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Xiao, H. R., Hu, W., Zhou, H. L., & Liu, G. P. (2023) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3242198 · Cited by 24 · see citing works

TruVR: Trustworthy Cybersickness Detection using Explainable Machine Learning

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A three-level view of readiness models: Statistical and managerial insights on industry 4.0

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Where does AI play a major role in the new product development and product management process?

Witkowski, A., & Wodecki, A. (2025) · Management Review Quarterly · doi:10.1007/s11301-025-00533-5 · Cited by 23 · see citing works

A Trustable LSTM-Autoencoder Network for Cyberbullying Detection on Social Media Using Synthetic Data

Akter, M. S., Shahriar, H., Cuzzocrea, A., Wu, F., & Rodriguez-Cardenas, J. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386719 · Cited by 14 · see citing works

Beyond the Sensor: A Systematic Review of AI’s Role in Next-Generation Machine Health Monitoring

Sufi, F. K. (2025) · Applied Sciences · doi:10.3390/app151910494 · Cited by 12 · see citing works

Early Prediction of Cryptocurrency Price Decline: A Deep Learning Approach

Akter, M. S., Shahriar, H., Rahman, M. A., Rahman, M. S., & Cuzzocrea, A. (2023) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit60459.2023.10441030 · Cited by 12 · see citing works

An Exploration of the Applications, Challenges, and Success Factors in AI-Driven Product Development and Management

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Augmenting post-hoc explanations for predictive process monitoring with uncertainty quantification via conformalized Monte Carlo dropout

Mehdiyev, N., Majlatow, M., & Fettke, P. (2024) · Data & Knowledge Engineering · doi:10.1016/j.datak.2024.102402 · Cited by 18 · see citing works

Multimodal transformer for early alarm prediction

Strem, N., Dhami, D. S., Schmidt, B., & Kersting, K. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109643 · Cited by 11 · see citing works

From physics to machine learning and back: Part I - Learning with inductive biases in prognostics and health management (PHM)

Fink, O., Sharma, V., Nejjar, I., Krannichfeldt, L. V., Garmaev, S., Zhang, Z., Wei, A., Frusque, G., Forest, F., Zhao, M., et al. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.112213 · Cited by 10 · see citing works

An Efficient Siamese Network and Transfer Learning-Based Predictive Maintenance System for More Sustainable Manufacturing

Çalışkan, A., O’Brien, C., Panduru, K., Walsh, J., & Riordan, D. (2023) · Sustainability · doi:10.3390/su15129272 · Cited by 10 · see citing works

A financial anomaly prediction approach using semantic space of news flow on twitter

Bodaghi, A., & Oliveira, J. (2024) · Decision Analytics Journal · doi:10.1016/j.dajour.2024.100422 · Cited by 10 · see citing works

Application of Explainable AI (Xai) For Anomaly Detection and Prognostic of Gas Turbines with Uncertainty Quantification.Preprint

Nor, A. K. M., Pedapati, S. R., & Muhammad, M. B. (2021) · Preprints.org · doi:10.20944/preprints202109.0034.v1 · Cited by 8 · see citing works

APT: Alarm Prediction Transformer

Strem, N., Dhami, D. S., Schmidt, B., Klöpper, B., & Kristian Kersting, B. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125521 · Cited by 7 · see citing works

Digital twin and AI enabled predictive maintenance in building industry

Hu, W. (2024) · doi:10.32657/10356/180365 · Cited by 8 · see citing works

An interpretable fault diagnosis method for aeroengine bearings based on belief rule based with a dynamic power set

Li, J., He, W., & Zhu, H. (2024) · Scientific Reports · doi:10.1038/s41598-024-82804-x · Cited by 6 · see citing works

Optimal Transport for Efficient, Unsupervised Anomaly Detection on Industrial Data

Langbridge, A., O’Donncha, F., Rayfield, J. T., & Eck, B. J. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825081 · Cited by 6 · see citing works

Cyberbullying Detection, Prevention, and Analysis on Social Media via Trustable LSTM-Autoencoder Networks over Synthetic Data: The TLA-NET Approach

Cuzzocrea, A., Akter, M. S., Shahriar, H., & Bringas, P. G. (2025) · Future Internet · doi:10.3390/fi17020084 · Cited by 6 · see citing works

Toward Explainable AI in Spacecraft Health Monitoring: Comparative Benchmarking of Anomaly Detection Models and Open-Source Datasets

Tahir, H., Jabbarpour, M. R., Vo, Q. B., Kowalczyk, R., Barr, J., & Bessell, T. (2025) · IEEE Access · doi:10.1109/access.2025.3639551 · Cited by 7 · see citing works

Anomaly Attribution with Likelihood Compensation

Idé, T., Dhurandhar, A., Navrátil, J., Singh, M., & Abe, N. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i5.16535 · Cited by 6 · see citing works

Secured UAV Navigation: A Novel Intrusion Detection System Based on PWM Signal Analysis

Alva, A., Moreno, L. M., Asif, M., Khalil, A. A., Rahman, M. A., Cuzzocrea, A., & Shahriar, H. (2024) · IEEE Swiss Conference on Data Science (SDS) · doi:10.1109/sds60720.2024.00032 · Cited by 5 · see citing works

exHAR

Kianpisheh, M., Mariakakis, A. T., & Truong, K. N. (2024) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3643500 · Cited by 4 · see citing works

LTL-based runtime verification framework for cyber-attack anomaly prediction in cyber–physical systems

Akande, A. J., Hóu, Z., Foo, E., & Li, Q. (2025) · Computers & Security · doi:10.1016/j.cose.2025.104455 · Cited by 5 · see citing works

Long Horizon Anomaly Prediction in Multivariate Time Series with Causal Autoencoders

Asres, M. W., Cummings, G., Khukhunaishvili, A., Parygin, P., Cooper, S. I., Yu, D., Dittmann, J., & Omlin, C. W. P. (2022) · PHM Society European Conference · doi:10.36001/phme.2022.v7i1.3367 · Cited by 4 · see citing works

Explanation of clustering result based on multi-objective optimization

Chen, L., Zhong, C., & Zhang, Z. (2023) · PLoS ONE · doi:10.1371/journal.pone.0292960 · Cited by 4 · see citing works

An Automated Machine Learning Approach for Predicting Chemical Laboratory Material Consumption

Silva, A. J., & Cortez, P. C. (2021) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-79150-6_9 · Cited by 4 · see citing works

Explainable Artificial Intelligence for Anomaly Detection and Prognostic of Gas Turbines using Uncertainty Quantification with Sensor-Related DataPreprint

Nor, A. K. M., Pedapati, S. R., Muhammad, M. B., & Leiva, V. (2021) · Preprints.org · doi:10.20944/preprints202109.0034.v2 · Cited by 4 · see citing works

A Location Analytics Algorithm to Analyze Geographic and Demographic Data for Restaurant Analytics

Geng, C., LaFreniere, G., Le, H. K., Leung, C. K.-S., Wong, Q. C., & Cuzzocrea, A. (2024) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac61105.2024.00282 · Cited by 3 · see citing works

A systemic survey towards predictive maintenance in the building industry based on network analysis

Wei, H., Xie, Z., & Cai, Y. (2025) · Journal of Building Engineering · doi:10.1016/j.jobe.2025.113889 · Cited by 3 · see citing works

A robust logistic regression approach enhanced by hyperparameter optimization techniques through swarm intelligence and genetic algorithms: Advancing cancer diagnosis

Benghazouani, S., Nouh, S., & Zakrani, A. (2025) · High-Confidence Computing · doi:10.1016/j.hcc.2025.100378 · Cited by 3 · see citing works

An Anomaly Detection Method for Metro Signal and Control Systems

Huang, Y., Li, K. Y. H., Pei, C., Wang, L., Ming, W. L. L., Cheung, K. M. S., Lau, K. K. M., Chan, C. C. P., Ma, Z., Peng, Z., et al. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.09.633 · Cited by 3 · see citing works

Exploring Task-Specific Dimensions in Word Embeddings Through Automatic Rule Learning

Gao, L., Zhan, H., & Sheng, V. S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72341-4_14 · Cited by 3 · see citing works

A Big Data Management and Analytics Framework for Supporting Machine Learning, OLAP, and Visualization on Big COVID-19 Data

Cuzzocrea, A., Leung, C. K.-S., Shang, S., & Wen, Y. (2025) · Journal of Database Management · doi:10.4018/jdm.386132 · Cited by 3 · see citing works

Machinery Faults Prediction Using Ensemble Tree Classifiers: Bagging or Boosting?

Ramezani, S. B., Amirlatifi, A., Kirby, T., Rahimi, S., & Seale, M. A. (2021) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2021.v13i1.3063 · Cited by 3 · see citing works

Towards Highly Efficient Anomaly Detection for Predictive Maintenance

Klüttermann, S., Peka, V., Doebler, P., & Müller, E. (2024) · International Conference on Machine Learning and Applications · doi:10.1109/icmla61862.2024.00261 · Cited by 3 · see citing works

Interactive Explainable Anomaly Detection for Industrial Settings

Gramelt, D., Höfer, T., & Schmid, U. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92805-5_9 · Cited by 2 · see citing works

Efficient Failure Prediction: A Transfer Learning-Based Solution for Imbalanced Data Classification

Çalışkan, A., Badem, H., Walsh, J. L., & Riordan, D. (2025) · Electronics · doi:10.3390/electronics14244957 · Cited by 2 · see citing works

Drill-CODA: A Framework for Supporting Drill-Across Multidimensional Big Data Analytics Over Big Co-Occurrence Aggregate Hierarchical Data

Cuzzocrea, A. (2025) · SN Computer Science · doi:10.1007/s42979-025-04440-0 · Cited by 2 · see citing works

An interpretable prediction of FCM driven by small samples for energy analysis based on air quality prediction

Peng, Z., Zhang, C., Cao, B., Hong, Z., & Han, X. (2022) · Journal of the Air & Waste Management Association · doi:10.1080/10962247.2022.2064006 · Cited by 2 · see citing works

TLAQ: Enhanced Big Healthcare Data Analytics via Lazy Aggregations

Cuzzocrea, A., Belmerabet, I., & Hafsaoui, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-06524-7_6 · Cited by 1 · see citing works

Convergence Behaviors and Variabilities of Loss Functions in Quantum GANs and GANs

Rahman, M. A., Cuzzocrea, A., & Shahriar, H. (2025) · IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW) · doi:10.1109/icdcsw63273.2025.00013 · Cited by 1 · see citing works

Integrating LSTM Autoencoders in Medical Monitoring Systems for Early Diabetes Detection

Vaishnavi Dali., A., David Samuel Azariya., S., Sundaram.P, Hemalatha., K., Thatoi, P., & Soms, N. (2025) · International Conference on Expert Clouds and Applications (ICOECA) · doi:10.1109/icoeca66273.2025.00176 · Cited by 1 · see citing works

Effective and Efficient Big OLAP Data Cube Compression in Mobile Cloud Environments: The IQTS Algorithm

Cuzzocrea, A., & Hajian, M. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386278 · Cited by 1 · see citing works

Uncovering the Interpretation of Large Language Models

Akter, M. S., Shahriar, H., Cuzzocrea, A., & Wu, F. (2024) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac61105.2024.00143 · Cited by 1 · see citing works

A SOLID-Based Framework for Supporting Privacy-Preserving Identity Management in Multi-Cloud Environments

Cuzzocrea, A., & Belmerabet, I. (2024) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac61105.2024.00375 · Cited by 1 · see citing works

Towards Analysis and Interpretation of Large Language Models for Arithmetic Reasoning

Akter, M. S., Shahriar, H., & Cuzzocrea, A. (2024) · IEEE Swiss Conference on Data Science (SDS) · doi:10.1109/sds60720.2024.00049 · Cited by 1 · see citing works

Cellular clarity: a logistic regression approach to identify root epidermal regulators of iron deficiency response

Schmittling, S., Muhammad, D., Haque, S., Long, T. A., & Williams, C. (2023) · BMC Genomics · doi:10.1186/s12864-023-09714-6 · Cited by 1 · see citing works

Multi-Resolution Inter-Level Refinement (MR-ILR) Architecture for Anomaly Prediction in IoT Data

Cikmazel, R. O., Saylam, A., Rodoplu, V., & Guzelis, C. (2022) · International Conference on Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista55318.2022.9894159

Towards Explainable Machine Learning for Anomaly Detection in Real-World Contexts

González, A. B. (2023) · doi:10.20868/upm.thesis.73247

BigData Fusion for Trajectory Prediction of Multi-Sensor Surveillance Information Systems

Cascavilla, G., Cuzzocrea, A., Pascale, D., Omidbakhsh, M., & Tamburri, D. A. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386779

Digital Transformation Using Industry 4.0 and Artificial Intelligence

Keerthika, M., Pragadeesh, M., Santhiya, M., Jebamalar, G. B., & Venu, H. (2024) · Wiley eBooks · doi:10.1002/9781394216147.ch2 · Cited by 1 · see citing works

Prediction of Anomalous Events with Data Augmentation and Hybrid Deep Learning Approach

Raihan, A. S. (2024) · doi:10.33915/etd.12347

Mining and Forecasting Energy Consumption Based on Weather Data

Giesbrecht, N., Hnylycia, O. A., Leung, C. K.-S., Lu, J., Mai, T. H. D., Jiang, F., & Cuzzocrea, A. (2024) · IEEE Swiss Conference on Data Science (SDS) · doi:10.1109/sds60720.2024.00011

Turning Multidimensional Big Data Analytics into Practice: Design and Implementation of ClustCube Big-Data Tools in Real-Life Scenarios

Cuzzocrea, A., Hafsaoui, A., & Benlaredj, I. (2025) · Lecture notes in social networks · doi:10.1007/978-3-031-85386-9_16

Combining Columnar-OLAP and Apache Hive for Speeding-Up Multidimensional Big Data Analytics Tools

Cuzzocrea, A. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-3352-4_15

Predicting Corrosion Inhibitor Usage in Coke Plants: A Logistic Regression Approach Based on Real-Time Water Quality Monitoring

Galkina, O., Shevchenko, T., & Kunytskyi, S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-06832-3_15

Additional file 2 of Cellular clarity: a logistic regression approach to identify root epidermal regulators of iron deficiency response

Schmittling, S., Muhammad, D., Haque, S., Long, T. A., & Williams, C. (2024) · Figshare · doi:10.6084/m9.figshare.24355119

Additional file 1 of Cellular clarity: a logistic regression approach to identify root epidermal regulators of iron deficiency response

Schmittling, S., Muhammad, D., Haque, S., Long, T. A., & Williams, C. (2023) · Figshare · doi:10.6084/m9.figshare.24355008

Stock Market Prediction Using Machine-Learning Models

Shree, S. K. M., Shreya, V., Santharoopan, J., Kuresan, H., Bala, A. A., & Vijayalakshmi, D. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-7508-1_22

Explainable algorithms for anomaly detection and time series forecasting

Kong, L. (2024) · doi:10.70675/f3f56534zcb22z4c9fzb5fez6b38e2066740

Swamping and masking rate in binomial regression model

Saleh, N. N. (2026) · Boletim da Sociedade Paranaense de Matemática · doi:10.5269/bspm.80163

Neuro-Symbolic Video Anomaly Detection via Attribute-Based Reasoning

Filippova, S., Korevaar, S., Dau, S. H., Pham, N. T., Cao, T., & Tennakoon, R. (2026) · Proceedings - International Conference on Image Processing · doi:10.1109/icip61757.2026.11630160

A Flexible and Interpretable Framework for Predicting Anomalous Behavior in Industry 4.0 Environments

Langone, R., Cuzzocrea, A., & Skantzos, N. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-44041-1_61 · Cited by 2 · see citing works

Time-Series Anomaly Detection and Imbalanced Classification for Predictive Maintenance

Kong, F. (2025) · IEEE International Conference on Frontiers Technology of Information and Computer (ICFTIC) · doi:10.1109/icftic68075.2025.11324991

A novel unsupervised method for anomaly detection in time series based on statistical features for industrial predictive maintenance

Arantes, J. D. S., Arantes, M. D. S., Fröhlich, H. B., Siret, L., & Bonnard, R. (2021) · International Journal of Data Science and Analytics · doi:10.1007/s41060-021-00283-z · Cited by 24 · see citing works

Long Short Term Memory-based anomaly detection applied to an industrial dosing pump

de Galatheau, A. F., Olteanu, A., Julien, N., & Garrec, S. L. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.04.200 · Cited by 3 · see citing works

Which Datasets for Predicting Anomalies in Industry 4.0?

Diallo, M. S., Mokeddem, S. A., Braud, A., Frey, G., & Lachiche, N. (2020) · doi:10.13140/rg.2.2.28452.83842