Volume 17 , Issue 1 , PP: 10-20, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Ali Subhi Alhumaima 1 , Waleed Khalid Al-Zubaidi 2 , El-Sayed M. El-Kenawy 3 * , Marwa M. Eid 4
Doi: https://doi.org/10.54216/JCIM.170102
In this study, we present an integrated approach to IoT-based environmental data analysis using a collection of unsupervised-learning techniques. We employed KMeans clustering in particular to identify natural groupings in environmental and behavioral features such as air quality, noise level, temperature, stress level, sleeping hours, and mood score. We then trained a Decision Tree classifier to predict and interpret cluster membership from raw sensor readings. The data of more than 30,000 observations in indoor school environments has multifaceted relationships between environmental factors and psychological well-being. KMeans consistently detected three environmental-behavioral states, and the Decision Tree classifier performed 87% classification accuracy, which indicated extremely high predictability power in addition to interpretability. The results indicated that sleep duration, air, and stress were the main factors for cluster discrimination. The hybrid model introduces the potential of observing real-time environmental and mental states for applications in smart cities. The approach is scalable, interpretable, and usable in IoT settings for proactivity-enabled wellness management.
IoT Sensor Data , Environmental Monitoring , KMeans Clustering , Decision Tree Classification , Behavioral Analysis , Air Quality , Stress Prediction , Machine Learning, Data Mining
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