ABSTRACT
The COVID-19 has placed pandemic modeling at the forefront of the whole world's public policymaking. Nonetheless, forecasting and modeling the COVID-19 medical waste with a detoxification center of the COVID-19 medical wastes remains a challenge. This work presents a Fuzzy Inference System to forecast the COVID-19 medical wastes. Then, people are divided into five categories are divided according to the symptoms of the disease into healthy people, suspicious, suspected of mild COVID-19, and suspicious of intense COVID-19. In this regard, a new fuzzy sustainable model for COVID-19 medical waste supply chain network for location and allocation decisions considering waste management is developed for the first time. The main purpose of this paper is to minimize supply chain costs, the environmental impact of medical waste, and to establish detoxification centers and control the social responsibility centers in the COVID-19 outbreak. To show the performance of the suggested model, sensitivity analysis is performed on important parameters. A real case study in Iran/Tehran is suggested to validate the proposed model. Classifying people into different groups, considering sustainability in COVID 19 medical waste supply chain network and examining new artificial intelligence methods based on TS and GOA algorithms are among the contributions of this paper. Results show that the decision-makers should use an FIS to forecast COVID-19 medical waste and employ a detoxification center of the COVID-19 medical wastes to reduce outbreaks of this pandemic. © 2023, Crown.
ABSTRACT
Diabetes has become a common and critical disease which generally occurs due to the presence of high sugar in blood for long time. A diabetic patient has to follow different rules and restrictions where he/she has to be under proper attention by measuring diabetes level frequently to avoid unexpected risk. The risk become more when patient even doesn't know that he/she is already having diabetes and doesn't follow those restrictions. To prevent this risk, everyone should check the diabetes status to be sure. With the same target different system using machine learning techniques have been introduced which can predict the diabetes status of a patient. But the challenging fact is that the performances and accuracy of those models are questionable where there may be a huge risk of patient's life. The conventional systems are not able to show that which level of diabetes a patient can have using the previous records. To solve this issue, through this paper an efficient system has been proposed with which the diabetes status can be predicted correctly. The proposed system can also show the complexity of diabetes as well as the Covid-19 risk percentage that can also be possible to measure. After comparing several machine learning techniques, the suitable model has been selected where high level of accuracy has been ensured in term of predicting the disease. © 2022 IEEE.
ABSTRACT
Purpose: Based on medical reports, it is hard to find levels of different hospitalized symptomatic COVID-19 patients according to their features in a short time. Besides, there are common and special features for COVID-19 patients at different levels based on physicians' knowledge that make diagnosis difficult. For this purpose, a hierarchical model is proposed in this paper based on experts' knowledge, fuzzy C-mean (FCM) clustering, and adaptive neuro-fuzzy inference system (ANFIS) classifier. Methods: Experts considered a special set of features for different groups of COVID-19 patients to find their treatment plans. Accordingly, the structure of the proposed hierarchical model is designed based on experts' knowledge. In the proposed model, we applied clustering methods to patients' data to determine some clusters. Then, we learn classifiers for each cluster in a hierarchical model. Regarding different common and special features of patients, FCM is considered for the clustering method. Besides, ANFIS had better performances than other classification methods. Therefore, FCM and ANFIS were considered to design the proposed hierarchical model. FCM finds the membership degree of each patient's data based on common and special features of different clusters to reinforce the ANFIS classifier. Next, ANFIS identifies the need of hospitalized symptomatic COVID-19 patients to ICU and to find whether or not they are in the end-stage (mortality target class). Two real datasets about COVID-19 patients are analyzed in this paper using the proposed model. One of these datasets had only clinical features and another dataset had both clinical and image features. Therefore, some appropriate features are extracted using some image processing and deep learning methods. Results: According to the results and statistical test, the proposed model has the best performance among other utilized classifiers. Its accuracies based on clinical features of the first and second datasets are 92% and 90% to find the ICU target class. Extracted features of image data increase the accuracy by 94%. Conclusion: The accuracy of this model is even better for detecting the mortality target class among different classifiers in this paper and the literature review. Besides, this model is compatible with utilized datasets about COVID-19 patients based on clinical data and both clinical and image data, as well. Highlights: • A new hierarchical model is proposed using ANFIS classifiers and FCM clustering method in this paper. Its structure is designed based on experts' knowledge and real medical process. FCM reinforces the ANFIS classification learning phase based on the features of COVID-19 patients. • Two real datasets about COVID-19 patients are studied in this paper. One of these datasets has both clinical and image data. Therefore, appropriate features are extracted based on its image data and considered with available meaningful clinical data. Different levels of hospitalized symptomatic COVID-19 patients are considered in this paper including the need of patients to ICU and whether or not they are in end-stage. • Well-known classification methods including case-based reasoning (CBR), decision tree, convolutional neural networks (CNN), K-nearest neighbors (KNN), learning vector quantization (LVQ), multi-layer perceptron (MLP), Naive Bayes (NB), radial basis function network (RBF), support vector machine (SVM), recurrent neural networks (RNN), fuzzy type-I inference system (FIS), and adaptive neuro-fuzzy inference system (ANFIS) are designed for these datasets and their results are analyzed for different random groups of the train and test data;• According to unbalanced utilized datasets, different performances of classifiers including accuracy, sensitivity, specificity, precision, F-score, and G-mean are compared to find the best classifier. ANFIS classifiers have the best results for both datasets. • To reduce the computational time, the effects of the Principal Component Analysis (PCA) feature reduction method are studied on th performances of the proposed model and classifiers. According to the results and statistical test, the proposed hierarchical model has the best performances among other utilized classifiers. Graphical : [Figure not available: see fulltext.] © 2023, The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering.
ABSTRACT
The COVID-19 pandemic is not over yet. The Coronavirus Disease 19 Pandemic due to the SARS-CoV-2 virus is spreading very quickly in almost every country in the world because of its human-to-human nature. The first COVID-19 case in Indonesia was detected in Depok, West Java, on March 2, 2020. To deal with this, the government must decide on an efficient policy by observing the atmosphere and situation in each region. In this research, we aim to determine the risk status of COVID-19 transmission in the East Java region using Tsukamoto Fuzzy Inference System. The data used are 38 district data groups consisting of four variables. The input variables are COVID-19 positive cases, suspect cases, and probable cases. The output variable is the risk status of COVID-19 transmission data. The results of this study, the Fuzzy Inference System Tsukamoto method, can be used to determine the risk status of COVID-19 transmission in all areas in East Java with an accuracy value of 95.51%. We implement the model of this research in Banten. The results of model calculations in Banten Province show that the model can be used to determine the zone status of each region in Banten with an accuracy rate above 97%. Therefore, the parameter values for each input and output variable in this study can be used in decision-making in areas that have the same zoning policy. © 2022 IEEE.
ABSTRACT
This article presents the development of a ventilator and its control algorithm. The main feature of the developed ventilator is compressed by a pneumatic drive. The control algorithm is based on the adaptive fuzzy inference system (ANFIS), which integrates the principles of fuzzy logic. The paper also presents a simulation model to test the designed control approach. The results of the experiment provide verification of the developed control system. The novelty of the article is, on the one hand, the implementation of the ANFIS controller, pressure control, with a description of the training process. On the other hand, in the article presented a draft ventilator with a detailed description of the hardware and control system. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
In this study, forecasting the number of immigrants on the Turkey's maritime line for use in a national security project carried out by Turkish Government within the scope of fight against uncontrolled immigration is discussed for the first time. Handling with the immigration problem is one of the biggest concerns of Turkey as unsupervised immigration can adversely affect the demographic and economic structure of the country. Precautions are needed as the short-, medium- and long-term impacts of undetected immigrants on the country's ecosystem are unpredictable, but due to the uncertainties inherent in immigration, the cost of using government resources such as patrol vehicles to capture undocumented immigrants can be extremely high. In order to both minimize the expenditure problem and keep immigration under control by providing a proper scan, forecasting the number of immigrants on the maritime line route is seen as an important problem and studied by probabilistic and non-probabilistic models. Since the data for 2020 and 2021 could not be attained yet due to COVID-19, in order to obtain forecasts and compare actual observations for 2019, which is the primarily focus of the research in this study, the dataset of interest on the number of daily immigrants between years 2016 and 2019 is obtained from Turkish Coast Guard Command within Ministry of Interior of Republic of Turkey. To obtain the most accurate forecasts, seven distinguished forecasting methods, from simple to complex, are implemented. Then, the forecast combination approach with meta-fuzzy functions which combines all methods is proposed. Consequently, the forecasting results are acquired and evaluated by using R. The evaluation of the results is made by using widely considered measurement accuracy metric root mean square error. According to the final assessments, the proposed approach gives more accurate forecasting results for the expected number of immigrants on the Turkey's maritime line and these results become an input to the national security project.
ABSTRACT
The COVID-19 influenza became a curse on the world. It has been around for two years, so no one needs to make a big introduction of it. It has became a significant challenge around the world. Owing to this, we made dynamic networks using an amalgamating of fuzzy logic and neural networks for the prediction of sufferers of COVID-19. These hybrid networks serve for the assessment of the COVID-19 victims and usefully serve for the assessment of the medical resources needed for future victims. This manuscript proposed Sugeno Adaptive Neuro-Fuzzy Inference System (SANFIS) prediction model for COVID-19 prediction in Andhra Pradesh, India. We gathered data on positive COVID-19 sufferers in Andhra Pradesh for this purpose. The data can be separated into three categories: training set, testing set and checking set. We have utilized Root Mean Square Deviation (RMSD) for prediction precision. If the prediction model has a lower RMSD value, it is regarded as the best forecast. In this study, we concluded that the 3 Triangular MFns for each input were excellent with the extreme precision for all of the districts based on our expertise. In the end, we deployed seven SANFIS replicas in Andhra Pradesh, but we discovered that SANFIS6 and SANFIS7 provided excellent COVID-19 prediction results. These findings will assist the government, healthcare agencies, and medical organizations in planning for future COVID-19 victims' medical requirements. These sorts of Sugeno Adaptive Neuro-Fuzzy Inference System (SANFIS) prediction models based on Artificial Intelligence (AI) will be beneficial in overcoming the COVID-19. © 2022 Slovak Academy of Sciences. All rights reserved.
ABSTRACT
The world is still currently facing a pandemic. In the Philippines, the number of cases is rapidly rising. Since there is yet a cure to be found, the best cure to such is prevention such as being aware of the adverse effects that it has on people along with the symptoms commonly felt by those who have the disease. Constant sanitation is also necessary to kill the bacteria causing the disease before it even has the chance to spread throughout the human body. In this research, a small scale AI program that could diagnose a person with the probability of having the disease was developed. Theprogram used patients' symptoms who have the disease, along with the corresponding severities of such, as input. Fuzzy logic was used in developing the program through the development and integration of a fuzzy inference system (FIS). Moreover, the testing accuracy of the proposed system was 70.83% which was based on the number of diagnoses that produced a medium or high verdict of a patient contracting the virus. The inputs for such diagnoses were the symptoms felt by confirmed COVID-19 patients along with their corresponding severities which were obtained from the data set acquired containing information regarding COVID-19 patients in the Philippines. Additionally, MATLAB was the software used to develop both the program and the FIS. © 2022 IEEE.
ABSTRACT
Triage is a fundamental process in hospitals and emergency care units, as it allows for the classification and prioritization of patient care based on the severity of their clinical conditions. In Brazil, the triage of suspected COVID-19 cases is performed using a specific protocol, which involves manual steps, requiring the completion of four different forms, by four health care professionals. Aiming to investigate the possibility of improving the triage processes in Brazil, this article proposes the use of computational techniques for decision-making based on fuzzy inference systems. We argue that fuzzy set theory is appropriate to the problem because it allows the use of natural language to express the patient's symptoms, making it easier for health care professionals. After modelling the problem in a fuzzy system we applied a pilot test. The model includes symptoms that health professionals currently use to analyse COVID-19 cases. The results suggest that the model presents convergence with the sample data, highlighting its potential application in supporting triage for the classification of the severity of COVID-19 cases. Among the benefits of the proposed model, we emphasize contributions as the reduction of the time and number of professionals required for triage as well as the reduction of exposure of health care professionals and other patients suspected of carrying the virus. In this context, this research provides an opportunity to obtain social contributions regarding the services in public hospitals improvement.
ABSTRACT
Fuzzy inference is a powerful tool used in many fields of science nowadays, including medical science. However, for applications where the number of fuzzy rules is very large, the increased computational complexity for systems with limited resources (such as low budget computers and embedded systems) can result in a very slow operation. In this paper, a new method is proposed to accelerate the operation of Fuzzy Inference Systems that is faster than the conventional sequential procedure, primarily for such computer systems. © 2022 IEEE.
ABSTRACT
At home fitness has rapidly risen recently due to the COVID-19 pandemic and stay-at-home-orders. This also produced a large set of first time users of gym equipment and structured exercise routines. Access to professional fitness trainers to assist beginners in proper exercise form has become increasingly difficult. According to the National Safety Council (NSC), approximately 468, 000 injuries occurred due to exercise in 2019 before the pandemic. Without proper guidance, this statistic is bound to increase. Therefore, there is a need for systems to monitor exercise performance for both short term and long term injury prevention. We present a novel mobile app called Verum Fitness which will use the camera from a smart phone to record a user performing an exercise. Then, the app will skeletonize the user, extract angles from specific joints, and feed this data into a Fuzzy Inference System (FIS), an inherently explainable model, to classify exercise performance. With the FIS, we can provide a description of each repetition performed to determine if it could cause injury and how to improve. From our synthetically generated data, we show a training and test Accuracy of 80.42% and 71.67%, respectively, as well as high Sensitivity and Specificity for the goblet squat. © 2021 IEEE.