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  • Open Access

    ARTICLE

    Fractional Order Modeling of Predicting COVID-19 with Isolation and Vaccination Strategies in Morocco

    Lakhlifa Sadek1, Otmane Sadek1, Hamad Talibi Alaoui2, Mohammed S. Abdo3, Kamal Shah4,5, Thabet Abdeljawad4,6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1931-1950, 2023, DOI:10.32604/cmes.2023.025033 - 06 February 2023

    Abstract In this work, we present a model that uses the fractional order Caputo derivative for the novel Coronavirus disease 2019 (COVID-19) with different hospitalization strategies for severe and mild cases and incorporate an awareness program. We generalize the SEIR model of the spread of COVID-19 with a private focus on the transmissibility of people who are aware of the disease and follow preventative health measures and people who are ignorant of the disease and do not follow preventive health measures. Moreover, individuals with severe, mild symptoms and asymptomatically infected are also considered. The basic reproduction… More >

  • Open Access

    ARTICLE

    LexDeep: Hybrid Lexicon and Deep Learning Sentiment Analysis Using Twitter for Unemployment-Related Discussions During COVID-19

    Azlinah Mohamed1,3,*, Zuhaira Muhammad Zain2, Hadil Shaiba2,*, Nazik Alturki2, Ghadah Aldehim2, Sapiah Sakri2, Saiful Farik Mat Yatin1, Jasni Mohamad Zain1

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1577-1601, 2023, DOI:10.32604/cmc.2023.034746 - 06 February 2023

    Abstract The COVID-19 pandemic has spread globally, resulting in financial instability in many countries and reductions in the per capita gross domestic product. Sentiment analysis is a cost-effective method for acquiring sentiments based on household income loss, as expressed on social media. However, limited research has been conducted in this domain using the LexDeep approach. This study aimed to explore social trend analytics using LexDeep, which is a hybrid sentiment analysis technique, on Twitter to capture the risk of household income loss during the COVID-19 pandemic. First, tweet data were collected using Twint with relevant keywords… More >

  • Open Access

    ARTICLE

    Optimal Machine Learning Driven Sentiment Analysis on COVID-19 Twitter Data

    Bahjat Fakieh1, Abdullah S. AL-Malaise AL-Ghamdi1,2,3, Farrukh Saleem1, Mahmoud Ragab2,4,5,6,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 81-97, 2023, DOI:10.32604/cmc.2023.033406 - 06 February 2023

    Abstract The outbreak of the pandemic, caused by Coronavirus Disease 2019 (COVID-19), has affected the daily activities of people across the globe. During COVID-19 outbreak and the successive lockdowns, Twitter was heavily used and the number of tweets regarding COVID-19 increased tremendously. Several studies used Sentiment Analysis (SA) to analyze the emotions expressed through tweets upon COVID-19. Therefore, in current study, a new Artificial Bee Colony (ABC) with Machine Learning-driven SA (ABCML-SA) model is developed for conducting Sentiment Analysis of COVID-19 Twitter data. The prime focus of the presented ABCML-SA model is to recognize the sentiments More >

  • Open Access

    ARTICLE

    Effect of Family Cohesion on Depression of Chinese College Students in the COVID-19 Pandemic: Chain Mediation Effect of Perceived Social Support and Intentional Self-Regulation

    Jingjing Wang1, Xiangli Guan1,*, Yue Zhang2, Yang Li1, Md Zahir Ahmed3, Mary C. Jobe4, Oli Ahmed5

    International Journal of Mental Health Promotion, Vol.25, No.2, pp. 223-235, 2023, DOI:10.32604/ijmhp.2022.025570 - 02 February 2023

    Abstract Individuals’ perceptions, attitudes, and patterns of getting along with family members are important factors influencing Chinese people’s self-evaluation. The aim of this study was to investigate the effect of family cohesion on depression and the role of perceived social support and intentional self-regulation in this association. A hypothesized model of the association of family cohesion, perceived social support, intentional self-regulation, and depression was examined. A convenience sampling method was used to survey 1,180 college students in Yunnan Province using self-report. Data were collected using the Family Cohesion Scale, the Perceived Social Support Scale, the Intentional More >

  • Open Access

    ARTICLE

    Moderating Mechanism in the Relationship between Social Isolation and Mental Health among College Students during High-Risk Period of COVID-19 Transmission in Hubei, China

    Dong Yang1, Chia Ching Tu1, Zhengyan Guo2, Xiao Dai1, Chia Feng Tu3,*

    International Journal of Mental Health Promotion, Vol.25, No.2, pp. 193-206, 2023, DOI:10.32604/ijmhp.2022.022130 - 02 February 2023

    Abstract This study explored the effect of perceived social isolation on the mental health of college students during the high-risk period of COVID-19 transmission in Hubei, China and the role of social support from online friends in alleviating this effect. The questionnaire responses of 213 college students from four universities in Hubei were included. Measurement and structural models were constructed using structural equation modeling. The findings revealed that perceived social isolation while under home quarantine was a negative predictor of the mental health of college students in Hubei. Low social support from online friends may lead More >

  • Open Access

    ARTICLE

    Exploring the Impact of Crisis and Trauma on the Mental Health and Psychological Well-Being of University Students in Northern Haiti

    Michael Galvin1,2,3,*, Guesly Michel3, Henri-Claude Saintelmond4, Carolyn Lesorogol5, Jean-François Trani5, Lora Iannotti5

    International Journal of Mental Health Promotion, Vol.25, No.2, pp. 173-191, 2023, DOI:10.32604/ijmhp.2023.018800 - 02 February 2023

    Abstract In recent decades, Haiti has been subject to man-made and natural disasters that have left its citizens vulnerable to a range of shocks. With a weak state unable to protect its populace, Haitians are exposed to some of the highest levels of poverty and violence in the Western Hemisphere. In recent years, Haitians have experienced two crises that this study analyzes: the instability and political violence of “peyi lòk” as well as the global pandemic of COVID-19. This community-based assessment explores the impact of these two crises on the mental health and psychological well-being of… More >

  • Open Access

    ARTICLE

    Al-Biruni Earth Radius Optimization for COVID-19 Forecasting

    El-Sayed M. El-kenawy1, Abdelaziz A. Abdelhamid2,3, Abdelhameed Ibrahim4, Mostafa Abotaleb5, Tatiana Makarovskikh5, Amal H. Alharbi6,*, Doaa Sami Khafaga6

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 883-896, 2023, DOI:10.32604/csse.2023.034697 - 20 January 2023

    Abstract Several instances of pneumonia with no clear etiology were recorded in Wuhan, China, on December 31, 2019. The world health organization (WHO) called it COVID-19 that stands for “Coronavirus Disease 2019,” which is the second version of the previously known severe acute respiratory syndrome (SARS) Coronavirus and identified in short as (SARSCoV-2). There have been regular restrictions to avoid the infection spread in all countries, including Saudi Arabia. The prediction of new cases of infections is crucial for authorities to get ready for early handling of the virus spread. Methodology: Analysis and forecasting of epidemic patterns… More >

  • Open Access

    ARTICLE

    Quantum Inspired Differential Evolution with Explainable Artificial Intelligence-Based COVID-19 Detection

    Abdullah M. Basahel, Mohammad Yamin*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 209-224, 2023, DOI:10.32604/csse.2023.034449 - 20 January 2023

    Abstract Recent advancements in the Internet of Things (Io), 5G networks, and cloud computing (CC) have led to the development of Human-centric IoT (HIoT) applications that transform human physical monitoring based on machine monitoring. The HIoT systems find use in several applications such as smart cities, healthcare, transportation, etc. Besides, the HIoT system and explainable artificial intelligence (XAI) tools can be deployed in the healthcare sector for effective decision-making. The COVID-19 pandemic has become a global health issue that necessitates automated and effective diagnostic tools to detect the disease at the initial stage. This article presents… More >

  • Open Access

    ARTICLE

    SNELM: SqueezeNet-Guided ELM for COVID-19 Recognition

    Yudong Zhang1, Muhammad Attique Khan2, Ziquan Zhu1, Shuihua Wang1,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 13-26, 2023, DOI:10.32604/csse.2023.034172 - 20 January 2023

    Abstract (Aim) The COVID-19 has caused 6.26 million deaths and 522.06 million confirmed cases till 17/May/2022. Chest computed tomography is a precise way to help clinicians diagnose COVID-19 patients. (Method) Two datasets are chosen for this study. The multiple-way data augmentation, including speckle noise, random translation, scaling, salt-and-pepper noise, vertical shear, Gamma correction, rotation, Gaussian noise, and horizontal shear, is harnessed to increase the size of the training set. Then, the SqueezeNet (SN) with complex bypass is used to generate SN features. Finally, the extreme learning machine (ELM) is used to serve as the classifier due… More >

  • Open Access

    ARTICLE

    A Multi-Modal Deep Learning Approach for Emotion Recognition

    H. M. Shahzad1,3, Sohail Masood Bhatti1,3,*, Arfan Jaffar1,3, Muhammad Rashid2

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1561-1570, 2023, DOI:10.32604/iasc.2023.032525 - 05 January 2023

    Abstract In recent years, research on facial expression recognition (FER) under mask is trending. Wearing a mask for protection from Covid 19 has become a compulsion and it hides the facial expressions that is why FER under the mask is a difficult task. The prevailing unimodal techniques for facial recognition are not up to the mark in terms of good results for the masked face, however, a multimodal technique can be employed to generate better results. We proposed a multimodal methodology based on deep learning for facial recognition under a masked face using facial and vocal… More >

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