ABSTRACT
The continuous increase in COVID-19 positive cases in the Philippines might further weaken the local healthcare system. As such, an efficient system must be implemented to further improve the immunization efforts of the country. In this paper, a contactless digital electronic device is proposed to assess the vaccine and booster brand compatibility. Using Logisim 2.7.1, the logic diagrams were designed and simulated with the help of truth tables and Boolean functions. Moreover, the finalized logic circuit design was converted into its equivalent complementary metal-oxide semiconductor (CMOS) and stick diagrams to help contextualize how the integrated circuits will be designed. Results have shown that the proposed device was able to accept three inputs of the top three COVID-19 vaccine brands (Sinovac, AstraZeneca, and Pfizer) and assess the compatibility of heterologous vaccinations. With the successful results of the circuit, it can be concluded that this low-power device can be used to manufacture a physical prototype for use in booster vaccination sites. © 2022 IEEE.
ABSTRACT
In this study, a type-2 fuzzy logic-based decision support system comprising clinical examination and blood test results that health professionals can use in addition to existing methods in the diagnosis of COVID-19 has been developed. The developed system consists of three fuzzy units. The first fuzzy unit produces COVID-19 positivity as a percentage according to the respiratory rate, loss of smell, and body temperature values, and the second fuzzy unit according to the C-reactive protein, lymphocyte, and D-dimer values obtained as a result of the blood tests. In the third fuzzy unit, the COVID-19 positivity risks according to the clinical examination and blood analysis results, which are the outputs of the first and second fuzzy units, are evaluated together and the result is obtained. As a result of the evaluation of the trials with 60 different scenarios by physicians, it has been revealed that the system can detect COVID-19 risk with 86.6% accuracy. © 2023 TÜBÍTAK.
ABSTRACT
Safety-critical infrastructures must operate in a safe and reliable way. Fault tree analysis is a widespread method used for risk assessment of these systems: fault trees (FTs) are required by, e.g., the Federal Aviation Administration and the Nuclear Regulatory Commission. In spite of their popularity, little work has been done on formulating structural queries about and analyzing these, e.g., when evaluating potential scenarios, and to give practitioners instruments to formulate queries on in an understandable yet powerful way. In this paper, we aim to fill this gap by extending [37], a logic that reasons about Boolean. To do so, we introduce a Probabilistic Fault tree Logic is a simple, yet expressive logic that supports easier formulation of complex scenarios and specification of FT properties that comprise probabilities. Alongside, we present, a domain specific language to further ease property specification. We showcase and by applying them to a COVID-19 related FT and to a FT for an oil/gas pipeline. Finally, we present theory and model checking algorithms based on binary decision diagrams (BDDs). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
This paper presents a supervised learning method for paranoid detection in French tweets. A classifier uses four groups of features (textual, linguistic, meta-data, timeline) that exploit a hybrid approach. This approach uses information obtained from the text of tweets by applying Natural Language Processing (NLP) techniques to analyse them, such as morphological analysis, syntactic analysis and sentence embedding. Thus, information about the user such as the number of followers and the number of shared posts. Besides, information about tweets such as the number of symbols and the number of hashtags. Moreover, information about the publication date of tweets such as the number of postings in the morning. Finally, statistical techniques to combine and filter the different types of features extracted from the previous steps in order to calculate the distance between the training corpus (the labelled data) and the test corpus (unlabelled data). In addition, the state mentioned statistical techniques are used for detecting the writing style of patients. In general, our method benefits from different types of features and preserves the principle of relativity by taking advantage of fuzzy logic. Our results are encouraging with an accuracy of 78% for the detection of paranoid people and 70% for the detection of the behaviour of these people towards Covid-19. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
ABSTRACT
Safety-critical infrastructures must operate in a safe and reliable way. Fault tree analysis is a widespread method used for risk assessment of these systems: fault trees (FTs) are required by, e.g., the Federal Aviation Administration and the Nuclear Regulatory Commission. In spite of their popularity, little work has been done on formulating structural queries about and analyzing these, e.g., when evaluating potential scenarios, and to give practitioners instruments to formulate queries on in an understandable yet powerful way. In this paper, we aim to fill this gap by extending [37], a logic that reasons about Boolean. To do so, we introduce a Probabilistic Fault tree Logic is a simple, yet expressive logic that supports easier formulation of complex scenarios and specification of FT properties that comprise probabilities. Alongside, we present, a domain specific language to further ease property specification. We showcase and by applying them to a COVID-19 related FT and to a FT for an oil/gas pipeline. Finally, we present theory and model checking algorithms based on binary decision diagrams (BDDs). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Internet is almost a necessary facility and tool to solve daily life problems in every field life. Whether at the individual level or national and international level sale purchase of any kind of object has always been of much importance, especially after Corona Pandemic, when online business is at its peak. Because of the enhancement of online sales and purchases, various businessmen are looking for suitable internet websites for their businesses, and the selection of the most suitable internet websites is one of the multi-attribute decision-making (MADM) dilemmas. Thus, in this script, we take benefits of three various concepts that are Bonferroni mean (BM) operator which is a significant technique to catch the interrelatedness among any number of inputs, Dombi operations which are based on Dombi t-norm and t-conorm and the ability to create an aggregation procedure more flexible because of the parameter, bipolar complex fuzzy set (BCFS) which is an outstanding model for tackling two-dimensional information with negative aspect and interpret bipolar complex fuzzy (BCF) Dombi Bonferroni mean (BCFDBM), BCF weighted Dombi Bonferroni mean (BCFWDBM), BCF Dombi geometric Bonferroni mean (BCFDGBM), and BCF weighted Dombi geometric Bonferroni mean (BCFWDGBM) operators. After ward, in this script, for tackling MADM dilemmas in the setting of BCFS, we investigate a MADM procedure based on the investigated operators and solve a MADM dilemma (selection of a suitable internet website for businessmen). Further, to display the superiority and efficiency of our work, we compare our approach and operators with a few current approaches and operators. Author
ABSTRACT
The studies done on lung protective strategies in medical ventilators have shown that tidal volume of 6-ml/ kg predicted body weight protects the lungs of a patient during the invasive ventilation for acute respiratory distress syndrome (ARDS) patients in intensive care unit. Corona virus disease 2019 has increased the need for mechanical ventilation, which are operated manually, in changing the settings on the mechanical ventilators. In this study, fuzzy logic method is used to develop a computer-aided decision-making to improve on the accuracy of the reasoning done during the ventilator setting adjustment, by adding the fuzzy reasoning concept into the ARDS Berlin definition. The ARDS positive end-expiratory pressure (PEEP) values were used in building the fuzzy rules of the fuzzy algorithm. From the experimental results, the algorithm mimics the recommended ARDS PEEP values with respect to the values of fraction of inspired oxygen (FiO2);the algorithm as well increases the respiratory rate and tidal volume for potential of Hydrogen (pH) less than 7.2;maintains the respiratory rate and tidal volume for pH between 7.2 and 7.4;decreases the respiratory rate;and maintains the tidal volume for pH greater than 7.4. The developed fuzzy system can therefore be applied as a physician–ventilator interface to guide the clinician/physician during the ventilation, so as, to reduce the human errors and ensure lung protection. © 2023, The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association.
ABSTRACT
The scenario of online learning is a very urgent need in the world of future knowledge. Since the Corona Virus Disease-19 pandemic, the world economy has started to plummet and caused many adults to lose their jobs. The advantage is the flexibility and rapid development of the internet. In 2020, the number of unemployed increased significantly. This reason makes people strive to improve their ability to meet job requirements by taking online courses. Online courses are a way that people can choose to improve their skills anywhere and anytime. The sustainability of online course material that is offered to the course user and issued by the company will be discussed in this study. The novelty of this research is to obtain a decision support model based on fuzzy logic for determining online courses. The method used is decision-making based on UML and fuzzy logic for the final decision. The fuzzy inference model process begins by determining the decision parameters then using fuzzification with absolute input then refracted with fuzzy criteria, and ends with defuzzification with absolute output. There are two groups of parameters in this study, company profits which consist of 5 parameters and user benefits, which consist of 9 parameters. Once the model is verified and valid, the final decision is useful for users looking for online course and also useful for the decision unit of online course companies in determining the sustainability of online course materials. © 2022 IEEE.
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Recently, COVID-19 is spreading rapidly and fast detection of COVID-19 can save millions of lives. Further, the COVID-19 can be detected easily from computed tomography (CT) images using artificial intelligence methods. However, the performance of these application and methods are reduced due to noises presented in the CT images, which degrading the performance of overall systems. Therefore, this article is focused on implementation of an innovative method for quickly processing CT images of low quality, which enhances the contrast using fuzzy logic. This method makes use of tuned fuzzy intensification operators and is intended to speed up the processing time. Extensive experiments were carried out to test the processing capacity of the method that was proposed, and the results obtained demonstrated that it was capable of filtering a variety of images that had become degraded. © 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
Prioritizing candidate genes is essential for genome-based diagnostics of various hereditary disorders. Furthermore, it is a difficult task with particular and noisy information about genes, illnesses, and relationships. Although several computer methods for disease gene prioritization have been developed, their efficiency is limited by manually created traits, network architecture, or pre-established data fusion criteria. Hence, this research proposes a unique gene prioritization and disease prediction model. Initially, the gathered information is pre-processed by a data cleaning model. In the proposed gene prioritization phase, the pre-processed data are tokenized. Then a new knowledge-based ontology structure is constructed with the improved skewness-based semantic similarity function. The ensemble classifier is constructed along Recurrent Neural Network (RNN), optimized fuzzy logic, and also Deep Belief Network (DBN) to forecast the gene disorders in the prediction phase. The retrieved features from the feature extraction phase are used to train RNN;while the extracted knowledge bases are used to train the DBN, then the results are fed into the optimized fuzzy logic. The fuzzy logic is the primary indication;its fuzzification function is fine-tuned employing a methodology to improve illness prediction accuracy. A recommended new hybrid system, named as Cauchy's Mutated Corona Virus Optimization Algorithm (CMCOA), is the upgraded version of the CVOA, a typical coronavirus optimization technique. Finally, to evaluate the efficiency of the projected model, a comparison of the suggested and existent models is performed with respect to various measures. In particular, the proposed model has recorded the highest accuracy as 93 % at 60 % of training, which is 42.5 %, 36.1 %, 33.3 %, 41.1 %, 48.5 %, 48.5 %, 9 %, 8 %, 8 %, 8 %, 8 %, and 14.5 % improved over existing models like GCN, GCN [6], SVM, CNN, Bi-LSTM, LSTM, GRU, fuzzy, EC + GOA, EC + SSO, EC + CMBO, EC + SMA and EC + CCVOA, respectively. The precision of the suggested work with improved features &CMCOA is 15.5 %, and 14.42 % superior to the proposed work without existing features & CMCOA and proposed work with existing features & CMCOA approaches. © 2022 Elsevier Ltd
ABSTRACT
In this article, the prediction of COVID-19 based on a combination of fractal theory and interval type-3 fuzzy logic is put forward. The fractal dimension is utilized to estimate the time series geometrical complexity level, which in this case is applied to the COVID-19 problem. The main aim of utilizing interval type-3 fuzzy logic is for handling uncertainty in the decision-making occurring in forecasting. The hybrid approach is formed by an interval type-3 fuzzy model structured by fuzzy if then rules that utilize as inputs the linear and non-linear values of the dimension, and the forecasts of COVID-19 cases are the outputs. The contribution is the new scheme based on the fractal dimension and interval type-3 fuzzy logic, which has not been proposed before, aimed at achieving an accurate forecasting of complex time series, in particular for the COVID-19 case. Publicly available data sets are utilized to construct the interval type-3 fuzzy system for a time series. The hybrid approach can be a helpful tool for decision maker in fighting the pandemic, as they could use the forecasts to decide immediate actions. The proposed method has been compared with previous works to show that interval type-3 fuzzy systems outperform previous methods in prediction. © 2022, The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association.
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This paper proposes augmenting the Multi-Criteria Decision-Making (MCDM) hybrid methodology of AHP-TOPSIS with dynamic-case handling (DCH) calculations and fuzzy logic. This method is evaluated with an illustrative example of three interrelated scenarios that rank 20 countries based on regional safety assessment related to the COVID-19 pandemic. The proposed method is compared to related work in the field. Additionally, sensitivity analysis is performed to evaluate the robustness of the proposed methodology. Empirical results demonstrate that the AHP-TOPSIS method coupled with fuzzy logic and DCH calculations is a realistic decision-making approach. © 2022 IEEE.
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The effectiveness of passenger attraction policies designed to induce a modal shift from private mode of transport to public mode of transport is tested frequently as a solution to the constantly dropping ridership rates in public transportation (PT). However, with COVID-19 pandemic in the picture, will the policies that were previously tested effective stand the test of time? Using the work-trip data collected from employees working in Thiruvananthapuram City, this study compares the effectiveness of six passenger attraction policies, aimed at decreasing the travel time and travel cost parameters, in a pre-COVID-19 and a post-lockdown scenario. Fuzzy logic-based mode choice models are developed to perform policy sensitivity analysis. The policies such as improving PT coverage and supply, introducing parking prohibition on major streets, operating non-stop bus services, reducing return-trip fares, early bird pre-peak hour discounts, and providing monthly PT season tickets are tested. The results show that, compared to the pre-COVID-19 model, the effectiveness of two out of the six policies reduces for the post-lockdown model, and the two policies being related to the travel cost parameter. The six policies are found to induce a private to public modal shift ranging from 5.8 to 7.9% for the pre-COVID-19 scenario, while the post-lockdown model gives a shift ranging from 5.8 to 7.1%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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
The risk of Coronavirus disease (COVID-19) was reported to be higher in the indoor environment due to poor ventilation systems. A good and efficient ventilation system in enclosed spaces can help reduce the risk of infection. Thus, it is important to monitor the efficiency of the ventilation system. Therefore, this research aims to develop an indoor air quality (IAQ) monitoring and control system using the fuzzy logic controller (FLC). Three IAQ parameters were selected in this study (temperature, relative humidity (RH), and carbon dioxide (CO2) concentration). In addition, benchmark testing was done to test the efficiency of the IAQ monitoring and control system. The system's engine is a microcontroller, which collects data on IAQ parameters, and is equipped with an exhaust fan as the ventilation strategy. The device aids in monitoring IAQ parameters and is equipped with an exhaust fan as the ventilation strategy. The device, which aids in monitoring IAQ, was created using a machine learning technique, fuzzy logic controller. The performance of the proposed air quality monitoring and control system was also investigated and validated through several experiments. The system was tested by modifying each parameter individually while keeping the controlled parameters safe. In addition, the tests were changed to include the existence of a controller in the system to see how ventilation affects the measured metrics. The test revealed that without the controller, the parameters take a long time to return to their initial values, however with the controller, the readings return to their original values faster than normal. The system also demonstrated that by following the fuzzy rules set, it is capable of handling two parameters at the same time. © 2022 IEEE.
ABSTRACT
Corona Virus or Covid-19 Disease, a term which creates a mass affection to all the countries in both Human life as well as economy wise. This disease causes a huge destruction in many person's individual life and most of the people around the world died due to this cause. Several assessments and researches are going on to predict the disease in fine manner as well as identify the disease over earlier stages to save the life of people without any delay. However, the most prominent and acceptable way of predicting the Corona Virus is by using the Lung Computed Tomography (CT) images. The Lung based CT images provides a huge support to identify the Covid virus on earlier stages, in which the people are advised to take such type of scanning while infected with corona virus. An earlier stage identification of Corona Virus is the basic need now-a-days, in which the disease is identified initially, means it can easily be cured. The identification of Covidvirus over lung CT images is of course a complex task because the CT images contains low-intensity pixels and the contrast level variations are different on various images. So it is complex to manipulate such images in practical, due to this a novel Digital Image Processing scheme is required to provide an efficient support to the respective physician to identify the Corona Virus on earlier stages in clear manner. The concept of machine learning is adopted over this paper to provide a proper predictions as well as the logic of dual classification algorithms are combined together to form a new machine learning strategy to attain high accuracy with enhanced prediction probabilities. The logic of Deep Neural Network (DNN) is modulated with respect to the logic of Random Forest (RF) Classification algorithm to make a new methodology called Hybrid Learning based Disease Prediction Scheme (HLDPS). In which this proposed approach associates the benefits of both DNN and RF into this prediction strategy to make an appropriate predictions over lung CT images and report the level of severity based on the cell vector distance. The resulting section of this paper provides proper experimental proof of the mentioned things in clear manner with graphical representations. For all the proposed approach of HLDPS is sufficient to predict the Corona Virus on earlier stages based on lung CT images in fine manner and the associated proofs are specified clearly on resulting section of this paper. © 2022 IEEE.
ABSTRACT
Serious game has been a very potential tool for learning, especially for online learning in the Covid 19 pandemic era. Using interactivity and experience as the main advantage, serious games offer a fun way to learn that can enhance the understanding of the learning material. However, the design of the serious game is difficult due to the different nature between learning and gaming. Game activity is one of the game elements that is difficult to develop. Appreciative serious game is the type of serious game that is using Appreciative Learning concept to design the activity. Appreciative serious game has four main stages, namely Discovery, Dream, Design and Destiny. Design stage is one of the most difficult stages, because it has the most activities compared to other stage, thus it should maintain the balance between boredom and frustration. Item often only viewed as a gimmick, even though it has potential to support the balance. This research is using fuzzy logic to produce adaptive item behavior, called fuzzy adaptive items. The fuzzy adaptive item takes life point and the number of errors, which resulted in the dynamic frequency of appearing relevant items. The results shows that items appear dynamically according to player performance. © 2022 IEEE.
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The European Erasmus+ project ARC – Automated Reasoning in the Class aims at improving the academic education in disciplines related to Computational Logic by using Automated Reasoning tools. We present the technical aspects of the tools as well as our education experiments, which took place mostly in virtual lectures due to the COVID pandemics. Our education goals are: to support the virtual interaction between teacher and students in the absence of the blackboard, to explain the basic Computational Logic algorithms, to study their implementation in certain programming environments, to reveal the main relationships between logic and programming, and to develop the proof skills of the students. For the introductory lectures we use some programs in C and in Mathematica in order to illustrate normal forms, resolution, and DPLL (Davis-Putnam-Logemann-Loveland) with its Chaff version, as well as an implementation of sequent calculus in the Theorema system. Furthermore we developed special tools for SAT (propositional satisfiability), some based on the original methods from the partners, including complex tools for SMT (Satisfiability Modulo Theories) that allow the illustration of various solving approaches. An SMT related approach is natural-style proving in Elementary Analysis, for which we developed and interesting set of practical heuristics. For more advanced lectures on rewrite systems we use the Coq programming and proving environment, in order on one hand to demonstrate programming in functional style and on the other hand to prove properties of programs. Other advanced approaches used in some lectures are the deduction based synthesis of algorithms and the techniques for program transformation. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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In this paper we propose a fuzzy logic-based approach to analyze UK National Health Service (NHS) public administrative data related to pre-and post-pandemic claims filed by patients, analyzing the legal and ethical issues connected to the use of Artificial Intelligence systems, including our own, to take critical decisions having a significant impact on patients, such as employing computational intelligence to justify the management choices related to Intensive Care Unit (ICU) bed allocation. Differently from previous papers, in this work we follow an unsupervised approach and, specifically, we perform an analysis of UK hospitals by means of a computational intelligence algorithm integrating Fuzzy C-Means and swarm intelligence. The dataset that we analyse allows us to compare pre-and post-pandemic data, to analyze the ethical and legal challenges of the use of computational intelligence for critical decision-making in the health care field. © 2022 IEEE.