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1. IoT Augmented Physical Scale Model of a Suburban Home

   https://doi.org/10.1109/ICCWorkshops49005.2020.9145040  

   Burns, Thomas; Fichthorn, Gregory; Zehtabian, Sharare; Bacanli, Salih Safa; Razghandi, Mina; Boloni, Ladislau; Turgut, Damla (June 2020, IEEE ICC 2020 Workshop on Convergent IoT (C-IoT))

   null (Ed.)

   Green homes require informed energy management decisions. For instance, it is preferable that a comfortable internal temperature is achieved through natural, energy-efficient means such as opening doors or lowering shades as opposed to turning on the air conditioning. This requires the control agent to understand the complex system dynamics of the home: will opening the window raise or lower the temperature in this particular situation? Unfortunately, developing mathematical models of a suburban home situated in its natural environment is a significant challenge, while performing real-world experiments is costly, takes a long time and depends on external circumstances beyond the control of the experimenter. In this paper, we describe the architecture of a physical, small scale model of a suburban home and its immediate exterior environment. Specific scenarios can be enacted using Internet of Things (IoT) actuators that control the doors and windows. We use a suite of IoT sensors to collect data during the scenario. We use deep learning-based temporal regression models to make predictions about the impact of specific actions on the temperature and humidity in the home. 

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2. A Predictive Analytics Approach for Nursing Home Hurricane Evacuation

   Sakib, Nazmus; Hyer, Kathryn; Dobbs, Debra; Peterson, Lindsay; Jester, Dylan J.; Kong, N (July 2019, Proceedings of the 2019 IISE Annual Conference)

   Whether to evacuate a nursing home (NH) or shelter in place in response to the approaching hurricane is one of the most complex and difficult decisions encountered by nursing home administrators. A variety of factors may affect the evacuation decision, including storm and environmental conditions, nursing home characteristics, and the dwelling residents’ health conditions. Successful prediction of evacuation decision is essential to proactively prepare and manage resources to meet the surge in nursing home evacuation demands. In current nursing home emergency preparedness literature, there is a lack of analytical models and studies for nursing home evacuation demand prediction. In this paper, we propose a predictive analytics framework by applying machine learning techniques, integrated with domain knowledge in NH evacuation research, to extract, identify and quantify the effects of relevant factors on NH evacuation from heterogeneous data sources. In particular, storm features are extracted from Geographic Information System (GIS) data to strengthen the prediction accuracy. To further illustrate the proposed work and demonstrate its practical validity, a real-world case study is given to investigate nursing home evacuation in response to recent Hurricane Irma in Florida. The prediction performance among different predictive models are also compared comprehensively. 

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3. Real-Time Posture Classification on Resource-Constrained Runtime Environments: A Comparative Study of Traditional and Deep Learning Models

   https://doi.org/10.1109/SoutheastCon56624.2025.10971620  

   Shejuti, Zarin; Fuad, Muztaba (March 2025, IEEE)

   While machine learning models perform well on offline data, assessing their performance in real-world, resource-constrained environments-considering accuracy, prediction time, power consumption, and memory usage-is crucial for practical applications. This research implements a mobile-based Human Activity Recognition solution to classify three postures-sitting, standing, and walking-using smartphone sensors, specifically accelerometer, gyroscope, and magnetometer. Time-domain features extracted from these sensors were used, with Random Forest employed for feature selection. One traditional machine learning model, Logistic Regression, and one deep learning model, Convolutional Neural Network, were trained and deployed via an Android application for real-time evaluation. While the Convolutional Neural Network achieved higher accuracy and better memory efficiency, Logistic Regression demonstrated faster prediction times during real-time use. Both models showed reduced accuracy for standing and walking postures in real-world conditions, emphasizing the challenges of deploying machine learning models in dynamic environments. This study highlights the importance of evaluating machine learning models in real-world settings to ensure reliability and efficiency, particularly in resource-constrained environments. 

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4. Modeling Climate Management in a Smart Home using a Scaled Testbed with Accelerated Time

   https://doi.org/10.1109/ICC45855.2022.9882274  

   Sanchez, Estefania; Petro, Caitlin; Bacanli, Salih Safa; Cimen, Furkan; Boloni, Ladislau; Turgut, Damla (May 2022, IEEE International Conference on Computer Communications (ICC))

   A smart home with a controller that can understandand predict the interaction between the external environment and the user’s behavior and preferences can provide significant energy efficiency and savings. Unfortunately, experimentation of real world homes for the development of such a controller is prohibitively expensive. In this paper we describe techniques through which such experiments can be performed on scaled testbed with an accelerated time. We illustrate how the modeling of different geographical areas can be performed by the mapping of the model’s temperature and time to their real-world equivalents. We train three different machine learning models for predicting different sensor readings in the testbed, and find that the achieved predictive accuracy supports the feasibility of the development of future smart climate controllers. 

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5. Predicting the formation of fractionally doped perovskite oxides by a function-confined machine learning method

   https://doi.org/10.1038/s43246-022-00269-9  

   Zhai, Ximei; Ding, Fei; Zhao, Zeyu; Santomauro, Aaron; Luo, Feng; Tong, Jianhua (December 2022, Communications Materials)

   Abstract Fractionally doped perovskites oxides (FDPOs) have demonstrated ubiquitous applications such as energy conversion, storage and harvesting, catalysis, sensor, superconductor, ferroelectric, piezoelectric, magnetic, and luminescence. Hence, an accurate, cost-effective, and easy-to-use methodology to discover new compositions is much needed. Here, we developed a function-confined machine learning methodology to discover new FDPOs with high prediction accuracy from limited experimental data. By focusing on a specific application, namely solar thermochemical hydrogen production, we collected 632 training data and defined 21 desirable features. Our gradient boosting classifier model achieved a high prediction accuracy of 95.4% and a high F1 score of 0.921. Furthermore, when verified on additional 36 experimental data from existing literature, the model showed a prediction accuracy of 94.4%. With the help of this machine learning approach, we identified and synthesized 11 new FDPO compositions, 7 of which are relevant for solar thermochemical hydrogen production. We believe this confined machine learning methodology can be used to discover, from limited data, FDPOs with other specific application purposes. 

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