Introduction

Satapathy, Sandeep Kumar; Dehuri, Satchidananda; Jagadev, Alok Kumar; Mishra, Shruti

doi:10.1016/b978-0-12-817426-5.00001-6

Cited by 17 publications

(8 citation statements)

References 35 publications

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“…KNN, LWL, LibSVM (c), and LibSVM (nu) had lower accuracy and Cohen’s κ values, whereas PNN was the least accurate ML tool and had the lowest Cohen’s κ value in our case. PNN is an implementation of a statistical procedure known as kernel discriminant analysis [ 133 ]. It has a number of drawbacks, including delayed network execution due to several layers and high memory needs, among others.…”

Section: Discussionmentioning

confidence: 99%

Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors

et al. 2022

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Background: The unprecedented global spread of coronavirus disease 2019 (COVID-19) has imposed huge challenges on the healthcare facilities, and impacted every aspect of life. This has led to the development of several vaccines against COVID-19 within one year. This study aimed to assess the attitudes and the side effects among Arab communities after receiving a COVID-19 vaccine and use of machine learning (ML) tools to predict post-vaccination side effects based on predisposing factors. Methods: An online-based multinational survey was carried out via social media platforms from June 14 to 31 August 2021, targeting individuals who received at least one dose of a COVID-19 vaccine from 22 Arab countries. Descriptive statistics, correlation, and chi-square tests were used to analyze the data. Moreover, extensive ML tools were utilized to predict 30 post vaccination adverse effects and their severity based on 15 predisposing factors. The importance of distinct predisposing factors in predicting particular side effects was determined using global feature importance employing gradient boost as AutoML. Results: A total of 10,064 participants from 19 Arab countries were included in this study. Around 56% were female and 59% were aged from 20 to 39 years old. A high rate of vaccine hesitancy (51%) was reported among participants. Almost 88% of the participants were vaccinated with one of three COVID-19 vaccines, including Pfizer-BioNTech (52.8%), AstraZeneca (20.7%), and Sinopharm (14.2%). About 72% of participants experienced post-vaccination side effects. This study reports statistically significant associations (p < 0.01) between various predisposing factors and post-vaccinations side effects. In terms of predicting post-vaccination side effects, gradient boost, random forest, and XGBoost outperformed other ML methods. The most important predisposing factors for predicting certain side effects (i.e., tiredness, fever, headache, injection site pain and swelling, myalgia, and sleepiness and laziness) were revealed to be the number of doses, gender, type of vaccine, age, and hesitancy to receive a COVID-19 vaccine. Conclusions: The reported side effects following COVID-19 vaccination among Arab populations are usually non-life-threatening; flu-like symptoms and injection site pain. Certain predisposing factors have greater weight and importance as input data in predicting post-vaccination side effects. Based on the most significant input data, ML can also be used to predict these side effects; people with certain predicted side effects may require additional medical attention, or possibly hospitalization.

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Section: Discussionmentioning

confidence: 99%

Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors

et al. 2022

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“…SVM learns by assigning labels to objects and is widely used in biological fields. It is used for both classification and regression challenges [ 102 , 103 ]. In SVM, each data item is plot as a point in an n-dimensional space with the value of each feature related to the value of a specific coordinate.…”

Section: Methodsmentioning

confidence: 99%

A Machine Learning Tool to Predict the Antibacterial Capacity of Nanoparticles

et al. 2021

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The emergence and rapid spread of multidrug-resistant bacteria strains are a public health concern. This emergence is caused by the overuse and misuse of antibiotics leading to the evolution of antibiotic-resistant strains. Nanoparticles (NPs) are objects with all three external dimensions in the nanoscale that varies from 1 to 100 nm. Research on NPs with enhanced antimicrobial activity as alternatives to antibiotics has grown due to the increased incidence of nosocomial and community acquired infections caused by pathogens. Machine learning (ML) tools have been used in the field of nanoinformatics with promising results. As a consequence of evident achievements on a wide range of predictive tasks, ML techniques are attracting significant interest across a variety of stakeholders. In this article, we present an ML tool that successfully predicts the antibacterial capacity of NPs while the model’s validation demonstrates encouraging results (R2 = 0.78). The data were compiled after a literature review of 60 articles and consist of key physico-chemical (p-chem) properties and experimental conditions (exposure variables and bacterial clustering) from in vitro studies. Following data homogenization and pre-processing, we trained various regression algorithms and we validated them using diverse performance metrics. Finally, an important attribute evaluation, which ranks the attributes that are most important in predicting the outcome, was performed. The attribute importance revealed that NP core size, the exposure dose, and the species of bacterium are key variables in predicting the antibacterial effect of NPs. This tool assists various stakeholders and scientists in predicting the antibacterial effects of NPs based on their p-chem properties and diverse exposure settings. This concept also aids the safe-by-design paradigm by incorporating functionality tools.

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“…Until now, there has been little academic research on emojis (Li et al, 2019), especially in the context of consumers’ responses to emojis in advertisements (Das et al, 2019) to better understand any effects and underlying reasons of any impact (Das et al, 2019; Wang et al, 2014). The information extracted through neuromarketing can better reflect the potential preferences of consumers, reduce the biases that tend to occur in traditional surveys and studies (Meyerding and Mehlhose, 2018), and capture subtle differences in consumer behaviour (Stasi et al, 2018). Hence, this study will explore the impact of emojis as subliminal stimuli on consumers’ brain activity through experimental research design.…”

Section: Prior Studiesmentioning

confidence: 99%

Neuromarketing, Subliminal Advertising, and Hotel Selection: An EEG Study

Hsu

Chen

2020

Australasian Marketing Journal

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This study aims to understand how hotel videos embedded with a smiling face emoji as a subliminal message affect consumers’ selection of hotels, with their brain activities measured and collected while they watched the videos. Data was collected from sixteen participants who completed two rounds of experiments. A chi-square test of homogeneity, paired sample t-test, and Bayes factor were performed to address the two proposed research questions. The results of this study reveal that participants’ selection of hotels would be significantly affected by the subliminal stimuli of a smiling face emoji. Meanwhile, neuroscientific data identifies significant differences between participants’ two (theta and beta) out of five bands of brainwaves while they were viewing hotel videos with and without the subliminal message. Suggestions for future studies and practical operations are also discussed.

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Introduction

Cited by 17 publications

References 35 publications

Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors

Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors

A Machine Learning Tool to Predict the Antibacterial Capacity of Nanoparticles

Neuromarketing, Subliminal Advertising, and Hotel Selection: An EEG Study

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