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  • Open Access


    Depression Detection on COVID 19 Tweets Using Chimp Optimization Algorithm

    R. Meena1,*, V. Thulasi Bai2

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1643-1658, 2022, DOI:10.32604/iasc.2022.025305

    Abstract The Covid-19 outbreak has an unprecedented effects on people's daily lives throughout the world, causing immense stress amongst individuals owing to enhanced psychological disorders like depression, stress, and anxiety. Researchers have used social media data to detect behaviour changes in individuals with depression, postpartum changes and stress detection since it reveals critical aspects of mental and emotional diseases. Considerable efforts have been made to examine the psychological health of people which have limited performance in accuracy and demand increased training time. To conquer such issues, this paper proposes an efficient depression detection framework named Improved Chimp Optimization Algorithm based Convolution… More >

  • Open Access


    Forecasting Mental Stress Using Machine Learning Algorithms

    Elias Hossain1, Abdulwahab Alazeb2,*, Naif Al Mudawi2, Sultan Almakdi2, Mohammed Alshehri2, M. Gazi Golam Faruque3, Wahidur Rahman3

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4945-4966, 2022, DOI:10.32604/cmc.2022.027058

    Abstract Depression is a crippling affliction and affects millions of individuals around the world. In general, the physicians screen patients for mental health disorders on a regular basis and treat patients in collaboration with psychologists and other mental health experts, which results in lower costs and improved patient outcomes. However, this strategy can necessitate a lot of buy-in from a large number of people, as well as additional training and logistical considerations. Thus, utilizing the machine learning algorithms, patients with depression based on information generally present in a medical file were analyzed and predicted. The methodology of this proposed study is… More >

  • Open Access


    Decision Level Fusion Using Hybrid Classifier for Mental Disease Classification

    Maqsood Ahmad1,2, Noorhaniza Wahid1, Rahayu A Hamid1, Saima Sadiq2, Arif Mehmood3, Gyu Sang Choi4,*

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5041-5058, 2022, DOI:10.32604/cmc.2022.026077

    Abstract Mental health signifies the emotional, social, and psychological well-being of a person. It also affects the way of thinking, feeling, and situation handling of a person. Stable mental health helps in working with full potential in all stages of life from childhood to adulthood therefore it is of significant importance to find out the onset of the mental disease in order to maintain balance in life. Mental health problems are rising globally and constituting a burden on healthcare systems. Early diagnosis can help the professionals in the treatment that may lead to complications if they remain untreated. The machine learning… More >

  • Open Access


    A Novel Deep Learning Based Healthcare Model for COVID-19 Pandemic Stress Analysis

    Ankur Dumka1, Parag Verma2, Rajesh Singh3, Anil Kumar Bisht4, Divya Anand5,6,*, Hani Moaiteq Aljahdali7, Irene Delgado Noya6,8, Silvia Aparicio Obregon6,9

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 6029-6044, 2022, DOI:10.32604/cmc.2022.024698

    Abstract Coronavirus (COVID-19) has impacted nearly every person across the globe either in terms of losses of life or as of lockdown. The current coronavirus (COVID-19) pandemic is a rare/special situation where people can express their feelings on Internet-based social networks. Social media is emerging as the biggest platform in recent years where people spend most of their time expressing themselves and their emotions. This research is based on gathering data from Twitter and analyzing the behavior of the people during the COVID-19 lockdown. The research is based on the logic expressed by people in this perspective and emotions for the… More >

  • Open Access


    Depression, Anxiety, Stress and Their Association with the Use of Electronic Devices among Adolescents during the COVID-19 Pandemic

    Ahmad Y. Alqassim*, Mohamed S. Mahfouz, Mohammed M. Hakami, Abdullah A. Al Faqih, Ahmad A. Shugairi, Malek R. Alsanosy, Ahmed Y. Rayyani, AbdulAziz Y. Albrraq, Mohammed A. Muaddi, Abdullah A. Alharbi

    International Journal of Mental Health Promotion, Vol.24, No.2, pp. 251-262, 2022, DOI:10.32604/ijmhp.2022.019000

    Abstract Background: Adolescence is a critical, multifactorial developmental phase. With the current pandemic of COVID-19, excessive using of electronic devices is a public health concern. The aim of this study is to investigate the relationship between depression and the use of electronic devices among secondary school children in Jazan, Saudi Arabia during the COVID-19 pandemic. Materials and Methods: The study is an observational, cross-sectional study. Data was collected using an anonymous online survey instrument. including the Depression Anxiety Stress Scale. Results: A total of 427 participants were included in the study. The prevalence of depression, anxiety, and stress in our study… More >

  • Open Access


    Emotion Based Signal Enhancement Through Multisensory Integration Using Machine Learning

    Muhammad Adnan Khan1,2, Sagheer Abbas3, Ali Raza3, Faheem Khan4, T. Whangbo4,*

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5911-5931, 2022, DOI:10.32604/cmc.2022.023557

    Abstract Progress in understanding multisensory integration in human have suggested researchers that the integration may result into the enhancement or depression of incoming signals. It is evident based on different psychological and behavioral experiments that stimuli coming from different perceptual modalities at the same time or from the same place, the signal having more strength under the influence of emotions effects the response accordingly. Current research in multisensory integration has not studied the effect of emotions despite its significance and natural influence in multisensory enhancement or depression. Therefore, there is a need to integrate the emotional state of the agent with… More >

  • Open Access


    Be Called and Be Healthier: How Does Calling Influence Employees’ Anxiety and Depression in the Workplace?

    Wenyuan Jin1, Jialing Miao2, Yuanfang Zhan3,*

    International Journal of Mental Health Promotion, Vol.24, No.1, pp. 1-12, 2022, DOI:10.32604/IJMHP.2022.018624

    Abstract Despite limited studies have found the negative relationships between calling and mental health symptoms, its underlying mechanism is still unknown. Drawing on the conservation of resources theory (COR), this study developed the resources model that explains the relationships between career calling, anxiety and depression, and the underlying mechanism. With a sample of 628 employees from the two-wave survey, the theorized model was tested. The results showed that career calling was able to decrease the levels of employees’ anxiety and depression, and two important resources (i.e., personal growth, and meaningful work) provided explanatory mechanisms for the relationships. The findings highlight the… More >

  • Open Access


    The Influence of Body Investment on Depression in Chinese College Students: A Moderated Mediating Effect

    Jingjing Wang1, Xiangli Guan1,*, Sumei Yin1, Sha Shen2, Xuejiao Li1, Md Zahir Ahmed3, Mary C. Jobe4, Oli Ahmed5

    International Journal of Mental Health Promotion, Vol.24, No.1, pp. 39-50, 2022, DOI:10.32604/ijmhp.2022.019635

    Abstract An individual’s perception, attitude, feeling and behavior about their body are important factors for mental health (depression). This study aims to explore the impact of body investment on depression, and the role of self-efficacy and self-esteem in this connection. A hypothetical model about the relationship between body investment, selfefficacy, self-esteem and depression was tested. Using convenient sampling methods, a self-rated cross-sectional survey comprised of paper-based and online modes was conducted among 1,164 college students in Yunnan Province, China from July 2021 to August 2021. The data collection used the body investment scale, self-efficacy scale, self-esteem scale and depression scale. The… More >

  • Open Access


    Efficacy of a Community-Based Trauma Recovery Program after a Fire Disaster

    Yun-Jung Choi1, Mi-Ra Won2, Dong-Hee Cho1,*

    International Journal of Mental Health Promotion, Vol.24, No.1, pp. 85-94, 2022, DOI:10.32604/ijmhp.2022.018017

    Abstract As industries develop, fire disasters and their associated damage are increasing. Investigating the mental health of victims is imperative because this is an essential issue for community recovery after a disaster. This study was conducted to determine the efficacy of a program implemented by a community mental health center based on the investigation of the victims’ depression and post-traumatic stress disorder (PTSD) levels immediately after the disaster and at one-year follow-up. As a result, victims’ depression and PTSD recovered over time, and more changes were confirmed. In particular, the high-risk group for PTSD showed a high program participation rate, and… More >

  • Open Access


    Attention-Based Bi-LSTM Model for Arabic Depression Classification

    Abdulqader M. Almars*

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3091-3106, 2022, DOI:10.32604/cmc.2022.022609

    Abstract Depression is a common mental health issue that affects a large percentage of people all around the world. Usually, people who suffer from this mood disorder have issues such as low concentration, dementia, mood swings, and even suicide. A social media platform like Twitter allows people to communicate as well as share photos and videos that reflect their moods. Therefore, the analysis of social media content provides insight into individual moods, including depression. Several studies have been conducted on depression detection in English and less in Arabic. The detection of depression from Arabic social media lags behind due the complexity… More >

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