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Topic Review
Big Data Analytics in COVID-19
The COVID-19 epidemic has caused a large number of human losses and havoc in the economic, social, societal, and health systems around the world. Controlling such epidemic requires understanding its characteristics and behavior, which can be identified by collecting and analyzing the related big data. Big data analytics tools play a vital role in building knowledge required in making decisions and precautionary measures. However, due to the vast amount of data available on COVID-19 from various sources, there is a need to review the roles of big data analysis in controlling the spread of COVID-19, presenting the main challenges and directions of COVID-19 data analysis, as well as providing a framework on the related existing applications and studies to facilitate future research on COVID-19 analysis.
  • 2.6K
  • 04 Jun 2021
Topic Review
HVAC Systems of Smart Building
Early fault detection and diagnosis in heating, ventilation and air conditioning (HVAC) systems may reduce the damage of equipment, improving the reliability and safety of smart buildings, generating social and economic benefits. Data models for fault detection and diagnosis are increasingly used for extracting knowledge in the supervisory tasks. This article proposes an autonomic cycle of data analysis tasks (ACODAT) for the supervision of the building’s HVAC systems. Data analysis tasks incorporate data mining models for extracting knowledge from the system monitoring, analyzing abnormal situations and automatically identifying and taking corrective actions. This article shows a case study of a real building’s HVAC system, for the supervision with our ACODAT, where the HVAC subsystems have been installed over the years, providing a good example of a heterogeneous facility. The proposed supervisory functionality of the HVAC system is capable of detecting deviations, such as faults or gradual increment of energy consumption in similar working conditions. The case study shows this capability of the supervisory autonomic cycle, usually a key objective for smart buildings.
  • 2.5K
  • 01 Jun 2021
Topic Review
Data Ownership in Healthcare
This section briefly discusses this issue of data ownership in the light of recent privacy laws. These laws have a very large impact on the topic of data sharing. It shows that these privacy laws provide rights to the patient, but they do not necessarily make clear who is the owner of the data. They only provide a legal framework for the handling of the data.
  • 2.4K
  • 05 Feb 2021
Topic Review
Comparison Between Esperanto and Interlingua
Esperanto and Interlingua are two planned languages which have taken radically different approaches to the problem of providing an International auxiliary language (IAL). Although they are both classed as IALs, the intellectual bases of Esperanto and Interlingua are quite different. It has been argued that each language is a successful implementation of a particular IAL model. However, in both language communities there is a polemical tradition of using external criteria to critique the perceived opponent language (that is, judging Interlingua by Esperantist criteria and vice versa). In practical use, moreover, language usage in the two communities has sometimes shown convergences despite divergent theory.
  • 2.4K
  • 18 Oct 2022
Topic Review
Decentralized Smart IoT
Decentralized smart Internet of Things (IoT) refers to future IoT powered by blockchain-enabled edge intelligence. This new form of IoT is motivated by the recent advancement of distributed ledger technology (DLT), multi-access edge computing (MEC) and artificial intelligence (AI). The idea is to empower all kinds of IoT devices to observe, identify, and understand the world not by the help of humans but by cooperation and consensus among edge devices, in a secure and verifiable manner. Decentralized smart IoT will provide trust and intelligence to satisfy the sophisticated needs of industries and society.
  • 2.4K
  • 26 Apr 2021
Topic Review
HBase Storage Architecture
HBase is the top option for storing huge data. HBase has been selected for several purposes, including its scalability, efficiency, strong consistency support, and the capacity to support a broad range of data models.
  • 2.3K
  • 21 Feb 2021
Topic Review
Pandora Radio
Pandora Media Inc. (also known as Pandora Internet Radio or simply Pandora) is a music streaming and automated music recommendation internet radio service powered by the Music Genome Project. As of August 1, 2017, the service, operated by Pandora Media, Inc., is available only in the United States. The service plays songs that have similar musical traits. The user then provides positive or negative feedback (as thumbs up or thumbs down) for songs chosen by the service, and the feedback is taken into account in the subsequent selection of other songs to play. The service can be accessed either through a web browser or with its mobile app. Pandora is a freemium service; basic features are free with advertisements or limitations, while additional features, such as improved streaming quality, music downloads and offline channels are offered via paid subscriptions. On September 24, 2018, Sirius XM Holdings announced its intent to acquire Pandora for $3.5 billion.
  • 2.3K
  • 15 Nov 2022
Topic Review
Fashion Recommendation Systems
Image-based fashion recommendation systems (FRSs) have attracted a huge amount of attention from fast fashion retailers as they provide a personalized shopping experience to consumers. With the technological advancements, this branch of artificial intelligence exhibits a tremendous amount of potential in image processing, parsing, classification, and segmentation.
  • 2.3K
  • 12 Aug 2021
Topic Review
Industry 4.0 Readiness Models
It is critical for organizations to self-assess their Industry 4.0 readiness to survive and thrive in the age of the Fourth Industrial Revolution. Thereon, conceptualization or development of an Industry 4.0 readiness model with the fundamental model dimensions is needed. This paper used a systematic literature review (SLR) methodology with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and content analysis strategy to review 97 papers in peer-reviewed academic journals and industry reports published from 2000 to 2019. The review identifies 30 Industry 4.0 readiness models with 158 unique model dimensions. Based on this review, there are two theoretical contributions. First, this paper proposes six dimensions (Technology, People, Strategy, Leadership, Process and Innovation) that can be considered as the most important dimensions for organizations. Second, this review reveals that 70 (44%) out of total 158 total unique dimensions on Industry 4.0 pertain to the assessment of technology alone. This establishes that organizations need to largely improve on their technology readiness, to strengthen their Industry 4.0 readiness. In summary, these six most common dimensions, and in particular, the dominance of the technology dimension provides a research agenda for future research on Industry 4.0 readiness.
  • 2.1K
  • 25 Jun 2021
Topic Review
Cybersecurity
Cyberspace has become an indispensable factor for all areas of the modern world. The world is becoming more and more dependent on the internet for everyday living. The increasing dependency on the internet has also widened the risks of malicious threats. On account of growing cybersecurity risks, cybersecurity has become the most pivotal element in the cyber world to battle against all cyber threats, attacks, and frauds. The expanding cyberspace is highly exposed to the intensifying possibility of being attacked by interminable cyber threats. The objective of this survey is to bestow a brief review of different machine learning (ML) techniques to get to the bottom of all the developments made in detection methods for potential cybersecurity risks. These cybersecurity risk detection methods mainly comprise of fraud detection, intrusion detection, spam detection, and malware detection. In this review paper, we build upon the existing literature of applications of ML models in cybersecurity and provide a comprehensive review of ML techniques in cybersecurity. To the best of our knowledge, we have made the first attempt to give a comparison of the time complexity of commonly used ML models in cybersecurity. We have comprehensively compared each classifier’s performance based on frequently used datasets and sub-domains of cyber threats. This work also provides a brief introduction of machine learning models besides commonly used security datasets. Despite having all the primary precedence, cybersecurity has its constraints compromises, and challenges. This work also expounds on the enormous current challenges and limitations faced during the application of machine learning techniques in cybersecurity.
  • 2.1K
  • 10 Feb 2021
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