Min Hyung Park¹, Dongyan Nan^2,3, Yerin Kim¹, Jang Hyun Kim^1,2,3,*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 121-134, 2023, DOI:10.32604/csse.2023.033247

Abstract After the outbreak of COVID-19, the global economy entered a deep freeze. This observation is supported by the Volatility Index (VIX), which reflects the market risk expected by investors. In the current study, we predicted the VIX using variables obtained from the sentiment analysis of data on Twitter posts related to the keyword “COVID-19,” using a model integrating the bidirectional long-term memory (BiLSTM), autoregressive integrated moving average (ARIMA) algorithm, and generalized autoregressive conditional heteroskedasticity (GARCH) model. The Linguistic Inquiry and Word Count (LIWC) program and Valence Aware Dictionary for Sentiment Reasoning (VADER) model were utilized as sentiment analysis methods. The… More >

Anahita Ali^*, Santosh Kumar

International Journal of Mental Health Promotion, Vol.25, No.7, pp. 833-845, 2023, DOI:10.32604/ijmhp.2023.028799

Abstract Since the coronavirus pandemic, many factors led to the change in the mental well-being of hospital administrators and their staff. The pandemic negatively impacted the availability and capability of health professionals to deliver essential services and meet rising demand. Therefore, this study aimed to understand the perspective of hospital administrators about issues and challenges that negatively impacted their staff’s mental health and hospital administrators’ coping response to mitigate those challenges and issues. An exploratory qualitative study was conducted with 17 hospital administrators (superintendents, deputy superintendents, nursing in charge and hospital in charge) working in a government district hospital of Rajasthan… More > Graphic Abstract

Yoshikiyo Kanada^1,#, Shota Suzumura^1,2,#, Soichiro Koyama¹, Kazuya Takeda¹, Kenta Fujimura¹, Takuma Ii¹, Shigeo Tanabe¹, Hiroaki Sakurai^1,*

International Journal of Mental Health Promotion, Vol.25, No.7, pp. 855-861, 2023, DOI:10.32604/ijmhp.2023.028956

Abstract Mental health difficulties can impact students’ motivation, focus, and ability to communicate with others. Students attending medical universities are more likely to experience anxiety, depression, and other mood changes for the first time. However, no study has examined their prevalence among Japanese rehabilitation students. This study investigated the prevalence of anxiety among Japanese rehabilitation students and aimed to identify its predictors. A cross-sectional study was conducted among 148 first-year physical and occupational therapy students at a private medical university in Japan in June 2022. Data on sociodemographic and personal characteristics, such as gender, age, subject major, regular exercise, place of… More >

B. Ducrocq

Psycho-Oncologie, Vol.16, No.3, pp. 313-317, 2022, DOI:10.3166/pson-2022-0209

Abstract The world population is lockdown in March 2020 because of the COVID-19 pandemic due to the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) virus. The impact on mental and sexual health adds to the effects on public health and physical health. Individuals’ projects were disrupted like the parenthood of couples. The sexuality adapted to loneliness, and we observe important reshuffles linked to anxiety and uncertainty of worldwide pandemic.

Résumé La pandémie de Covid-19 liée au virus SARSCoV-2 a imposé un confinement mondial des populations en mars 2020. Outre les effets sur la santé publique et la santé physique, la… More >

L. Amaadour, I. Lahrch, O. Siyouri, K. Oualla, Z. Benbrahim, S. Arifi, C. Aarab, S. El Fakir, N. Mellas

Psycho-Oncologie, Vol.17, No.1, pp. 38-43, 2023, DOI:10.3166/pson-2022-0221

Abstract Background: The Covid-19 disease is a cause for several mental alterations mainly in cancer patients who are already categorized as a vulnerable population.
Aim: The objective of this study is to characterize psychological disorders caused by Covid-19 infection among cancer patients on systemic treatment.
Methods: It is a cross-sectional study performed at the Department of Medical Oncology of Hassan-II University Hospital of Fez, Morocco, during a period of four months (peak of the pandemic). Symptoms of anxiety/depression and post-traumatic stress disorder in patients were screened using HADS (Hospital Anxiety and Depression Scale) and PCL-5 (post-traumatic stress disorder checklist version DSM-5)… More >

Aoli Li^1,#, Yan You^1,2,#, Kunli Wu³, Huibin Shan⁴, Younglee Kim⁵, Qilian He^1,*

International Journal of Mental Health Promotion, Vol.25, No.6, pp. 783-797, 2023, DOI:10.32604/ijmhp.2023.026956

Abstract Objective: To explore the double psychosocial threats of the COVID-19 pandemic, targeted behavior toward Chinese Americans, and the correlates to their mental health. Methods: A quantitative, cross-sectional, and descriptive design was utilized by using a purposive convenience sample of 301 Chinese Americans over the age of 18 residing in the United States. Online data collection was conducted through the social media platform WeChat from April 8–21, 2021. Descriptive statistical analysis was used for the participants’ demographic characteristics, Multidimensional Scale of Perceived Social Support (MSPSS), Double Threat Situations, COVID-19 Racial Discrimination, and General Anxiety Disorder-7 (GAD-7). Stepwise logistic regression was conducted… More > Graphic Abstract

Asma Qaiser¹, Saman Hina¹, Abdul Karim Kazi^1,*, Saad Ahmed², Raheela Asif³

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 73-90, 2023, DOI:10.32604/iasc.2023.036784

Abstract In the past few years, social media and online news platforms have played an essential role in distributing news content rapidly. Consequently. verification of the authenticity of news has become a major challenge. During the COVID-19 outbreak, misinformation and fake news were major sources of confusion and insecurity among the general public. In the first quarter of the year 2020, around 800 people died due to fake news relevant to COVID-19. The major goal of this research was to discover the best learning model for achieving high accuracy and performance. A novel case study of the Fake News Classification using… More >

Meilin Wu^1,2, Lianggui Tang^1,2,*, Qingda Zhang^1,2, Ke Yan^1,2

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 179-198, 2023, DOI:10.32604/iasc.2023.036684

Abstract As COVID-19 poses a major threat to people’s health and economy, there is an urgent need for forecasting methodologies that can anticipate its trajectory efficiently. In non-stationary time series forecasting jobs, there is frequently a hysteresis in the anticipated values relative to the real values. The multilayer deep-time convolutional network and a feature fusion network are combined in this paper’s proposal of an enhanced Multilayer Deep Time Convolutional Neural Network (MDTCNet) for COVID-19 prediction to address this problem. In particular, it is possible to record the deep features and temporal dependencies in uncertain time series, and the features may then… More >

Shroog Alshomrani^*, Muhammad Arif, Mohammed A. Al Ghamdi

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5717-5742, 2023, DOI:10.32604/cmc.2023.038059

Abstract Coronavirus has infected more than 753 million people, ranging in severity from one person to another, where more than six million infected people died worldwide. Computer-aided diagnostic (CAD) with artificial intelligence (AI) showed outstanding performance in effectively diagnosing this virus in real-time. Computed tomography is a complementary diagnostic tool to clarify the damage of COVID-19 in the lungs even before symptoms appear in patients. This paper conducts a systematic literature review of deep learning methods for classifying the segmentation of COVID-19 infection in the lungs. We used the methodology of systematic reviews and meta-analyses (PRISMA) flow method. This research aims… More >

Sobia Nawaz¹, Sidra Rasheed², Wania Sami³, Lal Hussain^4,5,*, Amjad Aldweesh^6,*, Elsayed Tag eldin⁷, Umair Ahmad Salaria^8,9, Mohammad Shahbaz Khan¹⁰

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5213-5228, 2023, DOI:10.32604/cmc.2023.037543

Abstract This study is designed to develop Artificial Intelligence (AI) based analysis tool that could accurately detect COVID-19 lung infections based on portable chest x-rays (CXRs). The frontline physicians and radiologists suffer from grand challenges for COVID-19 pandemic due to the suboptimal image quality and the large volume of CXRs. In this study, AI-based analysis tools were developed that can precisely classify COVID-19 lung infection. Publicly available datasets of COVID-19 (N = 1525), non-COVID-19 normal (N = 1525), viral pneumonia (N = 1342) and bacterial pneumonia (N = 2521) from the Italian Society of Medical and Interventional Radiology (SIRM), Radiopaedia, The… More >