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Abstract Efficient and exact traffic flow forecasting is critical for intelligent transportation systems. The number of model generations increases the computational complexity of deep neural network (DNN) models. Overfitting occurs when hyperparameters are used excessively to train neural network models, and this has a major influence on prediction accuracy. To address these limitations, this approach employed H2O grid-based stochastic gradient descent with ridge regression in deep neural network (GSGDRR-DNN). This technique efficiently distributes memory across several clusters, runs independently, and simultaneously creates numerous DNN models. To remove multicollinearity and achieve better computational efficiency and lower variance, GSGDRR-DNN utilizes stochastic gradient descent (SGD) with ridge regression in the H2O cluster. Finally, we evaluate the performance of the recommended GSGDRR-DNN approach against several DNN methods, including LSTM, Bi-LSTM, GRU, and current state-of-the-art methods. Additionally, the run-time performance of the parallel GSGDRR-DNN model was compared with the run-time performance of the sequential GSGDRR-DNN model. The suggested system has a minimum MSE, a minimum RMSE, a minimum MAE, a minimum RMSLE, and maximum R2 values of 0.012, 0.108, 0.096, 0.015, and 0.99. This demonstrates that the GSGDRR-DNN model of traffic flows outperforms other state-of-the-art approaches in terms of prediction accuracy.

This study aims to explore the food management strategies among caregivers/family members of children with Prader-Willi Syndrome (PWS) using the lens of 'familialisation' of a health problem and the sociology of food socialization. Food intake among individuals with PWS is a main concern for parents, caregivers, and medical practitioners as it affects their physical, mental, and social well-being throughout their lives. Earlier studies on PWS and food intake centered around dietary management, dietary intake and growth, nutritional treatment and pharmacological approaches, nutritional phases, and weight gain. However, little has been done to understand the challenges of managing children with PWS from the sociological lens of food management strategies and socialization among families in Malaysia. This study is based on an investigation involving eight children with PWS and 46 family members and caregivers through lab observations and reflexive interviews. Ten food management strategies were identified that were adopted by the caregivers and families, which were influenced by cultural factors, family norms, and formal and informal support systems. The findings will influence future behavioral interventions to ensure the empowerment and well-being of individuals with PWS and their families.