Advances and opportunities in machine learning for process data analytics

Support-vector networks

Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421

Support-Vector Networks

Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565

Regularization and Variable Selection Via the Elastic Net

Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957

Reducing the dimensionality of data with neural networks

Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928

Mastering the game of Go with deep neural networks and tree search

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612

Learning Deep Architectures for AI

Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,330

Untitled work

(2011) · Cited by 5,203

A survey of industrial model predictive control technology

Qin, S. J., & Badgwell, T. A. (2003) · Control Engineering Practice · doi:10.1016/s0967-0661(02)00186-7 · Cited by 5,111

Learning Deep Architectures for AI

Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 5,050

Detecting influenza epidemics using search engine query data

Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009) · Nature · doi:10.1038/nature07634 · Cited by 4,295

Data Management

Bresciani, S., Ferraris, A., Romano, M., & Santoro, G. (2021) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-80043-171-320211008

On-line estimation of circulating fluidized bed boiler fuel composition

Ikonen, E., Neuvonen, M., Selek, I., Salo, M., & Liukkonen, M. (2022) · UKACC International Conference on Control · doi:10.1109/control55989.2022.9781460

Framework for Suppressing Transient Fault Alarms Online

Xu, S., & Nixon, M. S. (2022) · Computer-aided chemical engineering/Computer aided chemical engineering · doi:10.1016/b978-0-323-85159-6.50258-x

To examine the application of data science to physics

Balaji, D. K. (2022) · Technoarete Transactions on Advances in Data Science and Analytics · doi:10.36647/ttadsa/01.02.a004

WORKSHOP: Data Analytics with Python for Decision-Making in Organizations

Ramos, R. R. R., Luna, A., Hitas, D. Z. H., Ortiz, K. N. T., Chong, M., Arzaluz, F. A., & Robles, R. E. (2023) · IEEE World Engineering Education Conference (EDUNINE) · doi:10.1109/edunine57531.2023.10102889

O PROFESSOR NO AMBIENTE VIRTUAL DE APRENDIZAGEM: APONTAMENTOS ACERCA DA POSTURA E ATUAÇÃO

de Paula, V. S. (2023) · Editora Inovar eBooks · doi:10.36926/editorainovar-978-65-5388-162-4_008

AS TICS COMO FERRAMENTA DE ENSINO-APRENDIZAGEM: UMA ANÁLISE EM ESCOLAS DO MUNICÍPIO DE ARACRUZ/ES

de Souza, J. A. M., & Nunes, M. A. D. C. (2023) · Editora Inovar eBooks · doi:10.36926/editorainovar-978-65-5388-162-4_003

USO DE RECURSOS DIGITAIS EDUCATIVOS NA APRENDIZAGEM DOS ALUNOS COM DEFICIÊNCIA DA ESCOLA MUNICIPAL PROFESSORA ALDA AMAZONAS MARTINS

Batista, A. N., & Costa, A. (2023) · Editora Inovar eBooks · doi:10.36926/editorainovar-978-65-5388-162-4_014

APRENDIZAGEM ATRAVÉS DO ENSINO REMOTO E SUAS IMPLICAÇÕES

Reis, S. G. (2023) · Editora Inovar eBooks · doi:10.36926/editorainovar-978-65-5388-162-4_002

Tecnologias educacionais em ação

Inovar, E. (2023) · Editora Inovar eBooks · doi:10.36926/editorainovar-978-65-5388-162-4

PROPRIEDADE INTELECTUAL E O (DES)CONHECIMENTO NA EDUCAÇÃO DIGITAL: A TECNOLOGIA COMO FIO CONDUTOR

Ferreira, A. D. S. S., Santos, K. S., Pinheiro, M. A., & Ramos, R. M. D. P. Q. (2023) · Editora Inovar eBooks · doi:10.36926/editorainovar-978-65-5388-162-4_010

PSICOLOGIA ESCOLAR E NEUROEDUCAÇÃO: UM ESTUDO DA COGNIÇÃO HUMANA A PARTIR DA UTILIZAÇÃO DE IAS NA PERSPECTIVA DAS FATIAS DE EXPLORAÇÃO COGNITIVA

Curado, E. P. D. O., & Miranda, F. D. (2023) · Editora Inovar eBooks · doi:10.36926/editorainovar-978-65-5388-162-4_011

PROPOSTA DE OTIMIZAÇÃO DO ENSINO DE QUÍMICA NA EDUCAÇÃO BÁSICA ATRAVÉS DE UMA METODOLOGIA EDUCACIONAL DESENVOLVIDA COM O FRAMEWORK DJANGO

Beviláqua, B. C. D. S., Carvalho, K. F. D. A., & Alves, F. D. A. F. (2023) · Editora Inovar eBooks · doi:10.36926/editorainovar-978-65-5388-162-4_009

INTELIGÊNCIA ARTIFICIAL E ROBÓTICA: BENEFÍCIOS E POSSIBILIDADES EDUCACIONAIS

Bender, A. L., Oliveira, A. M. D. J., da Fonseca, E. C. B., de Araújo, H. A. S., da Silva, J. E., & Serejo, M. B. S. (2023) · Editora Inovar eBooks · doi:10.36926/editorainovar-978-65-5388-162-4_007

GAMIFICAÇÃO E APRENDIZADO LÚDICO NA FORMAÇÃO DE PROFESSORES: UMA ABORDAGEM INOVADORA PARA O DESENVOLVIMENTO PROFISSIONAL DOCENTE

de Abreu, A. K. V., de Oliveira, J. N., & de Oliveira, A. S. (2023) · Editora Inovar eBooks · doi:10.36926/editorainovar-978-65-5388-162-4_005

RECURSOS DIDÁTICOS COM ÊNFASE NOS TECNOLÓGICOS NO ENSINO E APRENDIZAGEM EM CIÊNCIAS DA NATUREZA

Ferreira, M. D. S. T., Rocha, A. C., Santos, T. D. S., dos Santos, B. C., Mauriz, T. R. D. M., Ayres, M. C. C., Ramos, J. C. F., de Andrades, E. O., & dos Santos, R. C. (2023) · Editora Inovar eBooks · doi:10.36926/editorainovar-978-65-5388-162-4_012

GERAÇÃO DIGITAL COM USO DE TECNOLOGIAS

Ferraz, E. G. (2023) · Editora Inovar eBooks · doi:10.36926/editorainovar-978-65-5388-162-4_006

A INTELIGÊNCIA ARTIFICIAL (IA) NA EDUCAÇÃO A DISTÂNCIA: IMPLEMENTAÇÃO, BENEFÍCIOS E PROBLEMÁTICAS

de Paula, V. S. (2023) · Editora Inovar eBooks · doi:10.36926/editorainovar-978-65-5388-162-4_001

SALA DE AULA INVERTIDA: VANTAGENS E DESAFIOS PARA O DOCENTE

Medeiro, G. L., & Follador, K. J. (2023) · Editora Inovar eBooks · doi:10.36926/editorainovar-978-65-5388-162-4_013

AULAS EXPOSITIVAS DIALOGADAS: UMA ALTERNATIVA PARA A CONTINUIDADE DO MÉTODO EXPOSITIVO DE ENSINO

Kuhn, L. L., & Baldissera, R. D. R. (2023) · Editora Inovar eBooks · doi:10.36926/editorainovar-978-65-5388-162-4_004

Desenvolvimento de um sistema de tutoria inteligente e interativo baseada na metodologia PBL aplicado em ambiente virtual de aprendizagem

Meira, M. C. (2022) · doi:10.47749/t/unicamp.2022.1258871

Web log augmented analytics and extraction for e-learning environment

Mokhtar, N. A. M., Sulaiman, S., & Pranolo, A. (2023) · Science in Information Technology Letters · doi:10.31763/sitech.v4i2.1224

Design of a novel deep network model for spinal cord injury prediction

Raju, P. R. S. S. V., Asanambigai, V., & Mudunuri, S. B. (2024) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v13.i2.pp2131-2142

Foundation of analytics

Khare, V., Dwivedi, S. K., & Bhatia, M. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-16078-3.00004-6

THE ROLE OF SURROGATE MODELS AND MACHINE LEARNING IN MODERN ULTRASONIC FLOW MEASUREMENT

Gryshanova, I. (2024) · Bulletin of Kyiv Polytechnic Institute Series Instrument Making · doi:10.20535/1970.67(1).2024.306728

Dynamic Soft Sensor Development Based on Hierarchical Spatiotemporal Extreme Learning Machine for Anaerobic Digestion Process

Zhang, N., Wang, Y., Gai, M., & Yan, P. (2024) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc62350.2024.10587981

Advancements in Deep Learning Architectures for Natural Language Processing Tasks

Reddy, E. K. (2024) · International Journal of Computer Science and Engineering · doi:10.14445/23488387/ijcse-v11i6p101

D-Impact of the Various Strategies on the Author's Impact Score

Ahammad, S. H., Saradhi, P. P., Pande, S. D., Gurav, M. N., & Software Test Engineer, S. C. I. P. L. (2024) · Journal of Engineering Education/Journal of engineering education transformations/Journal of engineering education transformation · doi:10.16920/jeet/2024/v38i1/24171

Advanced Methods in Diagnostics and Prognostics

Mohr, F., Sun, W., & Braatz, R. D. (2024) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc60939.2024.10644895

Sentiment analysis using graph-based Quickprop method for product quality enhancement

Subramani, R. K. V., & Kumaresan, T. (2024) · Network Computation in Neural Systems · doi:10.1080/0954898x.2024.2410777

Proposal for the implementation of a system based on augmented reality for the monitoring of an industrial process in real time

Encarnación-Díaz, J. D., Jiménez-Quevedo, C. G., Chasi-Pesantez, P. A., Guerrero-Vásquez, L. F., Torres-Torres, F. A., & Ordoñez-Ordoñez, J. O. (2024) · IEEE Colombian Conference on Communications and Computing (COLCOM) · doi:10.1109/colcom62950.2024.10720281

Using Deep Learning to Build Prediction Models of Carbon Emissions and Leadership Ethics for Stock Returns - Based on BERT and LSTM Models

Chen, T., & Wu, C. (2024) · International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) · doi:10.1109/iceccme62383.2024.10796502

Fit for Future – Die Erfolgsfaktoren der Fraunhofer-Lernlabore

Ebert, S., Harbers, C., Poxleitner, E., Schätz, R., Schnepper, T., & Waldner, K. (2024) · Springer eBooks · doi:10.1007/978-3-658-46481-3_16

A Novel Fault Diagnosis Method for Multimode Hot Rolling Process Based on MGU and BLS

Zhang, C. J., Yin, W., Zhang, H., Liu, R., Zhang, Z., & Zhao, Z. (2024) · China Automation Congress (CAC) · doi:10.1109/cac63892.2024.10864790

Algorithms Facilitating the Observation of Urban Residential Vacancy Rates: Technologies, Challenges and Breakthroughs

Liu, B., Zeng, W., Liu, W., Peng, Y., & Yao, N. (2025) · Algorithms · doi:10.3390/a18030174

Knowledge-Guided Reasoning Chain of Pre-trained LLM in Industrial Domain

Lou, J., Ren, H., Ma, G., Liang, X., Yang, C., & Gui, W. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-4509-1_26

Technology Opportunity Discovery of Cloud Computing Based on Latent Dirichlet Allocation Topic Model and Explainable Artificial Intelligence Model

Xu, Y., Zhang, X., Xu, Y., & Xia, D. (2025) · International Conference on Cloud Computing and Big Data Analytics (ICCCBDA) · doi:10.1109/icccbda64898.2025.11030488

Dynamic Latent Variable Analysis for Multirate Closed-Loop Process Monitoring

Chen, X., & He, X. Y. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3588991

Technology Opportunity Discovery Based on Biterm Topic Model and Explainable Artificial Intelligence Model

Xu, Y., Zhang, X., & Xu, Y. (2025) · International Conference on Artificial Intelligence and Big Data (ICAIBD) · doi:10.1109/icaibd64986.2025.11081999

Exploration of Big Data Analytics Using Machine Learning (ML): A Comprehensive Survey

JohnAlex, E., Venkatapathi, P., Cury, C., Yasmeen, Samatha, S., & Krishnaveni, B. V. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-95-0269-1_56

Scientific Machine Learning for Industrial Process Simulation: Applications, Strategies, and ChallengesPreprint

Kamali, M., & Tu, Q. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5797705

Canonical Correlation Analysis-Based Fault Detection Methods

Chen, Z., & Zhang, K. (2025) · Lecture notes in control and information sciences · doi:10.1007/978-981-96-9033-6_20

Nonlinear bidirectional dynamic latent modeling for closed-loop process monitoring

Chen, X., He, X., & Qin, S. J. (2025) · Automatica · doi:10.1016/j.automatica.2025.112751

Evaluating humachine dynamics in process industry AI applications

Cuzzola, F. A., Quadrini, W., Teocchi, S., Pinzone, M., & Fumagalli, L. (2026) · Journal of Manufacturing Technology Management · doi:10.1108/jmtm-05-2025-0406

Machine Learning-Based Predictive Maintenance for Industrial Machinery

Suyahman, S., Wicaksono, A., & Iswavigra, D. U. (2025) · Journal of Multidisciplinary Research and Technology · doi:10.63891/j-mart.v1i3.32

Membrane Separation Processes Using Machine Learning Based Mathematical Programming Models

Macali, A. (2023) · doi:10.70675/37b5e277z691fz4d37zb5a4zcfd4ceeaa517

Robust ATR-FTIR Calibration for Concentration Monitoring in Mixed-Solvent Crystallization Processes

Lima, F. A. R. D., Murr, M. E., Griffin, D. J., Grover, M. A., & Rousseau, R. W. (2026) · Organic Process Research & Development · doi:10.1021/acs.oprd.5c00338

A knowledge-enhanced graph transformer network for fault prediction in chemical processes

Chen, Z., Hou, Y., Chen, Z., & Lv, F. (2026) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2026.109729

Case studies: Breakthroughs at the intersection of ML and chemical engineering

Tanimu, G., & Hussain, I. M. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-33943-1.00013-7

A method for integrated monitoring of process multiple indicators based on quality‐aware network

Chen, Z., Cheng, X., Tan, S., Qiu, Y., Wang, J., Pan, Z., & Zhang, Z. (2026) · The Canadian Journal of Chemical Engineering · doi:10.1002/cjce.70484

Intelligent process systems engineering (iPSE) in the era of artificial intelligence and process electrification

Qi, M., & Wang, X. (2026) · Green Energy & Environment · doi:10.1016/j.gee.2026.07.002

Artificial Intelligence in Chemical Engineering: A Bibliometric and Foresight Analysis with Insights from Peru

Gusukuma, M., & Hernández-Cenzano, C. G. (2026) · LACCEI International Multi-Conference for Engineering, Education and Technology (LACCEI) · doi:10.18687/laccei2026.1.1.2153

Joint prediction and classification within a single multitask architecture for early fault diagnosis in chemical processes

J., D. F. C., & Chioua, M. (2026) · Journal of Process Control · doi:10.1016/j.jprocont.2026.103818

Design and Expert Validation of the MIGEAS Model: A Data Analytics-Driven Framework for Strategic Management and Functional Integration in Higher Education

Egas, P. F. B., Rodríguez, G. L., & Toasa, R. M. (2026) · Education Sciences · doi:10.3390/educsci16081313

Artificial Intelligence in Chemical Process Safety: Applications, Challenges, and Trustworthy DeploymentPreprint

Hu, Y., Meng, Y., Zhang, C., Meng, L., & Zhao, D. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7512578

Two-dimensional attention-based dynamic latent variable modeling for real-time batch process monitoring

Liu, J., Gan, F., & Chen, J. (2026) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2026.109686

Digital Twin Applications for Predictive Maintenance, Operational Safety, and Gas-Plant Reliability

Asibor, E. (2026) · INTERNATIONAL JOURNAL OF ENGINEERING AND MODERN TECHNOLOGY · doi:10.56201/ijemt.v9.no3.2023.pg287.336

Branch Voting and Hierarchical Activation for Anomaly Inference in Refinery Units

Ma, J., Wang, Z., Fan, J., Wang, X., & Wang, L. (2026) · Processes · doi:10.3390/pr14203370

Data Analytics and Machine Learning for Smart Process Manufacturing: Recent Advances and Perspectives in the Big Data Era

Shang, C., & You, F. (2019) · Engineering · doi:10.1016/j.eng.2019.01.019 · Cited by 336

Data Mining and Analytics in the Process Industry: The Role of Machine Learning

Ge, Z., Song, Z., Ding, S. X., & Huang, B. (2017) · IEEE Access · doi:10.1109/access.2017.2756872 · Cited by 922

Data Mining, Spidering and Analysis with Python

Kamble, T., Garde, S., & Kadam, S. (2022) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.21556305

Support industrial processes through machine learning

Curo, G. G., Giribaldi, A. R. B., Quispe, M. A. R., Pineda, L. H. T., & Sullca, E. M. G. (2021) · Journal of Scientific and Technological Research Industrial · doi:10.47422/jstri.v2i2.18

A general paradigm for applying machine learning in automated manufacturing processes

Zhang, W., & Tjoelker, R. (1998)