LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 3 of total 3

Search options

  1. Article ; Online: The effects of negative social media connotations on subjective wellbeing of an ageing population: A stressor-strain-outcome perspective.

    Zolkepli, Izzal Asnira / Tariq, Rehan / Isawasan, Pradeep / Shamugam, Lalitha / Mustafa, Hasrina

    PloS one

    2024  Volume 19, Issue 1, Page(s) e0296973

    Abstract: In recent years, users' privacy concerns and reluctance to use have posed a challenge for the social media and wellbeing of its users. There is a paucity of research on elderly users' negative connotations of social media and the way these connotations ... ...

    Abstract In recent years, users' privacy concerns and reluctance to use have posed a challenge for the social media and wellbeing of its users. There is a paucity of research on elderly users' negative connotations of social media and the way these connotations contribute to developing passive behaviour towards social media use, which, in turn, affects subjective wellbeing. To address this research vacuum we employed the stressor-strain-outcome (SSO) approach to describe the evolution of passive social media use behaviour from the perspective of communication overload, complexity, and privacy. We conceptualized subjective wellbeing as a combination of three components-negative feelings, positive feelings, and life satisfaction. Negative and positive feelings were used to derive an overall affect balance score that fluctuates between 'unhappiest possible' and 'happiest possible'. The proposed research framework was empirically validated through 399 valid responses from elderly social media users. Our findings reveal that communication overload and complexity raise privacy concerns among social media users, which leads to passive usage of social media. This passive social media use improved the subjective wellbeing favourably by lowering negative feelings and raising positive feelings and life satisfaction. The findings also revealed that respondents' overall affect balance leans towards positive feelings as a consequence of passive social media use. This study contributes to the field of technostress by illuminating how the SSO perspective aid the comprehension of the way passive social media use influences the subjective wellbeing of its users.
    MeSH term(s) Humans ; Aged ; Social Media ; Emotions ; Communication ; Mental Processes ; Aging
    Language English
    Publishing date 2024-01-30
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0296973
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

  2. Article ; Online: Predicting dengue transmission rates by comparing different machine learning models with vector indices and meteorological data.

    Ong, Song Quan / Isawasan, Pradeep / Ngesom, Ahmad Mohiddin Mohd / Shahar, Hanipah / Lasim, As'malia Md / Nair, Gomesh

    Scientific reports

    2023  Volume 13, Issue 1, Page(s) 19129

    Abstract: Machine learning algorithms (ML) are receiving a lot of attention in the development of predictive models for monitoring dengue transmission rates. Previous work has focused only on specific weather variables and algorithms, and there is still a need for ...

    Abstract Machine learning algorithms (ML) are receiving a lot of attention in the development of predictive models for monitoring dengue transmission rates. Previous work has focused only on specific weather variables and algorithms, and there is still a need for a model that uses more variables and algorithms that have higher performance. In this study, we use vector indices and meteorological data as predictors to develop the ML models. We trained and validated seven ML algorithms, including an ensemble ML method, and compared their performance using the receiver operating characteristic (ROC) with the area under the curve (AUC), accuracy and F1 score. Our results show that an ensemble ML such as XG Boost, AdaBoost and Random Forest perform better than the logistics regression, Naïve Bayens, decision tree, and support vector machine (SVM), with XGBoost having the highest AUC, accuracy and F1 score. Analysis of the importance of the variables showed that the container index was the least important. By removing this variable, the ML models improved their performance by at least 6% in AUC and F1 score. Our result provides a framework for future studies on the use of predictive models in the development of an early warning system.
    MeSH term(s) Humans ; Machine Learning ; Algorithms ; Support Vector Machine ; ROC Curve ; Dengue/epidemiology
    Language English
    Publishing date 2023-11-05
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-023-46342-2
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

  3. Article ; Online: Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time.

    Ong, Song-Quan / Ahmad, Hamdan / Nair, Gomesh / Isawasan, Pradeep / Majid, Abdul Hafiz Ab

    Scientific reports

    2021  Volume 11, Issue 1, Page(s) 9908

    Abstract: Classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using ... ...

    Abstract Classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.
    MeSH term(s) Adult ; Aedes/anatomy & histology ; Aedes/classification ; Aedes/virology ; Animals ; Datasets as Topic ; Deep Learning ; Dengue/prevention & control ; Dengue/transmission ; Dengue/virology ; Entomology/methods ; Entomology/statistics & numerical data ; Female ; Humans ; Image Interpretation, Computer-Assisted/methods ; Image Interpretation, Computer-Assisted/statistics & numerical data ; Insecticide Resistance ; Male ; Middle Aged ; Mosquito Control/methods ; Mosquito Vectors/anatomy & histology ; Mosquito Vectors/classification ; Mosquito Vectors/virology ; Video Recording
    Language English
    Publishing date 2021-05-10
    Publishing country England
    Document type Comparative Study ; Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-021-89365-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

To top