Possibilities of Applying Neural Network Models to Assess Quality of Life

This study examines quality of life, its varied evaluations, and the role of objective and subjective perspectives. Additionally, neural network models are discussed as a means to assess quality of life indicators. Statistical indicators and private criteria are also explored in relation to evaluating quality of life. Finally, the study touches on generational theory and its relevance to quality of life evaluation. One facet worth exploring is how the TCN model can discern temporal patterns in the generational experience, dissecting the impact of historical events and societal shifts on quality of life indicators. By comprehending the temporal dependencies encoded in TCN, policies can be tailored to meet the evolving needs and preferences of distinct generational cohorts. The synthesis of generational classification and TCN applications underscores the potential for a more sophisticated, dynamic, and adaptable approach to quality of life assessment. As we navigate the complexities of societal development, acknowledging the temporal dimensions and generational intricacies becomes imperative, and TCN emerges as a valuable tool in unraveling these intricate patterns for informed decision-making and policy formulation.