scholarly journals Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing

2020 ◽  
Vol 17 (15) ◽  
pp. 5330 ◽  
Author(s):  
Israel Edem Agbehadji ◽  
Bankole Osita Awuzie ◽  
Alfred Beati Ngowi ◽  
Richard C. Millham
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Industrial Revolution ◽  
Big Data Analytics ◽  
Contact Tracing ◽  
Case Detection ◽  
Human Beings ◽  
Accurate Detection ◽  
Nature Inspired Computing ◽  
Computing Models

The emergence of the 2019 novel coronavirus (COVID-19) which was declared a pandemic has spread to 210 countries worldwide. It has had a significant impact on health systems and economic, educational and social facets of contemporary society. As the rate of transmission increases, various collaborative approaches among stakeholders to develop innovative means of screening, detecting and diagnosing COVID-19’s cases among human beings at a commensurate rate have evolved. Further, the utility of computing models associated with the fourth industrial revolution technologies in achieving the desired feat has been highlighted. However, there is a gap in terms of the accuracy of detection and prediction of COVID-19 cases and tracing contacts of infected persons. This paper presents a review of computing models that can be adopted to enhance the performance of detecting and predicting the COVID-19 pandemic cases. We focus on big data, artificial intelligence (AI) and nature-inspired computing (NIC) models that can be adopted in the current pandemic. The review suggested that artificial intelligence models have been used for the case detection of COVID-19. Similarly, big data platforms have also been applied for tracing contacts. However, the nature-inspired computing (NIC) models that have demonstrated good performance in feature selection of medical issues are yet to be explored for case detection and tracing of contacts in the current COVID-19 pandemic. This study holds salient implications for practitioners and researchers alike as it elucidates the potentials of NIC in the accurate detection of pandemic cases and optimized contact tracing.

scholarly journals Big data for whose sake? Governing migration through artificial intelligence

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Tuba Bircan ◽  
Emre Eren Korkmaz
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
New Technologies ◽  
Control Function ◽  
Big Data Analytics ◽  
Fundamental Rights ◽  
High Tech ◽  
Human Beings ◽  
Security Threat ◽  
Data Accessibility

AbstractAlthough human activity constantly generates massive amounts of data, these data can only be analysed by mainly the private sector and governmental institutes due to data accessibility restrictions. However, neither migrants (as the producers of this data) nor migration scholars (as scientific experts on the topic) are in a position to monitor or control how governments and corporations use such data. Big Data analytics and Artificial Intelligence (AI) technologies are promoted as cutting-edge solutions to ongoing and emerging social, economic and governance challenges. Meanwhile, states increasingly rely on digital and frontier technologies to manage borders and control migratory movements, and the defence industry and military–intelligence sectors provide high-tech tools to support these efforts. Worryingly, during the design and testing of algorithmic tools, migrants are often portrayed as a security threat instead of human beings with fundamental rights and liberties. Thus, privacy, data protection, and confidentiality issues continue to pose risks and challenges to migrant communities and raise important questions for the public and decision-makers alike. This comment seeks to shed light on the lack of effective regulation of AI and Big Data as they are applied in migration ‘management’. Additionally, from the perspective of privacy issues and immigrant rights (seeking asylum as a human right, it aims at advocating improved access to Big Data for scientific research which might act as a social control function for the smart border and existing/ongoing migration governance practices of countries. We argue that the use of Big Data and AI for migration governance requires much better collaboration between migrants (including the civil society and grassroots organisations solidarity that represent them), data scientists, migration scholars and policymakers if the potential of these technologies is to be reached in a way that is reasonable and ethical. Numerous critical privacy questions arise are regarding the legal requirements, confidentiality, and rules of engagement as well as the ethical concerns of (mis)use of new technologies. When the secretive nature of the ongoing exploitation of migrant data by states and corporations is considered raising such questions is essential for progress.

scholarly journals KURIKULUM PENDIDIKAN VOKASI PADA ERA REVOLUSI INDUSTRI 4.0

2019 ◽  
Vol 20 (1) ◽  
pp. 82
Author(s):  
Unung Vera Wardina ◽  
Nizwardi Jalinus ◽  
Lise Asnur
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Blended Learning ◽  
Vocational Education ◽  
Industrial Revolution ◽  
Technology Literacy ◽  
Business Practices ◽  
Education Curriculum ◽  
The Government ◽  
Good Learning

Vocational education purpose is to produce ready-to-work graduates who have the relevant skills for current job employment. Entering the industrial revolution era 4.0 there were massive changes in various industries and workers' ability needs. This article intends to examine the implications of the industrial revolution 4.0 era for vocational education curriculum. Based on the study of various sources and business practices, it is necessary to develop vocational education curriculum that are in accordance with the era of industrial revolution 4.0 and relevant to answering the needs of new skills, such as the ability to create and manage coding, big data, and artificial intelligence. The vocational curriculum needs to apply blended learning, which integrates face-to-face and online learning, so as to more effectively build graduates' abilities and skills. The curriculum also needs to contain mastery of 4.0 competencies such as data literacy, technology literacy and human literacy. In order for the vocational education curriculum to have a broad impact, the government, educational institutions, industries must work together to revitalize the approach and content of the vocational education curriculum. Teachers must also be able to implement good learning to produce optimal graduate performance. Pendidikan vokasi merupakan pendidikan yang menghasilkan lulusan siap kerja yang memiliki keterampilan sesuai kebutuhan dunia kerja. Memasuki era revolusi indusri 4.0 terjadi perubahan yang masif pada perbagai industri dan kebutuhan kemampuan pekerja. Artikel ini bermaksud mengkaji implikasi era revolusi industri 4.0 bagi kurikulum pendidikan vokasi. Berdasarkan kajian berbagai sumber dan praktek bisnis, diperlukan pengembangan kurikulum pendidikan vokasi yang sesuai dengan era revolusi industri 4.0 dan relevan menjawab kebutuhan keterampilan baru, seperti kemampuan membuat dan mengelola coding, big data, dan artificial intelligence. Kurikulum vokasi perlu menerapkan pembelajaran blended learning, yang mengintegrasikan pembelajaran tatap muka dan online, supaya lebih efektif membangun kemampuan dan ketrampilan lulusan. Kurikulum juga perlu memuat penguasaan kompetensi 4.0 seperti literasi data, literasi teknologi dan literasi manusia. Agar kurikulum pendidikan vokasi menghasilkan dampak yang luas, pemerintah, lembaga pendidikan, industri harus bersinergi untuk merevitalisasi pendekatan dan isi kurikulum pendidikan vokasi. Pengajar juga harus dapat menyelenggarakan pembelajaran yang baik untuk menghasilkan kinerja optimal lulusan.

Big Data and Machine Learning

2018 ◽  
pp. 225-239
Author(s):  
Fernando Enrique Lopez Martinez ◽  
Edward Rolando Núñez-Valdez
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Complete Solution ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Healthcare Organizations ◽  
High Level

IoT, big data, and artificial intelligence are currently three of the most relevant and trending pieces for innovation and predictive analysis in healthcare. Many healthcare organizations are already working on developing their own home-centric data collection networks and intelligent big data analytics systems based on machine-learning principles. The benefit of using IoT, big data, and artificial intelligence for community and population health is better health outcomes for the population and communities. The new generation of machine-learning algorithms can use large standardized data sets generated in healthcare to improve the effectiveness of public health interventions. A lot of these data come from sensors, devices, electronic health records (EHR), data generated by public health nurses, mobile data, social media, and the internet. This chapter shows a high-level implementation of a complete solution of IoT, big data, and machine learning implemented in the city of Cartagena, Colombia for hypertensive patients by using an eHealth sensor and Amazon Web Services components.

Predictive Analysis for Digital Marketing Using Big Data

2017 ◽  
pp. 259-283
Author(s):  
Balamurugan Balusamy ◽  
Priya Jha ◽  
Tamizh Arasi ◽  
Malathi Velu
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Big Data ◽  
Statistical Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Predictive Analysis ◽  
Cognitive Computing ◽  
Internet Resources ◽  
To Come

Big data analytics in recent years had developed lightning fast applications that deal with predictive analysis of huge volumes of data in domains of finance, health, weather, travel, marketing and more. Business analysts take their decisions using the statistical analysis of the available data pulled in from social media, user surveys, blogs and internet resources. Customer sentiment has to be taken into account for designing, launching and pricing a product to be inducted into the market and the emotions of the consumers changes and is influenced by several tangible and intangible factors. The possibility of using Big data analytics to present data in a quickly viewable format giving different perspectives of the same data is appreciated in the field of finance and health, where the advent of decision support system is possible in all aspects of their working. Cognitive computing and artificial intelligence are making big data analytical algorithms to think more on their own, leading to come out with Big data agents with their own functionalities.

Records Management in the Fourth Industrial Revolution

2021 ◽  
pp. 1-24
Author(s):  
Adeyinka Tella ◽  
Oluwakemi Titilola Olaniyi ◽  
Aderinola Ololade Dunmade
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Internet Of Things ◽  
Industrial Revolution ◽  
Records Management ◽  
Technological Advancement ◽  
Fourth Industrial Revolution ◽  
Industrial Revolutions ◽  
The Way

The chapter looked at records management in the fourth industrial revolution (4IR) with the challenges and the way forward. The chapter discussed the industrial revolutions, records management, and the fourth industrial revolution (4IR), and described the advancement in records management in the 4IR based on the 4IR tools and technologies including artificial intelligence, blockchain, internet of things (IoT), robotics, and big data. The chapter also identified and discussed the benefits of technological advancement in the management of records; challenges of records management at the wake of 4IR and charted the way forward. In the context of document and records management, and taking into account all characteristics of the 4IR technologies and tools as well as its underlying technologies and concepts, the chapter concluded that the 4IR tools can be used to save time to create and process records, secure records from being damaged or destroyed, confirm the integrity of records, among others.

Intelligent Manufacturing Systems Driven by Artificial Intelligence in Industry 4.0

2022 ◽  
pp. 406-428
Author(s):  
Lejla Banjanović-Mehmedović ◽  
Fahrudin Mehmedović
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Industry 4.0 ◽  
Data Analytics ◽  
Manufacturing Systems ◽  
System Modeling ◽  
Big Data Analytics ◽  
Intelligent Manufacturing ◽  
Intelligent Manufacturing Systems ◽  
Intelligent Decision Making

Intelligent manufacturing plays an important role in Industry 4.0. Key technologies such as artificial intelligence (AI), big data analytics (BDA), the internet of things (IoT), cyber-physical systems (CPSs), and cloud computing enable intelligent manufacturing systems (IMS). Artificial intelligence (AI) plays an essential role in IMS by providing typical features such as learning, reasoning, acting, modeling, intelligent interconnecting, and intelligent decision making. Artificial intelligence's impact on manufacturing is involved in Industry 4.0 through big data analytics, predictive maintenance, data-driven system modeling, control and optimization, human-robot collaboration, and smart machine communication. The recent advances in machine and deep learning algorithms combined with powerful computational hardware have opened new possibilities for technological progress in manufacturing, which led to improving and optimizing any business model.

The Nature of Intelligent Analytics

2021 ◽  
pp. 1-21
Author(s):  
Zhaohao Sun ◽  
Andrew Stranieri
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Research And Development ◽  
Business Intelligence ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Business Analytics ◽  
Novel Approach ◽  
Advanced Analytics

Intelligent analytics is an emerging paradigm in the age of big data, analytics, and artificial intelligence (AI). This chapter explores the nature of intelligent analytics. More specifically, this chapter identifies the foundations, cores, and applications of intelligent big data analytics based on the investigation into the state-of-the-art scholars' publications and market analysis of advanced analytics. Then it presents a workflow-based approach to big data analytics and technological foundations for intelligent big data analytics through examining intelligent big data analytics as an integration of AI and big data analytics. The chapter also presents a novel approach to extend intelligent big data analytics to intelligent analytics. The proposed approach in this chapter might facilitate research and development of intelligent analytics, big data analytics, business analytics, business intelligence, AI, and data science.

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