Telematics and Informatics Reports最新文献

The classification of White Blood Cells (WBCs) is crucial for diagnosing diseases, monitoring treatment effectiveness, and understanding how the immune system functions. In this paper, we propose a deep learning approach to classify WBCs using Super Resolution Generative Adversarial Network (SRGAN) and Visual Geometry Group 19 (VGG19). Firstly, microscopic images of WBCs are generated using the SRGAN to obtain more precise and high-resolution images, which are then classified with a pretrained VGG19 classifier. Low-resolution (LR) images are inputted into the generator of SRGAN, and its discriminator compares the High-resolution (HR) image with LR, generating super-resolution images to minimize misclassification risks. A large dataset of 12,447 images containing four classes of WBCs (Eosinophil, Lymphocyte, Monocyte, and Neutrophil) is utilized to train and validate our proposed model. Following extensive experimental analysis, our proposed model achieves a test accuracy of 94.87 %, surpassing traditional Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Hybrid CNN-RNN models, and other conventional approaches. The generated images of SRGAN overcome challenges associated with misclassification due to the poor resolution of microscopic images, while the use of a pretrained model as a classifier reduces classification complexity. The source code of the entire work is available at https://github.com/Jannatul-Ferdousi/SRGAN_VGG19_WBC.git.

Smart medical waste management is significant during the ongoing COVID-19 pandemic. This research presents a smart medical waste management system with real-time monitoring of medical waste data and an end-user application. The functional and technical design of a smart trolley by integrating various sensors for authenticated automatic lid opening and closing, waste level, weight, and storage time monitoring are discussed. Subsequently, the implementation of a cloud server and web application that provide automatic notifications to stakeholders are also presented. The users can monitor the entire process to take actions and approvals whenever necessary. The experiments are conducted to test the functionalities of the smart trolley and web application prototype. The results showed that the sensing system is efficient and avoids manual checking by sending necessary notifications. Healthcare institutions can adopt the proposed system for medical waste transfer to ensure process transparency, worker safety, and environmental cleanliness.

This study investigated the politicization of COVID-19 vaccination on Twitter in the context of the 2020 U.S. Presidential Election. I analyzed 2,100 English tweets related to COVID-19 antivaccine misinformation from October 28th, 2020, to November 3rd, 2020 (one week before Election Day). Those tweets clustered into 12 topics.

The result indicated that conspiracy theories about population control have raised concerns and become prevalent in perpetuating anti-vaccine beliefs. It also reflects the rise of a new trend in anti-vaccine topics. The study highlights the influence of partisan reasoning on vaccination decision-making in the context of a public health crisis caused by COVID-19, and the difficulty in correcting partisan-driven conspiracy theories and misinformation.

The harmful political, economic and health effects of fake news on social media are well known. The present study examines the impact of two socio-cultural variables (political orientation and parents' educational attainment), one media literacy variable and two media use variables (news control, platform usage frequency and media addiction) on fake videos detection. The ability to detect fake videos among Hungarian secondary school students (N = 507) was assessed using a 16-item video test. The results of the online survey are partly consistent and partly contradictory to the literature. There is no gender difference in the ability to detect fake videos in the age group studied, and media literacy and media use do not influence the ability to detect fake videos. However, a more conservative worldview and higher parental education are associated with better detection of fake videos. The paper concludes with recommendations.

In the contemporary landscape of healthcare, the rapid integration of digital technologies is reshaping traditional practices. As healthcare systems increasingly embrace digitalization, there arises a critical need to comprehend the dynamic and ever-evolving role played by Information and Communication Technologies (ICTs). Amidst the rapid digitization of healthcare systems, understanding the evolving role of Information and Communication Technologies (ICTs) is paramount. This study explores the impact of Information and Communication Technologies (ICTs) on healthcare, with a focus on Health Information Technology (HIT) and its role in enhancing patient-centered care. Specifically, the research centers on Personal Health Records (PHRs), which empower patients to control their health information securely. The study aims to identify key features influencing patients’ behavioral intention to adopt HIT, utilizing a structured questionnaire and a conceptual framework based on the Technology Acceptance Model (TAM). Feature selection analysis, conducted using the Weka tool, employed various classifiers to predict users’ perceptions and behavioral intentions towards HIT adoption. Results highlight the significance of trust, comfort, usefulness, and technophobia in shaping patients’ acceptance of HIT, with Random Forest and Bayes Net classifiers emerging as key predictors. This research provides valuable insights into the critical factors driving patients’ acceptance of HIT, contributing to the ongoing discourse on the adoption of healthcare technologies.

The rapid development of digital mobile payment platforms (DMPPs) has significantlytransformed the financial services landscape, offering consumers unparalleled convenience and efficiency in payment options. This study delves into the acceptance and usability factors among digital enthusiast consumers, employing fuzzy set Qualitative Comparative Analysis (fsQCA) to uncover key determinants. Analyzing data from 325 digital enthusiasts, the research identified four distinct configural paths that significantly influence DMPP usage, revealing two primary user typologies: “systems readiness and ease of use” and “affection and secured platforms.” Performance expectancy emerged as a core condition, highlighting its critical role in driving DMPP adoption among digital enthusiasts. The study also underscored the importance of grievance redress and perceived trust as essential conditions influencing user adoption and continuous usage, emphasizing their pivotal role in enhancing user experience and fostering trust. These insights suggest that addressing users' concerns and building trust are fundamental for the sustained success of DMPPs. By tailoring DMPP features to align with the distinct preferences of the identified user typologies, service providers can significantly boost adoption rates. The fsQCA approach offers novel insights into the nuances of emerging technology acceptance and usage, underscoring the necessity of categorizing users to better understand DMPP adoption dynamics. This research provides valuable contributions to both businesses and policymakers,offering strategies to optimize DMPP design, ensure superior user experiences, and promote broader adoption. The findings highlight the dynamic interplay of user expectations and technological capabilities, essential for navigating the evolving digital financial landscape.

Impulsive buying behavior is crucial for understanding the psychological dynamics of consumer purchasing in the online marketplace. Identifying antecedents and moderating factors is vital to comprehensively understanding impulsive buying behavior. This research explores the influence of the Big Five personality traits on impulsive buying behavior in e-commerce settings and investigates the moderating effects of time pressure and emotions on these relationships. Data was collected through online questionnaires from 342 Indonesian participants with e-commerce purchasing experience. Structural Equation Modeling was utilized to validate the research hypotheses. The findings reveal that personality traits such as agreeableness, openness to experience, extroversion, and conscientiousness significantly drive impulsive buying behavior in e-commerce. Additionally, the study highlights that the interaction between emotions and neuroticism, as well as time pressure and agreeableness, significantly impacts impulsive buying behavior. The findings contribute valuable theoretical insights by demonstrating how specific Big Five personality traits influence impulsive buying behavior, while also providing practical implications for e-commerce platforms to effectively manage and leverage time pressure and emotional triggers to enhance consumer engagement and drive impulsive purchases.

Nomophobia has been reported as a prevalence among especially emerging adults by a wide variety of studies. The current study aims to investigate the personality and psychosocial antecedents of nomophobia. Specifically, the mediating roles of perceived stress and ostracism in the association of loneliness and nomophobia were investigated with the inclusion of narcissism through structural equation modeling. The study was conducted with the participation of 602 university students. The findings first indicated that loneliness is an indirect predictor of nomophobia with the mediation of perceived stress. It was also revealed that narcissism is a significant predictor of nomophobia; but not of its psychosocial antecedents. It was concluded that as university students experience more feelings of loneliness, they perceive more stress and demonstrate more nomophobic behaviors. Considering the influence of narcissism on nomophobia among university students, further research is suggested on the possible mediators and moderators in this relationship.

Drawing on a qualitative analysis of official documents, field observations, and interviews with citizens and planners, this article examines how Kashiwanoha International Campus Town has attempted to actively engage its new residents in smart city making despite being built from scratch. The study draws on recent scholarship advocating for a human-centered approach in which smart technologies are used as tools to address local social issues, meet resident needs, and engage citizens in the co-creation of the smart city. This perspective favors context-sensitive and collaborative approaches to smart city development, but building or rebuilding smart cities from scratch continues to capture the interest of enterprises and governments despite widespread criticism for lacking citizen input and struggling to foster community. The findings of the case indicate that actively investing in citizen participation and placemaking promotes community formation and fosters an open environment for innovation and experimentation. However, challenges remain in empowering grassroots innovation, implying that balancing community-driven and technology-driven smartification is difficult in such contexts. Overall, the paper contributes empirical evidence to limited understanding about the actual experiences of citizens living in similar developments, and it makes recommendations to shift the bleak narrative surrounding new smart cities in more positive directions.