The advent of COVID-19 has disrupted all facets of human lives. As of September 2020, there is no effective viral therapy for the disease, thus necessitating research efforts toward providing solutions to the diverse areas where the pandemic has wreaked havoc. As a way of reducing the spread of the disease, the South African government declared COVID-19 a national disaster and implemented nationwide lockdowns with several regulations. Nevertheless, the success of such synergized efforts primarily depends on the people's attitudes and perceptions toward the multifaceted management of the pandemic. Therefore, this current study aims to discover what topical issues relating to the pandemic are being discussed by the populace and what impacts these issues have on compliance with regulations, including how they can aid in the implementation of the measures put in place by the government, as we analyze discussions relating to COVID-19 using data harvested from Twittersocial media and opinion mining platform. The Latent Dirichlet Allocation (LDA) algorithm was applied for the extraction of noteworthy topics. From the experiments conducted, it was observed that alcohol sale and consumption, staying home, daily statistics tracing, police brutality, 5G and vaccines conspiracy theories were among the topics discussed and around which attitudes and perceptions were formed by the citizens. The findings also revealed people's resistance to measures that affect their economic activities, and their unwillingness to take tests or vaccines as a result of fake news and conspiracy theories. These findings can assist the government and policymakers in redirecting their efforts by addressing the citizens' concerns and reactions to the instituted measures toward an anticipated overall success.
Since the end of 2019, the world faced a major health crisis in the form of the Coronavirus (COVID-19) pandemic. To mitigate the impact of the pandemic, governments across the globe instituted measures such as restricting local and international travel and in many cases, ordering citizens to stay indoors. Considering the social and economic impact of these restrictions it becomes crucial to investigate internet citizens’ (netizens) perception about the precautionary measures adopted. The study is anchored in the digital public sphere theory, which treats social media applications as virtual platforms where netizens commune to share ideas and debate about issues that affect them. Social media platforms already have critical public views on the current pandemic. However, the majority of this data is unstructured and difficult to interpret. Natural language processing (NLP), on the other hand, makes the task of gathering and analysing vast amounts of textual data feasible. Extracting structured knowledge from natural language, however, comes with unique challenges due to diverse linguistic properties including abbreviation, spelling mistakes, punctuations, stop words and non-standard text. In this work, The Latent Dirichlet Allocation (LDA) algorithm was applied to tweeter data to extract topics discussed by netzens from Zimbabwe and South Africa. The primary focus of this paper, is to comparatively explore the variety of topics that occupied twitter communities from the two countries. We examine whether or not the national identities that define and differentiate citizens of these countries also exist on Twitter as evident in the emerging topics. Furthermore, this work investigated public opinion by analysing how citizens discuss the issues around the COVID-19 pandemic on social media
It is everyone's dream to have network connectivity anywhere at all times. This dream can only be realized provided there are feasible solutions that are put in place for the next generation of wireless works. Wireless Mesh Networks (WMNs) configures itself and because of its cost effectiveness, it is therefore seen as a solution for the next generation networks. However, this field still has a lot of limitations and the main constrain is that the energy of the nodes is very limited, especially when the network is deployed in rural areas where electricity is a scares resource. This research therefore presents an energy optimization based path selection algorithm for IEEE 802.11s WMNs which is aimed at addressing the above mentioned constrains. As a newly proposed standard specifically designed for WMN, the IEEE 802.11s does not consider energy conservation as a priority in its protocol. As a result, the main goal for this research is that paths with enough energy for transmission in the network must be selected when transmitting packets. Our simulation results obtained from the experiments prove that there is a substantial increase with regard to the network's operational time when energy is considered by the protocol used in IEEE 802.11s.
The rapid and pervasive growth of Cloud computing and SOA has given rise to a lot of other remarkable offshoots. One interesting feature of this trend has been the emergence of the Ad-hoc Mobile Cloud (AMC) paradigm. Ad-hoc Mobile Cloud allows users to collaborate and share web services in a platform that enables mobile devices to play the role of service providers. This peculiar functional architecture that relies on resource-limited devices to host and perform discovery operations introduces another dimension to the service discovery challenge in conventional mobile environments. The conventional Cloud and Mobile Cloud environments leverage on the limitless cloud storage and computing power, therefore, techniques designed for such environments are generally resource-unaware. Consequently, the traditional service discovery mechanisms designed for Cloud computing cannot be adopted into AMC. In this review, we present an analysis of how service discovery solutions in Ad-hoc Mobile Cloud have adapted to meet the unique challenges of this resource-constrained environment. We classified, at a generic level, the solution space of the current service discovery techniques in Ad-hoc mobile cloud, and the identify opportunities for advancing research in the domain.
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