After Thematic Analysis: Introducing the Fuzzy Thematic Network Analysis in Psychological Research

Farahani, Hojjatollah; Azadfallah, Parviz; Rashidi, Kazhal

doi:10.1007/978-3-030-74772-5_35

Cited by 2 publications

(4 citation statements)

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“…The values in the input layer that connect to the hidden node are multiplied by pre-determined weights, and then all of the results are summed to create a single number. This number is passed as an argument to a nonlinear mathematical function, called the activation function (AF) in ANNs (56). The AF returns an output between 0 and 1 which is the sum of the weighted input values that enter a node.…”

Section: The Ann Approachmentioning

confidence: 99%

“…The MLP network training procedure is used to minimize an objective function with regard to its criteria, which is connected to the task that the MLP is used for. The feedforward algorithm is used to quickly complete the prediction by an MLP (56).…”

Section: The Multilayer Perceptron Approachmentioning

confidence: 99%

“…The equations consider the number of inputs and the desired degree of fit, and can be used to determine the necessary number of parameters for MLP neural networks. Typically, one hidden layer is enough, but if two hidden layers are needed to meet the required constraints, the equations can help determine the appropriate number of hidden units for each layer (56,62,63).…”

Section: The Number Of the Necessary Hidden Unitsmentioning

confidence: 99%

“…Farahani et al (56) describe that the MLP model was trained using a supervised learning approach, where the model is presented with a labeled dataset and iteratively adjusts its weights and biases to minimize prediction errors. The error function used for training is the Sum of Squares, which calculates the squared difference between the predicted output and the true target value for each data point.…”

Section: Model Training and Error Functionmentioning

confidence: 99%

See 3 more Smart Citations

Multilayer perceptron modeling for social dysfunction prediction based on general health factors in an Iranian women sample

Bagheri,

Taridashti,

Farahani

et al. 2023

Front. Psychiatry

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In the year 2022, this research conducted an in-person study involving 780 single or widowed women, aged between 20 and 70, falling within the bottom three economic deciles and possessing varying levels of education. All participants held educational qualifications below a high school diploma and were beneficiaries of charitable financial support in Khorasan province, Iran. The study aimed to investigate the predictive factors of social dysfunction in this specific demographic. Data collection spanned a 12-month period throughout 2022, with participants completing the GHQ-28 questionnaire during their visits to the charity office. Clinical in-person interviews were also conducted to gather comprehensive data. Data analysis was carried out using IBM SPSS version 27. The research employed a Multilayer Perceptron (MLP) neural network model, considering an extensive set of input factors and covariates. These factors included cognitive functioning, anxiety, depression, age, and education levels. The MLP model exhibited robust performance, achieving high overall accuracy and sensitivity in identifying cases of high social dysfunction. The findings emphasized the significance of cognitive functioning, anxiety, and depression as pivotal predictors of social dysfunction within this specific demographic, while education and age displayed relatively lower importance. The normalized importance scores provided a relative measure of each covariate’s impact on the model’s predictions. These results furnish valuable insights for the development of targeted interventions and evidence-based policies aimed at addressing social dysfunction and promoting societal well-being among economically disadvantaged, single or widowed women. Notably, the research underscores the potential of MLP modeling in social science research and suggests avenues for further research and refinement to enhance the model’s predictive accuracy, particularly for cases of low social dysfunction.

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Section: The Ann Approachmentioning

confidence: 99%

Section: The Multilayer Perceptron Approachmentioning

confidence: 99%