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.

How did consumers' self-protective behavior formed during the COVID-19 pandemic?

Introduction: This study explored the formation mechanism of consumers' self-protective behavior during the COVID-19 pandemic, which is very important for policy settings to regulate consumer behavior. Based on the basic framework of the Protective Action Decision Model (PADM), this study analyzed the formation mechanism of consumers' self-protective willingness from the perspective of risk information, and explained the deviation between consumers' self-protective willingness and behavior from the perspective of protective behavior attributes. Methods: Based on 1,265 consumer survey data during the COVID-19 pandemic, the empirical test was carried out. Results and Discussion: The amount of risk information has a significant positive impact on the consumers' self-protective willingness, where the credibility of risk information plays a positive moderating role between them. Risk perception plays a positive mediating role between the amount of risk information and the consumers' self-protective willingness, and the positive mediating effect of risk perception is negatively moderated by the credibility of risk information. In the protective behavior attributes, hazard-related attributes play a positive moderating role between the consumers' self-protective willingness and behavior, while resource-related attributes play the opposite role. Consumers pay more attention to hazard-related attributes than resource-related attributes, and they are willing to consume more resources to reduce risk.

Cost-effectiveness of future lockdown policies against the COVID-19 pandemic.

AIM: While the European Union (EU) has approved several COVID-19 vaccines, new variants of concern may be able to escape immunity. The purpose of this study is to project the cost-effectiveness of future lockdown policies in conjunction with a variant-adapted vaccine booster. The exemplary scenario foresees a 25% decline in the vaccine protection against severe disease. METHODS: A decision model was constructed using, for example, information on age-specific fatality rates, intensive care unit (ICU) costs and outcomes, and herd protection threshold. The costs and benefits of a future lockdown strategy were determined from a societal viewpoint under three future scenarios-a booster shot's efficacy of 0%, 50%, and 95%. RESULTS: The cost-effectiveness ratio of a lockdown policy in conjunction with a booster dose with 95% efficacy is €44,214 per life year gained. A lockdown is cost-effective when the probability of approving a booster dose with 95% efficacy is at least 48% (76% when considering uncertainty in input factors). CONCLUSION: In this exemplary scenario, a future lockdown policy appears to be cost-effective if the probability of approving a variant-adapted vaccine booster with an efficacy of 95% is at least 48%.

Not Only for Contact Tracing: Use of Belgium's Contact Tracing App among Young Adults

Many countries developed and deployed contact tracing apps to reduce the spread of the COVID-19 coronavirus. Prior research explored people's intent to install these apps, which is necessary to ensure effectiveness. However, adopting contact tracing apps is not enough on its own, and much less is known about how people actually use these apps. Exploring app use can help us identify additional failures or risk points in the app life cycle. In this study, we conducted 13 semi-structured interviews with young adult users of Belgium's contact-tracing app, Coronalert. The interviews were conducted approximately a year after the onset of the COVID-19 pandemic. Our findings offer potential design directions for addressing issues identified in prior work - such as methods for maintaining long-term use and better integrating with the local health systems - and offer insight into existing design tensions such as the trade-off between maintaining users' privacy (by minimizing the personal data collected) and users' desire to have more information about an exposure incident. We distill from our results and the results of prior work a framework of people's decision points in contact-tracing app use that can serve to motivate careful design of future contact tracing technology. © 2023 Owner/Author.

Emergency Decision Model by Combining Preference Relations with Trapezoidal Pythagorean Fuzzy Probabilistic Linguistic Priority Weighted Averaging PROMETHEE Approach

The outbreak of COVID-19 in 2019 has brought greater international attention to emergency decision making and management. Since emergency situations are often uncertain, prevention and control are crucial. For better prevent and control, according to the characteristics of emergency incidents, the paper proposes a new form of linguistic expression trapezoidal Pythagorean fuzzy probabilistic linguistic variables to express decision-making information. Next, the paper develops the operational rules, value index and ambiguity of trapezoidal Pythagorean fuzzy probabilistic linguistic variables. Then, the new trapezoidal Pythagorean fuzzy probabilistic linguistic priority weighted averaging PROMETHEE approach is introduced to aggregate the trapezoidal Pythagorean fuzzy probabilistic linguistic information combining with preference relation. Finally, an emergency decision making case of prevention of infectious diseases analysis illustrate the necessity and effectiveness of this method, the results of comparative and experimental analyses demonstrate that the constructed new trapezoidal Pythagorean fuzzy probabilistic linguistic priority weighted averaging PROMETHEE approach owns better performances in terms of effectiveness and reasonability. © 2022. International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Is Implementing a Biotech Ban Correct or Not? Analysis of Farmer Perceptions and Attitudes on the Philippine Supreme Court’s Ban on Biotech Crops

Several studies have explored the effects of restrictive policies in different case-use instances;however, studies focusing on restrictive agricultural policies and their effects on major stakeholders are scarce. While the Philippines has been increasing its support for biotech-related technologies in agriculture, such as the recent approval of Golden Rice and Bt (Bacillus thuringiensis) eggplant for cultivation, the years prior to 2020 have not been as lenient in the acceptance of biotech crops. This paper explored the perceptions and attitudes of biotech corn farmers on the Philippine Supreme Court’s ban on biotech crops in 2015 and discussed how this restrictive agricultural policy could affect rural Filipino communities. A bifurcation was observed regarding the farmers’ ban perception, with almost half indicating that implementing the ban was an incorrect decision. The effects of the decision-making stages and influential factors on farmers’ perceived correctness of the ban were modeled using ordinal logistic regression and Spearman correlation. It was observed that while farmers’ initial instinct is directly related to their ban perception, succeeding decision-making stages enforce the notion of a pragmatic point of view leading to innate resistance effects towards the ban. Furthermore, internal factors (such as income and satisfaction) and external family-related factors perturb their ban perception. This information can offer guidance on how future restrictive agricultural policies may be framed to avoid conflicting interests between policymakers and stakeholders. This also highlights the need to understand farmer perspectives and attitudes to gain critical information regarding technology adoption and development.

Predicted long-term impact of COVID-19 pandemic-related care delays on cancer mortality in Canada.

The COVID-19 pandemic has affected cancer care worldwide. This study aimed to estimate the long-term impacts of cancer care disruptions on cancer mortality in Canada using a microsimulation model. The model simulates cancer incidence and survival using cancer incidence, stage at diagnosis and survival data from the Canadian Cancer Registry. We modeled reported declines in cancer diagnoses and treatments recorded in provincial administrative datasets in March 2020 to June 2021. Based on the literature, we assumed that diagnostic and treatment delays lead to a 6% higher rate of cancer death per 4-week delay. After June 2021, we assessed scenarios where cancer treatment capacity returned to prepandemic levels, or to 10% higher or lower than prepandemic levels. Results are the median predictions of 10 stochastic simulations. The model predicts that cancer care disruptions during the COVID-19 pandemic could lead to 21 247 (2.0%) more cancer deaths in Canada in 2020 to 2030, assuming treatment capacity is recovered to 2019 prepandemic levels in 2021. This represents 355 172 life years lost expected due to pandemic-related diagnostic and treatment delays. The largest number of expected excess cancer deaths was predicted for breast, lung and colorectal cancers, and in the provinces of Ontario, Québec and British Columbia. Diagnostic and treatment capacity in 2021 onward highly influenced the number of cancer deaths over the next decade. Cancer care disruptions during the COVID-19 pandemic could lead to significant life loss; however, most of these could be mitigated by increasing diagnostic and treatment capacity in the short-term to address the service backlog.

Determinant Factors in Personal Decision-Making to Adopt COVID-19 Prevention Measures in Chile.

The pandemic has challenged countries to develop stringent measures to reduce infections and keep the population healthy. However, the greatest challenge is understanding the process of adopting self-care measures by individuals in different countries. In this research, we sought to understand the behavior of individuals who take self-protective action. We selected the risk homeostasis approach to identify relevant variables associated with the risk of contagion and the Protective Action Decision Model to understand protective decision-making in the pandemic. Subsequently, we conducted an exploratory survey to identify whether the same factors, as indicated in the literature, impact Chile's adoption of prevention measures. The variables gender, age, and trust in authority behave similarly to those found in the literature. However, socioeconomic level, education, and media do not impact the protection behaviors adopted to avoid contagion. Furthermore, the application of the Protective Action Decision Model is adequate to understand the protective measures in the case of a pandemic. Finally, women have a higher risk perception and adopt more protective measures, and in contrast, young people between 18 and 30 years of age are the least concerned about COVID-19 infection.

Revisiting level II sleep studies in the era of COVID-19: a theoretical economic decision model in patients with suspected obstructive sleep apnea.

BACKGROUND: The recent pandemic has made it more challenging to assess patients with suspected obstructive sleep apnea (OSA) with in laboratory polysomnography (PSG) due to concerns of patient and staff safety. The purpose of this study was to assess how Level II sleep studies (LII, full PSG in the home) might be utilized in diagnostic algorithms of suspected OSA using a theoretical decision model. METHODS: We examined four diagnostic algorithms for suspected OSA: an initial PSG approach, an initial LII approach, an initial Level III approach (LIII, limited channel home sleep study) followed by PSG if needed, and an initial LIII approach followed by LII if needed. Costs per patient assessed was calculated as a function of pretest OSA probability and a variety of other variables (e.g. costs of tests, failure rate of LIII/LII, sensitivity/specificity of LIII). The situation in British Columbia was used as a case study. RESULTS: The variation in cost per test was calculated for each algorithm as a function of the above variables. For British Columbia, initial LII was the least costly across a broad range of pretest OSA probabilities (< 0.80) while initial LIII followed by LII as needed was least costly at very high pretest probability (> 0.8). In patients with a pretest OSA probability of 0.5, costs per patient for initial PSG, initial LII, initial LIII followed by PSG, and initial LIII followed by LII were: $588, $417, $607, and $481 respectively. CONCLUSIONS: Using a theoretical decision model, we developed a preliminary cost framework to assess the potential role of LII studies in OSA assessment. Across a broad range of patient pretest probabilities, initial LII studies may provide substantial cost advantages. LII studies might be especially useful during pandemics as they combine the extensive physiologic information characteristic of PSG with the ability to avoid in-laboratory stays. More empiric studies need to be done to test these different algorithms. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41606-021-00063-5.

Do data from mechanical Turk subjects replicate accuracy, response time, and diffusion modeling results?

Online data collection is being used more and more, especially in the face of the COVID crisis. To examine the quality of such data, we chose to replicate lexical decision and item recognition paradigms from Ratcliff et al. (Cognitive Psychology, 60, 127-157, 2010) and numerosity discrimination paradigms from Ratcliff and McKoon (Psychological Review, 125, 183-217, 2018) with subjects recruited from Amazon Mechanical Turk (AMT). Along with these tasks, we collected data from either an IQ test or a math computation test. Subjects in the lexical decision and item recognition tasks were relatively well-behaved, with only a few giving a significant number of responses with response times (RTs) under 300 ms at chance accuracy, i.e., fast guesses, and a few with unstable RTs across a session. But in the numerosity discrimination tasks, almost half of the subjects gave a significant number of fast guesses and/or unstable RTs across the session. Diffusion model parameters were largely consistent with the earlier studies as were correlations across tasks and correlations with IQ and age. One surprising result was that eliminating fast outliers from subjects with highly variable RTs (those eliminated from the main analyses) produced diffusion model analyses that showed patterns of correlations similar to the subjects with stable performance. Methods for displaying data to examine stability, eliminating subjects, and implementing RT data collection on AMT including checks on timing are also discussed.