Applications of Machine Learning in Viral Disease Diagnosis

Viral diseases are common and natural in human it spreads from animals and other humans. It seeks to identify the proper, reliable, and effective disease detection as quickly as possible so that patients can receive the right care. It becomes vital for medical field searches to have assistance from other disciplines like statistics and computer science because this detection is frequently a challenging process. These fields must overcome the difficulty of learning novel, non-traditional methodologies. Because so many new techniques are being developed, a thorough overview must be given while avoiding some specifics. In order to do this, we suggest a thorough analysis of machine learning which is used for the diagnosis of viral diseases caused in humans as well as plans. Predictions are made which is not obvious at the first glance does machine learning will be more helpful in making decisions. The study focuses on the machine learning algorithms for diagnosis of viral diseases for early diagnosis and treatment of viral diseases with greater accuracy. The work helps the researchers and medical professionals for learning and to give treatment for determining the applications of different machine learning techniques run to evaluate the parameters. Through examination of various parameters new machine learning model is proposed understanding the applications of machine learning in viral disease diagnosis like imaging techniques, plant virus diagnosis and the solution for the problem, Covid 19 diagnosis. © 2023 Bharati Vidyapeeth, New Delhi.

AI in Tourism: Leveraging Machine Learning in Predicting Tourist Arrivals in Philippines using Artificial Neural Network

Tourism is one of the most prominent and rapidly expanding sectors that contribute significantly to the growth of a country's economy. However, the tourism industry has been most adversely affected during the coronavirus pandemic. Thus, a reliable and accurate time series prediction of tourist arrivals is necessary in making decisions and strategies to develop the competitiveness and economic growth of the tourism industry. In this sense, this research aims to examine the predictive capability of artificial neural networks model, a popular machine learning technique, using the actual tourism statistics of the Philippines from 2008-2022. The model was trained using three distinct data compositions and was evaluated utilizing different time series evaluation metrics, to identify the factors affecting the model performance and determine its accuracy in predicting arrivals. The findings revealed that the ANN model is reliable in predicting tourist arrivals, with an R-squared value and MAPE of 0.926 and 13.9%, respectively. Furthermore, it was determined that adding training sets that contain the unexpected phenomenon, like COVID-19 pandemic, increased the prediction model's accuracy and learning process. As the technique proves it prediction accuracy, it would be a useful tool for the government, tourism stakeholders, and investors among others, to enhance strategic and investment decisions © 2023, International Journal of Advanced Computer Science and Applications.All Rights Reserved.

LIME Explainability on Flower Classification

Artificial Intelligence is becoming more advanced with increasing complexity in generating the predictions and as a result it is becoming more challenging for the users to understand and retrace how the algorithm is predicting the outcomes. Artificial intelligence has also been contributing in making decisions. There are many flowers in the world so the botanist scientists need help in identifying or recognizing which type of flower. The paper presents an x-ray diagnostic model and the explained with Local interpretable model-agnostic explanations LIME method. The model is trained with various COVID as well as non-COVID images. Whereas chest X-rays are segmented to extract the lungs and the model predictions are tested with perturbated images that are generated using LIME. This paper opens a wide area of research in the field of XAI. © 2022 IEEE.

IoT-Based Telemedicine Health Monitoring System with a Fuzzy Inference-Based Medical Decision Support Module for Clinical Risk Evaluation

This paper discusses the design and implementation of an Internet of Things (IoT)-based telemedicine health monitoring system (THMS) with an early warning scoring (EWS) function that reads, assesses, and logs physiological parameters of a patient such as body temperature, oxygen saturation level, systemic arterial pressure, breathing patterns, pulse (heart) rate, supplemental oxygen dependency, consciousness, and pain level using Particle Photon microcontrollers interfaced with biosensors and switches. The Mandami fuzzy inference-based medical decision support system (FI-MDSS) was also developed using MATLAB to assist medical professionals in evaluating a patient's health risk and deciding on the appropriate clinical intervention. The patient's physiological measurements, EWS, and health risk category are stored on the Particle cloud and Thing Speak cloud platforms and can be accessed remotely and in real-time via the Internet. Furthermore, a RESTful application programming interface (API) was developed using GO language and PostgreSQL database to enhance data presentation and accessibility. Based on the paired samples t-tests obtained from 6 sessions with 10 trials for each vital sign per session, there were no significant differences between the clinical data obtained from the designed prototype and the commercially sold medical equipment. The mean differences between the compared samples for each physiological data were not more than 0.40, the standard deviations were less than 2.3, and the p-values were greater than 0.05. With a 96.67% accuracy, the FI-MDSS predicted health risk levels that were comparable to conventional EWS techniques such as the Modified National Early Warning Score (m-NEWS) and NEWS2, which are used in the clinical decision-making process for managing patients with COVID-19 and other infectious illnesses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Performance Analysis of GAN Approaches in the Portable Chest X-Ray Synthetic Image Generation for COVID-19 Screening

COVID-19 mainly affects lung tissues, aspect that makes chest X-ray imaging useful to visualize this damage. In the context of the global pandemic, portable devices are advantageous for the daily practice. Furthermore, Computer-aided Diagnosis systems developed with Deep Learning algorithms can support the clinicians while making decisions. However, data scarcity is an issue that hinders this process. Thus, in this work, we propose the performance analysis of 3 different state-of-the-art Generative Adversarial Networks (GAN) approaches that are used for synthetic image generation to improve the task of automatic COVID-19 screening using chest X-ray images provided by portable devices. Particularly, the results demonstrate a significant improvement in terms of accuracy, that raises 5.28% using the images generated by the best image translation model. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COVID-19 pandemic and job interviews: Conducting an online job interview

This chapter uses sources from previous research that illustrates how various organizations have applied online interviews as part of the decision-making process. In addition, quotes from conversations with interviewers and interviewees are presented for better understanding their online experience. Several suggestions are made for improving the validity of the information obtained. As in face-to-face interviews, it is important to ask questions in various areas which are important to the organization or for examining his/her abilities in the job context. Although in the usual interview interaction, non-verbal messages are easily observed, in an online interview it is considerably more difficult. Interviewers here must try to see as much of the candidate as possible and should ask the interviewee to direct the camera from the shoulders up. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Strategic thinking and behavior during a pandemic

This paper introduces a novel theoretical model and measure of strategic thinking in social decision making. The model distinguishes four strategic orientations: egocentric (thinking about how one's actions shape one's outcomes), impact (thinking about how one's actions shapes others' outcomes), dependency (thinking about how others' actions shape one's outcomes), and altercentric (thinking about how others' actions shape their outcomes). Applying this model to explain social behavior in the context of the COVID-19 pandemic, an exploratory study finds that the more people think about how their actions shape others' outcomes, the more likely they are to: (a) comply with social distancing restrictions designed to curb the spread of the virus, and (b) donate money they received in the study to charitable organizations. These findings advance understanding of the multifaceted nature of strategic thinking and highlight the usefulness of the Strategic Thinking Scale for explaining social behavior. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Know better, do better: The role of exponential growth understanding in decision making during the COVID-19 pandemic

People mistakenly use the term "exponential growth" to depict a fast-growing process rather than a specific mathematical concept with implications for the spread of the COVID-19 pandemic. Policies promulgated by the authorities during this period were misunderstood and resulted, in many cases, with shocking results worldwide. Biases associated with lack of complete understanding of the speed that the virus was spreading had an impact on the decision-making process. In particular, policy makers had to determine the proper balance between life-saving guidelines and economic costs associated with containment measures. In the future, governments must learn to manage such situations by better appreciating the impact of exponential growth to respond properly when a pandemic may reoccur. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

An integrated interval type-2 fuzzy rough technique for emergency decision making

This paper aims to develop a hybrid emergency decision-making (EDM) method by combining best–worst method (BWM), multi-attributive border approximation area comparison (MABAC) and prospect theory (PT) in trapezoidal interval type-2 fuzzy rough (TrIT2FR) environment. In this hybrid method, the decision information is represented by trapezoidal interval type-2 fuzzy rough numbers (TrIT2FRNs). Firstly, this paper defines the TrIT2FRN and analyzes its desirable properties. Then, the TrIT2FR-BWM is developed to determine criteria weights. To develop the TrIT2FR-BWM, this paper completes the following three core issues: (i) propose an effective theorem to normalize the TrIT2FR weights;(ii) build a crisp programming model to transform the minmax objective of weight-determining model for the TrIT2FR-BWM;(iii) design a consistency ratio for the TrIT2FR-BWM to check the reliability of the determined criteria weights. Afterwards, this paper extends the classical MABAC into TrIT2FR environment to calculate the border approximation area (BAA). Subsequently, the PT is used to rank the alternatives, in which the calculated BAA is selected as the reference point. Lastly, the validity of the proposed method is certificated with a real site selection case of makeshift hospitals on COVID-19. Sensitivity analysis and comparative analyses are conducted to illustrate the robustness and superiorities of the proposed method. Some valuable results are summarized as follows: (i) the best alternative determined by the proposed method conforms with the actual selection result, (ii) the proposed models in the TrIT2FR-BWM have strong robustness, (iii) PT is helpful to improve the decision quality of EDM. © 2023 Elsevier B.V.

Towards a Strategic Decision Support Tool for Innovation Ecosystems Stakeholder Management

The COVID-19 pandemic has highlighted how the success of the appropriation of various healthcare innovations was due to agile and responsive innovation ecosystems. However, some progress was deterred in various country and regional contexts due to the delay in ascertaining the right stakeholders to work collaboratively with for health promotion. What has become evident is that stakeholder management plays a central role in the agility of an innovation ecosystem. This paper proposes a decision support tool that includes assessing the innovation capabilities and sustainability related activities of stakeholders. The tool is evaluated by two subject matter experts and applied to a case study of the key innovation ecosystem actors participating in the development of South Africa's first mRNA technology transfer hub. The strategic technology hub management tool is of a generic and agile nature to ensure that it can be applied to any project context for stakeholder management and decision making across various industries. The overall aim being that of informing an organization that has the role of being an ecosystem builder on other tools that they can quickly utilize to assess stakeholders. © 2022 IEEE.

The epistemic uncertainty of COVID-19: Failures and successes of heuristics in clinical decision-making

The brief article deals with the following questions: Was the adaptive toolbox of heuristics ecologically rational and specifically accurate in the initial stages of COVID-19, which was characterized by epistemic uncertainty? In other words, in dealing with COVID-19 did the environmental structural variables allow the success of a given heuristic strategy? (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Avoiding the Worst Decisions: A Simulation and Experiment

Many practical decisions are more realistic concerning preventing bad decisions than seeking better ones. However, there has been no behavioral decision theory research on avoiding the worst decisions. This study is the first behavioral decision research on decision strategies from the perspective of avoiding the worst decisions. We conducted a computer simulation with the Mersenne Twister method and a psychological experiment using the monitoring information acquisition method for two-stage decision strategies of all combinations for different decision strategies: lexicographic, lexicographic semi-order, elimination by aspect, conjunctive, disjunctive, weighted additive, equally weighted additive, additive difference, and a majority of confirming dimensions. The rate of choosing the least expected utility value among the alternatives was computed as the rate of choosing the worst alternative in each condition. The results suggest that attention-based decision rules such as disjunctive strategy lead to a worse decision, and that striving to make the best choice can conversely often lead to the worst outcome. From the simulation and the experiment, we concluded that simple decision strategies such as considering what is most important can lead to avoiding the worst decisions. The findings of this study provide practical implications for decision support in emergency situations.

Todim-Vikor Methods with Pythagorean Fuzzy Information Based Emergency Decision Support Model for Economic Growth Factor Selection

Because India's economy has shrunk to a low level during COVID-19, building an emergency decision support model (EDSM) for economic growth factors is the main objective of this study. We develop the TODIM-VIKOR method under Pythagorean fuzzy information. For dealing with comparison problems, the Pythagorean fuzzy scoring function is presented. We also include a new entropy metric for assessing the degree of fuzziness in PyFS. We also present a new Jensen Shannon divergence metric for PyFS that can be used to compare the discrimination information of two PyFSs. In this article, we introduced entropy and divergence measures to derive objective weight in the TODIM-VIKOR approach. Establishes a novel emergency decision making (EDM) strategy under the Pythagorean fuzzy atmosphere, using economic growth considerations. We used TODIM to determine the overall dominance degree, which takes into account the bounded rationality of decision makers, and VIKOR to calculate the compromise ranking of alternatives. © 2023 IEEE.

What can managers learn from professional poker players about decision-making?

This paper aims to investigate how past decision-making experiences can improve future decisionmaking. Nine semi-structured interviews were conducted with profitable professional Poker players. The results point out that it is the knowledge background of the decision-maker that makes him make sense of the situations he experiences. The research findings allowed the identification of three mechanisms that facilitate and make future decisions faster and more appropriate based on past experiences: (1) memory, (2) reflection, and (3) tools and analytical approach. The research contributes by showing evidence that, when supported by analytical tools, decision-makers can improve the quality and speed of the decision-making process. For organizations and supply chains, the paper highlights the importance of recognizing patterns based on the past to make sense of the future. For operations management, in events like COVID-19, companies can take advantage of memory to enact over unprecedented scenarios, prevent disruptions, and recover. © 2023 IGI Global. All rights reserved.

Decision Support System for Student Selection Recipients of COVID-19 Impacts using MOORA

SMPN 174 Jakarta is a junior high school in East Jakarta that has 719 students and 22 classrooms, including being one of the schools with government programs in the form of assistance such as Smart Jakarta Card, Smart Indonesia Program, and School Operational Assistance. To meet educational needs and distribute to students who are entitled to assistance. Currently, the selection process for student beneficiaries at SMPN 174 Jakarta is still done manually, so there is a risk of making decisions that are not on target and require a longer time. Therefore, a decision support system was made to select students who receive assistance. The development of the decision support system used in this research is the Multi-Objective Optimization Method on the Basic of Ratio (MOORA). The tests include black-box testing and comparison testing of calculation results with manual calculations. The Decision Support System for Selection of Student Recipients for the Impact of COVID-19 has been successfully created, and the results of selecting recipients of this assistance are obtained based on the calculation of the highest optimization value. The verifier has approved them with a percentage of 66.7% strongly agreeing and 33.3% agreeing. © 2022 IEEE.

What drives patients to choose a physician online? A study based on tree models and SHAP values

Smart healthcare is changing our lives. As an emerging medical pattern, online medical platform is arising from the combination of traditional medical resources and Internet platform, which largely resolve the disequilibrium of offline medical resources in China. Compared with offline healthcare, online platforms shorten the distance between patients and medical resources and give patients more options to seek medical treatment during the COVID-19 epidemic. In order to better help and guide patients in making decisions, the platform provides physicians' treatment information for patients' reference. This information describes the physician's diagnostic capability and service level from different dimensions, such as the physician's specialty, the number of gifts received from patients, etc., which are important basis for patients to choose a physician. For the platform and physicians, it is crucial to understand patients' preferences for different characteristics of physicians in the consultation process, in order to manage data more targeted. This paper use machine learning methods to build a prediction model of physician's characteristics data on incremental volume of consultation to study patients' preferences in medical consultation. Most existing studies use linear models, but given the complexity of patient preferences, they may have greater limitations in reflecting patients' choice logic. Therefore, this paper turn to more complex models on the training data. For the lack of interpretation of complex models, this paper uses a Shapley Value-based approach to parse the model's feature contributions to obtain patients' preferences for physician information. From the perspectives of local interpretation, global interpretation and interaction effect, this paper obtains regular conclusions on patients' preferences for physicians' information, and discusses the management insights in the context of online platform management and physicians' word-of-mouth maintenance. © 2022 IEEE.

Optimal Resource Allocation during Epidemics using Reinforcement Learning

Epidemics can prove to be disastrous, which has been further emphasized by the recent COVID-19 pandemic, and several countries like India lack sufficient resources to meet the population's needs. It is therefore important that the limited testing and protective resources are utilized such that the disease spread is minimized and their reach to the most vulnerable demographic is maximized. This paper studies the scope of intelligent agents in aiding authorities with such policy-making decisions. This is done by exploring the performance of various action selection methods on custom environments dealing with socio-economic groups and Indian states. Experiments using multi-armed bandit techniques provide greater insight into administrative decisions surrounding resource allocation and their future potential for greater use in similar scenarios. © 2022 IEEE.

Machine Learning-Based Predictors for ICU Admission of COVID-19 Patients

The burden on the health sector has increased when covid-19 was declared as a critical pandemic, making the decision-taking more crucial. This study aimed mainly to build predictors to aid in making decisions for severe patients to predict whether a patient has to be admitted to the intensive care unit (ICU) based only on the vital records. Statistical techniques were used on the electrical health records (EHR) that were accessible for the covid-19 patients. Samples were processed and then extracted based on criteria that support data imputation. Then, several feature selection techniques were utilized based on the field knowledge, Pearson correlation coefficient, and finally by taking the permutation importance of a hypothetical model to retain features that have the highest relationship with the target variable. Then two versions of data were obtained as stateless and grouped data with and without feature selection which were used to build models with various machine learning algorithms;logistic regression, linear support vector machine SVM, SVM with radial basis function RBF, and artificial neural network ANN. In this respect, the models reached an accuracy of more than 95% in most of the used classifiers and the best one scored is RBF-SVM with accuracy up to 98% and achieve 0.95 areas under curve (AUC) performance. These results indicate that trustworthy models were built to fulfill the high demand for accuracy that is more or less commensurate with the cost of accuracy in the health sector relying only on vital information. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Implications for the design of a Diagnostic-Decision Support System to reduce time and cost to diagnosis in paediatric shoulder instability

Keywords: Shoulder;Instability;Diagnostic Decision Support System Purpose: Diagnosis of shoulder instability in children, is difficult and recurrent rates of instability are high (70–90%). Time to a formal diagnosis is normally two years diagnostic delays can lead to poorer outcome and long-term complications e.g. shoulder arthritis (odds ratio 19.3). There is a need to improve diagnostic accuracy and prevent the development of long-term complications. Healthcare services are increasingly drawing upon technological solutions to improve diagnostic accuracy and efficiency, particularly within the context of the COVID-19 pandemic and subsequent ‘Rebuilding of the NHS’ strategy. A Diagnostic Decision Support System (DDSS) has the potential to reduce time to diagnosis and improve outcomes for patients. The aim of this study was to elicit physiotherapists clinical decision-making processes and develop a concept map for a future DDSS in shoulder instability. Methods: A qualitative study, using modified nominal focus group technique, involving three clinical vignettes, was used to elicit physiotherapists decision-making processes. Participants from across four separate clinical sites were recruited within their capacity as physiotherapists with a specialist interest in paediatric shoulder instability. All focus group sessions were audio recorded and transcribed verbatim. Thematic analysis was conducted according to the stages of Braune and Clarke. Results: Twenty-five physiotherapists, (18F:7M) from four separate clinical sites participated. The themes identified related to • Variability in diagnostic processes and lack of standardised practice 1. Differences in diagnoses and diagnostic processes 2. Differences in diagnoses and diagnostic processes 3. Diagnostic process occurs over a long period of time 4. Diagnostic test choices influenced by factors beyond objective markers associated with the patient injury 5. Planning for prognosis influenced by factors beyond assessment findings 6. Trust in staff relationships • General distrust in individuals or modes of medicine used outside of the department • Unity within the department • Knowledge and attitudes towards novel technologies for facilitating assessment and clinical decision making and 1. Lack of knowledge and rejection of 3D motion capture. Conclusion(s): No common structured approach towards assessment and diagnosis was identified. Decision-making processes were not explicit, therefore, limiting the ability to develop a DDSS around current practice. Several systematic biases were identified in the assessment of paediatric shoulder instability, most notably regarding gender. Lack of knowledge, perceived usefulness, access, and cost were identified as barriers to adoption of new technology. Impact: Based on the information elicited a conceptual design of a future DDSS has been developed. Implementation of a DDSS may act as a vehicle for establishing wider consensus in practice and alert clinical end users of potential bias in order to mitigate against it. These findings have wider implications for the training and education of physiotherapists regarding assessment and clinical decision-making. Use of more objective measures, derived from technology, and used alongside an appropriate DDSS may reduce bias and the negative effects on patient outcomes. Development of any subsequent DDSS and software will need to address the barriers identified which are likely to limit the use of novel technology in practice. There is a risk that even if additional information and technology was available to clinicians, they would not use it. Funding acknowledgements: This work was supported by the Keele University, Faculty of Natural Sciences Research Development Fund under Grant C3700-0958.