A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Cites
1 retracted
RETRACTED: Signal fusion-based deep fast random forest method for machine health assessment
Yuan, N., Yang, W., Kang, B. H., Xu, S., & Li, C. (2018) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2018.05.004 · Cited by 21 · see citing works
RetractedMulti-Interval Discretization of Continuous-Valued Attributes for Classification Learning
Fayyad, U. M., & Irani, K. B. (1993) · International Joint Conference on Artificial Intelligence · Cited by 2,870 · see citing works
Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks
Kıranyaz, S., İnce, T., & Gabbouj, M. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2468589 · Cited by 1,925 · see citing works
Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges
Gravina, R., Alinia, P., Ghasemzadeh, H., & Fortino, G. (2017) · Information Fusion · doi:10.1016/j.inffus.2016.09.005 · Cited by 899 · see citing works
Data mining applications in healthcare
Ogwueleka, F. N. (2010) · International Journal of Natural and Applied Sciences · doi:10.4314/ijonas.v5i1.49926 · Cited by 558 · see citing works
Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers
Atkinson, J., & Campos, D. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.10.049 · Cited by 510 · see citing works
Banaee, H., Ahmed, M., & Loutfi, A. (2013) · Sensors · doi:10.3390/s131217472 · Cited by 452 · see citing works
Enabling Effective Programming and Flexible Management of Efficient Body Sensor Network Applications
Fortino, G., Giannantonio, R., Gravina, R., Kuryloski, P., & Jafari, R. (2013) · IEEE Transactions on Human-Machine Systems · doi:10.1109/tsmcc.2012.2215852 · Cited by 412 · see citing works
A survey of depth and inertial sensor fusion for human action recognition
Chen, C., Jafari, R., & Kehtarnavaz, N. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-015-3177-1 · Cited by 356 · see citing works
Mursalin, M., Zhang, Y., Chen, Y., & Chawla, N. V. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.02.053 · Cited by 313 · see citing works
Human emotion recognition using deep belief network architecture
Hassan, M. M., Alam, M. G. R., Uddin, M. Z., Huda, S., Almogren, A., & Fortino, G. (2018) · Information Fusion · doi:10.1016/j.inffus.2018.10.009 · Cited by 307 · see citing works
Enabling IoT interoperability through opportunistic smartphone-based mobile gateways
Aloi, G., Caliciuri, G., Fortino, G., Gravina, R., Pace, P., Russo, W., & Savaglio, C. (2016) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2016.10.013 · Cited by 303 · see citing works
Random Forests and Kernel Methods
Scornet, E. (2016) · IEEE Transactions on Information Theory · doi:10.1109/tit.2016.2514489 · Cited by 293 · see citing works
A framework for collaborative computing and multi-sensor data fusion in body sensor networks
Fortino, G., Galzarano, S., Gravina, R., & Li, W. (2014) · Information Fusion · doi:10.1016/j.inffus.2014.03.005 · Cited by 283 · see citing works
Agent-Oriented Cooperative Smart Objects: From IoT System Design to Implementation
Fortino, G., Russo, W., Savaglio, C., Shen, W., & Zhou, M. (2017) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2017.2780618 · Cited by 271 · see citing works
Improving Human Action Recognition Using Fusion of Depth Camera and Inertial Sensors
Chen, C., Jafari, R., & Kehtarnavaz, N. (2014) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2014.2362520 · Cited by 269 · see citing works
Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions
Maurer, U., Smailagic, A., Siewiorek, D. P., & Deisher, M. (2006) · doi:10.21236/ada534437 · Cited by 266 · see citing works
A Novel Secure IoT-Based Smart Home Automation System Using a Wireless Sensor Network
Pirbhulal, S., Zhang, H., Alahi, M. E. E., Ghayvat, H., Mukhopadhyay, S. C., Zhang, Y., & Wu, W. (2016) · Sensors · doi:10.3390/s17010069 · Cited by 247 · see citing works
Self-citationDurrant-Whyte, H. F., & Henderson, T. C. (2016) · Springer handbooks · doi:10.1007/978-3-319-32552-1_35 · Cited by 199 · see citing works
Capela, N. A., Lemaire, E. D., & Baddour, N. (2015) · PLoS ONE · doi:10.1371/journal.pone.0124414 · Cited by 197 · see citing works
Mobile edge computing based QoS optimization in medical healthcare applications
Sodhro, A. H., Luo, Z., Sangaiah, A. K., & Baik, S. W. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2018.08.004 · Cited by 183 · see citing works
Self-citationActivity Recognition on Smartphones via Sensor-Fusion and KDA-Based SVMs
Khan, A., Tufail, A., Khattak, A. M., & Laine, T. H. (2014) · International Journal of Distributed Sensor Networks · doi:10.1155/2014/503291 · Cited by 149 · see citing works
Martini, N., Menicucci, D., Sebastiani, L., Bedini, R., Pingitore, A., Vanello, N., Milanesi, M., Landini, L., & Gemignani, A. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.060 · Cited by 148 · see citing works
Multisensor Data Fusion for Physical Activity Assessment
Liu, S., Gao, R. X., John, D., STAUDENMAYER, J. W., & Freedson, P. S. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2178070 · Cited by 147 · see citing works
Convergence of IoT and product lifecycle management in medical health care
Sodhro, A. H., Pirbhulal, S., & Sangaiah, A. K. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.03.052 · Cited by 141 · see citing works
Self-citationA hierarchical approach to real-time activity recognition in body sensor networks
Wang, L., Gu, T., Tao, X., & Lü, J. (2010) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2010.12.001 · Cited by 118 · see citing works
Kanjo, E., Younis, E. M. G., & Sherkat, N. (2018) · Information Fusion · doi:10.1016/j.inffus.2017.05.005 · Cited by 118 · see citing works
Hash Based Encryption for Keyframes of Diagnostic Hysteroscopy
Hamza, R., Muhammad, K., Arunkumar, N., & Ramírez-González, G. (2017) · IEEE Access · doi:10.1109/access.2017.2762405 · Cited by 115 · see citing works
Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology
Hsu, Y., Chou, P., Chang, H.-C., Lin, S., Yang, S.-C., Su, H.-Y., Chang, C.-C., Cheng, Y., & Kuo, Y.-C. (2017) · Sensors · doi:10.3390/s17071631 · Cited by 110 · see citing works
Wu, W., Zhang, H., Pirbhulal, S., Mukhopadhyay, S. C., & Zhang, Y. (2015) · IEEE Sensors Journal · doi:10.1109/jsen.2015.2470638 · Cited by 92 · see citing works
Self-citationA Weighted Belief Entropy-Based Uncertainty Measure for Multi-Sensor Data Fusion
Tang, Y., Zhou, D., Xu, S., & He, Z. (2017) · Sensors · doi:10.3390/s17040928 · Cited by 92 · see citing works
Modeling and Simulating Internet-of-Things Systems: A Hybrid Agent-Oriented Approach
Fortino, G., Gravina, R., Russo, W., & Savaglio, C. (2017) · Computing in Science & Engineering · doi:10.1109/mcse.2017.3421541 · Cited by 91 · see citing works
Wrapper method for feature selection to classify cardiac arrhythmia
Mustaqeem, A., Anwar, S. M., Majid, M., & Khan, A. R. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8037650 · Cited by 82 · see citing works
Automatic Methods for the Detection of Accelerative Cardiac Defense Response
Gravina, R., & Fortino, G. (2016) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2016.2515094 · Cited by 81 · see citing works
A Joint Transmission Power Control and Duty-Cycle Approach for Smart Healthcare System
Sodhro, A. H., Pirbhulal, S., Sodhro, G. H., Gurtov, A., Muzammal, M., & Luo, Z. (2018) · IEEE Sensors Journal · doi:10.1109/jsen.2018.2881611 · Cited by 76 · see citing works
Self-citationVehicle Tracking Using Surveillance With Multimodal Data Fusion
Zhang, Y., Song, B., Du, X., & Guizani, M. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2787101 · Cited by 73 · see citing works
Adaptive mining prediction model for content recommendation to coronary heart disease patients
Kim, J., Lee, J.-S., Park, D.-K., Lim, Y.-S., Lee, Y., & Jung, E.-Y. (2013) · Cluster Computing · doi:10.1007/s10586-013-0308-1 · Cited by 72 · see citing works
Knowledge discovery in cardiology: A systematic literature review
Kadi, I., Idri, A., & Fernández‐Alemán, J. L. (2016) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2016.09.005 · Cited by 67 · see citing works
Emotion-relevant activity recognition based on smart cushion using multi-sensor fusion
Gravina, R., & Li, Q. (2018) · Information Fusion · doi:10.1016/j.inffus.2018.08.001 · Cited by 67 · see citing works
A Framework for Creating Healthcare Monitoring Applications Using Wireless Body Sensor Networks
Iyengar, S., F, T. B., Gravina, R., Guerrieri, A., Fortino, G., & Sangiovanni-Vincentelli, A. L. (2008) · International Conference on Body Area Networks · doi:10.4108/icst.bodynets2008.2969 · Cited by 61 · see citing works
Power Control Algorithms for Media Transmission in Remote Healthcare Systems
Sodhro, A. H., Pirbhulal, S., Qaraqe, M., Lohano, S., Sodhro, G. H., Junejo, N. U. R., & Luo, Z. (2018) · IEEE Access · doi:10.1109/access.2018.2859205 · Cited by 61 · see citing works
Self-citationDeveloping a Body Sensor Network to Detect Emotions During Driving
Rebolledo‐Mendez, G., Reyes, A., Paszkowicz, S., Domingo, M. C., & Skrypchuk, L. (2014) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2335151 · Cited by 54 · see citing works
Predicting disease by using data mining based on healthcare information system
Huang, F., Wang, S., & Chan, C.-C. (2012) · IEEE International Conference on Granular Computing (GrC) · doi:10.1109/grc.2012.6468691 · Cited by 54 · see citing works
Domingo, M. C. (2011) · IEEE Communications Magazine · doi:10.1109/mcom.2011.5681022 · Cited by 49 · see citing works
Green media-aware medical IoT system
Sodhro, A. H., Sangaiah, A. K., Pirphulal, S., Sekhari, A., & Ouzrout, Y. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-5634-0 · Cited by 46 · see citing works
Self-citationA system for activity recognition using multi-sensor fusion
Gao, L., Bourke, A. K., & Nelson, J. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6091939 · Cited by 45 · see citing works
Wearable sensor data fusion for remote health assessment and fall detection
Yi, W.-J., Sarkar, O., Mathavan, S., & Saniie, J. (2014) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit.2014.6871782 · Cited by 44 · see citing works
Hassan, M. M., Huda, S., Yearwood, J. L., Jelinek, H. F., & Almogren, A. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.08.004 · Cited by 37 · see citing works
Trajectory Mining Using Uncertain Sensor Data
Muzammal, M., Gohar, M., Rahman, A. U., Qu, Q., Ahmad, A., & Jeon, G. (2017) · IEEE Access · doi:10.1109/access.2017.2778690 · Cited by 34 · see citing works
Self-citationNovel method and real-time system for detecting the Cardiac Defense Response based on the ECG
Covello, R., Fortino, G., Gravina, R., Aguilar, A., & Breslin, J. G. (2013) · IEEE International Symposium on Medical Measurements and Applications (MeMeA) · doi:10.1109/memea.2013.6549705 · Cited by 28 · see citing works
A Distributed Multiagent System Architecture for Body Area Networks Applied to Healthcare Monitoring
Felisberto, F., Laza, R., Fdez‐Riverola, F., & Pereira, A. M. D. J. (2015) · BioMed Research International · doi:10.1155/2015/192454 · Cited by 22 · see citing works
Online Recognition of Daily Activities by Color-Depth Sensing and Knowledge Models
Crispim-Junior, C. F., Covella, A. G. U., Strumia, C., Koperski, M., König, A., Negin, F., Coşar, S., Nghiem, A.-T., Chau, D. P., Charpiat, G., et al. (2017) · Sensors · doi:10.3390/s17071528 · Cited by 17 · see citing works
Towards Big Data for Activity Recognition: A Novel Database Fusion Strategy
Schuldhaus, D., Leutheuser, H., & Eskofier, B. M. (2014) · International Conference on Body Area Networks · doi:10.4108/icst.bodynets.2014.256946 · Cited by 16 · see citing works
Yu, X., Paul, M., Antink, C. H., Venema, B., Blažek, V., Bollheimer, C., Leonhardt, S. K., & Teichmann, D. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8512451 · Cited by 12 · see citing works
DF-SWin: Sliding windows for multi-sensor data fusion in wireless sensor networks
Duong-Viet, H., & Nguyen-Dinh, V. (2017) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse.2017.8119434 · Cited by 4 · see citing works
Flagged first, then by citations
2 of the 57 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Ali, F., El–Sappagh, S., Islam, S. M. R., Kwak, D., Ali, A., Imran, M. A., & Kwak, K.-S. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.06.008 · Cited by 840 · see citing works
Hybrid multimodal wearable sensors for comprehensive health monitoring
Mahato, K., Saha, T., Ding, S., Sandhu, S. S., Chang, A., & Wang, J. (2024) · Nature Electronics · doi:10.1038/s41928-024-01247-4 · Cited by 435 · see citing works
A Comprehensive Survey of the Internet of Things (IoT) and AI-Based Smart Healthcare
Alshehri, F., & Muhammad, G. (2020) · IEEE Access · doi:10.1109/access.2020.3047960 · Cited by 349 · see citing works
Trustworthiness in Industrial IoT Systems Based on Artificial Intelligence
Lv, Z., Han, Y., Singh, A. K., Manogaran, G., & Lv, H. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2994747 · Cited by 321 · see citing works
IoT-Cloud-Based Smart Healthcare Monitoring System for Heart Disease Prediction via Deep Learning
Nancy, A. A., Ravindran, D., Vincent, P. M. D. R., Srinivasan, K., & Reina, D. G. (2022) · Electronics · doi:10.3390/electronics11152292 · Cited by 290 · see citing works
A comprehensive survey on multimodal medical signals fusion for smart healthcare systems
Muhammad, G., Alshehri, F., Karray, F., Saddik, A. E., Alsulaiman, M., & Falk, T. H. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.06.007 · Cited by 287 · see citing works
An Overview of Patient’s Health Status Monitoring System Based on Internet of Things (IoT)
Kadhim, K. T., Alsahlany, A. M., Wadi, S. M., & Kadhum, H. T. (2020) · Wireless Personal Communications · doi:10.1007/s11277-020-07474-0 · Cited by 269 · see citing works
Bourechak, A., Zedadra, O., Kouahla, M. N., Guerrieri, A., Séridi, H., & Fortino, G. (2023) · Sensors · doi:10.3390/s23031639 · Cited by 295 · see citing works
khan, Z. F., & Alotaibi, S. R. (2020) · Journal of Healthcare Engineering · doi:10.1155/2020/8894694 · Cited by 235 · see citing works
Security and Privacy in IoT-Cloud-Based e-Health Systems—A Comprehensive Review
Butpheng, C., Yeh, K., & Xiong, H. (2020) · Symmetry · doi:10.3390/sym12071191 · Cited by 219 · see citing works
Holzinger, A., Dehmer, M., Emmert‐Streib, F., Cucchiara, R., Augenstein, I., Ser, J. D., Samek, W., Jurišica, I., & Díaz-Rodríguez, N. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.10.007 · Cited by 215 · see citing works
Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis
Kashinath, S. A., Mostafa, S. A., Mustapha, A., Mahdin, H., Lim, D., Mahmoud, M. A., Mohammed, M. A., Al‐rimy, B. A. S., Fudzee, M. F. M., & Yang, T. J. (2021) · IEEE Access · doi:10.1109/access.2021.3069770 · Cited by 203 · see citing works
A survey of emotion recognition methods with emphasis on E-Learning environments
Imani, M., & Montazer, G. A. (2019) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2019.102423 · Cited by 190 · see citing works
Zhang, M., Wang, X., Feng, H., Huang, Q., Xiao, X., & Zhang, X. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.127712 · Cited by 172 · see citing works
Ihnaini, B., Khan, M. A., Khan, T. A., Abbas, S., Daoud, M. S., Ahmad, M., & Khan, M. A. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/4243700 · Cited by 148 · see citing works
A systematic review of data fusion techniques for optimized structural health monitoring
Hassani, S., Dackermann, U., Mousavi, M., & Li, J. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102136 · Cited by 158 · see citing works
The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods
Jin, X.-B., Robert Jeremiah, R. J., Su, T.-L., Bai, Y.-T., & Kong, J.-L. (2021) · Sensors · doi:10.3390/s21062085 · Cited by 137 · see citing works
Heart disease risk prediction using deep learning techniques with feature augmentation
García-Ordás, M. T., Bayón-Gutiérrez, M., Benavides, C., Aveleira‐Mata, J., & Benítez‐Andrades, J. A. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14817-z · Cited by 136 · see citing works
Ali, M. H., Jaber, M. M., Khalil, S., Rehman, A. U., Awan, M. J., Damaševičius, R., & Bahaj, S. A. (2022) · Electronics · doi:10.3390/electronics11030494 · Cited by 132 · see citing works
COVID-19 and Non-COVID-19 Classification using Multi-layers Fusion From Lung Ultrasound Images
Muhammad, G., & Shamim Hossain, M. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.02.013 · Cited by 120 · see citing works
An effective approach for CT lung segmentation using mask region-based convolutional neural networks
Hu, Q., Souza, L. F. D. F., Holanda, G. B., Alves, S. S. A., Silva, F. H. S., Han, T., & Filho, P. P. R. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101792 · Cited by 125 · see citing works
Toward 6G Architecture for Energy-Efficient Communication in IoT-Enabled Smart Automation Systems
Sodhro, A. H., Pirbhulal, S., Luo, Z., Muhammad, K., & Zahid, N. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3024715 · Cited by 120 · see citing works
Self-citationHybrid Integration of Wearable Devices for Physiological Monitoring
Zhang, Y., Zheng, X. T., Zhang, X., Pan, J., & Thean, A. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00471 · Cited by 122 · see citing works
A Review on the State of the Art in Atrial Fibrillation Detection Enabled by Machine Learning
Rizwan, A., Zoha, A., Mabrouk, I. B., Sabbour, H. M., Al‐Sumaiti, A. S., Alomainy, A., Imran, M. A., & Abbasi, Q. H. (2020) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.2976507 · Cited by 118 · see citing works
A data-level fusion model for unsupervised attribute selection in multi-source homogeneous data
Zhang, P., Li, T., Yuan, Z., Luo, C., Wang, G., Liu, J., & Du, S. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.10.017 · Cited by 112 · see citing works
IoMT-Enabled Fusion-Based Model to Predict Posture for Smart Healthcare Systems
Ghazal, T. M., Hasan, M. K., Abdullah, S. N. H. S., Abubakkar, K. A., & Afifi, M. A. M. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.019706 · Cited by 109 · see citing works
A Joint Deep Learning and Internet of Medical Things Driven Framework for Elderly Patients
Zhang, T., Sodhro, A. H., Luo, Z., Zahid, N., Nawaz, M. W., Pirbhulal, S., & Muzammal, M. (2020) · IEEE Access · doi:10.1109/access.2020.2989143 · Cited by 105 · see citing works
Self-citationNaser, M. A., Majeed, A. A., Alsabah, M. Q., Al-Shaikhli, T. R., & Kaky, K. M. (2024) · Algorithms · doi:10.3390/a17020078 · Cited by 124 · see citing works
A framework for efficient brain tumor classification using MRI images
Guan, Y., Aamir, M., Rahman, Z.-U., Ali, A., Abro, W. A., Dayo, Z. A., Bhutta, M. S., & Hu, Z. (2021) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2021292 · Cited by 107 · see citing works
Sodhro, A. H., Pirbhulal, S., Muzammal, M., & Luo, Z. (2020) · Journal of Grid Computing · doi:10.1007/s10723-020-09527-x · Cited by 90 · see citing works
Self-citationUnderstanding Sensor Cities: Insights from Technology Giant Company Driven Smart Urbanism Practices
D’Amico, G., L’Abbate, P., Liao, W., Yiğitcanlar, T., & Ioppolo, G. (2020) · Sensors · doi:10.3390/s20164391 · Cited by 93 · see citing works
Mu, X., He, B., Wu, S., Zhang, X., Song, Y., & Yan, T. (2020) · Applied Ocean Research · doi:10.1016/j.apor.2020.102441 · Cited by 85 · see citing works
Multi-sensor real-time monitoring of dam behavior using self-adaptive online sequential learning
Ren, Q., Li, M., Kong, T., & Ma, J. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104365 · Cited by 88 · see citing works
Tran, T. M. T., Yuen, K. F., Li, K. X., Balcı, G., & Ma, F. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.118401 · Cited by 89 · see citing works
AlShorman, O., Alshorman, B., Al-khassaweneh, M., & Alkahtani, F. (2020) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v20.i1.pp414-422 · Cited by 84 · see citing works
Kuthadi, V. M., Selvaraj, R., Baskar, S., Shakeel, P. M., & Ranjan, A. (2021) · Wireless Personal Communications · doi:10.1007/s11277-021-08583-0 · Cited by 80 · see citing works
IoT Based Smart Monitoring of Patients’ with Acute Heart Failure
Umer, M., Sadiq, S., Karamti, H., Karamti, W., Majeed, R., & Nappi, M. (2022) · Sensors · doi:10.3390/s22072431 · Cited by 80 · see citing works
A multi-source information fusion model for outlier detection
Zhang, P., Li, T., Wang, G., Wang, D., Lai, P. L., & Zhang, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2022.12.027 · Cited by 81 · see citing works
BTC-fCNN: Fast Convolution Neural Network for Multi-class Brain Tumor Classification
El-Wahab, B. S. A., NASR, M. E. M., Khamis, S., & Ashour, A. S. (2023) · Health Information Science and Systems · doi:10.1007/s13755-022-00203-w · Cited by 79 · see citing works
Heart failure patients monitoring using IoT-based remote monitoring system
Umer, M., Aljrees, T., Karamti, H., Ishaq, A., Alsubai, S., Omar, M., Bashir, A. K., & Ashraf, I. (2023) · Scientific Reports · doi:10.1038/s41598-023-46322-6 · Cited by 87 · see citing works
D’Amico, G., Arbolino, R., Ши, Л., Yiğitcanlar, T., & Ioppolo, G. (2021) · Sustainability · doi:10.3390/su13116043 · Cited by 78 · see citing works
Imran, M., Zaman, U., Imran, I., Imtiaz, J., Fayaz, M., & Gwak, J. (2021) · Electronics · doi:10.3390/electronics10202501 · Cited by 80 · see citing works
Phatak, A. A., Wieland, F.-G., Vempala, K., Volkmar, F., & Memmert, D. (2021) · Sports Medicine - Open · doi:10.1186/s40798-021-00372-0 · Cited by 74 · see citing works
Huynh-The, T., Hua, C.-H., Tu, N. A., & Kim, D.-S. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3013272 · Cited by 72 · see citing works
Wearable Assistive Robotics: A Perspective on Current Challenges and Future Trends
Martínez-Hernández, U., Metcalfe, B., Assaf, T., Jabban, L., Male, J., & Zhang, D. (2021) · Sensors · doi:10.3390/s21206751 · Cited by 74 · see citing works
D’Amico, G., Arbolino, R., Ши, Л., Yiğitcanlar, T., & Ioppolo, G. (2021) · Land Use Policy · doi:10.1016/j.landusepol.2021.105819 · Cited by 73 · see citing works
Wagan, S. A., Koo, J., Siddiqui, I. F., Attique, M., Shin, D. R., & Qureshi, N. M. F. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2022.09.005 · Cited by 76 · see citing works
Smart insoles review (2008-2021): Applications, potentials, and future
Almuteb, I., Hua, R., & Wang, Y. (2022) · Smart Health · doi:10.1016/j.smhl.2022.100301 · Cited by 69 · see citing works
Han, T., Nunes, V. X., Souza, L. F. D. F., Marques, A. G., Silva, I. C. L., Ferreira, M. A. A., Sun, J., & Filho, P. P. R. (2020) · IEEE Access · doi:10.1109/access.2020.2987932 · Cited by 68 · see citing works
Mu, X., Zhang, H., Zhang, C., Yang, S., Xu, J., Huang, Y., Xu, J., Zhang, Y., Li, Q., Wang, X., et al. (2021) · Journal of Materials Science · doi:10.1007/s10853-021-05937-8 · Cited by 67 · see citing works
Mobile edge-enabled trust evaluation for the Internet of Things
Wang, T., Wang, P., Cai, S., Zheng, X., Ma, Y., Jia, W., & Wang, G. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.04.007 · Cited by 66 · see citing works
A single-fibre computer enables textile networks and distributed inference
Gupta, N., Cheung, H., Payra, S., Loke, G. Z. J., Li, J., Zhao, Y., Balachander, L., Son, E., Li, V., Kravitz, S., et al. (2025) · Nature · doi:10.1038/s41586-024-08568-6 · Cited by 72 · see citing works
Bhutto, J. A., Tian, L., Du, Q., Sun, Z., Yu, L., & Tahir, M. F. (2022) · Entropy · doi:10.3390/e24030393 · Cited by 66 · see citing works
A Novel Adaptive Battery-Aware Algorithm for Data Transmission in IoT-Based Healthcare Applications
Magsi, H., Sodhro, A. H., Al-Rakhami, M. S., Zahid, N., Pirbhulal, S., & Wang, L. (2021) · Electronics · doi:10.3390/electronics10040367 · Cited by 65 · see citing works
Self-citationA decentralised approach to privacy preserving trajectory mining
Talat, R., Obaidat, M. S., Muzammal, M., Sodhro, A. H., Luo, Z., & Pirbhulal, S. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.07.068 · Cited by 62 · see citing works
Self-citationZhao, B., & Zhao, X. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3094465 · Cited by 62 · see citing works
Lakhan, A. R., Mohammed, M. A., Elhoseny, M., Alshehri, M. D., & Abdulkareem, K. H. (2022) · Soft Computing · doi:10.1007/s00500-022-07167-9 · Cited by 60 · see citing works
RetractedA deep learning based static taint analysis approach for IoT software vulnerability location
Niu, W., Zhang, X., Du, X., Zhao, L., Cao, R., & Guizani, M. (2019) · Measurement · doi:10.1016/j.measurement.2019.107139 · Cited by 58 · see citing works
AI-Enabled Framework for Fog Computing Driven E-Healthcare Applications
Sodhro, A. H., & Zahid, N. (2021) · Sensors · doi:10.3390/s21238039 · Cited by 57 · see citing works
Self-citationAnomaly detection using ensemble random forest in wireless sensor network
Biswas, P., & Samanta, T. (2021) · International Journal of Information Technology · doi:10.1007/s41870-021-00717-8 · Cited by 55 · see citing works
Toward ML-Based Energy-Efficient Mechanism for 6G Enabled Industrial Network in Box Systems
Sodhro, A. H., Zahid, N., Wang, L., Pirbhulal, S., Ouzrout, Y., Sekhari, A., Neto, A. V. L., de Macedo, A. R. L., & de Albuquerque, V. H. C. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3026663 · Cited by 54 · see citing works
Self-citationSafdar, M. F., Nowak, R. M., & Pałka, P. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107908 · Cited by 52 · see citing works
Abirami, L., & Karthikeyan, J. (2023) · IEEE Access · doi:10.1109/access.2023.3312278 · Cited by 53 · see citing works
Mastoi, Q.-U.-A., Wah, T. Y., Raj, R. G., & Lakhan, A. R. (2020) · Sensors · doi:10.3390/s20020441 · Cited by 49 · see citing works
A knowledge graph-supported information fusion approach for multi-faceted conceptual modelling
Chen, Z., Wan, Y., Liu, Y., & Valera-Medina, A. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101985 · Cited by 50 · see citing works
Energy-efficient secure data fusion scheme for IoT based healthcare system
Singh, S., & Kumar, D. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2022.12.040 · Cited by 45 · see citing works
Information fusion for multi-scale data: Survey and challenges
Zhang, Q., Yang, Y., Cheng, Y., Wang, G., Ding, W., Wu, W.-Z., & Pelusi, D. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101954 · Cited by 45 · see citing works
FogMed: a Fog-based Framework for Disease Prognosis Based Medical Sensor Data Streams
Sun, L., Yu, Q., Peng, D., Subramani, S., & Wang, X. (2020) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2020.012515 · Cited by 44 · see citing works
An efficient cardiovascular disease prediction model through AI-driven IoT technology
Marengo, A., Pagano, A., & Santamato, V. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109330 · Cited by 42 · see citing works
HBoost: A heterogeneous ensemble classifier based on the Boosting method and entropy measurement
Kadkhodaei, H., Moghadam, A. M. E., & Dehghan, M. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113482 · Cited by 41 · see citing works
Ullah, I., Youn, J., & Han, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3063242 · Cited by 40 · see citing works
Ullah, I., Youn, H. Y., & Han, Y. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.06.012 · Cited by 40 · see citing works
Towards Cognitive Authentication for Smart Healthcare Applications
Sodhro, A. H., Sennersten, C. C., & Ahmad, A. (2022) · Sensors · doi:10.3390/s22062101 · Cited by 40 · see citing works
Self-citationMonitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
Almujally, N. A., Aljrees, T., Saidani, O., Umer, M., Faheem, Z. B., Abuzinadah, N. E., Alnowaiser, K., & Ashraf, I. (2023) · Sensors · doi:10.3390/s23104580 · Cited by 40 · see citing works
Lakhan, A. R., Dootio, M. A., Sodhro, A. H., Pirbhulal, S., Grønli, T., Khokhar, M. S., & Wang, L. (2021) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2021363 · Cited by 37 · see citing works
Self-citationSamuel, O. W., Yang, B., Geng, Y., Asogbon, M. G., Pirbhulal, S., Mzurikwao, D., Idowu, O. P., Ogundele, T. J., Li, X., Chen, S., et al. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.10.034 · Cited by 37 · see citing works
Self-citationToward QoS Monitoring in IoT Edge Devices Driven Healthcare—A Systematic Literature Review
Younas, M. I., Iqbal, M. J., Aziz, A., & Sodhro, A. H. (2023) · Sensors · doi:10.3390/s23218885 · Cited by 35 · see citing works
Self-citationCao, L. J., Cai, Y., Yue, Y., Cai, S., & Hang, B. (2020) · IEEE Access · doi:10.1109/access.2020.2967118 · Cited by 35 · see citing works
Weighted adaptive Kalman filtering-based diverse information fusion for hole edge crack monitoring
Wang, Y., He, M., Sun, L., Wu, D., Wang, Y., & Qing, X. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.108534 · Cited by 35 · see citing works
Classifying students based on cognitive state in flipped learning pedagogy
Shaw, R., & Patra, B. K. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.08.018 · Cited by 35 · see citing works
A review of feature set partitioning methods for multi-view ensemble learning
Kumar, A., & Yadav, J. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101959 · Cited by 34 · see citing works
Application of stacking ensemble learning model in quantitative analysis of biomaterial activity
Cao, H., Gu, Y., Fang, J., Hu, Y., Ding, W., He, H., & Chen, G. (2022) · Microchemical Journal · doi:10.1016/j.microc.2022.108075 · Cited by 34 · see citing works
AFSense-ECG: Atrial Fibrillation Condition Sensing From Single Lead Electrocardiogram (ECG) Signals
Ukil, A., Marín, L., Mukhopadhyay, S. C., & Jara, A. J. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3162691 · Cited by 32 · see citing works
Consensus-based distributed two-target tracking over wireless sensor networks
Zhang, C., Qin, J., Li, H., Wang, Y., Wang, S., & Zheng, W. X. (2022) · Automatica · doi:10.1016/j.automatica.2022.110593 · Cited by 32 · see citing works
Progression and Challenges of IoT in Healthcare: A Short Review
Rahman, S., Ibtisum, S., Podder, P., & Hossain, S. (2023) · International Journal of Computer Applications · doi:10.5120/ijca2023923168 · Cited by 31 · see citing works
Feng, H., Fu, Y., Huang, S., Glamuzina, B., & Zhang, X. (2023) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2023.115211 · Cited by 31 · see citing works
Zhou, C., Hou, A., Dai, P., Li, A., Zhang, Z., Mu, Y., & Liu, L. (2023) · Information Sciences · doi:10.1016/j.ins.2023.04.011 · Cited by 31 · see citing works
A Decentralized System for Green Energy Distribution in a Smart Grid
Talat, R., Muzammal, M., Qu, Q., Zhou, W. J., Najam-ul-Islam, M., Bamakan, S. M. H., & Qiu, J. (2019) · Journal of Energy Engineering · doi:10.1061/(asce)ey.1943-7897.0000623 · Cited by 30 · see citing works
Self-citationZhou, Y., Yang, Z., Zhang, X., & Wang, Y. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3196170 · Cited by 29 · see citing works
Intelligent Health Monitoring System for Detection of Symptomatic/Asymptomatic COVID-19 Patient
Nandy, S., & Adhikari, M. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3096425 · Cited by 27 · see citing works
Dual-Layer Deep Ensemble Techniques for Classifying Heart Disease
Prakash, J., V, & Karthikeyan, N. (2022) · Information Technology And Control · doi:10.5755/j01.itc.51.1.30083 · Cited by 27 · see citing works
Dhiman, H. S., Deb, D., & Guerrero, J. M. (2022) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2022.100795 · Cited by 27 · see citing works
Jijesh, J. J., Shivashankar, & Keshavamurthy, K. (2020) · Wireless Personal Communications · doi:10.1007/s11277-020-07762-9 · Cited by 26 · see citing works
Khan, M. H. R., & Hossain, A. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.561801 · Cited by 26 · see citing works
Cyber-physical system with IoT-based smart vehicles
Alshdadi, A. A. (2021) · Soft Computing · doi:10.1007/s00500-021-05908-w · Cited by 26 · see citing works
Hu, F., Huang, M., Sun, J., Zhang, X., & Liu, J. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.02.016 · Cited by 26 · see citing works
Machine Learning-based sEMG Signal Classification for Hand Gesture Recognition
Aarotale, P. N., & Rattani, A. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822133 · Cited by 25 · see citing works
MDS: Multi-level decision system for patient behavior analysis based on wearable device information
Tolba, A. K. R., Said, O., & Al-Makhadmeh, Z. (2019) · Computer Communications · doi:10.1016/j.comcom.2019.08.022 · Cited by 25 · see citing works
Low-Cost and Device-Free Human Activity Recognition Based on Hierarchical Learning Model
Chen, J., Huang, X., Jiang, H., & Miao, X. (2021) · Sensors · doi:10.3390/s21072359 · Cited by 25 · see citing works
FedSPL: federated self-paced learning for privacy-preserving disease diagnosis
Wang, Q., & Zhou, Y. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab498 · Cited by 25 · see citing works
- Authors
- Muzammal, M., Talat, R., Sodhro, A. H., & Pirbhulal, S.
- Affiliations
- Bahria University (PK)
- Linköping University (SE)
- Chinese Academy of Sciences (CN)
- Corresponding author
- Pirbhulal, Sandeep
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 53
- Pages
- 155-164
- ISSN
- 1566-2535 (ISSN-L)
- OpenAlex
- W2949754992
- Cited by
- 311
- References
- 57
- Citations by year
- 2026: 12 · 2025: 38 · 2024: 39 · 2023: 52 · 2022: 45 · 2021: 70 · 2020: 44 · 2019: 11
- FWCI
- 46.56
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Information Fusion (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Engineering › Biomedical Engineering › Wireless Body Area Networks
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- wireless body sensor networks, sensor fusion, ensemble classifiers, fog computing, wearable sensors, activity data, health data
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review