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Advice Online presents a comprehensive study of advice-giving in one particular American Internet advice column, referred to as 'Lucy Answers'. The discursive practice investigated is part of a professional and educational health program managed by an American university. The study provides insights into the linguistic realization of both asking for and giving advice in a written form and thus adds to the literature on advice columns as a specific text genre, on advice in health care contexts, and on Internet communication. The book offers a comprehensive literature review of advice in health encounters and other contexts, and uses this knowledge as a basis for comparison. Advice Online demonstrates how qualitative and quantitative research methods can be successfully combined to arrive at a comprehensive analysis of a discursive practice. It provides essential information on advice-giving for researchers, academics and students in the fields of (Internet) communication, media studies, pragmatics, social psychology and counseling. Health educators who work for advice columns or use similar forms of communication will also benefit from the insights gained in this study.

Based firmly on research, The Internet and Health Communication provides an in-depth analysis of the changes in human communication and health care resulting from the Internet revolution. The contributors, representing a wide range of expertise, provide an extensive variety of examples to vividly illustrate their findings and conclusions.

Despite a strong commitment to delivering quality health care, persistent problems involving medical errors and ineffective treatment continue to plague the industry.Many of these problems are the consequence of poor information and technology (IT) capabilities, and most importantly, the lack cognitive IT support.

The outbreak of chronic diseases such as COVID-19 has made a renewed call for providing urgent healthcare facilities to the citizens across the globe. The recent pandemic exposes the shortcomings of traditional healthcare system, i.e., hospitals and clinics alone are not capable to cope with this situation. One of the major technology that aids contemporary healthcare solutions is the smart and connected wearables. The advancement in Internet of Things (IoT) has enabled these wearables to collect data on an unprecedented scale. These wearables gather context-oriented information related to our physical, behavioural and psychological health. The big data generated by wearables and other healthcare devices of IoT is a challenging task to manage that can negatively affect the inference process at the decision centres. Applying big data analytics for mining information, extracting knowledge and making predictions/inferences has recently attracted significant attention. Machine learning is another area of research that has successfully been applied to solve various networking problems such as routing, traffic engineering, resource allocation, and security. Recently, we have seen a surge in the application of ML-based techniques for the improvement of various IoT applications. Although, big data analytics and machine learning are extensively researched, there is a lack of study that exclusively focus on the evolution of ML-based techniques for big data analysis in the IoT healthcare sector. In this paper, we have presented a comprehensive review on the application of machine learning techniques for big data analysis in the healthcare sector. Furthermore, strength and weaknesses of existing techniques along with various research challenges are highlighted. Our study will provide an insight for healthcare practitioners and government agencies to keep themselves well-equipped with the latest trends in ML-based big data analytics for smart healthcare.

In the dynamic environment of the Internet of Things (IoT), edge and cloud computing play critical roles in analysing and storing data from numerous connected devices to produce valuable insights. Efficient resource allocation and workload distribution are vital to ensuring continuous and reliable service in growing IoT ecosystems with increasing data volumes and changing application demands. This study proposes a novel optimisation approach utilising deep learning to tackle these challenges. The integration of Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) offers a practical approach to addressing the dynamic characteristics of IoT applications. The hybrid algorithm's primary characteristic is its capacity to simultaneously fulfil multiple objectives, including reducing response times, enhancing resource efficiency, and decreasing operational costs. DQN facilitates the formulation of optimal resource allocation strategies in intricate and unpredictable environments. PPO enhances policies in continuous action spaces to guarantee reliable performance in real-time, dynamic IoT settings. This method achieves an optimal equilibrium between policy learning and optimisation, rendering it suitable for contemporary IoT systems. This method improves numerous IoT applications, including smart cities, industrial automation, and healthcare. The hybrid DQN-PPO-GNN-RL model addresses bottlenecks by dynamically managing computing and network resources, allowing for efficient operations in low-latency, high-demand environments such as autonomous systems, sensor networks, and real-time monitoring. The use of Graph Neural Networks (GNNs) improves the accuracy of resource representation, while reinforcement learning-based scheduling allows for seamless adaptation to changing workloads. Simulations using real-world IoT data on the iFogSim platform showed significant improvements: task scheduling time was reduced by 21%, operational costs by 17%, and energy consumption by 22%. The method reliably provided equitable resource distribution, with values between 0.93 and 0.99, guaranteeing efficient allocation throughout the network. This hybrid methodology establishes a novel benchmark for scalable, real-time resource management in extensive, data-centric IoT ecosystems, consequently enhancing system performance and operational efficiency.

Recurrent neural networks (RNNs) have significantly advanced the field of machine learning (ML) by enabling the effective processing of sequential data. This paper provides a comprehensive review of RNNs and their applications, highlighting advancements in architectures, such as long short-term memory (LSTM) networks, gated recurrent units (GRUs), bidirectional LSTM (BiLSTM), echo state networks (ESNs), peephole LSTM, and stacked LSTM. The study examines the application of RNNs to different domains, including natural language processing (NLP), speech recognition, time series forecasting, autonomous vehicles, and anomaly detection. Additionally, the study discusses recent innovations, such as the integration of attention mechanisms and the development of hybrid models that combine RNNs with convolutional neural networks (CNNs) and transformer architectures. This review aims to provide ML researchers and practitioners with a comprehensive overview of the current state and future directions of RNN research.

The World Health Organization considers coronavirus disease (COVID-19) to be a public emergency threatening global health. During the crisis, the public's need for web-based information and communication is a subject of focus. Digital inequality research has shown that internet access is not evenly distributed among the general population. The aim of this study was to provide a timely understanding of how different people use the internet to meet their information and communication needs and the outcomes they gain from their internet use in relation to the COVID-19 pandemic. We also sought to reveal the extent to which gender, age, personality, health, literacy, education, economic and social resources, internet attitude, material access, internet access, and internet skills remain important factors in obtaining internet outcomes after people engage in the corresponding uses. We used a web-based survey to draw upon a sample collected in the Netherlands. We obtained a dataset with 1733 respondents older than 18 years. Men are more likely to engage in COVID-19-related communication uses. Age is positively related to COVID-19-related information uses and negatively related to information and communication outcomes. Agreeableness is negatively related to both outcomes and to information uses. Neuroticism is positively related to both uses and to communication outcomes. Conscientiousness is not related to any of the uses or outcomes. Introversion is negatively related to communication outcomes. Finally, openness relates positively to all information uses and to both outcomes. Physical health has negative relationships with both outcomes. Health perception contributes positively to information uses and both outcomes. Traditional literacy has a positive relationship with information uses and both outcomes. Education has a positive relationship with information and communication uses. Economic and social resources played no roles. Internet attitude is positively related to information uses and outcomes but negatively related to communication uses and outcomes. Material access and internet access contributed to all uses and outcomes. Finally, several of the indicators and outcomes became insignificant after accounting for engagement in internet uses. Digital inequality is a major concern among national and international scholars and policy makers. This contribution aimed to provide a broader understanding in the case of a major health pandemic by using the ongoing COVID-19 crisis as a context for empirical work. Several groups of people were identified as vulnerable, such as older people, less educated people, and people with physical health problems, low literacy levels, or low levels of internet skills. Generally, people who are already relatively advantaged are more likely to use the information and communication opportunities provided by the internet to their benefit in a health pandemic, while less advantaged individuals are less likely to benefit. Therefore, the COVID-19 crisis is also enforcing existing inequalities.