Health monitoring and its related technologies is an attractive research area. The electrocardiogram (ECG) has always been a popular measurement scheme to assess and diagnose cardiovascular diseases (CVDs). The number of ECG monitoring systems in the literature is expanding exponentially. Hence, it is very hard for researchers and healthcare experts to choose, compare, and evaluate systems that serve their needs and fulfill the monitoring requirements. This accentuates the need for a verified reference guiding the design, classification, and analysis of ECG monitoring systems, serving both researchers and professionals in the field. In this paper, we propose a comprehensive, expert-verified taxonomy of ECG monitoring systems and conduct an extensive, systematic review of the literature. This provides evidence-based support for critically understanding ECG monitoring systems’ components, contexts, features, and challenges. Hence, a generic architectural model for ECG monitoring systems is proposed, an extensive analysis of ECG monitoring systems’ value chain is conducted, and a thorough review of the relevant literature, classified against the experts’ taxonomy, is presented, highlighting challenges and current trends. Finally, we identify key challenges and emphasize the importance of smart monitoring systems that leverage new technologies, including deep learning, artificial intelligence (AI), Big Data and Internet of Things (IoT), to provide efficient, cost-aware, and fully connected monitoring systems.

Twitter popularity has increasingly grown in the last few years making influence on the social, political and business aspects of life. Therefore, sentiment analysis research has put special focus on Twitter. Tweet data have many peculiarities relevant to the use of informal language, slogans, and special characters. Furthermore, training machine learning classifiers from tweets data often faces the data sparsity problem primarily due to the large variety of Tweets expressed in only 140-character. In this work, we evaluate the performance of various classifiers commonly used in sentiment analysis to show their effectiveness in sentiment mining of Twitter data under different experimental setups. For the purpose of the study the Stanford Testing Sentiment dataset STS is used. Results of our analysis show that multinomial Naïve Bayes outperforms other classifiers in Twitter sentiment analysis and is less affected by data sparsity.

Even though, most of the early efforts in personalized learning focused on formal learning, there is a growing undeniable demand for personalized support for informal learning. Wikis among other information-oriented platforms are experiencing an increasing attention for informal learning, especially Wikipedia. Link-based navigation and keyword-based search methods used on wiki environments suffer from many limitations. To support informal learning on these environments, it is important to provide easy and fast access to relevant content. However, the massive diversity of unstructured content and user base on these environments pose major challenges when designing recommendation models. To the best of our knowledge, no effective personalized content recommendation approach has yet been defined to support informal learning on wikis. We propose an effective personalized content recommendation framework (PCRF) in addition to an evaluation framework that can be used to evaluate the impact of personalized content recommendations on informal learning from wikis. PCRF implements an efficient structural recommendation model by integrating fuzzy thesauri with adaptive users' interest models generated using structural analysis of topical navigational graphs. We design user studies with multiple strategies and treatments to evaluate the effectiveness of the framework and assess the impact of recommendations on informal learning. Experiments show that PCRF generates highly relevant recommendations adaptive to changes in users' interests using the HARD model with MAP@k scores 86.4-100%. An evaluation of informal learning revealed that users of Wikipedia with personalized support could achieve higher scores on a conceptual knowledge assessment with an average score of 14.9 compared with 10.0 for users who used the encyclopedia without recommendations. Results confirm that PCRF can effectively support informal learning on wikis and similar environments.

Cardio Vascular Diseases (CVD) is the leading cause of death globally and is increasing at an alarming rate, according to the American Heart Association's Heart Attack and Stroke Statistics-2021. This increase has been further exacerbated because of the current coronavirus (COVID-19) pandemic, thereby increasing the pressure on existing healthcare resources. Smart and Connected Health (SCH) is a viable solution for the prevalent healthcare challenges. It can reshape the course of healthcare to be more strategic, preventive, and custom-designed, making it more effective with value-added services. This research endeavors to classify state-of-the-art SCH technologies via a thorough literature review and analysis to comprehensively define SCH features and identify the enabling technology-related challenges in SCH adoption. We also propose an architectural model that captures the technological aspect of the SCH solution, its environment, and its primary involved stakeholders. It serves as a reference model for SCH acceptance and implementation. We reflected the COVID-19 case study illustrating how some countries have tackled the pandemic differently in terms of leveraging the power of different SCH technologies, such as big data, cloud computing, Internet of Things, artificial intelligence, robotics, blockchain, and mobile applications. In combating the pandemic, SCH has been used efficiently at different stages such as disease diagnosis, virus detection, individual monitoring, tracking, controlling, and resource allocation. Furthermore, this review highlights the challenges to SCH acceptance, as well as the potential research directions for better patient-centric healthcare.

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