Machine Learning/Artificial Intelligence for Sensor Data Fusion–Opportunities and Challenges

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Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey

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Multi-source information fusion: Progress and future

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Real-Time Digital Twins: Vision and Research Directions for 6G and Beyond

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Remote Sensing and Geospatial Analysis in the Big Data Era: A Survey

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The Role of Machine Learning and the Internet of Things in Smart Buildings for Energy Efficiency

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UAV based defect detection and fault diagnosis for static and rotating wind turbine blade: a review

Zhang, S., He, Y., Gu, Y., He, Y., Wang, H., Wang, H., Yang, R., Chady, T., & Zhou, B. (2024) · Nondestructive Testing And Evaluation · doi:10.1080/10589759.2024.2395363 · Cited by 136

Backdoor attacks and defenses in federated learning: Survey, challenges and future research directions

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A Survey of the Real-Time Metaverse: Challenges and Opportunities

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CYBERSECURITY AWARENESS AND EDUCATION PROGRAMS: A REVIEW OF EMPLOYEE ENGAGEMENT AND ACCOUNTABILITY

Abrahams, T. O., Farayola, O. A., Kaggwa, S., Uwaoma, P. U., Hassan, A. O., & Dawodu, S. O. (2024) · Computer Science & IT Research Journal · doi:10.51594/csitrj.v5i1.708 · Cited by 89

Recent Advances in Artificial Intelligence and Wearable Sensors in Healthcare Delivery

Junaid, S. B., Imam, A. A., Abdulkarim, M., Surakat, Y. A., Balogun, A. O., Kumar, G., Shuaibu, A. N., Garba, A., Sahalu, Y., Mohammed, A., et al. (2022) · Applied Sciences · doi:10.3390/app122010271 · Cited by 65

Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments

Majeed, A., & Hwang, S. O. (2021) · Symmetry · doi:10.3390/sym14010016 · Cited by 73

The Innovation of Ideological and Political Education Integrating Artificial Intelligence Big Data with the Support of Wireless Network

Du, G., Sun, Y., & Zhao, Y. (2023) · Applied Artificial Intelligence · doi:10.1080/08839514.2023.2219943 · Cited by 55

Avionics Systems Panel Research and Innovation Perspectives

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Metal multi-material additive manufacturing: Overcoming barriers to implementation

Clare, A. T., Woizeschke, P., Rankouhi, B., Pfefferkorn, F. E., Bartels, D., Schmidt, M., & Wits, W. W. (2025) · CIRP Annals · doi:10.1016/j.cirp.2025.05.004 · Cited by 49

EcoTaskSched: a hybrid machine learning approach for energy-efficient task scheduling in IoT-based fog-cloud environments

Khan, A., Ullah, F., Shah, D., Khan, M. H., Ali, S., & Tahir, M. N. (2025) · Scientific Reports · doi:10.1038/s41598-025-96974-9 · Cited by 43

Fusion-driven deep feature network for enhanced object detection and tracking in video surveillance systems

Jain, D. K., Zhao, X., Gan, C., Shukla, P. K., Jain, A., & Sharma, S. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102429 · Cited by 45

Environmental energy harvesting boosts self-powered sensing

Luo, H., Yang, T., Jing, X., Cui, Y., & Qin, W. (2024) · Materials Today Energy · doi:10.1016/j.mtener.2024.101502 · Cited by 40

A roadmap for model-based bioprocess development

Mu'azzam, K., da Silva, F. V. S., Murtagh, J., & Sousa-Gallagher, M. J. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108378 · Cited by 40

AI in Context: Harnessing Domain Knowledge for Smarter Machine Learning

Miller, T. I., Durlik, I., Łobodzińska, A., Dorobczyński, L., & Jasionowski, R. (2024) · Applied Sciences · doi:10.3390/app142411612 · Cited by 46

Advanced Sensor Technologies in CAVs for Traditional and Smart Road Condition Monitoring: A Review

Khanmohamadi, M., & Guerrieri, M. (2024) · Sustainability · doi:10.3390/su16198336 · Cited by 42

Real‐time monitoring of tunnel structures using digital twin and artificial intelligence: A short overview

Afrazi, M., Armaghani, D. J., Afrazi, H., Fattahi, H., & Samui, P. (2025) · Deep Underground Science and Engineering · doi:10.1002/dug2.70029 · Cited by 36

The Microverse: A Task-Oriented Edge-Scale Metaverse

Qu, Q., Hatami, M., Xu, R., Nagothu, D., Chen, Y., Li, X., Blasch, E., Ardiles-Cruz, E., & Chen, G. (2024) · Future Internet · doi:10.3390/fi16020060 · Cited by 33

Enhancing fraud detection in accounting through AI: Techniques and case studies

Adelakun, B. O., Onwubuariri, E. R., Adediran, G. A., & Ntiakoh, A. (2024) · Finance & Accounting Research Journal · doi:10.51594/farj.v6i6.1232 · Cited by 32

A paradigm shift in crisis management: The nexus of AGI‐driven intelligence fusion networks and blockchain trustworthiness

Yue, Y., & Shyu, J. Z. (2024) · Journal of Contingencies and Crisis Management · doi:10.1111/1468-5973.12541 · Cited by 35

A Critical AI View on Autonomous Vehicle Navigation: The Growing Danger

Miller, T. I., Durlik, I., Kostecka, E., Borkowski, P., & Łobodzińska, A. (2024) · Electronics · doi:10.3390/electronics13183660 · Cited by 38

Modeling of two-stage anaerobic onsite wastewater sanitation system to predict effluent soluble chemical oxygen demand through machine learning

Mathur, R., Sharma, M., Loganathan, K., Abbas, M. A. E., Hussain, S., Kataria, G., Alqahtani, M. S., & Rao, K. S. (2024) · Scientific Reports · doi:10.1038/s41598-023-50805-x · Cited by 28

Marketing Strategies in Nanomaterials for Sensor Applications: Bridging Lab to Market

Singh, A., Ahmed, E., Rather, M. U. D., Sundararajan, A., Sharma, A., Choudhary, F. S., Sundramoorthy, A. K., Dixit, S., Vatin, N. I., & Arya, S. K. (2025) · Global Challenges · doi:10.1002/gch2.202400294 · Cited by 28

Anti‐Crosstalk Materials and Structural Decoupling Strategies for Multimodal Flexible Sensors

Wu, W. H. (2025) · Advanced Functional Materials · doi:10.1002/adfm.202524222 · Cited by 30

End-to-End Multimodal Sensor Dataset Collection Framework for Autonomous Vehicles

Gu, J., Lind, A., Chhetri, T. R., Bellone, M., & Sell, R. (2023) · Sensors · doi:10.3390/s23156783 · Cited by 30

UGV-UAV Integration Advancements for Coordinated Missions: A Review

Shahar, F. S., Sultan, M. T. H., Nowakowski, M., & Łukaszewicz, A. (2025) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-025-02273-w · Cited by 25

A Contemporary Survey on Multisource Information Fusion for Smart Sustainable Cities: Emerging Trends and Persistent Challenges

Orchi, H., Diallo, A. B., Elbiaze, H., Sabir, E., & Sadik, M. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102667 · Cited by 24

Internet of Things and Artificial Intelligence for Secure and Sustainable Green Mobility: A Multimodal Data Fusion Approach to Enhance Efficiency and Security

Reis, M. J. C. S. (2025) · Multimodal Technologies and Interaction · doi:10.3390/mti9050039 · Cited by 26

Strain Sensing Technology to Enable Next-Generation Industry and Smart Machines for the Factories of the Future: A Review

Hamed, Y., O’Donnell, G. E., Lishchenko, N. V., & Munina, I. V. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3313013 · Cited by 23

Experience in psychological counseling supported by artificial intelligence technology

Ping, Y. (2024) · Technology and Health Care · doi:10.3233/thc-230809 · Cited by 22

Artificial intelligence meets radar resource management: A comprehensive background and literature review

Hashmi, U. S., Akbar, S., Adve, R., Moo, P. W., & Ding, J. B. (2022) · IET Radar Sonar & Navigation · doi:10.1049/rsn2.12337 · Cited by 26

Machine Learning and IoT based Performance Improvement of Energy Efficiency in Smart Buildings

Jagadeesan, S., Ravi, C. N., Sujatha, M., Sree Southry, S., Sundararajan, J., & Reddy, C. V. K. (2023) · International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) · doi:10.1109/icscds56580.2023.10104874 · Cited by 22

IFNet: Data-driven multisensor estimate fusion with unknown correlation in sensor measurement noises

Wang, M., Liu, H., Tang, H., Zhang, M., & Shen, X. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102750 · Cited by 25

Integration of AI with artificial sensory systems for multidimensional intelligent augmentation

Tian, C., Cho, Y., Song, Y., Park, S., Kim, I. H., & Cho, S. (2025) · International Journal of Extreme Manufacturing · doi:10.1088/2631-7990/adbd98 · Cited by 21

LPI Waveform Recognition Using Adaptive Feature Construction and Convolutional Neural Networks

Huang, H., Li, Y., Liu, J., Shen, D., Chen, G., Blasch, E., & Pham, K. D. (2023) · IEEE Aerospace and Electronic Systems Magazine · doi:10.1109/maes.2023.3238704 · Cited by 21

AI-Powered Arabic Language Education in the Era of Society 5.0

Anwar, M. R., & Ahyarudin, H. A. (2023) · IAIC Transactions on Sustainable Digital Innovation (ITSDI) · doi:10.34306/itsdi.v5i1.607 · Cited by 21

A Quarter Century of Covariance Intersection: Correlations Still Unknown? [Lecture Notes]

Forsling, R., Noack, B., & Hendeby, G. (2024) · IEEE Control Systems · doi:10.1109/mcs.2024.3358658 · Cited by 21

Dynamic Pricing Strategies Using Artificial Intelligence Algorithm

Başal, M., Saraç, E., & Özer, K. (2024) · Open Journal of Applied Sciences · doi:10.4236/ojapps.2024.148128 · Cited by 20

Challenges and Opportunities of Using Data Fusion Methods for Travel Time Estimation

Guido, G., Haghshenas, S. S., Vitale, A., & Astarita, V. (2022) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit55151.2022.9804014 · Cited by 20

Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis

Braca, P., Millefiori, L. M., Aubry, A., Maranò, S., Maio, A. D., & Willett, P. (2022) · IEEE Open Journal of Signal Processing · doi:10.1109/ojsp.2022.3232284 · Cited by 20

Digital Transformation and Sustainability Artificial Intelligence and Sustainable Development

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A Deep Learning-Enhanced Multi-Modal Sensing Platform for Robust Human Object Detection and Tracking in Challenging Environments

Peng, C., Xiong, Z., Bao, Y., Zhuang, P., Zhang, Y., Blasch, E., & Chen, G. (2023) · Electronics · doi:10.3390/electronics12163423 · Cited by 17

Augmented Physics-Based Machine Learning for Navigation and Tracking

Imbiriba, T., Straka, O., Duník, J., & Closas, P. (2023) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2023.3328853 · Cited by 21

Uncertain about ChatGPT: enabling the uncertainty evaluation of large language models

Jousselme, A.-L., de Villiers, J. P., de Freitas, A., Blasch, E., Dragos, V., Pavlin, G., Costa, P., Laskey, K. B., & Laudy, C. (2023) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion52260.2023.10224086 · Cited by 16

Harnessing the power of artificial intelligence in pharmaceuticals: Current trends and future prospects

Aritra, S., Shikha, C. B., & Indu, S. (2025) · Intelligent Pharmacy · doi:10.1016/j.ipha.2024.12.001 · Cited by 18

IoBT-OS: Optimizing the Sensing-to-Decision Loop for the Internet of Battlefield Things

Liu, D., Abdelzaher, T., Wang, T., Hu, Y. C., Li, J., Liu, S., Caesar, M., Kalasapura, D., Bhattacharyya, J., Srour, N., et al. (2022) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn54977.2022.9868920 · Cited by 14

Wearable smart sensors integration with AI and machine learning for tracking human health

Xianyong, S., Mishra, D. K., Özcan, Ö. Ö., Karahan, M., Panpan, T., Zhongshan, X., & Kumar, P. V. (2025) · Biosensors and Bioelectronics X · doi:10.1016/j.biosx.2025.100711 · Cited by 17

Heterogeneous sensing for target tracking: architecture, techniques, applications and challenges

Li, Z., Liu, J., Chen, K., Gao, X. J., Tang, C., Xie, C., & Lu, X. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/acc267 · Cited by 13

Sensor Fusion and Machine Learning for Seated Movement Detection With Trunk Orthosis

Rao, A. Z., Siddique, S. S., Mujib, M. D., Hasan, M. A., Alokaily, A. O., & Tahira, T. (2024) · IEEE Access · doi:10.1109/access.2024.3377111 · Cited by 15

Design of a Machine Learning Approach to Anomaly Detection in Tyre-Road Interaction

Sakhnevych, A., Pasquino, N., & Sperlí, G. (2025) · IEEE Access · doi:10.1109/access.2025.3539923 · Cited by 13

Fraud Detection in Fintech Leveraging Machine Learning and Behavioral AnalyticsPreprint

Josyula, H. P. (2023) · Research Square · doi:10.21203/rs.3.rs-3548343/v1 · Cited by 18

Adaptive Traffic Light Management for Mobility and Accessibility in Smart Cities

Almaliki, M., Bamaqa, A., Badawy, M., Farrag, T. A., Balaha, H. M., & Elhosseini, M. A. (2025) · Sustainability · doi:10.3390/su17146462 · Cited by 12

UAV-based deep learning for biodiversity monitoring: Advances, applications, and future directions

Wang, S., Chen, K., Wei, Z., Yang, L., Wang, Q., Liu, M., Cao, K., Zhao, C., Chang, R., Wang, Z., et al. (2026) · Ecological Informatics · doi:10.1016/j.ecoinf.2026.103710 · Cited by 11

Classroom Design and Application of Art Design Education Based on Artificial Intelligence

Zhao, Y., & Gao, L. (2023) · International Journal of Information Technology and Web Engineering · doi:10.4018/ijitwe.334008 · Cited by 16

ContextLLM: Meaningful Context Reasoning from Multi-Sensor and Multi-Device Data Using LLMs

Post, K., Kuchida, R., Olapade, M., Yin, Z., Nurmi, P., & Flores, H. (2025) · IEEE Workshop on Mobile Computing Systems and Applications (WMCSA) · doi:10.1145/3708468.3711892 · Cited by 11

Resonance-Based Optical Gas Sensors

Martínez, E. E. G., Zamarreño, C. R., & Matias, I. R. (2025) · IEEE Sensors Reviews · doi:10.1109/sr.2025.3584750 · Cited by 10

MultiSenseNet: Multi-Modal Deep Learning for Machine Failure Risk Prediction

Rahman, M., Hossain, M. S., Rozario, U., Roy, S., Mridha, M. F., & Dey, N. (2025) · IEEE Access · doi:10.1109/access.2025.3586978 · Cited by 18

Digital Twins for Cognitive Situation Awareness

Blasch, E. (2024) · IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA) · doi:10.1109/cogsima61085.2024.10553721 · Cited by 10

A review on AI integration with FDM printing to enhance precision, efficiency, and process optimization

Rajendran, S., Palani, G., Sanjeevi, S., Veerasimman, A., Trilaksana, H., Sundaram, V., Uthayakumar, M., Yang, Y.-L., & Shanmugam, V. (2025) · Journal of Reinforced Plastics and Composites · doi:10.1177/07316844251358587 · Cited by 10

GASx: Explainable Artificial Intelligence For Detecting GPS Spoofing Attacks

Fan, Z., Tian, X. L., Wei, S., Shen, D., Chen, G., Pham, K. D., & Blasch, E. (2024) · Proceedings of the Institute of Navigation ... International Technical Meeting/Proceedings of the ... International Technical Meeting of The Institute of Navigation · doi:10.33012/2024.19543 · Cited by 10

A prospective study: Advances in chaotic characteristics of serum Raman spectroscopy in the field of assisted diagnosis of disease

Liu, Y., Chen, C., Tian, X., Zuo, E., Cheng, Z., Su, Y., Chang, C., Li, M., Chen, C., & Lv, X. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121787 · Cited by 11

Calibration of Marine Pressure Sensors with a Combination of Temperature and Pressure: A Case Study of SBE 37-SM

Zhang, M., Sun, Q., Bai, X., Yang, B., Zhao, W., & Wu, C. J. (2024) · Journal of Marine Science and Engineering · doi:10.3390/jmse12122366 · Cited by 9

Synergizing Machine Learning and fluorescent biomolecules: A new era in sensing platforms

Saini, N., Kriti, N. A., Thakur, A., Saini, S., Kaur, N., & Singh, N. (2025) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2025.118196 · Cited by 9

The Future of Artificial Intelligence and Machine Learning in Online Social Networking

Tyagi, A. K., Lakkshmanan, A., & Ahmad, S. S. (2024) · doi:10.1002/9781394231126.ch9 · Cited by 9

On digital twins in defense: overview and applications

Giberna, M., Voos, H., Tavares, P. J., Nunes, J., Sorg, T., Masini, A., & Sánchez-López, J. L. (2026) · The Journal of Defense Modeling and Simulation Applications Methodology Technology · doi:10.1177/15485129261441817 · Cited by 19

A review of trajectory planning for high speed vehicles: Current advancements and future prospects

Yang, Q., Liu, S., Wu, X., Han, T., Shin, H., & Tekin, R. (2026) · Aerospace Science and Technology · doi:10.1016/j.ast.2026.112667 · Cited by 8

Artificial Intelligence Fusion of Information for Aerospace (AIFIA) Systems

Blasch, E., Shen, D., Chen, G., Insaurralde, C. C., & Niu, R. (2022) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero53065.2022.9843668 · Cited by 8

Fusion of heterogeneous industrial data using polygon generation & deep learning

Elhefnawy, M., Ouali, M.-S., Ragab, A., & Amazouz, M. (2023) · Results in Engineering · doi:10.1016/j.rineng.2023.101234 · Cited by 8

Use of the URREF towards Information Fusion Accountability Evaluation

Blasch, E., de Villiers, J. P., Pavin, G., Jousselme, A.-L., Costa, P., Laskey, K. B., & Ziegler, J. (2021) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion49465.2021.9626847 · Cited by 8

Data fusion information group (DFIG) model meets AI+ML

Blasch, E., Sullivan, N., Chen, G., Chen, Y., Shen, D., Yu, W., & Chen, H.-M. (2022) · Signal processing, sensor fusion, and target recognition. Conference · doi:10.1117/12.2619624 · Cited by 8

Exploring Purpose-Driven Methods and a Multifaceted Approach in Dam Health Monitoring Data Utilization

Li, Z., Khailah, E. Y., Liu, X., & Liang, J. (2025) · Buildings · doi:10.3390/buildings15152803 · Cited by 8

Multi-step Bayesian Optimization-based intelligent task planning for an ozone observation satellite

Kabir, R. H., Peng, H., & Bai, X. (2023) · Acta Astronautica · doi:10.1016/j.actaastro.2023.04.003 · Cited by 7

Fusion Orchestration Guidelines (FOG) for Collaborative Computing and Network Data Fusion

Blasch, E. (2023) · National Aerospace and Electronics Conference · doi:10.1109/naecon58068.2023.10365788 · Cited by 7

Transfer learning approach to reduce similar IOT sensor data for industrial applications

Ramesh, K., Rajarao, B., Chaudhari, H., Mary, S. A. L., Venkatanaresh, M., & Dhiman, T. K. (2023) · Measurement Sensors · doi:10.1016/j.measen.2023.100985 · Cited by 7

Supporting Agile User Fusion Analytics through Human-Agent Knowledge Fusion

Braines, D., Preece, A. D., Roberts, C. H., & Blasch, E. (2021) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion49465.2021.9627072 · Cited by 7

Multimodal Heterogeneous Data Sensing and Communication Integration for CIoT

Chen, N., Zhang, C., Fall, D., Liu, C., Urakami, T., Okada, M., Fu, M., Wang, Q., Bai, Y. B., Sun, L., et al. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3576162 · Cited by 6

Self-Diagnosing Circuit Breakers Using Embedded Optical Fiber Sensors in Medium Voltage Systems

Garg, N. (2025) · International Workshop on Fiber Optics in Access Networks (FOAN) · doi:10.1109/foan66962.2025.11189116 · Cited by 6

Deep learning for multisource medical information processing

Gezimati, M., & Singh, G. K. (2024) · Elsevier eBooks · doi:10.1016/b978-0-44-313233-9.00009-6 · Cited by 6

Crop Health Monitoring Through AI-Based Crop Leaf Disease Detection Systems

Bhuyan, P., Singh, P. K., & Das, S. K. (2025) · Advances in environmental engineering and green technologies book series · doi:10.4018/979-8-3693-8019-2.ch005 · Cited by 6

Enhancing Sensor Perception: Integrating Multi-Sensor Data for Robust Sensor Perception

Gupta, K., Singhal, A., Kumar, A., & Maji, P. (2025) · doi:10.1109/comsnets63942.2025.10885686 · Cited by 6

An initiative to promote the integration of artificial intelligence in transforming the diagnosis and management of Wilson disease in the 21st century

Stremmel, W., & Weiskirchen, R. (2025) · Metabolism and Target Organ Damage · doi:10.20517/mtod.2024.138 · Cited by 6

Advancing numerical models for land-to-ocean litter transport: A review of hydrodynamic processes and inland contributions

Baci, N. O., Santiago-Collazo, F. L., Woodson, C. B., & Jambeck, J. (2025) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2025.179013 · Cited by 6

Trusted Entropy-Based Information Maneuverability for AI Information Systems Engineering

Blasch, E., Schuck, T. M., & Gagne, O. B. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-89385-9_3 · Cited by 6

Convergent Sensing: Integrating Biometric and Environmental Monitoring in Next-Generation Wearables

Guarnaccia, M. C., Spampinato, A. G., Alessi, E., & Cavallaro, S. (2026) · Biosensors · doi:10.3390/bios16010043 · Cited by 7

AI-based sensing solutions in the food industry: A review

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Special Issue on Arificial Intelligence for Data Fusion

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