Mammadov, SarkanKucukkulahli, Enver2025-10-112025-10-1120252076-3417https://doi.org/10.3390/app15073792https://hdl.handle.net/20.500.12684/21592University libraries are essential academic spaces, yet existing smart systems often overlook user perception in environmental optimization. A key challenge is the lack of adaptive frameworks balancing objective sensor data with subjective user experience. This study introduces an Internet of Things (IoT)-powered framework integrating real-time sensor data, image-based occupancy tracking, and user feedback to enhance study conditions via machine learning (ML). Unlike prior works, our system fuses objective measurements and subjective input for personalized assessment. Environmental factors-including air quality, sound, temperature, humidity, and lighting-were monitored using microcontrollers and image processing. User feedback was collected via surveys and incorporated into models trained using Logistic Regression, Decision Trees, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNNs), Extreme Gradient Boosting (XGBoost), and Naive Bayes. KNNs achieved the highest F1 score (99.04%), validating the hybrid approach. A user interface analyzes environmental factors, identifying primary contributors to suboptimal conditions. A scalable fog-cloud architecture distributes computation between edge devices (fog) and cloud servers, optimizing resource management. Beyond libraries, the framework extends to other smart workspaces. By integrating the IoT, ML, and user-driven optimization, this study presents an adaptive decision support system, transforming libraries into intelligent, user-responsive environments.en10.3390/app15073792info:eu-repo/semantics/openAccessuniversity libraryenvironmental qualityIoTmachine learninguser feedbackKNNfog-cloud architectureArticle1572-s2.0-105002276789WOS:001463702100001Q1Q2