Development of a Web Application for Telecommuting Capability Assessment Embedded with Fuzzy Model

By early 2020, COVID-19 has caused a global pandemic which led to an enormous number of challenges worldwide in various sectors. The Philippine government has implemented multiple quarantine guidelines and travel restrictions to ensure the people's health and safety. However, the International Labour Organization projected an initial economic and labor market disruption affecting 11 million workers, or about 25% of the Philippine workforce, due to the pandemic. Therefore, the government, thru the concerned agencies continues to encourage employers to implement alternative work plans such as a work-from-home (WFH) operation in compliance with the established regulations in line with existing laws and policies. In line with the telecommuting concept, various research has already been performed, however, some were regarded inconclusive and require further study. Hence, in this study, a Web application was developed along with an embedded fuzzy model to evaluate the telecommuting capability assessment of employees. The proposed web application with embedded fuzzy model is capable of providing capability assessment using the four main input variables which are also relatively characterized for possible telecommuting cost assessment. © 2022 IEEE.

A quantitative framework for health‐care service quality assessment in India

PurposeThe present situation of COVID-19 pandemic has put the health-care systems under tremendous stress and stringent tests for their ability to offer expected quality of health-care services, as it decides the sustainability and growth of health-care service providers. This study aims to deliver a quantitative framework for service quality assessment in the health-care industry by classifying the health-care service quality parameters into four balanced scorecard (BSC) perspectives.Design/methodology/approachTo determine the service quality for the Indian health-care system, decision-making trial and evaluation laboratory and analytical network process are integrated in a fuzzy environment to contemplate the interaction among BSC perspectives and respective performance measures.FindingsThe results indicate "internal processes” perspective assumes the key role within BSC perspectives, while performance measures "nursing staff turnover” and "staff training” play the key roles. The results also signify that "patient satisfaction” is the most vital issue and can be strongly influenced by measures belonging to the "learning and growth” perspective. In "learning and growth” perspective, "staff training” is the most decisive criteria, very highly influencing "patient satisfaction”, highly influencing "profitability,” "change of cost per patient (both in and out patients)” and "outpatient waiting time” while moderately influencing "staff satisfaction,” "bed occupancy” and "nursing staff turnover”. Moreover, "staff training” criteria have a positive influence on "nursing staff turnover.”Originality/valueThe contributions of this study are in two folds in the domain of quantification of service quality for the health-care system. First, it delivers an assessment framework for Indian health-care service quality. Second, it demonstrates an application of the framework for a case situation and validates the proposed framework.

DIBR–DOMBI–FUZZY MAIRCA Model for Strategy Selection in the System of Defense

Strategic management has applications in many areas of social life. One of the basic steps in the process of strategic management is formulating a strategy by choosing the optimal strategy. Improving the process of selecting the optimal strategy with MCDM methods and theories that treat uncertainty well in this process, as well as the application of other and different selection criteria, is the basic idea and goal of this research. The improvement of the process of the aforementioned selection in the defense system was carried out by applying a hybrid model of multicriteria decision-making based on methods defining interrelationships between ranked criteria (DIBR) and multiattributive ideal-real comparative analysis (MAIRCA) modified by triangular fuzzy numbers–"DIBR–DOMBI–Fuzzy MAIRCA model.” The DIBR method was used to determine the weight coefficients of the criteria, while the selection of the optimal strategy, from the set of offered methods, was carried out by the MAIRCA method. This was done in a fuzzy environment with the aim of better treatment of imprecise information and better translation of quantitative data into qualitative data. In the research, an analysis of the model's sensitivity to changes in weight coefficients was performed. Additionally, a comparison of the obtained results with the results obtained using other multicriteria decision-making methods was conducted, which validated the model and confirmed stable results. In the end, it was concluded that the proposed MCDM methodology can be used for choosing a strategy in the defense system, that the results of the MCDM model are stable and valid, and that the process has been improved by making the choice easier for decision makers and by defining new and more comprehensive criteria for selection.

Digital Contact Tracing for COVID-19: Overview of Existing Systems and Framework for Future Innovation

Background: The COVID-19 pandemic catalyzed innovation in infection control measures, including widespread deployment of digital contact tracing systems. However, these technologies were not well understood by the general public and were complex for the public health community to implement, hampering adoption. Objective(s): To provide an overview of existing digital contact tracing systems, creating a framework for understanding design elements that impact their effectiveness as public health tools and offering a rubric for decision-makers to evaluate different systems for selection and implementation. Method(s): Scientific literature and publicly available information from relevant health authorities and other stakeholders was reviewed. Information was synthesized to develop a conceptual framework explaining how key design elements impact effectiveness of digital contact tracing systems and highlighting opportunities for future improvement. Result(s): A range of digital contact tracing interventions were deployed by governments worldwide and several professional sports leagues. Key design elements of the systems include: (1) data architecture (i.e., centralized versus decentralized systems, impacting privacy guarantees and data availability);(2) proximity detection technology (e.g., type of device signaling);(3) alert logic and timing (e.g., time- and distance-based criteria affecting sensitivity and specificity of alerts;real-time proximity alerts and/or bidirectional contact tracing, determining scope of infection prevention);(4) population (eligibility and availability);and (5) the structural and public health context of intervention (e.g., availability and timeliness of testing). Several systems demonstrated effectiveness in preventing transmission during COVID-19, though numerous limitations have also been documented in the literature. Conclusion(s): Digital contact tracing systems have the potential to mitigate the economic and public health impact of future infectious disease outbreaks, reducing community transmission and detecting potential cases earlier in the disease course. Lessons learned from solutions deployed during the COVID-19 pandemic provide an opportunity to improve multiple aspects of these systems, enhancing preparedness for future outbreaks.Copyright © 2023

Service-Dominant Logic and Service Management 4.0

Contemporary service environments characterized by advanced technologies augmenting customer-frontline interactions present significant changes in the working environment of service managers. The COVID-19 pandemic further transformed the way of doing business in the service industry. This chapter explicates how complex contemporary service environments can be better understood, when applying service-dominant (S-D) logic informed strategies and methodologies that promote value cocreation processes and the engagement of broad sets of actors. It points toward—what we coin Service Management 4.0—a possible future for service management that embraces human-centered technologies, smart (cyber-physical) service ecosystems, inclusive and nature-positive service. © The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022.

The meatpacking industry's corporate exceptionalism: racialized logics of food chain worker disposability during the COVID-19 crisis

Building on theories of biopower and necropolitics, we detail how the meatpacking industry expanded corporate exceptionalism amidst the U.S. COVID-19 pandemic. Our analysis argues that the industry utilized three strategies to assert exceptionalism and secure increased production and profitability despite significant risks for meatpacking workers. First, the industry constructed COVID-19 as an urgent threat to the nation's meat supply, casting themselves as a critical economic linchpin. Second, the industry aligned themselves with heroic portrayals of meatpacking workers, deflecting criticism of their handling of the crisis. Third, the industry promoted images of themselves as competent stewards, meriting unfettered autonomy to manage workers' health risks. Detailing these strategies sheds light on how corporate exceptionalism functions within late capitalist food systems to further racialized logics of worker disposability. [ FROM AUTHOR] Copyright of Food, Culture & Society is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

COVID-19 Vaccine and Booster Shots Compatibility Analyzer using CMOS-based Monochrome Encoding

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.

COMPARISON OF PERFORMANCES OF FUZZY LOGIC AND ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) FOR ESTIMATING EMPLOYEE LABOR LOSS

As a result of artificial intelligence research that started in the 1950s, the need for human beings in all sectors and labor markets constantly decreases. The increase in the total cost of the labor force increases the productivity pressure on the labor. For this reason, the workforce participating in production is expected to be more efficient and productive. For this reason, the loss of labor is carefully monitored and tried to be reduced as much as possible. However, with each passing day, labor losses are inevitable due to personnel turnover, work accidents, dismissals, and absenteeism. Humanity is still struggling, mainly due to the contagious covid-19 virus, which has recently affected the world. Since it is a condition that affects human health, its adverse effects have been observed in many areas where people are present. Especially in this period, unpredictable workforce losses have occurred in the production and service sectors since people are mostly the primary workforce. Since there is no plan and measure for such a situation in most risk planning, it also brings labor losses and costs. In this study, In order to examine the relationship between health problems and loss of labor, the amount of lost labor due to employees who could not come to work due to health-related reasons was tried to be estimated by Fuzzy Logic and ANFIS methods. This study examined three-year absenteeism data of employees in a courier company, and twenty-eight reasons for absenteeism were determined. The amount of labor loss was estimated using Fuzzy Logic and ANFIS methods, using five factors that cause absenteeism. Estimated and actual values were statistically compared with MAD MAPE, MSE, and RMSE performance measurement values. With fuzzy logic, the MAD value is 4.76;the MAPE value is 155.7;The MSE value was calculated as 52.7, and the RMSE value as 7.26. In ANFIS, the MAD value is 3.2, the MAPE value of 86.24, MSE of 27.5;The RMSE value was calculated as 5.25. When the results are compared, it has been seen that the ANFIS method obtains closer estimations than the fuzzy logic method.

Web server self-examination for detection of COVID-19 cases with fuzzy classifiers using chest X-ray image

The coronavirus has spread fast resulting in a worldwide pandemic. Early discovery of positive patients is critical in preventing the pandemic from spreading further, leading to the development of diagnostic technologies that provide rapid and reliable responses for COVID-19 detection. Previous research has shown that chest x-rays are an essential tool for the detection and diagnosis of sirivanoroC (COVID-19) patients. A radiological finding known as ground-glass opacity (GGO), which causes color and texture changes, was discovered in the lung of a person with COVID-19 as a consequence of x-ray tests. An automatic method to assist radiologists is required due to the carelessness of radiologists who work a long time and misdiagnosis resulting in the confusion of findings with different diseases, in this study, were described a new technique to help us with the early diagnosis of COVID-19 using x-rays that is based on fuzzy classification. The skewness, kurtosis, and average statistical features of x-rays of patients in two classes, COVID and Normal, are calculated in the suggested method, and the value ranges for both classes are identified. In the building of a fuzzy logic classifier, three statistical characteristics and value ranges are used as membership functions. The suggested solution, which uses a user-friendly interface, allows for quick and accurate COVID vs Normal (binary classification). Experiments show that our method has a lot of promise for radiologists to validate their initial screening and enhance early diagnosis, isolation, and therapy, which helps prevent infection and contain the pandemic. © 2023 Author(s).

Organic Electrochemical Transistors (OECTs): Advancements and Exciting Prospects for Future Biosensing Applications

Over the past few decades, the field of organic electronics has depicted proliferated growth, due to the advantageous characteristics of organic semiconductors, such as tunability through synthetic chemistry, simplicity in processing, cost-effectiveness, and low-voltage operation, to cite a few. Organic electrochemical transistors (OECTs) have recently emerged as a highly promising technology in the area of biosensing and flexible electronics. OECT-based biosensors are capable of sensing brain activities, tissues, monitoring cells, hormones, DNAs, and glucose. Sensitivity, selectivity, and detection limit are the key parameters adopted for measuring the performance of OECT-based biosensors. This article highlights the advancements and exciting prospects of OECTs for future biosensing applications, such as cell-based biosensing, chemical sensing, DNA/ribonucleic acid (RNA) sensing, glucose sensing, immune sensing, ion sensing, and pH sensing. OECT-based biosensors outperform other conventional biosensors because of their excellent biocompatibility, high transconductance, and mixed electronic-ionic conductivity. At present, OECTs are fabricated and characterized in millimeter and micrometer dimensions, and miniaturizing their dimensions to nanoscale is the key challenge for utilizing them in the field of nanobioelectronics, nanomedicine, and nanobiosensing.

A multimodal AI-based non-invasive COVID-19 grading framework powered by deep learning, manta ray, and fuzzy inference system from multimedia vital signs.

The COVID-19 pandemic has presented unprecedented challenges to healthcare systems worldwide. One of the key challenges in controlling and managing the pandemic is accurate and rapid diagnosis of COVID-19 cases. Traditional diagnostic methods such as RT-PCR tests are time-consuming and require specialized equipment and trained personnel. Computer-aided diagnosis systems and artificial intelligence (AI) have emerged as promising tools for developing cost-effective and accurate diagnostic approaches. Most studies in this area have focused on diagnosing COVID-19 based on a single modality, such as chest X-rays or cough sounds. However, relying on a single modality may not accurately detect the virus, especially in its early stages. In this research, we propose a non-invasive diagnostic framework consisting of four cascaded layers that work together to accurately detect COVID-19 in patients. The first layer of the framework performs basic diagnostics such as patient temperature, blood oxygen level, and breathing profile, providing initial insights into the patient's condition. The second layer analyzes the coughing profile, while the third layer evaluates chest imaging data such as X-ray and CT scans. Finally, the fourth layer utilizes a fuzzy logic inference system based on the previous three layers to generate a reliable and accurate diagnosis. To evaluate the effectiveness of the proposed framework, we used two datasets: the Cough Dataset and the COVID-19 Radiography Database. The experimental results demonstrate that the proposed framework is effective and trustworthy in terms of accuracy, precision, sensitivity, specificity, F1-score, and balanced accuracy. The audio-based classification achieved an accuracy of 96.55%, while the CXR-based classification achieved an accuracy of 98.55%. The proposed framework has the potential to significantly improve the accuracy and speed of COVID-19 diagnosis, allowing for more effective control and management of the pandemic. Furthermore, the framework's non-invasive nature makes it a more attractive option for patients, reducing the risk of infection and discomfort associated with traditional diagnostic methods.

Online Fuzzy Logic Prediction of Electrical Load Based on Real-Time Measurements During the Covid-19 Pandemic

As technologies advance and the population grows, electrical energy became one of the necessities for many peoples. Because the availability of electrical energy is limited, it requires various ways to be used efficiently. Electrical load monitoring usage in Indonesia still require an electrical officer to come to an electric panel location to record electrical usage. During the COVID-19 pandemic, it is not feasible to locally visit an electric panel because of the many restrictions. Remote monitoring using Internet of Things (IoT) can be used to address the problem. Going further, by knowing the electrical load usage, prediction can be done using fuzzy logic as a way to understand how to use electricity efficiently. Thus, a fuzzy logic load forecasting system IoT is developed in this research. Fuzzy variables used in this system are time of day, days of the week, measured loads, and forecasted loads. The research produced a system that predicts electrical load with one hour of accuracy based on the previous week's data. The average prediction error rate of the system is 9.48%. The implemented system is available on a web server and can be accessed via a web browser, either via a computer or cellphone. The system allows users to monitor and predict electrical load usage regardless of time and place. © 2023, The authors.

Making cultural and tourist attractions accessible and inclusive for people with disability through value co-creation amidst COVID-19: a critical discourse analysis

People with disabilities (PwD) are a COVID-19 vulnerable group, and globally they are experiencing even higher rates of social exclusion than before the pandemic. Value co-creation is a process whereby firms and their customers work together to develop service offerings and provides a tool for service improvement during disruptions such as health crises. Although many cultural and tourist attractions have access and inclusion as part of their strategic plans not all of them have turned to value co-creation to address access and inclusion in response to the COVID-19 pandemic. They also have varying degrees of understandings about what facilitates social inclusion. Using Critical Discourse Analysis, this study explores how museums have addressed access and inclusion in response to the COVID-19 pandemic, the degree of uptake, discourses of value co-creation, and how their responses can be categorised. The research design included semi-structured, participatory interviews with 15 managers from eight museums;and ethnographic observation and semi-structured, post-museum visit interviews with 12 PwD. Then, an iterative data analysis process using ATLAS-ti was undertaken. The results provide insight into the social inclusion of PwD in museums during the COVID-19 pandemic.

Zero-Based Budget in Restrictive Contexts: Work Methodology Applying Fuzzy Logic for Smes

Purpose: The objective of the research is proposed a methodology to prepare a Zero-Based Budget ( ZBB) for Small and Medium-sized Enterprises (SMEs) in Ecuador, applying fuzzy logic. Design/methodology/approach: A quantitative approach is assumed to show findings derived from the work carried out in these Ecuadorian business units, belonging to non-essential sectors such as wood, textiles and footwear. Fuzzy logic, the technique of expertise, and Trapezoidal Fuzzy Numbers ( TpFN) are used to capture true budget levels. Findings: The results recommend that optimal budget levels can be obtained for SMEs in restrictive and health emergency contexts. Originality/value: As a result of COVID-19 pandemic, markets and demand are contracting causing variations in income and demanding greater rationalization at the level of expenditures. For SMEs is essential prepared income and disbursements estimates. Based on the methodology proposed, predictions are made to achieve the objectives of SMEs. Directors will be able to make more successful decisions for the benefit of their companies, to streamline operations, direct the achievement of objectives, rationalize expenses (costs and expenses), and to project better scenarios in the future before carrying out cost-benefit analysis.

An innovative approach for predicting pandemic hotspots in complex wastewater networks using graph theory coupled with fuzzy logic.

Early prediction of COVID-19 infected communities (potential hotspots) is essential to limit the spread of virus. Diagnostic testing has limitations in big populations because it cannot deliver information at a fast enough rate to stop the spread in its early phases. Wastewater based epidemiology (WBE) experiments showed promising results for brisk detection of 'SARS CoV-2' RNA in urban wastewater. However, a systematic and targeted approach to track COVID-19 virus in the complex wastewater networks at a community level is lacking. This research combines graph network (GN) theory with fuzzy logic to determine the chances of a specific community being a COVID-19 hotspot in a wastewater network. To detect 'SARS-CoV-2' RNA, GN divides wastewater network into communities and fuzzy logic-based inference system is used to identify targeted communities. For the propose of tracking, 4000 sample cases from Minnesota (USA) were tested based on various contributing factors. With a probability score of greater than 0.8, 42% of cases were likely to be designated as COVID-19 hotspots based on multiple demographic characteristics. The research enhances the conventional WBE approach through two novel aspects, viz. (1) by integrating graph theory with fuzzy logic for quick prediction of potential hotspot along with its likelihood percentage in a wastewater network, and (2) incorporating the uncertainty associated with COVID-19 contributing factors using fuzzy membership functions. The targeted approach allows for rapid testing and implementation of vaccination campaigns in potential hotspots. Consequently, governmental bodies can be well prepared to check future pandemics and variant spreading in a more planned manner. Supplementary Information: The online version contains supplementary material available at 10.1007/s00477-023-02468-3.

Jaya Honey Badger optimization-based deep neuro-fuzzy network structure for detection of (SARS-CoV) Covid-19 disease by using respiratory sound signals

PurposeThe Covid-19 prediction process is more indispensable to handle the spread and death occurred rate because of Covid-19. However early and precise prediction of Covid-19 is more difficult because of different sizes and resolutions of input image. Thus these challenges and problems experienced by traditional Covid-19 detection methods are considered as major motivation to develop JHBO-based DNFN.Design/methodology/approachThe major contribution of this research is to design an effectual Covid-19 detection model using devised JHBO-based DNFN. Here, the audio signal is considered as input for detecting Covid-19. The Gaussian filter is applied to input signal for removing the noises and then feature extraction is performed. The substantial features, like spectral roll-off, spectral bandwidth, Mel-frequency cepstral coefficients (MFCC), spectral flatness, zero crossing rate, spectral centroid, mean square energy and spectral contract are extracted for further processing. Finally, DNFN is applied for detecting Covid-19 and the deep leaning model is trained by designed JHBO algorithm. Accordingly, the developed JHBO method is newly designed by incorporating Honey Badger optimization Algorithm (HBA) and Jaya algorithm.FindingsThe performance of proposed hybrid optimization-based deep learning algorithm is estimated by means of two performance metrics, namely testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219.Research limitations/implicationsThe JHBO-based DNFN approach is developed for Covid-19 detection. The developed approach can be extended by including other hybrid optimization algorithms as well as other features can be extracted for further improving the detection performance.Practical implicationsThe proposed Covid-19 detection method is useful in various applications, like medical and so on.Originality/valueDeveloped JHBO-enabled DNFN for Covid-19 detection: An effective Covid-19 detection technique is introduced based on hybrid optimization–driven deep learning model. The DNFN is used for detecting Covid-19, which classifies the feature vector as Covid-19 or non-Covid-19. Moreover, the DNFN is trained by devised JHBO approach, which is introduced by combining HBA and Jaya algorithm.

Construction of Hyperbolic Fuzzy Set and its Applications in Diverse COVID-19 Associated Problems

This paper's core objective is to introduce a novel notion called hyperbolic fuzzy set (HFS) where, the grades follow the stipulation that the product of optimistic and pessimistic degree must be less than or equal to one (1), rather than their sum not exceeding one (1) as in case of IFSs. The concept of HFS originates from a hyperbola, which provides extreme flexibility to the decision makers in the representation of vague and imprecise information. It is observed that IFSs, Pythagorean fuzzy sets (PFSs), and q-rung orthopair fuzzy sets (Q-ROFSs) often failed to express the uncertain information properly under some specific situations, while HFS tends to overcome such limitations by being applicable under those perplexed situations too. In this paper, we first define some basic operational laws and few desirable properties of HFSs. Second, we define a novel score function, accuracy function, and also establish some of their properties. Third, a novel similarity and distance measure is proposed for HFSs that are capable of distinguishing between different physical objects or alternatives based on the grounds of "similitude degree” and "farness coefficient”, respectively. Later, the advantages of all of these newly defined measures have been showcased by performing a meticulous comparative analysis. Finally, these measures have been successfully applied in various COVID-19 associated problems such as medical decision-making, antivirus face-mask selection, efficient sanitizer selections, and effective medicine selection for COVID-19. The final results obtained with our newly defined measures comply with several other existing methods that we considered and the decision strategy adopted is simple, logical, and efficient. The significant findings of this study are certain to aid the healthcare department and other frontline workers to take necessary measures to reduce the intensity of the coronavirus transmission, so that we can hopefully progress toward the end of this ruthless pandemic.

A Hybridized Machine Learning Approach for Predicting COVID-19 Using Adaptive Neuro-Fuzzy Inference System and Reptile Search Algorithm.

This research is aimed to escalate Adaptive Neuro-Fuzzy Inference System (ANFIS) functioning in order to ensure the veracity of existing time-series modeling. The COVID-19 pandemic has been a global threat for the past three years. Therefore, advanced forecasting of confirmed infection cases is extremely essential to alleviate the crisis brought out by COVID-19. An adaptive neuro-fuzzy inference system-reptile search algorithm (ANFIS-RSA) is developed to effectively anticipate COVID-19 cases. The proposed model integrates a machine-learning model (ANFIS) with a nature-inspired Reptile Search Algorithm (RSA). The RSA technique is used to modulate the parameters in order to improve the ANFIS modeling. Since the performance of the ANFIS model is dependent on optimizing parameters, the statistics of infected cases in China and India were employed through data obtained from WHO reports. To ensure the accuracy of our estimations, corresponding error indicators such as RMSE, RMSRE, MAE, and MAPE were evaluated using the coefficient of determination (R2). The recommended approach employed on the China dataset was compared with other upgraded ANFIS methods to identify the best error metrics, resulting in an R2 value of 0.9775. ANFIS-CEBAS and Flower Pollination Algorithm and Salp Swarm Algorithm (FPASSA-ANFIS) attained values of 0.9645 and 0.9763, respectively. Furthermore, the ANFIS-RSA technique was used on the India dataset to examine its efficiency and acquired the best R2 value (0.98). Consequently, the suggested technique was found to be more beneficial for high-precision forecasting of COVID-19 on time-series data.

Artificial Bee Colony for Logic Mining in Credit Scoring

During the SARS-CoV-2 (Covid-19) pandemic, credit applications skyrocketed unimaginably. Thus, creditors or financial entities were burdened with information overload to ensure they provided the proper credit to the right person. The existing methods employed by financial entities were prone to overfitting and did not provide any information regarding the behavior of the creditor. However, the outcome did not consider the attribute of the creditor that led to the default outcome. In this paper, a swarm intelligence-based algorithm named Artificial Bee Colony has been implemented to optimize the learning phase of the Hopfield Neural Network with 2 Satisfiability-based Reverse Analysis Methods. The proposed hybrid model will be used to extract logical information in the credit data with more than 80% accuracy compared to the existing method. The effectiveness of the proposed hybrid model was evaluated and showed superior results compared to other models.

Fuzzy logic controller development for prediction of student performance based on activity parameters

Owing to the COVID-19 pandemic, most of the academic education has suddenly shifted from traditional teaching methods to advanced technological methods on the internet. Many teachers encountered difficulties in successfully evaluating and monitoring their students. We address these challenges and propose a fuzzy logic-based controller that can assists teachers during classes and support allocation of appropriate resources to students. The purpose of the controller is to provide early warning about students who have performed poorly in the initial part of the course assessment. The controller makes predictions based on 5 input parameters which, by applying statistical tools, have been proven to accurately reflect the students' achievements. The model was tested on a group of 50 students and the results indicate 82% prediction accuracy. There is a possibility for additional improvements related to the built-in parameters, both in terms of their selection and in terms of their number.