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Viruses, worms, Trojan horses, spywares have been effective for quite sometime in the domain of digital computers. These malicious software cause millions of dollars of…
Viruses, worms, Trojan horses, spywares have been effective for quite sometime in the domain of digital computers. These malicious software cause millions of dollars of loss in assets, revenue, opportunity, cleanup cost, and lost productivity. To stop virus attacks, organizations frame up different security policies. These policies work only within the limited domain of the organization’s network. However, the emergence of wireless technologies, and the seamless mobility features of the wireless devices from one network to the other have created a challenge to uphold the security policies of a particular network. Hence, in this digital society, while mobile devices roam in foreign networks, they get infected through viruses in the foreign network. Anti‐virus software is not so effective for novel viruses. There have been no reports of mobile‐phone viruses in the wild as yet. However, with the emergence of execution environments on mobile phones, it will be possible to write viruses and worms for mobile devices in cellular networks. We should be prepared to fight against viruses in the cellular networks. All the technologies available to fight against viruses are specific to virus signatures. We propose that this fight needs to be multilayered. In this paper the authors have proposed a novel philosophy in cellular network called Artificial Hygiene (AH), which is virus neutral and will work at the class level. With this process a device and the network will take the necessary steps to keep the digital environment safe.
Traditional public health methods for tracking contagious diseases are increasingly complemented with digital tools, which use data mining, analytics and crowdsourcing to…
Traditional public health methods for tracking contagious diseases are increasingly complemented with digital tools, which use data mining, analytics and crowdsourcing to predict disease outbreaks. In recent years, alongside these public health tools, commercial mobile apps such as Sickweather have also been released. Sickweather collects information from across the web, as well as self-reports from users, so that people can see who is sick in their neighborhood. The purpose of this paper is to examine the privacy and surveillance implications of digital disease tracking tools.
The author performed a content and platform analysis of two apps, Sickweather and HealthMap, by using them for three months, taking regular screenshots and keeping a detailed user journal. This analysis was guided by the walkthrough method and a cultural-historical activity theory framework, taking note of imagery and other content, but also the app functionalities, including characteristics of membership, “rules” and parameters of community mobilization and engagement, monetization and moderation. This allowed me to study HealthMap and Sickweather as modes of governance that allow for (and depend upon) certain actions and particular activity systems.
Draw on concepts of network power, the surveillance assemblage, and Deleuze’s control societies, as well as the data gathered from the content and platform analysis, the author argues that disease tracking apps construct disease threat as omnipresent and urgent, compelling users to submit personal information – including sensitive health data – with little oversight or regulation.
Disease tracking mobile apps are growing in popularity yet have received little attention, particularly regarding privacy concerns or the construction of disease risk.
The healthcare sector in India is witnessing phenomenal growth, such that by the year 2022, it will be a market worth trillions of INR. Increase in income levels…
The healthcare sector in India is witnessing phenomenal growth, such that by the year 2022, it will be a market worth trillions of INR. Increase in income levels, awareness regarding personal health, the occurrence of lifestyle diseases, better insurance policies, low-cost healthcare services, and the emergence of newer technologies like telemedicine are driving this sector to new heights. Abundant quantities of healthcare data are being accumulated each day, which is difficult to analyze using traditional statistical and analytical tools, calling for the application of Big Data Analytics in the healthcare sector. Through provision of evidence-based decision-making and actions across healthcare networks, Big Data Analytics equips the sector with the ability to analyze a wide variety of data. Big Data Analytics includes both predictive and descriptive analytics. At present, about half of the healthcare organizations have adopted an analytical approach to decision-making, while a quarter of these firms are experienced in its application. This implies the lack of understanding prevalent in healthcare sector toward the value and the managerial, economic, and strategic impact of Big Data Analytics. In this context, this chapter on “Predictive Analytics in Healthcare” discusses sources, areas of application, possible future areas, advantages and limitations of the application of predictive Big Data Analytics in healthcare.
The purpose of this paper is to utilize co-query volumes of brands as relatedness measurement to understand the market structure and demonstrate the usefulness of brand relatedness via a real-world case.
Using brand relatedness measurement obtained using data from Google Trends as data inputs into a multidimensional scaling method, the market structure of the automobile industry is presented to reveal its competitive landscape. The relatedness with brands involved in product-harm crisis is further incorporated in empirical models to estimate the influence of crisis on future sales performance of each brand. A representative incident of a product-harm crisis in the automobile industry, which is the 2009 Toyota recall, is investigated. A panel regression analysis is conducted using US and world sales data.
The use of co-query as brand relatedness measurement is validated. Results indicate that brand relatedness with a brand under crisis is positively associated with future sales for both US and global market. Potential presence of negative spillovers from an affected brand to innocent brands sharing common traits such as same country of origin is shown.
The brand relatedness measured from co-query volumes is considered as a broad concept, which encompasses all associative relationships between two brands perceived by the consumers. This study contributes to the literature by clarifying the concept of brand relatedness and proposing a measure with readily accessible data. Compared to previous studies relying on a vast amount of online data, the proposed measure is proven to be efficient and enhance predictions about the future performance of brands in a turbulent market.