidemiological trends of the disease is rapidly expanding because of the coronavirus disease (COVID-19) pandemic. The COVID-19 datasets provided by Johns Hopkins University were included in the analysis. This dataset contains some missing data that is imputed using the multi-objective particle swarm optimization method. A time series model based on nonlinear autoregressive exogenou (NARX) neural network is proposed to predict the recovered and death COVID-19 cases. This model is trained and evaluated for two modes: predicting the situation of the affected areas for the next day and the next month. After training the model based on the data from January 22 to February 27, 2020, the performance of the proposed model was evaluated in predicting the situation of the areas in the coming two weeks. The error rate was less than 5%. The prediction of the proposed model for April 9, 2020, was compared with the actual data for that day. The absolute percentage error (AE) worldwide was 12%. The lowest mean absolute error (MAE) of the model was for South America and Australia with 3 and 3.3, respectively. In this paper, we have shown that geographical areas with mortality and recovery of COVID-19 cases can be predicted using a neural network-based model.
Coronavirus disease 2019 detection using deep features learningIJECEIAES
A Coronavirus disease 2019 (COVID-19) pandemic detection considers a critical and challenging task for the medical practitioner. The coronavirus disease spread so rapidly between people and infected more than one hundred and seventy million people worldwide. For this reason, it is necessary to detect infected people with coronavirus and take action to prevent virus spread. In this study, a COVID-19 classification methodology was adopted to detect infected people using computed tomography (CT) images. Deep learning was applied to recognize COVID-19 infected cases for different patients by employing deep features. This methodology can be beneficial for medical practitioners to diagnose infected patients. The results were based on a new data collection named BasrahDataset that includes different CT scan videos for Iraqi patients. The proposed system gave promised results with a 99% F1-score for detecting COVID-19.
A Predictive Factor For Short-Term Outcome In Patients With COVID-19: CT Scor...JohnJulie1
This study enrolled 253 patients (63 died in the hospital, 190 were discharged). Compared to survivors, non-survivors were older, mostly male, had a higher prevalence of preexisting comorbidity, higher incidences of hypoxemia, lymphopenia and bacterial coinfection (p<0.001 for each). Regarding CT evaluations, non-survivors had higher CT scores (14.3±3.4 vs. 8.1±2.9), higher incidences of bronchial dilation with mosaic (34.9% vs. 10.5%), emphysema (28.6% vs. 10.5%), and diffuse opacity distribution (76.1% vs. 36.8%; all p<0.001).
A Predictive Factor For Short-Term Outcome In Patients With COVID-19: CT Scor...suppubs1pubs1
This study enrolled 253 patients (63 died in the hospital, 190 were discharged). Compared to survivors, non-survivors were older, mostly male, had a higher prevalence of preexisting comorbidity, higher incidences of hypoxemia, lymphopenia and bacterial coinfection (p<0.001 for each). Regarding CT evaluations, non-survivors had higher CT scores (14.3±3.4 vs. 8.1±2.9), higher incidences of bronchial dilation with mosaic (34.9% vs. 10.5%), emphysema (28.6% vs. 10.5%), and diffuse opacity distribution (76.1% vs. 36.8%; all p<0.001).
A Predictive Factor For Short-Term Outcome In Patients With COVID-19: CT Scor...suppubs1pubs1
This study enrolled 253 patients (63 died in the hospital, 190 were discharged). Compared to survivors, non-survivors were older, mostly male, had a higher prevalence of preexisting comorbidity, higher incidences of hypoxemia, lymphopenia and bacterial coinfection (p<0.001 for each). Regarding CT evaluations, non-survivors had higher CT scores (14.3±3.4 vs. 8.1±2.9), higher incidences of bronchial dilation with mosaic (34.9% vs. 10.5%), emphysema (28.6% vs. 10.5%), and diffuse opacity distribution (76.1% vs. 36.8%; all p<0.001).
Predicting the status of COVID-19 active cases using a neural network time s...IJECEIAES
The design of intelligent systems for analyzing information and predicting the epidemiological trends of the disease is rapidly expanding because of the coronavirus disease (COVID-19) pandemic. The COVID-19 datasets provided by Johns Hopkins University were included in the analysis. This dataset contains some missing data that is imputed using the multi-objective particle swarm optimization method. A time series model based on nonlinear autoregressive exogenou (NARX) neural network is proposed to predict the recovered and death COVID-19 cases. This model is trained and evaluated for two modes: predicting the situation of the affected areas for the next day and the next month. After training the model based on the data from January 22 to February 27, 2020, the performance of the proposed model was evaluated in predicting the situation of the areas in the coming two weeks. The error rate was less than 5%. The prediction of the proposed model for April 9, 2020, was compared with the actual data for that day. The absolute percentage error (AE) worldwide was 12%. The lowest mean absolute error (MAE) of the model was for South America and Australia with 3 and 3.3, respectively. In this paper, we have shown that geographical areas with mortality and recovery of COVID-19 cases can be predicted using a neural network-based model.
Clinical characteristics of children with COVID-19 pneumoniaAI Publications
For this purpose, 75 children under the age of 18 were included in the study. The test for SARS-CoV-2 virus RNA was positive in the pathological material from the nasopharynx in all examined patients. The control group consisted of 15 healthy children. The patients included in the study were divided into 2 groups according to severity of disease: 49 (65.3%) patients with moderate COVID-19 were included in group I, 26 (34.7%) patients with severe COVID-19 - in group 2. Examination methods included anamnestic, clinical, instrumental and laboratory studies. The most common symptoms among children examined were fever (66 (88.0%)) and cough (74 (98.7%)). Rarely, muscle pains, loss of sense of smell and taste, headaches have been observed in older children. Intergroup comparison revealed higher levels of ferritin, D-dimer and fibrinogen in group II compared with group I. According to the results obtained, the course of the disease in children, in contrast to adults, is more favorable.
Coronavirus disease (COVID-19) is a pandemic disease, which has already caused
thousands of causalities and infected several millions of people worldwide. Any technological tool
enabling rapid screening of the COVID-19 infection with high accuracy can be crucially helpful to the
healthcare professionals. The main clinical tool currently in use for the diagnosis of COVID-19 is the
Reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less-sensitive and requires
specialized medical personnel. X-ray imaging is an easily accessible tool that can be an excellent alternative
in the COVID-19 diagnosis. This research was taken to investigate the utility of artificial intelligence (AI)
in the rapid and accurate detection of COVID-19 from chest X-ray images
Can Lung Ultrasound in Patients with Fever of Unknown Origin Detect Early Sig...navasreni
The increasing interest in Lung Ultrasound (LUS) over the last years led to a great diffusion and better experience in using this technique, which became an essential tool for clinicians. During the current Coronavirus Disease 2019 (COVID-19) pandemic, LUS is being extensively applied to the evaluation and monitoring....
Can Lung Ultrasound in Patients with Fever of Unknown Origin Detect Early Sig...clinicsoncology
The increasing interest in Lung Ultrasound (LUS) over the last years led to a great diffusion and better experience in using this technique, which became an essential tool for clinicians. During the current Coronavirus Disease 2019 (COVID-19) pandemic
Can Lung Ultrasound in Patients with Fever of Unknown Origin Detect Early Sig...pateldrona
The increasing interest in Lung Ultrasound (LUS) over the last years led to a great diffusion and better experience in using this technique, which became an essential tool for clinicians. During the current Coronavirus Disease 2019 (COVID-19) pandemic, LUS is being extensively applied to the evaluation and monitoring....
Can Lung Ultrasound in Patients with Fever of Unknown Origin Detect Early Sig...georgemarini
The increasing interest in Lung Ultrasound (LUS) over the last years led to a great diffusion and better experience in using this technique, which became an essential tool for clinicians. During the current Coronavirus Disease 2019 (COVID-19) pandemic
Can Lung Ultrasound in Patients with Fever of Unknown Origin Detect Early Sig...SarkarRenon
The increasing interest in Lung Ultrasound (LUS) over the last years led to a great diffusion and better experience in using this technique, which became an essential tool for clinicians. During the current Coronavirus Disease 2019 (COVID-19) pandemic, LUS is being extensively applied to the evaluation and monitoring....
Can Lung Ultrasound in Patients with Fever of Unknown Origin Detect Early Sig...komalicarol
In this case report we describe the detection of very early ultrasonographic signs of lung involvement in a patient who presented no clinical signs of Severe Acute Respiratory Syndrome
Coronavirus 2 (SARS-CoV-2) pneumonia, but who developed respiratory symptoms and tested
positive for SARS-CoV-2 infection 22 days later
Can Lung Ultrasound in Patients with Fever of Unknown Origin Detect Early Sig...AnonIshanvi
The increasing interest in Lung Ultrasound (LUS) over the last years led to a great diffusion and better experience in using this technique, which became an essential tool for clinicians. During the current Coronavirus Disease 2019 (COVID-19) pandemic, LUS is being extensively applied to the evaluation and monitoring....
Detect COVID-19 with Deep Learning- A survey on Deep Learning for Pulmonary M...JumanaNadir
Who knew Deep Learning can come so handy to us during this period of global crisis?
There has yet been no vaccine or any effective treatment for the 2019 novel Coronavirus (COVID-19), but generative deep learning is helping in detecting and monitoring coronavirus patients by chest CT screening.
CT scans still play a critical role in managing COVID-19. Patients with a severe coronavirus infection show different features on their computed tomography
Modeling and Forecasting the COVID-19 Temporal Spread in Greece: An Explorato...Konstantinos Demertzis
Within the complex framework of anti-COVID-19 health management, where the criteria of diagnostic testing, the availability of public-health resources and services, and the applied anti-COVID-19 policies vary between countries, the reliability and accuracy in the modeling of temporal spread can prove to be effective in the worldwide fight against the disease. This paper applies an exploratory time-series analysis to the evolution of the disease in Greece, which currently suggests a success story of COVID-19 management. The proposed method builds on a recent conceptualization of detecting connective communities in a time-series and develops a novel spline regression model where the knot vector is determined by the community detection in the complex network. Overall, the study contributes to the COVID-19 research by proposing a free of disconnected past-data and reliable framework of forecasting, which can facilitate decision-making and management
of the available health resources.
This paper presents a time series analysis of a novel coronavirus, COVID-19, discovered in China in December 2019 using intuitionistic fuzzy logic system with neural network learning capability. Fuzzy logic systems are known to be universal approximation tools that can estimate a nonlinear function as closely as possible to the actual values. The main idea in this study is to use intuitionistic fuzzy logic system that enables hesitation and has membership and non-membership functions that are optimized to predict COVID-19 outbreak cases. Intuitionistic fuzzy logic systems are known to provide good results with improved prediction accuracy and are excellent tools for uncertainty modelling. The hesitation-enabled fuzzy logic system is evaluated using COVID-19 pandemic cases for Nigeria, being part of the COVID-19 data for African countries obtained from Kaggle data repository. The hesitation-enabled fuzzy logic model is compared with the classical fuzzy logic system and artificial neural network and shown to offer improved performance in terms of root mean squared error, mean absolute error and mean absolute percentage error. Intuitionistic fuzzy logic system however incurs a setback in terms of the high computing time compared to the classical fuzzy logic system.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Covid 19 Health Prediction using Supervised Learning with Optimizationijtsrd
The assessment of infection is significant for Covid 19 as the antigen pack and RTPCR are imperfect and ought to be better for diagnosing such sickness. Continuous Return Transcription constant talk record polymerase chain . Medical services rehearse incorporate the assortment of different kinds of patient information to assist the doctor with diagnosing the patients wellbeing. This information could be basic side effects, first analysis by a specialist, or an inside and out research facility test. This information is in this manner utilized for examinations simply by a specialist, who thusly utilizes his specific clinical abilities to track down the illness. To group Covid 19 sickness datasets like gentle, center and serious infections, the proposed model uses the idea of controlled machine training and GWO advancement to manage in the event that the patient is impacted or not. Effectiveness investigation is determined and thought about of infection information for the two calculations. The consequences of the reenactments outline the compelling nature and intricacy of the informational index for the reviewing strategies. Contrasted with SVM, the proposed model gives 7.8 percent further developed forecast exactness. The forecast exactness is 8 better than the SVM. This outcome F1 score of 2 is better than an SVM conjecture. Akash Malvi | Nikesh Gupta "Covid-19 Health Prediction using Supervised Learning with Optimization" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-6 , December 2023, URL: https://www.ijtsrd.com/papers/ijtsrd61266.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/61266/covid19-health-prediction-using-supervised-learning-with-optimization/akash-malvi
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
More Related Content
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Clinical characteristics of children with COVID-19 pneumoniaAI Publications
For this purpose, 75 children under the age of 18 were included in the study. The test for SARS-CoV-2 virus RNA was positive in the pathological material from the nasopharynx in all examined patients. The control group consisted of 15 healthy children. The patients included in the study were divided into 2 groups according to severity of disease: 49 (65.3%) patients with moderate COVID-19 were included in group I, 26 (34.7%) patients with severe COVID-19 - in group 2. Examination methods included anamnestic, clinical, instrumental and laboratory studies. The most common symptoms among children examined were fever (66 (88.0%)) and cough (74 (98.7%)). Rarely, muscle pains, loss of sense of smell and taste, headaches have been observed in older children. Intergroup comparison revealed higher levels of ferritin, D-dimer and fibrinogen in group II compared with group I. According to the results obtained, the course of the disease in children, in contrast to adults, is more favorable.
Coronavirus disease (COVID-19) is a pandemic disease, which has already caused
thousands of causalities and infected several millions of people worldwide. Any technological tool
enabling rapid screening of the COVID-19 infection with high accuracy can be crucially helpful to the
healthcare professionals. The main clinical tool currently in use for the diagnosis of COVID-19 is the
Reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less-sensitive and requires
specialized medical personnel. X-ray imaging is an easily accessible tool that can be an excellent alternative
in the COVID-19 diagnosis. This research was taken to investigate the utility of artificial intelligence (AI)
in the rapid and accurate detection of COVID-19 from chest X-ray images
Can Lung Ultrasound in Patients with Fever of Unknown Origin Detect Early Sig...navasreni
The increasing interest in Lung Ultrasound (LUS) over the last years led to a great diffusion and better experience in using this technique, which became an essential tool for clinicians. During the current Coronavirus Disease 2019 (COVID-19) pandemic, LUS is being extensively applied to the evaluation and monitoring....
Can Lung Ultrasound in Patients with Fever of Unknown Origin Detect Early Sig...clinicsoncology
The increasing interest in Lung Ultrasound (LUS) over the last years led to a great diffusion and better experience in using this technique, which became an essential tool for clinicians. During the current Coronavirus Disease 2019 (COVID-19) pandemic
Can Lung Ultrasound in Patients with Fever of Unknown Origin Detect Early Sig...pateldrona
The increasing interest in Lung Ultrasound (LUS) over the last years led to a great diffusion and better experience in using this technique, which became an essential tool for clinicians. During the current Coronavirus Disease 2019 (COVID-19) pandemic, LUS is being extensively applied to the evaluation and monitoring....
Can Lung Ultrasound in Patients with Fever of Unknown Origin Detect Early Sig...georgemarini
The increasing interest in Lung Ultrasound (LUS) over the last years led to a great diffusion and better experience in using this technique, which became an essential tool for clinicians. During the current Coronavirus Disease 2019 (COVID-19) pandemic
Can Lung Ultrasound in Patients with Fever of Unknown Origin Detect Early Sig...SarkarRenon
The increasing interest in Lung Ultrasound (LUS) over the last years led to a great diffusion and better experience in using this technique, which became an essential tool for clinicians. During the current Coronavirus Disease 2019 (COVID-19) pandemic, LUS is being extensively applied to the evaluation and monitoring....
Can Lung Ultrasound in Patients with Fever of Unknown Origin Detect Early Sig...komalicarol
In this case report we describe the detection of very early ultrasonographic signs of lung involvement in a patient who presented no clinical signs of Severe Acute Respiratory Syndrome
Coronavirus 2 (SARS-CoV-2) pneumonia, but who developed respiratory symptoms and tested
positive for SARS-CoV-2 infection 22 days later
Can Lung Ultrasound in Patients with Fever of Unknown Origin Detect Early Sig...AnonIshanvi
The increasing interest in Lung Ultrasound (LUS) over the last years led to a great diffusion and better experience in using this technique, which became an essential tool for clinicians. During the current Coronavirus Disease 2019 (COVID-19) pandemic, LUS is being extensively applied to the evaluation and monitoring....
Detect COVID-19 with Deep Learning- A survey on Deep Learning for Pulmonary M...JumanaNadir
Who knew Deep Learning can come so handy to us during this period of global crisis?
There has yet been no vaccine or any effective treatment for the 2019 novel Coronavirus (COVID-19), but generative deep learning is helping in detecting and monitoring coronavirus patients by chest CT screening.
CT scans still play a critical role in managing COVID-19. Patients with a severe coronavirus infection show different features on their computed tomography
Modeling and Forecasting the COVID-19 Temporal Spread in Greece: An Explorato...Konstantinos Demertzis
Within the complex framework of anti-COVID-19 health management, where the criteria of diagnostic testing, the availability of public-health resources and services, and the applied anti-COVID-19 policies vary between countries, the reliability and accuracy in the modeling of temporal spread can prove to be effective in the worldwide fight against the disease. This paper applies an exploratory time-series analysis to the evolution of the disease in Greece, which currently suggests a success story of COVID-19 management. The proposed method builds on a recent conceptualization of detecting connective communities in a time-series and develops a novel spline regression model where the knot vector is determined by the community detection in the complex network. Overall, the study contributes to the COVID-19 research by proposing a free of disconnected past-data and reliable framework of forecasting, which can facilitate decision-making and management
of the available health resources.
This paper presents a time series analysis of a novel coronavirus, COVID-19, discovered in China in December 2019 using intuitionistic fuzzy logic system with neural network learning capability. Fuzzy logic systems are known to be universal approximation tools that can estimate a nonlinear function as closely as possible to the actual values. The main idea in this study is to use intuitionistic fuzzy logic system that enables hesitation and has membership and non-membership functions that are optimized to predict COVID-19 outbreak cases. Intuitionistic fuzzy logic systems are known to provide good results with improved prediction accuracy and are excellent tools for uncertainty modelling. The hesitation-enabled fuzzy logic system is evaluated using COVID-19 pandemic cases for Nigeria, being part of the COVID-19 data for African countries obtained from Kaggle data repository. The hesitation-enabled fuzzy logic model is compared with the classical fuzzy logic system and artificial neural network and shown to offer improved performance in terms of root mean squared error, mean absolute error and mean absolute percentage error. Intuitionistic fuzzy logic system however incurs a setback in terms of the high computing time compared to the classical fuzzy logic system.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Covid 19 Health Prediction using Supervised Learning with Optimizationijtsrd
The assessment of infection is significant for Covid 19 as the antigen pack and RTPCR are imperfect and ought to be better for diagnosing such sickness. Continuous Return Transcription constant talk record polymerase chain . Medical services rehearse incorporate the assortment of different kinds of patient information to assist the doctor with diagnosing the patients wellbeing. This information could be basic side effects, first analysis by a specialist, or an inside and out research facility test. This information is in this manner utilized for examinations simply by a specialist, who thusly utilizes his specific clinical abilities to track down the illness. To group Covid 19 sickness datasets like gentle, center and serious infections, the proposed model uses the idea of controlled machine training and GWO advancement to manage in the event that the patient is impacted or not. Effectiveness investigation is determined and thought about of infection information for the two calculations. The consequences of the reenactments outline the compelling nature and intricacy of the informational index for the reviewing strategies. Contrasted with SVM, the proposed model gives 7.8 percent further developed forecast exactness. The forecast exactness is 8 better than the SVM. This outcome F1 score of 2 is better than an SVM conjecture. Akash Malvi | Nikesh Gupta "Covid-19 Health Prediction using Supervised Learning with Optimization" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-6 , December 2023, URL: https://www.ijtsrd.com/papers/ijtsrd61266.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/61266/covid19-health-prediction-using-supervised-learning-with-optimization/akash-malvi
Similar to Calculating the area of white spots on the lungs of patients with COVID-19 using the Sauvola thresholding method (20)
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
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Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Calculating the area of white spots on the lungs of patients with COVID-19 using the Sauvola thresholding method
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 1, February 2023, pp. 315~324
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i1.pp315-324 315
Journal homepage: http://ijece.iaescore.com
Calculating the area of white spots on the lungs of patients with
COVID-19 using the Sauvola thresholding method
Retno Supriyanti1
, Muhammad Rifqi Kurniawan1
, Yogi Ramadhani1
, Haris Budi Widodo2
1
Department of Electrical Engineering, Faculty of Engineering, Jenderal Soedirman University, Purwokerto, Indonesia
2
Department of Dentistry, Faculty of Medical, Jenderal Soedirman University, Purwokerto, Indonesia
Article Info ABSTRACT
Article history:
Received Jan 22, 2022
Revised Aug 23, 2022
Accepted Sep 23, 2022
Coronavirus desease 2019 (COVID-19) is a pandemic that has occurred in
the world since 2019. Researchers have carried out various ways in dealing
with this disease, starting from the screening stage to the stage of treatment
and therapy for COVID-19 patients. As the gateway to the COVID-19
problem, screening has an essential role in a diagnosis that leads to
appropriate treatment. In this paper, we will focus on the screening stage
using digital image processing techniques, namely in calculating the area of
white spots in the lungs of COVID-19 patients. The white patches are an
early indication of how badly COVID-19 is attacking the patient. We use X-
Ray Thorax image objects as research data in this paper. Although the
current experimental results show that this method has a successful
performance of 71.11%, it is pretty promising for further development due to
its simplicity.
Keywords:
COVID-19
Sauvola Thresholding
Screening
White spots
X-Ray image
This is an open access article under the CC BY-SA license.
Corresponding Author:
Retno Supriyanti
Department of Electrical Engineering, Faculty of Engineering, Jenderal Soedirman University
Blater Campus, Jl. Mayjend Sungkono KM 5, Blater, Purbalingga-53371, Indonesia
Email: retno_supriyanti@unsoed.ac.id
1. INTRODUCTION
At the end of December 2019, there was an outbreak of an unknown pneumonia disease with no
known cause in Wuhan, Hubei Province, China. A group of patients is admitted with an initial diagnosis of
pneumonia of unknown etiology; these patients are epidemiologically associated with seafood and wet
animals from a wholesale market in Wuhan, Hubei Province, China [1], [2]. In early January 2020, the virus
that causes this mysterious pneumonia was identified as a new type of coronavirus (nCov) named Severe
Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), while the name of the disease is called
coronavirus disease 2019 (COVID-19). COVID-19 has spread globally rapidly, so that in March 2020,
COVID-19 was officially declared a pandemic by the World Health Organization (WHO) [3]. The number of
COVID-19 cases in Indonesia as of November 2021 is the number of confirmed positive patients; 4,251,423
people, 4,099,399 recovered patients, and 143,685 dead patients [4].
The SARS-Cov 2 virus can be transmitted through physical contact and respiratory droplets. This
virus attacks the human respiratory system, especially the lungs. At the beginning of infection, the victim will
experience general symptoms such as fever, cough, fatigue, difficulty breathing, sputum production,
dyspnoea, hemoptysis, headache, diarrhea, and lymphopenia. These symptoms will appear after an
incubation period of about 5.2 days, depending on the condition of the immune system and the patient's age
[2]. In order to detect the SARS-Cov 2 virus in the body, there are two methods can be done, namely a rapid
test by taking a blood sample to check whether Immunoglobulin G (IgG) and Immunoglobulin M (IgM)
antibodies are formed in the body and a polymerase chain reaction (PCR) swab test by testing a sample of
2. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 1, February 2023: 315-324
316
mucus produced by the body taken from the nose and throat [5]. In addition to conducting a clinical
examination, diagnosis of lung disease can be made through a chest x-ray, the image of the Thorax X-ray will
be diagnosed to determine the patient's lung condition. On X-ray results, normal lungs will look like black
shadows. However, in patients infected with the coronavirus, white spots indicate the presence of fluid in the
lung cavity, known as ground glass opacity (GGO). GGO in patients with COVID-19 is located in the
periphery or posterior, especially in the lower lobe; GGO with inter/intra-lobular septal thickening or bilateral,
peripheral, and basal consolidation can also be found. This accumulation of fluid can cause the sufferer to have
difficulty breathing and even cause death [6].
Some research regarding GGO are as follows: Kang et al. [7] conducted preliminary research on
optimizing computed tomography parameters in detecting CGNs in lung cancer screening cases. Shao et al.
[8] performed feature extraction on PET images and CT scans to monitor the growth of invasive
adenocarcinoma (IAC) in early-stage lung cancer. Huang et al. [9] attempted to clarify the difference
between pure GGO nodules and prognosis by using patients who had pure GGO to participate in their
research. In addition, they also reviewed 404 lung cancer patients who had received cancer resection from
July 2014 to March 2015 to verify the conclusions of their research. Ichikawa et al. [10] investigated the
relationship between GGO visibility and signal-to-noise-based physical detection index in the low dose
computed tomography (LDCT) model by analyzing a set of images obtained from 12 types of multidetector
row computed tomography (MDCT). Hotta et al. [11] conducted a study by reviewing 34 adenocarcinoma
patients with multiple ground-glass nodules in the Southeast Asian population in order to obtain individual
characteristics. Xue et al. [12] conducted a study to determine the relationship between neutrophil-
lymphocyte ratio (NLR) and the growth of GGO in lung cancer. The method they used included all patients
with acute renal failure (ARF) in this study, monitored, and followed up on the patients based on the
variation in ARF growth recorded. The parameters used were age, sex, smoking history, histology, tumor
size, and stage of cancer present in the patient. Next, they calculated signal to noise using SPSS software.
Chen et al. [13] identified the GGO model's innovation by sequencing the size of the GGO to determine the
priority scale of surgical operations by doctors based on the results of CT scans. Li et al. [14] carried out
research to extract CT features associated with ground-glass nodule pathology so that they could provide an
accurate diagnosis. The method used was patients with ground glass nodules from March 2016 to October
2019 who had undergone surgery and then monitored their GGO progress based on the extracted CT features.
Cheng et al. [15] reviewed synchronous multiple primary lung cancer (SMPLC) cases in patients who had
undergone surgery and then underwent epidermal growth factor receptor tyrosine kinase inhibitors
(EFGR-TKI) within 12 months of surgery. Wang et al. [16] in their research, built an objective and accurate
prediction in assessing the pathology of GGO by extracting the parameter features of p53 expression.
Ye et al. [17] conducted research using deep learning methods in identifying GGO. Their research used
images from the lung image database consortium and image database resource initiative (LIDC-IDRI).
Qu et al. [18], Their research is motivated by the increasing number of cases due to GGO. So, they
researched to investigate and evaluate surgical resection procedures related to ARF cases in the hospital
where they worked. Firmino et al. [19] highlighted the importance of conducting a review of the use of
computer-aided diagnosis (CAD) in identifying lung cancer, particularly the identification of GGO.
Chillakuru et al. [20] developed a deep learning model to evaluate computer vision in identifying axial slices
of the lung for less surgical resection. Pizzi et al. [21] extracted radiomic features from CT scanned GGO
images using machine learning as an early diagnosis of acute lung disease. Toledo et al. [22] developed a
small optical depth sensor (ODS) instrument that collects the daily average aerosol optical depth (AOD) and
detects cloud characteristics both on Earth and on Mars from their observatory. Yi-Feng et al. [23] evaluated
lung biopsies' diagnostic performance and safety under CT fluoroscopy control by performing automated
biopsies on several patients. Wang et al. [24] analyzed high-resolution computed tomography (HRCT)
features of pure ground-glass nodules (GGN) to treat patients with adenocarcinoma. Peng et al. [25]
conducted research using the lung inflammation index to score the level of lung inflammation associated with
the severity of COVID-19.
Referring to all the research results above, we can be concluded that GGO can be a reference in
predicting the presence of abnormalities in the lungs. Various methods have been carried out in analyzing the
relationship between GGO and lung-related diseases. Previously, we had conducted research based on image
processing on several types of medical image modalities [26]–[29]. However, to the best of our knowledge,
there has been no research on the relationship between lung infection and COVID-19 based on X-ray image
segmentation of the lungs, especially the presence of GGO. In other hand, GGO in the lungs can be analyzed
using image processing digital, where currently digital image processing is growing rapidly and can be used
in the medical world to analyze X-ray images, so it can assist medical personnel in identifying an
abnormality or disease. Therefore, this paper develops a system for determining the degree of lung infection
due to COVID-19 using the Sauvola thresholding method. In addition, this system will calculate the area and
3. Int J Elec & Comp Eng ISSN: 2088-8708
Calculating the area of white spots on the lungs of patients with COVID-19 using … (Retno Supriyanti)
317
number of white spots found in the lungs of COVID-19 sufferers. The purpose of this research is to transfer
the knowledge of medical personnel who are experienced in detecting white patches X-rays of the lungs into
a system so that less experienced medical personnel can detect them more quickly and accurately.
2. PROPOSED METHOD
In this research, we focus our algorithm on pre-processing a lung X-ray image based on the tuned
tri-threshold fuzzy intensification operators method before segmenting; then, we will segment the lungs and
white patches on the X-ray images of the thorax using the Sauvola thresholding method. After successfully
segmenting the lungs, we calculated the lung area and spots white with pixel units using the Sauvola
thresholding method. Then we evaluate the performance of the Sauvola thresholding method in image
processing X-rays of normal lungs and X-ray images of the lungs of patients with COVID-19. The algorithm
proposed in this paper is described in Figure 1.
Figure 1. Proposed algorithm
3. METHOD
3.1. Data
In this experiment, we used secondary data in the form of X-ray images of the lungs which became
the standard database in research with lung image objects for normal patients. As for COVID-19 patients, we
also use X-ray images of the lungs, which is also a database of lung objects affected by COVID-19. There are
two kinds of data type and divided into two groups of data are used: X-ray image of the lungs of a patient
with COVID-19 obtained from the Italian Society of Medical and Interventional Radiology [30] and X-ray
image data of patients' lungs normal data obtained from the websites www.kaggle.com [31], and
radiopaedia.org [32]. The amount of data used in this study is as much as 95 data, of which 45 data are X-ray
image data of the patient's lungs COVID-19 and 50 other data are X-ray image data of normal patients.
Table 1 shows an example of the research data we used in this experiment. In this research, we only use
95 X-ray images of the lungs because this number is sufficient to represent the object's condition in actual
conditions. We hope that with the pilot data of 95, this data will be used as a template when implementing
this system on actual data in relevant conditions.
3.2. Tuned tri-threshold fuzzy intensification operator
This method will modify the histogram value using fuzzy techniques to increase the sharpness of the
image. This method uses a simple fuzzy membership function that assigns the pixel value of a given channel
to a range between zero and one depending on the threshold value. This method will be applied to each color
channel of the image to be processed to obtain an image with smooth and precise color quality. This stage
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begins with cropping the input image. Cropping algorithm using the polygonal method crop, where the
process is to cut an image using a rectangular shape many to be mapped using points with coordinates X and
Y. The function of cropping in this experiment is to separate X-ray images of the lung organs from other
areas using the polygonal crop method. By using the polygonal crop method, the mapping will be carried out
using points with X and Y coordinates which will form a polygonal shape that can select parts of the lungs
from the X-ray image, as shown in Figure 2.
Table 1. Examples of input data
Normal Patient COVID-19 Patient
Figure 2. An example of the cropping process in this research
3.3. Sauvola thresholding
Segmentation is a process to obtain the area of the desired object in an image by separating the
object from its background. This separation process aims to facilitate the classification and area calculation
processes more precisely and accurately [33]. Thresholding is an image segmentation method in which the
process is based on differences in the image's gray level to separate the object and its background. An image
resulting from thresholding can be presented in the form of a histogram to determine the distribution of pixel
intensity values in an image-specific part of the image so that the histogram can be properly partitioned and
the threshold value can be determined [34]. Sauvola thresholding is the development of the Niblack
algorithm. Sauvola is a valuable local thresholding technique for images with non-uniform backgrounds,
especially for text recognition. This method will calculate multiple thresholds for each pixel using a unique
formula that considers the mean and standard deviation of the local environment [35]. In the Sauvola method,
the threshold value T (x, y) is calculated using (1):
𝑡(𝑥, 𝑦) = 𝑚(𝑥, 𝑦) [1 + 𝑘 (
𝑠(𝑥,𝑦)
𝑅
− 1)] (1)
with 𝑡(𝑥, 𝑦) is thresholding, 𝑘 is parameters that are positive in the range [0,2, 0,5], 𝑅 is maximum value of
standard deviation, and 𝑠(𝑥, 𝑦) is standard deviation.
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4. RESULTS AND DISCUSSION
Pre-processing is the initial process in digital image processing that aims to improve image quality
by removing noise, increasing contrast/brightness, sharpening object edge intensity, and removing blurry
effects. In this research, pre-processing is carried out because the input image contains noise, affecting the
experimental results. In this research, all input images will be resized to a size of 500×500 pixels. As
described above, in the pre-processing, we used the tuned tri-threshold fuzzy intensification operator method
to improve image quality. Figure 3 is an example histogram of an X-ray image of the lungs of a COVID-19
patient. This histogram serves to express the distribution of the pixel intensity of an image. Figure 3(a) is an
original image, Figure 3(b) is a histogram of the original image, while Figure 3(c) is an image histogram after
processing using a tuned tri-threshold fuzzy intensification operator. If we compare the histogram in
Figure 3(b) with Figure 3(c), it can be seen that there are differences in the distribution of the intensity of the
image pixels. In Figure 3(b), it can be seen that the distribution of pixel intensity is limited to a specific
value. In contrast, in Figure 3(c), it can be seen that the histogram with pixel intensities is evenly distributed
over the entire range. This case indicates that after processing using the tuned tri-threshold fuzzy
intensification operator method, an image with better quality will be obtained than the original image.
(a) (b) (c)
Figure 3. An example of the results of a tuned tri-threshold fuzzy intensification operator on an image of the
lungs of a patient with COVID-E (a) original image, (b) original histogram before processing, and
(b) histogram after processing 19
In this research, not all parts of the X-ray image of the lungs are used; only parts of the lungs are
used, so parts other than the lungs will be discarded. Therefore, cropping is done using the polygonal crop
method to determine precisely which part of the image contains the desired object area to be separated
between the required object area and other parts that are not needed. This case can help detect the desired
part, namely the lung part. As described above, the algorithm used to perform cropping uses the polygonal
crop method, an example shown in Figure 1.
The segmentation in this research uses the Sauvola thresholding method, which is a modified local
thresholding technique from the Niblack method [35]. The selection of the Sauvola thresholding method as a
segmentation method is very fast in computing the threshold for each n-pixel. In addition, this Sauvola
method can be used to segment images with non-uniform and blurred backgrounds. The Sauvola
Thresholding method identifies image pixels based on the contrast approach at the edges of the image to
minimize background variations. Table 2 is examples of the X-ray image segmentation results of the lungs of
patients with COVID-19. The image used in this segmentation process is the cropped image.
Post-processing is the final stage in image processing, where the system can recognize the processed
image. In this research, post-processing was carried out to mark the white spot objects in the X-ray image of
the lungs using the labeling method. In this research, the labeled object is the white spot object contained in
the image resulting from the white spot segmentation. After being segmented, the area of the lung object and
white spots is calculated, as well as counting the number of white spots. The system will determine the
coordinates, and the number of white spots detected for further search for parameters such as centroid, area,
perimeter, and rectangle coordinates. Then using these parameters, the system can label the red rectangle on
the white spot object and calculate the lung area and white spot. To calculate the percentage of white spots
using (2). Table 3 shows an example of labeling results on X-ray image segmentation of the lungs using the
Sauvola thresholding method.
𝑊ℎ𝑖𝑡𝑒 𝑆𝑝𝑜𝑡𝑠 𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝑎𝑔𝑒 =
𝑊ℎ𝑖𝑡𝑒 𝑆𝑝𝑜𝑡𝑠 𝐴𝑟𝑒𝑎
𝐿𝑢𝑛𝑔 𝐴𝑟𝑒𝑎
𝑥 100% (2)
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Table 2. Example results of lung segmentation and white spots on COVID-19 patients X-Ray images using
the Sauvola thresholding method
Image name Cropped image Image of lung segmentation Image of white spots segmentation
Image 1
Image 2
Image 3
Table 3. Example of labeling results on lung X-ray image segmentation
Image Labelling result Lung area
(pixel)
White spot area
(pixel)
Number of white
spots
White spots
Percentage (%)
Covid1 55361.3 25689.8 163 46.4039
Covid2 78108.1 41204.9 124 52.7537
Covid3 39584.8 20753.5 83 52.428
Normal1 47548.5 22500.4 32 47.3209
Normal2 58381.5 26340 52 45.117
Normal3 48185.4 21511.1 37 44.6424
Referring to Table 3, in some data samples obtained test results that are not under the initial
hypothesis, where should the X-ray image data of patients with COVID-19 obtained a smaller lung area, a
larger white spot area, and the number of white spots are more when compared to the test results on normal
patient lung X-ray images, and vice versa. In addition, several anomalies of test results from data samples,
both lung X-ray image data of patients with COVID-19 and normal patients, can be seen in Table 4.
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Table 4. Examples of the results on the X-ray image of the lungs
Image name Image Lung area
(pixel)
White spot area
(pixel)
Number of
white spots
White spots
Percentage (%)
Test result
Covida 68645.4 34584.4 40 50,3812 Failed
Covidb 36918.5 18110.9 55 49,0564 Failed
Covidc 94542.6 43854.3 170 46,3858 Failed
Normala 61121.6 35870.4 114 58,6869 Failed
Normalb 81084.1 44363.1 195 54,7125 Failed
Normalc 75849.1 39885.9 183 52,5859 Failed
The inaccuracy of the test results with this initial hypothesis can occur due to various things, one of
which is image quality. Image quality consists of several parameters, including brightness, contrast,
sharpness, and the image's resolution is not good. In this experiment, we use pre-processing to improve
image quality, but there is no improvement in quality in some data after pre-processing. This case can happen
because the quality parameters of the image are inferior, so the quality cannot be improved using the
pre-processing method contained in the system. In addition to the parameters mentioned above, another thing
that can cause the poor quality of an X-ray image is the poor quality of the machine and the paper film used
during the X-ray process, so that the final result is an image with poor quality.
Based on the sample test results in Table 4, several test results were not following the initial
hypothesis. For example, in the X-ray image of the lungs of patients with COVID-19, the parameter values of
the test results in the form of area and number of white spots are the same as the test results for the normal
patient lung. Even if the image is seen visually, the image appears to have many spots white. On the other
hand, in some test results found in normal lungs, the parameter values of the test results in the form of area
and number of white spots are the same as the test results for the lung test data group of patients with
COVID-19, even though if the image is seen visually, the image appears to have clean lungs. For the system
to work optimally and obtain segmentation results following the initial hypothesis used as a reference for
system test results, it is necessary to have an input image with image quality parameters such as brightness,
contrast, sharpness, and resolution-the good one. In addition, because there is a process of resizing the image
to a size of 500×500 pixels, the input image should be at least the same size as the resize resolution of
500×500 pixels. The image does not experience information degradation. We compared segmentation using
the Souvola Thresholding method and conventional segmentation methods to measure the algorithm's
performance that we propose in this paper; the results are shown in Table 5.
According to Table 5, it can be seen that there is a significant difference in lung segmentation using
the conventional segmentation method compared to the Souvola Thresholding method. In the conventional
segmentation method, we cannot calculate the lung area as a whole, but we must calculate the right and left
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parts of the lung one by one. Calculating lung area separately for right and left gives results in the system not
being able to automatically calculate the area and number of white spots on the lungs. Even though these two
variables are pretty decisive in classifying the severity of COVID-19 later, meanwhile, by using the Souvula
Thresholding segmentation method, we can measure the lung area as a whole without having to separate the
left or right parts so that it will be easier to calculate the number and area of white spots in the lungs. Based
on the advantages of using the Souvola Thresholding method, we used this method in calculating the lung
area and the white spots contained therein. The implementation of the Souvola thresholding method in
calculating lung area, area, and the number of white spots is shown in Table 6.
Referring to Table 6, in the Sauvola thresholding method, the test success rate on chest X-ray
images of patients with COVID-19 is 71.11%, and the test success rate on chest X-ray images of normal
patients is 54%. From the results we got, it can be concluded that the Sauvola thresholding method obtained
an incomplete success rate in testing chest X-ray images of patients with COVID-19. This case can happen
because the Sauvola thresholding method has several disadvantages for images that have low contrast; some
objects from images that have low contrast will be lost, so that it will affect the accuracy of the segmentation
results. In addition, when calculating the threshold of n-pixels, the Sauvola method uses interpolation for
other pixels to speed up the computational process, thus reducing the accuracy in thresholding [34]. Apart
from the factor of the Sauvola thresholding method algorithm, other factors that affect the accuracy of the
test's success are that there are data anomalies in the input image used in the study, the poor quality of the
input image will cause test results that are not as expected.
Table 5. Souvola thresholding method vs conventional method
Conditions Lung Area Range
Sobel Prewitt Robert Canny Souvola
Right Normal patient 35533 to 66646 35533 to 66646 35192 to 66562 35184 to 66437 -
COVID-19 5607 to 59011 5607 to 59011 4452 to 61314 4896 to 65517 -
Left Normal patient 31066 to 57600 31066 to 57600 30693 to 68288 30695 to 58206 -
COVID-19 5868 to 53751 5868 to 53751 8485 to 52961 8457 to 52923 -
Whole lungs Normal patient - - - - 27303,1 to 81084,1
COVID-19 - - - - 36918,5 to 121943
Table 6. Sauvola Thresholding method performance
Succeed Failed Success Percentage
COVID-19 lung X-ray image 32 images 13 images 71%
Normal lung X-ray image 27 images 23 images 54%
5. CONCLUSION
In this research, pre-processing uses the tuned tri-threshold fuzzy intensification operator method,
which modifies the histogram value of an image using a fuzzy technique carried out before the segmentation
process can be used to improve the quality of the chest X-ray image so that the results are very influential in
detecting lung objects and white patches from a chest X-ray image. Meanwhile, in the segmentation process,
we use the Sauvola thresholding method, which is used to segment the lungs, and the white spots found on
the X-ray image of the lungs can produce good segmentation results. However, in calculating the percentage
of white spots in lung X-ray images using the Sauvola thresholding method, the average percentage of white
spots for patients with COVID-19 is 56.35837111%, with a test success rate of 71.11% and the percentage of
white spots for normal patients. of 50.941716% with a test success rate of 54%. This value is not maximized,
but after comparison was made on the average result value of the percentage of white spots in patients with
COVID-19 and normal patients, a significant difference was obtained, namely 5.41665511%, so it can be
concluded that the system is said to be successful in testing the two data samples that have been given. So, it
can be said that the results of white spot segmentation using the Sauvola Thresholding method can simplify
the process of analyzing the image of a thorax X-ray for COVID-19 sufferers, thus obtaining more accurate,
precise, and thorough image information compared to the results of analysis using the human sense of sight.
Although the results of the white spot segmentation test on thorax X-ray images of patients with COVID-19
using the Sauvola method are not optimal, the Sauvola method is sensitive to low-contrast images, the
interpolation method used in thresholding, and the input image quality is not good. So that the quality of the
input image significantly affects the experimental results. In addition, the accuracy and precision at the time
of cropping are very influential on image segmentation results. Anomalies of test results that occur in some
test data samples can be caused by the poor quality of the input image. However, overall, the results of this
research can be a means of transferring knowledge from medical personnel who are experienced in detecting
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white spots on lung X-rays into a system so that inexperienced medical personnel can detect them quickly,
precisely, and accurately.
ACKNOWLEDGEMENTS
We would like to thank the Ministry of Education, Culture, Research, and Technology, who
provided funding for this research through the “Fundamental Research” scheme.
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BIOGRAPHIES OF AUTHORS
Retno Supriyanti is a professor at Electrical Engineering Department, Jenderal
Soedirman University, Indonesia. She received her PhD in March 2010 from Nara Institute of
Science and Technology Japan. Also, she received her M.S degree and Bachelor degree in
2001 and 1998, respectively, from Electrical Engineering Department, Gadjah Mada
University Indonesia. Her research interests include image processing, computer vision,
pattern recognition, biomedical application, e-health, tele-health and telemedicine. She can be
contacted at email: retno_supriyanti@unsoed.ac.id.
Muhammad Rifqi Kurniawan received his Bachelor degree from Electrical
Engineering Department, Jenderal Soedirman University Indonesia. His research interest
Image Processing field. He can be contacted at email: rifqikurniawan76@gmail.com.
Yogi Ramadhani is an academic staff at Electrical Engineering Department,
Jenderal Soedirman University, Indonesia. He received his MS Gadjah Mada University
Indonesia, and his Bachelor degree from Jenderal Soedirman University Indonesia. His
research interest including computer network, decision support system, telemedicine and
medical imaging. He can be contacted at email: yogi.ramadhani@unsoed.ac.id.
Haris Budi Widodo is an academic staff at Public Health Department, Jenderal
Soedirman University, Indonesia. He received his Ph.D from Airlangga University Indonesia.
Also, He received his M.S degree and bachelor degree from Gadjah Mada University
Indonesia. His research interest including public health, e-health and telemedicine. He can be
contacted at email: haris.bwidodo@unsoed.ac.id.