Keeping the Resident in the Loop: Adapting the Smart Home to the User

Reinforcement Learning: An Introduction

Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225

Reinforcement Learning: An Introduction

Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268

Introduction to Reinforcement Learning

Sutton, R. S., & Barto, A. G. (1998) · The MIT Press eBooks · Cited by 6,855

Mining sequential patterns

Agrawal, R., & Srikant, R. (2002) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.1995.380415 · Cited by 5,150

Activity Recognition in the Home Using Simple and Ubiquitous Sensors

Tapia, E. M., Intille, S. S., & Larson, K. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24646-6_10 · Cited by 1,323

The Gator Tech Smart House: a programmable pervasive space

Helal, S., Mann, W., Elzabadani, H., King, J., Kaddoura, Y., & Jansen, E. (2005) · Computer · doi:10.1109/mc.2005.107 · Cited by 891

The Neural Network House: An Environment that Adapts to its Inhabitants

Mozer, M. C. (1998) · Cited by 500

A Survey of Temporal Knowledge Discovery Paradigms and Methods

Roddick, J., & Spiliopoulou, M. (2002) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2002.1019212 · Cited by 491

Activity monitoring

Fawcett, T. E., & Provost, F. J. (1999) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/312129.312195 · Cited by 429

The human experience [of ubiquitous computing]

Abowd, G. D., Mynatt, E. D., & Rodden, T. A. (2002) · IEEE Pervasive Computing · doi:10.1109/mprv.2002.993144 · Cited by 353

Smart and interactive healthcare system based on speech recognition using soft margin formulation and kernel trick

Alshudukhi, J. S. (2022) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-022-01728-9 · Cited by 3

A Novel Human Activity Recognition Framework Based on Pre-Trained Foundation Model

Xiong, F., Wang, J., Liu, Y.-S., Yan, X., Ivanov, K., Wang, L., & Yan, Y. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822159 · Cited by 3

Futuring Digital Health and Wellness

Mokhtari, M., Tiberghien, T., Biswas, J., & Aung, A. P. W. (2014) · IEEE International Conference on Ubiquitous Intelligence and Computing, Autonomic and Trusted Computing, and Scalable Computing and Communications (UIC-ATC-ScalCom) · doi:10.1109/uic-atc-scalcom.2014.170 · Cited by 3

Prototype development of responsive kinetic façade control system for the elderly based on Ambient Assisted Living

Ham, S., Cho, H., Lee, H., & Lee, G. (2019) · Architectural Science Review · doi:10.1080/00038628.2019.1614902 · Cited by 3

Resident's activity at different abstraction levels: Proposition of a general theoretical framework

Pomponio, L., Goc, M. L., Pascual, É., & Anfosso, A. (2011) · IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) · doi:10.1109/idaacs.2011.6072825 · Cited by 3

SAT based enforcement of domotic effects in smart environments

Corno, F., & Razzak, F. (2013) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-013-0183-x · Cited by 3

Mining sequential patterns to efficiently manage Energy Storage Systems within smart home buildings

Botón-Fernández, V., Lozano-Tello, A., Pérez-Romero, M., & Romero‐Cadaval, E. (2016) · Journal of Ambient Intelligence and Smart Environments · doi:10.3233/ais-160381 · Cited by 3

A Sliding Window Method for Online Tracking of Spatiotemporal Event Patterns

Zhang, J., Zhu, S., Zang, D., & Zhou, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-45940-0_48 · Cited by 3

EDAWS: A distributed framework with efficient data analytics workspace towards discriminative services for critical infrastructures

Wu, R., Huang, L., Yu, P., & Zhou, H. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.11.009 · Cited by 3

Hexagram Linkage: An Ambient Assistive Living System with Healthcare for Elderly People Living Alone

Fan, X., Huang, H., Xie, Q., Pang, X., & Xie, C. M. (2020) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-44751-9_29 · Cited by 3

Discovering Models of Human's Behavior from Sensor's Data

Pomponio, L., Goc, M. L., Pascual, É., & Anfosso, A. (2011) · Ambient intelligence and smart environments · doi:10.3233/978-1-60750-795-6-17 · Cited by 3

Can we Take Advantage of Time-Interval Pattern Mining to Model Students Activity?

Dermy, O., & Brun, A. (2020) · HAL · Cited by 3

Automating Routine Tasks in Smart Environments. A Context-aware Model-driven Approach

Serral, E. (2011) · doi:10.4995/thesis/10251/11550 · Cited by 3

Real-time Load Prediction with High Velocity Smart Home Data StreamPreprint

Doblander, C., Strohbach, M., Ziekow, H., & Jacobsen, H. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.04613 · Cited by 3

An Overview of Real World Applications with Concept Drifting Data StreamsPreprint

Mittal, V., & Kashyap, I. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3170324 · Cited by 3

Context-aware smart home monitoring through the analysis of pressure sequences

Arcelus, A. (2010) · doi:10.22215/etd/2010-09724 · Cited by 3

Affordable Audio Hardware and Artificial Intelligence Can Transform the Dementia Care Pipeline

Potamitis, I. (2025) · Algorithms · doi:10.3390/a18120787 · Cited by 2

Living Lab Assessment for the Development of a Non-Invasive Sensor Network for Measuring Activities and Comfort in Multi-Resident Contexts

Casaccia, S., Meletani, S., SARTINI, G., & Revel, G. M. (2025) · IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) · doi:10.1109/metroxraine66377.2025.11340333 · Cited by 2

Healthcare robotics across home and hospital settings: innovations, challenges, and future prospects

Alanazi, F., Gaffar, A., & Eulenstein, O. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3659 · Cited by 2

Home Networking: Smart but Complicated

Moallem, A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-07227-2_70 · Cited by 2

Data-Driven End-to-End Lighting Automation Based on Human Residential Trajectory Analysis

Zhu, J., Tan, J., & Wu, W. (2024) · International Conference on Smart Applications, Communications and Networking (SmartNets) · doi:10.1109/smartnets61466.2024.10577722 · Cited by 2

Training the Behaviour Preferences on Context Changes

Benţa, K., Cremene, M., & Hoszu, A. (2024) · Technische Universität Berlin – Universitätsbibliothek · doi:10.14279/tuj.eceasst.28.400 · Cited by 2

Statistical Evaluation of Four Technologies used for Intelectualization of Smart Home Environment

Paulauskaitė-Tarasevičienė, A., Jukavičius, V., Morkevičius, N., Jasinevičius, R., Petrauskas, V., & Kazanavicius, V. (2015) · Information Technology And Control · doi:10.5755/j01.itc.44.3.11965 · Cited by 2

Q-Aura: A Quantitative Model for Managing Mutual Awareness of Smart Social Artifacts

Loke, S. W., Ling, S., Indrawan, M., & Leung, E. (2010) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2010.2055150 · Cited by 2

Enhancement of Human-Preference Assisted Activity Recognition Using a Cooperative ADL Infrastructure

Lu, C.-H., & Fu, L. (2012) · Autonomic and Trusted Computing · doi:10.1109/uic-atc.2012.51 · Cited by 2

Automated In-Home Assistive Monitoring with Privacy-Enhanced Video

Edgcomb, A., & Vahid, F. (2013) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi.2013.29 · Cited by 2

Novel model for inhabitants prediction in smart houses

Kadouche, R., & Abdulrazak, B. (2012) · International Journal of Pervasive Computing and Communications · doi:10.1108/17427371211262644 · Cited by 2

Accurate and Efficient Algorithms that Adapt to Privacy-Enhanced Video for Improved Assistive Monitoring

Edgcomb, A., & Vahid, F. (2013) · ACM Transactions on Management Information Systems · doi:10.1145/2523025.2523026 · Cited by 2

Activity awareness based on simple sensors

Tsai, C.-M., Zhao, Q., Chen, R.-C., & Taya, M. (2014) · International Conference on Awareness Science and Technology (iCAST) · doi:10.1109/icawst.2014.6981827 · Cited by 2

Identification of multiple harmonic sources in cyber-physical energy system using supervised independent component analysis

Wu, J., Wang, X., Liu, Y., & Sun, X. (2014) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc.2014.6860795 · Cited by 2

Design of Remote Control System for Smart Home Based on Z-Wave

Gao, C., Jiang, M. Y., Zhang, N., Huang, F. L., & Li, Y. L. (2013) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.397-400.1833 · Cited by 2

Temporal modeling and its application for anomaly detection in smart homes

Alam, M. R. (2011) · AFRICAN JOURNAL OF BUSINESS MANAGEMENT · doi:10.5897/ijps11.1410 · Cited by 2

Simple objects tracking system for smart homes

Bergeron, F., Bouchard, K., Giroux, S., Gaboury, S., & Bouchard, B. (2015) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2015.7359805 · Cited by 2

Machine Learning On Real-Time Data to Enhance Home Automation

Shaikh, S., Attar, A., Pathan, S., & Shaikh, R. (2016) · International Journal of Engineering Trends and Technology · doi:10.14445/22315381/ijett-v42p202 · Cited by 2

Applications of Natural Language Techniques in Solving SmartHome Use-Cases

Chanda, P. K., Varshney, N., & Subash, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59569-6_24 · Cited by 2

Activity Recognition and Dementia Care in Smart Home

Gayathri, K., Easwarakumar, K. S., & Elias, S. (2018) · Advances in theory and practice of emerging markets · doi:10.1007/978-3-319-78378-9_2 · Cited by 2

Development of an Eye- and Gaze-Tracking Mechanism in an Active and Assisted Living Ecosystem

Cheng, A. L., Vega, N. L., & Latorre, G. (2019) · International Symposium on Automation and Robotics in Construction (ISARC) · doi:10.22260/isarc2019/0101 · Cited by 2

Cloud-based Machine Learning Framework for Residential HVAC Control System

Issaraviriyakul, A., Pora, W., & Panitantum, N. (2021) · International Conference on Knowledge and Smart Technology · doi:10.1109/kst51265.2021.9415840 · Cited by 2

Intention as a Context: An Activity Intention Model for Adaptable Development of Applications in the Internet of Things

Ponce, V., & Abdulrazak, B. (2021) · IEEE Access · doi:10.1109/access.2021.3126000 · Cited by 2

Earning Extra Performance From Restrictive Feedbacks

Li, J., Pan, Y., Lyu, Y., Yao, Y., Sui, Y., & Tsang, I. W.-H. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3273249 · Cited by 2

Virtual Agents in Next Generation Interactive Homes

Del-Hoyo, R., Sanagustín, L. M., Benito, C., Hupont, I., & Abadía, D. (2012) · Advances in intelligent and soft computing · doi:10.1007/978-3-642-28795-4_2 · Cited by 2

Pursuit Estimator Learning Automata Based Approach for Online Event Pattern Tracking

Jiang, W., Ge, H., Tianrong, W., Wang, F., & Li, S. (2016) · IEEE International Conference on Data Science in Cyberspace · doi:10.1109/dsc.2016.121 · Cited by 2

A Cloud Framework Design for A Disease Symptom Self-inspection Service

Yan, L., & Xiong, D. (2020) · Information Resources Management Journal · doi:10.4018/irmj.2020040101 · Cited by 2

A daily activity feature extraction approach based on time series of sensor events

Liu, Y., Yang, H., Gong, S., Liu, Y., & Xiong, X. (2020) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2020280 · Cited by 2

Inhabitant prediction in smart houses

Kadouche, R., & Abdulrazak, B. (2011) · doi:10.1145/2030045.2030055 · Cited by 2

Scaling activity discovery and recognition to large, complex datasets

Cook, D. J., & Rashidi, P. (2011) · ProQuest Demo Repository · Cited by 2

Intelligent Learning Automata-based Strategies Applied to Personalized Service Provisioning in Pervasive Environments

Yazidi, A. (2011) · NORA - Norwegian Open Research Archives · Cited by 2

Energy saving in smart homes based on consumer behaviour data

Zehnder, M. (2015) · Institutional Repository (IHS Vienna) · doi:10.26041/fhnw-1172 · Cited by 2

Definition of a human-machine learning process from timed observations : application to the modelling of human behaviourfor the detection of abnormal behaviour of old people at home

Pomponio, L. (2012) · theses.fr (ABES) · doi:10.70675/72e7758bzb41az4312za57az618bf0a0d11c · Cited by 2

The Intelligent Home: A Systematic Review of Technological Pillars, Emerging Paradigms, and Future Directions

Abdelnaby, K. M., Al-Husainy, M. A. F., Alhawarat, M. O., Rohaim, M. A., Assar, K. M., & Elshafey, K. (2026) · Symmetry · doi:10.3390/sym18050718 · Cited by 1

Sensors Signal Processing Techniques

Mukhopadhyay, S. C. (2013) · Smart sensors, measurement and instrumentation · doi:10.1007/978-3-642-37027-4_6 · Cited by 1

Self-configuring topology switch in smart home network

Bae, M., Lee, D. K., Kim, H., & Kim, H. (2013) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc.2013.6675405 · Cited by 1

Aprendizaje de Secuencias de AcciÓN Para la Toma de Decisiones en Instalaciones DomÓTicas

Botón-Fernández, V., Redondo-Garcí, J. L., & Lozano-Tello, A. (2012) · Computer Science and Engineering · doi:10.5923/j.computer.20120001.01 · Cited by 1

Discovering and Tracking Patterns of Interest in Security Sensor Streams

Rashidi, P., Krishnan, N. C., & Cook, D. J. (2012) · Elsevier eBooks · doi:10.1016/b978-0-12-415815-3.00019-4 · Cited by 1

Fuzzy control of intelligent indoor exercise environment in air-polluted area

Zhi, S., Wei, Y., Wang, J., & Zhao, W. (2016) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz-ieee.2016.7737752 · Cited by 1

Distributed Classifier System for Smart Home’s Machine Learning

Irvan, M., & Terano, T. (2017) · Springer eBooks · doi:10.1007/978-981-10-3662-0_15 · Cited by 1

Applying End User Software Product Line Engineering for Smart Spaces

Tzeremes, V., & Gomaa, H. E. M. (2018) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2018.721 · Cited by 1

Activity Strength Recognition Using a Binary Infrared Sensor Array

Shoichi, I., Ota, R., & Zhao, Q. (2018) · International Conference on Awareness Science and Technology (iCAST) · doi:10.1109/icawst.2018.8517193 · Cited by 1

Exploitational Interaction

García–Herranz, M., Alamán, X., & Haya, P. A. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-19757-4_8 · Cited by 1

Introduction

Sayed‐Mouchaweh, M. (2018) · Studies in big data · doi:10.1007/978-3-319-89803-2_1 · Cited by 1

Achieving Energy Efficiency in Life-Logging Applications of Internet of Things Using Data Compression Through Incorporation of Machine Learning and Edge-Cloud Architecture

Barot, V., & Patel, R. P. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-10551-7_17 · Cited by 1

L’habitat intelligent : un lieu de conception des technologies pour l’autonomie des personnes

Brulin, D., Campo, É., Val, T., BOSSCHE, A. V. D., Vella, F., & Vigouroux, N. (2023) · Revue Ouverte d Intelligence Artificielle · doi:10.5802/roia.49 · Cited by 1

Human Activity Recognition using Convolutional Neural Network

Dugar, M. (2023) · Computer Applications & Technological Solutions (CATS) · doi:10.1109/cats58046.2023.10424362 · Cited by 1

Multiple Feature Fusion in Particle Filter Framework for Visual Tracking

Singaravelan, S., Selvakumar, V., Balaganesh, S., Gopalsamy, P., & Arun, R. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-2141-6.ch012 · Cited by 1

Game of LLMs: Discovering Structural Constructs in Activities using Large Language Models

Hiremath, S. K., & Plötz, T. (2024) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3675094.3678444 · Cited by 1

Extracting behavioral motifs for characterizing human daily activities in smart environments

Liu, K., Cheung, W. K., & Liu, J. (2012) · HKBU Institutional Repository (Hong Kong Baptist University) · Cited by 1

Consumo residencial: uma proposta de modelo de negócio para medidores inteligentes

de Almeida, T. D., & Fettermann, D. C. (2019) · Revista Produção Online · doi:10.14488/1676-1901.v19i3.3617 · Cited by 1

Introduction

Bock, T. A., Linner, T., Güttler, J., & Iturralde, K. (2019) · Cambridge University Press eBooks · doi:10.1017/9781139872034.001 · Cited by 1

Introduction

Klonovs, J., Haque, M. A., Krueger, V., Nasrollahi, K., Andersen‐Ranberg, K., Moeslund, T. B., & Spaich, E. G. (2016) · SpringerBriefs in computer science · doi:10.1007/978-3-319-27024-1_1 · Cited by 1

Sistema de e-terapia inteligente. Un nuevo paradigma de psicoterapia asistida por ordenador

Álvarez, I. Z. (2012) · doi:10.4995/thesis/10251/16801 · Cited by 1

Wireless network system based multi-non-invasive sensors for smart home

Ahmed, R. I. (2013) · doi:10.22215/etd/2013-07201 · Cited by 1

Fine-grained Access Control for Internet of Things Smart Spaces Driven by User Inputs

Al‐Shaboti, M. (2021) · doi:10.26686/wgtn.16564053.v1 · Cited by 1

Future Trends and Developments

Bock, T. A., Linner, T., Güttler, J., & Iturralde, K. (2019) · Cambridge University Press eBooks · doi:10.1017/9781139872034.006 · Cited by 1

Semantic Reasoning for Ubiquitous Smart Living Framework for Well-being and Digital Health

Kodyš, M. (2020) · doi:10.70675/8ea4cd0bz46ecz42e0za9fdzf124b0f02054 · Cited by 1

Predicting user behavior using transition probability

Heukels, R. (2012)

Technologies for Autonomic Dependable Services Platform: Achievements and Future Challenges

Ovaska, E., Dobrica, L., Purhonen, A., & Jaakola, M. (2013) · Communications in computer and information science · doi:10.1007/978-3-642-36177-7_13

The case for platform-integrated sensors and people-centric sensing

Tran, H. Q., Dang, T., & Milenković, M. (2014) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc.2014.6940495

EvA - A Self Adaptable Event-Based Recognition Framework for Three-Dimensional Activity Zones

Frey, J., Neurohr, C., Britz, J., & Brandherm, B. (2014) · International Conference on Intelligent Environments (IE) · doi:10.1109/ie.2014.20

Describing and Classifying Situations with Description Logics in Ubiquitous Robotics

Scalmato, A., Sgorbissa, A., & Zaccaria, R. (2012) · IFAC Proceedings Volumes · doi:10.3182/20120905-3-hr-2030.00035

Proceedings of the Fourth International Workshop onKnowledge Discovery from Sensor Data (SensorKDD’10)

Omitaomu, O. A., Chandola, V., Ganguly, A. R., da Gama, J. M. P., Vatsavai, R. R., Gaber, M. M., & Chawla, N. V. (2010)

A system architecture for accessing residential multimedia services

Zheng, X., Guo, W., Chen, N., & Chen, G. (2012) · IEEE International Conference on Cloud Computing Technology and Science · doi:10.1109/cloudcom.2012.6427557

Exploring Appropriate Clusters in Subspace for Human Activity Recognition

Zhang, H., Luo, S., & Yoshie, O. (2013) · IEEJ Transactions on Electronics Information and Systems · doi:10.1541/ieejeiss.133.2282

Has FQAS Something to Say on Taking Good Care of Our Elders?

Ros, M., Molina-Solana, M., & Delgado‐Rodríguez, M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40769-7_5

Ambient Assisted Living using Sensor and Mobile Technologies for Elderly People

Bhuiya, M. B. (2015) · IJARCCE · doi:10.17148/ijarcce.2015.412157

Activity recognition based on importance degree reduction of decision table

Yu, X., Wang, C., & Yang, B. (2017) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2017.7978491

Fixed-order output feedback controller design for discrete-time T-S fuzzy affine systems using quantized measurements

Ji, W., Qiu, J., Wang, T., & Wang, A. (2017) · International Conference on Control and Automation · doi:10.1109/icca.2017.8003125

Automatic Recognition Of Activities In Smart Environment

Karthika, L., & Sumathi, A. C. (2011) · i-manager’s Journal on Software Engineering · doi:10.26634/jse.6.2.2895

An Adaptive Activity Recognition Approach in Smart Environments

Ahmed, A. M., A., T., & Amr, T. (2018) · International Journal of Computer Applications · doi:10.5120/ijca2018917738

Investigating telemonitoring technologies for the detection of activities and the application of BLE in smart homes for elderly independent living

Ball, S. (2017) · Queensland University of Technology · doi:10.5204/thesis.eprints.123510

Evaluating Rule-based Programming and Reinforcement Learning for Personalising an Intelligent System

Liu, R., Sarkar, A., Solovey, E., & Tschiatschek, S. (2019) · IUI Workshops

Motion Prediction with Artificial Neural Networks Using Wearable Strain Sensors Based on Flexible Thin Graphite Films

Kanokoda, T., Kushitani, Y., Shimada, M., & Shirakashi, J. (2019) · Key engineering materials · doi:10.4028/www.scientific.net/kem.826.111

Adoption of the activation function fusion approach to identify human activity recognition in a semi-supervised neural network

Hernandez-Cruz, N., Nugent, C., McChesney, I., Zhang, S., & Favela, J. (2019) · EAI Endorsed Transactions on Pervasive Health and Technology · doi:10.4108/eai.30-10-2018.161669

Egocentryzm inteligentnych domów. Analiza smart-interfejsów jako przekładu egzotyzującego

Okulska, I. (2018) · Zagadnienia Informacji Naukowej - Studia Informacyjne · doi:10.36702/zin.382

Knowledge Representation and Reasoning for Sensory-rich Smart Environments to Support Independent Living

Alessandra, M., Merico, D., & Bisiani, R. (2012) · Ambient intelligence and smart environments · doi:10.3233/978-1-61499-123-6-50

Federated Classification with Low Complexity Reproducing Kernel Hilbert Space Representations

Peifer, M., & Ribeiro, A. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053733

A Taxonomy of Methods for Handling Data Streams in Presence of Concepts Drifts

Mittal, V., & Srivastava, R. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-4451-4_41

Enabling Edge Cloud Intelligence for Activity Learning in Smart HomePreprint

Huang, B. S., Bouguettaya, A., & Dong, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.06885

Federated Classification using Parsimonious Functions in Reproducing Kernel Hilbert SpacesPreprint

Peifer, M., & Ribeiro, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.03768

Multiple Kernel-Based Online Federated LearningPreprint

Chae, J., & Hong, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.10861

Keeping the intelligent environment resident in the loop

Rashidi, P., & Cook, D. J. (2008) · International Conference on Intelligent Environments (IE) · doi:10.1049/cp:20081170 · Cited by 28

Adapting to resident preferences in smart environments

Rashidi, P., & Cook, D. J. (2008)

User in the Loop: Adaptive Smart Homes Exploiting User Feedback—State of the Art and Future Directions

Karami, A., Fleury, A., Boonært, J., & Lecœuche, S. (2016) · Information · doi:10.3390/info7020035 · Cited by 28

A user-guided personalization methodology for new smart homes

Ali, S. M. (2022) · Middlesex University Research Repository (Middlesex University Of London)

Design and Implementation of Tracking a user’s Behavior in a Smart Home

Abbas, A. F., & Abdullah, M. Z. (2021) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1094/1/012008 · Cited by 8