TY  - JOUR
AU  - Milon, Md Hasanujjaman
AU  - Raja, Mamunur R
AU  - Papel, Md Saidul Islam
AU  - Hossain, Zakir
PY  - 2025
DA  - 2025/06/28
TI  - Predictive Neural Computing Framework for Assessing Mental Health Conditions within Intelligent and Data-Driven Smart City Ecosystems
JO  - ICCK Transactions on Neural Computing
T2  - ICCK Transactions on Neural Computing
JF  - ICCK Transactions on Neural Computing
VL  - 1
IS  - 2
SP  - 98
EP  - 107
DO  - 10.62762/TNC.2025.421125
UR  - https://www.icck.org/article/abs/TNC.2025.421125
KW  - predictive analytics
KW  - mental health
KW  - smart city
KW  - IoT
KW  - federated learning
AB  - Mental health poses a growing concern in metropolitan areas where the speedy urbanization and societal demands are the chief causes of psychological discomfort. The context of intelligent cities, through their capabilities of advanced technologies and interconnecting networks, facilitates the approach of predictive analytic resolution of such issues. This paper is research regarding the implementation of machine learning in conjunction with Artificial Intelligence (AI) inter-operation for the prompt identification and management of mental health anomalies in smart cities. By using information from wearable gadgets, social networks, and the Internet of Things (IoT) based health monitoring systems, the proposed methodology tries to find trends and determinants of the mental health related applications. Federated learning models address data privacy and security requirements by enabling collaborative data analytics across organizations without exposing end-user identities. The results confirm that predictive analytics can boost mental wellness by means of individual approaches and precautionary measures. AI-supported initiatives are the possibility for acquiring mental health and sustaining the ability to get over the traumatic attacks of the smart city society.
SN  - 3068-7519
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Milon2025Predictive,
  author = {Md Hasanujjaman Milon and Mamunur R Raja and Md Saidul Islam Papel and Zakir Hossain},
  title = {Predictive Neural Computing Framework for Assessing Mental Health Conditions within Intelligent and Data-Driven Smart City Ecosystems},
  journal = {ICCK Transactions on Neural Computing},
  year = {2025},
  volume = {1},
  number = {2},
  pages = {98-107},
  doi = {10.62762/TNC.2025.421125},
  url = {https://www.icck.org/article/abs/TNC.2025.421125},
  abstract = {Mental health poses a growing concern in metropolitan areas where the speedy urbanization and societal demands are the chief causes of psychological discomfort. The context of intelligent cities, through their capabilities of advanced technologies and interconnecting networks, facilitates the approach of predictive analytic resolution of such issues. This paper is research regarding the implementation of machine learning in conjunction with Artificial Intelligence (AI) inter-operation for the prompt identification and management of mental health anomalies in smart cities. By using information from wearable gadgets, social networks, and the Internet of Things (IoT) based health monitoring systems, the proposed methodology tries to find trends and determinants of the mental health related applications. Federated learning models address data privacy and security requirements by enabling collaborative data analytics across organizations without exposing end-user identities. The results confirm that predictive analytics can boost mental wellness by means of individual approaches and precautionary measures. AI-supported initiatives are the possibility for acquiring mental health and sustaining the ability to get over the traumatic attacks of the smart city society.},
  keywords = {predictive analytics, mental health, smart city, IoT, federated learning},
  issn = {3068-7519},
  publisher = {Institute of Central Computation and Knowledge}
}