Guest Editors
Prof. Dr. Abdul Rahman, Universitas Negeri Makassar, Indonesia.
Dr. Rusli, Universitas Negeri Makassar, Indonesia.
Dr. Luca Di Nunzio, University of Rome “Tor Vergata”, Italy.
Summary
At present, the world has entered the era of the Industrial Revolution 4.0 (RI4), an era in which everything is associated with technology. This RI4 era prioritizes work automation using technology, such as the use of the Internet of Think (IoT). The COVID-19 pandemic has provided shock therapy for the world of education because face-to-face education cannot be carried out as usual. This certainly has an impact on the learning process. Using AI/ML/IoT/BigData techniques in education, providing optimal solution becomes important. It aims to find new challenges and overcome the difficulties of new models/techniques of learning and learning process using AI/ML/IoT/BigData to achieve learning goals. This calls for bringing new ideas in AI/ML/IoT/BigData based on COVID-19 and RI 4.0. which will provide value to researchers and scientists. The proposal aims to collect innovative and unpublished work that focuses on methods and technologies suitable for use in the learning process, or other online learning models for COVID-19 and RI 4.0. This special issue provides an opportunity for researchers around the world to share their new ideas in an interesting field.
Keywords
• Artificial Intelligence methods for Learning Process
• Learning methods for COVID-19 and Revolution Industries 4.0
• Technology learning assessment
• Learning Technology
• Novel methods with AI/ML/IoT for the learning process in COVID-19 and Revolution Industries 4.0
• Decision making
Published Papers
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Open Access
ARTICLE
Sparse Crowd Flow Analysis of Tawaaf of Kaaba During the COVID-19 Pandemic
Durr-e-Nayab, Ali Mustafa Qamar, Rehan Ullah Khan, Waleed Albattah, Khalil Khan, Shabana Habib, Muhammad Islam
CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5581-5601, 2022, DOI:10.32604/cmc.2022.022153
（This article belongs to this Special Issue:
Application of Artificial Intelligence, Internet of Things, and Learning Approach for Learning Process in COVID-19/Industrial Revolution 4.0)
Abstract The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related tasks. Video surveillance and crowd management using video analysis techniques have significantly impacted today's research, and numerous applications have been developed in this domain. This research proposed an anomaly detection technique applied to Umrah videos in Kaaba during the COVID-19 pandemic through sparse crowd analysis. Managing the Kaaba rituals is crucial since the crowd gathers from around the world and requires proper analysis during these days of the pandemic. The Umrah videos are analyzed, and a…
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Open Access
ARTICLE
Clustering Indoor Location Data for Social Distancing and Human Mobility to Combat COVID-19
Yuan Ai Ho, Chee Keong Tan, Yin Hoe Ng
CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 907-924, 2022, DOI:10.32604/cmc.2022.021756
（This article belongs to this Special Issue:
Application of Artificial Intelligence, Internet of Things, and Learning Approach for Learning Process in COVID-19/Industrial Revolution 4.0)
Abstract The world is experiencing the unprecedented time of a pandemic caused by the coronavirus disease (
i.e., COVID-19). As a countermeasure, contact tracing and social distancing are essential to prevent the transmission of the virus, which can be achieved using indoor location analytics. Based on the indoor location analytics, the human mobility on a site can be monitored and planned to minimize human’s contact and enforce social distancing to contain the transmission of COVID-19. Given the indoor location data, the clustering can be applied to cluster spatial data, spatio-temporal data and movement behavior features for proximity detection or contact tracing applications.…
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Open Access
ARTICLE
Prediction of COVID-19 Transmission in the United States Using Google Search Trends
Meshrif Alruily, Mohamed Ezz, Ayman Mohamed Mostafa, Nacim Yanes, Mostafa Abbas, Yasser El-Manzalawy
CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1751-1768, 2022, DOI:10.32604/cmc.2022.020714
（This article belongs to this Special Issue:
Application of Artificial Intelligence, Internet of Things, and Learning Approach for Learning Process in COVID-19/Industrial Revolution 4.0)
Abstract Accurate forecasting of emerging infectious diseases can guide public health officials in making appropriate decisions related to the allocation of public health resources. Due to the exponential spread of the COVID-19 infection worldwide, several computational models for forecasting the transmission and mortality rates of COVID-19 have been proposed in the literature. To accelerate scientific and public health insights into the spread and impact of COVID-19, Google released the Google COVID-19 search trends symptoms open-access dataset. Our objective is to develop 7 and 14-day-ahead forecasting models of COVID-19 transmission and mortality in the US using the Google search trends for COVID-19…
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Open Access
ARTICLE
Intelligent Integrated Model for Improving Performance in Power Plants
Ahmed Ali Ajmi, Noor Shakir Mahmood, Khairur Rijal Jamaludin, Hayati Habibah Abdul Talib, Shamsul Sarip, Hazilah Mad Kaidi
CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5783-5801, 2022, DOI:10.32604/cmc.2022.021885
（This article belongs to this Special Issue:
Application of Artificial Intelligence, Internet of Things, and Learning Approach for Learning Process in COVID-19/Industrial Revolution 4.0)
Abstract Industry 4.0 is expected to play a crucial role in improving energy management and personnel performance in power plants. Poor performance problem in maintaining power plants is the result of both human errors, human factors and the poor implementation of automation in energy management. This problem can potentially be solved using artificial intelligence (AI) and an integrated management system (IMS). This article investigates the current challenges to improving personnel and energy management performance in power plants, identifies the critical success factors (CSFs) for an integrated intelligent framework, and develops an intelligent framework that enables power plants to improve performance. The…
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Open Access
ARTICLE
SutteARIMA: A Novel Method for Forecasting the Infant Mortality Rate in Indonesia
Ansari Saleh Ahmar, Eva Boj del Val, M. A. El Safty, Samirah AlZahrani, Hamed El-Khawaga
CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6007-6022, 2022, DOI:10.32604/cmc.2022.021382
（This article belongs to this Special Issue:
Application of Artificial Intelligence, Internet of Things, and Learning Approach for Learning Process in COVID-19/Industrial Revolution 4.0)
Abstract This study focuses on the novel forecasting method (SutteARIMA) and its application in predicting Infant Mortality Rate data in Indonesia. It undertakes a comparison of the most popular and widely used four forecasting methods: ARIMA, Neural Networks Time Series (NNAR), Holt-Winters, and SutteARIMA. The data used were obtained from the website of the World Bank. The data consisted of the annual infant mortality rate (per 1000 live births) from 1991 to 2019. To determine a suitable and best method for predicting Infant Mortality rate, the forecasting results of these four methods were compared based on the mean absolute percentage error…
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Open Access
ARTICLE
Sustainable Supplier Selection Model in Supply Chains During the COVID-19 Pandemic
Chia-Nan Wang, Chao-Fen Pan, Viet Tinh Nguyen, Syed Tam Husain
CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3005-3019, 2022, DOI:10.32604/cmc.2022.020206
（This article belongs to this Special Issue:
Application of Artificial Intelligence, Internet of Things, and Learning Approach for Learning Process in COVID-19/Industrial Revolution 4.0)
Abstract As global supply chains become more developed and complicated, supplier quality has become increasingly influential on the competitiveness of businesses during the Covid-19 pandemic. Consequently, supplier selection is an increasingly important process for any business around the globe. Choosing a supplier is a complex decision that can result in lower procurement costs and increased profits without increasing the cost or lowering the quality of the product. However, these decision-making problems can be complicated in cases with multiple potential suppliers. Vietnam's textile and garment industry, for example, has made rapid progress in recent years but is still facing great difficulties as…
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Open Access
ARTICLE
Soft -Rough Set and Its Applications in Decision Making of Coronavirus
M. A. El Safty, Samirah Al Zahrani, M. K. El-Bably, M. El Sayed
CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 267-285, 2022, DOI:10.32604/cmc.2022.019345
（This article belongs to this Special Issue:
Application of Artificial Intelligence, Internet of Things, and Learning Approach for Learning Process in COVID-19/Industrial Revolution 4.0)
Abstract In this paper, we present a proposed method for generating a soft rough approximation as a modification and generalization of Zhaowen et al. approach. Comparisons were obtained between our approach and the previous study and also. Eventually, an application on Coronavirus (COVID-19) has been presented, illustrated using our proposed concept, and some influencing results for symptoms of Coronavirus patients have been deduced. Moreover, following these concepts, we construct an algorithm and apply it to a decision-making problem to demonstrate the applicability of our proposed approach. Finally, a proposed approach that competes with others has been obtained, as well as realistic…
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Open Access
ARTICLE
Real-time Privacy Preserving Framework for Covid-19 Contact Tracing
Akashdeep Bhardwaj, Ahmed A. Mohamed, Manoj Kumar, Mohammed Alshehri, Ahed Abugabah
CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1017-1032, 2022, DOI:10.32604/cmc.2022.018736
（This article belongs to this Special Issue:
Application of Artificial Intelligence, Internet of Things, and Learning Approach for Learning Process in COVID-19/Industrial Revolution 4.0)
Abstract The recent unprecedented threat from COVID-19 and past epidemics, such as SARS, AIDS, and Ebola, has affected millions of people in multiple countries. Countries have shut their borders, and their nationals have been advised to self-quarantine. The variety of responses to the pandemic has given rise to data privacy concerns. Infection prevention and control strategies as well as disease control measures, especially real-time contact tracing for COVID-19, require the identification of people exposed to COVID-19. Such tracing frameworks use mobile apps and geolocations to trace individuals. However, while the motive may be well intended, the limitations and security issues associated…
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Open Access
ARTICLE
Prediction of BRIC Stock Price Using ARIMA, SutteARIMA, and Holt-Winters
Ansari Saleh Ahmar, Pawan Kumar Singh, Nguyen Van Thanh, Nguyen Viet Tinh, Vo Minh Hieu
CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 523-534, 2022, DOI:10.32604/cmc.2022.017068
（This article belongs to this Special Issue:
Application of Artificial Intelligence, Internet of Things, and Learning Approach for Learning Process in COVID-19/Industrial Revolution 4.0)
Abstract The novel coronavirus has played a disastrous role in many countries worldwide. The outbreak became a major epidemic, engulfing the entire world in lockdown and it is now speculated that its economic impact might be worse than economic deceleration and decline. This paper identifies two different models to capture the trend of closing stock prices in Brazil (BVSP), Russia (IMOEX.ME), India (BSESN), and China (SSE), i.e., (BRIC) countries. We predict the stock prices for three daily time periods, so appropriate preparations can be undertaken to solve these issues. First, we compared the ARIMA, SutteARIMA and Holt-Winters (H-W) methods to determine…
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Open Access
ARTICLE
A Model for Selecting a Biomass Furnace Supplier Based on Qualitative and Quantitative Factors
Chia-Nan Wang, Hsin-Pin Fu, Hsien-Pin Hsu, Van Thanh Nguyen, Viet Tinh Nguyen, Ansari Saleh Ahmar
CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2339-2353, 2021, DOI:10.32604/cmc.2021.016284
（This article belongs to this Special Issue:
Application of Artificial Intelligence, Internet of Things, and Learning Approach for Learning Process in COVID-19/Industrial Revolution 4.0)
Abstract In developing countries, solar energy is the largest source of energy, accounting for 35%–45% of the total energy supply. This energy resource plays a vital role in meeting the energy needs of the world, especially in Vietnam. Vietnam has favorable natural conditions for this energy production. Because it is hot and humid, and it has much rainfall and fertile soil, biomass develops very quickly. Therefore, byproducts from agriculture and forestry are abundant and continuously increasing. However, byproducts that are considered natural waste have become the cause of environmental pollution; these include burning forests, straw, and sawdust in the North; and…
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Open Access
ARTICLE
Implications COVID-19 on Performance and Energy Management in the Production Electricity
Noor Shakir Mahmood, Ahmed Ali Ajmi, Shamsul Sarip, Khairur Rijal Jamaludin, Hazilah Mad Kaidi, Hayati Abdul Talib
CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 895-911, 2021, DOI:10.32604/cmc.2021.018012
（This article belongs to this Special Issue:
Application of Artificial Intelligence, Internet of Things, and Learning Approach for Learning Process in COVID-19/Industrial Revolution 4.0)
Abstract The COVID-19 pandemic has directly impacted the electric power industry; the energy sector has experienced huge losses in electricity production. These losses have also affected the reliability of communication and employees’ performance, hence destabilizing the electric power system. This article aims at achieving two objectives. First, analyzing the impact of the COVID-19 pandemic on the communication of performance (human error and human factors) and energy management in electricity production. Second, to develop a conceptual framework model to alleviate effects of the pandemic on the power sector and then improve energy management and human performance. This paper involves investigating the influence…
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Open Access
ARTICLE
Minimizing Warpage for Macro-Size Fused Deposition Modeling Parts
Thanh Thuong Huynh, Tien V. T. Nguyen, Quoc Manh Nguyen, Trieu Khoa Nguyen
CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2913-2923, 2021, DOI:10.32604/cmc.2021.016064
（This article belongs to this Special Issue:
Application of Artificial Intelligence, Internet of Things, and Learning Approach for Learning Process in COVID-19/Industrial Revolution 4.0)
Abstract In this study, we investigated warpage and corner lifting minimization for three-dimensional printed parts generated by macro-size fused deposition modeling (FDM). First, the reasons for warpage were theoretically elucidated. This approach revealed that the thermal deformation and differential volumetric shrinkage of the extruded molten plastic resulted in warpage of FDM parts. In addition, low adhesion between the deposited model and the heated or non-heated printing bed may intensify warpage further. As a next step, initial small-size and medium-size models were used to identify parameters to manage and minimize warpage in a way that would reduce material consumption and running time.…
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Open Access
ARTICLE
Rasch Model Assessment for Bloom Digital Taxonomy Applications
Mohd Effendi Ewan Mohd Matore
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1235-1253, 2021, DOI:10.32604/cmc.2021.016143
（This article belongs to this Special Issue:
Application of Artificial Intelligence, Internet of Things, and Learning Approach for Learning Process in COVID-19/Industrial Revolution 4.0)
Abstract Assessment using Bloom’s taxonomy levels has evolved in a variety of contexts and uses. In the era of the COVID-19 pandemic, which necessitates use of online assessment, the need for teachers to use digital-based taxonomy skills or Bloom’s Digital Taxonomy (BDT) has increased even more. However, the existing studies on validity and reliability of BDT items are limited. To overcome this limitation, this study aims to test whether BDT has good psychometric characteristics as a teacher’s self-assessment tool using the Rasch model analysis and to investigate the pattern of BDT usage in teaching and learning. By using a quantitative online…
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